CN120633977A - An adaptive pipeline relocation intelligent planning method and system - Google Patents
An adaptive pipeline relocation intelligent planning method and systemInfo
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Abstract
本申请提供一种自适应管线迁改智能规划方法和系统，其中，该方法包括：获取地面以上既有管线的激光扫描点云数据和穿透探测数据；将激光扫描点云数据和穿透探测数据进行融合，形成既有管线的多源探测数据；基于多源探测数据，建立包含三维位置信息及材质属性的数据集；针对既有管线中被遮挡的管线部分，基于多源探测数据重构被遮挡的管线部分的空间弯曲信息；基于数据集与重构后的空间弯曲信息，利用降维计算模型生成保留既有管线连接结构的低维表达数据；基于低维表达数据与预设的新建管线约束条件，生成新建管线的避让规划方案。本申请提升了密集城区管线迁改规划的精度和效率。
The present application provides an adaptive pipeline relocation intelligent planning method and system, wherein the method includes: obtaining laser scanning point cloud data and penetration detection data of existing pipelines above the ground; fusing the laser scanning point cloud data and penetration detection data to form multi-source detection data of the existing pipelines; establishing a data set containing three-dimensional position information and material properties based on the multi-source detection data; for the obscured pipeline portions of the existing pipelines, reconstructing the spatial curvature information of the obscured pipeline portions based on the multi-source detection data; based on the data set and the reconstructed spatial curvature information, using a dimensionality reduction calculation model to generate low-dimensional expression data that retains the connection structure of the existing pipelines; and generating an avoidance planning scheme for the new pipelines based on the low-dimensional expression data and preset constraints on the new pipelines. The present application improves the accuracy and efficiency of pipeline relocation planning in dense urban areas.
Description
Technical Field
The application relates to the technical field of intelligent planning of urban underground pipelines, in particular to a self-adaptive pipeline migration intelligent planning method and system.
Background
With the continuous improvement of urban underground space development density, the problem of cross conflict between a newly built pipeline and an existing pipeline is increasingly remarkable, and particularly in dense urban areas, the existing pipeline is often blocked by buildings or strata, the traditional detection means are difficult to acquire complete three-dimensional data, and an intelligent planning method capable of fusing multi-source data, accurately reconstructing the blocked pipeline form and automatically generating an avoidance scheme is needed.
At present, a technical scheme of laser scanning and geological radar layered detection is adopted, the scheme aligns two types of data through a time sequence registration algorithm, pipeline characteristics are extracted based on point cloud density threshold segmentation, and finally a three-dimensional path searching algorithm is utilized to generate an avoidance path.
The method has the advantages that the topological relation of the pipeline connection structure is kept insufficient, key space constraint is easy to lose during dimension reduction processing, the generated avoidance path has curvature mutation or space violation risk, and the calculation complexity is increased sharply along with the increase of the number of pipeline nodes.
Disclosure of Invention
The application provides a self-adaptive pipeline migration and modification intelligent planning method and system, which are used for solving the problems of low precision and poor efficiency of dense urban pipeline migration and modification planning in the prior art.
In a first aspect, the present application provides an adaptive pipeline migration intelligent planning method, including:
Acquiring laser scanning point cloud data and penetration detection data of an existing pipeline above the ground;
Fusing the laser scanning point cloud data and the penetration detection data to form multi-source detection data of the existing pipeline;
based on the multi-source detection data, establishing a data set containing three-dimensional position information and material properties;
reconstructing spatial bending information of the blocked pipeline portion based on the multi-source probe data for the blocked pipeline portion in the existing pipeline;
generating low-dimensional expression data for reserving the existing pipeline connection structure by using a dimension-reduction calculation model based on the data set and the reconstructed space bending information;
And generating an avoidance planning scheme of the new pipeline based on the low-dimensional expression data and a preset constraint condition of the new pipeline.
Optionally, reconstructing, for the occluded pipeline portion in the existing pipeline, spatial bending information of the occluded pipeline portion based on the multi-source probe data, including:
Separating a penetration probe data segment corresponding to the occluded pipeline portion from the multi-source probe data;
constructing a spatial reference axis based on trend trends of adjacent visible pipe sections;
and comparing the penetration detection data segment with the space reference axis, and reconstructing space bending information based on a comparison result to obtain the reconstructed space bending information.
Optionally, reconstructing the spatial bending information based on the comparison result to obtain reconstructed spatial bending information, including:
calculating vertical distance values from each spatial point of the penetration detection data segment to the reference axis according to the comparison result to form a vertical distance value sequence;
Generating a point position offset direction sequence according to the positive and negative signs of each vertical distance value in the vertical distance value sequence;
constructing a continuous morphological curve based on the point offset direction sequence;
And carrying out smoothing treatment on the continuous morphological curve, and taking the smoothed continuous morphological curve as reconstructed space bending information.
Optionally, the smoothing the continuous morphology curve includes:
Equidistant segmentation is carried out on the smoothed continuous morphological curve to generate a plurality of curve segments;
Calculating the bending direction variation of each curve segment end point;
Detecting whether the bending direction variation of the adjacent curve segment endpoints meets a preset consistency condition;
and connecting curve segments meeting the consistency condition according to the detection result to form a smooth continuous morphological curve.
Optionally, the fusing the laser scanning point cloud data and the penetration detection data to form multi-source detection data of an existing pipeline includes:
Establishing a spatial position corresponding relation between a surface coordinate system of the laser scanning point cloud data and an underground coordinate system of the penetration detection data;
identifying a common coverage area of the laser scanning point cloud data and the penetration detection data describing the same existing pipeline part according to the spatial position corresponding relation;
Mapping the underground depth data in the penetration detection data to the surface coordinate system according to the corresponding relation of the space positions in a common coverage area;
For non-common coverage areas, preserving original subsurface depth data in the penetration detection data;
And fusing the mapped underground depth data in the common coverage area with the original underground depth data in the non-common coverage area to form multi-source detection data.
Optionally, the generating, based on the data set and the reconstructed spatial bending information, the low-dimensional expression data of the existing pipeline connection structure by using a dimension-reduction calculation model includes:
encoding three-dimensional position information and material properties in the dataset into a first feature vector;
encoding the reconstructed spatial bending information into a second feature vector;
Combining the first feature vector and the second feature vector, and inputting the combined result into a dimension reduction calculation model, wherein the dimension reduction calculation model applies spatial position constraint of a pipeline connecting point to the combined result in the dimension reduction process;
and outputting low-dimensional expression data according to the spatial position constraint.
Optionally, the generating the avoidance planning scheme of the new pipeline based on the low-dimensional expression data and the preset constraint condition of the new pipeline includes:
resolving space condition and form condition from the newly-built pipeline constraint condition;
converting the low-dimensional expression data into an obstacle constraint boundary, and constructing a path feasible region space of a newly built pipeline by combining the obstacle constraint boundary and the space position condition;
generating a candidate path node sequence of a newly built pipeline in the path feasible region space;
Screening a critical path node sequence conforming to the morphological condition from the candidate path node sequences;
And converting the critical path node sequence into an original three-dimensional coordinate system through inverse mapping, and generating an avoidance planning scheme.
In a second aspect, the present application provides an adaptive pipeline migration intelligent planning system, comprising:
The acquisition module is used for acquiring laser scanning point cloud data and penetration detection data of the existing pipeline above the ground;
the fusion module is used for fusing the laser scanning point cloud data and the penetration detection data to form multi-source detection data of the existing pipeline;
the establishing module is used for establishing a data set containing three-dimensional position information and material properties based on the multi-source detection data;
a reconstruction module for reconstructing spatial bending information of the blocked pipeline portion based on the multi-source probe data for the blocked pipeline portion in the existing pipeline;
The input module is used for generating low-dimensional expression data which reserves the existing pipeline connection structure by using a dimension reduction calculation model based on the data set and the reconstructed space bending information;
The generation module is used for generating an avoidance planning scheme of the new pipeline based on the low-dimensional expression data and a preset new pipeline constraint condition.
In a third aspect, the present application provides a computing device comprising a processor and a memory, the memory having stored therein a computer program, the processor being arranged to run the computer program to perform an adaptive pipeline migration intelligent planning method according to any of the first aspects.
In a fourth aspect, the present application provides a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement an adaptive pipeline migration intelligent planning method according to any one of the first aspects.
The application provides a self-adaptive pipeline migration intelligent planning method which comprises the steps of obtaining laser scanning point cloud data and penetration detection data of an existing pipeline above the ground, fusing the laser scanning point cloud data and the penetration detection data to form multisource detection data of the existing pipeline, establishing a data set containing three-dimensional position information and material properties based on the multisource detection data, reconstructing space bending information of the blocked pipeline part based on the multisource detection data aiming at the blocked pipeline part in the existing pipeline, generating low-dimensional expression data for reserving an existing pipeline connecting structure by using a dimension reduction calculation model based on the data set and the reconstructed space bending information, and generating an avoidance planning scheme of a new pipeline based on the low-dimensional expression data and a preset new pipeline constraint condition.
The technical scheme provided by the application has the following beneficial effects:
The method realizes synchronous acquisition of ground and underground pipeline data, provides a basis for multi-source data fusion, solves the field of view limitation of a single detection means, improves the integrity of three-dimensional information of the pipeline, stores key characteristics of the pipeline in a structured mode, supports subsequent analysis and calculation, fills up data blind areas, restores the true three-dimensional form of a hidden pipeline section, compresses data dimensions while maintaining the pipeline topological relation, reduces calculation complexity, automatically outputs a compliance path based on constraint conditions, and improves planning efficiency and precision.
Furthermore, the continuous and accurate space bending information is reconstructed by separating the penetration detection data segments of the blocked pipe sections and combining the trends of adjacent visible pipe sections to construct a space reference axis and comparing the offset distribution of the data segments and the axis.
And the reconstruction process of the shielded part is guided by utilizing the space reference axis of the visible pipe section, so that the natural connection of the bending form and the existing pipeline is ensured, and the path planning deviation caused by data loss is avoided.
These and other aspects of the application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an adaptive pipeline migration intelligent planning method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a system for adaptive pipeline migration and modification intelligent planning according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computing device according to an embodiment of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.
In some of the flows described in the specification and claims of the present application and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
In the current pipeline migration planning technology, although the existing scheme based on laser scanning and geological radar layered detection can realize data fusion, the processing logic which depends on time sequence registration and point cloud density segmentation has inherent limitations that the topological relation of a pipeline connection structure is easy to break in the process of dimension reduction, so that the generated avoidance path curvature is discontinuous or the distance is illegal, meanwhile, the algorithm complexity grows exponentially along with the number of pipeline nodes, and the real-time planning requirement of a large-scale pipeline network in a dense urban area is difficult to meet. These drawbacks result from the lack of retention of pipeline space constraints by existing methods and from non-optimization of the computing architecture.
Aiming at the problems, the application provides a self-adaptive pipeline migration intelligent planning method, which reconstructs the three-dimensional topology of a pipeline through depth fusion of multi-source detection data and dimension reduction calculation driven by space constraint. The method comprises the steps of firstly establishing a complete data set containing material properties by fusing laser scanning and penetration detection data, reconstructing bending information based on a spatial reference axis for a blocked pipe section, then maintaining the low-dimensional expression data by means of connection structure maintainability, and finally outputting an avoidance planning scheme in combination with constraint conditions. The method takes the pipeline connection relation as the space constraint embedded dimension reduction process in a breakthrough manner, solves the problem of path curvature mutation caused by topology information loss in the prior art, greatly improves planning efficiency through low-dimensional space calculation, and realizes high-precision and real-time intelligent planning of the dense urban complex pipeline network.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.
Fig. 1 is a flowchart of an adaptive pipeline migration intelligent planning method according to an embodiment of the present application, as shown in fig. 1, where the method includes:
And 101, acquiring laser scanning point cloud data and penetration detection data of an existing pipeline above the ground.
In step 101, laser scan point cloud data represents a dense three-dimensional set of coordinates of a surface pipeline surface acquired by a laser scanner, each point containing XYZ location information. The penetration detection data represent underground pipeline reflection signal data acquired by geological radar and other equipment, and comprise parameters such as depth, material reflection intensity and the like.
In the embodiment of the application, a vehicle-mounted laser scanner is adopted to scan along the trend of the pipeline to obtain high-density point cloud of the ground visible part, a geological radar vehicle is synchronously used for underground detection, the laser scanning records the geometric form of the exposed part of the pipeline, the geological radar obtains the buried depth and contour data of the underground pipeline through electromagnetic wave reflection, and the two types of equipment realize synchronous data acquisition through unified space-time reference so as to ensure the space-time consistency of subsequent fusion.
For example, in urban road reconstruction, an A-type laser scanner is used for scanning and acquiring point cloud data of a ground water supply pipeline at fixed intervals, a B-type geological radar is used for detecting an underground area at set frequency, a laser scanning is used for acquiring three-dimensional coordinates of a pipeline bracket, the geological radar is used for measuring pipeline burial depth data, and both types of data are recorded in a database with uniform time stamps. Wherein the scanning interval is determined according to the pipeline diameter, and the sampling theorem requirement is met.
And 102, fusing the laser scanning point cloud data and the penetration detection data to form multi-source detection data of the existing pipeline.
In step 102, the multi-source probe data represents a complete data set that includes three-dimensional coordinates of the earth's surface and subsurface depth information after fusion.
In the embodiment of the application, a conversion relation between a laser point cloud earth surface coordinate system and a radar underground coordinate system is established, an overlapping area of the same pipeline segment is described in two types of data is identified, radar depth data are mapped to the earth surface coordinate system in the overlapping area, original radar data are reserved for a non-overlapping area, and the data are finally combined into a unified data set containing overground and underground information.
For example, the laser point cloud coordinate system obtained in the step 101 is associated with a radar coordinate system through a transformation matrix, wherein a ground reference point is used as a common origin, the pipeline vertex burial depth measured by the radar is transformed into a ground surface Z coordinate in an overlapping section in the X direction, and original radar depth data is reserved for a building shielding section to form a fusion data set.
Step 103, based on the multi-source detection data, a data set containing three-dimensional position information and material properties is established.
In step 103, the three-dimensional position information refers to specific coordinate data of the existing pipeline in space, and includes coordinate values of X, Y, Z directions of key points on the central line of the pipeline, wherein X and Y represent the planar position of the pipeline on the ground surface, Z represents the vertical height or burial depth of the pipeline, and the data are obtained by extracting after fusion of laser scanning and penetration detection. The material property represents a pipeline material class code divided according to electromagnetic reflection intensity. The data set refers to a structured and stored existing pipeline feature set, and comprises three-dimensional coordinate information of pipeline key points and corresponding material property codes, wherein the material property is determined by matching the intensity of a reflected signal penetrating through detection data with a standard material library and is used for subsequent dimension reduction calculation and path planning.
In the embodiment of the application, three-dimensional coordinates of key points of a pipeline central line are extracted from the fusion data, a material type library is matched according to radar reflection signal intensity, and the three-dimensional coordinates are stored as a coordinate-material association data set in a structuring mode.
For example, the coordinates XYZ of the central point of the water supply pipe segment in the fusion data are extracted every set distance, the material library is matched according to the intensity value of the reflected signal, the cast iron pipe segment code is determined to be the set value, and finally a structured table containing the coordinates and the material code is generated.
Step 104, reconstructing the space bending information of the shielded pipeline part based on the multi-source detection data aiming at the shielded pipeline part in the existing pipeline.
In step 104, the occluded pipeline section refers to a hidden pipeline area that cannot be directly detected by laser scanning due to the presence of ground buildings, other pipelines or obstructions in the urban dense area, which areas need to be supplemented by penetration detection data (e.g., radar). The spatial bending information refers to actual three-dimensional trend and curvature data of the shielded pipeline, and can be understood as three-dimensional morphological information. The information is obtained by carrying out space analysis and geometric calculation on the penetration detection data, is used for accurately describing the real bending form of the pipeline, and provides accurate space constraint for path planning.
In the embodiment of the application, radar data fragments of the shielded section are separated from fusion data, a reference axis is fitted according to adjacent visible pipe sections, the offset of radar data points relative to the axis is calculated, and a continuous bending form is generated through curve fitting.
For example, in a building shelter, a reference straight line is built by taking the average value of the trend angles of adjacent visible pipe sections, radar data points of the shelter are perpendicularly projected to an axis, offset distances are calculated, a three-time polynomial is adopted to fit offset distribution, and a bending curve equation z=ax3+bx2+cx+d is obtained, wherein coefficients are determined through a least square method.
And 105, generating low-dimensional expression data which reserves the existing pipeline connection structure by using a dimension-reduction calculation model based on the data set and the reconstructed space bending information.
In step 105, the existing pipeline connection structure refers to the topological connection relationship among the pipe sections in the pipeline system, including the space positions and connection modes of branch points and junction points, while the existing pipeline structure refers to the overall three-dimensional form of the pipeline, and includes physical characteristics such as pipe diameter, trend, burial depth and the like. The connection structure is a core component of the pipeline structure, which determines the network topology characteristics of the pipeline, and the pipeline structure is a specific spatial representation of the connection structure. The low-dimensional expression data refers to a feature vector compressed by a dimension reduction calculation model, and the vector maintains a key connection structure and a space topological relation of a pipeline while reducing the dimension of the data, so that the subsequent path planning calculation is more efficient, and the rationality of an avoidance scheme is ensured.
In the embodiment of the application, three-dimensional coordinates and materials are encoded into a first feature vector, bending information is encoded into a second vector, the first feature vector and the second feature vector are combined, a dimension reduction model is input, position constraint is applied to the coordinates of connecting points in the dimension reduction process, and a low-dimension vector with a topological relation reserved is output.
For example, the coordinates and material data of the water supply pipeline are encoded into a vector, the curve sampling point is encoded into another vector, the constraint branch point coordinate error does not exceed a set threshold value during dimension reduction calculation, and the compressed feature vector is finally output.
And 106, generating an avoidance planning scheme of the new pipeline based on the low-dimensional expression data and a preset constraint condition of the new pipeline.
In step 106, the newly built pipeline constraint conditions refer to engineering specification requirements that must be met during planning, including constraints such as minimum safe distance from existing pipelines, maximum allowable radius of curvature, and material compatibility, and the like, and are used for guiding generation and optimization of avoidance paths. The avoidance planning scheme represents a three-dimensional pipeline path that satisfies pitch and curvature constraints.
In the embodiment of the application, the space and curvature constraint of a newly built pipeline are analyzed, a path node sequence meeting the constraint is searched in a low-dimensional space, the selected sequence is inversely mapped into three-dimensional coordinates, and a continuous path is generated through spline interpolation.
For example, setting minimum spacing according to the design specification of the gas pipeline, screening node combinations meeting the conditions in a low-dimensional space, converting the nodes back to three-dimensional coordinates, and generating a final path by using spline curves, wherein curve parameters are determined by solving an interpolation equation set.
According to the method, through the multi-source data fusion and space constraint maintaining technology, accurate reconstruction of the pipeline shielding section of the dense urban area is achieved, the generated avoidance scheme meets engineering specification requirements, the topological rationality of the pipeline network is maintained, and the accuracy and efficiency of pipeline migration planning in a complex environment are improved.
To solve the problem of insufficient accuracy of the morphological reconstruction of the occluded pipeline, in some embodiments, the reconstructing the spatial bending information of the occluded pipeline portion based on the multi-source detection data for the occluded pipeline portion in the existing pipeline, step 104 includes:
Step 201, separating a penetration detection data segment corresponding to the occluded pipeline portion from the multi-source detection data.
In step 201, the penetration detection data segment refers to local detection data extracted from the complete penetration data, corresponding to the area occluded by the building or the stratum, including depth and profile information of the section pipeline.
In the embodiment of the application, the shielding range is determined by analyzing the signal interruption area in the multi-source detection data, the penetration detection data corresponding to the section is intercepted from the fusion data, and the depth and reflection intensity characteristics of the penetration detection data are reserved for subsequent reconstruction.
Step 202, constructing a space reference axis based on trend trends of adjacent visible pipe sections.
In step 202, the trend of the adjacent visible pipe section refers to the rule of the extending direction of the pipeline deduced by the geometric features of the non-occluded pipeline section, specifically, the line extending direction prediction model is obtained by calculating the angle change rate and the curvature radius of the tangential vector of the central line of the adjacent visible pipe section and adopting least square fitting. The space reference axis refers to a theoretical central line which is constructed according to the geometric characteristic extension of the visible pipe sections at the two sides of the shielding area and is used for guiding the morphological reconstruction of the shielded part.
In the embodiment of the application, the coordinates of the central points of the visible pipe sections in front and behind the shielding area are taken, the trend angle and curvature change trend are calculated, and a reference axis extending to the shielding area is generated through polynomial fitting, wherein the axis is kept in natural connection with the visible pipe sections.
And 203, comparing the penetration detection data segment with the space reference axis, and reconstructing space bending information based on a comparison result to obtain the reconstructed space bending information.
In step 203, the reconstruction process of the spatial bending information refers to restoring the true three-dimensional shape of the blocked pipe section by comparing the deviation distribution of the actual detection data and the theoretical axis.
In the embodiment of the application, each point penetrating through the detection data segment is perpendicularly projected to a reference axis, the distance offset and the direction of each projection point are calculated, a continuous smooth pipeline bending form is generated by adopting a curve fitting algorithm based on an offset mode, and smooth transition with an adjacent pipe section is ensured.
The following is a specific example:
In the urban road reconstruction engineering A, for a section of an underground water supply pipeline blocked by a building foundation B, firstly, a geological radar detection data segment corresponding to the blocking section is extracted from fused multi-source detection data, the segment comprises depth information of 8 detection points, and the distance between the points along the pipeline trend is 0.5 meter. Based on the central line coordinates of the visible pipe sections on two sides of the shielding area, taking the azimuth average value of 32 degrees and the inclination angle of 5 degrees of the first 5-meter pipe section, and constructing a space reference axis equation z=tan5°. X+1.85, wherein the x-axis direction is determined according to the azimuth angle of 32 degrees. Comparing the three-dimensional coordinates of 8 detection points with a reference axis, and calculating the vertical distance d_i= |0.087x_i-z_i+1.85|/[ v (0.0872+1) of each point from the axis, wherein x_i and z_i are the detection point coordinates, so as to obtain a distance sequence of 0.12 meter, 0.15 meter, 0.18 meter, 0.22 meter, 0.25 meter, 0.23 meter, 0.20 meter and 0.17 meter. Fitting a cubic curve equation z=1.85+0.002x3-0.015 x2+0.0322 according to the distance sequence, wherein coefficients are determined by solving a normal equation set Σx_i Σ6·a+Σx_i Σ5·b+Σx_i Σx_i Σ4·c=Σx_i Σ3·d_i, and a fitting error is ensured to be smaller than 0.04 meters.
In the embodiment of the application, the high-precision reconstruction of the bending form of the shielded pipeline is realized through the collaborative analysis of the penetrating data segment and the reference axis, the visual field limitation of a single data source is overcome, the natural connection of the reconstruction result and the existing pipeline is ensured, and a reliable space constraint basis is provided for the subsequent avoidance planning.
In order to further improve the reconstruction accuracy of the occluded pipeline bending form, in some embodiments, step 203 comprises reconstructing the spatial bending information based on the comparison result to obtain reconstructed spatial bending information, including:
And step 301, calculating vertical distance values from each spatial point of the penetration detection data segment to the reference axis according to the comparison result, and forming a vertical distance value sequence.
In step 301, the vertical distance value sequence refers to a numerical value sequence formed by arranging vertical distances from each spatial point in the penetration detection data to the reference axis according to the detection sequence, and reflects the deviation degree of the actual trend of the pipeline from the theoretical axis.
In the embodiment of the application, the vertical distance from each detection point in the penetration detection data segment to the reference axis is calculated, and a distance change sequence is formed according to the spatial sequence of the detection points, and the sequence is used for analyzing the amplitude change characteristics of the pipeline bending.
And 302, generating a point position offset direction sequence according to the positive and negative signs of each vertical distance value in the vertical distance value sequence.
In step 302, the dot offset direction sequence refers to a line offset direction record determined according to the sign of the vertical distance value, with positive values being biased to one side of the axis and negative values being biased to the other side.
In the embodiment of the application, each numerical value in the vertical distance value sequence is subjected to symbol judgment, a sequence consisting of signs is generated, the left-right offset trend of the pipeline relative to the reference axis is reflected, and direction guidance is provided for subsequent curve construction.
And 303, constructing a continuous morphological curve based on the point position offset direction sequence.
In step 303, the continuous morphology curve refers to a smooth three-dimensional curve constructed based on the sequence of offset directions and distance values, describing the actual curved morphology of the occluded pipe segment.
In the embodiment of the application, the curve trend is determined according to the offset direction sequence, and a continuous curve which is naturally connected with the adjacent pipe section is generated by adopting a curve fitting method in combination with the amplitude change of the vertical distance value sequence, so that the physical rationality of the pipeline form is ensured.
And 304, smoothing the continuous morphological curve, and taking the smoothed continuous morphological curve as reconstructed space bending information.
In the embodiment of the application, the position of the curve control point is adjusted through a mathematical smoothing algorithm, so that the curvature change is uniformly transited, and finally smooth and continuous space bending information meeting engineering requirements is output.
The following is a specific example:
In the urban underground pipeline reconstruction project A, for a water supply pipeline segment with the diameter of 600 mm which is blocked by a building C, based on the obtained 8 detection point vertical distance sequences of 0.12 meter, 0.15 meter, 0.18 meter, 0.22 meter, 0.25 meter, 0.23 meter, 0.20 meter and 0.17 meter, each distance value is firstly determined to be positive, and an offset direction sequence consisting of 8 right is generated, wherein the whole pipeline is positioned on the right side of a reference axis. Based on the sequence, an initial morphological curve is constructed by adopting a cubic polynomial fitting, the curve equation coefficient is determined by solving an equation set of Σx_i-6-a+Σx_i-5-b+Σx-i-4-c=Σx_i-3-d_i, wherein x_i represents the horizontal position of the ith detection point and d_i represents a corresponding vertical distance value, and the curve equation is calculated to be z=1.85+0.002x3-0.015 x2+0.0322 x. When the curve is subjected to smoothing treatment, a moving average method is adopted to average the coordinates of the adjacent three points, the curvature change rate of the adjusted curve is controlled within 0.15 per meter, and finally the smoothed space bending information is output for subsequent planning, wherein the curve smoothness is verified by calculating the curvature difference of the adjacent sections, so that the curve smoothness does not exceed the engineering-allowed 0.05 per meter change rate standard.
In the embodiment of the application, the high-fidelity reconstruction of the bending form of the shielded pipeline is realized by the collaborative analysis of the vertical distance sequence and the offset direction sequence and the combination of curve fitting and smooth optimization, so that the natural connection with the adjacent pipeline section is ensured, the precision requirement of engineering application is met, and a reliable space data basis is provided for the follow-up pipeline migration planning.
To further improve the smoothness and continuity of the pipeline curvature profile, in some embodiments, step 304 comprises smoothing the continuous profile curve, including:
and 401, equally dividing the smoothed continuous morphology curve to generate a plurality of curve segments.
In step 401, the curve segmentation refers to a plurality of segments formed by dividing the continuous morphology curve by a fixed length, and each segment includes a start point and an end point.
In the embodiment of the application, the whole curve is divided into a plurality of paragraphs according to the set segmentation length, so that the consistent length of each paragraph is ensured, and the subsequent unified processing and analysis are convenient.
Step 402, calculating the bending direction change quantity of each curve segment end point.
In step 402, the bending direction change refers to the difference in angle between the tangential directions at the two ends of the curve segment, reflecting the bending degree of the segment.
In the embodiment of the application, the tangential direction at the starting point and the ending point of each curve segment is calculated, and the angle difference between the starting point and the ending point is obtained to be used as the bending characteristic quantity of the segment for evaluating the intensity of curve change.
Step 403, detecting whether the bending direction variation of the adjacent curve segment end points meets a preset consistency condition.
In step 403, the consistency condition is that the difference of the bending direction variation amounts of the adjacent curve segments does not exceed a set threshold value, so as to ensure that the curve transition is natural.
In the embodiment of the application, the difference value of the bending direction variation of the adjacent segments is compared, whether the smooth transition requirement is met is judged, and curve paragraph combinations meeting the standard are screened.
And step 404, connecting curve segments meeting the consistency condition according to the detection result to form a smoothed continuous morphological curve.
In step 404, the smoothed continuous morphology curve refers to the final optimized curve formed by concatenating the satisfactory curve segments.
In the embodiment of the application, the end point matching and the connection are carried out on the adjacent curve segments meeting the consistency condition, and local adjustment is carried out at the connection part if necessary, so that the overall smooth pipeline bending form is generated.
The following is a specific example:
In the engineering of modifying the underground pipe network of the urban area A, smooth optimization treatment is carried out on the reconstructed bending curve z=1.85+0.002x3-0.015 x2+0.0322 of the water supply pipeline. Firstly, equally dividing the curve in the horizontal direction at intervals of 0.6 meter to obtain 5 curve segments, wherein the x coordinates of the segment points are respectively 0 meter, 0.6 meter, 1.2 meter, 1.8 meter and 2.4 meter. Calculating the bending direction variation at each segment end point, obtaining each end point angle value theta_i=arctan (0.006x_i2-0.03x_i+0.032) by calculating the curve first derivative tanθ=dz/dx=0.006x2-0.03x+0.032, and calculating the adjacent segment angle difference delta θ=θ_i+1 } - θ_i to obtain a variation sequence of 3.0 degrees, 2.7 degrees, 2.2 degrees and 1.8 degrees. And setting a consistency condition that the difference value of adjacent delta theta is not more than 1.2 degrees, and detecting all the segments to meet the requirement. The segments meeting the conditions are connected through end point coordinate matching, wherein the curvature continuity is ensured by adopting cubic spline interpolation at the connection part, interpolation parameters are determined by solving an equation set S '' (x_i) =S '' (x_ { i+1 }), and finally a smoothed continuous curve is formed. Wherein the radius of curvature R is calculated by the formula R= [1+ (dz/dx) 2] (3/2)/|d2z/dx2| where dz/dx represents the first derivative of the curve and d2z/dx2 represents the second derivative.
In the embodiment of the application, the local mutation points in the morphological curve are effectively eliminated by the connection mode of curve segmentation processing and consistency detection, the natural transition of the bending change of the pipeline is ensured, the accuracy of original data is maintained, the overall smoothness of the curve is improved, and a more reliable geometric foundation is provided for pipeline migration.
In order to further improve the accuracy and integrity of the multi-source data fusion, in some embodiments, step 102, the fusing the laser scanning point cloud data and the penetration detection data to form multi-source detection data of the existing pipeline includes:
Step 501, establishing a spatial position corresponding relation between a surface coordinate system of the laser scanning point cloud data and an underground coordinate system of the penetration detection data.
In step 501, the surface coordinate system of the laser scanning point cloud data is a coordinate system directly established when the laser scanner performs three-dimensional scanning on the above-ground pipeline, and the coordinate system is configured by measuring XYZ coordinates of the laser reflection point with the scanner position as the origin. The underground coordinate system of the penetration detection data is a coordinate system established when the penetration detection equipment such as a geological radar collects the underground data, and the coordinate system takes a detection starting point as a reference, and the depth direction extends downwards perpendicular to the ground. Depth is referred to herein specifically as the vertical distance value from the surface of the underground pipeline, i.e., pipeline burial depth data, described in the penetration test data. The spatial position corresponding relation refers to a conversion rule between an earth surface coordinate system and an underground coordinate system, and comprises an alignment mode of a coordinate origin and an axial corresponding relation.
In the embodiment of the application, the conversion relation of two coordinate systems is established by selecting the ground reference point as a common origin, wherein the Z axis of the ground surface coordinate system is upward, the Z axis of the underground coordinate system is downward, and the two coordinate systems are connected through the height difference.
Step 502, identifying a common coverage area of the laser scanning point cloud data and the same existing pipeline part described in the penetration detection data according to the spatial position corresponding relation.
In step 502, the common coverage area refers to a section of pipeline where both laser scanning and penetration detection can be detected, as evidenced by the effective detection of both types of data in that area.
In the embodiment of the application, the sections which both contain effective detection data are found out by comparing the distribution ranges of the two types of data in space, and the sections are the common coverage areas.
And 503, mapping the underground depth data in the penetration detection data to the surface coordinate system according to the spatial position corresponding relation in the common coverage area.
In step 503, the underground depth information in the penetration detection data refers to underground pipeline buried depth data obtained by a penetration detection device such as a geological radar, and the information is calculated by using a reflection time difference of electromagnetic waves emitted by the device after encountering a pipeline, and the specific value is the round trip time of the electromagnetic waves multiplied by half of the wave velocity in the medium. The relation between the underground depth information and the underground coordinate system is that the underground depth information is a core parameter in the underground coordinate system, the system takes a detection starting point as an origin, the depth direction extends vertically downwards, the depth information represents the vertical coordinate value of a pipeline in the coordinate system, and the underground depth information and the underground coordinate system are the relation between data and a carrier frame. The mapping process of subsurface depth data refers to a process of converting subsurface depth values obtained by penetration detection into a surface coordinate system.
In the embodiment of the application, in the common coverage area, the underground depth value obtained by penetration detection is converted into the height value in the surface coordinate system according to the established coordinate conversion relation, so that unified expression of data is realized.
Step 504, for non-common coverage areas, preserving original subsurface depth data in the penetration detection data.
In step 504, raw subsurface depth data refers to unconverted depth information obtained from penetration detection in non-common coverage areas.
In the embodiment of the application, the original depth measurement result of the region which can be detected only by penetration detection is reserved, so that the data integrity is ensured.
And 505. Fusing the mapped underground depth data in the common coverage area with the original underground depth data in the non-common coverage area to form multi-source detection data.
In step 505, the data fusion process refers to a process of integrating the transformed common coverage data with the original non-common coverage data into a unified data set.
In the embodiment of the application, the common coverage area data subjected to coordinate transformation and the non-common coverage area data with the original form reserved are combined according to the space position to form a complete multi-source detection data set.
The following is a specific example:
In the project of reforming the underground pipeline of the main road of the urban area A, a C-type laser scanner is adopted to scan and acquire the point cloud data of the ground water supply pipeline bracket at intervals of 0.2 meter, and a D-type geological radar is used to detect the underground pipeline at the density of 5 measuring points per meter. The ground datum point at the road starting point is selected as a coordinate origin, a conversion relation between the Z-axis direction of the ground surface coordinate system and the Z-axis direction of the underground coordinate system is established, and coordinate unification is realized specifically through a formula Z_ground= -Z_underground +2.15, wherein 2.15 meters is an actual measurement height difference from the ground to the radar detection starting surface. In the section of 18-23 m in the X direction, laser scanning shows that the center coordinate X=20.5 m and Y=4.2 m of the ground manhole correspond to the point of the pipeline detected by the radar with the burial depth of 1.92 m, and the ground coordinate Z= -1.92+2.15=0.23 m is obtained through conversion. And in the X-direction 25-30 m building shielding section, the original depth data of the radar is kept for 1.85-2.03 m. The finally formed multi-source detection data comprise a common coverage area coordinate X=20.5 meters, Y=4.2 meters and Z=0.23 meters, and a non-coverage area depth value of 1.85-2.03 meters, wherein the coordinate conversion precision is checked through a ground control point, and the error is controlled within 0.015 meters.
In the embodiment of the application, the seamless fusion of the above-ground and underground data is realized by establishing an accurate coordinate conversion relation, so that the original characteristics of various data are reserved, the accurate expression of the data under a unified coordinate system is ensured, and a complete and reliable data foundation is provided for the three-dimensional reconstruction of the follow-up pipeline.
In order to further improve the efficiency of pipeline data processing and maintain the topological relation of the pipeline network, in some embodiments, step 105, generating low-dimensional expression data of the reserved existing pipeline connection structure by using a dimension-reduction calculation model based on the data set and the reconstructed spatial bending information includes:
and 601, encoding three-dimensional position information and material properties in the dataset into a first feature vector.
In step 601, a first feature vector refers to a sequence of values that are converted from three-dimensional coordinates and texture attributes of a pipeline, and is used to characterize the basis space and texture characteristics of the pipeline.
In the embodiment of the application, three-dimensional coordinate values of the pipeline key points and corresponding material codes are extracted from the data set and are arranged according to a fixed sequence to form a numerical vector, so that the basic characteristics of each pipeline segment are ensured to be expressed completely.
And step 602, encoding the reconstructed spatial bending information into a second feature vector.
In step 602, the second feature vector refers to a numerical sequence into which the reconstructed spatial bending information is converted, and is used to describe the bending morphology feature of the pipeline.
In the embodiment of the application, equidistant sampling is carried out on the reconstructed bending curve, the spatial position parameters of each sampling point are recorded, and numerical sequences describing the bending characteristics of the pipeline are formed by sequential arrangement.
And 603, combining the first feature vector and the second feature vector, and inputting the combined result into a dimension-reduction calculation model, wherein the dimension-reduction calculation model applies spatial position constraint of a pipeline connecting point to the combined result in the dimension-reduction process.
In step 603, the spatial position constraint refers to the relative positional relationship of the pipeline connection points that is forcedly maintained in the dimension reduction process, so as to ensure that the topology is not destroyed.
In the embodiment of the application, the coordinate change range of the pipeline intersection point and the branch point is limited during dimension reduction calculation, and the key points are required to keep the relative position relationship unchanged before and after dimension reduction.
Step 604, outputting low-dimensional expression data according to the spatial position constraint.
In the embodiment of the application, the feature vectors with reduced output dimension and containing pipeline space structures and connection relations are used for subsequent efficient processing through constrained dimension reduction calculation.
The following is a specific example:
In the reconstruction engineering of an A urban underground water supply pipeline network, aiming at an established data set containing coordinates and material properties and a reconstructed bending curve z=1.82+0.0018x3-0.014x2+0.03x, firstly, selecting three-dimensional coordinates and material codes of key nodes of the pipeline to form a first feature vector, wherein the material cast iron code is 0.84, and the steel pipe code is 0.91. 15 points are sampled according to the reconstructed bending curve at intervals of 0.5 meter, and the spatial position parameters of the points are arranged to form a second feature vector. The two vectors are combined and then input into a dimension reduction model, the coordinate change of 2 branch points is restrained to be not more than 0.04 m in the calculation process, the restraint is realized by optimizing an objective function of min sigma I WX-Y I2 + lambda sigma I C_j (X) -D_j I2, wherein W represents a projection matrix, X is input data, Y is output data, C_j (X) represents a j-th constraint condition, D_j is a constraint target value, and lambda takes 0.5 for balancing a reconstruction error and constraint satisfaction. Finally outputting a 6-dimensional feature vector, wherein the position error of a detected branch point is 0.035 m, the curved feature remains intact, the weight of each dimension of the feature vector after the dimension reduction is determined by solving a feature value problem (W TW+lambdac TC) v=gammav, v represents the feature vector, and gamma represents the feature value.
In the embodiment of the application, the efficient compression processing of the pipeline data is realized by the feature vector combination and the constraint dimension reduction method, so that the data dimension is greatly reduced, the spatial connection relation of the pipeline is completely reserved, and a simplified and accurate data basis is provided for subsequent avoidance planning.
In order to further improve the rationality and feasibility of the avoidance planning of the new pipeline, in some embodiments, step 106 includes generating a avoidance planning scheme of the new pipeline based on the low-dimensional expression data and a preset constraint condition of the new pipeline, including:
And 701, analyzing space position conditions and morphology conditions from the newly-built pipeline constraint conditions.
In step 701, the spatial location condition refers to the minimum safe distance requirement that the newly built pipeline should maintain from the existing pipeline. Morphological conditions refer to the limit of maximum bend allowed by the newly built pipeline.
In the embodiment of the application, the minimum distance standard of a newly built pipeline and various existing pipelines and the curvature radius limit determined according to the pipeline materials and the purposes are extracted from engineering specifications to be used as basic constraint conditions of planning.
Step 702, converting the low-dimensional expression data into an obstacle constraint boundary, and constructing a path feasible region space of a newly-built pipeline by combining the obstacle constraint boundary and the space position condition.
In step 702, the obstacle constraint boundary refers to a spatial range boundary that is needed to be avoided for converting existing pipeline features expressed in low-dimensional data into new pipelines. The path feasible region space refers to a three-dimensional space range in which a newly built pipeline is allowed to be laid after the avoidance boundary and the safety distance of the existing pipeline are deducted, and the space ensures that the newly built pipeline meets the safety distance requirement of the existing facility and meets the layout specification of engineering design.
In the embodiment of the application, the space influence range of the existing pipeline is restored according to the low-dimensional feature vector, and the forbidden area which can not be accessed by the newly built pipeline is defined by combining the safety distance requirement, so that a clear avoidance boundary is formed.
Step 703, generating a candidate path node sequence of the newly built pipeline in the path feasible region space.
In step 703, candidate path node sequences refer to a set of key control points for a number of possible paths that are initially generated in the feasible region space, each sequence representing a potential pipeline strike scheme, including the spatial coordinates of the path turning points and feature points.
In the embodiment of the application, the area range where the pipeline can be arranged is determined as the basic space of path searching according to engineering design requirements in the space left after the constraint boundary of the obstacle is deducted.
Step 704, screening the critical path node sequence meeting the morphological condition from the candidate path node sequences.
In step 704, the critical path node sequence refers to an optimal path control point sequence which is screened from the candidate solutions and accords with morphological constraints (such as curvature, gradient and the like), and the optimal path control point sequence can meet avoidance requirements and ensure engineering rationality of pipeline trend.
In the embodiment of the application, a plurality of possible path trends are generated in a feasible region space according to a certain rule, and key turning point coordinates of each path are recorded to form an alternative scheme set.
Step 705, converting the critical path node sequence to an original three-dimensional coordinate system through inverse mapping, and generating an avoidance planning scheme.
In step 705, the original three-dimensional coordinate system is a unified spatial reference established by fusing a surface coordinate system with an underground coordinate system, wherein the surface coordinate system provides XY plane coordinates of the above-ground pipeline, the underground coordinate system provides depth Z values of the pipeline, and the two coordinate systems are associated by a coordinate conversion formula X ' = X, Y ' = Y, Z ' = -z+h (H is the height difference from the ground to the detection reference surface), so as to finally form the unified three-dimensional coordinate system containing the complete above-ground underground information.
In the embodiment of the application, the screened optimal path node sequence is converted back to the original coordinate system, and a continuous smooth three-dimensional pipeline path is generated through curve fitting, so that an executable engineering scheme is formed.
The following is a specific example:
In the new construction of the A urban underground gas pipeline, an avoidance scheme is generated based on the 6-dimensional low-dimensional characteristic vector of the existing water supply pipeline. Firstly, analyzing constraint conditions according to a gas design specification, determining that the minimum safety distance between the gas design specification and a water supply pipeline is 0.5 meter, and the maximum allowable curvature radius is 2 meters. The low-dimensional feature vector is restored to an obstacle boundary in a three-dimensional space through a transformation matrix, wherein the boundary range is obtained through calculation of a formula R= v (x2+y2+z2) +0.5, R represents the radius of the safety boundary, and x, y and z are coordinate offsets after transformation of the low-dimensional vector. After deducting the obstacle boundary, a feasible layout area of the newly built pipeline is constructed, wherein the area is in the range of 18-25 m in the X direction, 3-5 m in the Y direction and-1.8-1.2 m in the Z direction. And 3 candidate paths are generated in a feasible domain by adopting a grid search method, wherein each path comprises 7 key nodes, and the node spacing is 1.2 meters. And (3) screening out the optimal path node sequences with the curvature radius larger than 2.1 meters by calculating the curvature radius R= [1+ (dz/dx) 2] (3/2)/|d2z/dx 2|. The sequence is transformed to an original coordinate system through inverse mapping, a final avoidance path is generated through cubic spline interpolation, interpolation parameters are determined through solving an equation set S '' (x_i) =S '' (x_ { i+1 }), wherein S represents a piecewise cubic polynomial, and x_i is a node coordinate.
In the embodiment of the application, the safety avoidance of the newly built pipeline and the existing pipeline is realized by converting the low-dimensional data into the space constraint and optimizing the path selection in the feasible domain, so that the engineering specification requirements are met, and the rationality and the construction feasibility of the pipeline arrangement are ensured.
Fig. 2 is a schematic structural diagram of an adaptive pipeline migration intelligent planning system according to an embodiment of the present application, where, as shown in fig. 2, the system includes:
The acquisition module 21 is used for acquiring laser scanning point cloud data and penetration detection data of the existing pipeline above the ground.
And the fusion module 22 is configured to fuse the laser scanning point cloud data and the penetration detection data to form multi-source detection data of an existing pipeline.
The establishing module 23 is configured to establish a dataset including three-dimensional location information and texture attributes based on the multi-source detection data.
A reconstruction module 24, configured to reconstruct, for an occluded pipeline portion in the existing pipeline, spatial bending information of the occluded pipeline portion based on the multi-source probe data.
And an input module 25, configured to generate low-dimensional expression data that retains the existing pipeline connection structure by using a dimension-reduction calculation model based on the data set and the reconstructed spatial bending information.
The generating module 26 is configured to generate an avoidance planning scheme for the new pipeline based on the low-dimensional expression data and a preset constraint condition of the new pipeline.
An adaptive pipeline migration intelligent planning system shown in fig. 2 may execute an adaptive pipeline migration intelligent planning method shown in the embodiment shown in fig. 1, and its implementation principle and technical effects are not described again. The specific manner in which the various modules and units perform operations in an adaptive pipeline migration intelligent planning system in accordance with the above embodiments has been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
In one possible design, an adaptive pipeline migration intelligent planning system of the embodiment of FIG. 2 may be implemented as a computing device, as shown in FIG. 3, which may include a storage component 31 and a processing component 32;
The storage component 31 stores one or more computer instructions for execution by the processing component 32.
The processing component 32 is configured to perform an adaptive pipeline migration intelligent planning method according to the embodiment of fig. 1.
Wherein the processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps of the methods described above. Of course, the processing component may also be implemented as one or more Application-specific integrated circuits (ASICs), digital signal processors (DIGITAL SIGNAL processes, DSPs), digital signal processing devices (DIGITAL SIGNAL Process devices, DSPDs), programmable logic devices (Programmable Logic Device, PLDs), field programmable gate arrays (Field Programmable GATE ARRAY, FPGA), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above method.
The storage component 31 is configured to store various types of data to support operations at the terminal. The Memory component may be implemented by any type or combination of volatile or nonvolatile Memory devices such as Static Random-Access Memory (SRAM), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ ONLY MEMORY, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read Only Memory (ROM), magnetic Memory, flash Memory, magnetic or optical disk.
Of course, the computing device may necessarily include other components as well, such as input/output interfaces, display components, communication components, and the like.
The input/output interface provides an interface between the processing component and a peripheral interface module, which may be an output device, an input device, etc.
The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, and the like.
The computing device may be a physical device or an elastic computing host provided by the cloud computing platform, and at this time, the computing device may be a cloud server, and the processing component, the storage component, and the like may be a base server resource rented or purchased from the cloud computing platform.
The embodiment of the application also provides a computer storage medium which stores a computer program, and the computer program can realize the self-adaptive pipeline migration intelligent planning method of the embodiment shown in the figure 1 when being executed by a computer.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same, and although the present application has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present application.
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