CN120726257B - Three-dimensional data acquisition management system and method - Google Patents
Three-dimensional data acquisition management system and methodInfo
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Abstract
本发明涉及数据采集技术领域，公开了一种用于三维数据采集管理系统及方法，包括以下步骤：S1、获取待采集区域的三维空间模型，并将该空间离散为多个采样单元构成体素网格；S2、识别所述三维空间模型中的遮挡区域，构建有效可达的三维采集空间；S3、根据设备视场参数生成初始布设点位；S4、基于设备的采集参数和采样单元位置；S5、构建联合优化目标函数；S6、在约束条件下，通过优化方法求解出最优的设备布设点位和路径控制函数。本发明中，通过构建三维空间采集模型，采用联合优化目标函数，设备布设点与路径的动态优化，能耗及遮挡区域惩罚等多个因素，得到最优的设备布设方案与路径控制策略，提高采集效率和路径规划的合理性。
This invention relates to the field of data acquisition technology and discloses a three-dimensional data acquisition management system and method, comprising the following steps: S1, acquiring a three-dimensional spatial model of the area to be acquired, and discretizing the space into multiple sampling units to form a voxel mesh; S2, identifying occlusion areas in the three-dimensional spatial model and constructing an effectively reachable three-dimensional acquisition space; S3, generating initial deployment points based on the device's field of view parameters; S4, based on the device's acquisition parameters and sampling unit positions; S5, constructing a joint optimization objective function; S6, under constraints, solving for the optimal device deployment points and path control function using optimization methods. In this invention, by constructing a three-dimensional spatial acquisition model, employing a joint optimization objective function, dynamic optimization of device deployment points and paths, and considering multiple factors such as energy consumption and occlusion area penalties, the optimal device deployment scheme and path control strategy are obtained, improving acquisition efficiency and the rationality of path planning.
Description
Technical Field
The invention relates to the technical field of data acquisition, in particular to a system and a method for three-dimensional data acquisition management.
Background
With the rapid development of three-dimensional data acquisition technology, how to efficiently and accurately acquire and process three-dimensional space data has become a research hot spot in many application fields such as environmental monitoring, city modeling, robot navigation and the like. However, conventional three-dimensional data acquisition methods typically rely on fixed deployment points and simple path planning strategies, lacking dynamic optimization of acquisition area and equipment configuration. The method has a plurality of problems in practical application, particularly the problems of energy consumption of equipment, influence of shielding objects, acquisition coverage and the like become more prominent along with the increase of the complexity of an acquisition area, so that the efficiency of a data acquisition process is low, the energy consumption is high, and available resources cannot be fully utilized.
In the prior art, a common three-dimensional data acquisition management system usually adopts a static path planning mode, and lacks of dynamic selection of equipment setting points and real-time optimization of paths. Even though some approaches consider optimization of acquisition paths, only a single factor is typically of concern, such as energy minimization or acquisition coverage maximization, ignoring the need for multi-objective optimization. In addition, due to the presence of obstacles in the environment, conventional systems fail to effectively address occlusion problems, resulting in frequent cases of missing data or incomplete acquisition in practical applications.
Disclosure of Invention
In order to make up for the defects, the invention provides a system and a method for three-dimensional data acquisition management, which aim to solve the problems that the prior method is usually focused on a single target, and how to balance a plurality of optimization targets is ignored, so that acquisition coverage is insufficient or energy consumption is too high, and an optimal scheme cannot be provided after various factors are comprehensively considered.
In a first aspect, the present invention provides a method for three-dimensional data acquisition management, including the following steps:
S1, acquiring a three-dimensional space model of a region to be acquired, and dispersing the space into a plurality of sampling units to form a voxel grid;
s2, identifying a shielding region in the three-dimensional space model, and constructing an effectively reachable three-dimensional acquisition space;
s3, generating initial layout points according to the field-of-view parameters of the equipment, and establishing an acquisition path control function of each equipment;
S4, establishing a vision coverage relation based on acquisition parameters of the equipment and the positions of the sampling units, and judging whether each sampling unit is effectively covered by the equipment;
s5, constructing a joint optimization objective function, wherein the objective function simultaneously considers acquisition coverage rate, path energy consumption cost and space conflict penalty of the path and the shielding area;
and S6, under the constraint condition, solving the optimal equipment layout point positions and the path control function through an optimization method, and outputting an optimal result for guiding the layout and the scheduling of the actual three-dimensional data acquisition equipment.
Preferably, in S2, the effective reachable space is obtained by voxel modeling of the occlusion region and excluding the point where occlusion exists in any acquisition direction based on ray analysis, so as to obtain the equipment judgment routable region.
Preferably, in S3, the device path control function is established by a kinematic model, where the kinematic model is a time continuous function, and the speed input is constrained by the maximum speed and the acceleration of the device, and the initial set point is taken as the path starting point.
Preferably, in S4, the established coverage relationship is based on the following conditions:
the distance between the device and the sampling unit does not exceed the effective acquisition radius of the device, the device is directed to the unit, and no blocking voxel exists between the line of sight and the unit;
and constructing a supporting graph structure between the views of the devices, wherein each node in the supporting graph corresponds to one of the views of the devices, and if the views of the two devices are overlapped spatially, connecting edges are established in the graph, so that a continuously nested view field chain structure is formed.
Preferably, the constructed joint optimization objective function includes the following three sub-objectives:
s501, the proportion of sampling units which are not covered by any equipment in the total sampling units;
s502, collecting total energy consumption costs of paths by all devices, wherein the energy consumption costs are represented by integral of a path speed function on a path interval;
s503, penalty values generated by all device paths crossing the shielding region in the execution process are determined by the spatial intersection measure of the path track and the shielding voxel set.
Preferably, in S6, the optimization method includes an alternate direction multiplier method or a mixed integer programming method, and the joint objective function value is minimized by performing joint optimization solution on the layout point variable, the path variable and the coverage relation variable;
The path optimization is based on optimal control theory modeling, and an optimal control variable is determined by constructing a Hamiltonian and combining a Lagrangian multiplier method, so that the energy consumption of the equipment path is minimized.
Preferably, the initial layout point generation in the method adopts a heuristic sampling method, the method generates a plurality of candidate points according to space boundary conditions and barrier distribution, and the point with the highest visible coverage is selected as an initial layout position.
In a second aspect, the present invention provides the following technical solutions, a system for three-dimensional data acquisition management, the system comprising:
the space modeling module is used for carrying out grid discrete modeling on the acquisition region and identifying the obstacle region to form an acquisition space;
the layout generation module is used for generating initial layout points according to the equipment parameters and establishing an equipment path control function;
The view modeling module is used for constructing a view coverage relation between the equipment and the space sampling unit and constructing a support diagram structure;
The optimization solving module is used for constructing a joint optimization target of coverage rate, path energy consumption and shielding penalty and solving an optimal equipment layout scheme and path;
And the scheduling control module is used for transmitting the optimal result to three-dimensional data acquisition equipment, and the equipment layout and track execution task scheduling control.
In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements a method for three-dimensional data acquisition management as described above when the processor executes the computer program.
In a fourth aspect, the present invention provides a readable storage medium having a computer program stored thereon, where the computer program when executed by a processor implements a method for three-dimensional data acquisition management as described above.
The invention has the following beneficial effects:
1. according to the method, the three-dimensional space acquisition model is constructed, the dynamic optimization of the equipment layout points and the paths is realized by adopting the combined optimization objective function, a plurality of factors such as acquisition coverage, energy consumption and shielding area punishment are considered, an optimal equipment layout scheme and path control strategy are obtained, and the acquisition efficiency and the rationality of path planning are effectively improved.
2. According to the invention, through the establishment of the coverage relation between the field of view of the equipment and the spatial voxels, the acquisition coverage of each voxel can be comprehensively evaluated, and by combining with the optimal control and path optimization algorithm, the higher spatial acquisition coverage rate and accuracy are realized, the uncovered area is reduced, and the integrity and accuracy of the acquisition task are improved.
3. According to the invention, by introducing the Hamiltonian optimal control theory and the path energy consumption model, the energy consumption of the equipment in the path execution process can be effectively reduced while the equipment acquisition path is optimized, the energy consumption and the path conflict of the equipment operation are reduced to the greatest extent, and the operation efficiency and the sustainability of the whole acquisition system are improved.
Drawings
FIG. 1 is a flow chart of a method for three-dimensional data acquisition management method according to the present invention;
Fig. 2 is a diagram of a system module relationship architecture for a three-dimensional data acquisition management system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made in detail and with reference to the accompanying drawings, wherein it is apparent that the embodiments described are only some, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, in a first embodiment of the present invention, the present invention provides a method for three-dimensional data acquisition management, comprising the steps of:
S1, acquiring a three-dimensional space model of a region to be acquired, and dispersing the space into a plurality of sampling units to form a voxel grid;
S2, identifying a shielding region in the three-dimensional space model, and constructing an effectively reachable three-dimensional acquisition space;
s3, generating initial layout points according to the field-of-view parameters of the equipment, and establishing an acquisition path control function of each equipment;
S4, establishing a vision coverage relation based on acquisition parameters of the equipment and the positions of the sampling units, and judging whether each sampling unit is effectively covered by the equipment;
s5, constructing a joint optimization objective function, wherein the objective function simultaneously considers acquisition coverage rate, path energy consumption cost and space conflict penalty of the path and the shielding area;
and S6, under the constraint condition, solving the optimal equipment layout point positions and the path control function through an optimization method, and outputting an optimal result for guiding the layout and the scheduling of the actual three-dimensional data acquisition equipment.
And S2, the effective reachable space is obtained by carrying out voxel modeling on the shielding region and eliminating the point positions with shielding in any acquisition direction based on ray analysis, thereby obtaining the equipment judgment routable region.
Specifically, the construction of an effective reachable space depends on the voxel modeling result of the shielding area, and combines a multi-directional ray projection analysis mode to realize the accurate screening of the equipment layout position, and the system firstly carries out discrete processing on all known obstacle objects in the three-dimensional space, maps the known obstacle objects into a voxel grid form and forms an obstacle voxel set. Each voxel unit has a volume boundary definition, and the voxel center is marked as. The obstacle area is not processed continuously in a surface or body form, but is converted into a point set expression with uniform spatial scale, the conversion has direction independence and is convenient for subsequent algorithm processing, and a key processing link is based on a directional projection model (ray-tracing based visibility check) to judge whether occlusion voxel blocking exists in each direction. The process uses a certain point to be laid outStarting from a plurality of preset directionsPerforming space path simulation:
;
Wherein, the Vector for maximum throw distance (typically set as the upper limit of the maximum perceived range of the device)Can be uniformly distributed on the spherical surface to ensure the uniformity of directional coverage. For each pathDiscrete it into steps ofChecking, point by point, whether it crosses the set of occlusion voxels。
If any point in the path in a certain direction falls within the range of the obstacle voxels, the direction view is considered to be limited. This determination not only depends on whether the center points overlap, but also considers voxel boundary buffer values (can be introduced-A neighbor judgment mechanism) to prevent occlusion judgment from being too severe and eventually, only the points satisfying the following conditions are reserved as routable locations:
;
This means that the device issues from this point that there is at least one unobstructed direction in which the perceived task can be performed. If the barrier is present in all directions, the point is rejected from the layout candidate set. It is worth noting that the method based on voxel shielding judgment is more robust than the traditional method based on geometric model or triangle mesh shielding judgment, and can effectively avoid boundary geometric calculation errors. Through uniform voxel scale, the consistency of shielding analysis of different space regions can be ensured, and uniform processing is facilitated. The shielding judgment supports parallel execution, the path judgment mode is naturally suitable for GPU or spatial index optimization, and after the construction is completed, the system outputs the voxel set As the actual deployable area for the device. The set has stable spatial distribution, does not fluctuate severely with modeling error, and has repeatability and engineering feasibility.
In S3, the equipment path control function is established through a kinematic model, the kinematic model is a time continuous function, the speed input of the kinematic model is constrained by the maximum speed and the acceleration of the equipment, and an initial setting point is taken as a path starting point.
In particular, in a device scheduling system, the generation of paths is not an isolated process, which relies on dynamic behavior modeling on a routable area basis. After finishing the judgment of the equipment layout candidate point set, the invention enters the next stage, namely, the equipment path control function is constructed. The path is not only a connecting line in geometric sense, but also a time sequence continuous action plan limited by a motion rule and execution constraint. The introduction of kinematic modeling is key, the device path control function adopts a continuous time domain expression form, and the position change process of the device in the three-dimensional space is described by a function curve. To make the equipment at the timeThe path function in is noted as:
;
Wherein the method comprises the steps of Indicating at the momentIs a spatial location of (c). The initial condition is that the equipment is distributed from the distribution pointDeparture, terminal timeThe change of the path control function, which can be seen as a planning cycle or a task completion moment, is limited by the actual movement capabilities of the device, including in particular the maximum linear velocity and the maximum acceleration. To this end, a speed function is defined:
;
Acceleration function:
;
the following constraints are imposed in the present invention-maximum speed constraint:
;
-maximum acceleration constraint:
;
Wherein, the And (3) withRespectively, the upper limit preset in the system according to the physical structure of the equipment or the specification of the platform. Such constraints avoid non-physical movements in the path, such as momentary accelerations, retraces, etc.
The specific construction of the path function may vary depending on the device class. For example, for a flying-type device (e.g., a drone), it is preferable to use spline curves (e.g., cubic B-splines) to interpolate the path, as it naturally satisfies the continuity of position and speed. The spline parameter node number can be dynamically adjusted according to the complexity degree of the sampling area, so that the expression capacity of the path is reserved, excessive redundancy is avoided, and the path initial value constraint ensures that the path starts from the equipment setting point and has no drift error. Namely:
;
In the process of establishing a path control model, in order to avoid the problems of overlarge curvature, frequent direction change and the like of the path, a curvature constraint or soft constraint punishment item is introduced, an adjusting factor is applied to the second derivative of the path function, the item can be used as an energy consumption agent item in a subsequent optimization model, natural, smooth and executable path generation is facilitated, the path function not only has time continuity on data expression, but also fully reflects dynamic constraint conditions faced by equipment in actual operation in the modeling process. And a path control function is established on the routable region, which is the expression of the spatial degree of freedom and the rationality of the path function, so that the floor-standing property and the high-quality acquisition guarantee capability of the final scheduling scheme are determined.
In S4, the established view coverage relationship is based on the following conditions:
the distance between the device and the sampling unit does not exceed the effective acquisition radius of the device, the device is directed to the unit, and no blocking voxel exists between the line of sight and the unit;
And constructing a supporting graph structure between the views of the devices, wherein each node in the supporting graph corresponds to one of the views of the devices, and if the views of the two devices are overlapped spatially, connecting edges are established in the graph, so that a continuously nested view field chain structure is formed.
Specifically, first, the distance condition. The straight line distance between the device and the target point must not exceed its preset maximum acquisition radius. This constraint reflects the physical limits of the device's perceptibility. In practical application, the sensing radius can be flexibly set according to the type of equipment, resolution requirements or optical conditions;
And secondly, judging the directivity. Not all voxels within a radius are perceivable. The device must be oriented towards this point and the target voxel needs to fall within its viewing angle range. In other words, the voxel positions and their included angles from the current orientation of the device need to lie within a sector of the area that allows acquisition. If the device hindbrain scoop is over against the voxel, the device is regarded as ineffective even if the device hindbrain scoop is further away, and third, the device hindbrain scoop is free of shielding of the sight. Even if the distance is appropriate, the direction is opposite, and if the line of sight passes halfway through an obstacle (previously noted as occlusion voxel), then this point is still considered invisible. The system will determine if the connection between the device and the voxel is penetrated by any obstruction. Judging as being shielded as long as shielding exists;
Only voxel units that meet both of these conditions will be marked as effectively covered by the device. Such mapping results have a direct impact on the acquisition coverage, path benefit assessment, etc., of subsequent computations. The system uniformly defines all voxel sets which can be covered by the equipment as a vision supporting set thereof, and the vision supporting set is used as a perception effective range in the current path control state;
A support graph structure between inter-device views is also introduced. This is an abstract data structure describing the spatial coupling relationship between fields of view between multiple devices;
The view that each device has at any time of the path is considered a node in the graph. If the views of the two devices have an intersection, i.e. there is at least one voxel that can be perceived by both devices at the same time, an undirected connecting edge is established between the two nodes. This connection represents an information exchange or overlay relationship between markets. The construction of the support graph not only helps to judge coverage redundancy, but also provides structural input for the subsequent establishment of the joint optimization model. The method can reveal which devices are locally coordinated and which areas have vision islands, and can also find paths with high overlapping degree, thereby assisting in dynamically adjusting path planning and point allocation. In addition, the support graph has expandability naturally, and along with the increase of the number of the devices or the change of the paths, the graph structure can be updated in real time, so that the timeliness and the accuracy of the field-of-view topological relation of the whole system are ensured.
The constructed joint optimization objective function comprises the following three sub-objectives:
s501, the proportion of sampling units which are not covered by any equipment in the total sampling units;
s502, collecting total energy consumption costs of paths by all devices, wherein the energy consumption costs are represented by integral of a path speed function on a path interval;
S503, penalty values generated by all device paths crossing the shielding region in the execution process are determined by spatial intersection measures of the path track and the shielding voxel set.
Specifically, firstly, in order to realize efficient, complete and stable data acquisition in a three-dimensional space, the system needs to synthesize multiple targets, and under the given conditions of a routable area and a path control function, a joint optimization model is solved to obtain a scheduling scheme with optimal coverage, reasonable energy consumption and obstacle avoidance safety. For this purpose, the following joint objective function is constructed:
;
Wherein- Ratio of uncovered voxelsPath energy consumptionBlocking conflict penaltyWeight coefficient for adjusting the importance degree of the three
Second, the term measures how many target voxels are ultimately not perceptually covered by any device. Defining a set of target sampled voxels asIf a certain voxelNot covered by either device, it is marked as "not acquired". Defining a binary functionWhen (when)When covered, 1, otherwise 0:
;
this reflects the overall sampling accuracy, with lower values indicating more comprehensive coverage, and the system considers the kinetic energy consumption generated by each device running along its path, rather than a simple path length measurement, and instead models the energy consumption integrally based on a velocity function. Set the first The path of the station apparatus being a continuous function of timeAt a speed of Its energy consumption can be approximated as a square integral of the velocity norm:
;
Wherein the method comprises the steps of Is the firstPath execution time of the station apparatus. The model implies a preference for a smooth path, avoiding frequent acceleration/deceleration if the device path crosses an occlusion region during execution, i.e. trajectory and trajectoryThere is a non-empty intersection, a penalty is generated. Defined herein is:
;
Wherein the method comprises the steps of Is an indication function of the obstacle region, and takes a value of 1 when the path point falls into the shielding region, and takes a value of 0 otherwise. The integration result represents the interaction 'time length' of the device with the obstacle area during operation, and the larger value indicates that more paths have impassable traversing behaviors.
S6, the optimization method comprises an alternate direction multiplier method or a mixed integer programming method, and the joint objective function value is enabled to be minimum by carrying out joint optimization solution on the layout point variables, the path variables and the coverage relation variables;
The path optimization is based on optimal control theory modeling, and an optimal control variable is determined by constructing a Hamiltonian and combining a Lagrangian multiplier method, so that the energy consumption of the equipment path is minimized.
Specifically, in order to minimize the energy consumption of the device path, a static planning or heuristic path approximation mode is not adopted, and the path optimization process is converted into a typical optimal control problem to solve. The modeling mode can finely regulate and control the system performance index while meeting the motion constraint, and the equipment path functionIs defined as a time-varying state variable, the control variable of which is the instantaneous speed of the deviceThe goal is how the control device "walks" to walk an optimal trajectory with minimum energy consumption and satisfied constraints between the start point and the end point, wherein the state control model is defined as the state variables:;
control variable: the dynamic equation is written as:
;
boundary condition, the initial position is the layout point: if there is an endpoint constraint, then specify Otherwise, for free termination in space, the objective function is the minimum energy consumption path, namely the quadratic cost function of the control input:
;
in order to introduce an optimal control theory framework, the invention constructs a Hamiltonian:
;
Wherein, the Is a covariate (i.e. Lagrangian multiplier) representing the sensitivity of the system state to the objective function, optimally controlled according to the Pontrisia minimum principleThe hamiltonian must be minimized at each time and the following set of requirements is met:
;
;
;
From the above conditions, a linear proportional relationship exists between the optimal speed and the cooperative variable, which in this example is a constant vector. This means that the optimal path corresponds to a smooth track with evenly distributed energy, and the control input changes linearly with time, so that the optimal path accords with the power model characteristics of the actual equipment.
The initial layout point generation in the method adopts a heuristic sampling method, the method generates a plurality of candidate points according to space boundary conditions and obstacle distribution, and the point with the highest visible coverage is selected as an initial layout position.
Specifically, the initial layout point generation in the method adopts a heuristic sampling method, the method generates a plurality of candidate points according to space boundary conditions and obstacle distribution, the point with the highest visible coverage is selected as the initial layout position, and the generation area of the candidate points is limited in the previous step and is determined to be inside the routable space area, namely, the voxel setOn the basis, the system constructs a candidate point set according to the following heuristic rule, wherein boundary constraint is that all candidate points need to meet space closed boundary conditions, and equipment is prevented from being close to high-risk areas such as walls, site edges and the like. The critical points can be automatically removed by setting the edge buffer distance, so that obstacle avoidance is realized, wherein the minimum Euclidean distance between a candidate point and any obstacle voxel is not smaller than the safety radius of the equipment volume and the boundary of a perception vision field, the initial crossing shielding condition in a subsequent path is prevented, and the direction distribution uniformity is realized;
After generating the candidate points, the system will evaluate the visibility of each point, use the point as the virtual layout position of the device, and combine the viewing angle range and the acquisition radius of the device to detect how many effective sampling voxels can be covered from the position. The number is the visual coverage index of the point. In the vision detection process, the shielding and eliminating mechanism is also applied, so that all the counted voxels are target units which are free of shielding, can reach and fall into the effective field of view of the equipment. The detection mode can be light projection or a direction sampling beam method, and depends on the equipment performance. After obtaining the visible coverage of all the candidate points, the system ranks according to the indexes, and preferably a group of points with highest coverage are used as initial layout points. The method aims at enabling the initial layout to have larger global view field supporting capacity and improving the searching quality of the whole dispatching system.
Embodiment two:
referring to fig. 2, in a second embodiment of the present invention, the present invention provides a system for three-dimensional data acquisition management, the system comprising:
the space modeling module is used for carrying out grid discrete modeling on the acquisition region and identifying the obstacle region to form an acquisition space;
the layout generation module is used for generating initial layout points according to the equipment parameters and establishing an equipment path control function;
The view modeling module is used for constructing a view coverage relation between the equipment and the space sampling unit and constructing a support diagram structure;
The optimization solving module is used for constructing a joint optimization target of coverage rate, path energy consumption and shielding penalty and solving an optimal equipment layout scheme and path;
And the scheduling control module is used for transmitting the optimal result to the three-dimensional data acquisition equipment, and the equipment layout and the track execute the scheduling control of the task.
Specifically, the space modeling module is responsible for carrying out grid discrete modeling on the acquisition area, identifying the obstacle area and finally forming a complete acquisition space. The gridded space facilitates subsequent path optimization and visibility analysis. The identification of the obstacle region can be realized according to the known obstacle position or by real-time feedback information of a sensor;
And the layout generation module is used for generating a plurality of initial layout points according to equipment parameters (such as the maximum acquisition radius, the perception capability and the like of the equipment) and establishing a path control function of the equipment. The generation of the initial setting points ensures that the selected setting points can maximize the coverage area and avoid barriers through a heuristic sampling method;
And the vision modeling module is responsible for constructing a vision coverage relation between the equipment and the space sampling unit. The view coverage relationship considers the influence of the perceived range, directionality, and occlusion of the device. Through vision modeling, the system can determine which areas each device can cover, and build a support diagram structure among the devices, and the support diagram structure represents the overlapping of fields of view and the possibility of information exchange among the devices;
And the optimization solving module is used for considering multiple factors such as coverage rate, path energy consumption, shielding penalty and the like by constructing a joint optimization objective function. By optimizing the objective function, an optimal equipment layout scheme and path are solved, so that path planning with minimum energy consumption, maximum coverage rate and optimal obstacle avoidance performance is realized;
And the dispatching control module is used for finally transmitting the optimal result output by the optimization solving module to the three-dimensional data acquisition equipment through the dispatching control module. The scheduling control module is responsible for scheduling and controlling the equipment layout and track execution tasks, ensuring that the equipment performs path execution according to an optimal scheme and completing the data acquisition tasks;
Example III
A third embodiment of the present invention is based on the same inventive concept, and the present invention proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of a method for three-dimensional data acquisition management of the above embodiments.
Example IV
The fourth embodiment of the invention provides a computer device based on the same inventive concept, which comprises a processor and a memory, wherein the processor and the memory are communicated with each other, the memory is used for storing instructions, and the processor is used for executing the instructions in the memory and executing the three-dimensional data acquisition management method.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
It should be noted that the foregoing description is only a preferred embodiment of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood that modifications, equivalents, improvements and modifications to the technical solution described in the foregoing embodiments may occur to those skilled in the art, and all modifications, equivalents, and improvements are intended to be included within the spirit and principle of the present invention.
Claims (4)
1. The three-dimensional data acquisition management method is characterized by comprising the following steps of:
S1, acquiring a three-dimensional space model of a region to be acquired, and dispersing the space into a plurality of sampling units to form a voxel grid;
S2, identifying a shielding region in the three-dimensional space model, and constructing an effectively reachable three-dimensional acquisition space, wherein in S2, the effectively reachable space is obtained by carrying out voxel modeling on the shielding region and eliminating the point positions with shielding in any acquisition direction based on ray analysis, so as to obtain a device judgment routable region;
s3, generating initial layout points according to field parameters of the equipment, and establishing an acquisition path control function of each equipment, wherein in S3, the equipment path control function is established through a kinematic model, the kinematic model is a continuous time function, the speed input of the kinematic model is constrained by the maximum speed and the acceleration of the equipment, and the initial layout points are taken as path starting points;
s4, establishing a view coverage relation based on acquisition parameters of the equipment and the positions of the sampling units, wherein the view coverage relation is used for judging whether each sampling unit is effectively covered by the equipment, and the established view coverage relation in S4 is based on the following conditions:
the distance between the device and the sampling unit does not exceed the effective acquisition radius of the device, the device is directed to the unit, and no blocking voxel exists between the line of sight and the unit;
The method comprises the steps of constructing a supporting graph structure between equipment views, wherein each node in the supporting graph corresponds to one equipment view, and if the views of two equipment are overlapped spatially, a connecting edge is established in the graph, so that a continuously nested view field chain structure is formed;
s5, constructing a joint optimization objective function, wherein the objective function simultaneously considers acquisition coverage rate, path energy consumption cost and space conflict penalty of a path and a shielding region, and the constructed joint optimization objective function comprises the following three sub-objectives:
s501, the proportion of sampling units which are not covered by any equipment in the total sampling units;
s502, collecting total energy consumption costs of paths by all devices, wherein the energy consumption costs are represented by integral of a path speed function on a path interval;
s503, penalty values generated by all equipment paths crossing the shielding region in the execution process are determined by the space intersection measure of the path track and the shielding voxel set;
S6, under constraint conditions, solving optimal equipment layout points and path control functions through an optimization method, and outputting an optimal result for guiding layout and scheduling of actual three-dimensional data acquisition equipment;
S6, the optimization method comprises an alternate direction multiplier method or a mixed integer programming method, and the joint objective function value is enabled to be minimum by carrying out joint optimization solution on the layout point variable, the path variable and the coverage relation variable;
the path optimization is modeled based on an optimal control theory, and an optimal control variable is determined by constructing a Hamiltonian and combining a Lagrangian multiplier method, so that the energy consumption of the equipment path is minimized;
the initial layout point generation in the method adopts a heuristic sampling method, the method generates a plurality of candidate points according to space boundary conditions and obstacle distribution, and the point with the highest visible coverage is selected as an initial layout position.
2. A system for three-dimensional data acquisition management, characterized in that it is for a method for three-dimensional data acquisition management according to claim 1, said system comprising:
the space modeling module is used for carrying out grid discrete modeling on the acquisition region and identifying the obstacle region to form an acquisition space;
the layout generation module is used for generating initial layout points according to the equipment parameters and establishing an equipment path control function;
The view modeling module is used for constructing a view coverage relation between the equipment and the space sampling unit and constructing a support diagram structure;
The optimization solving module is used for constructing a joint optimization target of coverage rate, path energy consumption and shielding penalty and solving an optimal equipment layout scheme and path;
And the scheduling control module is used for transmitting the optimal result to three-dimensional data acquisition equipment, and the equipment layout and track execution task scheduling control.
3. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a method for three-dimensional data acquisition management as claimed in claim 1 when executing the computer program.
4. A readable storage medium, wherein a computer program is stored on the readable storage medium, which computer program, when being executed by a processor, implements a method for three-dimensional data acquisition management as claimed in claim 1.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105531995A (en) * | 2013-05-10 | 2016-04-27 | 罗伯特·博世有限公司 | Systems and methods for object and event recognition using multiple cameras |
| CN119886493A (en) * | 2025-03-26 | 2025-04-25 | 国网浙江省电力有限公司永嘉县供电公司 | Method and system for arranging nests of power transmission line inspection unmanned aerial vehicle |
| CN119987408A (en) * | 2025-04-11 | 2025-05-13 | 南京工业大学 | A method, device and equipment for optimizing the inspection path of a group of unmanned aerial vehicles in a chemical park |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113449448B (en) * | 2021-05-31 | 2022-02-15 | 天津大学 | Layout optimization method for optical distributed measurement system |
| CN114510865A (en) * | 2021-12-06 | 2022-05-17 | 国网四川省电力公司眉山供电公司 | Video monitoring visualization layout optimization method based on Unity3D |
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| CN118433551A (en) * | 2024-04-25 | 2024-08-02 | 中信银行股份有限公司 | Monitoring layout optimization method, device, equipment and storage medium |
| CN118643679B (en) * | 2024-08-14 | 2024-11-26 | 中建国际城市建设有限公司 | Visual display engineering monitoring method |
| CN119440050B (en) * | 2024-11-04 | 2025-07-22 | 河海大学 | Coverage path planning method of multiple unmanned aerial vehicles in dam abnormal environment inspection |
| CN120111380A (en) * | 2025-02-17 | 2025-06-06 | 深圳市华宇特科技有限公司 | A modeled camera layout method and system |
| CN120103855A (en) * | 2025-03-05 | 2025-06-06 | 安徽大学 | Collaborative path planning method for heterogeneous multi-UAVs based on multi-agent deep reinforcement learning |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105531995A (en) * | 2013-05-10 | 2016-04-27 | 罗伯特·博世有限公司 | Systems and methods for object and event recognition using multiple cameras |
| CN119886493A (en) * | 2025-03-26 | 2025-04-25 | 国网浙江省电力有限公司永嘉县供电公司 | Method and system for arranging nests of power transmission line inspection unmanned aerial vehicle |
| CN119987408A (en) * | 2025-04-11 | 2025-05-13 | 南京工业大学 | A method, device and equipment for optimizing the inspection path of a group of unmanned aerial vehicles in a chemical park |
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