CN110012069A - A kind of vehicle data processing unit, method and vehicle monitoring platform - Google Patents

Abstract

The present invention relates to a kind of vehicle data processing unit, method and vehicle monitoring platforms, wherein, the vehicle data processing unit includes: data acquisition module, and for obtaining the basic data information of vehicle in target fleet, the basic data information includes travel time data and driving behavior data；It travels timeliness module and determines the punctuality rate of the target fleet for the presupposed information of class's line according to corresponding to vehicle in the travel time data and the target fleet；Driving behavior analysis module, for determining the driving characteristics of driver corresponding to vehicle in the target fleet, and establish the corresponding relationship of the driving characteristics and target component according to the driving behavior data.The monitoring to vehicle and corresponding driver in target fleet may be implemented in the present invention, provides the foundation examined to vehicle and corresponding driver for the manager of fleet, is conducive to improve the efficiency of management and effect for target fleet.

Description

A vehicle data processing device, method and vehicle monitoring platform

technical field

The present invention relates to the technical field of vehicle management, and in particular, to a vehicle data processing device, method and vehicle monitoring platform.

Background technique

The Internet of Things is connected to the Internet by means of different types of detection devices, radio frequency identification and other sensing devices to form an environment covering people and things. Based on the development of Internet of Things technology, it is now possible to monitor vehicles in logistics distribution.

However, the monitoring business scope of vehicles in logistics distribution in the prior art is limited, so that the information obtained by the managers of the logistics distribution fleet is limited, which reduces the management efficiency and effect of the managers on the logistics distribution fleet.

SUMMARY OF THE INVENTION

In order to solve the problems in the prior art, the embodiments of the present invention provide a vehicle data processing apparatus, method, and vehicle monitoring platform. The technical solution is as follows:

In one aspect, a vehicle data processing device is provided, the device comprising:

a data acquisition module for acquiring basic data information of vehicles in the target fleet, where the basic data information includes travel time data and driving behavior data;

a driving time-limiting module, configured to determine the on-time rate of the target fleet according to the driving time data and preset information of the shift line corresponding to the vehicles in the target fleet;

The driving behavior analysis module is used for determining the driving characteristics of the drivers corresponding to the vehicles in the target fleet according to the driving behavior data, and establishing the corresponding relationship between the driving characteristics and the target parameters.

Further, the travel time data includes the actual arrival time of the vehicle in the corresponding shift line, and the preset information includes the planned arrival time of the vehicle in the corresponding shift line;

The driving aging module includes an on-time determination module and an on-time rate calculation module,

The on-time determination module is used to calculate the difference between the actual arrival time of the vehicle on the corresponding shift line and the planned arrival time, and determine that the vehicle arrives on time when the difference meets the first preset condition;

The on-time rate calculation module is configured to calculate the on-time rate of the target fleet according to the number of on-time arrivals of vehicles in the target fleet.

Specifically, the punctuality rate calculation module includes a first statistics module and a first calculation sub-module, and the punctuality rate of the target team includes the first punctuality rate;

The first statistical module is used to count the on-time arrival times of all vehicles in the target fleet and the total arrival times of all vehicles;

The first calculation submodule is configured to calculate the ratio of the number of on-time arrivals of all vehicles in the target fleet to the total number of arrivals of all vehicles, and the ratio is recorded as the first on-time rate.

Specifically, the punctuality rate calculation module includes a second statistics module and a second calculation sub-module, and the punctuality rate of the target team includes the second punctuality rate;

The second statistics module is used to count the on-time arrival times of a single vehicle in the target fleet and the total arrival times of the single vehicle;

The second calculation submodule is configured to calculate the ratio of the number of on-time arrivals of the single vehicle to the total number of arrivals of the single vehicle, and the ratio is recorded as the second on-time rate.

Specifically, the punctuality rate calculation module includes a third statistics module and a third calculation sub-module, and the punctuality rate of the target team includes a third punctuality rate;

The third statistical module is used to count the on-time arrival times of vehicles of the target fleet in the preset shift line and the total arrival times of all vehicles in the preset shift line;

The third calculation sub-module is used to calculate the ratio of the number of on-time arrivals of vehicles of the target team in the preset shift line and the total number of arrivals of all vehicles in the preset shift line, and the ratio is recorded as The third punctuality rate.

Further, the driving aging module further includes an alarm module, and the alarm module is configured to generate alarm information when the difference satisfies a second preset condition.

Further, the driving behavior data includes the acceleration of the vehicle, the fuel consumption of the vehicle, the speed of the vehicle, and the pedal operation data of the vehicle;

The driving behavior analysis module includes a driving behavior data screening module, a driving feature determination module, and a corresponding relationship establishment module;

The driving behavior data screening module is configured to obtain the driving behavior data corresponding to the completion of multiple complete accelerations of the vehicle according to the acceleration of the vehicle, and obtain screening data;

the driving characteristic determining module, configured to determine the driving characteristic of the driver corresponding to the vehicle according to the screening data;

The corresponding relationship establishing module is configured to calculate an average fuel consumption rate according to the fuel consumption of the vehicle in the screening data, and establish a corresponding relationship between the driving characteristics of the driver and the average fuel consumption rate.

Further, the driving feature determination module includes a data division module, a feature data acquisition module, a clustering module and a determination submodule,

the data division module, configured to divide the screening data into a plurality of data sets according to the speed of the vehicle in the screening data;

The characteristic data acquisition module is used to acquire pedal operation data in each data set, and all the pedal operation data form a driving characteristic data set;

The clustering module is configured to perform cluster analysis on the driving feature data set by using a preset clustering algorithm;

The determining submodule is configured to determine the driving characteristics of the driver corresponding to the vehicle according to the result of the cluster analysis.

In another aspect, a vehicle data processing method is provided, the method comprising:

Acquire basic data information of vehicles in the target fleet, where the basic data information includes travel time data and driving behavior data;

determining the on-time rate of the target fleet according to the travel time data and preset information of the shift line corresponding to the vehicles in the target fleet;

According to the driving behavior data, the driving characteristics of the drivers corresponding to the vehicles in the target fleet are determined, and the corresponding relationship between the driving characteristics and the target parameters is established.

In another aspect, a vehicle monitoring platform is provided, and the vehicle monitoring platform includes the vehicle data processing apparatus described in any one of the above.

A vehicle data processing device, method and vehicle monitoring platform of the present invention have the following beneficial effects:

The vehicle data processing device of the present invention obtains the basic data information of the vehicles in the target fleet through the data acquisition module, and uses the driving time module to determine the target based on the travel time data in the basic data information and the preset information of the corresponding shift lines of the vehicles in the target fleet The punctuality rate of the fleet, the driving behavior analysis module is used to determine the driving characteristics of the drivers corresponding to the vehicles in the target fleet based on the driving behavior data in the basic data information, and establish the corresponding relationship between the driving characteristics and the target parameters, so as to realize the target fleet. The monitoring of vehicles and corresponding drivers provides a basis for fleet managers to evaluate vehicles and corresponding drivers, which is beneficial to improve the management efficiency and effect of the target fleet.

Description of drawings

In order to illustrate the technical solutions of the present invention more clearly, the following will briefly introduce the accompanying drawings that are required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

FIG. 1 is an architecture diagram of a vehicle monitoring platform provided by an embodiment of the present invention;

2 is a schematic structural diagram of a vehicle data processing device provided by an embodiment of the present invention;

3 is a schematic structural diagram of a driving aging module provided by an embodiment of the present invention;

FIG. 4 is another schematic structural diagram of a driving aging module provided by an embodiment of the present invention;

5 is a schematic structural diagram of a driving behavior analysis module provided by an embodiment of the present invention;

6 is a schematic structural diagram of a driving feature determination module provided by an embodiment of the present invention;

7 is a schematic structural diagram of another vehicle data processing device provided by an embodiment of the present invention;

FIG. 8 is a schematic flowchart of a vehicle data processing method provided by an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

It should be understood that "vehicle" or other similar words described in the embodiments of this specification include general motor vehicles, such as passenger vehicles including sport utility vehicles, buses, trucks, various commercial vehicles, and Includes hybrid, electric, plug-in hybrid, hydrogen and other alternative fuel vehicles.

It should be noted that the vehicle data processing apparatus in the embodiment of the present specification may be configured in the vehicle monitoring platform of the embodiment of the present invention, for example, may be configured in the new energy vehicle monitoring platform to process new energy vehicle data.

Specifically, the overall architecture of the vehicle monitoring platform can be as shown in FIG. Of course, in addition to the above five layers, the platform can also include a data open layer 600 and a platform security control layer 700. The data open layer 600 can flexibly provide external API interfaces for third-party business system calls, and the platform security control layer 700 can Identity authentication and authorization to ensure the safe operation of the monitoring platform. The vehicle data processing device 310 in the embodiment of the present specification can be specifically placed in the data analysis layer 300 of the above-mentioned vehicle monitoring platform, and the processing result thereof can be used by the business service layer 400 .

The data collection layer 100 can communicate with the vehicle-mounted terminal of the monitored vehicle to collect monitoring data sent by the vehicle-mounted terminal of the monitored vehicle. The vehicle terminal can obtain the monitoring data of the vehicle in real time through various sensors and other detection devices installed on the vehicle. For new energy vehicles, the monitoring data category can include vehicle data (vehicle speed, accumulated mileage, acceleration, fuel consumption, gear, etc.) , drive motor data (drive motor speed, torque, temperature, etc.), fuel cell data (fuel cell voltage, current, fuel consumption, etc.), engine data (transmitter status, crankshaft speed, fuel consumption, etc.), position data (longitude , latitude, etc.), extreme value data (battery cell voltage highest/low value, highest/low temperature value, etc.), alarm data (temperature difference alarm, vehicle-mounted energy storage device overvoltage/undervoltage alarm, etc.).

The data storage layer 200 can store the monitoring data collected by the data collection layer 100. Specifically, the data storage layer 200 can adopt database forms such as MySQL, SQL Server, and Oracle9i.

The data analysis layer 300 can obtain the data to be analyzed from the data storage layer 200 for analysis and processing. For example, the vehicle data processing device in the embodiment of this specification can be used to analyze and process the monitoring data of vehicles in the target fleet.

The business service layer 400 can realize functions such as the management of the fleet involved in the monitoring platform, the setting of the platform, and the response to business requests. Shift line setting, wherein the identification of the fleet is the unique representation of the fleet in the vehicle monitoring platform, and the identifier of the vehicles in the fleet is the unique representation of the vehicle in the vehicle monitoring platform. When setting the shift line of vehicles in the fleet, the same vehicle can be configured with multiple shift lines, and each shift line can be set with preset information such as the vehicle's planned departure time, planned arrival time, and shift line mileage. The display layer 500 may include the display of the client connected and communicated with the monitoring platform, for example, it may be a display in the form of a large screen, or it may be a display on a PC side, and so on.

Please refer to FIG. 2 , which shows a schematic structural diagram of a vehicle data processing apparatus provided in an embodiment of the present specification. As shown in FIG. 2 , the vehicle data processing apparatus 310 may include a data acquisition module 3110 , a driving aging module 3120 and a driving Behavior Analysis Module 3130.

In the embodiment of this specification, the data acquisition module 3110 may be used to acquire basic data information of vehicles in the target fleet, and the basic data information may include travel time data and driving behavior data. Specifically, the data acquisition module 3110 can acquire the basic data information of the vehicles in the target fleet collected by the data acquisition layer 100 from the data storage layer 200 of the vehicle monitoring platform. Detection devices such as a plurality of sensors are detected, and are acquired and uploaded to the data collection layer 100 through a vehicle-mounted terminal such as a T-BOX configured on the vehicles in the target fleet.

Specifically, the travel time data may include the actual arrival time of the vehicles in the target fleet on the corresponding shift line. Of course, other time data, such as the actual departure time of the vehicle on the corresponding shift line, can also be obtained as required. The driving behavior data may include the acceleration of the vehicle in the target fleet, the speed of the vehicle, the fuel consumption of the vehicle, the pedal operation data of the vehicle, etc., wherein the pedal operation data of the vehicle may include the accelerator pedal operation data and the brake pedal operation data. Of course, it is also possible to Obtain other monitoring data related to the driving behavior of the driver as required, which is not limited in the present invention.

In the embodiment of this specification, the driving time module 3120 is configured to determine the punctuality rate of the target fleet according to the driving time data and the preset information of the shift lines corresponding to the vehicles in the target fleet, and the on-time rate can reflect the The target fleet and the drivers corresponding to the vehicles in the target fleet control the shift line time. Based on the on-time rate, the fleet administrator can conduct the assessment of the drivers in the fleet, as well as the deployment and management of the fleet drivers.

In a specific embodiment, the travel time data includes the actual arrival time of the vehicle on the corresponding shift line, and the preset information includes the planned arrival time of the vehicle on the corresponding shift line. The driving time period module 3120, as shown in FIG. 3, may include an on-time determination module 3121 and an on-time rate calculation module 3122.

The punctuality determination module 3121 is used to calculate the difference between the actual arrival time of the vehicle on the corresponding shift line and the planned arrival time, and determine that the vehicle arrives on time when the difference meets the first preset condition. The first preset condition may be configured when the business service layer 400 sets the vehicles in the fleet. The first preset condition may be that the difference does not exceed the preset first threshold, and the specific form of the preset first threshold may be: The specific time value, for example, 0.5 minutes, may also be in the form of a percentage of the difference in the planned arrival time, for example, 1%. Of course, the above are only two examples of the first preset threshold, and other values may also be set according to actual requirements, which are not limited in the present invention.

The on-time rate calculation module 3122 is configured to calculate the on-time rate of the target fleet according to the number of on-time arrivals of vehicles in the target fleet. Specifically, as shown in FIG. 3 , the punctuality rate calculation module 3122 may include a first statistics module 3122a and a first calculation sub-module 3122b. Correspondingly, the punctuality rate of the target team includes the first punctuality rate. The first statistics module 3122a is used to count the on-time arrival times of all vehicles in the target fleet and the total number of arrivals of all vehicles; the first calculation sub-module 3122b is used to calculate the punctuality of all vehicles in the target fleet. The ratio of the number of on-time arrivals to the total number of arrivals of all vehicles, the ratio is recorded as the first on-time rate, and the specific calculation formula is as follows:

Among them, A ₁ % represents the first on-time rate; n represents the total number of vehicles in the target fleet; _i represents any vehicle _i in the target fleet; Ni ′ represents the number of on-time arrivals of vehicle i in the target fleet; The number of arrivals of vehicle i in the target fleet. It can be seen that the first punctuality rate takes the entire target fleet as the analysis object, which can facilitate the manager to understand the punctuality of the entire fleet, and then make adjustments between the fleets.

In another specific embodiment, as shown in FIG. 3 , the punctuality rate calculation module 3122 may further include a second statistics module 3122c and a second calculation sub-module 3122d, and accordingly, the punctuality rate of the target team includes the second punctuality rate. The second statistics module 3122c is used to count the number of on-time arrivals of a single vehicle in the target fleet and the total number of arrivals of the single vehicle; the second calculation sub-module 3122d is used to calculate the number of on-time arrivals of the single vehicle and the number of on-time arrivals of the single vehicle. The ratio of the total number of arrivals of a single vehicle, the ratio is recorded as the second on-time rate, and the specific calculation formula is as follows:

Among them, A ₂ % represents the second on-time rate; i represents any vehicle i in the target fleet; N _i ′ represents the number of on-time arrivals of the vehicle i in the target fleet; N _i represents the total arrival times of the vehicle i in the target fleet number of stops. It can be seen that the second punctuality rate takes a single vehicle in the target fleet as the analysis object, which can facilitate the manager to understand the punctuality of each vehicle in the fleet, and then adjust the vehicles in the fleet.

In another specific embodiment, as shown in FIG. 3 , the punctuality rate calculation module 3122 may further include a third statistics module 3122e and a third calculation sub-module 3122f, and the punctuality rate of the target team includes a third punctuality rate. The third statistics module 3122e is used to count the on-time arrival times of vehicles of the target fleet in the preset shift line and the total number of arrivals of all vehicles in the preset shift line; the third calculation sub-module 3122f is used to calculate all The ratio of the number of on-time arrivals of vehicles of the target team in the preset shift line to the total number of arrivals of all vehicles in the preset shift line, the ratio is recorded as the third on-time rate, and the specific calculation formula is as follows :

Among them, A ₃ % represents the third punctuality rate; p represents the total number of vehicles in the target fleet in the preset shift line; i represents any vehicle i in the target fleet in the preset shift line; j represents any one of the preset shift lines Vehicle j; N _i ′ represents the on-time arrival times of vehicle j of the target fleet in the preset shift line; k represents the total number of vehicles in the preset shift line; N _j represents the arrival times of vehicle j in the preset shift line . It can be seen that the third punctuality rate takes the preset class line as the analysis object, which can facilitate the manager to understand the punctuality of each class line, and then adjust between the class lines.

In the embodiment of this specification, the driving time limit module 3120 may also include an alarm module 3123. As shown in FIG. 4 , the alarm module 3123 is used for the difference between the actual arrival time and the planned arrival time of the vehicle on the corresponding shift line, which satisfies the Alarm information is generated when the second preset condition occurs. The second preset condition may be that the difference exceeds a preset second threshold, and the specific form of the preset second threshold may be a specific time value, such as 3 minutes, or the difference may account for the planned arrival time as a percentage, such as 10%. Of course, the above are only two examples of the second preset threshold, and other values may also be set according to actual requirements, which are not limited in the present invention.

In the embodiment of this specification, the driving behavior analysis module 3130 is configured to determine the driving characteristics of the drivers corresponding to the vehicles in the target fleet according to the driving behavior data, and establish the corresponding relationship between the driving characteristics and the target parameters. The driving characteristics of the driver are used to reflect the driving habit of the driver, for example, the driving habit may include a smooth type, a normal type, and an adventurous type. The target parameter is a vehicle-related parameter determined according to actual needs and affected by the driver's driving characteristics. For example, the target parameter may be a parameter related to the fuel consumption of the vehicles in the target fleet, or a The parameters related to the accident situation of the vehicle, etc., of course, are not limited in the present invention, and any target parameters that can establish a corresponding relationship with the driving characteristics are included in the protection scope of the present invention.

In a specific embodiment, as shown in FIG. 5 , the driving behavior analysis module 3130 may include a behavior data screening module 3131 , a driving feature determination module 3132 , and a corresponding relationship establishment module 3133 .

Among them, the behavior data screening module 3131 is configured to obtain the driving behavior data corresponding to the completion of multiple complete accelerations of the vehicle according to the acceleration of the vehicle, and obtain the screening data. Specifically, the start of acceleration (that is, the acceleration from being equal to zero to being greater than zero) to the end of acceleration (that is, the next acceleration being from being greater than zero to being equal to zero) is recorded as a complete acceleration of the vehicle. It should be noted that, in order to improve the reliability and integrity of the data, the behavior data screening module 3131 may also perform data cleaning on the data to be screened before performing data screening. Specifically, data cleaning can be performed by means of a limited period of time. For example, each period of data whose start time is time_flag1(i) and end time is time_flag2(i) can be selected, and the rest of the data can be removed.

The driving characteristic determining module 3132 is configured to determine the driving characteristic of the driver corresponding to the vehicle according to the screening data. Specifically, as shown in FIG. 6 , the driving feature determination module 3132 may include a data division module 3132a, a feature data acquisition module 3132b, a clustering module 3132c, and a determination submodule 3132d.

The data dividing module 3132a is configured to divide the screening data into multiple data sets according to the speed of the vehicle in the screening data. Specifically, the screening data corresponding to the time interval from the moment when the speed of the vehicle changes from greater than zero to equal to zero to the next moment when the speed of the vehicle changes from greater than zero to equal to zero can be divided into a data set, so that the screening data can be divided into for multiple data sets.

The characteristic data acquisition module 3132b is configured to acquire pedal operation data in each data set, and all the pedal operation data form a driving characteristic data set. Specifically, the pedal operation data may include accelerator pedal operation, or accelerator pedal operation and brake pedal operation data, or brake pedal operation data. Specifically, the accelerator pedal operation data may include the average acceleration acc_avg1 when the accelerator pedal is pressed and the average acceleration dec_avg1 when the accelerator pedal is released. Thus, the accelerator pedal operation data of each data set can be expressed as xi=[acc_avg1, dec_avg1], all The accelerator pedal operation data xi corresponding to the data set constitutes the driving feature data set X=[xi].

The clustering module 3132c uses a preset clustering algorithm to perform cluster analysis on the driving feature data set, and obtains a result of the cluster analysis. The preset clustering algorithm may use the cluster analysis function kmeans of matlab. Specifically, the cluster analysis function kmeans may be used to divide the driving feature data set X into three categories: mild type, common type and passionate type. Of course, the preset clustering algorithm may also be other clustering methods, such as density-based clustering method, maximum expectation clustering using Gaussian mixture model, agglomerative hierarchical clustering, etc., which is not limited in the present invention.

The determination sub-module 3132d is configured to determine the driving characteristics of the driver corresponding to the vehicle according to the result of the cluster analysis. Specifically, the percentage of the number of data sets belonging to the mild type, the normal type and the passionate type to the total number of data sets can be counted separately, and the type with the highest percentage is determined as the driving characteristics of the driver corresponding to the vehicle. For example, the statistics get If the mild type accounts for 80%, the normal type accounts for 15%, and the passionate type accounts for 5%, it can be determined that the driving characteristic of the driver of the corresponding vehicle is the mild type.

The corresponding relationship establishing module 3133 is configured to calculate an average fuel consumption rate according to the fuel consumption of the vehicle in the screening data, and establish a corresponding relationship between the driving characteristics of the driver and the average fuel consumption rate. Specifically, the average fuel consumption rate may be the average fuel consumption in the screening data or the fuel consumption per unit mile. When it is the fuel consumption per unit mile, the driving behavior data also needs to include the mileage of the vehicle corresponding to the fuel consumption of the vehicle, for example, the average fuel consumption The rate can be fuel consumption per 100 meters. The corresponding relationship may be a one-to-one correspondence between the driver, the driving characteristics of the driver, and the average fuel consumption rate. Through the establishment of the corresponding relationship, it is convenient for the manager to understand the driver's daily driving behavior, and it is convenient for the assessment of the driver; in addition, because the driving characteristics are corresponding to the average fuel consumption rate, it is convenient for the manager to understand the fuel consumption corresponding to different driving characteristics. , which is conducive to improving the efficiency and effect of fleet management.

In another specific embodiment, as shown in FIG. 7 , the vehicle data processing apparatus 310 may further include a fuel consumption statistics module 3140, and the fuel consumption statistics module 3140 is configured to perform statistical analysis on the fuel consumption of the target fleet according to the fuel consumption of the vehicles in the target fleet . Specifically, statistical analysis can be performed on the fuel consumption of each vehicle in the target fleet within a preset time period to obtain the maximum value, minimum value and average value of the fuel consumption of each vehicle within the preset time period, wherein the preset time period Segments can be set according to actual needs. Of course, the maximum value, minimum value and average value of the fuel consumption of the target fleet within the preset time period can also be calculated according to the fuel consumption of each vehicle in the target fleet within the preset time period, so as to facilitate the manager to know that the target fleet is in the forecast period. Fuel consumption over a set period of time to make adjustments to vehicles in the target fleet.

It should be noted that, when implementing the functions of the device provided in the above-mentioned embodiments, only the division of the above-mentioned functional modules is used as an example. The internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

To sum up, the vehicle data processing device of the present invention obtains the basic data information of the vehicles in the target fleet through the data acquisition module, and uses the driving time module based on the travel time data in the basic data information and the preset information of the corresponding shift lines of the vehicles in the target fleet. To determine the punctuality rate of the target fleet, use the driving behavior analysis module to determine the driving characteristics of the drivers corresponding to the vehicles in the target fleet based on the driving behavior data in the basic data information, and establish the corresponding relationship between the driving characteristics and the target parameters, so as to realize the The monitoring of vehicles and corresponding drivers in the target fleet provides the fleet manager with a basis for evaluating vehicles and corresponding drivers, which is beneficial to improve the management efficiency and effect of the target fleet.

Please refer to FIG. 8 , which is a schematic flowchart of a vehicle data processing method provided by an embodiment of the present invention. It should be noted that this specification provides the method operation steps as described in the embodiment or the flowchart, but based on conventional Or non-creative work may involve more or fewer operational steps. The sequence of steps enumerated in the embodiments is only one of the execution sequences of many steps, and does not represent the only execution sequence. When an actual device or product is executed, the methods shown in the embodiments or the accompanying drawings may be executed sequentially or in parallel (for example, a parallel processor or a multi-threaded processing environment). Specifically, as shown in FIG. 8 , the method may include:

S801: Acquire basic data information of vehicles in a target fleet, where the basic data information includes travel time data and driving behavior data.

S803: Determine the on-time rate of the target fleet according to the travel time data and preset information of the shift lines corresponding to the vehicles in the target fleet.

S805, according to the driving behavior data, determine the driving characteristics of the drivers corresponding to the vehicles in the target fleet, and establish the corresponding relationship between the driving characteristics and the target parameters.

It should be noted that, step S805 may be executed first, and then step S803 may be executed, or step S805 and step S803 may be executed simultaneously, and the present invention does not limit the execution order of the two.

In addition, since the vehicle data processing method provided by the embodiments of the present invention corresponds to the vehicle data processing apparatuses provided in the above-mentioned embodiments, the foregoing implementations of the vehicle data processing apparatuses are also applicable to the vehicle data processing methods provided in this embodiment. It will not be described in detail in this embodiment.

The foregoing description has fully disclosed specific embodiments of the present invention. It should be pointed out that any changes made by those skilled in the art to the specific embodiments of the present invention will not depart from the scope of the claims of the present invention. Accordingly, the scope of the claims of the present invention is not limited to the foregoing specific embodiments.

Claims (10)

1. a kind of vehicle data processing unit, which is characterized in that described device includes:

Data acquisition module, for obtaining the basic data information of vehicle in target fleet, the basic data information includes row Sail time data and driving behavior data；

Timeliness module is travelled, for the pre- of class's line according to corresponding to vehicle in the travel time data and the target fleet If information, the punctuality rate of the target fleet is determined；

Driving behavior analysis module, for determining in the target fleet corresponding to vehicle according to the driving behavior data The driving characteristics of driver, and establish the corresponding relationship of the driving characteristics and target component.

2. vehicle data processing unit according to claim 1, which is characterized in that the travel time data includes vehicle Practical arrival time in corresponding class's line, when the presupposed information includes that plan of the vehicle in corresponding class's line is arrived at a station Between；

The traveling timeliness module includes determining module and punctuality rate computing module on schedule,

The determining module on schedule, for calculating vehicle in the practical arrival time of corresponding class's line and the difference of plan arrival time Value, and determine that the vehicle is to arrive at a station on schedule when the difference meets the first preset condition；

The punctuality rate computing module, for calculating the target carriage according to the number that arrives at a station of vehicle in the target fleet on schedule The punctuality rate of team.

3. vehicle data processing unit according to claim 2, which is characterized in that the punctuality rate computing module includes the One statistical module and the first computational submodule, the punctuality rate of the target fleet include the first punctuality rate；

First statistical module, for counting arrive at a station number and all vehicles of all vehicles in the target fleet on schedule The number that always arrives at a station；

First computational submodule, for calculating arrive at a station number and all vehicles of all vehicles in the target fleet on schedule The number that always arrives at a station ratio, the ratio is denoted as first punctuality rate.

4. vehicle data processing unit according to claim 2, which is characterized in that the punctuality rate computing module includes the Two statistical modules and the second computational submodule, the punctuality rate of the target fleet include the second punctuality rate；

Second statistical module, for counting arrive at a station number and the single unit vehicle of single unit vehicle in target fleet on schedule The number that always arrives at a station；

Second computational submodule, arrive at a station on schedule number and the single unit vehicle for calculating the single unit vehicle always arrive It stands the ratio of number, the ratio is denoted as second punctuality rate.

5. vehicle data processing unit according to claim 2, which is characterized in that the punctuality rate computing module includes the Three statistical modules and third computational submodule, the punctuality rate of the target fleet include third punctuality rate；

The third statistical module, the number and described pre- of arriving at a station on schedule of the vehicle for counting default Ban Xianzhong target fleet If the number that always arrives at a station of all vehicles in class's line；

The third computational submodule, for calculate the default Ban Xianzhong target fleet vehicle arrive at a station on schedule number and The ratio of the number that always arrives at a station of all vehicles, the ratio are denoted as third punctuality rate in default class's line.

6. vehicle data processing unit according to claim 2, which is characterized in that the traveling timeliness module further includes report Alert module, the alarm module are used to generate warning message when the difference meets the second preset condition.

7. vehicle data processing unit according to claim 1, which is characterized in that the driving behavior data include vehicle Acceleration, the oil consumption of vehicle, the speed of vehicle and vehicle pedal operation data；

The driving behavior analysis module includes driving behavior data screening module, driving characteristics determining module and corresponding relationship Establish module；

The driving behavior data screening module completes multiple complete acceleration for obtaining the vehicle according to the acceleration of vehicle Corresponding driving behavior data, obtain garbled data；

The driving characteristics determining module, the driving for determining the corresponding driver of the vehicle according to the garbled data are special Sign；

The corresponding relation building module calculates mean-fuelconsumption rate for the oil consumption according to vehicle in the garbled data, and builds Found the driving characteristics of the driver and the corresponding relationship of the mean-fuelconsumption rate.

8. vehicle data processing unit according to claim 7, which is characterized in that the driving characteristics determining module includes Data division module, characteristic obtain module, cluster module and determine submodule,

The garbled data is divided into more by the data division module for the speed according to vehicle in the garbled data A data acquisition system；

The characteristic obtains module, for obtaining the pedal operation data in each data acquisition system, all pedal operations Data form driving characteristics data set；

The cluster module, for carrying out clustering to the driving characteristics data set using default clustering algorithm；

The determining submodule determines that the driving of driver corresponding to the vehicle is special for the result according to clustering Sign.

9. a kind of vehicle data processing method, which is characterized in that the described method includes:

The basic data information of vehicle in target fleet is obtained, the basic data information includes travel time data and driving row For data；

According to the presupposed information of class's line corresponding to vehicle in the travel time data and the target fleet, the mesh is determined Mark the punctuality rate of fleet；

According to the driving behavior data, the driving characteristics of driver corresponding to vehicle in the target fleet are determined, and build Found the corresponding relationship of the driving characteristics and target component.

10. a kind of vehicle monitoring platform, which is characterized in that including the processing of vehicle data described in claim 1-8 any one Device.