Vehicle driving data processing method and device and electronic equipment
By segmenting and comparing the vehicle driving data one by one, we can judge whether the error of the data point exceeds the expected value, and solve the problem that abnormal data cannot be effectively discovered in the prior art, and achieve more efficient vehicle management and data accuracy.
Patent Information
- Application Number
- CN202510167819.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-14
- Publication Date
- 2025-06-06
AI Technical Summary
The existing vehicle driving data processing methods are too simple to effectively detect abnormal data, resulting in poor vehicle management and inability to meet business needs.
By obtaining the motion trajectory data of the target vehicle, processing the data in segments, comparing the azimuth and coordinate information of adjacent data points one by one, we judge whether the error exceeds the expected value to determine whether there is an abnormality in the data.
Quickly and effectively discover abnormal data, improve vehicle management effect, improve the accuracy of displaying operating trajectories on the map, and reduce the misjudgment rate of violation information by functions such as electronic fences.
Smart Images
Figure CN120108175A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present application relate to the field of data processing technology, and in particular to a vehicle driving data processing method, device and electronic device. Background Art
[0002] Vehicle management refers to the use of processed vehicle driving data to manage vehicles in real time, including vehicle location, trajectory display, and electronic fencing.
[0003] At present, the methods for processing massive vehicle driving data include deleting redundant and repeated data within a unit of time and deleting unnecessary static data.
[0004] The existing vehicle driving data processing method is too simple and cannot effectively detect abnormal data, resulting in poor vehicle management results and failure to meet business needs. For example, problems such as trajectory path anomalies, electronic fence misjudgment, and inefficient database query may occur. Summary of the invention
[0005] The embodiments of the present application provide a vehicle driving data processing method, device and electronic device for quickly and effectively discovering abnormal data and improving vehicle management effects.
[0006] The present application embodiment adopts the following technical solutions: In a first aspect, a vehicle driving data processing method is provided, the method comprising: Obtain the motion trajectory data of the target vehicle during the target period; Segmenting the motion trajectory data to obtain at least one set of trajectory data; For each set of trajectory data in the at least one set of trajectory data, compare a next piece of trajectory data with a previous piece of trajectory data one by one to determine an error between the next piece of trajectory data and first information in the previous piece of trajectory data, wherein the first information includes azimuth information and coordinate information; When an error of at least one item of the first information exceeds an expected value, it is determined that an abnormality exists in the next track data.
[0007] In a second aspect, a vehicle travel data processing device is provided, the device comprising: A data acquisition module is used to acquire the motion trajectory data of the target vehicle in the target period; A data segmentation module, used for segmenting the motion trajectory data to obtain at least one set of trajectory data; a data comparison module, for comparing, for each set of trajectory data in the at least one set of trajectory data, a next piece of trajectory data with a previous piece of trajectory data one by one, to determine an error between the next piece of trajectory data and first information in the previous piece of trajectory data, wherein the first information includes azimuth information and coordinate information; The abnormality judgment module is used to determine that the next track data has an abnormality when an error of at least one item of the first information exceeds an expected value.
[0008] In a third aspect, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the method described in the first aspect are implemented.
[0009] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.
[0010] In a fifth aspect, a computer program product is provided. The program product is stored in a storage medium and is executed by at least one processor to implement the method as described in the first aspect.
[0011] A vehicle driving data processing method proposed in an embodiment of the present application can obtain the motion trajectory data of the target vehicle in the target time period, segment the motion trajectory data to obtain at least one group of trajectory data; then, for each group of trajectory data in the at least one group of trajectory data, determine the error of the first information between the next trajectory data and the previous trajectory data by comparing the next trajectory data with the previous trajectory data one by one, wherein the first information includes azimuth information and coordinate information; when the error of at least one item of information in the first information exceeds the expected value, determine that the next trajectory data is abnormal, and the abnormal data can be quickly and effectively discovered, thereby improving the vehicle management effect and better meeting business needs. For example, the accuracy of the running trajectory displayed on the map can be improved, and the misjudgment rate of violation information by functions such as electronic fences can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 It is a flow chart of a vehicle driving data processing method provided in one embodiment of the present application.
[0013] Figure 2It is a flow chart of a vehicle driving data processing method provided in another embodiment of the present application.
[0014] Figure 3 It is a flow chart of a vehicle driving data processing method provided in another embodiment of the present application.
[0015] Figure 4 It is a structural schematic diagram of a vehicle driving data processing device provided in one embodiment of the present application.
[0016] Figure 5 It is a structural schematic diagram of a vehicle driving data processing device provided in another embodiment of the present application.
[0017] Figure 6 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION
[0018] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.
[0019] In order to quickly and effectively discover abnormal data and improve vehicle management effects, the embodiments of the present application provide a vehicle driving data processing method and device. The method can be executed by an electronic device, such as a terminal device or a server, or the method can be executed by software installed in the electronic device.
[0020] like Figure 1 As shown, a vehicle driving data processing method provided by an embodiment of the present application may include: Step 101, obtaining the motion trajectory data of the target vehicle in the target time period.
[0021] The target vehicle can be any vehicle that needs to be managed. In a specific embodiment, the target vehicle is an official vehicle.
[0022] The target period can be any period during which the target vehicle needs to be monitored. Typically, the target period can be a period of the day, such as a working period of the day.
[0023] In some embodiments, the motion trajectory data of the target vehicle in the target time period may be directly obtained.
[0024] In some embodiments, it may be determined whether there is vehicle application information about the target vehicle in the target time period; if the vehicle application information exists, the movement trajectory data of the target vehicle in the target time period is obtained. For example, if the target vehicle is a business vehicle of an enterprise or other institution, the vehicle application form for the target vehicle may be queried, and if there is an approved vehicle application form for the target vehicle, the movement trajectory data of the target vehicle in the target time period is obtained.
[0025] Optionally, if there is no vehicle application information about the target vehicle, it means that the target vehicle should be in a stationary state, and the redundant stationary state data of the target vehicle in the target period can be deleted. In specific implementation, it can be queried by calling the interface of a third-party system (such as an official vehicle management platform), and querying whether there is a vehicle application form for the target vehicle according to the license plate number and time conditions (such as the day before the target period). If there is no approved vehicle application form, the redundant stationary state data is deleted. If there is a vehicle application form, the movement trajectory data of the target vehicle in the target period is obtained.
[0026] Wherein, in the absence of vehicle application information about the target vehicle in the target time period, deleting the redundant static state data of the target vehicle in the target time period may include: batch deleting the redundant static state data of the target vehicle in the target time period without a vehicle application form. Because in general, in the enterprise vehicle use process regulations, official vehicles cannot be used without a vehicle application form, so such vehicles are in a static state, and there is no need to save all data. By setting a scheduled task, the same static state data of such vehicles can be saved every first time interval (such as 10 minutes), and the redundant static state data can be deleted to meet the positioning query vehicle location requirements. Wherein, a static state data may include azimuth information, coordinate information, cumulative mileage, cumulative fuel consumption, etc.
[0027] It can be understood that deleting the redundant stationary state data of the target vehicle in the target period can reduce the amount of data stored about the target vehicle, which can improve the query efficiency of the target vehicle.
[0028] Optionally, in the case where there is vehicle application information about the target vehicle in the target time period, before step 101, the method may also include: deleting redundant static state data of the target vehicle in the target time period. It can be understood that even if there is vehicle application information, the target vehicle may not be in motion all the time in the target time period, but in a static state during some periods of the target time period. Although the static state data can be used as an important basis for segmenting the running trajectory, most of the continuous static state data is redundant data and can be deleted to further reduce the amount of data stored about the target vehicle, which can improve the query efficiency of the target vehicle.
[0029] Wherein, in the case that there is vehicle application information about the target vehicle in the target time period, deleting redundant stationary state data of the target vehicle in the target time period may include: Acquire ignition event information and flameout event information of the target vehicle during the target time period; Determining at least one period of time during which the target vehicle is continuously stationary based on the ignition event information and the flameout event information; For each of the at least one time period, the first and last pieces of static state data are retained, and the remaining static state data are deleted. Alternatively, for each of the at least one time period, the first and last pieces of static state data are retained, and for the data between the first and last pieces of static state data, one piece of the same static state data is retained every second time interval (such as every 5 minutes), and the remaining static state data is deleted, so as to meet the data segmentation requirements.
[0030] Optionally, the static state data and the moving state data of the target vehicle are stored in a database with static and moving as the state fields, respectively. Accordingly, the acquisition of the moving track data of the target vehicle in the target period may include: reading from the database a number of data of the target vehicle in the target period with the state field being moving, and obtaining the moving track data of the target vehicle in the target period. That is, the moving track data of the target vehicle in the target period is filtered out from the database with the "movement" field as the operating condition, and there is a field in the database indicating whether a piece of data is static state data or moving state data.
[0031] It should be noted that a piece of motion status data (or a piece of trajectory data) may include azimuth information, coordinate information, accumulated mileage, accumulated fuel consumption, etc.
[0032] Step 102: segment the motion trajectory data to obtain at least one set of trajectory data.
[0033] In some embodiments, step 102 may include: Acquire ignition event information and flameout event information of the target vehicle during the target time period; The motion trajectory data is segmented according to the ignition event information and the flameout event information to obtain at least one group of trajectory data, wherein the motion trajectory data between an adjacent ignition event and a flameout event is one segment / one group.
[0034] That is, in step 102, each ignition record within the target time period can be searched by combining the ignition event information and the flameout event information, and the driving trajectory data of the corresponding time point of each ignition record can be searched in the target vehicle driving trajectory table (the table is stored in the database) as the first trajectory data of the driving trajectory; each flameout record within the target time period can be searched, and the driving trajectory data of the corresponding time point of each flameout record can be searched in the vehicle driving trajectory table as the last data of the driving trajectory, and all driving trajectory data can be segmented and grouped, with one running trajectory as a group.
[0035] Step 103: for each set of trajectory data in the at least one set of trajectory data, compare the next trajectory data with the previous trajectory data one by one to determine an error between the next trajectory data and first information in the previous trajectory data, wherein the first information includes azimuth information and coordinate information.
[0036] The azimuth information is used to characterize the driving direction of the target vehicle; the coordinate information is used to characterize the position of the target vehicle, and the coordinate information can be represented by longitude and latitude information.
[0037] In some embodiments, in step 103, determining the error between the next piece of trajectory data and the first information in the previous piece of trajectory data may include: Determining an azimuth error between the next track data and the previous track data; and Determine the error between the coordinates in the next track data and the previous track data.
[0038] Usually, the difference between the azimuth in the next trajectory data and the previous trajectory data can be used as the error between the azimuth in the next trajectory data and the previous trajectory data; similarly, the difference between the coordinates in the next trajectory data and the previous trajectory data can be used as the error between the coordinates in the next trajectory data and the previous trajectory data. The coordinates include longitude and latitude, and the errors of the two can be calculated separately or combined together.
[0039] Step 104: When the error of at least one item of the first information exceeds an expected value, determine that the next track data is abnormal.
[0040] In the first embodiment, when the error of at least one item of the first information exceeds an expected value, determining that the next track data is abnormal includes: When the error of the azimuth exceeds the first expected value and the error of the coordinate does not exceed the second expected value, it is determined that the azimuth information of the next track data is abnormal.
[0041] In a second embodiment, when an error of at least one item of the first information exceeds an expected value, determining that the next track data is abnormal includes: When the error of the azimuth angle does not exceed the first expected value and the error of the coordinate exceeds the second expected value, it is determined that there is an abnormality in the coordinate information of the next track data.
[0042] In a third embodiment, when an error of at least one item of the first information exceeds an expected value, determining that the next track data is abnormal includes: When the error of the azimuth exceeds the first expected value, and the error of the coordinate exceeds the second expected value, it is determined that both the azimuth information and the coordinate information of the next track data are abnormal.
[0043] The first expected value and the second expected value can be preset based on experience. It can be understood that, whether it is the azimuth or the coordinate, in two adjacent trajectory data, the error between them should not be too large. If it is too large, it means that one of the trajectory data is abnormal.
[0044] A vehicle driving data processing method proposed in an embodiment of the present application can obtain the motion trajectory data of the target vehicle in the target time period, segment the motion trajectory data to obtain at least one group of trajectory data; then, for each group of trajectory data in the at least one group of trajectory data, determine the error of the first information between the next trajectory data and the previous trajectory data by comparing the next trajectory data with the previous trajectory data one by one, wherein the first information includes azimuth information and coordinate information; when the error of at least one item of information in the first information exceeds the expected value, determine that the next trajectory data is abnormal, and the abnormal data can be quickly and effectively discovered, thereby improving the vehicle management effect and better meeting business needs. For example, the accuracy of the running trajectory displayed on the map can be improved, and the misjudgment rate of violation information by functions such as electronic fences can be reduced.
[0045] Alternatively, if Figure 2 As shown, a vehicle driving data processing method provided by another embodiment of the present application may also include: Step 105, when it is determined that the next trajectory data is abnormal, the next trajectory data is corrected according to the second information and the first information in the previous trajectory data, wherein the second information includes the mileage information, the driving speed information and the sampling time interval of the trajectory data of the target vehicle at the collection time point of the next trajectory data.
[0046] It can be understood that step 105 is intended to calculate relatively correct next trajectory data through the first information in the previous trajectory data, the mileage information and speed information of the target vehicle at the collection time point of the next trajectory data, and the sampling time interval of the trajectory data.
[0047] In the first embodiment, when it is determined that the coordinate information of the next track data is abnormal, the coordinate correction value of the next track data can be determined according to the second information and the coordinate information of the previous track data; and the next track data can be corrected according to the coordinate correction value. For example, the coordinate information in the next track data is replaced by the coordinate correction value.
[0048] In the second embodiment, when it is determined that the azimuth information of the next trajectory data is abnormal, the azimuth correction value of the next trajectory data can be determined according to the second information and the azimuth information of the previous trajectory data; and the next trajectory data can be corrected according to the azimuth correction value. For example, the azimuth information in the next trajectory data is replaced by the azimuth correction value.
[0049] Optionally, a vehicle driving data processing method provided in an embodiment of the present application may further include: saving the corrected next trajectory data.
[0050] It can be understood that after correcting and saving the next track data with an abnormality, the accuracy of the running track displayed on the map can be improved, and the misjudgment rate of violation information by functions such as electronic fences can be reduced.
[0051] The following example takes the target vehicle as a company's official car. Figure 3 , a vehicle driving data processing method provided by another embodiment of the present application is described. The data processing method can be executed by a back-end scheduled task, without the need for front-end user intervention, and without the need to manually delete redundant data, and is a non-sensing operation for both the user and the front-end. The processing units are based on specific vehicles and specific dates, and the data between different vehicles will not be affected. The overall data processing flow includes two parts, rough processing and fine processing, and both parts of data processing can be executed by Springboot scheduled tasks. Optionally, the data processing cycle of all scheduled tasks can be the data of the day before the current date (that is, the target period is the day before the current date). The following is a detailed description.
[0052] like Figure 3 As shown, a vehicle driving data processing method provided by another embodiment of the present application may include: Step 300, start.
[0053] Step 301 , determine whether there is a vehicle application form for the target official vehicle; if so, proceed to step 302 , otherwise proceed to step 309 .
[0054] For example, the relevant approval system of the enterprise where the target official vehicle is located can be used to query the vehicle application form for the target official vehicle. If there is an approved vehicle application form for the target official vehicle, the movement trajectory data of the target official vehicle during the target period can be obtained.
[0055] In specific implementation, the interface of the third-party system (such as the official vehicle management platform) can be called to query whether there is a vehicle application form for the target official vehicle according to the license plate number and time conditions (such as the day before the target time period). If there is no approved vehicle application form, the system will enter the Figure 3 If there is a vehicle application form, enter the rough processing process shown in Figure 3 The finishing process shown.
[0056] Step 309, deleting the redundant static state data of the target official vehicle in the target time period.
[0057] Specifically, the redundant static state data of the target official vehicles that did not have a vehicle application form the day before are deleted in batches. Because the official vehicles are generally not allowed to be used without a vehicle application form in the enterprise vehicle use process, such vehicles are in a static state and there is no need to save all data. By setting a scheduled task, the same static state data of such vehicles can be saved every first time interval (such as 10 minutes), and the redundant static state data can be deleted to meet the needs of positioning and querying the vehicle position. Among them, a static state data may include azimuth information, coordinate information, cumulative mileage, cumulative fuel consumption, etc.
[0058] It can be understood that deleting the redundant static state data of the target official vehicle in the target period can reduce the amount of data stored about the target official vehicle, which can improve the query efficiency of the target official vehicle.
[0059] Step 302 , delete the redundant static state data of the target official vehicle in the target period, and then proceed to step 303 .
[0060] It is understandable that even if there is a vehicle application form, the target official vehicle may not be in motion all the time during the target period, but may be stationary during some periods of the target period. Although the stationary state data can be used as an important basis for segmenting the running trajectory, most of the continuous stationary state data is redundant data and can be deleted to further reduce the amount of data stored about the target official vehicle, which can improve the query efficiency of the target official vehicle.
[0061] In this case, deleting the redundant static state data of the target official vehicle in the target period may include: Obtaining ignition event information and flameout event information of the target official vehicle during the target time period; Determine at least one period of time during which the target official vehicle is continuously stationary based on the ignition event information and the flameout event information; For each of the at least one time period, the first and last pieces of static state data are retained, and the remaining static state data are deleted. Alternatively, for each of the at least one time period, the first and last pieces of static state data are retained, and for the data between the first and last pieces of static state data, one piece of the same static state data is retained every second time interval (such as every 5 minutes), and the remaining static state data is deleted, so as to meet the data segmentation requirements.
[0062] Step 303, obtaining the motion trajectory data of the target official vehicle in the target time period.
[0063] Optionally, the static state data and motion state data of the target official vehicle are stored in the database with static and motion as the state fields, respectively. Accordingly, the acquisition of the motion trajectory data of the target official vehicle in the target period may include: reading from the database a number of data of the target official vehicle in the target period with the state field being motion, and obtaining the motion trajectory data of the target official vehicle in the target period. That is, the motion trajectory data of the target official vehicle in the target period is filtered out from the database with the "motion" field as the operating condition, and there is a field in the database indicating whether a piece of data is static state data or motion state data.
[0064] It should be noted that a piece of motion status data (or a piece of trajectory data) may include azimuth information, coordinate information, accumulated mileage, accumulated fuel consumption, etc.
[0065] Step 304: segment the motion trajectory data to obtain at least one set of trajectory data.
[0066] In some embodiments, step 304 may include: Obtaining ignition event information and flameout event information of the target official vehicle during the target time period; The motion trajectory data is segmented according to the ignition event information and the flameout event information to obtain at least one group of trajectory data, wherein the motion trajectory data between an adjacent ignition event and a flameout event is one segment / one group.
[0067] That is, in step 304, each ignition record within the target time period can be searched by combining the ignition event information and the flameout event information, and the driving trajectory data of the corresponding time point of each ignition record can be searched in the target official vehicle driving trajectory table (the table is stored in the database) as the first trajectory data of the driving trajectory; each flameout record within the target time period can be searched, and the driving trajectory data of the corresponding time point of each flameout record can be searched in the vehicle driving trajectory table as the last data of the driving trajectory, and all driving trajectory data can be segmented and grouped, with one segment of the driving trajectory as a group.
[0068] Step 305, for each set of trajectory data, determine whether the errors of the azimuth and longitude and latitude of the next trajectory data and the previous trajectory data exceed their respective expected values; if both are no, proceed to step 310; if the error of the azimuth does not exceed the first expected value, but the error of the longitude and latitude exceeds the second expected value, proceed to step 306.
[0069] Step 306: When the error of the azimuth angle does not exceed the first expected value and the error of the longitude and latitude exceeds the second expected value, it is determined that the longitude and latitude information of the next track data is abnormal.
[0070] Step 307: modify the next piece of trajectory data according to the second information and the first information of the previous piece of trajectory data.
[0071] The second information includes the mileage information, the driving speed information and the sampling time interval of the trajectory data of the target vehicle at the collection time point of the next trajectory data.
[0072] Step 308, saving the corrected next track data.
[0073] It can be understood that if the azimuth in the next trajectory data is compared with the azimuth in the previous trajectory data, it is found that the change is within the allowable error range, but the longitude and latitude data are not within the allowable error range, which means that the longitude and latitude information in the next trajectory data is abnormal information, that is, there may be abnormal GPS positioning data caused by environmental factors. The odometer information, driving speed information and sampling time interval of the trajectory data of the target official vehicle at the collection time point corresponding to the next trajectory data can be used to calculate the next trajectory data based on the longitude and latitude information in the previous trajectory data, and the next trajectory data at the current time point replaces the original longitude and latitude values in the next trajectory data, so as to achieve the purpose of correcting the abnormal longitude and latitude information. Optionally, after the overall processing of the group of data is completed, the group of changed data information is saved back to the database.
[0074] Figure 3The illustrated embodiment proposes a vehicle driving data processing method, which, in the face of massive driving trajectory data of official vehicles and combined with vehicle application conditions, can quickly and effectively discover abnormal data and delete redundant static data, thereby improving vehicle management effects and better meeting business needs. For example, it can improve the accuracy of the display of the operating trajectory on the map, improve the query efficiency of the driving data storage database, and reduce the misjudgment rate of violation information by functions such as electronic fences, thereby improving the efficiency of official vehicle management and preventing the occurrence of official vehicles for private use.
[0075] Corresponding to a vehicle driving data processing method proposed in an embodiment of the present application, an embodiment of the present application also proposes a vehicle driving data processing device, which is described below.
[0076] like Figure 4 As shown, the embodiment of the present application also proposes a vehicle driving data processing device 400. In a software implementation, the device 400 may include: a data acquisition module 401, a data segmentation module 402, a data comparison module 403 and an abnormality judgment module 404.
[0077] The data acquisition module 401 is used to acquire the motion trajectory data of the target vehicle in the target period.
[0078] The target vehicle can be any vehicle that needs to be managed. In a specific embodiment, the target vehicle is an official vehicle.
[0079] The target period can be any period during which the target vehicle needs to be monitored. Typically, the target period can be a period of the day, such as a working period of the day.
[0080] In some embodiments, the data acquisition module 401 may directly acquire the motion trajectory data of the target vehicle in the target time period.
[0081] In some embodiments, the data acquisition module 401 may first determine whether there is vehicle application information about the target vehicle in the target time period; if the vehicle application information exists, then obtain the movement trajectory data of the target vehicle in the target time period. For example, if the target vehicle is a business vehicle of an enterprise or other institution, the vehicle application form for the target vehicle may be queried, and if there is an approved vehicle application form for the target vehicle, the movement trajectory data of the target vehicle in the target time period may be obtained.
[0082] Optionally, the device 400 may further include: a first deletion module, which is used to indicate that the target vehicle should be in a stationary state when there is no vehicle application information about the target vehicle, and to delete the redundant stationary state data of the target vehicle in the target time period. In specific implementation, it can be queried by calling the interface of a third-party system (such as an official vehicle management platform), and inquiring whether there is a vehicle application form for the target vehicle according to the license plate number and time conditions (such as the day before the target time period). If there is no approved vehicle application form, the redundant stationary state data is deleted, and if there is a vehicle application form, the movement trajectory data of the target vehicle in the target time period is obtained.
[0083] Among them, the first deletion module can be specifically used to: batch delete the redundant static state data of target vehicles without vehicle application forms in the target time period. Because in general, in the enterprise vehicle use process regulations, official vehicles cannot be used without vehicle application forms, so such vehicles are in a static state, and there is no need to save all data. By setting a scheduled task, the same static state data of such vehicles can be saved every first time interval (such as 10 minutes), and the redundant static state data can be deleted to meet the needs of positioning and querying the vehicle position. Among them, a static state data may include azimuth information, coordinate information, cumulative mileage, cumulative fuel consumption, etc.
[0084] It can be understood that deleting the redundant stationary state data of the target vehicle in the target period can reduce the amount of data stored about the target vehicle, which can improve the query efficiency of the target vehicle.
[0085] Optionally, the device 400 may further include: a second deletion module, which is used to delete the redundant static state data of the target vehicle in the target period when there is vehicle application information about the target vehicle in the target period. It can be understood that even if there is vehicle application information, the target vehicle may not be in motion all the time in the target period, but in a static state during some periods in the target period. Although the static state data can be used as an important basis for segmenting the running trajectory, most of the continuous static state data is redundant data and can be deleted to further reduce the amount of data stored about the target vehicle, which can improve the query efficiency of the target vehicle.
[0086] The second deletion module can be specifically used for: Acquire ignition event information and flameout event information of the target vehicle during the target time period; Determining at least one period of time during which the target vehicle is continuously stationary based on the ignition event information and the flameout event information; For each of the at least one time period, the first and last pieces of static state data are retained, and the remaining static state data are deleted. Alternatively, for each of the at least one time period, the first and last pieces of static state data are retained, and for the data between the first and last pieces of static state data, one piece of the same static state data is retained every second time interval (such as every 5 minutes), and the remaining static state data is deleted, so as to meet the data segmentation requirements.
[0087] Optionally, the static state data and the moving state data of the target vehicle are stored in a database with static and moving as the state fields, respectively. Accordingly, the acquisition of the moving track data of the target vehicle in the target period may include: reading from the database a number of data of the target vehicle in the target period with the state field being moving, and obtaining the moving track data of the target vehicle in the target period. That is, the moving track data of the target vehicle in the target period is filtered out from the database with the "movement" field as the operating condition, and there is a field in the database indicating whether a piece of data is static state data or moving state data.
[0088] It should be noted that a piece of motion status data (or a piece of trajectory data) may include azimuth information, coordinate information, accumulated mileage, accumulated fuel consumption, etc.
[0089] The data segmentation module 402 is used to segment the motion trajectory data to obtain at least one set of trajectory data.
[0090] In some embodiments, the data segmentation module 402 may be specifically configured to: Acquire ignition event information and flameout event information of the target vehicle during the target time period; The motion trajectory data is segmented according to the ignition event information and the flameout event information to obtain at least one group of trajectory data, wherein the motion trajectory data between an adjacent ignition event and a flameout event is one segment / one group.
[0091] The data comparison module 403 is used to compare the next trajectory data with the previous trajectory data one by one for each set of trajectory data in the at least one set of trajectory data, and determine the error between the first information in the next trajectory data and the previous trajectory data, wherein the first information includes azimuth information and coordinate information.
[0092] The azimuth information is used to characterize the driving direction of the target vehicle; the coordinate information is used to characterize the position of the target vehicle, and the coordinate information can be represented by longitude and latitude information.
[0093] In some embodiments, in the data comparison module 403, determining the error between the next piece of trajectory data and the first information in the previous piece of trajectory data may include: Determining an azimuth error between the next track data and the previous track data; and Determine the error between the coordinates in the next track data and the previous track data.
[0094] Usually, the difference between the azimuth in the next trajectory data and the previous trajectory data can be used as the error between the azimuth in the next trajectory data and the previous trajectory data; similarly, the difference between the coordinates in the next trajectory data and the previous trajectory data can be used as the error between the coordinates in the next trajectory data and the previous trajectory data. The coordinates include longitude and latitude, and the errors of the two can be calculated separately or combined together.
[0095] The abnormality judgment module 404 is used to determine that the next track data has an abnormality when the error of at least one item of the first information exceeds an expected value.
[0096] In the first embodiment, the abnormality judgment module 404 may be specifically configured to determine that the azimuth information of the next trajectory data is abnormal when the azimuth error exceeds a first expected value and the coordinate error does not exceed a second expected value.
[0097] In the second embodiment, the abnormality judgment module 404 may be specifically configured to determine that there is an abnormality in the coordinate information of the next track data when the error of the azimuth angle does not exceed the first expected value and the error of the coordinate exceeds the second expected value.
[0098] In the third embodiment, the abnormality judgment module 404 can be specifically used to: when the error of the azimuth exceeds the first expected value, and the error of the coordinate exceeds the second expected value, determine that both the azimuth information and the coordinate information of the next trajectory data are abnormal.
[0099] The first expected value and the second expected value can be preset based on experience. It can be understood that, whether it is the azimuth or the coordinate, in two adjacent trajectory data, the error between them should not be too large. If it is too large, it means that one of the trajectory data is abnormal.
[0100] It should be noted that Figure 4 The vehicle driving data processing device 400 shown can realize Figure 1 The method of the method embodiment of the present invention can achieve the same technical effect, and the specific reference can be made to the above Figure 1The introduction of the vehicle driving data processing method of the illustrated embodiment will not be repeated here.
[0101] like Figure 5 As shown, optionally, a vehicle travel data processing device 400 proposed in an embodiment of the present application may also include: an abnormality correction module 405, which is used to correct the next trajectory data according to the second information and the first information in the previous trajectory data when it is determined that the next trajectory data has an abnormality.
[0102] The second information includes the mileage information, the driving speed information and the sampling time interval of the trajectory data of the target vehicle at the collection time point of the next trajectory data.
[0103] It can be understood that the anomaly correction module 405 is intended to calculate relatively correct next trajectory data through the first information in the previous trajectory data, the mileage information and driving speed information of the target vehicle at the collection time point of the next trajectory data, and the sampling time interval of the trajectory data.
[0104] In the first embodiment, the abnormal correction module 405 can determine the coordinate correction value of the next trajectory data according to the second information and the coordinate information of the previous trajectory data, and correct the next trajectory data according to the coordinate correction value. For example, the coordinate information in the next trajectory data is replaced by the coordinate correction value.
[0105] In the second embodiment, the abnormal correction module 405 can determine the azimuth correction value of the next trajectory data according to the second information and the azimuth information of the previous trajectory data, and correct the next trajectory data according to the azimuth correction value. For example, the azimuth information in the next trajectory data is replaced by the azimuth correction value.
[0106] Optionally, a vehicle travel data processing device 400 provided in an embodiment of the present application may further include: a saving module, used to save the corrected next trajectory data.
[0107] It can be understood that after correcting and saving the next track data with an abnormality, the accuracy of the running track displayed on the map can be improved, and the misjudgment rate of violation information by functions such as electronic fences can be reduced.
[0108] Figure 6 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 6At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.
[0109] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0110] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.
[0111] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a vehicle driving data processing device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations: Obtain the motion trajectory data of the target vehicle during the target period; Segmenting the motion trajectory data to obtain at least one set of trajectory data; For each set of trajectory data in the at least one set of trajectory data, compare a next piece of trajectory data with a previous piece of trajectory data one by one to determine an error between the next piece of trajectory data and first information in the previous piece of trajectory data, wherein the first information includes azimuth information and coordinate information; When an error of at least one item of the first information exceeds an expected value, it is determined that an abnormality exists in the next track data.
[0112] The above application Figure 1The vehicle driving data processing method disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in one or more embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in one or more embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.
[0113] The electronic device may also perform Figure 1 The vehicle driving data processing method is not described in detail in this application.
[0114] The embodiment of the present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figure 1 The method of the embodiment shown is specifically used to perform the following operations: Obtain the motion trajectory data of the target vehicle during the target period; Segmenting the motion trajectory data to obtain at least one set of trajectory data; For each set of trajectory data in the at least one set of trajectory data, compare a next piece of trajectory data with a previous piece of trajectory data one by one to determine an error between the next piece of trajectory data and first information in the previous piece of trajectory data, wherein the first information includes azimuth information and coordinate information; When an error of at least one item of the first information exceeds an expected value, it is determined that an abnormality exists in the next track data.
[0115] The embodiment of the present application also proposes a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the vehicle driving data processing method provided in the embodiment of the present application.
[0116] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0117] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0120] It should be noted that the various embodiments in this application are described in a related manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0121] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0122] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A vehicle driving data processing method, characterized in that: The method comprises: Obtain the motion trajectory data of the target vehicle during the target period; Segmenting the motion trajectory data to obtain at least one set of trajectory data; For each set of trajectory data in the at least one set of trajectory data, compare a next piece of trajectory data with a previous piece of trajectory data one by one to determine an error between the next piece of trajectory data and first information in the previous piece of trajectory data, wherein the first information includes azimuth information and coordinate information; When an error of at least one item of the first information exceeds an expected value, it is determined that an abnormality exists in the next track data.
2. The method according to claim 1, characterized in that The method further comprises: When it is determined that there is an abnormality in the next trajectory data, the next trajectory data is corrected according to the second information and the first information in the previous trajectory data, wherein the second information includes the mileage information, the driving speed information and the sampling time interval of the trajectory data of the target vehicle at the collection time point of the next trajectory data.
3. The method according to claim 2, characterized in that The determining an error between the next piece of trajectory data and the first information in the previous piece of trajectory data includes: Determining an azimuth error between the next track data and the previous track data; and Determine the error between the coordinates in the next track data and the previous track data.
4. The method according to claim 3, characterized in that When the error of at least one item of the first information exceeds an expected value, determining that the next track data is abnormal includes: When the error of the azimuth angle does not exceed the first expected value and the error of the coordinate exceeds the second expected value, it is determined that there is an abnormality in the coordinate information of the next track data.
5. The method according to claim 4, characterized in that The step of modifying the next piece of trajectory data according to the second information and the first information in the previous piece of trajectory data includes: Determine a coordinate correction value of the next track data according to the second information and the coordinate information of the previous track data; The next track data is corrected according to the coordinate correction value.
6. The method according to claim 2, characterized in that The method further comprises: The corrected next track data is saved.
7. The method according to any one of claims 1 to 6, characterized in that: The step of segmenting the motion trajectory data to obtain at least one set of trajectory data includes: Acquire ignition event information and flameout event information of the target vehicle during the target time period; The motion trajectory data is segmented according to the ignition event information and the flameout event information to obtain at least one group of trajectory data, wherein the motion trajectory data between adjacent ignition events and flameout events is one segment.
8. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: The redundant stationary state data of the target vehicle in the target time period is deleted.
9. The method according to any one of claims 1 to 6, characterized in that: The step of obtaining the motion trajectory data of the target vehicle in the target period includes: Determine whether there is vehicle application information about the target vehicle in the target time period; In response to the existence of the vehicle application information, the step of obtaining the movement trajectory data of the target vehicle in the target time period is performed.
10. A vehicle travel data processing device, characterized in that: The device comprises: A data acquisition module is used to acquire the motion trajectory data of the target vehicle in the target period; A data segmentation module, used for segmenting the motion trajectory data to obtain at least one set of trajectory data; a data comparison module, for comparing, for each set of trajectory data in the at least one set of trajectory data, a next piece of trajectory data with a previous piece of trajectory data one by one, to determine an error between the next piece of trajectory data and first information in the previous piece of trajectory data, wherein the first information includes azimuth information and coordinate information; The abnormality judgment module is used to determine that the next track data has an abnormality when an error of at least one item of the first information exceeds an expected value.
11. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1-9.
12. A computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 9.
13. A computer program product, characterized in that The program product is stored in a storage medium, and the program product is executed by at least one processor to implement the method according to any one of claims 1 to 9.