Trajectory data quality optimization method and device for automobile data recorder

Abstract

The invention discloses an automobile data recorder trajectory data quality optimization method and device, and the method comprises the steps: carrying out the preprocessing of collected trajectory data, and carrying out the restoration and filtering of abnormal positioning data; performing local preliminary statistics on the repaired and filtered trajectory data according to the defined indexes, generating basic indexes, and writing the basic indexes into a message queue; and reading the data from the message queue through a big data statistical service, performing multi-dimensional statistical analysis, storing the data in a database, and performing multi-dimensional multi-report cross-cycle analysis display through an operation management system to obtain an automobile data recorder trajectory data quality optimization result. According to the method, the accuracy of the positioning data is improved, the integrity and reliability of the data are improved by means of reasonable data filtering, abnormity repairing and the like, meanwhile, the equipment software quality is improved, and the method is suitable for track management and quality control of the automobile data recorder in large-scale vehicle networking application.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking and automotive electronic technology, and in particular to a method and device for optimizing the quality of trajectory data of a driving recorder. Background Art

[0002] There are many problems with the quality of current dashcam trajectory data, such as poor positioning data quality. Common poor positioning data quality is manifested in data duplication, interruptions in time and space, inconsistent timestamps, and other issues.

[0003] These problems reduce the overall quality of the trajectory and make it impossible to provide users with accurate and reliable driving data support. For example, time interruption and space interruption problems cause the positioning trajectory to be broken, making it difficult to form a continuous and complete record.

[0004] The existing technologies for solving these problems are relatively limited. They usually adopt simple data filtering and cleaning methods, but cannot achieve global optimization of trajectory quality. Specifically:

[0005] 1. It is inconvenient to query trajectory statistics and analysis. It is necessary to wait for the device to report data for a period of time, and then the monitoring system will query the records that meet the conditions according to the time conditions. Then, the trajectory is drawn on the map and the trajectory changes are manually checked to make a unified analysis. The lag is very obvious.

[0006] 2. Checking the trajectory afterwards cannot reflect the changes in the device trajectory in real time, and it is impossible to statistically analyze whether all devices have the same problem. When the number of devices and data reaches a certain scale, it cannot support the demand for high-quality trajectories of new businesses;

[0007] 3. With the development of mobile Internet technology, smart car machines have more and more functions. In addition to conventional tracking, there will be more display requirements on the user side. The backend needs to use high-quality trajectory data to perform big data analysis and generate accurate reports that meet industry characteristics. At this time, ordinary trajectory requirements cannot meet the accuracy requirements of new businesses.

[0008] Conventional post-event trajectory analysis has obvious shortcomings. The implementation of these methods has some restrictions. It is difficult to achieve the ideal goal when conducting big data analysis on massive data.

[0009] Therefore, the prior art needs to be improved. Summary of the invention

[0010] The technical problem to be solved by the present invention is that, in view of the defects of the prior art, the present invention provides a method and device for optimizing the quality of driving recorder trajectory data, so as to solve the problem that the existing method for optimizing the quality of driving recorder trajectory data cannot achieve global optimization of trajectory quality.

[0011] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0012] In a first aspect, the present invention provides a method for optimizing the quality of driving recorder trajectory data, comprising:

[0013] Pre-process the collected trajectory data and repair and filter the abnormal positioning data;

[0014] Perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue;

[0015] The data is read from the message queue through the big data statistics service, and is stored after multi-dimensional statistical analysis. The operation management system is used to perform multi-dimensional and multi-report cross-cycle analysis and display to obtain the optimization results of the driving recorder trajectory data quality.

[0016] In one implementation, the preprocessing of the collected trajectory data and the repairing and filtering of abnormal positioning data include:

[0017] Whether the trajectory data meets the legality standard is detected from the time dimension and the space dimension.

[0018] In one implementation, the preprocessing of the collected trajectory data and repairing and filtering of abnormal positioning data include:

[0019] Preprocessing the collected trajectory data;

[0020] The data with time duplication, space duplication, time interruption and space interruption in the trajectory data are repaired, and the data beyond the repair range in the trajectory data are discarded.

[0021] In one implementation, repairing the data with time duplication, space duplication, time interruption, and space interruption in the trajectory data, and discarding the data in the trajectory data that exceeds the repair range, includes:

[0022] For the data with time duplication and time interruption in the trajectory data, the predicted time is determined by the previous and next time points and the system time point, and the predicted time is used for time repair;

[0023] For data with spatial duplication and spatial interruption in the trajectory data, interpolation points are determined by a linear algorithm, and spatial repair is performed using the interpolation points;

[0024] The data with longer positioning time and longer distance from the current point to the previous point in the trajectory data are discarded; or other data that cannot be repaired are discarded.

[0025] In one implementation, performing local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generating basic indicators and writing them into the message queue includes:

[0026] Based on the defined indicators, basic statistical calculations are performed within the service by device and organization dimensions, and the calculation results are written to the message queue.

[0027] In one implementation, the defined indicators include: one or a combination of positioning effectiveness, time continuity, space continuity, mileage continuity, time consistency, positioning real-time, trajectory change rate, trajectory reissue rate, and trajectory delivery rate.

[0028] In one implementation, the data is read from the message queue by the big data statistics service, and stored after multi-dimensional statistical analysis, including:

[0029] Perform aggregation calculation based on the defined indicator items, and perform multi-dimensional statistics based on device, organization, and date;

[0030] According to the weight of each indicator, the trajectory quality score of each device is calculated using the configured quality configuration formula;

[0031] Business indicators used to view the trajectory quality change trend of each device on different days are obtained by aggregating or merging basic indicators;

[0032] The calculation results are written to a database to generate trajectory quality reports.

[0033] In a second aspect, the present invention provides a device for optimizing the quality of vehicle driving recorder trajectory data, comprising:

[0034] The trajectory data preprocessing module is used to preprocess the collected trajectory data and repair and filter the abnormal positioning data;

[0035] The basic indicator module is used to perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue;

[0036] The multi-dimensional statistical analysis module is used to read data from the message queue through the big data statistical service, store the data after multi-dimensional statistical analysis, and perform multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the quality optimization results of the driving recorder trajectory data.

[0037] In a third aspect, the present invention provides a terminal, comprising: a processor and a memory, wherein the memory stores a driving recorder trajectory data quality optimization program, and when the driving recorder trajectory data quality optimization program is executed by the processor, it is used to implement the operation of the driving recorder trajectory data quality optimization method as described in the first aspect.

[0038] In a fourth aspect, the present invention further provides a medium, which is a computer-readable storage medium, and which stores a driving recorder trajectory data quality optimization program. When the driving recorder trajectory data quality optimization program is executed by a processor, it is used to implement the operation of the driving recorder trajectory data quality optimization method as described in the first aspect.

[0039] The present invention adopts the above technical solution to achieve the following effects:

[0040] The present invention pre-processes the collected trajectory data, and repairs and filters the abnormal positioning data. It can perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue; and read data from the message queue through the big data statistics service, perform multi-dimensional statistical analysis and then store it in the warehouse. It can perform multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the optimization results of the driving recorder trajectory data quality. The present invention not only improves the accuracy of positioning data, but also improves the integrity and reliability of data through reasonable data filtering, abnormality repair and other means, while improving the quality of equipment software, and is suitable for driving recorder trajectory management and quality control in large-scale vehicle networking applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0042] Figure 1 It is a flow chart of the method for optimizing the quality of vehicle driving recorder trajectory data in the present invention.

[0043] Figure 2 It is a functional principle diagram of a terminal in one implementation of the present invention.

[0044] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] Exemplary Methods

[0047] The existing technology has limited ways to solve these problems. It usually adopts simple data filtering and cleaning methods, but it cannot achieve global optimization of trajectory quality. Specifically, it is inconvenient to query trajectory statistics and analysis. It is necessary to wait for the device to report data for a period of time, and then the monitoring system will query the records that meet the conditions according to the time conditions. Then, the trajectory is drawn on the map and the trajectory changes are manually checked before unified analysis can be performed. The lag is very obvious. Second, by checking the trajectory afterwards, it cannot reflect the changes of the device trajectory in real time, and it is impossible to statistically analyze whether all devices have the same problem. When the number of devices and data reaches a certain scale, it cannot support the demand for high-quality trajectories of new businesses. Third, with the development of mobile Internet technology, the functions of smart car machines are increasing. In addition to conventional whereabouts tracking, there will be more display requirements on the user side. The back end needs to use high-quality trajectory data for big data analysis to generate accurate reports that meet industry characteristics. At this time, ordinary trajectory requirements cannot meet the accuracy requirements of new businesses. Conventional post-event trajectory analysis has obvious shortcomings. The implementation of these methods has some restrictions. It is difficult to achieve the ideal purpose when performing big data analysis on massive data.

[0048] In response to the above technical problems, an embodiment of the present invention provides a method for optimizing the quality of dashcam trajectory data. The method pre-processes the collected trajectory data, and repairs and filters the abnormal positioning data. The repaired and filtered trajectory data can be subjected to local preliminary statistics according to the defined indicators, and the basic indicators are generated and written into the message queue; and the data is read from the message queue through the big data statistics service, and multi-dimensional statistical analysis is performed before storage. The operation management system can perform multi-dimensional and multi-report cross-cycle analysis and display to obtain the dashcam trajectory data quality optimization results. The present invention not only improves the accuracy of positioning data, but also improves the integrity and reliability of data through reasonable data filtering, abnormality repair and other means, while improving the quality of equipment software, and is suitable for dashcam trajectory management and quality control in large-scale vehicle networking applications.

[0049] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the quality of driving recorder trajectory data, comprising the following steps:

[0050] Step S100, pre-processing the collected trajectory data, and repairing and filtering the abnormal positioning data.

[0051] In this embodiment, a method for improving the quality of dashcam trajectories is proposed. This method forms a series of indicators on trajectory quality through years of operation of mature vehicle networking systems. These indicators include: time consistency, spatial continuity, mileage continuity, etc. After forming a series of indicators on trajectory quality, these indicators are used to comprehensively evaluate and optimize the trajectory quality. Therefore, this embodiment not only improves the accuracy of positioning data, but also improves the integrity and reliability of data through reasonable data filtering, abnormal repair and other means, while improving the quality of equipment software, and is suitable for dashcam trajectory management and quality control in large-scale vehicle networking applications.

[0052] In this embodiment, the trajectory data of the vehicle during driving is collected by a driving recorder; for the trajectory data collected by the driving recorder, data legitimacy detection is also required, that is, the system first detects whether the trajectory data meets the legitimacy standard, so as to determine whether the time and space of the trajectory data collected by the driving recorder are abnormal.

[0053] Specifically, in an implementation of this embodiment, the following steps are included before step S100:

[0054] Step S101a: detecting whether the trajectory data meets the legality standard from the time dimension and the space dimension.

[0055] In this embodiment, in the process of detecting whether the trajectory data meets the legality standard, it is possible to detect whether the time of the trajectory data is close to the current system time, for example, whether the time period of the trajectory data is a preset time period before the current system time; or, whether time duplication or time interruption data appears in the trajectory data, so as to determine whether the trajectory data collected by the driving recorder is abnormal in time.

[0056] In the process of detecting whether the trajectory data meets the legality standards, it is also possible to detect whether the space of the trajectory data is consistent with the space where the current vehicle is located. For example, the space where the current vehicle is located is a parking lot and the vehicle is stationary. It is possible to detect whether the space in the trajectory data is stationary; or, detect whether there is spatial duplication or spatial interruption in the trajectory data, so as to determine whether the trajectory data collected by the driving recorder is abnormal in space.

[0057] Specifically, in an implementation of this embodiment, step S100 includes the following steps:

[0058] Step S101, preprocessing the collected trajectory data;

[0059] Step S102: repairing the data with time duplication, space duplication, time interruption and space interruption in the trajectory data, and discarding the data in the trajectory data that exceeds the repair range.

[0060] In this embodiment, after detecting whether the trajectory data meets the legality standard, the trajectory distribution system pre-processes the collected trajectory data to ensure that the trajectory data meets the storage requirements; for example, the format of the trajectory data is uniformly set, or the resolution, frame rate, duration, etc. of the trajectory data are uniformly set. After the collected trajectory data is pre-processed, it is also necessary to repair and filter the trajectory data, that is, repair the trajectory data with time duplication, space duplication, time interruption, and space interruption, and directly discard the trajectory data beyond the repair range.

[0061] Specifically, in an implementation of the present embodiment, the data with time duplication, space duplication, time interruption, and space interruption in the trajectory data are repaired, and the data beyond the repair range in the trajectory data are discarded, including: for the data with time duplication and time interruption in the trajectory data, the predicted time is determined by the previous and next time points and the system time points, and the predicted time is used for time repair; for the data with space duplication and space interruption in the trajectory data, the interpolation points are determined by the linear algorithm, and the space repair is performed by the interpolation points; the data with a longer positioning time and a longer distance from the current point to the previous point in the trajectory data are discarded; or other data that cannot be repaired are discarded.

[0062] In this embodiment, the data with time duplication and time interruption in the trajectory data are defined as data with positioning time errors. For the data with positioning time errors, the predicted time can be given by the front and back points and the system time, and then the data with positioning time errors are time-repaired according to the predicted time to ensure the accuracy of the time in the trajectory data; the data with spatial duplication and spatial interruption in the trajectory data are defined as drift data. For the drift data or individual defect data, interpolation points are given by a linear algorithm, and then the data with spatial positioning errors are spatially repaired by the interpolation points.

[0063] In addition to repairing the data with time duplication, space duplication, time interruption, and space interruption in the trajectory data, this embodiment directly discards trajectory data with a positioning time longer than the current time point, data with a current point distance from the previous point too long, or other data that cannot be repaired, that is, the data is no longer distributed to other business services.

[0064] After repairing the data with time duplication, space duplication, time interruption, and space interruption, and discarding the records beyond the repair range, in this embodiment, it is also necessary to perform a trajectory delivery rate detection, that is, to detect whether the trajectory is successfully transmitted to the remote server, so as to ensure the stability and reliability of data transmission.

[0065] In this embodiment, the continuity and integrity of the trajectory are guaranteed by repairing and filtering the abnormal time and space data, so as to facilitate comprehensive calculation through a series of trajectory indicators (such as positioning validity, time continuity, space continuity, mileage continuity, etc.).

[0066] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the quality of driving recorder trajectory data, comprising the following steps:

[0067] Step S200: Perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue.

[0068] Specifically, in an implementation of this embodiment, step S200 includes the following steps:

[0069] Step S201, according to the defined indicators, basic statistical calculations are performed within the service in terms of device dimensions and organization dimensions, and the calculation results are written into a message queue.

[0070] In this embodiment, after calculating single trajectory indicators such as data legitimacy detection, data repair and filtering, and trajectory delivery rate detection on the collected trajectory data, trajectory indicator forwarding can be performed; the trajectory indicator forwarding refers to forwarding the trajectory data to the remote server through the trajectory distribution service, specifically: the trajectory distribution service performs basic statistical calculations in the device dimension and the organization dimension within the service according to the defined indicators, and writes the calculation results into the message queue.

[0071] The defined indicators include one or a combination of positioning effectiveness, time continuity, space continuity, mileage continuity, time consistency, positioning real-time, trajectory change rate, trajectory reissue rate, and trajectory delivery rate.

[0072] Specifically, the indicators used to measure the trajectory quality in this embodiment include but are not limited to:

[0073] Positioning validity: used to filter out invalid positioning points (such as those beyond the national borders, geographical area boundaries, administrative jurisdictions, time zone anomalies, etc.) to ensure the validity of the data and accumulate the number of this indicator;

[0074] Time continuity: used to compare the difference between the positioning time and the current time; compare the time interval difference between adjacent points; if the interval between adjacent points is greater than the return interval configured by the device, the device return is judged to be abnormal, ensuring the continuity of the trajectory and avoiding interruptions due to abnormal network signals or APK restarts; and accumulate the number of this indicator;

[0075] Spatial continuity: used to check the spatial distance between adjacent points (calculate the distance between the device based on the positioning speed, the distance between the two points, and the duration between the two points. If it exceeds the reasonable distance threshold, it is judged to be interrupted) to ensure that the positioning points are continuous within a reasonable spatial range.

[0076] Mileage continuity: It is used to calculate the continuous and uninterrupted mileage, obtain the ratio of continuous mileage to total mileage, and infer the stability of trajectory reporting.

[0077] Time consistency: used to ensure the consistency between the recorded positioning time and the device system time. This item is generally used to determine the real-time nature of trajectory upload.

[0078] Positioning real-time performance: when the difference between positioning time and receiving time is within the reasonable time configured by the device (according to the feedback interval configured by the device, the latest positioning time and receiving time interval of the same batch of trajectories are greater than the feedback interval), the cumulative number of occurrences is the real-time number of positioning time.

[0079] Track change rate: It is used to detect the track change rate, that is, the speed of change of the track point. It counts the ratio of the number of non-repetitive points to the total number of tracks. The larger the ratio, the greater the track change. This is used to evaluate whether the device is in a stopped state.

[0080] Track retransmission rate: The distribution service records the last normal positioning time of each device. If the uploaded track time is less than the last positioning time, it is considered a retransmission and the number of retransmissions is accumulated. For track data uploaded out of order, the retransmission rate (the ratio of the number of retransmission tracks to the total number of tracks) is used to evaluate the stability of device data upload.

[0081] Track delivery rate: used to detect whether the track is successfully transmitted to other remote track service servers to ensure the stability and reliability of data transmission.

[0082] In this embodiment, the trajectory distribution service performs basic statistical calculations on the defined indicators in the device dimension and the organization dimension within the service, and writes the calculation results into the message queue so that the subsequent big data statistical service can perform multi-dimensional and higher-level quality assessment and analysis.

[0083] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the quality of driving recorder trajectory data, comprising the following steps:

[0084] Step S300, read data from the message queue through the big data statistics service, perform multi-dimensional statistical analysis and then store the data in the warehouse, and perform multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the quality optimization result of the driving recorder trajectory data.

[0085] Specifically, in an implementation of this embodiment, step S300 includes the following steps:

[0086] Step S301, performing aggregation calculation according to the defined indicator items, and performing multi-dimensional statistics with device, organization and date as dimensions;

[0087] Step S302, according to the weight of each indicator, the trajectory quality score of each device is calculated using the configured quality configuration formula;

[0088] Step S303, obtaining a business indicator for checking the trajectory quality change trend of each device on different days by aggregating and calculating or merging the basic indicators;

[0089] Step S304, writing the calculation results into a database to generate a trajectory quality report.

[0090] In this embodiment, after the trajectory data is forwarded to the remote server, big data statistical processing and trajectory quality report and analysis processing can be performed; wherein, the big data statistical processing means that the trajectory data quality indicators are written into the message queue after preprocessing, and the big data statistical service reads the data from the queue and performs multi-dimensional statistical analysis. Among them, in the remote server, the data is statistically analyzed by the big data statistical model, and the main processing steps include:

[0091] First, aggregate calculations are performed based on different indicator items, and multi-dimensional statistics are performed based on equipment, organization, and date.

[0092] Then, according to the weight of the indicator, the configured quality configuration formula (the quality configuration formula is: various business indicators * weight) is used to calculate the trajectory quality score of each device. Among them, the business indicators are calculated or merged by the aggregation of basic indicators, which is convenient for operators to view the trajectory quality change trend of each device on different days according to the report;

[0093] Finally, the calculation results (i.e., the trajectory quality score of each device) are written into the database so that the trajectory quality report service can generate reports.

[0094] In this embodiment, the track quality report and analysis means that the operation management system reads the track quality indicator data from the database in real time, and generates a track quality report chart according to the date and organizational structure for users to view and analyze.

[0095] It is worth mentioning that in the process of repairing the data with time duplication, space duplication, time interruption and space interruption in the trajectory data, this embodiment can also detect and repair through a more intelligent anomaly detection mechanism: for example, introducing a machine learning algorithm to further optimize the accuracy of anomaly detection and repair.

[0096] And in the process of big data statistical processing, this embodiment can also calculate the trajectory quality score of each device by dynamically adjusting the weight: for example, for different types of trajectory data, the weight of each indicator is dynamically adjusted to better adapt to different business scenarios.

[0097] In the process of trajectory quality reporting and analysis, this embodiment can also use more intelligent data analysis technology for analysis: for example, big data analysis technology is introduced to conduct more in-depth mining and analysis of trajectory data to provide users with more intelligent driving data support. Another example is the introduction of large model analysis technology to monitor the quality changes of trajectories in real time and notify operators in real time to make batch adjustments to the equipment.

[0098] The following uses actual application scenarios as examples to illustrate the method in this embodiment:

[0099] In an actual application scenario of this embodiment, the method for optimizing the quality of driving recorder trajectory data includes the following steps:

[0100] Step S11, parsing the original positioning data;

[0101] Step S12, reading device configuration cache information;

[0102] The above steps S11 to S12 are the process of positioning data processing in this embodiment.

[0103] Step S13, start data legitimacy detection;

[0104] Step S14, determine whether the positioning time is abnormal; if yes, execute step S21; if no, execute step S15;

[0105] Step S15, determine whether the positioning position is abnormal; if yes, execute step S21; if no, execute step S16;

[0106] Step S16, determine whether the positioning time is interrupted; if yes, execute step S21; if no, execute step S17;

[0107] Step S17, determine whether the positioning is interrupted; if yes, execute step S21; if no, execute step S18;

[0108] Step S18, determine whether the real-time performance, change rate, and reissue rate meet the repair conditions; if yes, execute step S19; if no, abandon the data;

[0109] Step S19, repairing the trajectory data;

[0110] Step S20, writing the trajectory into the local queue to be sent;

[0111] The above steps S13 to S20 are the process of calculating a single trajectory index in this embodiment.

[0112] Step S21, writing into local statistics cache;

[0113] Step S22, calculating the index according to the device dimension;

[0114] Step S23, writing into the big data message queue;

[0115] The above steps S21 to S23 are the process of forwarding the trajectory indicator in this embodiment.

[0116] Step S24, querying equipment, organization, and other related information;

[0117] Step S25, combining the trajectory quality data to form an analysis report;

[0118] Step S26, querying the trajectory quality score and its change;

[0119] Step S27, displaying the optimization information to the operation personnel and equipment personnel.

[0120] The above steps S24 to S26 are the process of trajectory quality reporting and analysis performed by the operation management system in this embodiment.

[0121] After years of Internet of Vehicles operation, this embodiment proposes a set of basic indicators for the core data of the trajectory, and comprehensively evaluates and optimizes the quality of the trajectory data; repairs the abnormal data in time and space to ensure the continuity and integrity of the trajectory; through real-time monitoring and analysis of the trajectory data of each device, abnormalities are discovered in time and further measures are taken to ensure the accuracy and reliability of the data; and through comprehensive calculation of a series of trajectory indicators (such as positioning effectiveness, time continuity, space continuity, mileage continuity, etc.), the overall quality of the trajectory data is improved; in actual application scenarios, this embodiment can find out potential problems in batches of equipment, facilitate terminal departments to promptly repair problems with recorder software or positioning chips, and improve data accuracy and reliability.

[0122] This embodiment achieves the following technical effects through the above technical solution:

[0123] This embodiment pre-processes the collected trajectory data, and repairs and filters the abnormal positioning data. It can perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue; and read data from the message queue through the big data statistics service, perform multi-dimensional statistical analysis and then store it in the warehouse. It can perform multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the optimization results of the dashcam trajectory data quality. This embodiment not only improves the accuracy of positioning data, but also improves the integrity and reliability of data through reasonable data filtering, abnormality repair and other means, while improving the quality of equipment software, and is suitable for dashcam trajectory management and quality control in large-scale vehicle networking applications.

[0124] Exemplary Devices

[0125] Based on the above embodiments, the present invention further provides a device for optimizing the quality of vehicle driving recorder trajectory data, comprising:

[0126] The trajectory data preprocessing module is used to preprocess the collected trajectory data and repair and filter the abnormal positioning data;

[0127] The basic indicator module is used to perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue;

[0128] The multi-dimensional statistical analysis module is used to read data from the message queue through the big data statistical service, store the data after multi-dimensional statistical analysis, and perform multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the quality optimization results of the driving recorder trajectory data.

[0129] This embodiment achieves the following technical effects through the above technical solution:

[0130] This embodiment pre-processes the collected trajectory data, and repairs and filters the abnormal positioning data. It can perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue; and read data from the message queue through the big data statistics service, perform multi-dimensional statistical analysis and then store it in the warehouse. It can perform multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the optimization results of the dashcam trajectory data quality. This embodiment not only improves the accuracy of positioning data, but also improves the integrity and reliability of data through reasonable data filtering, abnormality repair and other means, while improving the quality of equipment software, and is suitable for dashcam trajectory management and quality control in large-scale vehicle networking applications.

[0131] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be as follows: Figure 2 shown.

[0132] The terminal includes: a processor, a memory, an interface, a display screen and a communication module connected through a system bus; wherein the processor of the terminal is used to provide computing and control capabilities; the memory of the terminal includes a storage medium and an internal memory; the storage medium stores an operating system and a computer program; the internal memory provides an environment for the operation of the operating system and the computer program in the storage medium; the interface is used to connect to external devices; the display screen is used to display corresponding information; and the communication module is used to communicate with a cloud server or other devices.

[0133] When the computer program is executed by a processor, it is used to implement the operation of the method for optimizing the quality of the vehicle driving recorder trajectory data.

[0134] It can be understood by those skilled in the art that Figure 2 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the terminal to which the scheme of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] In one embodiment, a terminal is provided, which includes: a processor and a memory, wherein the memory stores a driving recorder trajectory data quality optimization program, and the driving recorder trajectory data quality optimization program is used to implement the operation of the driving recorder trajectory data quality optimization method as described above when executed by the processor.

[0136] In one embodiment, a storage medium is provided, wherein the storage medium stores a driving recorder trajectory data quality optimization program, and the driving recorder trajectory data quality optimization program is used to implement the operation of the driving recorder trajectory data quality optimization method as described above when executed by a processor.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and volatile memory.

[0138] In summary, the present invention provides a method and device for optimizing the quality of dashcam trajectory data, including: preprocessing the collected trajectory data, repairing and filtering the abnormal positioning data; performing local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generating basic indicators and writing them into the message queue; reading data from the message queue through the big data statistics service, performing multi-dimensional statistical analysis and then storing it in the warehouse, and performing multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the dashcam trajectory data quality optimization results. The present invention not only improves the accuracy of positioning data, but also improves the integrity and reliability of data through reasonable data filtering, abnormality repair and other means, while improving the quality of equipment software, and is suitable for dashcam trajectory management and quality control in large-scale vehicle networking applications.

[0139] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for optimizing the quality of vehicle driving recorder trajectory data, characterized in that: include: Pre-process the collected trajectory data and repair and filter the abnormal positioning data; Perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue; The data is read from the message queue through the big data statistics service, and is stored after multi-dimensional statistical analysis. The operation management system is used to perform multi-dimensional and multi-report cross-cycle analysis and display to obtain the optimization results of the driving recorder trajectory data quality.

2. The method for optimizing the quality of driving recorder trajectory data according to claim 1, characterized in that: The preprocessing of the collected trajectory data, and repairing and filtering of abnormal positioning data, previously includes: Whether the trajectory data meets the legality standard is detected from the time dimension and the space dimension.

3. The method for optimizing the quality of driving recorder trajectory data according to claim 1, characterized in that: The preprocessing of the collected trajectory data, and repairing and filtering of abnormal positioning data, include: Preprocessing the collected trajectory data; The data with time duplication, space duplication, time interruption and space interruption in the trajectory data are repaired, and the data beyond the repair range in the trajectory data are discarded.

4. The method for optimizing the quality of driving recorder trajectory data according to claim 2, characterized in that: The repairing of the data with time duplication, space duplication, time interruption, and space interruption in the trajectory data, and discarding the data in the trajectory data that exceeds the repair range, includes: For the data with time duplication and time interruption in the trajectory data, the predicted time is determined by the previous and next time points and the system time point, and the predicted time is used for time repair; For data with spatial duplication and spatial interruption in the trajectory data, interpolation points are determined by a linear algorithm, and spatial repair is performed using the interpolation points; The data with longer positioning time and longer distance from the current point to the previous point in the trajectory data are discarded; or other data that cannot be repaired are discarded.

5. The method for optimizing the quality of driving recorder trajectory data according to claim 1, characterized in that: The method of performing local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators and generating basic indicators to write into the message queue includes: Based on the defined indicators, basic statistical calculations are performed within the service by device and organization dimensions, and the calculation results are written to the message queue.

6. The method for optimizing the quality of driving recorder trajectory data according to claim 1 or 5, characterized in that: The defined indicators include: one or a combination of positioning effectiveness, time continuity, space continuity, mileage continuity, time consistency, positioning real-time, trajectory change rate, trajectory reissue rate, and trajectory delivery rate.

7. The method for optimizing the quality of driving recorder trajectory data according to claim 1, characterized in that: The data is read from the message queue through the big data statistics service, and stored after multi-dimensional statistical analysis, including: Perform aggregation calculation based on the defined indicator items, and perform multi-dimensional statistics based on device, organization, and date; According to the weight of each indicator, the trajectory quality score of each device is calculated using the configured quality configuration formula; Business indicators used to view the trajectory quality change trend of each device on different days are obtained by aggregating or merging basic indicators; The calculation results are written to a database to generate trajectory quality reports.

8. A device for optimizing the quality of vehicle driving recorder trajectory data, characterized in that: include: The trajectory data preprocessing module is used to preprocess the collected trajectory data and repair and filter the abnormal positioning data; The basic indicator module is used to perform local preliminary statistics on the repaired and filtered trajectory data according to the defined indicators, generate basic indicators and write them into the message queue; The multi-dimensional statistical analysis module is used to read data from the message queue through the big data statistical service, store the data after multi-dimensional statistical analysis, and perform multi-dimensional and multi-report cross-cycle analysis and display through the operation management system to obtain the quality optimization results of the driving recorder trajectory data.

9. A terminal, characterized in that: include: A processor and a memory, wherein the memory stores a driving recorder trajectory data quality optimization program, and when the driving recorder trajectory data quality optimization program is executed by the processor, it is used to implement the operation of the driving recorder trajectory data quality optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a driving recorder trajectory data quality optimization program, and when the driving recorder trajectory data quality optimization program is executed by the processor, it is used to implement the operation of the driving recorder trajectory data quality optimization method as described in any one of claims 1-7.

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