Driving trajectory analysis method, device, equipment and storage medium

Abstract

The present invention discloses a driving trajectory analysis method, device, equipment and storage medium, the method comprising: confirming abnormal screening conditions according to abnormal conditions of a vehicle fleet; performing trajectory analysis on the abnormal screening conditions based on a driving trajectory analysis model to obtain trajectory analysis results, the driving trajectory analysis model using vehicle migration data of the vehicle fleet as input, obtaining multi-vehicle traffic data information by performing divergent analysis on single-vehicle data; and identifying vehicle fleet abnormalities through the trajectory analysis results. The present invention confirms abnormal screening conditions according to abnormal conditions of the vehicle fleet, performs driving trajectory analysis based on the driving trajectory analysis model, and identifies vehicle fleet abnormalities through the trajectory analysis results. Compared with traditional trajectory analysis methods, the method of analyzing through the driving trajectory analysis model can monitor the distribution range, status and historical operation trajectory of vehicles, realize the storage and analysis of the ever-increasing Internet of Vehicles data, and meet the actual needs of large-scale fleet analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent vehicle traffic management, and in particular to a vehicle trajectory analysis method, device, equipment and storage medium. Background Art

[0002] To help logistics companies and large fleets better manage vehicle delivery timeliness, safety, and cost control, they need to analyze vehicle movement. During operation, vehicles transmit operational data via onboard equipment back to the connected vehicle platform for analysis, storage, and review. The increasing number of vehicle terminals, high-frequency data collection methods, and persistent storage requirements have driven the demand for vehicle trajectory data analysis applications among commercial vehicle logistics companies and large fleets.

[0003] Currently, vehicle trajectory data is affected by a variety of factors, and data quality can be subpar. Secondly, due to a lack of standardization, data formats and content can vary across suppliers and vehicles, making unified analysis difficult. Furthermore, processing vehicle trajectory data is challenging, requiring specialized skills and tools. Therefore, a vehicle trajectory analysis method is urgently needed to store and analyze the ever-growing volume of connected vehicle data, addressing the practical needs of logistics companies and large fleets for big data analysis.

[0004] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present invention is to provide a vehicle trajectory analysis method, device, equipment and storage medium, aiming to solve the technical problems in the prior art that the processing of vehicle trajectory data is difficult and accurate vehicle trajectory analysis is difficult.

[0006] To achieve the above object, the present invention provides a vehicle trajectory analysis method, which comprises the following steps:

[0007] Confirm abnormal screening conditions based on fleet abnormalities;

[0008] Performing trajectory analysis on the abnormal screening condition based on a driving trajectory analysis model to obtain a trajectory analysis result, wherein the driving trajectory analysis model uses fleet vehicle migration data as input and obtains multi-vehicle traffic flow data information by performing divergent analysis on single-vehicle data;

[0009] Fleet anomaly identification is performed based on the trajectory analysis results.

[0010] Optionally, the method further includes:

[0011] Monitor the traffic migration data corresponding to the fleet;

[0012] storing the traffic flow migration data in a first database, and using the data in the first database as a data source;

[0013] Pre-calculate the data source based on data dimensions and data metrics to obtain a bicycle data cube;

[0014] Storing the bicycle data cube in a second database;

[0015] A multi-dimensional analysis is performed on the single-vehicle data cube in the second database to obtain multi-vehicle traffic data information and a driving trajectory analysis model.

[0016] Optionally, the step of pre-calculating the data source based on data dimensions and data metrics to obtain a bicycle data cube includes:

[0017] Obtaining the migration data and positioning data corresponding to the bicycle from the data source;

[0018] The migration data and positioning data are multidimensionalized based on data dimensions and data metrics to obtain a bicycle data cube.

[0019] Optionally, the step of performing multi-dimensional analysis on the single-vehicle data cube in the second database to obtain multi-vehicle traffic flow data information includes:

[0020] Obtaining the bicycle data cube in the second database;

[0021] Integrate the positioning data corresponding to the bicycle data cube as global positioning data;

[0022] Based on the global positioning data, a multi-dimensional analysis is performed on the single-vehicle data cube to obtain multi-vehicle traffic data information.

[0023] Optionally, the method further includes:

[0024] detecting the traffic migration data;

[0025] When the traffic migration data changes, the REST API is used to automatically trigger the incremental construction of the corresponding data cube;

[0026] The driving trajectory analysis model is updated based on the incremental construction.

[0027] Optionally, the step of confirming abnormal screening conditions according to the abnormal situation of the fleet includes:

[0028] Determine the abnormal scope based on the abnormal situation of the fleet;

[0029] The abnormality screening condition is determined according to the type of the abnormal range and the time interval corresponding to the abnormal situation.

[0030] Optionally, the step of determining the abnormal range according to the abnormal situation of the fleet includes:

[0031] Determine the corresponding vehicle type, vehicle model condition, engine, horsepower and drive mode based on the abnormal situation of the fleet;

[0032] The abnormal range is determined based on the vehicle type, vehicle model condition, engine, horsepower and driving form.

[0033] In addition, to achieve the above-mentioned purpose, the present invention further proposes a vehicle trajectory analysis device, the device comprising:

[0034] Condition confirmation module, used to confirm abnormal screening conditions based on abnormal conditions of the fleet;

[0035] a trajectory analysis module, configured to perform trajectory analysis on the abnormal screening condition based on a vehicle trajectory analysis model to obtain a trajectory analysis result, wherein the vehicle trajectory analysis model uses fleet vehicle migration data as input and obtains multi-vehicle traffic flow data information by performing divergent analysis on single-vehicle data;

[0036] The anomaly identification module is used to identify fleet anomalies based on the trajectory analysis results.

[0037] In addition, to achieve the above-mentioned purpose, the present invention also proposes a driving trajectory analysis device, which includes: a memory, a processor, and a driving trajectory analysis program stored in the memory and executable on the processor, wherein the driving trajectory analysis program is configured to implement the steps of the driving trajectory analysis method described above.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a driving trajectory analysis program is stored. When the driving trajectory analysis program is executed by a processor, the steps of the driving trajectory analysis method described above are implemented.

[0039] The present invention discloses a driving trajectory analysis method, device, equipment and storage medium, the method comprising: confirming abnormal screening conditions according to abnormal conditions of a vehicle fleet; performing trajectory analysis on the abnormal screening conditions based on a driving trajectory analysis model to obtain trajectory analysis results, the driving trajectory analysis model using vehicle migration data of the vehicle fleet as input, obtaining multi-vehicle traffic data information by performing divergent analysis on single-vehicle data; and identifying vehicle fleet abnormalities through the trajectory analysis results. The present invention confirms abnormal screening conditions according to abnormal conditions of the vehicle fleet, performs driving trajectory analysis based on the driving trajectory analysis model, and identifies vehicle fleet abnormalities through the trajectory analysis results. Compared with traditional trajectory analysis methods, the method of analyzing through the driving trajectory analysis model can monitor the distribution range, status and historical operation trajectory of vehicles, realize the storage and analysis of the ever-increasing Internet of Vehicles data, and meet the actual needs of large-scale fleet analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of the structure of a vehicle trajectory analysis device in a hardware operating environment according to an embodiment of the present invention;

[0041] Figure 2 This is a flow chart of a first embodiment of a vehicle trajectory analysis method according to the present invention;

[0042] Figure 3 A schematic diagram of the functional architecture of a vehicle trajectory analysis model according to a first embodiment of the vehicle trajectory analysis method of the present invention;

[0043] Figure 4 This is a flow chart of a second embodiment of the vehicle trajectory analysis method of the present invention;

[0044] Figure 5 Schematic diagram of the flow of the third embodiment of the vehicle trajectory analysis method of the present invention;

[0045] Figure 6 This is a structural block diagram of the first embodiment of the vehicle trajectory analysis device of the present invention.

[0046] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a vehicle trajectory analysis device in the hardware operating environment involved in an embodiment of the present invention.

[0049] like Figure 1As shown, the driving trajectory analysis device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 1003 may optionally include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the vehicle trajectory analysis device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0051] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module and a driving trajectory analysis program.

[0052] exist Figure 1 In the driving trajectory analysis device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the driving trajectory analysis device of the present invention can be set in the driving trajectory analysis device. The driving trajectory analysis device calls the driving trajectory analysis program stored in the memory 1005 through the processor 1001 and executes the driving trajectory analysis method provided by the embodiment of the present invention.

[0053] The embodiment of the present invention provides a method for analyzing vehicle trajectory. Figure 2 , Figure 2 FIG. 1 is a flow chart of a first embodiment of a vehicle trajectory analysis method according to the present invention.

[0054] In this embodiment, the driving trajectory analysis method includes the following steps:

[0055] Step S10: confirming abnormal screening conditions according to the abnormal situation of the fleet.

[0056] It should be noted that the execution entity of the method of this embodiment can be a trajectory analysis device with data processing, network communication, and program execution functions, such as a vehicle trajectory analysis device; it can also be other electronic devices with the same or similar functions, or a vehicle trajectory analysis system equipped with such an electronic device. This embodiment and the following embodiments will use the vehicle trajectory analysis device as the execution entity to illustrate the vehicle trajectory analysis method of the present invention.

[0057] It is understandable that a fleet anomaly can be a situation where a vehicle breaks down in the fleet. For example: Vehicle loss of connection or lost: The vehicle may lose connection due to poor signal or other reasons and be unable to provide real-time location information in a timely manner, or get lost and be unable to reach the destination on time. Vehicle breakdown or accident: The vehicle may be unable to continue driving due to mechanical failure or traffic accident and require repair or rescue. Vehicle violation: The vehicle may violate traffic regulations due to speeding, fatigue driving, severe overloading, etc., causing safety hazards or economic losses. Cargo loss or damage: Cargo may be stolen, damaged or lost during transportation, causing economic losses and customer dissatisfaction.

[0058] It should be understood that based on the vehicle type, vehicle model conditions, engine, horsepower, drive mode, and search time range, the vehicle's migration trajectory during that period will be reproduced on the map. Reproducing the traffic migration trajectory will help company personnel conduct fault analysis on faulty vehicles, conduct route and road condition analysis, and locate problems in a timely manner, thereby confirming abnormal screening conditions and further optimizing solutions.

[0059] Step S20: performing trajectory analysis on the abnormal screening condition based on a driving trajectory analysis model to obtain a trajectory analysis result. The driving trajectory analysis model uses fleet vehicle migration data as input and obtains multi-vehicle traffic flow data information by performing divergent analysis on single-vehicle data.

[0060] It should be noted that the development of the driving trajectory analysis model adopts the browser and server architecture (B / S architecture) and the front-end and back-end separation technology architecture.

[0061] See also Figure 3 , Figure 3This is a functional architecture diagram of the driving trajectory analysis model of the first embodiment of the driving trajectory analysis method of the present invention. Among them, in the view layer, Hypertext Markup Language (html) and WebServiceAPI technology are mainly used. The Web layer includes a control layer, a business logic layer and an interface layer, which separates the logical functions. This is a typical design pattern of a software system. The view layer uses Spring Boot to write the path mapping of the management layer (Controller) and related logical functions to return the JavaScript Object Notation (JSON) data related to the Web side, including the production information, driving data, etc. of the vehicle. The interface layer uses the Java Database Connectivity (JDBC) of the Remote Dictionary Server (Redis) to access the data in the cube, and uses the REST API to automatically trigger the incremental construction of the cube.

[0062] Step S30: performing fleet abnormality identification based on the trajectory analysis results.

[0063] It is understandable that the driving trajectory analysis device reproduces the migration trajectory of abnormal vehicles during the period on the map based on the driving trajectory analysis results to identify fleet anomalies.

[0064] In a specific implementation, for example, if an abnormal vehicle is discovered during a fleet transport, data on the abnormal vehicle during this transport process is obtained and extracted as anomaly screening criteria. This anomaly screening criteria is analyzed based on a vehicle trajectory analysis model to obtain the abnormal vehicle's latitude and longitude and other driving data during the transport process. Using a "map + one-way migration route map" visualization method, the abnormal vehicle data is distributed to regional vehicles, ultimately obtaining global data on the abnormal vehicle during the transport process, and anomaly identification is performed based on this global data.

[0065] In this embodiment, abnormal screening conditions are confirmed based on abnormal conditions of the fleet; trajectory analysis is performed on the abnormal screening conditions based on a driving trajectory analysis model to obtain trajectory analysis results, the driving trajectory analysis model uses fleet vehicle migration data as input, and obtains multi-vehicle traffic data information by performing divergent analysis on single-vehicle data; and fleet abnormality identification is performed based on the trajectory analysis results. The present invention confirms abnormal screening conditions based on abnormal conditions of the fleet, performs driving trajectory analysis based on a driving trajectory analysis model, and identifies fleet abnormality based on the trajectory analysis results. Compared with traditional trajectory analysis methods, the method of analysis using the driving trajectory analysis model can monitor the distribution range, status, and historical operation trajectory of vehicles, realize the storage and analysis of the ever-growing Internet of Vehicles data, and meet the actual needs of large-scale fleet analysis.

[0066] refer to Figure 4 , Figure 4 FIG. 4 is a flow chart of a second embodiment of a vehicle trajectory analysis method according to the present invention.

[0067] Furthermore, based on the first embodiment above, in this embodiment, the vehicle trajectory analysis method further includes:

[0068] Step S201: monitoring the traffic migration data corresponding to the fleet.

[0069] It should be noted that vehicle migration data can be data about vehicles in a fleet during transportation, typically including vehicle type, vehicle model, engine, horsepower, and drive type. This information can be used to determine the vehicle range, and the search time is added to determine the data time. Combined with the migration data, a corresponding migration map is generated.

[0070] Step 202: Storing the traffic flow migration data in a first database, and using the data in the first database as a data source.

[0071] It is understood that the first database can be a lightweight database. The first database includes a MySQL database, which can be used to provide more query capabilities and facilitate multi-condition query statistics as a storage for migration data. The driving trajectory analysis device stores the traffic migration data in the first database and uses the data in the first database as a data source.

[0072] Step S203: Pre-calculate the data source based on the data dimension and data metric to obtain a bicycle data cube.

[0073] It should be noted that the data source is pre-calculated based on data dimensions and data metrics to obtain a bicycle data cube, which can be obtained by the driving trajectory analysis device performing data multidimensionalization on the data source based on data dimensions and data metrics to obtain a bicycle data cube.

[0074] Furthermore, in order to obtain accurate data corresponding to the bicycle during the fleet transportation process, the step S203 also includes: obtaining the migration data and positioning data corresponding to the bicycle in the data source; performing data multidimensional processing on the migration data and positioning data based on data dimensions and data metrics to obtain a bicycle data cube.

[0075] It is understandable that building a single-vehicle data cube model based on data dimensions and data metrics can usually be done using tools such as online analytical processing (OLAP). By multidimensionalizing the precise data corresponding to each vehicle, a single-vehicle data cube is obtained.

[0076] Step S204: storing the bicycle data cube in a second database.

[0077] It should be noted that the second database can be a distributed database. The second database includes an HBase database, which can be used for mass data storage related services. The precise bicycle data cube can be stored in the second database.

[0078] It should be understood that the data computing layer uses Redis to obtain vehicle driving data on the MySQL database, and generates a single vehicle data cube (OLAP Cube) and finally stores it in the HBase database in the form of key-value pairs.

[0079] Step S205: performing a multi-dimensional analysis on the single-vehicle data cube in the second database to obtain multi-vehicle traffic flow data information and obtain a vehicle trajectory analysis model.

[0080] Furthermore, the precise data of multiple bicycles are dispersed to multiple vehicles and multiple areas. Step S205 also includes: obtaining the bicycle data cube in the second database; integrating the positioning data corresponding to the bicycle data cube as global positioning data; and performing multi-dimensional analysis on the bicycle data cube based on the global positioning data to obtain multi-vehicle traffic data information.

[0081] It is understandable that the driving trajectory analysis device obtains the bicycle data cube in the second database, and the driving trajectory analysis device integrates the positioning data corresponding to the bicycle data cube as global positioning data; the driving trajectory analysis device performs multi-dimensional analysis on the bicycle data cube based on the global positioning data to obtain multi-vehicle traffic data information.

[0082] In this embodiment, the traffic migration data corresponding to the fleet is monitored; the traffic migration data is stored in a first database, and the data in the first database is used as a data source; the data source is pre-calculated based on data dimensions and data metrics to obtain a single-vehicle data cube; the single-vehicle data cube is stored in a second database; a multi-dimensional analysis is performed on the single-vehicle data cube in the second database to obtain multi-vehicle traffic data information and a driving trajectory analysis model. This embodiment monitors the traffic migration data and stores it in a first database, performs pre-calculation based on the data source in the first database, and performs a multi-dimensional analysis on the calculation results. It is possible to obtain more accurate driving trajectory data during the fleet transportation process.

[0083] refer to Figure 5 , Figure 5 FIG. 4 is a flow chart of a third embodiment of a vehicle trajectory analysis method according to the present invention.

[0084] Based on the above embodiments, in this embodiment, step S10 includes:

[0085] Step S101: Determine the abnormal range according to the abnormal situation of the fleet.

[0086] It should be understood that the driving trajectory analysis device determines the possible scope of abnormal conditions based on the abnormal conditions of the fleet, such as abnormal positioning, abnormal driving speed, and abnormal vehicle status.

[0087] Furthermore, in order to determine the accurate abnormal situation, the step S101 further includes:

[0088] Step S1011: Determine the corresponding vehicle type, vehicle model condition, engine, horsepower and drive mode according to the abnormal situation of the fleet.

[0089] Step S1012: Determine the abnormal range based on the vehicle type, vehicle model condition, engine, horsepower and driving mode.

[0090] It is understood that the vehicle trajectory analysis device determines the vehicle data corresponding to the abnormal situation based on the abnormal situation of the fleet, including vehicle type, vehicle model condition, engine, horsepower, and driving mode, etc. The abnormal range is further determined based on the vehicle data corresponding to the abnormal situation.

[0091] Step S102: determining an abnormality screening condition according to the type of the abnormal range and the time interval corresponding to the abnormal situation.

[0092] It should be noted that the vehicle trajectory analysis device determines the anomaly screening criteria based on the type of the anomaly range and the time interval corresponding to the anomaly. By accurately screening the anomaly based on the vehicle trajectory analysis model, the device monitors the real-time distribution range, real-time status, and historical operation trajectory of all vehicles, enabling the storage and analysis of the ever-growing volume of Internet of Vehicles data.

[0093] In a specific implementation, the vehicle trajectory analysis device determines the corresponding vehicle type, vehicle model conditions, engine, horsepower, and drive mode based on the fleet abnormality. The vehicle trajectory analysis device determines the abnormal range based on the vehicle type, vehicle model conditions, engine, horsepower, and drive mode. The vehicle trajectory analysis device determines abnormality screening conditions based on the type of the abnormal range and the time interval corresponding to the abnormal situation.

[0094] In this embodiment, the corresponding vehicle type, vehicle model condition, engine, horsepower, and drive mode are determined based on the fleet abnormality; the abnormal range is determined based on the vehicle type, vehicle model condition, engine, horsepower, and drive mode; and the abnormality screening conditions are determined based on the type of the abnormal range and the time interval corresponding to the abnormality. This embodiment determines the abnormality screening conditions based on the type of the abnormal range and the time interval corresponding to the abnormality, further achieving more accurate fleet abnormality identification.

[0095] In addition, an embodiment of the present invention further provides a storage medium, on which a driving trajectory analysis program is stored. When the driving trajectory analysis program is executed by a processor, the steps of the driving trajectory analysis method described above are implemented.

[0096] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the vehicle trajectory analysis device of the present invention.

[0097] like Figure 6 As shown, the vehicle trajectory analysis device proposed in the embodiment of the present invention includes: a condition confirmation module 501, a trajectory analysis module 502 and an abnormality identification module 503.

[0098] The condition confirmation module 501 is used to confirm abnormal screening conditions according to abnormal conditions of the fleet.

[0099] It is understandable that a fleet anomaly can be a situation where a vehicle breaks down in the fleet. For example: Vehicle loss of connection or lost: The vehicle may lose connection due to poor signal or other reasons and be unable to provide real-time location information in a timely manner, or get lost and be unable to reach the destination on time. Vehicle breakdown or accident: The vehicle may be unable to continue driving due to mechanical failure or traffic accident and require repair or rescue. Vehicle violation: The vehicle may violate traffic regulations due to speeding, fatigue driving, severe overloading, etc., causing safety hazards or economic losses. Cargo loss or damage: Cargo may be stolen, damaged or lost during transportation, causing economic losses and customer dissatisfaction.

[0100] It should be understood that based on the vehicle type, vehicle model conditions, engine, horsepower, drive mode, and search time range, the vehicle's migration trajectory during that period will be reproduced on the map. Reproducing the traffic migration trajectory will help company personnel conduct fault analysis on faulty vehicles, conduct route and road condition analysis, and locate problems in a timely manner, thereby confirming abnormal screening conditions and further optimizing solutions.

[0101] The trajectory analysis module 502 is configured to perform trajectory analysis on the abnormal screening condition based on a driving trajectory analysis model to obtain a trajectory analysis result. The driving trajectory analysis model uses fleet vehicle migration data as input and obtains multi-vehicle traffic flow data information by performing divergent analysis on single-vehicle data.

[0102] It should be noted that the development of the driving trajectory analysis model adopts the browser and server architecture (B / S architecture) and the front-end and back-end separation technology architecture.

[0103] See also Figure 3 , Figure 3This is a functional architecture diagram of the driving trajectory analysis model of the first embodiment of the driving trajectory analysis method of the present invention. Among them, in the view layer, Hypertext Markup Language (html) and WebServiceAPI technology are mainly used. The Web layer includes a control layer, a business logic layer and an interface layer, which separates the logical functions. This is a typical design pattern of a software system. The view layer uses Spring Boot to write the path mapping of the management layer (Controller) and related logical functions to return the JavaScript Object Notation (JSON) data related to the Web side, including the production information, driving data, etc. of the vehicle. The interface layer uses the Java Database Connectivity (JDBC) of the Remote Dictionary Server (Redis) to access the data in the cube, and uses the REST API to automatically trigger the incremental construction of the cube.

[0104] The anomaly identification module 503 is used to identify fleet anomalies based on the trajectory analysis results.

[0105] It is understandable that the driving trajectory analysis device reproduces the migration trajectory of abnormal vehicles during the period on the map based on the driving trajectory analysis results to identify fleet anomalies.

[0106] In a specific implementation, for example, if an abnormal vehicle is discovered during a fleet transport, data on the abnormal vehicle during this transport process is obtained and extracted as anomaly screening criteria. This anomaly screening criteria is analyzed based on a vehicle trajectory analysis model to obtain the abnormal vehicle's latitude and longitude and other driving data during the transport process. Using a "map + one-way migration route map" visualization method, the abnormal vehicle data is distributed to regional vehicles, ultimately obtaining global data on the abnormal vehicle during the transport process, and anomaly identification is performed based on this global data.

[0107] In this embodiment, abnormal screening conditions are confirmed based on abnormal conditions of the fleet; trajectory analysis is performed on the abnormal screening conditions based on a driving trajectory analysis model to obtain trajectory analysis results, the driving trajectory analysis model uses fleet vehicle migration data as input, and obtains multi-vehicle traffic data information by performing divergent analysis on single-vehicle data; and fleet abnormality identification is performed based on the trajectory analysis results. The present invention confirms abnormal screening conditions based on abnormal conditions of the fleet, performs driving trajectory analysis based on a driving trajectory analysis model, and identifies fleet abnormality based on the trajectory analysis results. Compared with traditional trajectory analysis methods, the method of analysis using the driving trajectory analysis model can monitor the distribution range, status, and historical operation trajectory of vehicles, realize the storage and analysis of the ever-growing Internet of Vehicles data, and meet the actual needs of large-scale fleet analysis.

[0108] Other embodiments or specific implementations of the vehicle trajectory analysis device of the present invention can refer to the above-mentioned method embodiments and will not be described in detail here.

[0109] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0110] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0111] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0112] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A driving trajectory analysis method, characterized in that: The driving trajectory analysis method comprises the following steps: Confirm abnormal screening conditions based on fleet abnormalities; A trajectory analysis is performed on the abnormal screening condition based on a driving trajectory analysis model to obtain a trajectory analysis result. The driving trajectory analysis model uses fleet vehicle migration data as input and obtains multi-vehicle traffic data information by performing divergent analysis on single-vehicle data. The trajectory analysis result is the driving data of the abnormal vehicle during transportation, and the driving data includes longitude and latitude. Performing fleet anomaly identification based on the trajectory analysis results; The step of confirming abnormal screening conditions according to the abnormal situation of the fleet includes: Determine the corresponding vehicle type, vehicle model condition, engine, horsepower and drive mode based on the abnormal situation of the fleet; Determine the abnormal range based on the vehicle type, vehicle model condition, engine, horsepower and drive mode; The abnormality screening condition is determined according to the type of the abnormal range and the time interval corresponding to the abnormal situation.

2. The method according to claim 1, wherein The method further comprises: Monitor the traffic migration data corresponding to the fleet; storing the traffic flow migration data in a first database, and using the data in the first database as a data source; Pre-calculate the data source based on data dimensions and data metrics to obtain a bicycle data cube; Storing the bicycle data cube in a second database; A multi-dimensional analysis is performed on the single-vehicle data cube in the second database to obtain multi-vehicle traffic data information and a driving trajectory analysis model.

3. The method according to claim 2, wherein The step of pre-calculating the data source based on the data dimension and the data metric to obtain the bicycle data cube includes: Obtaining the migration data and positioning data corresponding to the bicycle from the data source; The migration data and positioning data are multidimensionalized based on data dimensions and data metrics to obtain a bicycle data cube.

4. The method according to claim 2, wherein The step of performing a multi-dimensional analysis on the single-vehicle data cube in the second database to obtain multi-vehicle traffic flow data information includes: Obtaining the bicycle data cube in the second database; Integrate the positioning data corresponding to the bicycle data cube as global positioning data; Based on the global positioning data, a multi-dimensional analysis is performed on the single-vehicle data cube to obtain multi-vehicle traffic data information.

5. The method according to claim 2, wherein The method further comprises: detecting the traffic migration data; When the traffic migration data changes, the REST API is used to automatically trigger the incremental construction of the corresponding data cube; The driving trajectory analysis model is updated based on the incremental construction.

6. A vehicle trajectory analysis device, characterized in that: The device comprises: Condition confirmation module, used to confirm abnormal screening conditions based on abnormal conditions of the fleet; a trajectory analysis module, configured to perform trajectory analysis on the abnormal screening conditions based on a driving trajectory analysis model to obtain a trajectory analysis result. The driving trajectory analysis model uses fleet vehicle migration data as input and obtains multi-vehicle traffic flow data information by performing divergent analysis on single-vehicle data. The trajectory analysis result is the driving data of the abnormal vehicle during transportation, and the driving data includes longitude and latitude; an anomaly identification module, configured to identify anomalies in the fleet based on the trajectory analysis results; Among them, the condition confirmation module is also used to determine the corresponding vehicle type, vehicle model conditions, engine, horsepower and drive mode according to the abnormal situation of the fleet; determine the abnormal range based on the vehicle type, vehicle model conditions, engine, horsepower and drive mode; determine the abnormal screening conditions according to the type of the abnormal range and the time interval corresponding to the abnormal situation.

7. A vehicle trajectory analysis device, characterized in that: The device includes: a memory, a processor, and a driving trajectory analysis program stored in the memory and executable on the processor, wherein the driving trajectory analysis program is configured to implement the steps of the driving trajectory analysis method according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a driving trajectory analysis program, which, when executed by a processor, implements the steps of the driving trajectory analysis method according to any one of claims 1 to 5.

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