Vehicle data analysis method and device, equipment, storage medium and product

Abstract

The invention discloses a vehicle data analysis method, device and equipment, a storage medium and a product, and belongs to the technical field of new energy automobiles. Comprising the following steps: acquiring national standard vehicle data of a plurality of vehicles, the national standard vehicle data being vehicle data meeting a national standard protocol; driving behavior data and charging behavior data of the multiple vehicles are determined from national standard vehicle data of the multiple vehicles, the driving behavior data of the vehicles comprise multiple frames of driving behavior messages, the driving behavior messages are messages generated after the vehicles are started, and the charging behavior data comprise multiple frames of charging behavior messages; the charging behavior message is a message generated after the vehicle is parked and charged; for any vehicle, determining the driving behavior characteristics of the vehicle based on the plurality of first burying points in the multi-frame driving behavior message of the vehicle; based on a plurality of second burying points in the multi-frame charging behavior message, determining charging behavior characteristics of the vehicle; and determining a data analysis result based on the driving behavior characteristics of the plurality of vehicles and the charging behavior characteristics of the plurality of vehicles.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle technology, and in particular to a vehicle data analysis method, device, equipment, storage medium and product. Background Art

[0002] With the development of the new energy vehicle industry, the market for new energy vehicles will reach 9 million units in 2023. Since new energy vehicles will generate vehicle data, the development of new energy vehicles will inevitably be accompanied by the development of big data. In order to standardize vehicle data, the vehicle data of new energy vehicles needs to meet the national standard protocol (GB / T 32960). Vehicle data that meets the national standard protocol can be called vehicle national standard data. Analyzing vehicle national standard data can provide a deeper understanding of new energy vehicles. Summary of the invention

[0003] The embodiments of the present application provide a vehicle data analysis method, device, equipment, storage medium and product. The technical solution is as follows:

[0004] In one aspect, a method for analyzing vehicle data is provided, the method comprising:

[0005] Acquire national standard vehicle data of multiple vehicles, where the national standard vehicle data is vehicle data that meets the national standard protocol;

[0006] Determine driving behavior data and charging behavior data of the multiple vehicles from the national standard vehicle data of the multiple vehicles, wherein the driving behavior data of the vehicles include multiple frames of driving behavior messages, which are messages generated after the vehicles are started, and the charging behavior data include multiple frames of charging behavior messages, which are messages generated after the vehicles are parked and charged;

[0007] For any vehicle, determining a driving behavior feature of the vehicle based on a plurality of first buried points in a multi-frame driving behavior message of the vehicle;

[0008] Determining a charging behavior feature of the vehicle based on a plurality of second buried points in the multi-frame charging behavior message;

[0009] Based on the driving behavior characteristics of the plurality of vehicles and the charging behavior characteristics of the plurality of vehicles, a data analysis result is determined.

[0010] In a possible implementation, the multiple first buried points include vehicle status buried points, collection time buried points, accumulated mileage buried points, power state SOC buried points and location buried points; the driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location;

[0011] The determining the driving behavior characteristics of the vehicle based on the multiple first buried points in the multi-frame driving behavior message includes:

[0012] Determine a first message and a second message based on the vehicle status buried points in the multi-frame driving behavior messages, the vehicle status corresponding to the vehicle status buried point of the first message is started, and the vehicle status corresponding to the vehicle status buried point of the previous frame message of the first message is not started, the vehicle status corresponding to the vehicle status buried point of the second message is not started, and the vehicle status corresponding to the vehicle status buried point of the previous frame message of the second message is started;

[0013] Determine the travel time of the vehicle based on the collection time corresponding to the collection time buried point in the first message;

[0014] Determine the mileage of the vehicle based on the cumulative mileage corresponding to the cumulative mileage buried point in the first message and the cumulative mileage corresponding to the cumulative mileage buried point in the second message;

[0015] Determining the power consumption of the vehicle based on the SOC corresponding to the SOC buried point in the first message and the SOC corresponding to the SOC buried point in the second message;

[0016] Based on the location information corresponding to the location buried point in the first message and the location information corresponding to the location buried point in the second message, the starting location and the ending location of the vehicle are determined.

[0017] In another possible implementation, the plurality of second buried points include charging state buried points, collection time buried points, SOC buried points, position buried points, current buried points and voltage buried points;

[0018] The charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and change trend of maximum single cell voltage during charging;

[0019] The determining the charging behavior characteristics of the vehicle based on the multiple second buried points in the multiple frames of charging behavior messages includes:

[0020] Determine a third message and a fourth message based on the charging status buried points in the multi-frame charging behavior message, the charging state corresponding to the charging state buried point of the third message is started, and the charging state corresponding to the charging state buried point of the previous frame message of the third message is not started, the charging state corresponding to the charging state buried point of the fourth message is not started, and the charging state corresponding to the charging state buried point of the previous frame message of the fourth message is started;

[0021] Determine the charging start time and the charging end time of the vehicle based on the collection time corresponding to the collection time buried point in the third message and the collection time corresponding to the collection time buried point in the fourth message;

[0022] Determine a charging start SOC and a charging end SOC of the vehicle based on the SOC corresponding to the SOC buried point in the third message and the SOC corresponding to the SOC buried point in the fourth message;

[0023] determining a charge amount of the vehicle based on a charge start SOC and a charge end SOC of the vehicle;

[0024] Determine the charging position of the vehicle based on the position information corresponding to the location buried point in the third message;

[0025] Determine a charging start voltage and a charging end voltage of the vehicle based on a charging voltage corresponding to a voltage buried point in the third message and a charging voltage corresponding to a voltage buried point in the fourth message;

[0026] Determine a plurality of fifth messages based on the collection time corresponding to the collection time buried point in the multiple frames of charging messages, where the collection time of the fifth message is between the collection time of the third message and the collection time of the fourth message;

[0027] Determining a charging speed type of the vehicle based on the charging currents corresponding to the current buried points in the plurality of fifth messages;

[0028] Based on the charging voltages corresponding to the voltage buried points in the multiple fifth messages, the maximum single cell voltage of the vehicle during the charging process is determined.

[0029] In another possible implementation, the driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location; the charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging location, charging speed type, charging start voltage, charging end voltage, and maximum single cell voltage during charging;

[0030] The determining of the data analysis result based on the driving behavior characteristics of the plurality of vehicles and the charging behavior characteristics of the vehicles includes:

[0031] Determine a travel time range, a travel mileage range, a power consumption range, a travel start location type, and a travel end location type based on the travel time, mileage, power consumption, travel start location, and travel end location of the multiple vehicles;

[0032] Generate a first Excel table based on the travel time range, the mileage range, the power consumption range, the travel start location type and the travel end location type;

[0033] Determine a charging start time range, a charging end time range, a charging amount range, a charging start SOC range, a charging end SOC range, a charging position type, a target charging speed type, a charging start voltage range, a charging end voltage range, and a changing trend of a maximum single cell voltage during charging based on the charging start time, charging end time, charging amount, charging start SOC range, charging end SOC range, charging position type, target charging speed type, charging start voltage range, charging end voltage range, and a changing trend of a maximum single cell voltage during charging of the plurality of vehicles;

[0034] Generate a second Excel spreadsheet based on the charging start time range, the charging end time range, the charging amount range, the charging start SOC range, the charging end SOC range, the charging position type, the target charging speed type, the charging start voltage range, the charging end voltage range and the overall change trend of the maximum single cell voltage during the charging process;

[0035] Based on at least one driving behavior option in the first Excel table and at least one charging behavior option in the second Excel table, a visualization report is generated, where the visualization report is used to represent the data analysis result.

[0036] In another possible implementation, the method further includes at least one of the following implementations:

[0037] Based on the data analysis result, determine first decision information for vehicle development, and based on the first decision information, provide data support for vehicle development; or,

[0038] Determine second decision information for vehicle sales based on the data analysis result, and provide data support for vehicle sales based on the second decision information; or

[0039] Based on the data analysis results, third decision information for vehicle promotion is determined, and based on the third decision information, data support is provided for vehicle promotion.

[0040] In another possible implementation, the obtaining of national standard vehicle data of a plurality of vehicles includes:

[0041] Determine the registration time of the vehicle in the data platform;

[0042] Based on the registration time of the vehicle in the data platform, determining from the data platform the national standard vehicle data of the vehicle whose registration time exceeds a preset time and the national standard vehicle data of the vehicle whose registration time does not exceed the preset time;

[0043] The determining of data analysis results based on the driving behavior characteristics of the plurality of vehicles and the charging behavior characteristics of the plurality of vehicles comprises:

[0044] Determining duration types of the multiple vehicles based on the registration time of the multiple vehicles, the duration type being used to indicate whether the vehicle is a new vehicle or an old vehicle;

[0045] Based on the duration types of the multiple vehicles, the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles are grouped to obtain a first group of vehicle characteristics and a second group of vehicle characteristics, wherein the first group of vehicle characteristics includes the driving behavior characteristics and the charging behavior characteristics of the new vehicle of the duration type, and the second group of vehicle characteristics includes the driving behavior characteristics and the charging behavior characteristics of the old vehicle of the duration type;

[0046] Based on the first set of vehicle characteristics and the second set of vehicle characteristics, a first data analysis result and a second data analysis result are determined.

[0047] In another aspect, a vehicle data analysis device is provided, the device comprising:

[0048] An acquisition module, used to acquire national standard vehicle data of multiple vehicles, wherein the national standard vehicle data is vehicle data that meets the national standard protocol;

[0049] A first determination module is used to determine driving behavior data and charging behavior data of the multiple vehicles from the national standard vehicle data of the multiple vehicles, wherein the driving behavior data of the vehicles include multiple frames of driving behavior messages, and the driving behavior messages are messages generated after the vehicles are started; and the charging behavior data include multiple frames of charging behavior messages, and the charging behavior messages are messages generated after the vehicles are parked and charged;

[0050] A second determination module is used to determine, for any vehicle, a driving behavior feature of the vehicle based on a plurality of first buried points in a multi-frame driving behavior message of the vehicle;

[0051] A third determination module, configured to determine a charging behavior feature of the vehicle based on a plurality of second buried points in the multi-frame charging behavior message;

[0052] The fourth determination module is used to determine the data analysis result based on the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles.

[0053] In a possible implementation, the multiple first buried points include vehicle status buried points, collection time buried points, accumulated mileage buried points, power state SOC buried points and location buried points; the driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location;

[0054] The second determination module is used to determine the first message and the second message based on the vehicle status buried points in the multi-frame driving behavior message, the vehicle status corresponding to the vehicle status buried points of the first message is started, and the vehicle status corresponding to the vehicle status buried points of the previous frame message of the first message is not started, the vehicle status corresponding to the vehicle status buried points of the second message is not started, and the vehicle status corresponding to the vehicle status buried points of the previous frame message of the second message is started; based on the collection time corresponding to the collection time buried points in the first message, the travel time of the vehicle is determined; based on the cumulative mileage corresponding to the cumulative mileage buried points in the first message and the cumulative mileage corresponding to the cumulative mileage buried points in the second message, the mileage of the vehicle is determined; based on the SOC corresponding to the SOC buried points in the first message and the SOC corresponding to the SOC buried points in the second message, the power consumption of the vehicle is determined; based on the location information corresponding to the location buried points in the first message and the location information corresponding to the location buried points in the second message, the starting location and the ending location of the travel of the vehicle are determined.

[0055] In another possible implementation, the plurality of second buried points include charging state buried points, collection time buried points, SOC buried points, position buried points, current buried points and voltage buried points;

[0056] The charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and change trend of maximum single cell voltage during charging;

[0057] The third determination module is used to determine the third message and the fourth message based on the charging status buried points in the multi-frame charging behavior messages, the charging status corresponding to the charging status buried point of the third message is started, and the charging status corresponding to the charging status buried point of the previous frame message of the third message is not started, the charging status corresponding to the charging status buried point of the fourth message is not started, and the charging status corresponding to the charging status buried point of the previous frame message of the fourth message is started; based on the acquisition time corresponding to the acquisition time buried point in the third message and the acquisition time corresponding to the acquisition time buried point in the fourth message, determine the charging start time and the charging end time of the vehicle; based on the SOC corresponding to the SOC buried point in the third message and the SOC corresponding to the SOC buried point in the fourth message, determine the charging start SOC and the charging end SOC of the vehicle; Determine the charge amount of the vehicle based on the charging start SOC and charging end SOC of the vehicle; determine the charging position of the vehicle based on the location information corresponding to the location buried point in the third message; determine the charging start voltage and charging end voltage of the vehicle based on the charging voltage corresponding to the voltage buried point in the third message and the charging voltage corresponding to the voltage buried point in the fourth message; determine multiple fifth messages based on the acquisition time corresponding to the acquisition time buried point in the multi-frame charging message, and the acquisition time of the fifth message is between the acquisition time of the third message and the acquisition time of the fourth message; determine the charging speed type of the vehicle based on the charging current corresponding to the current buried point in the multiple fifth messages; determine the maximum single cell voltage of the vehicle during the charging process based on the charging voltage corresponding to the voltage buried point in the multiple fifth messages.

[0058] In another possible implementation, the driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location; the charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging location, charging speed type, charging start voltage, charging end voltage, and maximum single cell voltage during charging;

[0059] The fourth determination module is used to determine the travel time range, mileage range, power consumption range, travel start position type and travel end position type based on the travel time, mileage, power consumption, travel start position and travel end position of the multiple vehicles; generate a first Excel table based on the travel time range, mileage range, power consumption range, travel start position type and travel end position type; determine the charging start time range, charging end time range, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and the maximum single cell voltage change trend during the charging process of the multiple vehicles , charging end SOC range, charging position type, target charging speed type, charging start voltage range, charging end voltage range and the changing trend of the maximum single cell voltage during the charging process; based on the overall changing trend of the charging start time range, the charging end time range, the charging amount range, the charging start SOC range, the charging end SOC range, the charging position type, the target charging speed type, the charging start voltage range, the charging end voltage range and the maximum single cell voltage during the charging process, generate a second excel table; based on at least one driving behavior option in the first excel table and at least one charging behavior option in the second excel table, generate a visualization report, the visualization report is used to characterize the data analysis results.

[0060] In another possible implementation manner, the device further includes at least one of the following implementation manners:

[0061] a fifth determination module, configured to determine first decision information for vehicle development based on the data analysis result, and provide data support for vehicle development based on the first decision information; or

[0062] a sixth determination module, configured to determine second decision information for vehicle sales based on the data analysis result, and provide data support for vehicle sales based on the second decision information; or

[0063] The seventh determination module is used to determine the third decision information of the vehicle promotion based on the data analysis result, and provide data support for the vehicle promotion based on the third decision information.

[0064] In another possible implementation, the acquisition module is used to determine the registration time of the vehicle in the data platform; based on the registration time of the vehicle in the data platform, determine from the data platform the national standard vehicle data of the vehicle whose registration time exceeds the preset time and the national standard vehicle data of the vehicle whose registration time does not exceed the preset time;

[0065] The fourth determination module is used to determine the duration types of the multiple vehicles based on the registration time of the multiple vehicles, and the duration type is used to indicate whether the vehicle is a new vehicle or an old vehicle; based on the duration types of the multiple vehicles, the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles are grouped to obtain a first group of vehicle characteristics and a second group of vehicle characteristics, the first group of vehicle characteristics including the driving behavior characteristics and the charging behavior characteristics of the duration type of a new vehicle, and the second group of vehicle characteristics including the driving behavior characteristics and the charging behavior characteristics of the duration type of an old vehicle; based on the first group of vehicle characteristics and the second group of vehicle characteristics, a first data analysis result and a second data analysis result are determined.

[0066] On the other hand, a computer device is provided, which includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the above-mentioned vehicle data analysis method.

[0067] On the other hand, a computer-readable storage medium is provided, wherein at least one program code is stored in the storage medium, and the at least one program code is loaded and executed by a processor to implement the above-mentioned vehicle data analysis method.

[0068] On the other hand, a computer program product is provided, wherein the product stores at least one program code, and the at least one program code is used to be executed by a processor to implement the above-mentioned vehicle data analysis method.

[0069] In the embodiment of the present application, since the national standard vehicle data is generated by the vehicle, the driving behavior data and the charging behavior data are obtained from the national standard vehicle data, so as to analyze the user's driving behavior based on the buried points in the driving behavior data, and analyze the user's charging behavior based on the buried points in the charging behavior data. It is possible to analyze the user's driving behavior and the user's charging behavior based on big data, thereby improving the accuracy of the user's driving behavior analysis and the user's charging behavior analysis.

[0070] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a schematic diagram of an implementation environment of a vehicle data analysis method shown in an exemplary embodiment of the present application;

[0072] Figure 2 is a flow chart of a vehicle data analysis method shown in an exemplary embodiment of the present application;

[0073] Figure 3 is a flow chart of a vehicle data analysis method shown in an exemplary embodiment of the present application;

[0074] Figure 4 is a schematic diagram of a data analysis result shown in an exemplary embodiment of the present application;

[0075] Figure 5 is a flow chart of a vehicle data analysis method shown in an exemplary embodiment of the present application;

[0076] Figure 6 is a block diagram of a vehicle data analysis device shown in an exemplary embodiment of the present application;

[0077] Figure 7 is a block diagram of a server shown in an exemplary embodiment of the present application;

[0078] Figure 8 It is a block diagram of a terminal shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to make the technical solutions and advantages of the present application clearer, the implementation methods of the present application are described in further detail below.

[0080] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0081] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions. For example, the national standard vehicle data involved in this application are all obtained with full authorization.

[0082] Please refer to Figure 1, which shows a schematic diagram of an implementation environment of a vehicle data analysis method shown in an exemplary embodiment of the present application. The implementation environment includes a computer device 101 and multiple vehicles 102. The computer device 101 is used to obtain national standard vehicle data of multiple vehicles 102 and perform analysis based on the national standard vehicle data of multiple vehicles 102. National standard vehicle data is vehicle data that meets the national standard protocol, and the national standard protocol can be updated as needed; for example, the national standard protocol is GB / T 32960.

[0083] The vehicle 102 is a new energy vehicle, such as a pure electric vehicle, a plug-in hybrid electric vehicle, or an extended-range electric vehicle, etc. The vehicle 102 is equipped with a vehicle controller for controlling the vehicle system, a battery for providing power to the vehicle 102, an on-board charger for charging the battery, and a battery management system for managing the battery.

[0084] Please refer to Figure 2 , which shows a flow chart of a vehicle data analysis method shown in an exemplary embodiment of the present application. Figure 2 , the method comprising:

[0085] Step 201: A computer device obtains national standard vehicle data of a plurality of vehicles, where the national standard vehicle data is vehicle data that meets the national standard protocol.

[0086] The national standard protocol can be updated as needed; for example, the national standard protocol is GB / T 32960. The vehicle in the embodiment of the present application is a new energy vehicle, and according to regulatory requirements, the national standard vehicle data of the new energy vehicle needs to be archived on the data platform; therefore, in this step, the computer device obtains the national standard vehicle data of multiple vehicles from the data platform.

[0087] In a possible implementation, the computer device may obtain national standard vehicle data of a portion of new users and national standard vehicle data of a portion of old users; accordingly, this step may be implemented by the following steps 2011-2022, including:

[0088] Step 2021: The computer device determines the registration time of the vehicle in the data platform.

[0089] The vehicle registration time can be the time when the vehicle is registered on the platform, or the time when the national standard vehicle data of the vehicle is first stored in the data platform.

[0090] Step 222: Based on the registration time of the vehicle in the data platform, the computer device determines from the data platform the national standard vehicle data of the vehicle whose registration time exceeds the preset time and the national standard vehicle data of the vehicle whose registration time does not exceed the preset time.

[0091] The preset duration can be set and changed as needed. In the embodiment of the present application, the preset duration is not specifically limited; for example, the preset duration is 30 days; a registration duration exceeding the preset duration refers to an old user, and a registration duration not exceeding the preset duration refers to a new user.

[0092] In another possible implementation, the computer device may also determine analysis requirements, and based on the analysis requirements, obtain national standard vehicle data of multiple vehicles that meet the data analysis requirements from the data platform. The analysis requirements may be set and changed as needed, and in the embodiment of the present application, the analysis requirements are not specifically limited. For example, the analysis requirements may be relevant information about vehicles in third-tier cities; the computer device determines the registration location information of the vehicle in the data platform, and based on the registration location information of the vehicle in the data platform, determines the national standard vehicle data of vehicles with registration locations in third-tier cities from the data platform. For another example, the analysis requirements may be relevant information about vehicles in first-tier cities; the computer device determines the registration location information of the vehicle in the data platform, and based on the registration location information of the vehicle in the data platform, determines the national standard vehicle data of vehicles with registration locations in first-tier cities from the data platform.

[0093] Step 202: The computer device determines the driving behavior data and charging behavior data of multiple vehicles from the national standard vehicle data of multiple vehicles, the driving behavior data of the vehicle includes multiple frames of driving behavior messages, and the driving behavior messages are messages generated after the vehicle is started; the charging behavior data includes multiple frames of charging behavior messages, and the charging behavior messages are messages generated after the vehicle is parked and charged.

[0094] Step 203: For any vehicle, the computer device determines the driving behavior characteristics of the vehicle based on multiple first buried points in the multi-frame driving behavior message of the vehicle.

[0095] The multiple first buried points include vehicle status buried points, collection time buried points, accumulated mileage buried points, SOC buried points and location buried points; driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location. Accordingly, this step can be implemented by the following steps 231-235, including:

[0096] Step 2031: The computer device determines the first message and the second message based on the vehicle status embedded points in the multi-frame driving behavior message, the vehicle status corresponding to the vehicle status embedded point of the first message is started, and the vehicle status corresponding to the vehicle status embedded point of the previous frame message of the first message is not started, the vehicle status corresponding to the vehicle status embedded point of the second message is not started, and the vehicle status corresponding to the vehicle status embedded point of the previous frame message of the second message is started.

[0097] Step 2032: The computer device determines the travel time of the vehicle based on the collection time corresponding to the collection time point in the first message.

[0098] The computer device determines the collection time corresponding to the collection time point in the first message as the travel time of the vehicle.

[0099] Step 2033: The computer device determines the mileage of the vehicle based on the cumulative mileage corresponding to the cumulative mileage points in the first message and the cumulative mileage corresponding to the cumulative mileage points in the second message.

[0100] The computer device determines the starting mileage of the vehicle based on the cumulative mileage corresponding to the cumulative mileage buried points in the first message, determines the ending mileage of the vehicle based on the cumulative mileage corresponding to the cumulative mileage buried points in the second message, and determines the mileage of the vehicle based on the starting mileage and the ending mileage of the vehicle. For example, the computer device determines the cumulative mileage corresponding to the cumulative mileage buried points in the first message as the starting mileage of the vehicle, determines the cumulative mileage corresponding to the cumulative mileage buried points in the second message as the ending mileage of the vehicle, and determines the difference between the starting mileage and the ending mileage as the mileage of the vehicle.

[0101] In one possible implementation, when the vehicle is a hybrid vehicle, the computer device may also determine the power type of the mileage, and the power type may be at least one of a motor and an engine; for example, the power type of the mileage is a motor; for another example, the power type of the mileage is an engine; for another example, the power type of the mileage is a motor + an engine.

[0102] Step 2034: The computer device determines the power consumption of the vehicle based on the SOC corresponding to the SOC buried point in the first message and the SOC corresponding to the SOC buried point in the second message.

[0103] The computer device determines the vehicle's travel start SOC based on the SOC corresponding to the SOC buried point in the first message, determines the vehicle's travel end SOC based on the SOC corresponding to the SOC buried point in the second message, and determines the vehicle's power consumption based on the vehicle's travel start SOC and travel end SOC. For example, the computer device determines the SOC corresponding to the SOC buried point in the first message as the vehicle's travel start SOC, determines the SOC corresponding to the SOC buried point in the second message as the vehicle's travel end SOC, determines the difference between the travel start SOC and the travel end SOC, and determines the vehicle's power consumption based on the difference and the vehicle's full vehicle range.

[0104] Step 2035: The computer device determines the starting position and the ending position of the vehicle based on the position information corresponding to the position point in the first message and the position information corresponding to the position point in the second message.

[0105] The computer device determines the location information corresponding to the location buried point in the first message as the starting location of the vehicle's travel, and determines the location information corresponding to the location buried point in the second message as the end location of the vehicle's travel. The location information includes accuracy information and latitude information.

[0106] In some embodiments, the multiple first buried points also include vehicle speed buried points and gear buried points; the computer device determines whether the vehicle is driving normally based on the vehicle speed corresponding to the vehicle speed buried point in the multi-frame driving behavior message; the vehicle gear status is determined based on the gear corresponding to the gear buried point in the multi-frame driving behavior message, thereby understanding the vehicle's operating condition information based on whether the vehicle is driving normally and the vehicle's current condition.

[0107] Step 204: The computer device determines the charging behavior characteristics of the vehicle based on the multiple second buried points in the multi-frame charging behavior message.

[0108] The multiple second buried points include charging state buried points, collection time buried points, SOC buried points, location buried points, current buried points and voltage buried points; the charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and maximum single cell voltage during charging. Accordingly, this step can be implemented by the following steps 2041-2049, including:

[0109] Step 2041: The computer device determines the third message and the fourth message based on the charging status buried points in the multi-frame charging behavior message. The charging status corresponding to the charging status buried point of the third message is started, and the charging status corresponding to the charging status buried point of the previous frame message of the third message is not started. The charging status corresponding to the charging status buried point of the fourth message is not started, and the charging status corresponding to the charging status buried point of the previous frame message of the fourth message is started.

[0110] Step 2042: The computer device determines the charging start time and the charging end time of the vehicle based on the collection time corresponding to the collection time buried point in the third message and the collection time corresponding to the collection time buried point in the fourth message.

[0111] The computer device determines the collection time corresponding to the collection time buried point in the third message as the charging start time of the vehicle, and determines the collection time corresponding to the collection time buried point in the fourth message as the charging end time of the vehicle.

[0112] Step 2043: The computer device determines the charging start SOC and charging end SOC of the vehicle based on the SOC corresponding to the SOC buried point in the third message and the SOC corresponding to the SOC buried point in the fourth message.

[0113] The computer device determines the SOC corresponding to the SOC buried point in the third message as the charging start SOC of the vehicle, and determines the SOC corresponding to the SOC buried point in the fourth message as the charging end SOC of the vehicle.

[0114] Step 2044: The computer device determines the charge amount of the vehicle based on the charge start SOC and charge end SOC of the vehicle.

[0115] The computer device determines the difference between the vehicle's charging start SOC and charging end SOC, and determines the vehicle's charge capacity based on the difference and the vehicle's full vehicle range.

[0116] Step 2045: The computer device determines the charging position of the vehicle based on the location information corresponding to the location point in the third message.

[0117] The computer device determines the location information corresponding to the location point in the third message as the charging position of the vehicle, which includes longitude information and latitude information.

[0118] Step 2046: The computer device determines the charging start voltage and charging end voltage of the vehicle based on the charging voltage corresponding to the voltage buried point in the third message and the charging voltage corresponding to the voltage buried point in the fourth message.

[0119] The computer device determines the charging voltage corresponding to the voltage buried point in the third message as the charging start voltage of the vehicle, and determines the charging voltage corresponding to the voltage buried point in the fourth message as the charging end voltage of the vehicle.

[0120] Step 2047: The computer device determines multiple fifth messages based on the collection time corresponding to the collection time points in the multiple frames of charging messages, and the collection time of the fifth message is between the collection time of the third message and the collection time of the fourth message.

[0121] Step 2048: The computer device determines the charging speed type of the vehicle based on the charging current corresponding to the current buried points in the multiple fifth messages.

[0122] The charging speed types include fast charging and slow charging. The charging current of fast charging is greater than the preset current, and the charging current of slow charging is less than the preset current. Accordingly, this step can determine the charging current corresponding to each current buried point in the fifth message for the computer device, and obtain multiple charging currents. When there is a charging current greater than the preset current among the multiple charging currents, the charging speed type of the vehicle is determined to be fast charging; when the multiple charging currents are all less than the preset current, the charging speed type of the vehicle is determined to be slow charging.

[0123] The charging behavior characteristics may also include charging frequency. The computer device determines the monthly charging times of the multiple vehicles based on the charging start times of the multiple vehicles, and determines the charging frequency based on the monthly charging times of the multiple vehicles and the number of days on which charging occurs in the month. For example, the computer device determines the charging frequency as the ratio of the monthly charging times to the number of days on which charging occurs.

[0124] Step 2049: The computer device determines the maximum single cell voltage of the vehicle during the charging process based on the charging voltages corresponding to the voltage buried points in the multiple fifth messages.

[0125] The maximum single cell voltage refers to the highest voltage value that the vehicle's battery cells can withstand; accordingly, this step can be: the computer device determines the maximum charging voltage based on the charging voltages corresponding to the voltage points in multiple fifth messages, and determines the maximum charging voltage as the maximum single cell voltage of the vehicle during the charging process.

[0126] Step 205: The computer device determines a data analysis result based on the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles.

[0127] The driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location; the charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging location, charging speed type, charging start voltage, charging end voltage, and maximum single cell voltage during charging; accordingly, this step can be implemented by the following steps 2051-2055, including:

[0128] Step 2051: The computer device determines the travel time range, mileage range, power consumption range, travel start location type and travel end location type based on the travel time, mileage, power consumption, travel start location and travel end location of multiple vehicles.

[0129] The step of determining the travel time range based on the travel times of multiple vehicles by the computer device may be as follows: In one possible implementation, the computer device combines the travel times of multiple vehicles into a travel time range. In another possible implementation, the computer device determines multiple first reference time ranges, determines the number of times the multiple first reference time ranges are hit based on the travel times of multiple vehicles, determines a first preset number of first reference time ranges with the largest number of hits based on the number of times the multiple first reference time ranges are hit, and determines the travel time range based on the first preset number of first reference time ranges. For example, the computer device combines the first preset number of first reference time ranges into a travel time range; or, the computer device determines the first preset number of first reference time ranges as the travel time range.

[0130] For example, multiple first reference time ranges include range 1, range 2, and range 3, range 1 is from 7 a.m. to 9 a.m., range 2 is from 9 a.m. to 4 p.m., and range 3 is from 4 p.m. to 7 p.m.; the travel times of multiple vehicles have a hit number of 200 in range 1, a hit number of 50 in range 2, and a hit number of 200 in range 3, then the computer device determines that the first reference time ranges with the largest number of hits are range 1 and range 3, and then determines range 1 and range 3 as the travel time range.

[0131] The step of determining the mileage range based on the mileage of multiple vehicles by the computer device may be as follows: In one possible implementation, the computer device combines the mileage of multiple vehicles into a mileage range. In another possible implementation, the computer device determines multiple mileage ranges, determines the number of times multiple reference mileage ranges are hit based on the mileage of multiple vehicles, determines a second preset number of reference mileage ranges with the largest number of hits based on the number of times multiple reference mileage ranges are hit, and determines the mileage range based on the second preset number of reference mileage ranges. For example, the computer device combines the second preset number of reference mileage ranges into a mileage range; or the computer device determines the second preset number of reference mileage ranges as the mileage range.

[0132] The step of determining the power consumption range based on the power consumption of multiple vehicles by the computer device may be: in one possible implementation, the computer device combines the power consumption of multiple vehicles into a power consumption range. In another possible implementation, the computer device determines multiple reference power consumption ranges, determines the number of times the multiple reference power consumption ranges are hit based on the power consumption of multiple vehicles, determines a third preset number of reference power consumption ranges with the largest number of hits based on the number of times the multiple reference power consumption ranges are hit, and determines the power consumption range based on the third preset number of reference power consumption ranges. For example, the computer device combines the third preset number of reference power consumption ranges into a power consumption range; or the computer determines the third preset number of reference power consumption ranges as the power consumption range.

[0133] The steps for a computer device to determine the types of travel starting locations based on the travel starting locations of multiple vehicles may be: in one possible implementation, the computer device determines the types of travel starting locations of multiple vehicles based on the travel starting locations of multiple vehicles, and the location types may be home, work, shopping mall, park, etc.; the computer device determines the fourth preset number of travel starting location types with the highest frequency based on the travel starting location types of multiple vehicles.

[0134] The steps for the computer device to determine the types of travel end locations based on the travel end locations of multiple vehicles may be: in one possible implementation, the computer device determines the types of travel end locations of multiple vehicles based on the travel end locations of multiple vehicles, and the location types may be home, work, shopping mall, park, etc.; the computer device determines the fifth preset number of travel end location types with the highest frequency based on the travel end location types of multiple vehicles.

[0135] When the travel start type and / or travel end location is a park and / or a shopping mall, the computer device may also determine the location information of the park and / or the shopping mall.

[0136] Step 2052: The computer device generates a first Excel table based on the travel time range, the mileage range, the power consumption range, the travel start location type and the travel end location type.

[0137] The first Excel table includes a plurality of driving behavior options, and the plurality of driving behavior options are respectively a travel time range, a mileage range, a power consumption range, a travel start location type, and a travel end location type.

[0138] Step 2053: The computer device determines the charging start time range, charging end time range, charging amount range, charging start SOC range, charging end SOC range, target charging position type, target charging speed type, charging start voltage range, charging end voltage range and the changing trend of the maximum single cell voltage during the charging process based on the charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and the changing trend of the maximum single cell voltage during the charging process of multiple vehicles.

[0139] The step of determining the charging start time range based on the charging start time of multiple vehicles by the computer device may be as follows: In one possible implementation, the computer device combines the charging start time of multiple vehicles into a charging start time range. In another possible implementation, the computer device determines multiple second reference time ranges, determines the number of times the multiple second reference time ranges are hit based on the charging start time of multiple vehicles, determines the fifth preset number of second reference time ranges with the largest number of hits based on the number of times the multiple second reference time ranges are hit, and determines the charging start time range based on the fifth preset number of second reference time ranges. For example, the computer device combines the fifth preset number of second reference time ranges into a charging start time range; or the computer device determines the fifth preset number of second reference time ranges as the charging start time range.

[0140] The step of determining the charging end time range based on the charging end time of multiple vehicles by the computer device may be as follows: In one possible implementation, the computer device combines the charging end time of multiple vehicles into a charging end time range. In another possible implementation, the computer device determines multiple third reference time ranges, determines the number of times the multiple third reference time ranges are hit based on the charging end time of multiple vehicles, determines the sixth preset number of third reference time ranges with the largest number of hits based on the number of times the multiple third reference time ranges are hit, and determines the charging end time range based on the sixth preset number of third reference time ranges. For example, the computer device combines the sixth preset number of third reference time ranges into a charging end time range; or the computer device determines the sixth preset number of third reference time ranges as the charging end time range.

[0141] The step of determining the charging start SOC range based on the charging start SOC of multiple vehicles by the computer device may be: in one possible implementation, the computer device combines the charging start SOCs of multiple vehicles into a charging start SOC range. In another possible implementation, the computer device determines multiple first SOC ranges, determines the number of times the multiple first SOC ranges are hit based on the charging start SOCs of multiple vehicles, determines the seventh preset number of first SOC ranges with the largest number of hits based on the number of times the multiple first SOC ranges are hit, and determines the charging start SOC range based on the seventh preset number of first SOC ranges. For example, the computer device combines the seventh preset number of first SOC ranges into a charging start SOC range; or, the computer device determines the seventh preset number of first SOC ranges as the charging start SOC range.

[0142] The step of determining the charging end SOC range based on the charging end SOC of multiple vehicles by the computer device may be: in one possible implementation, the computer device combines the charging end SOCs of multiple vehicles into a charging end SOC range. In another possible implementation, the computer device determines multiple second SOC ranges, determines the number of times the multiple second SOC ranges are hit based on the charging end SOCs of multiple vehicles, determines the eighth preset number of second SOC ranges with the largest number of hits based on the number of times the multiple second SOC ranges are hit, and determines the charging end SOC range based on the eighth preset number of second SOC ranges. For example, the computer device combines the eighth preset number of second SOC ranges into a charging end SOC range; or, the computer device determines the eighth preset number of second SOC ranges as the charging end SOC range.

[0143] The steps of determining the target charging location type based on the charging locations of multiple vehicles by the computer device may be: the computer device determines the charging location types of multiple vehicles based on the charging locations of multiple vehicles, and the charging location types may be home or public charging piles; the computer device determines the target charging location type with the highest frequency based on the charging location types of multiple vehicles.

[0144] The step of the computer device determining the target charging speed type based on the charging speed types of the multiple vehicles may be: the computer device determines the target charging speed type with the highest frequency based on the charging speed types of the multiple vehicles.

[0145] The step of determining the charging start voltage range based on the charging start voltages of multiple vehicles by the computer device may be as follows: In one possible implementation, the computer device combines the charging start voltages of multiple vehicles into a charging start voltage range. In another possible implementation, the computer device determines multiple first reference voltage ranges, determines the number of times the multiple first reference voltage ranges are hit based on the charging start voltages of multiple vehicles, determines the ninth preset number of first reference voltage ranges with the largest number of hits based on the number of times the multiple first reference voltage ranges are hit, and determines the charging start voltage range based on the ninth preset number of first reference voltage ranges. For example, the computer device combines the ninth preset number of first reference voltage ranges into a charging start voltage range; or the computer device determines the ninth preset number of first reference voltage ranges as the charging start voltage range.

[0146] The step of determining the charging end voltage range based on the charging end voltages of multiple vehicles by the computer device may be as follows: In one possible implementation, the computer device combines the charging end voltages of multiple vehicles into a charging end voltage range. In another possible implementation, the computer device determines multiple second reference voltage ranges, determines the number of times the multiple second reference voltage ranges are hit based on the charging end voltages of multiple vehicles, determines the tenth preset number of second reference voltage ranges with the largest number of hits based on the number of times the multiple second reference voltage ranges are hit, and determines the charging end voltage range based on the tenth preset number of second reference voltage ranges. For example, the computer device combines the tenth preset number of second reference voltage ranges into a charging end voltage range; or the computer device determines the tenth preset number of second reference voltage ranges as the charging end voltage range.

[0147] The computer device determines the change trend of the maximum single cell voltage during the charging process based on the maximum single cell voltage of multiple vehicles during the charging process, and the change trend is increasing or decreasing.

[0148] Step 2054: The computer device generates a second excel sheet based on the charging start time range, charging end time range, charging amount range, charging start SOC range, charging end SOC range, charging position type, target charging speed type, charging start voltage range, charging end voltage range and the overall change trend of the maximum single cell voltage during the charging process.

[0149] The second Excel table includes multiple charging behavior options, and the multiple driving behavior options are respectively charging start time range, charging end time range, charging amount range, charging start SOC range, charging end SOC range, charging position type, target charging speed type, charging start voltage range, charging end voltage range and the overall change trend of the maximum single cell voltage during the charging process.

[0150] Step 2055: The computer device generates a visualization report based on at least one driving behavior option in the first Excel table and at least one charging behavior option in the second Excel table, where the visualization report is used to represent the data analysis result.

[0151] For example, see Figure 3 The computer device obtains national standard vehicle data of multiple vehicles, processes the national standard vehicle data through a program algorithm, obtains driving behavior data and charging behavior data, outputs a first excel table based on the driving behavior data, outputs a second excel table based on the charging behavior data, and generates a visual report based on the first excel table and the second excel table.

[0152] In a possible implementation, the multiple vehicles include new user vehicles (referred to as new vehicles) and old user vehicles (referred to as old vehicles); accordingly, the embodiment of the present application can also analyze the new user vehicles and the old user vehicles separately; accordingly, step 205 can be implemented by the following steps (1) to (3), including:

[0153] (1) The computer device determines the duration type of the multiple vehicles based on the registration time of the multiple vehicles, where the duration type is used to indicate whether the vehicle is a new vehicle or an old vehicle.

[0154] (2) The computer device groups the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles based on the duration types of the multiple vehicles to obtain a first group of vehicle characteristics and a second group of vehicle characteristics. The first group of vehicle characteristics includes the driving behavior characteristics and the charging behavior characteristics of the new vehicles with a duration type, and the second group of vehicle characteristics includes the driving behavior characteristics and the charging behavior characteristics of the old vehicles with a duration type.

[0155] (3) The computer device determines a first data analysis result and a second data analysis result based on the first set of vehicle characteristics and the second set of vehicle characteristics.

[0156] The computer device determines a first data analysis result based on the first set of vehicle characteristics, and determines a second data analysis result based on the second set of vehicle characteristics, wherein the first data analysis result is a data analysis result corresponding to a new vehicle, and the second data analysis result is a data analysis result corresponding to an old vehicle. For example, please refer to Figure 4 ,The visualization report includes the user distribution based on the charging frequency and the user distribution based on the fast and slow charging selection. Figure 4 The leftmost column in each group of bar charts in the left figure is the user ratio of new users, the middle column in each group of bar charts is the user ratio of old users, and the rightmost column in each group of bar charts is the user ratio of all users. Figure 4 Among the three columns in the right figure, the bottom of each column is the proportion of fast charging only, the middle is the proportion of slow charging only, and the top is the proportion of both fast and slow charging.

[0157] In the embodiment of the present application, since the national standard vehicle data is generated by the vehicle, the driving behavior data and the charging behavior data are obtained from the national standard vehicle data, so as to analyze the user's driving behavior based on the buried points in the driving behavior data, and analyze the user's charging behavior based on the buried points in the charging behavior data. It is possible to analyze the user's driving behavior and the user's charging behavior based on big data, thereby improving the accuracy of the user's driving behavior analysis and the user's charging behavior analysis.

[0158] In an embodiment of the present application, driving behavior data and charging behavior data can be obtained from national standard vehicle data, so that the user's driving behavior can be analyzed based on the buried points in the driving behavior data, and the user's charging behavior can be analyzed based on the buried points in the charging behavior data, thereby improving the utilization rate of the national standard vehicle data without increasing costs.

[0159] Please refer to Figure 5 , which shows a flow chart of a vehicle data analysis method shown in an exemplary embodiment of the present application. The method includes:

[0160] Step 501: A computer device obtains national standard vehicle data of a plurality of vehicles, where the national standard vehicle data is vehicle data that meets the national standard protocol.

[0161] In some embodiments, this step is the same as step 201 and will not be repeated here.

[0162] Step 502: The computer device determines the driving behavior data and charging behavior data of multiple vehicles from the national standard vehicle data of multiple vehicles, the driving behavior data of the vehicle includes multiple frames of driving behavior messages, and the driving behavior messages are messages generated after the vehicle is started; the charging behavior data includes multiple frames of charging behavior messages, and the charging behavior messages are messages generated after the vehicle is parked and charged.

[0163] In some embodiments, this step is the same as step 202 and will not be repeated here.

[0164] Step 503: For any vehicle, the computer device determines the driving behavior characteristics of the vehicle based on multiple first buried points in the multi-frame driving behavior message of the vehicle.

[0165] In some embodiments, this step is the same as step 203 and will not be repeated here.

[0166] Step 504: The computer device determines the charging behavior characteristics of the vehicle based on the multiple second buried points in the multi-frame charging behavior message.

[0167] In some embodiments, this step is the same as step 204 and will not be repeated here.

[0168] Step 505: The computer device determines a data analysis result based on the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles.

[0169] In some embodiments, this step is the same as step 205 and will not be repeated here.

[0170] Step 506: The computer device provides decision information to the vehicle based on the data analysis results.

[0171] The decision information includes at least one of the first decision information, the second decision information and the third decision information; the first decision information is used to guide vehicle development, the second decision information is used to guide vehicle sales, and the third decision information is used to guide vehicle promotion. Accordingly, this step can be implemented in at least one of the following ways:

[0172] The first implementation method: the computer device determines the first decision information for vehicle research and development based on the data analysis results, and provides data support for vehicle research and development based on the first decision information.

[0173] For example, if the data analysis result is the charging start SOC range, the computer device determines the first decision information as the battery capacity information of the vehicle based on the charging start SOC range. For example, if the SOC of most users is 50% when they start charging, it means that the vehicle's range is sufficient, and the first decision information may be to set a smaller battery capacity, such as a battery with a range of 200km; for another example, if the SOC of most users is 10% when they start charging, it means that the vehicle's range may not be enough, and the first decision information may be to set a larger battery capacity.

[0174] For another example, if the data analysis result is a target charging speed type, the computer device determines that the first decision information is the charging power information of the vehicle based on the target charging speed type. For example, if the target charging speed type is slow charging, that is, most people choose slow charging, then the first decision information is to set a smaller charging power; for another example, if the target charging speed type is fast charging, that is, most people choose fast charging, then the first decision information is to set a larger charging power.

[0175] For example, if the result of data analysis is driving behavior habits, the computer equipment will update the vehicle services based on the driving behavior habits of the majority of people, thereby improving road safety and reducing the occurrence of traffic accidents.

[0176] The second implementation method: the computer device determines the second decision information for vehicle sales based on the data analysis results, and provides data support for vehicle sales based on the second decision information.

[0177] For example, if the data analysis result is the target charging location type, the computer device determines the second decision information as whether to give the user a charging pile based on the target charging location type. For example, if most users choose to charge at home, it means that it is necessary to give the user a charging pile, and the second decision information may be to give the user a charging pile when the vehicle is sold; for another example, if most users choose to charge at a public charging pile, it means that it is not necessary to give the user a charging pile, and the second decision information may be that the charging pile may not be given when the vehicle is sold, but something else may be given.

[0178] The third implementation method: the computer device determines the third decision information of the vehicle promotion based on the data analysis result, and provides data support for the vehicle promotion based on the third decision information.

[0179] For example, if the data analysis result is a place that users often go to on weekends, the computer device determines the third decision information to promote the vehicle at the place based on the place. For example, if most users often go to scenic spot A on weekends, the vehicle is promoted in scenic spot A, thereby improving the promotion effect of the vehicle.

[0180] In the embodiment of the present application, based on the data analysis results, a preliminary understanding of the user's driving behavior and charging behavior can be obtained, which can improve and expand the product definition and functions on the one hand. On the other hand, through the data analysis results, the working conditions of the vehicle during driving can be well understood, and some user-customized services and business expansion can be provided.

[0181] Please refer to Figure 6 , which shows a block diagram of a vehicle data analysis device shown in an exemplary embodiment of the present application. The device includes:

[0182] An acquisition module 601 is used to acquire national standard vehicle data of a plurality of vehicles, wherein the national standard vehicle data is vehicle data that meets the national standard protocol;

[0183] A first determination module 602 is used to determine driving behavior data and charging behavior data of the multiple vehicles from the national standard vehicle data of the multiple vehicles, wherein the driving behavior data of the vehicles include multiple frames of driving behavior messages, and the driving behavior messages are messages generated after the vehicles are started; and the charging behavior data include multiple frames of charging behavior messages, and the charging behavior messages are messages generated after the vehicles are parked and charged;

[0184] A second determination module 603 is used to determine, for any vehicle, a driving behavior feature of the vehicle based on a plurality of first buried points in a multi-frame driving behavior message of the vehicle;

[0185] A third determination module 604, configured to determine a charging behavior feature of the vehicle based on a plurality of second buried points in the multi-frame charging behavior message;

[0186] The fourth determination module 605 is used to determine the data analysis result based on the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles.

[0187] In a possible implementation, the multiple first buried points include vehicle status buried points, collection time buried points, accumulated mileage buried points, power state SOC buried points and location buried points; the driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location;

[0188] The second determination module 603 is used to determine the first message and the second message based on the vehicle status buried points in the multi-frame driving behavior message, the vehicle status corresponding to the vehicle status buried point of the first message is started, and the vehicle status corresponding to the vehicle status buried point of the previous frame message of the first message is not started, the vehicle status corresponding to the vehicle status buried point of the second message is not started, and the vehicle status corresponding to the vehicle status buried point of the previous frame message of the second message is started; based on the collection time corresponding to the collection time buried point in the first message, the travel time of the vehicle is determined; based on the cumulative mileage corresponding to the cumulative mileage buried point in the first message and the cumulative mileage corresponding to the cumulative mileage buried point in the second message, the mileage of the vehicle is determined; based on the SOC corresponding to the SOC buried point in the first message and the SOC corresponding to the SOC buried point in the second message, the power consumption of the vehicle is determined; based on the location information corresponding to the location buried point in the first message and the location information corresponding to the location buried point in the second message, the starting location and the ending location of the travel of the vehicle are determined.

[0189] In another possible implementation, the plurality of second buried points include charging state buried points, collection time buried points, SOC buried points, position buried points, current buried points and voltage buried points;

[0190] The charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and change trend of maximum single cell voltage during charging;

[0191] The third determination module 604 is used to determine the third message and the fourth message based on the charging status buried points in the multi-frame charging behavior message, the charging state corresponding to the charging state buried point of the third message is started, and the charging state corresponding to the charging state buried point of the previous frame message of the third message is not started, the charging state corresponding to the charging state buried point of the fourth message is not started, and the charging state corresponding to the charging state buried point of the previous frame message of the fourth message is started; based on the acquisition time corresponding to the acquisition time buried point in the third message and the acquisition time corresponding to the acquisition time buried point in the fourth message, determine the charging start time and charging end time of the vehicle; based on the SOC corresponding to the SOC buried point in the third message and the SOC corresponding to the SOC buried point in the fourth message, determine the charging start SOC and charging end SOC of the vehicle. C; determine the charge amount of the vehicle based on the charging start SOC and charging end SOC of the vehicle; determine the charging position of the vehicle based on the position information corresponding to the position buried point in the third message; determine the charging start voltage and charging end voltage of the vehicle based on the charging voltage corresponding to the voltage buried point in the third message and the charging voltage corresponding to the voltage buried point in the fourth message; determine multiple fifth messages based on the acquisition time corresponding to the acquisition time buried point in the multi-frame charging message, and the acquisition time of the fifth message is between the acquisition time of the third message and the acquisition time of the fourth message; determine the charging speed type of the vehicle based on the charging current corresponding to the current buried point in the multiple fifth messages; determine the maximum single cell voltage of the vehicle during the charging process based on the charging voltage corresponding to the voltage buried point in the multiple fifth messages.

[0192] In another possible implementation, the driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location; the charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging location, charging speed type, charging start voltage, charging end voltage, and maximum single cell voltage during charging;

[0193] The fourth determination module 605 is used to determine the travel time range, mileage range, power consumption range, travel start position type and travel end position type based on the travel time, mileage, power consumption, travel start position and travel end position of the multiple vehicles; generate a first excel table based on the travel time range, mileage range, power consumption range, travel start position type and travel end position type; determine the charging start time range, charging end time range, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and the maximum single cell voltage change trend during the charging process of the multiple vehicles. a second excel sheet based on the overall change trend of the charging start time range, the charging end time range, the charging amount range, the charging start SOC range, the charging end SOC range, the charging position type, the target charging speed type, the charging start voltage range, the charging end voltage range and the maximum single cell voltage during the charging process; and a visual report is generated based on at least one driving behavior option in the first excel sheet and at least one charging behavior option in the second excel sheet, the visual report being used to characterize the data analysis result.

[0194] In another possible implementation manner, the device further includes at least one of the following implementation manners:

[0195] a fifth determination module, configured to determine first decision information for vehicle development based on the data analysis result, and provide data support for vehicle development based on the first decision information; or

[0196] a sixth determination module, configured to determine second decision information for vehicle sales based on the data analysis result, and provide data support for vehicle sales based on the second decision information; or

[0197] The seventh determination module is used to determine the third decision information of the vehicle promotion based on the data analysis result, and provide data support for the vehicle promotion based on the third decision information.

[0198] In another possible implementation, the acquisition module 601 is used to determine the registration time of the vehicle in the data platform; based on the registration time of the vehicle in the data platform, determine the national standard vehicle data of the vehicle whose registration time exceeds the preset time and the national standard vehicle data of the vehicle whose registration time does not exceed the preset time from the data platform;

[0199] The fourth determination module 605 is used to determine the duration type of the multiple vehicles based on the registration time of the multiple vehicles, and the duration type is used to indicate whether the vehicle is a new vehicle or an old vehicle; based on the duration type of the multiple vehicles, the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles are grouped to obtain a first group of vehicle characteristics and a second group of vehicle characteristics, the first group of vehicle characteristics including the driving behavior characteristics and the charging behavior characteristics of the duration type of a new vehicle, and the second group of vehicle characteristics including the driving behavior characteristics and the charging behavior characteristics of the duration type of an old vehicle; based on the first group of vehicle characteristics and the second group of vehicle characteristics, a first data analysis result and a second data analysis result are determined.

[0200] In the embodiment of the present application, since the national standard vehicle data is generated by the vehicle, the driving behavior data and the charging behavior data are obtained from the national standard vehicle data, so as to analyze the user's driving behavior based on the buried points in the driving behavior data, and analyze the user's charging behavior based on the buried points in the charging behavior data. It is possible to analyze the user's driving behavior and the user's charging behavior based on big data, thereby improving the accuracy of the user's driving behavior analysis and the user's charging behavior analysis.

[0201] It should be noted that the vehicle data analysis device provided in the above embodiment only uses the division of the above functional modules as an example when analyzing vehicle data. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle data analysis device provided in the above embodiment and the vehicle data analysis method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0202] The computer device may be a server; see Figure 7 , Figure 7 The structural block diagram of a server 700 provided by an exemplary embodiment of the present application is shown. The server 700 includes a processor (central processing unit, CPU) 701 and a memory 702, wherein the memory 702 stores at least one program code, and the at least one program code is loaded and executed by the processor 701 to implement the vehicle data analysis method provided by the above-mentioned various method embodiments. Of course, the server 700 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The server 700 may also include other components for implementing device functions, which will not be described in detail here.

[0203] Those skilled in the art will understand that Figure 7 The structure shown in the figure does not constitute a limitation on the server 700, and the server 700 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0204] The computer device may be a terminal; Figure 8 It is a structural block diagram of a terminal provided in an embodiment of the present application. Generally, the terminal 800 includes: a processor 801, a memory 802, a voice receiving device 803 and a controller 804. The processor 801 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 801 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 801 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 801 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0205] The memory 802 may include one or more computer-readable storage media, which may be non-transitory. The memory 802 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 802 is used to store at least one instruction, which is used to be executed by the processor 801 to implement the lighting control method provided in the method embodiment of the present application.

[0206] In some embodiments, the terminal 800 may further optionally include: a peripheral device interface 805 and at least one peripheral device. The processor 801, the memory 802 and the peripheral device interface 805 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 805 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 806, an audio circuit 807 and a power supply 808.

[0207] The peripheral device interface 805 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 801 and the memory 802. In some embodiments, the processor 801, the memory 802, and the peripheral device interface 805 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 801, the memory 802, and the peripheral device interface 805 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0208] The radio frequency circuit 806 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 806 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 806 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 806 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 806 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 806 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0209] The audio circuit 807 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 801 for processing, or input them into the radio frequency circuit 806 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 800. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 801 or the radio frequency circuit 806 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 807 may also include a headphone jack.

[0210] The power supply 808 is used to power various components in the terminal 800. The power supply 808 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 808 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0211] Those skilled in the art will understand that Figure 8 The structure shown in the figure does not constitute a limitation on the terminal 800, and the terminal 800 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0212] The embodiment of the present application also provides a computer-readable storage medium, in which at least one program code is stored, and the at least one program code is loaded and executed by a processor to implement the vehicle data analysis method described in any of the above implementations. Optionally, the storage medium can be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium can be a ROM (Read-Only Memory), a RAM (Random Access Memory), a CD-ROM (Compact Disc Read-Only Memory), a magnetic tape, a floppy disk, and an optical data storage device.

[0213] An embodiment of the present application also provides a computer program product, which stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the vehicle data analysis method shown in the above embodiments.

[0214] In some embodiments, the computer program product involved in the embodiments of the present application may be deployed and executed on a computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed at multiple locations and interconnected by a communication network. Multiple computer devices distributed at multiple locations and interconnected by a communication network may constitute a blockchain system.

[0215] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0216] The above description is only for the purpose of facilitating those skilled in the art to understand the technical solution of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for analyzing vehicle data, characterized in that: The method comprises: Acquire national standard vehicle data of multiple vehicles, where the national standard vehicle data is vehicle data that meets the national standard protocol; Determine driving behavior data and charging behavior data of the multiple vehicles from the national standard vehicle data of the multiple vehicles, wherein the driving behavior data of the vehicles include multiple frames of driving behavior messages, which are messages generated after the vehicles are started, and the charging behavior data include multiple frames of charging behavior messages, which are messages generated after the vehicles are parked and charged; For any vehicle, determining a driving behavior feature of the vehicle based on a plurality of first buried points in a multi-frame driving behavior message of the vehicle; Determining a charging behavior feature of the vehicle based on a plurality of second buried points in the multi-frame charging behavior message; Based on the driving behavior characteristics of the plurality of vehicles and the charging behavior characteristics of the plurality of vehicles, a data analysis result is determined.

2. The method according to claim 1, characterized in that The multiple first buried points include vehicle status buried points, collection time buried points, accumulated mileage buried points, power state SOC buried points and location buried points; the driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location; The determining the driving behavior characteristics of the vehicle based on the multiple first buried points in the multi-frame driving behavior message includes: Determine a first message and a second message based on the vehicle status buried points in the multi-frame driving behavior messages, the vehicle status corresponding to the vehicle status buried point of the first message is started, and the vehicle status corresponding to the vehicle status buried point of the previous frame message of the first message is not started, the vehicle status corresponding to the vehicle status buried point of the second message is not started, and the vehicle status corresponding to the vehicle status buried point of the previous frame message of the second message is started; Determine the travel time of the vehicle based on the collection time corresponding to the collection time buried point in the first message; Determine the mileage of the vehicle based on the cumulative mileage corresponding to the cumulative mileage buried point in the first message and the cumulative mileage corresponding to the cumulative mileage buried point in the second message; Determining the power consumption of the vehicle based on the SOC corresponding to the SOC buried point in the first message and the SOC corresponding to the SOC buried point in the second message; Based on the location information corresponding to the location buried point in the first message and the location information corresponding to the location buried point in the second message, the starting location and the ending location of the vehicle are determined.

3. The method according to claim 1, characterized in that The plurality of second buried points include charging state buried points, collection time buried points, SOC buried points, position buried points, current buried points and voltage buried points; The charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging position, charging speed type, charging start voltage, charging end voltage and change trend of maximum single cell voltage during charging; The determining the charging behavior characteristics of the vehicle based on the multiple second buried points in the multiple frames of charging behavior messages includes: Determine a third message and a fourth message based on the charging status buried points in the multi-frame charging behavior message, the charging state corresponding to the charging state buried point of the third message is started, and the charging state corresponding to the charging state buried point of the previous frame message of the third message is not started, the charging state corresponding to the charging state buried point of the fourth message is not started, and the charging state corresponding to the charging state buried point of the previous frame message of the fourth message is started; Determine the charging start time and the charging end time of the vehicle based on the collection time corresponding to the collection time buried point in the third message and the collection time corresponding to the collection time buried point in the fourth message; Determine a charging start SOC and a charging end SOC of the vehicle based on the SOC corresponding to the SOC buried point in the third message and the SOC corresponding to the SOC buried point in the fourth message; determining a charge amount of the vehicle based on a charge start SOC and a charge end SOC of the vehicle; Determine the charging position of the vehicle based on the position information corresponding to the location buried point in the third message; Determine a charging start voltage and a charging end voltage of the vehicle based on a charging voltage corresponding to a voltage buried point in the third message and a charging voltage corresponding to a voltage buried point in the fourth message; Determine a plurality of fifth messages based on the collection time corresponding to the collection time buried point in the multiple frames of charging messages, where the collection time of the fifth message is between the collection time of the third message and the collection time of the fourth message; Determining a charging speed type of the vehicle based on the charging currents corresponding to the current buried points in the plurality of fifth messages; Based on the charging voltages corresponding to the voltage buried points in the multiple fifth messages, the maximum single cell voltage of the vehicle during the charging process is determined.

4. The method according to claim 1, characterized in that The driving behavior characteristics include travel time, mileage, power consumption, travel start location, and travel end location; the charging behavior characteristics include charging start time, charging end time, charging amount, charging start SOC, charging end SOC, charging location, charging speed type, charging start voltage, charging end voltage, and maximum single cell voltage during charging; The determining of the data analysis result based on the driving behavior characteristics of the plurality of vehicles and the charging behavior characteristics of the vehicles includes: Determine a travel time range, a travel mileage range, a power consumption range, a travel start location type, and a travel end location type based on the travel time, mileage, power consumption, travel start location, and travel end location of the multiple vehicles; Generate a first Excel table based on the travel start location type, the mileage range, the power consumption range, the travel start location type and the travel end location type; Determine a charging start time range, a charging end time range, a charging amount range, a charging start SOC range, a charging end SOC range, a charging position type, a target charging speed type, a charging start voltage range, a charging end voltage range, and a changing trend of a maximum single cell voltage during charging based on the charging start time, charging end time, charging amount, charging start SOC range, charging end SOC range, charging position type, target charging speed type, charging start voltage range, charging end voltage range, and a changing trend of a maximum single cell voltage during charging of the plurality of vehicles; Generate a second Excel spreadsheet based on the charging start time range, the charging end time range, the charging amount range, the charging start SOC range, the charging end SOC range, the charging position type, the target charging speed type, the charging start voltage range, the charging end voltage range and the overall change trend of the maximum single cell voltage during the charging process; Based on at least one driving behavior option in the first Excel table and at least one charging behavior option in the second Excel table, a visualization report is generated, where the visualization report is used to represent the data analysis result.

5. The method according to any one of claims 1 to 4, characterized in that: The method also includes at least one of the following implementations: Based on the data analysis result, determine first decision information for vehicle development, and based on the first decision information, provide data support for vehicle development; or, Determining second decision information for vehicle sales based on the data analysis result, and providing data support for vehicle sales based on the second decision information; or, Based on the data analysis results, third decision information for vehicle promotion is determined, and based on the third decision information, data support is provided for vehicle promotion.

6. The method according to any one of claims 1 to 4, characterized in that: The obtaining of national standard vehicle data of a plurality of vehicles includes: Determine the registration time of the vehicle in the data platform; Based on the registration time of the vehicle in the data platform, determining from the data platform the national standard vehicle data of the vehicle whose registration time exceeds a preset time and the national standard vehicle data of the vehicle whose registration time does not exceed the preset time; The determining of data analysis results based on the driving behavior characteristics of the plurality of vehicles and the charging behavior characteristics of the plurality of vehicles comprises: Determining duration types of the multiple vehicles based on the registration time of the multiple vehicles, the duration type being used to indicate whether the vehicle is a new vehicle or an old vehicle; Based on the duration types of the multiple vehicles, the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles are grouped to obtain a first group of vehicle characteristics and a second group of vehicle characteristics, wherein the first group of vehicle characteristics includes the driving behavior characteristics and the charging behavior characteristics of the new vehicle of the duration type, and the second group of vehicle characteristics includes the driving behavior characteristics and the charging behavior characteristics of the old vehicle of the duration type; Based on the first set of vehicle characteristics and the second set of vehicle characteristics, a first data analysis result and a second data analysis result are determined.

7. A vehicle data analysis device, characterized in that: The device comprises: An acquisition module, used to acquire national standard vehicle data of multiple vehicles, wherein the national standard vehicle data is vehicle data that meets the national standard protocol; A first determination module is used to determine driving behavior data and charging behavior data of the multiple vehicles from the national standard vehicle data of the multiple vehicles, wherein the driving behavior data of the vehicles include multiple frames of driving behavior messages, and the driving behavior messages are messages generated after the vehicles are started; and the charging behavior data include multiple frames of charging behavior messages, and the charging behavior messages are messages generated after the vehicles are parked and charged; A second determination module is used to determine, for any vehicle, a driving behavior feature of the vehicle based on a plurality of first buried points in a multi-frame driving behavior message of the vehicle; A third determination module, configured to determine a charging behavior feature of the vehicle based on a plurality of second buried points in the multi-frame charging behavior message; The fourth determination module is used to determine the data analysis result based on the driving behavior characteristics of the multiple vehicles and the charging behavior characteristics of the multiple vehicles.

8. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the vehicle data analysis method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the vehicle data analysis method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The product stores at least one program code, and the at least one program code is used to be executed by a processor to implement the vehicle data analysis method according to any one of claims 1 to 6.

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