Vehicle driving behavior scoring method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of Internet of Vehicles, and relates to a vehicle driving behavior scoring method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting Internet of Vehicles data of a new energy vehicle; based on the Internet of Vehicles data, determining at least one driving behavior scoring object of the vehicle in different travels; obtaining a scoring index value of each driving behavior scoring object; and scoring the vehicle driving behaviors in the target journey according to the driving behavior scoring objects and the corresponding scoring index values in the different journeys. By adopting the method, personalized accurate evaluation of the driving behavior can be realized for the operation condition of the new energy vehicle.

Description

Technical Field

[0001] The present application relates to the field of vehicle networking technology, and in particular to a vehicle driving behavior scoring method, device, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] In recent years, the importance of commercial vehicle driving behavior assessment has become increasingly prominent. By monitoring and evaluating driving behavior and promptly reminding drivers, drivers' safety awareness can be significantly improved, the occurrence of safety accidents can be reduced, and energy costs can be greatly saved by improving driving behavior.

[0003] The driving behavior evaluation method of traditional commercial vehicles can monitor and evaluate driving behavior based on safety, economy and other aspects. As the market penetration rate of new energy commercial vehicles accelerates, it is gradually found that different driving behaviors have a huge impact on power consumption under the same model and the same working conditions. However, due to the large differences between the operating conditions of new energy vehicles and traditional commercial vehicles, the key indicators for evaluating energy consumption and economy have also changed accordingly, and the driving behavior evaluation method of traditional commercial vehicles cannot meet the needs of new energy models.

[0004] Therefore, there is an urgent need for a vehicle driving behavior scoring method, device, computer equipment, computer-readable storage medium and computer program product that can achieve personalized and accurate evaluation of driving behavior for the operating conditions of new energy vehicles. Summary of the invention

[0005] Based on this, it is necessary to provide a vehicle driving behavior scoring method, device, computer equipment, computer-readable storage medium and computer program product that can achieve personalized and accurate evaluation of driving behavior for the operating conditions of new energy vehicles in response to the above technical problems.

[0006] In a first aspect, the present application provides a vehicle driving behavior scoring method, comprising:

[0007] Collect Internet of Vehicles data of new energy vehicles;

[0008] Determining at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data;

[0009] Obtain the scoring index value of each driving behavior scoring object respectively;

[0010] The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0011] In one embodiment, the Internet of Vehicles data includes vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data, cruise switch data, vehicle gross mass, road type, terrain and timestamp; the types of driving behavior scoring objects include vehicle speeding mileage, coasting mileage, auxiliary brake switch usage mileage, number of emergency brakes, accelerator pedal standard deviation and mileage with the accelerator pedal deeply depressed.

[0012] In one embodiment, after collecting the Internet of Vehicles data of new energy vehicles, the method further includes:

[0013] According to the timestamp, the starting time and stopping time of the vehicle are used as the basis for itinerary division, and the itinerary set corresponding to each vehicle is obtained;

[0014] The Internet of Vehicles data is sorted according to timestamps, and data mapping is performed between the sorted Internet of Vehicles data and each trip in the trip set.

[0015] In one embodiment, the step of obtaining the scoring index value of each driving behavior scoring object includes:

[0016] Determine at least one scoring dimension combination according to different road types, different terrains and different vehicle gross masses;

[0017] Determine the working condition value of each driving behavior scoring object under each scoring dimension combination according to the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data;

[0018] Based on the operating condition values ​​after eliminating outliers, the average value of each driving behavior scoring object under all scoring dimension combinations is comprehensively calculated and used as the index value.

[0019] In one embodiment, scoring the vehicle driving behavior in the target trip according to each driving behavior scoring object and the corresponding scoring index value in different trips includes:

[0020] Obtain the scoring benchmark value of each driving behavior scoring object in different trips;

[0021] The vehicle driving behavior in the target trip is scored based on the driving behavior scoring objects, scoring index values ​​and scoring benchmark values ​​in different trips.

[0022] In one embodiment, the scoring of the vehicle driving behavior in the target trip according to the driving behavior scoring objects, scoring index values ​​and scoring reference values ​​in different trips includes:

[0023] Obtain a separate score for each driving behavior scoring object according to each driving behavior scoring object, scoring index value, and scoring benchmark value in different trips;

[0024] Set the scoring weight of each driving behavior scoring object;

[0025] The total score of the vehicle driving behavior in the target trip is calculated based on the individual score and score weight of each driving behavior scoring object.

[0026] In a second aspect, the present application also provides a vehicle driving behavior scoring device, comprising:

[0027] Data acquisition module, used to collect Internet of Vehicles data of new energy vehicles;

[0028] A scoring object determination module, used to determine at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data;

[0029] A scoring index value acquisition module is used to obtain the scoring index value of each driving behavior scoring object respectively;

[0030] The scoring module is used to score the vehicle driving behavior in the target trip according to each driving behavior scoring object and the corresponding scoring index value in different trips.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Collect Internet of Vehicles data of new energy vehicles;

[0033] Determining at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data;

[0034] Obtain the scoring index value of each driving behavior scoring object respectively;

[0035] The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0037] Collect Internet of Vehicles data of new energy vehicles;

[0038] Determining at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data;

[0039] Obtain the scoring index value of each driving behavior scoring object respectively;

[0040] The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0041] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0042] Collect Internet of Vehicles data of new energy vehicles;

[0043] Determining at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data;

[0044] Obtain the scoring index value of each driving behavior scoring object respectively;

[0045] The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0046] The above-mentioned vehicle driving behavior scoring method, device, computer equipment, computer-readable storage medium and computer program product significantly improve the driver's safety awareness and reduce the occurrence of safety accidents by monitoring and evaluating driving behavior and reminding the driver in time. At the same time, by improving driving behavior, energy consumption costs are greatly saved. A new driving behavior evaluation method is proposed for the operating conditions of new energy commercial vehicles, which solves the problem that the traditional commercial vehicle driving behavior evaluation method cannot meet the needs of new energy models. Based on the Internet of Vehicles data transmitted back by Tbox, the key indicators affecting the vehicle power consumption are calculated, and the big data statistical baselines of different indicators under multi-dimensional conditions are extracted to achieve personalized and accurate evaluation of driving behavior. Two key indicators, namely, coasting use and auxiliary brake switch use, which are strongly related to the braking energy recovery of new energy pure electric vehicles, are added to effectively measure the utilization of energy recovery. According to different operating conditions (such as vehicle speed, road type, terrain, load, etc.), the big data statistical baselines of different indicators are extracted to achieve personalized and accurate evaluation of different conditions, making the driving behavior scoring fairer. Combined with multi-dimensional conditions such as road type, personalized baseline statistics based on big data are achieved, making the driving behavior scoring more fair and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A diagram showing an application environment of a vehicle driving behavior scoring method in one embodiment;

[0049] Figure 2 A schematic diagram of a process flow of a vehicle driving behavior scoring method in one embodiment;

[0050] Figure 3 is a flow chart of a vehicle driving behavior scoring method in another embodiment;

[0051] Figure 4 is a structural block diagram of a vehicle driving behavior scoring device in one embodiment;

[0052] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] The vehicle driving behavior scoring method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0055] The server 104 is used to collect Internet of Vehicles data of new energy vehicles through the terminal 102; determine at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data; obtain the scoring index value of each driving behavior scoring object respectively; and score the vehicle driving behavior in the target trip according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0056] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0057] In an exemplary embodiment, Figure 2 As shown, a vehicle driving behavior scoring method is provided, and the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps S202 to S208. Among them:

[0058] Step S202, collecting Internet of Vehicles data of new energy vehicles.

[0059] Specifically, various data generated by new energy vehicles during operation are collected in real time through on-board terminal devices. These data include but are not limited to driving behavior data, charging data, battery data, motor data, and DCDC data. These data can reflect the driving status of the vehicle and the behavior pattern of the driver.

[0060] Step S204: determining at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data.

[0061] Specifically, in the collected data, specific parameters or indicators for evaluating driving behavior are determined, which may be speeding mileage, coasting mileage, auxiliary brake switch use mileage, emergency braking times, throttle standard deviation, high throttle mileage, etc. These indicators can quantify the driver's behavior and serve as the basis for scoring.

[0062] Based on the Internet of Vehicles data, the driving behavior scoring objects of the vehicle in different trips can be determined. For example, typical dangerous behaviors such as sudden acceleration, sudden deceleration, sudden braking, speeding, etc. can be evaluated. These behaviors are considered to be key factors affecting driving safety and energy consumption.

[0063] Using the scoring objects and indicators determined above, a driving behavior scoring model is constructed. The model will score the driver's trip based on the safety and economy of the driving behavior. The model may use a clustering algorithm such as K-means++ to cluster driving behaviors for tendency analysis, or use a combined weighting method that combines the entropy weight method with the Analytic Hierarchy Process (AHP) to determine the weights of indicators at each level.

[0064] Step S206, respectively obtaining the scoring index value of each driving behavior scoring object.

[0065] Specifically, first, it is necessary to clarify which driving behaviors will be evaluated, which are called scoring objects. For example, possible scoring objects include speeding, sudden braking, sudden acceleration, coasting use, etc. For each scoring object, specific scoring indicators need to be defined. These indicators are quantifiable data points that are used to measure specific aspects of driving behavior. For example, the scoring indicator for speeding may be the total mileage driven over the speed limit.

[0066] The Internet of Vehicles technology is used to collect relevant vehicle operation data, such as vehicle speed, accelerator pedal opening, brake pedal opening, etc. These data are the basis for calculating the scoring index value.

[0067] Step S208 , scoring the vehicle driving behavior in the target trip according to the driving behavior scoring objects and corresponding scoring index values ​​in different trips.

[0068] Specifically, the score of each scoring object is calculated based on the deviation of the indicator value of each scoring object from a standard data (such as a benchmark value). The calculation of the score may involve simple comparisons (such as whether it exceeds the indicator value) or complex statistical or machine learning methods to determine the relative gap between the actual performance and the indicator value.

[0069] Different driving behaviors have different impacts on safety and economy, so each scoring object may be assigned a different weight to reflect its importance in the overall score. The scores of all scoring objects are weighted and summed according to their weights to obtain the total driving behavior score for the target trip. This total score can reflect the driver's overall performance in the trip, including safety and economy. Based on the total score, the driver's driving behavior can be analyzed, areas for improvement can be identified, and feedback can be provided. This can help drivers understand their driving habits and take appropriate measures to improve safety and economy.

[0070] In the above-mentioned vehicle driving behavior scoring method, by monitoring and evaluating driving behavior and reminding the driver in time, the driver's safety awareness is significantly improved, the occurrence of safety accidents is reduced, and by improving driving behavior, energy consumption costs are greatly saved. A new driving behavior evaluation method is proposed for the operating conditions of new energy commercial vehicles, which solves the problem that the traditional commercial vehicle driving behavior evaluation method cannot meet the needs of new energy models. Based on the Internet of Vehicles data transmitted back by Tbox, the key indicators affecting vehicle power consumption are calculated, and the big data statistical baselines of different indicators under multi-dimensional conditions are extracted to achieve personalized and accurate evaluation of driving behavior. Two key indicators, namely, coasting use and auxiliary brake switch use, which are strongly related to the braking energy recovery of new energy pure electric vehicles, are added to effectively measure the utilization of energy recovery. According to different operating conditions (such as vehicle speed, road type, terrain, load, etc.), the big data statistical baselines of different indicators are extracted to achieve personalized and accurate evaluation of different conditions, making the driving behavior scoring fairer. Combined with multi-dimensional conditions such as road type, personalized baseline statistics based on big data are achieved, making the driving behavior scoring fairer and more accurate.

[0071] In an exemplary embodiment, the Internet of Vehicles data includes vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data, cruise switch data, vehicle gross mass, road type, terrain and timestamp; the types of driving behavior scoring objects include vehicle speeding mileage, coasting mileage, auxiliary brake switch usage mileage, number of emergency brakes, accelerator pedal standard deviation and mileage with the accelerator pedal deeply depressed.

[0072] Specifically, Internet of Vehicles data includes:

[0073] Vehicle Identification Code: A code that uniquely identifies each vehicle and is used to distinguish data from different vehicles.

[0074] Vehicle speed: The vehicle’s current speed is a key parameter for evaluating driving behavior and safety.

[0075] Accelerator pedal opening: The depth to which the accelerator pedal is depressed, reflecting the force and frequency of the driver's acceleration.

[0076] Motor torque: The torque generated by the motor affects the vehicle's acceleration performance and is an important parameter for evaluating the performance of electric vehicles.

[0077] Brake pedal opening: The depth to which the brake pedal is depressed, used to assess the force and frequency of the driver's braking.

[0078] Auxiliary brake switch data: records whether the auxiliary brake system (such as ABS) is activated, reflecting the emergency braking situation.

[0079] Cruise switch data: records whether the cruise control is activated, reflecting whether the driver uses the cruise control function.

[0080] Gross Vehicle Mass: The total weight of the vehicle, including the vehicle's own weight and load, affects the vehicle's dynamic performance and braking performance.

[0081] Road type: The category of the road, such as highway, urban road, etc., affects driving behavior and safety.

[0082] Terrain: The terrain conditions of the road, such as flat, uphill, downhill, etc., affect the vehicle's energy consumption and driving style.

[0083] Timestamp: The specific time when the data is recorded, used for synchronization and analysis of data.

[0084] The types of driving behavior scoring objects include:

[0085] Vehicle speeding mileage: records the distance a vehicle travels in excess of the prescribed speed limit and is an important indicator for assessing driving safety.

[0086] Coasting mileage: records the distance the vehicle coasts without using the accelerator and brakes, reflecting the driver's use of energy recovery.

[0087] Auxiliary brake switch mileage: records the driving distance when the auxiliary brake system is activated, reflecting the frequency of emergency braking.

[0088] Number of emergency braking events: Records the number of emergency braking events, which is a key indicator for evaluating driving safety.

[0089] Accelerator pedal standard deviation: reflects the degree of variation in the accelerator pedal opening. A high standard deviation may mean unstable driving behavior.

[0090] Mileage with the accelerator pedal deeply depressed: records the driving distance with the accelerator pedal deeply depressed, reflecting the frequency of the driver's aggressive driving.

[0091] In this embodiment, the Internet of Vehicles data provides a comprehensive understanding of the vehicle's operating status and driver behavior, and the driving behavior scoring object is defined based on this data and is used to quantify and evaluate specific indicators of driving behavior. By analyzing these data and scoring objects, driving risks are evaluated through indicators such as speeding and sudden braking. Energy efficiency is evaluated through indicators such as coasting mileage and deep accelerator pedal depression. By evaluating the impact of driving behavior on vehicle performance and maintenance, operating costs are reduced. Combined with multi-dimensional working conditions such as road type and terrain, personalized and accurate evaluation is achieved. These data and scoring objects provide a comprehensive and quantitative framework for driving behavior evaluation, which helps to improve driving safety, reduce energy consumption and improve economy.

[0092] In an exemplary embodiment, after collecting the Internet of Vehicles data of new energy vehicles, the method further includes:

[0093] According to the timestamp, the starting time and stopping time of the vehicle are used as the basis for itinerary division, and the itinerary set corresponding to each vehicle is obtained;

[0094] Sort the Internet of Vehicles data according to timestamps, and perform data mapping between the sorted Internet of Vehicles data and each trip in the trip set.

[0095] Specifically, vehicle start time: the moment when the vehicle starts to move from a stationary state, which can usually be identified by the vehicle speed increasing from 0 to a certain threshold. Vehicle stop time: the moment when the vehicle changes from a moving state to a complete stop, which can be identified by the vehicle speed decreasing from a certain threshold to 0.

[0096] The continuous Internet of Vehicles data is divided into multiple independent trips according to the vehicle's start and stop times. Each trip represents the complete driving process of the vehicle from start to stop. This division helps to analyze the driving behavior of each trip separately, because different trips may involve different driving conditions and behavior patterns. The collected Internet of Vehicles data is sorted by timestamp. The timestamp is the specific time when each data point is recorded, ensuring that the order of the data is consistent with the actual time sequence. The sorted data can more accurately reflect the dynamic changes in the vehicle's driving process and provide accurate time series data for subsequent analysis.

[0097] Map the sorted IoV data to each trip in the trip set. This means assigning each data point to the corresponding trip. For example, if a trip starts at timestamp T1 and ends at timestamp T2, all data points within this time range will be mapped to this trip.

[0098] In this embodiment, the accuracy of data analysis can be ensured through precise trip division and data mapping, because the data of each trip is processed independently, reducing the possibility of data confusion. This processing method supports the evaluation of driving behavior for each trip and helps to identify driving problems in specific trips, such as speeding and sudden braking. Through detailed analysis of trips, fleet management can be optimized. For example, by identifying trips with high energy consumption, driving strategies can be adjusted to reduce energy consumption and costs. Based on trip-level data analysis, personalized driving improvement suggestions can be provided to drivers to improve driving safety and efficiency.

[0099] In an exemplary embodiment, Figure 3 As shown, the scoring index values ​​of each driving behavior scoring object are obtained respectively, including:

[0100] Step S302, determining at least one scoring dimension combination according to different road types, different terrains and different vehicle gross masses;

[0101] Step S304, determining the working condition value of each driving behavior scoring object under each scoring dimension combination according to the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data;

[0102] Step S306, based on the operating condition values ​​after removing abnormal values, the average values ​​of each driving behavior scoring object under all scoring dimension combinations are comprehensively calculated and used as the index value.

[0103] Specifically, at least one scoring dimension combination is determined according to different road types (e.g., urban roads, highways), different terrains (e.g., flat, uphill, downhill), and different vehicle gross masses (e.g., empty, fully loaded). These dimensions reflect the complexity of driving behavior evaluation because they affect the performance and energy consumption of the vehicle.

[0104] Based on the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data, these Internet of Vehicles data are used to determine the working condition value of each driving behavior scoring object under each scoring dimension combination. The working condition value refers to the actual performance of the driving behavior scoring object under specific conditions.

[0105] Before calculating the average value, outliers in the data need to be removed to avoid the impact of extreme data on the scoring results. Outliers may be caused by erroneous records, sensor failures, or other abnormal driving behaviors. Based on the operating condition values ​​after removing outliers, the average value of each driving behavior scoring object under all scoring dimension combinations is calculated comprehensively. This average value represents the general performance of the driving behavior scoring object under different conditions. The calculated average value is used as the scoring index value, which is a key reference for evaluating driving behavior. These index values ​​will be used to compare with the baseline value to determine the score of the driving behavior.

[0106] In this embodiment, by considering different road types, terrains, and gross vehicle mass, a more personalized and accurate evaluation of driving behavior can be performed. Eliminating outliers ensures the accuracy of the score and avoids the influence of erroneous data on the score results. By combining data from different dimensions, a more comprehensive driving behavior score can be obtained, which helps to more accurately evaluate the driver's performance. This method considers driving behavior under different conditions, making the score fairer because it reduces the influence of a single condition on the score result.

[0107] In an exemplary embodiment, the vehicle driving behavior in the target trip is scored according to each driving behavior scoring object and the corresponding scoring index value in different trips, including:

[0108] Obtain the scoring benchmark value of each driving behavior scoring object in different trips;

[0109] The vehicle driving behavior in the target trip is scored based on the driving behavior scoring objects, scoring index values ​​and scoring benchmark values ​​in different trips.

[0110] Specifically, first, determine which driving behaviors will be evaluated, which are called scoring objects, such as speeding mileage, coasting mileage, etc. Next, define specific scoring indicators for each scoring object, which are quantifiable data points used to measure driving behavior.

[0111] For each scoring object, a scoring benchmark value needs to be determined. This benchmark value can be a standard based on historical data analysis, industry standards, regulatory requirements, or expert opinions. The benchmark value represents the threshold of driving behavior that is considered safe or economical under specific conditions. For example, the benchmark value for speeding mileage may be the maximum speed limit stipulated by law.

[0112] Next, collect the Internet of Vehicles data for the specific target trip, including vehicle speed, accelerator pedal opening, motor torque, etc. Use the collected data to calculate the scoring index value for each scoring object. For example, if the scoring object is speeding mileage, then the specific mileage of speeding in the target trip needs to be calculated.

[0113] In this embodiment, a quantitative method is provided to evaluate driving behavior, so that the evaluation results are more objective and accurate. Personalized feedback is provided to drivers to help them understand their driving habits and guide them on how to improve them. By identifying bad driving behaviors, it helps to improve road safety and reduce energy consumption costs. It is used to evaluate and improve the driving behavior of new energy vehicles, aiming to improve driving safety and economy.

[0114] In an exemplary embodiment, the vehicle driving behavior in the target trip is scored according to each driving behavior scoring object, scoring index value and scoring reference value in different trips, including:

[0115] Obtain a separate score for each driving behavior scoring object according to each driving behavior scoring object, scoring index value, and scoring benchmark value in different trips;

[0116] Set the scoring weight of each driving behavior scoring object;

[0117] The total score of the vehicle driving behavior in the target trip is calculated based on the individual score and score weight of each driving behavior scoring object.

[0118] Specifically, for each driving behavior scoring object (such as speeding, sudden braking, etc.), its scoring index value (such as the mileage of speeding) is compared with the scoring benchmark value (such as the maximum speed limit stipulated by the law). According to the difference between the actual performance of the scoring object and the benchmark value, the individual score of each scoring object is calculated through the scoring formula. This formula takes into account the deviation between the actual performance and the benchmark value and converts it into a quantitative score. Among them, the scoring formula for the individual score of each scoring object is:

[0119] ; where x i is the scoring index value of the target trip, X i It is the scoring benchmark value for the target trip, and 75 (points) is the calculation coefficient, which can be customized.

[0120] Different driving behaviors have different impacts on safety and economy. Therefore, a weight is assigned to each scoring object to reflect its importance in the total score. For example, speeding may have a greater impact on safety and may be assigned a higher weight. Based on the individual scores and corresponding weights of each scoring object, the total driving behavior score for the target trip is calculated by weighted summation. This involves multiplying each individual score by its corresponding weight and then adding these weighted scores together. The total score can be calculated using the following formula:

[0121] ;in, is the weight value.

[0122] In this embodiment, a comprehensive driving behavior evaluation is provided by considering multiple driving behavior scoring objects. By weighting, it is identified which driving behaviors have a greater impact on safety and economy. By providing individual scores and overall scores, drivers are encouraged to identify and improve bad driving habits.

[0123] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0124] Based on the same inventive concept, the embodiment of the present application also provides a vehicle driving behavior scoring device for implementing the vehicle driving behavior scoring method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more vehicle driving behavior scoring device embodiments provided below can refer to the limitations of the vehicle driving behavior scoring method above, and will not be repeated here.

[0125] In an exemplary embodiment, Figure 4 As shown, a vehicle driving behavior scoring device is provided, comprising:

[0126] The data collection module 402 is used to collect the Internet of Vehicles data of new energy vehicles;

[0127] A scoring object determination module 406 is used to determine at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data;

[0128] The scoring index value acquisition module 408 is used to respectively acquire the scoring index value of each driving behavior scoring object;

[0129] The scoring module 410 is used to score the vehicle driving behavior in the target trip according to each driving behavior scoring object and the corresponding scoring index value in different trips.

[0130] In an exemplary embodiment, the Internet of Vehicles data includes vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data, cruise switch data, vehicle gross mass, road type, terrain and timestamp; the types of driving behavior scoring objects include vehicle speeding mileage, coasting mileage, auxiliary brake switch usage mileage, number of emergency brakes, accelerator pedal standard deviation and mileage with the accelerator pedal deeply depressed.

[0131] In an exemplary embodiment, the data processing module 404 is used to use the start time and stop time of the vehicle as the basis for dividing the itinerary according to the timestamp, and obtain a set of itineraries corresponding to each vehicle; sort the Internet of Vehicles data according to the timestamp, and perform data mapping between the sorted Internet of Vehicles data and each itinerary in the itinerary set.

[0132] In an exemplary embodiment, the scoring index value acquisition module 408 is specifically used to determine at least one scoring dimension combination according to different road types, different terrains and different vehicle gross masses; determine the operating condition value of each driving behavior scoring object under each scoring dimension combination according to the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data; based on the operating condition value after eliminating abnormal values, comprehensively calculate the average value of each driving behavior scoring object under all scoring dimension combinations and use it as the index value.

[0133] In an exemplary embodiment, the scoring module 410 is also used to obtain the scoring reference value of each driving behavior scoring object in different trips; and score the vehicle driving behavior in the target trip according to each driving behavior scoring object, scoring index value and scoring reference value in different trips.

[0134] In an exemplary embodiment, the scoring module 410 is also used to obtain a separate score for each driving behavior scoring object based on each driving behavior scoring object, scoring index value and scoring reference value in different trips; set a scoring weight for each driving behavior scoring object; and calculate a total score for the vehicle driving behavior in the target trip based on the separate score and scoring weight of each driving behavior scoring object.

[0135] Each module in the above-mentioned vehicle driving behavior scoring device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0136] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the Internet of Vehicles data of new energy vehicles. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a vehicle driving behavior scoring method is implemented.

[0137] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0138] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0139] Collect Internet of Vehicles data of new energy vehicles;

[0140] Based on the Internet of Vehicles data, determine at least one driving behavior scoring object of the vehicle in different trips;

[0141] Obtain the scoring index value of each driving behavior scoring object respectively;

[0142] The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0143] In one embodiment, the Internet of Vehicles data includes vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data, cruise switch data, vehicle gross mass, road type, terrain and timestamp; the types of driving behavior scoring objects include vehicle speeding mileage, coasting mileage, auxiliary brake switch usage mileage, number of emergency brakes, accelerator pedal standard deviation and mileage with the accelerator pedal deeply depressed.

[0144] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0145] According to the timestamp, the starting time and stopping time of the vehicle are used as the basis for itinerary division, and the itinerary set corresponding to each vehicle is obtained;

[0146] Sort the Internet of Vehicles data according to timestamps, and perform data mapping between the sorted Internet of Vehicles data and each trip in the trip set.

[0147] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0148] Determine at least one scoring dimension combination according to different road types, different terrains and different vehicle gross masses;

[0149] Determine the working condition value of each driving behavior scoring object under each scoring dimension combination according to the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data;

[0150] Based on the operating condition values ​​after eliminating outliers, the average value of each driving behavior scoring object under all scoring dimension combinations is comprehensively calculated and used as the index value.

[0151] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0152] According to the driving behavior scoring objects and corresponding scoring index values ​​in different trips, the vehicle driving behavior in the target trip is scored, including:

[0153] Obtain the scoring benchmark value of each driving behavior scoring object in different trips;

[0154] The vehicle driving behavior in the target trip is scored based on the driving behavior scoring objects, scoring index values ​​and scoring benchmark values ​​in different trips.

[0155] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0156] Obtain a separate score for each driving behavior scoring object according to each driving behavior scoring object, scoring index value, and scoring benchmark value in different trips;

[0157] Set the scoring weight of each driving behavior scoring object;

[0158] The total score of the vehicle driving behavior in the target trip is calculated based on the individual score and score weight of each driving behavior scoring object.

[0159] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0160] Collect Internet of Vehicles data of new energy vehicles;

[0161] Based on the Internet of Vehicles data, determine at least one driving behavior scoring object of the vehicle in different trips;

[0162] Obtain the scoring index value of each driving behavior scoring object respectively;

[0163] The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0164] In one embodiment, the Internet of Vehicles data includes vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data, cruise switch data, vehicle gross mass, road type, terrain and timestamp; the types of driving behavior scoring objects include vehicle speeding mileage, coasting mileage, auxiliary brake switch usage mileage, number of emergency brakes, accelerator pedal standard deviation and mileage with the accelerator pedal deeply depressed.

[0165] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0166] According to the timestamp, the starting time and stopping time of the vehicle are used as the basis for itinerary division, and the itinerary set corresponding to each vehicle is obtained;

[0167] Sort the Internet of Vehicles data according to timestamps, and perform data mapping between the sorted Internet of Vehicles data and each trip in the trip set.

[0168] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0169] Determine at least one scoring dimension combination according to different road types, different terrains and different vehicle gross masses;

[0170] Determine the working condition value of each driving behavior scoring object under each scoring dimension combination according to the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data;

[0171] Based on the operating condition values ​​after eliminating outliers, the average value of each driving behavior scoring object under all scoring dimension combinations is comprehensively calculated and used as the index value.

[0172] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0173] According to the driving behavior scoring objects and corresponding scoring index values ​​in different trips, the vehicle driving behavior in the target trip is scored, including:

[0174] Obtain the scoring benchmark value of each driving behavior scoring object in different trips;

[0175] The vehicle driving behavior in the target trip is scored based on the driving behavior scoring objects, scoring index values ​​and scoring benchmark values ​​in different trips.

[0176] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0177] Obtain a separate score for each driving behavior scoring object according to each driving behavior scoring object, scoring index value, and scoring benchmark value in different trips;

[0178] Set the scoring weight of each driving behavior scoring object;

[0179] The total score of the vehicle driving behavior in the target trip is calculated based on the individual score and score weight of each driving behavior scoring object.

[0180] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0181] Collect Internet of Vehicles data of new energy vehicles;

[0182] Based on the Internet of Vehicles data, determine at least one driving behavior scoring object of the vehicle in different trips;

[0183] Obtain the scoring index value of each driving behavior scoring object respectively;

[0184] The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

[0185] In one embodiment, the Internet of Vehicles data includes vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data, cruise switch data, vehicle gross mass, road type, terrain and timestamp; the types of driving behavior scoring objects include vehicle speeding mileage, coasting mileage, auxiliary brake switch usage mileage, number of emergency brakes, accelerator pedal standard deviation and mileage with the accelerator pedal deeply depressed.

[0186] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0187] According to the timestamp, the starting time and stopping time of the vehicle are used as the basis for itinerary division, and the itinerary set corresponding to each vehicle is obtained;

[0188] Sort the Internet of Vehicles data according to timestamps, and perform data mapping between the sorted Internet of Vehicles data and each trip in the trip set.

[0189] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0190] Determine at least one scoring dimension combination according to different road types, different terrains and different vehicle gross masses;

[0191] Determine the working condition value of each driving behavior scoring object under each scoring dimension combination according to the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data;

[0192] Based on the operating condition values ​​after eliminating outliers, the average value of each driving behavior scoring object under all scoring dimension combinations is comprehensively calculated and used as the index value.

[0193] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0194] According to the driving behavior scoring objects and corresponding scoring index values ​​in different trips, the vehicle driving behavior in the target trip is scored, including:

[0195] Obtain the scoring benchmark value of each driving behavior scoring object in different trips;

[0196] The vehicle driving behavior in the target trip is scored based on the driving behavior scoring objects, scoring index values ​​and scoring benchmark values ​​in different trips.

[0197] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0198] Obtain a separate score for each driving behavior scoring object according to each driving behavior scoring object, scoring index value, and scoring benchmark value in different trips;

[0199] Set the scoring weight of each driving behavior scoring object;

[0200] The total score of the vehicle driving behavior in the target trip is calculated based on the individual score and score weight of each driving behavior scoring object.

[0201] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0202] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0203] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0204] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle driving behavior scoring method, characterized in that: The method comprises: Collect Internet of Vehicles data of new energy vehicles; Determining at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data; Obtain the scoring index value of each driving behavior scoring object respectively; The vehicle driving behavior in the target trip is scored according to each driving behavior scoring object in different trips and the corresponding scoring index value.

2. The method according to claim 1, characterized in that The Internet of Vehicles data includes vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data, cruise switch data, vehicle gross mass, road type, terrain and timestamp; the types of driving behavior scoring objects include vehicle speeding mileage, coasting mileage, auxiliary brake switch usage mileage, number of emergency brakes, accelerator pedal standard deviation and mileage with the accelerator pedal deeply depressed.

3. The method according to claim 2, characterized in that After collecting the Internet of Vehicles data of new energy vehicles, the method further includes: According to the timestamp, the starting time and stopping time of the vehicle are used as the basis for itinerary division, and the itinerary set corresponding to each vehicle is obtained; The Internet of Vehicles data is sorted according to timestamps, and data mapping is performed between the sorted Internet of Vehicles data and each trip in the trip set.

4. The method according to claim 3, characterized in that The step of respectively obtaining the scoring index value of each driving behavior scoring object includes: Determine at least one scoring dimension combination according to different road types, different terrains and different vehicle gross masses; Determine the working condition value of each driving behavior scoring object under each scoring dimension combination according to the vehicle identification code, vehicle speed, accelerator pedal opening, motor torque, brake pedal opening, auxiliary brake switch data and cruise switch data; Based on the operating condition values ​​after eliminating outliers, the average value of each driving behavior scoring object under all scoring dimension combinations is comprehensively calculated and used as the indicator value.

5. The method according to claim 1, characterized in that Scoring the vehicle driving behavior in the target trip according to the driving behavior scoring objects and corresponding scoring index values ​​in different trips includes: Obtain the scoring benchmark value of each driving behavior scoring object in different trips; The vehicle driving behavior in the target trip is scored based on the driving behavior scoring objects, scoring index values ​​and scoring benchmark values ​​in different trips.

6. The method according to claim 5, characterized in that Scoring the vehicle driving behavior in the target trip according to the driving behavior scoring objects, scoring index values ​​and scoring reference values ​​in different trips includes: Obtain a separate score for each driving behavior scoring object according to each driving behavior scoring object, scoring index value, and scoring benchmark value in different trips; Set the scoring weight of each driving behavior scoring object; The total score of the vehicle driving behavior in the target trip is calculated based on the individual score and score weight of each driving behavior scoring object.

7. A vehicle driving behavior scoring device, characterized in that: The device comprises: Data acquisition module, used to collect Internet of Vehicles data of new energy vehicles; A scoring object determination module, used to determine at least one driving behavior scoring object of the vehicle in different trips based on the Internet of Vehicles data; A scoring index value acquisition module is used to obtain the scoring index value of each driving behavior scoring object respectively; The scoring module is used to score the vehicle driving behavior in the target trip according to each driving behavior scoring object and the corresponding scoring index value in different trips.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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