Energy consumption optimization method, device, equipment, storage medium and system

Abstract

The present application discloses a vehicle energy consumption optimization method, device, equipment, storage medium and system, the method comprising: generating a personnel tag based on the driver's personal information and historical driving data; analyzing the driver's driving behavior based on the personnel tag and current driving data to obtain the type of the driver's current driving behavior; obtaining the target driving data within a preset period before and after the high energy consumption event, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle-mounted monitoring terminal; and making energy consumption optimization suggestions for the driver and the vehicle based on the type and the correlation. The present application can more comprehensively mine energy consumption correlation factors, making the proposed energy consumption optimization suggestions more accurate and comprehensive.

Description

Technical Field

[0001] The present invention relates to the field of energy-saving driving technology, and in particular to a vehicle energy consumption optimization method, device, equipment, storage medium and system. Background Art

[0002] How to reduce the TCO (total cost of ownership) of operating vehicles is a key research topic for vehicle manufacturers. Among them, vehicle energy consumption is one of the key influencing factors in TCO. Therefore, the TCO of operating vehicles can be reduced by optimizing energy consumption.

[0003] In the prior art, the energy consumption optimization of traditional vehicles is based on the driver's driving behavior and the vehicle's status data, and the driver's driving behavior is evaluated to make energy consumption optimization suggestions. This energy consumption analysis method ignores the driver's personal factors. Therefore, this method does not fully explore the energy consumption related factors, resulting in inaccurate and incomplete energy consumption optimization suggestions.

[0004] Therefore, there is a technical problem in the prior art that the energy consumption optimization suggestions proposed are inaccurate and incomplete. Summary of the invention

[0005] The main purpose of the present invention is to provide a vehicle energy consumption optimization method, device, equipment, storage medium and system, aiming to solve the technical problem that the proposed energy consumption optimization suggestions are inaccurate and incomplete;

[0006] To achieve the above object, the present invention provides a vehicle energy consumption optimization method, the vehicle energy consumption optimization method comprising the following steps:

[0007] Generate a person label based on the driver’s personal information and historical driving data;

[0008] Based on the personnel tag and the current driving data, the driving behavior of the driver is evaluated to obtain an evaluation result;

[0009] Determining the correlation between the high energy consumption event and the driving data during the period when the high energy consumption event occurred;

[0010] Based on the evaluation results and the correlation, energy consumption optimization suggestions are provided to the driver and the vehicle.

[0011] In a possible implementation manner of the present application, the step of evaluating the driving behavior of the driver based on the personnel tag and the current driving data to obtain an evaluation result includes:

[0012] Generate a person label based on the driver’s personal information and historical driving data;

[0013] Analyzing the driver's driving behavior based on the personnel tag and the current driving data to obtain the type of the driver's current driving behavior;

[0014] Obtaining target driving data within a preset period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal;

[0015] Based on the types and the correlations, energy consumption optimization suggestions are made to the driver and the vehicle.

[0016] In a possible implementation manner of the present application, the step of analyzing the driving behavior of the driver based on the personnel tag and the current driving data to obtain the type of the driver's current driving behavior includes:

[0017] Inputting the current driving data into a preset driving behavior analysis model;

[0018] Based on the preset driving behavior analysis model, the current driving data is analyzed and processed to obtain the type of the driver's driving behavior, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels.

[0019] In a possible implementation manner of the present application, the step of analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driver's driving behavior includes:

[0020] Calculating the current driving data to obtain at least one current driving behavior indicator;

[0021] Determining the target score for each current driving behavior indicator based on the corresponding relationship between the preset driving behavior indicator and the target score;

[0022] Based on the entropy weight method, the target score is calculated to obtain the weight of each driving behavior indicator;

[0023] Calculating the target scores of the current driving behavior indicators based on the weights of the driving behavior indicators to obtain a first calculation result;

[0024] Determining an adjustment coefficient for the first calculation result based on a preset person tag and the current driving data;

[0025] Based on the adjustment coefficient, adjusting the first calculation result to obtain a second calculation result;

[0026] Based on the corresponding relationship between the preset second calculation result and the driving behavior type, the type of the driver's current driving behavior is determined.

[0027] In a possible implementation manner of the present application, the step of calculating the target score based on the entropy weight method to obtain the weight of each driving behavior indicator further includes:

[0028] Normalizing the target score to obtain a standardized target score;

[0029] Calculating the standardized target score to obtain the information entropy of each current driving behavior indicator;

[0030] Based on the information entropy and the target score, the weights corresponding to the current driving behavior indicators are obtained.

[0031] In a possible implementation manner of the present application, the step of determining the correlation between the high energy consumption event and the target driving data includes:

[0032] The change trend of each driving data within the period of occurrence of the high energy consumption event on the same time axis is analyzed to obtain the correlation between the high energy consumption event and the target driving data.

[0033] In a possible implementation manner of the present application, after the step of determining the correlation between the high energy consumption event and the target driving data, the method further includes:

[0034] Collecting original driving data of the current vehicle, and arranging the original driving data in chronological order to obtain a time series database;

[0035] Conduct frequency analysis on the time series data related to high energy consumption events in the time series database, determine the data characteristics of the time series data related to high energy consumption events, and generate an energy consumption experience library for use in vehicle research and development.

[0036] The present application also provides a vehicle energy consumption optimization device, the device comprising:

[0037] A generation module, used to generate a person label based on the driver's personal information and historical driving data;

[0038] A first determination module is used to analyze the driving behavior of the driver based on the personnel tag and the current driving data to obtain the type of the driver's current driving behavior;

[0039] A second determination module is used to obtain target driving data within a preset period before and after a high energy consumption event occurs, and determine the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal;

[0040] A proposal module is used to propose energy consumption optimization suggestions to the driver and the vehicle based on the type and the correlation.

[0041] The present application also provides a vehicle energy consumption optimization device, which includes: a memory, a processor, and a vehicle energy consumption optimization program stored in the memory and executable on the processor, wherein the vehicle energy consumption optimization program is configured to implement the steps of the vehicle energy consumption optimization method as described in any one of the above items.

[0042] The present application also provides a vehicle energy consumption optimization system, which includes: a vehicle-mounted monitoring terminal and any of the vehicle energy consumption optimization devices described above.

[0043] The present application provides a vehicle energy consumption optimization method, device, equipment, storage medium and system. Compared with the energy consumption analysis method in the prior art that evaluates the driver's driving behavior based on the driver's driving data to make energy consumption optimization suggestions, the present application generates a personnel label based on the driver's personal information and historical driving data; analyzes the driver's driving behavior based on the personnel label and current driving data to obtain the type of the driver's current driving behavior; obtains target driving data within a preset time period before and after the high energy consumption event occurs, and determines the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the on-board monitoring terminal; and makes energy consumption optimization suggestions for the driver and the vehicle based on the type and the correlation. It can be understood that the present application analyzes the driver's driving behavior not only based on the driver's current driving data, but also based on the driver's personal label, and performs correlation analysis on the driving data in a preset time period before and after a high-energy consumption event. Based on the types and correlations, energy consumption optimization suggestions are made to the driver and the vehicle, which can more comprehensively explore energy consumption related factors and make the proposed energy consumption optimization suggestions more accurate and comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a flow chart of a first embodiment of a vehicle energy consumption optimization method of the present application;

[0045] Figure 2 This is a schematic diagram of the first scenario involved in the vehicle energy consumption optimization method of this application;

[0046] Figure 3It is a structural schematic diagram of a vehicle energy consumption optimization device in a hardware operating environment involved in an embodiment of the present invention;

[0047] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0049] The present application embodiment provides a vehicle energy consumption optimization method, referring to Figure 1 , Figure 1 A schematic diagram of a flow chart of an embodiment of a vehicle energy consumption optimization method of the present application.

[0050] In this embodiment, the vehicle energy consumption optimization method includes:

[0051] Step S10: Generate a person tag based on the driver's personal information and historical driving data;

[0052] Step S20: Analyze the driver's driving behavior based on the personnel tag and the current driving data to obtain the type of the driver's current driving behavior;

[0053] Step S30: obtaining target driving data within a preset period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal;

[0054] Step S40: Based on the type and the correlation, energy consumption optimization suggestions are made to the driver and the vehicle.

[0055] This embodiment aims to solve the technical problem that the proposed energy consumption optimization suggestions are inaccurate and incomplete. That is, compared with the prior art in which the driver's driving behavior is evaluated based on the driver's driving data to propose energy consumption optimization suggestions, the driver's driving behavior is evaluated not only based on the driver's driving data, but also based on the driver's personal tag, and energy consumption optimization suggestions are proposed to the driver and the vehicle in combination with the correlation between the high energy consumption event and the driving data in the preset time period before and after the high energy consumption event, so that accurate and comprehensive energy consumption optimization suggestions can be proposed.

[0056] As an example, the vehicle energy consumption optimization method can be applied to a vehicle energy consumption optimization device, which belongs to a vehicle energy consumption optimization system.

[0057] As an example, vehicles include commercial vehicles and private cars, wherein the commercial vehicles may be trucks and passenger cars with more than nine seats, etc., without specific limitation.

[0058] For ease of description, the following uses a commercial vehicle as an example.

[0059] As an example, vehicle energy consumption refers to the energy consumed during the driving of the vehicle, which may be vehicle fuel consumption and alcohol consumption, etc., without specific limitation.

[0060] As an example, vehicle energy consumption optimization may be achieved by optimizing the energy consumption by the driver during driving, or by improving the quality of the vehicle during the vehicle development process to optimize the energy consumption, etc., without specific limitation.

[0061] As an example, the drivers of vehicles are centrally managed by a vehicle-related operating company, wherein the vehicle operating company manages the personal information and historical driving information of the drivers.

[0062] As an example, the driver's personal information includes the driver's age, gender, height, and weight, etc., which are not specifically limited.

[0063] As an example, historical driving data includes historical driving tracks and historical vehicle status, etc., which are not specifically limited.

[0064] As an example, see Figure 2 When the driver uses the vehicle, the driver needs to enter personal information such as age, height and weight through the user terminal, and upload personal photos through the user terminal. The user terminal uploads the information entered by the driver and the uploaded photos to the intelligent network platform, and then uploads them to the big data center through the intelligent network platform.

[0065] As an example, see Figure 2 When the driver starts the vehicle, the facial camera of the driving recorder in the car collects the driver's facial image and uploads it to the video or image server. The video or image server extracts information based on the facial image, such as gender and age group, and uploads it to the intelligent network platform, and then uploads it to the big data center through the intelligent network platform.

[0066] As an example, the driver can upload personal information and photos through a mobile terminal and an in-vehicle infotainment device, without specific limitation.

[0067] As an example, the intelligent connected platform verifies the information input by the user terminal and the uploaded photos based on the information extracted from the video or image server to ensure the validity of the information and archives it.

[0068] As an example, when a driver uses a vehicle, the driver needs to enter the basic vehicle information such as the VIN (Vehicle Identification Number) of the currently used vehicle through the user terminal. The intelligent network connection platform binds the driver to the vehicle and archives it so that the driver's historical driving data can be called up next time.

[0069] As an example, see Figure 2 During the driving process of the vehicle, the ECU (Electronic Control Unit, also known as the on-board computer) in the on-board monitoring terminal periodically broadcasts data such as the vehicle's driving status, speed, throttle opening, gear position, and brake opening to the CAN (Controller Area Network) bus, and uploads it to the big data center through the network module, where the network module can be a 4G network module or a 5G network module, etc., without specific limitation.

[0070] The specific steps are as follows:

[0071] Step S10: Generate a person tag based on the driver's personal information and historical driving data;

[0072] In this embodiment, the big data center obtains the driver's personal information and historical driving data. Based on the driver's personal information and historical driving data, the driver's personal information and historical driving behavior can be obtained, and a personnel label can be generated; wherein the personnel label includes a gender label, an age label, a driving experience label, a driving habit label, and a travel habit label, etc.

[0073] Step S20: obtaining target driving data within a preset period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal;

[0074] As an example, the current driving data is all driving data recorded during the process of the driver driving the current vehicle.

[0075] As an example, the current driving data includes vehicle operating status data, vehicle positioning data, vehicle actual load, vehicle external condition data, and the like.

[0076] In this embodiment, driving behavior types are divided into three types: safety type, economic type and loss type. Based on the current driving data and the personnel label, different driving behaviors of different drivers during driving can be classified, and based on the classification results, personalized energy consumption optimization suggestions can be made to the driver.

[0077] In this embodiment, the step of analyzing the driving behavior of the driver based on the personnel tag and the current driving data to obtain the type of the driver's current driving behavior includes:

[0078] Step A1: inputting the current driving data into a preset driving behavior analysis model;

[0079] Step A2: Based on the preset driving behavior analysis model, the current driving data is analyzed and processed to obtain the type of the driver's driving behavior, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels.

[0080] In this embodiment, the current driving data is input into a preset driving behavior analysis model, and the current driving data is analyzed and processed based on the preset driving behavior analysis model to obtain the type of the driver's driving behavior, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels.

[0081] As an example, historical driving data is obtained, and a type label of the historical driving data is generated. Based on the historical driving data and the type label of the historical driving data, a preset model to be trained is iteratively trained to obtain the driving behavior analysis model that meets the accuracy requirements.

[0082] The step of iteratively training the preset model to be trained based on the historical driving data and the type label of the historical driving data to obtain the driving behavior analysis model that meets the accuracy condition includes:

[0083] The historical driving data is input into the preset model to be trained to obtain the type of historical driving behavior; the difference between the historical driving data and the type label of the historical driving data is calculated to obtain an error result; based on the error result, it is determined whether the error result meets the error standard indicated by the preset error threshold range.

[0084] If the error result does not meet the error standard indicated by the preset error threshold range, return to the step of inputting the current driving data into the preset driving behavior analysis model, and analyzing and processing the current driving data based on the preset driving behavior analysis model until the training error result meets the error standard indicated by the preset error threshold range, then stop training to obtain the driving behavior analysis model.

[0085] In this embodiment, the step of analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driver's driving behavior includes:

[0086] Step B1: Calculating the current driving data to obtain at least one current driving behavior index;

[0087] Step B2: determining the target score of each current driving behavior indicator based on the corresponding relationship between the preset driving behavior indicator and the target score;

[0088] Step B3: Calculating the target score based on the entropy weight method to obtain the weight of each driving behavior indicator;

[0089] In this embodiment, current driving data is obtained, and the current driving data is calculated to obtain at least one current driving behavior indicator, wherein the driving behavior indicator may be the percentage of idling time in total driving time, average energy consumption, the percentage of speeding time in total driving time, number of braking times, etc., without specific limitation.

[0090] In this embodiment, the target score of each current driving behavior indicator is determined based on the correspondence between the preset driving behavior indicator and the target score, wherein the correspondence between the driving behavior indicator and the target score is determined based on the numerical value of the driving behavior indicator and historical experience effect data.

[0091] As an example, different values ​​of the percentage of idling time to total driving time correspond to different target scores.

[0092] In this embodiment, the target score is calculated based on the entropy weight method to obtain the weight of each driving behavior indicator.

[0093] Step B4: calculating the target scores of the current driving behavior indicators based on the weights of the driving behavior indicators to obtain a first calculation result;

[0094] Step B5: determining an adjustment coefficient for the first calculation result based on a preset person tag and the current driving data;

[0095] Step B6: adjusting the first calculation result based on the adjustment coefficient to obtain a second calculation result;

[0096] Step B7: Determine the type of the driver's current driving behavior based on the corresponding relationship between the preset second calculation result and the driving behavior type.

[0097] In this embodiment, the target score of each current driving behavior index is multiplied by the corresponding pre-generated weight, and the scores of each current driving behavior index after weight calculation are added to obtain the first calculation result of the driver.

[0098] In this embodiment, the driving behavior of the driver is scored based on the current driving data to obtain a first score, wherein the score is in percentage.

[0099] In this embodiment, since each driver has different gender, age, driving experience, driving habits and travel habits, and since the performance of different models of vehicles is different, analyzing the driving behavior only based on the current driving data of the vehicle is not comprehensive enough. Therefore, it is necessary to adjust the first score based on the personnel label and the above-mentioned vehicle information, so that the driver's driving behavior can be analyzed more targetedly and comprehensively.

[0100] As an example, based on the personnel tag and the above-mentioned vehicle information, the adjustment ratio of the first score can be adjusted based on the adjustment ratio set based on historical experience effect data and manually adjusted, and there is no specific limitation.

[0101] As an example, based on age labels, different adjustment ratios are defined for young, middle-aged and elderly people respectively; based on the weather conditions of the current driving cycle, different adjustment ratios are defined for sunny days, foggy days, rainy days and snowy days respectively; based on the vehicle production date and model, different adjustment ratios are defined respectively.

[0102] As an example, if the first score obtained above is 78 points, after adjustment based on the adjustment ratio, the second score obtained is 86 points.

[0103] In this embodiment, the driving behavior of the driver is classified based on the corresponding relationship between the preset second score and the driving behavior type.

[0104] As an example, the correspondence between the above-mentioned preset second score and the driving behavior type can be 60-80 points for safe driving behavior, 80-100 points for economical driving behavior, 0-60 points for lossy driving behavior, etc., without specific limitation.

[0105] As an example, if the second score obtained above is 86 points, then the type of driving behavior of the driver is economical; if the second score obtained above is 56 points, then the type of driving behavior of the driver is lossy.

[0106] As an embodiment, the type of driving behavior is obtained and the driver is classified into a novice driver, a skilled driver and an experienced driver, providing a humanized interactive experience and making it easier for the driver to accept reminders and notifications.

[0107] In this embodiment, the step of calculating the target score based on the entropy weight method to obtain the weight of each driving behavior indicator includes:

[0108] Step C1: normalizing the target score to obtain a standardized target score;

[0109] Step C2: Calculating the standardized target score to obtain the information entropy of each current driving behavior indicator;

[0110] Step C3: Based on the information entropy and the target score, the weights corresponding to the current driving behavior indicators are obtained.

[0111] In this embodiment, based on the correspondence between the preset driving behavior indicators and the target scores, the scores of each current driving behavior indicator are determined, and the scores of each current driving behavior indicator conform to the form of a normal distribution. Based on the expected value, the outliers far from the distribution are cleaned out, and the influence of some abnormal driving behaviors on the driving behavior score can be removed. The expected value of the indicator can be automatically updated by supplementing historical data and manually modified, without specific limitation.

[0112] In this embodiment, after cleaning the scores of the current driving behavior indicators, the cleaned scores are standardized to obtain standardized results;

[0113] As an example, the standardized target score is calculated to obtain the information entropy of each current driving behavior indicator; based on the information entropy and the target score, the weight corresponding to each current driving behavior indicator is obtained.

[0114] As an example, the weight may be calculated or manually set, and is not specifically limited.

[0115] Step S30: obtaining target driving data within a preset period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal;

[0116] In this embodiment, after the correlation between the driving behavior and economic energy consumption is obtained based on the evaluation of the driver's driving behavior, it is also necessary to evaluate the correlation between the current vehicle's driving data and energy consumption. To facilitate the evaluation, only the driving data within the period of high-energy consumption events with higher correlation is analyzed. The high-energy consumption event is determined based on the energy consumption data in the current driving data monitored by the on-board monitoring terminal. If the monitored energy consumption data is higher than the preset energy consumption threshold, it is determined that a high-energy consumption event has occurred.

[0117] The step of determining the correlation between the high energy consumption event and the driving data within the period of occurrence of the high energy consumption event comprises:

[0118] Step C1: analyzing the change trend of each driving data within the period of occurrence of the high energy consumption event on the same time axis to obtain the correlation between the high energy consumption event and the target driving data.

[0119] As an example, Figure 2 As shown, the on-board monitoring terminal listens to CAN data and monitors the average energy consumption of the current engine per 100 kilometers. When the energy consumption is higher than the preset energy consumption threshold, the high energy consumption event is recorded, and the CAN data during the period of high energy consumption event is stored locally. The data is compressed and stored in a compressed format and uploaded to the intelligent network connection platform through SFTP (Secret File Transfer Protocol). The intelligent network connection platform uploads the compressed data to the enterprise data lake of the vehicle operation enterprise platform. The data lake stores the compressed data in the original format. The data granularity (the degree of refinement and integration of the data stored in the data lake) is based on the definition in DBC (database management software), and continuous events are deduplicated at the same time.

[0120] As an example, the intelligent connected platform manages the DBC of the vehicle monitoring terminal and dynamically analyzes the data, and uploads it to the big data center after compression. The real-time analysis program of the big data center analyzes and stores the data, thereby locating high-energy consumption events and realizing dynamic data upload.

[0121] As an example, the time period when the high energy consumption event occurs may be within 30 seconds before and after the high energy consumption event occurs, or within 40 seconds before and after the high energy consumption event occurs, and is not specifically limited.

[0122] As an example, various driving data within the period when a high energy consumption event occurs are obtained from the data lake, and the above driving data are placed on the same time axis, and their changing trends are observed and analyzed to obtain the correlation between the high energy consumption event and the target driving data.

[0123] As an example, the correlation may be the degree of correlation and whether there is correlation, etc., which is not specifically limited.

[0124] Step S40: Based on the type and the correlation, energy consumption optimization suggestions are made to the driver and the vehicle.

[0125] In this embodiment, based on the above analysis results of the driver's driving behavior and the correlation between the above high energy consumption events and the driving data during the period when the high energy consumption events occurred, energy consumption optimization suggestions are made to the driver and the vehicle R&D.

[0126] As an example, the energy consumption optimization suggestion may be to regulate the driver's driving behavior and improve the vehicle performance and driving comfort, etc., without specific limitation.

[0127] As an example, when the driver finishes using the vehicle, he / she inputs the end of driving in the mobile terminal, and the mobile terminal feeds back the energy consumption optimization suggestions made to the driver, which serves as suggestions and prompts to the driver and can optimize the energy consumption of the driver.

[0128] As an example, when the driver finishes using the vehicle, the driving data of the vehicle during the driving process is stored in the above-mentioned enterprise data lake for R&D personnel to improve the performance of the vehicle on the R&D side.

[0129] In this embodiment, the driver's driving behavior is evaluated not only based on the driver's driving behavior and the vehicle's status data, but also based on the driver's personal tag. In combination with the correlation between high-energy consumption events and the driving data during the period when the high-energy consumption events occurred, energy consumption optimization suggestions are made to the driver and the vehicle. Energy consumption optimization can be performed from multiple angles, and accurate and comprehensive energy consumption optimization suggestions can be made.

[0130] Furthermore, based on the first embodiment and the second embodiment of the present application, another embodiment of the present application is provided. In this embodiment, after the step of determining the correlation between the high energy consumption event and the target driving data, the method further includes:

[0131] Step E1: collecting original driving data of the current vehicle, and arranging the original driving data in chronological order to obtain a time series database;

[0132] Step E2: Perform frequency analysis on the time series data related to high energy consumption events in the time series database, determine the data characteristics of the time series data related to high energy consumption events, and generate an energy consumption experience library for use in vehicle research and development.

[0133] As an example, the CAN bus collects driving data generated by the vehicle during driving in real time and stores it locally. Therefore, the CAN frame data is original and can truly reflect the driving status of the vehicle.

[0134] As an example, the original CAN frame data is collected. Due to network and other reasons, the CAN bus uploads vehicle driving data in a delayed manner. Therefore, it is necessary to arrange these CAN frame data in the order of occurrence to obtain a time series database.

[0135] As an example, frequency analysis is performed on the time series data related to high energy consumption events in the time series database to obtain the correlation between the time series data related to high energy consumption events and the high energy consumption events, and the data characteristics of the time series data related to high energy consumption events are determined. Based on the above data characteristics, an energy consumption experience database is generated.

[0136] , for use in vehicle development.

[0137] In this embodiment, the energy consumption experience database feeds back data when high energy consumption events occur to vehicle R&D personnel, so that vehicle R&D is closer to actual conditions, improving R&D efficiency and reducing R&D costs.

[0138] Reference Figure 3 , Figure 3 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present application.

[0139] like Figure 3 As shown, the vehicle energy consumption optimization device may include: a processor 1001 , a memory 1005 , and a communication bus 1002 . The communication bus 1002 is used to realize the connection and communication between the processor 1001 and the memory 1005 .

[0140] Optionally, the vehicle energy consumption optimization device may also include a user interface, a network interface, a face camera, an RF (Radio Frequency) circuit, a sensor, a WiFi module, etc. The user interface may include a display screen (Display), an input submodule such as a keyboard (Keyboard), and the optional user interface may also include a standard wired interface and a wireless interface. The network interface may include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0141] Those skilled in the art will understand that Figure 3 The vehicle energy consumption optimization device structure shown in the figure does not constitute a limitation on the vehicle energy consumption optimization device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0142] like Figure 3 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, and a vehicle energy consumption optimization program. The operating system is a program that manages and controls the hardware and software resources of the vehicle energy consumption optimization device, and supports the operation of the vehicle energy consumption optimization program and other software and / or programs. The network communication module is used to realize the communication between the components inside the memory 1005, and the communication with other hardware and software in the vehicle energy consumption optimization system.

[0143] exist Figure 3In the vehicle energy consumption optimization device shown, the processor 1001 is used to execute the vehicle energy consumption optimization program stored in the memory 1005 to implement the steps of any of the above-mentioned vehicle energy consumption optimization methods.

[0144] The specific implementation methods of the vehicle energy consumption optimization device of the present application are basically the same as the embodiments of the above-mentioned vehicle energy consumption optimization method, and will not be repeated here.

[0145] The present application also provides a vehicle energy consumption optimization device, the device comprising:

[0146] A first generation module, used to generate a person label based on the driver's personal information and historical driving data;

[0147] A first determination module is used to analyze the driving behavior of the driver based on the personnel tag and the current driving data to obtain the type of the driver's current driving behavior;

[0148] A second determination module is used to obtain target driving data within a preset period before and after the high energy consumption event occurs, and determine the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal;

[0149] A proposal module is used to propose energy consumption optimization suggestions to the driver and the vehicle based on the type and the correlation.

[0150] Optionally, in a possible implementation manner of the present application, the first determining module includes:

[0151] An input unit, used to input the current driving data into a preset driving behavior analysis model;

[0152] A processing unit is used to analyze and process the current driving data based on the preset driving behavior analysis model to obtain the type of the driver's driving behavior, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels.

[0153] Optionally, in a possible implementation of the present application, the processing unit is used to: calculate the current driving data to obtain at least one current driving behavior indicator; and to determine the target score of each current driving behavior indicator based on the correspondence between the preset driving behavior indicator and the target score; and to calculate the target score based on the entropy weight method to obtain the weight of each driving behavior indicator; and to calculate the target score of each current driving behavior indicator based on the weight of each driving behavior indicator to obtain a first calculation result; and to determine an adjustment coefficient for the first calculation result based on a preset personnel label and the current driving data; and to adjust the first calculation result based on the adjustment coefficient to obtain a second calculation result; and to determine the type of the driver's current driving behavior based on the correspondence between the preset second score and the driving behavior type.

[0154] And / or, the processing unit is also used to standardize the target score to obtain a standardized target score; is also used to calculate the standardized target score to obtain the information entropy of each current driving behavior indicator; and is also used to obtain the weight corresponding to each current driving behavior indicator based on the information entropy and the target score.

[0155] Optionally, in a possible implementation manner of the present application, the adjusting the second determining module includes:

[0156] The third determination module analyzes the change trend of each driving data within the period of occurrence of the high energy consumption event on the same time axis to obtain the correlation between the high energy consumption event and the target driving data.

[0157] Optionally, in a possible implementation manner of the present application, the device further includes:

[0158] The acquisition module is used to collect the original driving data of the current vehicle;

[0159] An arrangement module, used for arranging the original driving data in the order of occurrence to obtain a time series database;

[0160] An analysis module, used for frequency analysis of time series data related to high energy consumption events in a time series database;

[0161] A second determination module is used to determine data features of time series data related to high energy consumption events;

[0162] The second generation module is used to generate an energy consumption experience library for use in vehicle research and development.

[0163] The specific implementation methods of the vehicle energy consumption optimization method of the present application are basically the same as the various embodiments of the above-mentioned vehicle energy consumption optimization method, and will not be repeated here.

[0164] An embodiment of the present application provides a storage medium, and the storage medium stores one or more programs, and the one or more programs can also be executed by one or more processors to implement the steps of any of the above-mentioned vehicle energy consumption optimization methods.

[0165] The specific implementation method of the storage medium of the present application is basically the same as the above-mentioned embodiments of the vehicle energy consumption optimization method, and will not be repeated here.

[0166] An embodiment of the present application provides a vehicle energy consumption optimization system, the system comprising: a vehicle-mounted monitoring terminal and a vehicle energy consumption optimization device as claimed in any one of the above claims.

[0167] The specific implementation methods of the vehicle energy consumption optimization system of the present application are basically the same as the various embodiments of the above-mentioned vehicle energy consumption optimization method, and will not be repeated here.

[0168] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0169] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0170] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0171] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A vehicle energy consumption optimization method, It is characterized in that The vehicle energy consumption optimization method comprises the following steps: Generate a person tag based on the driver's personal information and historical driving data, wherein the historical driving data includes historical vehicle status; Inputting current driving data into a preset driving behavior analysis model; Based on the preset driving behavior analysis model, the current driving data is calculated to obtain a first calculation result, the first calculation result is adjusted based on the personnel label and the historical vehicle status, and the type of the driver's current driving behavior is determined based on the adjusted second calculation result, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels; Obtain target driving data within a preset period before and after a high energy consumption event occurs, and determine the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal; Based on the types and the correlations, energy consumption optimization suggestions are made to the driver and the vehicle.

2. The vehicle energy consumption optimization method according to claim 1, It is characterized in that The step of calculating the current driving data based on the preset driving behavior analysis model to obtain a first calculation result, adjusting the first calculation result based on the personnel tag and the historical vehicle status, and determining the type of the current driving behavior of the driver based on the adjusted second calculation result includes: Calculating current driving data to obtain at least one current driving behavior indicator; Determining the target score of each current driving behavior indicator based on the corresponding relationship between the preset driving behavior indicator and the target score; Based on the entropy weight method, the target score is calculated to obtain the weight of each current driving behavior indicator; Calculating the target scores of the current driving behavior indicators based on the weights of the current driving behavior indicators to obtain a first calculation result; Determining an adjustment coefficient for the first calculation result based on the personnel tag and the current driving data; Based on the adjustment coefficient, adjusting the first calculation result to obtain a second calculation result; Based on the corresponding relationship between the preset second calculation result and the driving behavior type, the type of the driver's current driving behavior is determined.

3. The vehicle energy consumption optimization method according to claim 2, It is characterized in that The step of calculating the target score based on the entropy weight method to obtain the weight of each current driving behavior index also includes: Normalizing the target score to obtain a standardized target score; Calculating the standardized target score to obtain the information entropy of each current driving behavior indicator; Based on the information entropy and the target score, the weights corresponding to the current driving behavior indicators are obtained.

4. The vehicle energy consumption optimization method according to claim 1, It is characterized in that The step of determining the correlation between the high energy consumption event and the target driving data comprises: The change trend of each driving data within the period of occurrence of the high energy consumption event on the same time axis is analyzed to obtain the correlation between the high energy consumption event and the target driving data.

5. The vehicle energy consumption optimization method according to claim 1, It is characterized in that After the step of determining the correlation between the high energy consumption event and the target driving data, the method further includes: Collecting original driving data of the current vehicle, and arranging the original driving data in chronological order to obtain a time series database; Conduct frequency analysis on the time series data related to high energy consumption events in the time series database, determine the data characteristics of the time series data related to high energy consumption events, and generate an energy consumption experience library for use in vehicle research and development.

6. A vehicle energy consumption optimization device, It is characterized in that The device comprises: A generating module, for generating a person tag based on the driver's personal information and historical driving data, wherein the historical driving data includes historical vehicle status; A first determination module is used to input current driving data into a preset driving behavior analysis model; based on the preset driving behavior analysis model, the current driving data is calculated to obtain a first calculation result, the first calculation result is adjusted based on the personnel label and the historical vehicle status, and the type of the current driving behavior of the driver is determined based on a second calculation result obtained after the adjustment, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels; A second determination module is used to obtain target driving data within a preset period before and after a high energy consumption event occurs, and determine the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data in the current driving data monitored by the vehicle monitoring terminal; A proposal module is used to propose energy consumption optimization suggestions to the driver and the vehicle based on the type and the correlation.

7. A vehicle energy consumption optimization device, It is characterized in that The device comprises: a memory, a processor, and a vehicle energy consumption optimization program stored in the memory and executable on the processor, wherein the vehicle energy consumption optimization program is configured to implement the steps of the vehicle energy consumption optimization method according to any one of claims 1 to 5.

8. A storage medium, It is characterized in that The storage medium stores a vehicle energy consumption optimization program, and when the vehicle energy consumption optimization program is executed by the processor, the steps of the vehicle energy consumption optimization method according to any one of claims 1 to 5 are implemented.

9. A vehicle energy consumption optimization system, It is characterized in that The system comprises: a vehicle-mounted monitoring terminal and the vehicle energy consumption optimization device as claimed in claim 6.

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