A vehicle energy consumption analysis database construction method, device and electronic equipment

Abstract

The present invention provides a vehicle energy consumption analysis database construction method, device and electronic equipment, which relates to the field of energy-saving control. The method includes: obtaining original vehicle data, dividing the original vehicle data into original energy consumption data and related parameter data; performing statistical analysis on the original energy consumption data to obtain energy consumption statistical data, performing data derivative statistical analysis on the related parameter data to obtain derived indicator data, performing cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistical data, performing correlation analysis on the derived indicator data and the cluster energy consumption statistical data to obtain cluster related indicator data; establishing a vehicle energy consumption analysis database according to the original vehicle data, energy consumption statistical data, derived indicator data, cluster energy consumption statistical data and cluster related indicator data. The present invention can provide high-quality vehicle data for energy consumption analysis and realize refined energy consumption analysis by performing multi-level data analysis and mining on the original vehicle data.

Description

Technical Field

[0001] The present invention relates to the field of energy-saving control technology, and in particular to a vehicle energy consumption analysis database construction method, device and electronic equipment. Background Art

[0002] With the advancement of sensing technology, various types of sensors can be used to monitor various types of vehicle data during driving. These vehicle data can be used in vehicle maintenance and diagnosis, driving behavior analysis, predictive maintenance, etc. Effectively organizing vehicle data and building a reasonable database can provide effective data support for various types of vehicle diagnosis.

[0003] In the task of vehicle energy consumption analysis, vehicle energy consumption is correlated with many factors, and the degree of influence of various factors on vehicle energy consumption varies under different circumstances, so it is necessary to mine and analyze data from multiple data sources. However, the existing vehicle energy consumption database is often only simple for monitoring and managing vehicle energy consumption and analyzing driving behavior, collecting corresponding vehicle sensor data for recording, lacking mining and analysis of multi-source sensor data, making it difficult to provide high-quality vehicle data for energy consumption analysis, and unable to conduct refined energy consumption analysis.

[0004] Therefore, the existing technology has technical problems such as lack of mining and analysis of multi-source sensor data in the vehicle energy consumption database, difficulty in providing high-quality vehicle data for energy consumption analysis, and inability to perform refined energy consumption analysis, which need to be improved. Summary of the invention

[0005] In view of this, it is necessary to provide a method, device and electronic equipment for constructing a vehicle energy consumption analysis database to solve the technical problems in the prior art that the vehicle energy consumption database lacks multi-source sensor data mining and analysis, is difficult to provide high-quality vehicle data for energy consumption analysis, and cannot perform refined energy consumption analysis.

[0006] In order to solve the above problems, on the one hand, the present invention provides a method for constructing a vehicle energy consumption analysis database, comprising: Obtaining original vehicle data, and dividing the original vehicle data into original energy consumption data and related parameter data; Perform statistical analysis on the original energy consumption data to obtain energy consumption statistics, perform data derivative statistical analysis on the relevant parameter data to obtain derived indicator data, perform cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistics, perform correlation analysis on the derived indicator data and cluster energy consumption statistics to obtain cluster related indicator data; A vehicle energy consumption analysis database is established based on original vehicle data, energy consumption statistics, derived indicator data, cluster energy consumption statistics and cluster related indicator data.

[0007] In one possible implementation, the original vehicle data includes an engine control domain signal, a dashboard control unit signal, a brake system control unit signal, a transmission control unit signal, an OBD fault code signal, a GPS geographic information signal, and atmospheric environment temperature data.

[0008] In a possible implementation, data derivative statistical analysis is performed on relevant parameter data to obtain derived indicator data, including: Carry out preliminary classification and statistics on relevant parameter data to obtain initial statistical data; Determine the corresponding derivative indicators according to the types of relevant parameters; The derived data are obtained by performing secondary statistical analysis on the initial statistical data according to the derived indicators; The initial statistical data and the derived data are combined to obtain the derived indicator data.

[0009] In a possible implementation, cluster statistical analysis is performed on the original vehicle data to obtain cluster energy consumption statistical data, including: Perform cluster analysis on the original vehicle data to obtain several application scenarios, and determine several vehicle types based on the data source of the original vehicle data; The original energy consumption data is divided into several energy consumption data clusters according to the application scenario and vehicle type, and statistical analysis is performed on each energy consumption data cluster to obtain cluster energy consumption statistical data.

[0010] In a possible implementation, correlation analysis is performed on the derived indicator data and the cluster energy consumption statistical data to obtain cluster-related indicator data, including: Divide the derived indicator data into a number of corresponding derived indicator clusters according to the energy consumption data clusters; The correlation analysis between the energy consumption data cluster and the derived indicators in the corresponding derived indicator cluster is performed to obtain the cluster-related indicator data.

[0011] In a possible implementation, the vehicle energy consumption analysis database construction method further includes: A historical energy consumption analysis data set is established based on the vehicle energy consumption analysis database, and regression analysis and time series analysis are performed on the historical energy consumption analysis data set to obtain energy consumption forecast data.

[0012] In a possible implementation, the vehicle energy consumption analysis database construction method further includes: Based on the decision tree model, the historical energy consumption analysis data set is used to identify the correlation between driving behavior and energy consumption to obtain high-energy consumption driving behavior, and energy consumption improvement suggestions are generated based on the high-energy consumption driving behavior.

[0013] On the other hand, the present invention also provides a vehicle energy consumption analysis database construction device, comprising: A raw data acquisition unit, used to acquire raw vehicle data, and divide the raw vehicle data into raw energy consumption data and related parameter data; A data statistical analysis unit is used to perform statistical analysis on the original energy consumption data to obtain energy consumption statistical data, perform data derivative statistical analysis on the relevant parameter data to obtain derived index data, perform cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistical data, and perform correlation analysis on the derived index data and the cluster energy consumption statistical data to obtain cluster related index data; The database construction unit is used to establish a vehicle energy consumption analysis database based on original vehicle data, energy consumption statistical data, derived indicator data, cluster energy consumption statistical data and cluster related indicator data.

[0014] On the other hand, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned vehicle energy consumption analysis database construction method is implemented.

[0015] On the other hand, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned vehicle energy consumption analysis database construction method is implemented.

[0016] The beneficial effects of the present invention are as follows: in the vehicle energy consumption analysis database construction method provided by the present invention, firstly, the original vehicle data is obtained, and the original vehicle data is divided into original energy consumption data and related parameter data; then, the original energy consumption data is statistically analyzed to obtain energy consumption statistical data, the related parameter data is subjected to data derivative statistical analysis to obtain derived index data, the original vehicle data is subjected to cluster statistical analysis to obtain cluster energy consumption statistical data, the derived index data and the cluster energy consumption statistical data are subjected to correlation analysis to obtain cluster related index data; finally, a vehicle energy consumption analysis database is established based on the original vehicle data, energy consumption statistical data, derived index data, cluster energy consumption statistical data and cluster related index data. The present invention performs multi-level data analysis and mining on the original vehicle data through statistical analysis, derivative statistical analysis, cluster statistical analysis and correlation analysis, which can provide high-quality vehicle data for energy consumption analysis and realize refined energy consumption analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1A schematic diagram of a flow chart of an embodiment of a method for constructing a vehicle energy consumption analysis database provided by the present invention; Figure 2 A schematic diagram of a process of performing data-derived statistical analysis on relevant parameter data according to an embodiment of the present invention; Figure 3 A schematic diagram of a cluster statistical analysis process according to an embodiment of the present invention; Figure 4 A schematic diagram of a correlation analysis process according to an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an embodiment of a vehicle energy consumption analysis database construction device provided by the present invention; Figure 6 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0021] The descriptions of "first" and "second" in the embodiments of the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] The present invention provides a vehicle energy consumption analysis database construction method, device and electronic equipment, which are described below respectively.

[0024] Figure 1A flow chart of an embodiment of a method for constructing a vehicle energy consumption analysis database provided by the present invention is shown in FIG. Figure 1 As shown, the vehicle energy consumption analysis database construction method includes: S101, obtaining original vehicle data, and dividing the original vehicle data into original energy consumption data and related parameter data; S102, performing statistical analysis on the original energy consumption data to obtain energy consumption statistical data, performing data derivative statistical analysis on the relevant parameter data to obtain derived index data, performing cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistical data, and performing correlation analysis on the derived index data and the cluster energy consumption statistical data to obtain cluster related index data; S103: Establish a vehicle energy consumption analysis database based on original vehicle data, energy consumption statistical data, derived indicator data, cluster energy consumption statistical data and cluster related indicator data.

[0025] Compared with the prior art, the vehicle energy consumption analysis database construction method provided by the embodiment of the present invention first obtains the original vehicle data, and divides the original vehicle data into original energy consumption data and related parameter data; then statistically analyzes the original energy consumption data to obtain energy consumption statistical data, performs data derivative statistical analysis on the related parameter data to obtain derived indicator data, performs cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistical data, performs correlation analysis on the derived indicator data and the cluster energy consumption statistical data to obtain cluster related indicator data; finally, a vehicle energy consumption analysis database is established based on the original vehicle data, energy consumption statistical data, derived indicator data, cluster energy consumption statistical data and cluster related indicator data. The present invention performs multi-level data analysis and mining on the original vehicle data through statistical analysis, derivative statistical analysis, cluster statistical analysis and correlation analysis, which can provide high-quality vehicle data for energy consumption analysis and realize refined energy consumption analysis.

[0026] In some embodiments of the present invention, the original vehicle data includes engine control domain signals, dashboard control unit signals, brake system control unit signals, transmission control unit signals, OBD fault code signals, GPS geographic information signals and atmospheric environment temperature data.

[0027] Specifically, in an embodiment, the original vehicle data comes from data obtained from various types of sensors in the vehicle, including engine control domain signals, dashboard control unit signals, brake system control unit signals, transmission control unit signals, OBD fault code signals, GPS geographic information signals and atmospheric ambient temperature data, as well as some data stored in the cloud platform database, such as map data.

[0028] Among them, the engine part can be divided into the intake and exhaust system, the cooling and lubrication system, and the emission after-treatment system. The engine control domain signals include the corresponding temperature and pressure signals collected in the above systems; the dashboard control unit collects signals such as vehicle speed, engine speed, and fuel quantity; the brake system control unit signal includes the brake pedal position signal, vehicle dynamic signal, and driver request signal; the transmission control unit signal includes the collected gear selection signal, clutch position signal, and driving mode selection signal; the OBD fault code signal is a collection of fault diagnosis signals from each control unit; the GPS geographic information signal includes the collected vehicle position signal (latitude and longitude coordinates) and GPS speed signal; the atmospheric ambient temperature data includes the atmospheric ambient temperature and altitude, which are collected and processed by external temperature sensors and atmospheric pressure sensors.

[0029] For each data collected, the embodiment uses the fuel quantity signal collected by the dashboard control unit as the original energy consumption data, and uses other data as related parameter data. In the embodiment, the data to be sent by the data bus is collected into the local memory through Tbox, packaged and compressed according to a predetermined format, and sent to the cloud platform. After receiving the data packet, the cloud platform server decompresses and decodes it, restores it to the original data format, and verifies the data, checks the integrity and accuracy of the data, handles missing values, outliers and duplicate data, and ensures data quality. Finally, the data is converted into a format suitable for storage and analysis.

[0030] Then, the table structure in the relational database is designed for the processed data to ensure efficient data storage and retrieval. The processed vehicle energy consumption, mileage information, vehicle status, engine status and other energy-related information, map geography static data, vehicle configuration static data, vehicle maintenance data, etc. are integrated into different tables in the database.

[0031] After integrating all the data, the vehicle energy consumption analysis database in the embodiment adopts various data analysis and mining technologies such as statistics, machine learning, deep learning and data mining. By performing various methods including but not limited to statistical analysis, pattern recognition, anomaly detection and association analysis on the vehicle energy consumption data, valuable information in the data is extracted and saved in the database for data extraction and display.

[0032] Among them, the embodiment performs four levels of analysis and mining in the data analysis and mining process.

[0033] In the first layer, the embodiment first performs basic mathematical statistical analysis on the original energy consumption data, including but not limited to calculating the mean, median, standard deviation and energy consumption analysis statistics of the energy consumption data.

[0034] In some embodiments of the present invention, Figure 2FIG. 1 is a flow chart of performing data-derived statistical analysis on relevant parameter data according to an embodiment of the present invention. Figure 2 As shown, the relevant parameter data are subjected to data derivative statistical analysis to obtain the derived indicator data, including: S201, performing preliminary classification and statistics on relevant parameter data to obtain initial statistical data; S202, determining corresponding derivative indicators according to the types of relevant parameters; S203, performing secondary statistical analysis on the initial statistical data according to the derived indicators to obtain derived data; S204: Combine the initial statistical data and the derived data to obtain derived indicator data.

[0035] Specifically, in the second layer, the embodiment performs data-derived statistical analysis on relevant parameters that affect energy consumption. First, the embodiment classifies the relevant parameters according to the factors that may affect energy consumption and performs preliminary statistical analysis. For example, driving behavior is a relatively common influencing factor. The embodiment can classify some relevant parameters into five major categories according to driving behavior: vehicle speed, engine speed, throttle, brake, and gear position, and then perform preliminary classification statistics on these five major categories. Then, for each major category, several subcategories can be derived based on common industry indicators and statistics in vehicle energy consumption analysis, and then a secondary statistical analysis can be performed. For example, the vehicle speed produces indicators such as the average vehicle speed, the average driving speed, the proportion of high speed, the proportion of low speed, the cumulative duration of sudden acceleration, the cumulative proportion of sudden acceleration, the cumulative duration of sudden deceleration, the cumulative proportion of sudden deceleration, the starting time and the number of starts and stops per 100 kilometers; the speed control produces indicators such as the proportion of economic speed time and the proportion of economic working condition time; the braking control produces indicators such as the proportion of braking time and the proportion of braking mileage; the gear control produces indicators such as the proportion of clutch opening and closing times greater than the preset value, the proportion of clutch disconnection time greater than the preset value, the gear shifting speed, the cumulative proportion of high-end and high-end time, the proportion of high-speed high-end time, the gear shifting frequency, the frequency of high-speed upshifting, the frequency of high-speed downshifting, the frequency of gear skipping, the frequency of gear skipping, the proportion of neutral gliding time, the proportion of gliding time with gear and the proportion of cruising time.

[0036] In some embodiments of the present invention, Figure 3 FIG. 1 is a flow chart of cluster statistical analysis according to an embodiment of the present invention. Figure 3 As shown in the figure, cluster statistical analysis is performed on the original vehicle data to obtain cluster energy consumption statistics, including: S301, performing cluster analysis on the original vehicle data to obtain several application scenarios, and determining several vehicle types according to the data source of the original vehicle data; S302 , dividing the original energy consumption data into a number of energy consumption data clusters according to application scenarios and vehicle types, and performing statistical analysis on each energy consumption data cluster to obtain cluster energy consumption statistical data.

[0037] Specifically, in the third layer, the embodiment performs cluster analysis and data statistics on the original vehicle data to analyze the energy consumption data in different modes. Among them, the embodiment identifies different application scenarios by performing cluster analysis on the original vehicle data, and then divides the original energy consumption data into several energy consumption data clusters according to the application scenarios and / or vehicle types. The statistical analysis of the first layer is applied to different clusters to obtain data such as average energy consumption and energy consumption distribution under different scenarios and / or different vehicle types. Among them, the vehicle type can be divided according to the engine model, power chain, and vehicle model.

[0038] In addition, in the embodiment, in addition to the clustering method of application scenarios and vehicle types, secondary clustering can be performed according to energy consumption, for example, energy consumption can be divided into three categories: excellent, normal and abnormal, and then statistical analysis can be performed separately.

[0039] In some embodiments of the present invention, Figure 4 FIG. 1 is a flow chart of correlation analysis according to an embodiment of the present invention. Figure 4 As shown, correlation analysis is performed on the derived indicator data and the cluster energy consumption statistics to obtain cluster related indicator data, including: S401, dividing the derived indicator data into a number of corresponding derived indicator clusters according to the energy consumption data clusters; S402: performing correlation analysis on the energy consumption data clusters and the derived indicators in the corresponding derived indicator clusters to obtain cluster-related indicator data.

[0040] Specifically, in the fourth layer, the derived indicator data is divided into corresponding derived indicator clusters under each cluster according to the different clusters established in the third layer. Then, the correlation analysis is performed on the energy consumption data cluster and each derived indicator in the corresponding derived indicator cluster under each cluster, and the derived indicators and corresponding data with strong correlation above the preset correlation threshold are read into the database together with the corresponding energy consumption data cluster, so as to realize the detailed energy consumption analysis of different clusters.

[0041] In some embodiments of the present invention, the vehicle energy consumption analysis database construction method further includes: A historical energy consumption analysis data set is established based on the vehicle energy consumption analysis database, and regression analysis and time series analysis are performed on the historical energy consumption analysis data set to obtain energy consumption forecast data.

[0042] In some embodiments of the present invention, the vehicle energy consumption analysis database construction method further includes: Based on the decision tree model, the historical energy consumption analysis data set is used to identify the correlation between driving behavior and energy consumption to obtain high-energy consumption driving behavior, and energy consumption improvement suggestions are generated based on the high-energy consumption driving behavior.

[0043] Specifically, based on the vehicle energy consumption analysis database, the embodiment can also perform energy consumption prediction and generate energy consumption improvement suggestions based on various types of energy consumption analysis data.

[0044] Among them, the energy consumption forecast establishes a forecast model based on the energy consumption analysis database of big data, and uses the database to construct a historical energy consumption analysis data set to predict future energy consumption trends. The embodiment predicts future fuel consumption trends by adopting regression analysis, time series analysis, etc. For example, by using the time information and energy consumption information in the database to establish a regression analysis model of time on energy consumption, it is possible to predict the current and even future energy consumption levels, the relationship between vehicle maintenance activities and fuel consumption changes. Integrate historical data and forecast data into a new data table for easy query and recommend the time for the next maintenance.

[0045] The energy consumption improvement suggestions are generated by using a decision tree model to divide the data under different clusters and the energy consumption data set into training sets and test sets for model training and verification. Select a decision tree algorithm, set the parameters of the decision tree, such as maximum depth, minimum sample split, minimum sample leaf node, etc., and use the training set data to train the decision tree model so that it can learn the relationship between energy consumption and influencing factors. Use the cross-validation method to evaluate the generalization ability of the model and reduce the risk of overfitting. Use regression performance indicators such as mean square error, root mean square error, mean absolute error, etc. to evaluate the prediction accuracy of the model. During the training process, use methods such as grid search or random search to adjust the parameters of the decision tree to find the best parameter combination for the model. According to the model results, identify the association between specific driving behaviors and high fuel consumption, and put forward improvement suggestions for reducing fuel consumption, such as controlling the vehicle speed, reducing the engine shift speed, etc., and establish a model output table.

[0046] In addition, the vehicle energy consumption analysis database in the embodiment also includes visualization and intelligent analysis reports and security, confidentiality and privacy protection mechanisms.

[0047] Visualization and intelligent analysis reports help users understand and analyze data intuitively by displaying fuel consumption data in a graphical and visual way. An interactive interface is established to provide users with personalized analysis functions, while automatically generating intelligent reports to provide corresponding decision support.

[0048] In the embodiment, the distribution and relationship of energy consumption are displayed by using charts (such as histograms, box plots, and scatter plots). The data in the cluster family tables established in the third and fourth layers of data mining are displayed in the form of scatter plots, such as the relationship between fuel consumption and driving speed, vehicle load, etc. By adding interactive elements, such as filters, drop-down menus, etc., users can customize the data viewing.

[0049] By designing report templates, including cover, table of contents, body, conclusion and other parts, and writing automated scripts to extract data from the database and fill it into the report template, dynamic content can be added to the report, such as real-time updating of charts, automatic generation of text analysis, etc. Visual reports can also be exported to PDF, PPT or other formats for easy distribution. Reports can also be shared through cloud platforms or internal platforms, allowing users to view and interact online.

[0050] The security, confidentiality and privacy protection mechanism takes into account that when developing a fuel consumption analysis database, the user's data security and privacy protection must be ensured. Measures such as data encryption, access control and standardized data flow can be taken to ensure that the user's data is not abused or leaked.

[0051] To achieve security, confidentiality and privacy protection, the embodiment identifies sensitive data, such as vehicle owner identity information, precise location information, etc., in each table built in the database, and labels information with different confidentiality levels, such as public data, internal data, sensitive data, etc. Sensitive data is marked and specially processed during data processing and transmission. Special processing methods include but are not limited to: using encryption protocols such as SSL / TLS to protect the security of data during transmission; encrypting sensitive data stored in the database to prevent unauthorized access; implementing key management strategies to ensure the security of encryption keys.

[0052] When accessing data at the same time, the implementation scheme uses an identity authentication mechanism and multi-factor authentication to ensure that only authorized users can access it; different roles and responsibilities are set for access users, different access rights are assigned, and the principle of least privilege is implemented; all access and operation behaviors of the database are recorded for tracking and auditing.

[0053] In addition, when intelligently generating reports, sensitive data is anonymized to remove or replace personal identification information, or to replace personal identification information with a pseudonym or code name to keep the data analyzable.

[0054] In summary, in order to construct a vehicle energy consumption analysis database containing high-quality vehicle data, the present invention first obtains original vehicle data, and divides the original vehicle data into original energy consumption data and related parameter data; then statistical analysis is performed on the original energy consumption data to obtain energy consumption statistical data, data derivative statistical analysis is performed on the related parameter data to obtain derived indicator data, cluster statistical analysis is performed on the original vehicle data to obtain cluster energy consumption statistical data, and correlation analysis is performed on the derived indicator data and cluster energy consumption statistical data to obtain cluster related indicator data; finally, a vehicle energy consumption analysis database is established based on the original vehicle data, energy consumption statistical data, derived indicator data, cluster energy consumption statistical data and cluster related indicator data. The present invention performs multi-level data analysis and mining on the original vehicle data through statistical analysis, derivative statistical analysis, cluster statistical analysis and correlation analysis, which can provide high-quality vehicle data for energy consumption analysis and realize refined energy consumption analysis.

[0055] In order to better implement the vehicle energy consumption analysis database construction method in the embodiment of the present invention, based on the vehicle energy consumption analysis database construction method, correspondingly, Figure 5 As shown, the present invention also provides a vehicle energy consumption analysis database construction device, and the vehicle energy consumption analysis database construction device 500 includes: The original data acquisition unit 501 is used to acquire original vehicle data and divide the original vehicle data into original energy consumption data and related parameter data; The data statistical analysis unit 502 is used to perform statistical analysis on the original energy consumption data to obtain energy consumption statistical data, perform data derivative statistical analysis on the relevant parameter data to obtain derived index data, perform cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistical data, and perform correlation analysis on the derived index data and the cluster energy consumption statistical data to obtain cluster related index data; The database construction unit 503 is used to establish a vehicle energy consumption analysis database according to the original vehicle data, energy consumption statistical data, derived index data, cluster energy consumption statistical data and cluster related index data.

[0056] The vehicle energy consumption analysis database construction device 500 provided in the above embodiment can implement the technical solution described in the above vehicle energy consumption analysis database construction method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above vehicle energy consumption analysis database construction method embodiment, which will not be repeated here.

[0057] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602 and a display 603. Figure 6Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0058] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 602, such as the vehicle energy consumption analysis database construction method of the present invention.

[0059] In some embodiments, the processor 601 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 601 may be local or remote. In some embodiments, the processor 601 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0060] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 600.

[0061] Furthermore, the memory 602 may include both an internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store application software installed in the electronic device 600 and various data.

[0062] In some embodiments, the display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 603 is used to display information of the electronic device 600 and to display a visual user interface. The components 601-603 of the electronic device 600 communicate with each other through a system bus.

[0063] In one embodiment, when the processor 601 executes the vehicle energy consumption analysis database construction program in the memory 602, the following steps may be implemented: Obtaining original vehicle data, and dividing the original vehicle data into original energy consumption data and related parameter data; Perform statistical analysis on the original energy consumption data to obtain energy consumption statistics, perform data derivative statistical analysis on the relevant parameter data to obtain derived indicator data, perform cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistics, perform correlation analysis on the derived indicator data and cluster energy consumption statistics to obtain cluster related indicator data; A vehicle energy consumption analysis database is established based on original vehicle data, energy consumption statistics, derived indicator data, cluster energy consumption statistics and cluster related indicator data.

[0064] It should be understood that: when the processor 601 executes the vehicle energy consumption analysis database construction program in the memory 602, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0065] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions in the vehicle energy consumption analysis database construction method provided in the above-mentioned method embodiments.

[0066] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0067] The vehicle energy consumption analysis database construction method, device, electronic device and storage medium provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for constructing a vehicle energy consumption analysis database, characterized in that: include: Acquire original vehicle data, and divide the original vehicle data into original energy consumption data and related parameter data; Performing statistical analysis on the original energy consumption data to obtain energy consumption statistical data, performing data derivative statistical analysis on the relevant parameter data to obtain derived index data, performing cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistical data, and performing correlation analysis on the derived index data and the cluster energy consumption statistical data to obtain cluster related index data; A vehicle energy consumption analysis database is established based on the original vehicle data, the energy consumption statistical data, the derived indicator data, the cluster energy consumption statistical data and the cluster related indicator data.

2. The vehicle energy consumption analysis database construction method according to claim 1, characterized in that: The original vehicle data includes engine control domain signals, dashboard control unit signals, brake system control unit signals, transmission control unit signals, OBD fault code signals, GPS geographic information signals and atmospheric environment temperature data.

3. The vehicle energy consumption analysis database construction method according to claim 1, characterized in that: The performing of data-derived statistical analysis on the relevant parameter data to obtain derived indicator data includes: Performing preliminary classification and statistics on the relevant parameter data to obtain initial statistical data; Determine the corresponding derivative indicators according to the types of relevant parameters; Performing secondary statistical analysis on the initial statistical data according to the derived indicators to obtain derived data; The initial statistical data and the derived data are combined to obtain derived indicator data.

4. The vehicle energy consumption analysis database construction method according to claim 1, characterized in that: The cluster statistical analysis of the original vehicle data to obtain cluster energy consumption statistical data includes: Performing cluster analysis on the original vehicle data to obtain a number of application scenarios, and determining a number of vehicle types according to the data source of the original vehicle data; The original energy consumption data is divided into a plurality of energy consumption data clusters according to the application scenario and the vehicle type, and statistical analysis is performed on each of the energy consumption data clusters to obtain cluster energy consumption statistical data.

5. The vehicle energy consumption analysis database construction method according to claim 4 is characterized in that: The performing correlation analysis on the derived indicator data and the cluster energy consumption statistical data to obtain cluster related indicator data includes: Dividing the derived indicator data into a number of corresponding derived indicator clusters according to the energy consumption data clusters; A correlation analysis is performed on the energy consumption data cluster and each derived indicator in the corresponding derived indicator cluster to obtain cluster-related indicator data.

6. The vehicle energy consumption analysis database construction method according to claim 1, characterized in that: The method further comprises: A historical energy consumption analysis data set is established according to the vehicle energy consumption analysis database, and regression analysis and time series analysis are performed on the historical energy consumption analysis data set to obtain energy consumption prediction data.

7. The vehicle energy consumption analysis database construction method according to claim 6, characterized in that: The method further comprises: Based on the decision tree model, the historical energy consumption analysis data set is used to identify the correlation between driving behavior and energy consumption to obtain high-energy consumption driving behavior, and energy consumption improvement suggestions are generated according to the high-energy consumption driving behavior.

8. A vehicle energy consumption analysis database construction device, characterized in that: include: A raw data acquisition unit, used to acquire raw vehicle data, and divide the raw vehicle data into raw energy consumption data and related parameter data; a data statistical analysis unit, configured to perform statistical analysis on the original energy consumption data to obtain energy consumption statistical data, perform data derivative statistical analysis on the relevant parameter data to obtain derived index data, perform cluster statistical analysis on the original vehicle data to obtain cluster energy consumption statistical data, and perform correlation analysis on the derived index data and the cluster energy consumption statistical data to obtain cluster related index data; The database construction unit is used to establish a vehicle energy consumption analysis database based on the original vehicle data, the energy consumption statistical data, the derived indicator data, the cluster energy consumption statistical data and the cluster related indicator data.

9. An electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the vehicle energy consumption analysis database construction method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle energy consumption analysis database construction method according to any one of claims 1 to 7 is implemented.

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