Vehicle battery multi-dimensional indicator analysis method, device and vehicle network server

Abstract

This application provides a method, device, and vehicle network server for multi-dimensional indicator analysis of vehicle batteries. The method includes: obtaining operational layer data based on battery data of vehicles corresponding to each vehicle cloud platform; scheduling various business tasks to process the operational layer data using a query language to obtain business data for each business task; converting the business data of each first business task into application layer data, and performing indicator analysis for each first business task based on the application layer data; converting the business data of each second business task into detailed layer data, waiting for the next or subsequent multiple conversions of the business data of each second business task into detailed layer data, until all detailed layer data possessed by each second business task conforms to the entire process, converting all detailed layer data possessed by each second business task into application layer data, and performing indicator analysis for each second business task based on the application layer data.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and in particular to a method, device, and vehicle networking server for multi-dimensional index analysis of vehicle batteries. Background Technology

[0002] New energy vehicles differ from traditional vehicles in that they are powered by batteries. By analyzing various battery indicators, their condition can be understood in a timely manner, and related control measures can be improved. However, current technology has a low utilization rate of battery data reported by the vehicle, resulting in incomplete and inaccurate analysis of various battery indicators. This poses a potential risk of battery accidents in new energy vehicles. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, apparatus, and vehicle network server for multi-dimensional index analysis of vehicle batteries, in order to solve the problem that the comprehensiveness and accuracy of the analysis of various indicators of new energy vehicle batteries in the prior art need to be improved.

[0004] A first aspect of this application provides a method for multi-dimensional indicator analysis of vehicle batteries, comprising: receiving battery data of vehicles corresponding to each vehicle cloud platform reported by each vehicle cloud platform at a first preset interval; storing the received battery data in a target data warehouse after parsing and processing until a second preset interval is reached, thereby obtaining operational layer data corresponding to the second preset interval; scheduling each business task to process the operational layer data using a query language to obtain business data for each business task; classifying each business task into a first business task belonging to a non-full process and a second business task belonging to a full process based on the business type of each business task; converting the business data of each first business task into application layer data; performing indicator analysis on each first business task based on the application layer data of each first business task; converting the business data of each second business task into detailed layer data; waiting for the next or subsequent multiple conversions of the business data of each second business task into detailed layer data until all the detailed layer data possessed by each second business task conforms to the full process; converting all the detailed layer data possessed by each second business task into application layer data; and performing indicator analysis on each second business task based on the application layer data of each second business task.

[0005] A second aspect of this application provides a vehicle battery multi-dimensional index analysis device, comprising: a receiving module configured to receive battery data of vehicles corresponding to each vehicle cloud platform reported by each vehicle cloud platform at a first preset interval, and store the received battery data in a target data warehouse after parsing and processing, until a second preset interval is reached to obtain the operation layer data corresponding to the second preset interval; a scheduling module configured to schedule various business tasks to process the operation layer data using a query language to obtain the business data of each business task; and a division module configured to divide each business task into a first business task belonging to a non-full process and a second business task belonging to the full process based on the business type of each business task. The system consists of two business tasks. The first analysis module is configured to convert the business data of each first business task into application-layer data, and perform indicator analysis on each first business task based on the application-layer data. The second analysis module is configured to convert the business data of each second business task into detailed-layer data, and wait for the next or subsequent conversion of the business data of each second business task into detailed-layer data multiple times until all the detailed-layer data of each second business task meets the requirements of the entire process. Then, it converts all the detailed-layer data of each second business task into application-layer data, and performs indicator analysis on each second business task based on the application-layer data.

[0006] A third aspect of this application provides a vehicle networking server, including a memory, an offline processing engine, and a computer program stored in the memory and executable on the offline processing engine. When the offline processing engine executes the computer program, it implements the steps of the method described above.

[0007] The beneficial effects of this application embodiment compared with the prior art include at least the following: This application embodiment receives battery data of the corresponding vehicles of each vehicle cloud platform reported by each vehicle cloud platform at a first preset interval, stores the received battery data in the target data warehouse after parsing and processing, until a second preset interval is reached, and obtains the operation layer data corresponding to the second preset interval; schedules each business task to process the operation layer data using a query language to obtain the business data of each business task; divides each business task into a first business task that is not part of the whole process and a second business task that is part of the whole process based on the business type of each business task; converts the business data of each first business task into application layer data, and performs indicator analysis of each first business task based on the application layer data of each first business task; converts the business data of each second business task into detailed layer data, waits for the next time or multiple times thereafter to convert the business data of each second business task into detailed layer data, until all the detailed layer data of each second business task conforms to the whole process, converts all the detailed layer data of each second business task into application layer data, and performs indicator analysis of each second business task based on the application layer data of each second business task. Therefore, by adopting the above-mentioned technical means, the problem that the comprehensiveness and accuracy of the analysis of various indicators of new energy vehicle batteries in the existing technology need to be improved can be solved, thereby improving the comprehensiveness and accuracy of the analysis of various indicators of new energy vehicle batteries. Attached Figure Description

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0009] Figure 1 This is a flowchart illustrating a method for analyzing multi-dimensional indicators of vehicle batteries provided in an embodiment of this application.

[0010] Figure 2 This is a flowchart illustrating a method for converting business data into application layer data, as provided in an embodiment of this application.

[0011] Figure 3 This is a schematic diagram of the structure of a multi-dimensional index analysis device for vehicle batteries provided in an embodiment of this application;

[0012] Figure 4 This is a schematic diagram of the structure of a vehicle networking server provided in an embodiment of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] Figure 1 This is a flowchart illustrating a method for analyzing multi-dimensional indicators of vehicle batteries provided in an embodiment of this application. Figure 1 The multi-dimensional index analysis method for vehicle batteries can be executed by an offline processing engine set up on the vehicle networking server. Optionally, Figure 1 The multi-dimensional indicator analysis method for vehicle batteries can also be executed by a computer or a regular server, or by software on a computer or a regular server. A vehicle networking server can be considered a server that provides services for vehicle networking. Taking an offline processing engine as the execution entity as an example, this multi-dimensional indicator analysis method for vehicle batteries includes:

[0015] S101, receive battery data of the corresponding vehicle of each vehicle cloud platform reported by each vehicle cloud platform at a first preset interval, and store the received battery data in the target data warehouse after parsing and processing until the second preset interval is reached, and obtain the operation layer data corresponding to the second preset interval.

[0016] S102, schedule each business task to process the operational layer data using query language and obtain the business data of each business task;

[0017] S103, based on the business type of each business task, divide each business task into a first business task that is not part of the whole process and a second business task that is part of the whole process.

[0018] S104, convert the business data of each first business task into application layer data, and perform indicator analysis of each first business task based on the application layer data of each first business task.

[0019] S105, convert the business data of each second business task into detailed layer data, wait for the next or subsequent multiple conversions of the business data of each second business task into detailed layer data, until all the detailed layer data of each second business task conforms to the whole process, convert all the detailed layer data of each second business task into application layer data, and perform indicator analysis on each second business task based on the application layer data of each second business task.

[0020] Battery data includes: charging status, individual cell current, individual cell temperature, slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, displayed battery level, reason for charging termination, number of cells, individual cell voltage value, temperature sensor value, highest individual cell voltage value, lowest individual cell voltage value, highest voltage cell code, lowest voltage cell code, charging extreme low temperature, lowest temperature probe number, charging extreme high temperature, highest temperature probe number, average charging temperature, module temperature polling number, vehicle speed, total mileage, ambient temperature, data collection time, data collection cycle, total battery power, battery drive power limit, battery feedback power limit, individual cell voltage, and battery health status. The system displays the following data: single charge amount, engine speed, current charge level, national standard insulation resistance, high-voltage interlock status, insulation status, relay status, anti-theft authentication status, charging request, heating request, BMS system fault level, high-voltage request under fault, fast charging positive relay control, fast charging negative relay control, vehicle status, battery pack total voltage, battery pack relay external voltage, battery energy status, maximum charging capacity, battery preheating status, charging mode, national standard charging status, DC system status, gear position, VCU status signal, rear electronic control status control, front electronic control status control, BMS status control, accelerator pedal position, throttle pedal position, charging connection status, and brake pedal status.

[0021] The Battery Management System (BMS), commonly known as a battery nanny or battery manager, is primarily designed for intelligent management and maintenance of individual battery cells. It prevents overcharging and over-discharging, extends battery life, and monitors battery status. DC stands for DC-DC converter, and VCU is the voltage control unit. A single cell is the smallest unit of the battery; multiple cells form a battery pack, and multiple battery packs constitute the entire battery system for the target vehicle.

[0022] Each vehicle is equipped with a TBOX (Battery Toll Collection Box). The TBOX collects battery data from the vehicle and reports it to the vehicle cloud platform. The vehicle cloud platform then forwards the data to the offline processing engine. It should be noted that the offline processing engine needs to process battery data from multiple vehicles. The format of battery data may differ between different vehicle models, so the battery data needs to be parsed. Data parsing converts the battery data into data that the offline processing engine can recognize. The offline processing engine can be any commonly used processing engine.

[0023] The second preset interval includes multiple first preset intervals. For example, if the first preset interval is 10 minutes and the second preset interval is one day, the second preset interval includes 144 first preset intervals. The vehicle cloud platform reports battery data every 10 minutes, storing the parsed and processed battery data in the target data warehouse each time. This process continues until the target data warehouse contains one day's worth of battery data, which is when the second preset interval is reached. For the first business task, the metrics analysis for that task can be performed based on one day's worth of battery data (one day's application layer data). For the second business task, the metrics analysis for that task can only be performed based on the battery data for the entire process (the application layer data for the entire process). If a charge-discharge cycle is 3 days, then after obtaining the detailed layer data from this conversion, it is necessary to wait for the detailed layer data from the next two conversions, for a total of 3 days of detailed layer data, to perform the metrics analysis for the second business task.

[0024] The scheduling of various business tasks utilizes query language to process operational layer data and obtain business data for each task. This can be understood as scheduling various business tasks according to pre-set task scheduling scripts, scheduling cycles, and scheduling dependencies. Each business task uses query language to search for its own business data from operational layer data.

[0025] Different business types require different scheduling scripts and different scheduling dependencies. For example, based on the needs of business one, script one from ODS to DWD and script two from DWD to ADS are written. Then, scheduling task one is set to be associated with script one and a scheduling period is set. Scheduling task two is set to be associated with script two and a scheduling period is set. Finally, script two is set to depend on script one. In this way, during actual scheduling, scheduling task two will be executed after script one has been scheduled.

[0026] The operation layer is the ODS layer, also known as the data operation layer or Operation Data Store. It can be considered as the raw data. In this embodiment, the battery data stored in the target data warehouse at the second preset interval is used as the operation layer data. The detail layer is the DWD layer, also known as the data warehouse details layer. This layer mainly performs data cleaning and normalization operations on the ODS layer, such as removing empty data, dirty data, outliers, etc. The application layer is the ADS layer, also known as the data application layer or Application Data Service. The application layer data is the data obtained after final processing. In this embodiment, the application layer data is used for indicator analysis.

[0027] The target data warehouse can be a Hive data warehouse, and the query language can be SQL query language.

[0028] The business types include: full-process and non-full-process.

[0029] Optionally, when parsing battery data, the battery data can also be filtered to select the data required for multiple business tasks.

[0030] According to the technical solution provided in the embodiments of this application, the system receives battery data of vehicles corresponding to each vehicle cloud platform reported by each vehicle cloud platform at a first preset interval. Each received battery data is parsed and processed, then stored in a target data warehouse until a second preset interval is reached, obtaining the operational layer data corresponding to the second preset interval. The system schedules each business task, using a query language to process the operational layer data, obtaining the business data of each business task. Based on the business type of each business task, each business task is divided into a first business task (not part of the entire process) and a second business task (part of the entire process). The business data of each first business task is converted into application layer data, and based on the application layer data of each first business task, indicator analysis is performed on each first business task. The business data of each second business task is converted into detailed layer data, and this process continues until all the detailed layer data of each second business task conforms to the entire process. Finally, all the detailed layer data of each second business task is converted into application layer data, and based on the application layer data of each second business task, indicator analysis is performed on each second business task. Therefore, by adopting the above-mentioned technical means, the problem that the comprehensiveness and accuracy of the analysis of various indicators of new energy vehicle batteries in the existing technology need to be improved can be solved, thereby improving the comprehensiveness and accuracy of the analysis of various indicators of new energy vehicle batteries.

[0031] Specifically: The business tasks include: single-cell voltage task during charging, single-cell current task during charging, single-cell temperature change task during discharging, single-cell temperature change task throughout the entire process, and charging interval duration task; the business data for the single-cell voltage task during charging includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and single-cell voltage; the business data for the single-cell current task during charging includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and single-cell current; the business data for the single-cell temperature change task during discharging includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and temperature; the business data for the single-cell temperature change task throughout the entire process includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and temperature; and the business data for the charging interval duration task includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and mileage.

[0032] The single-cell voltage task during charging analyzes the voltage changes of multiple cells during charging; the single-cell current task during charging analyzes the current changes of multiple cells during charging; the single-cell temperature change task during discharging analyzes the temperature changes of multiple cells during discharging; the charging interval days task analyzes the charging intervals or charging habits of the target vehicle's battery; and the single-cell temperature change task throughout the entire process analyzes the temperature changes of multiple cells throughout the entire process.

[0033] The current of a single cell includes charging current and discharging current; the voltage of a single cell includes output voltage, input voltage, and open-circuit voltage; the temperature of a single cell includes the temperature of the single cell when it is charging, discharging, open-circuit, and short-circuit.

[0034] Specifically: the full process refers to a complete charge and discharge cycle, while the non-full process refers to a process that is not a complete charge and discharge cycle; the first business task includes: the individual cell voltage task during the charging process, the individual cell current task during the charging process, and the individual cell temperature change task during the discharging process; the second business task includes: the charging interval duration task.

[0035] It should be noted that the first and second business tasks mentioned above are merely examples. In the analysis of indicators for new energy vehicle batteries, other business tasks are also required. Because the principles are similar and there are too many other business tasks to list them all, they will not be elaborated upon further. Each business task represents one indicator.

[0036] Figure 2 This is a flowchart illustrating a method for converting business data into application layer data, as provided in an embodiment of this application, executed by an offline processing engine, such as... Figure 2 As shown, it includes:

[0037] For each primary business task:

[0038] S201, group the business data of the first business task according to the vehicle identification number to obtain the group data of each vehicle, and sort the data within each group according to the data sampling time;

[0039] S202, use window functions to calculate the difference between every two adjacent data in each sorted group of data, and filter data from each group of data based on the difference between every two adjacent data;

[0040] S203, insert the data selected from multiple grouped data into the application layer to obtain the application layer data of the first business task.

[0041] Using window functions to calculate differences is a common technique and will not be elaborated further. For example, the first business task is the individual cell voltage task during the charging process. When a vehicle goes from not charging to entering charging mode, and from charging to exiting charging mode, there are changes in the slow charging state, slow charging gun connection state, fast charging state, or fast charging gun connection state. That is, there is a difference between two adjacent data points related to these four states (e.g., the data for these four states at 10:10 AM and the adjacent data for these four states at 10:15 AM). Based on this principle, data showing the vehicle is charging can be filtered from the grouped data (data that meets the signal changes at the start of charging). This filtered data is then inserted into the application layer to obtain application layer data.

[0042] The tasks related to single-cell current during charging, single-cell temperature change during discharging, and single-cell voltage during charging are similar and will not be elaborated further.

[0043] Use query language to filter data from the business data of each second business task; use UDTF functions to split the filtered data into rows, and use the split data as the detail layer data.

[0044] UDTF stands for User-Defined Table-Generating Functions. UDTF functions split filtered data row by row, essentially dividing a single row into multiple rows to obtain more detailed information.

[0045] Furthermore, all the detailed layer data possessed by each second business task is converted into application layer data, including: for each second business task: all the detailed layer data possessed by the first business task is grouped according to the vehicle identification number to obtain group data for each vehicle, and the data within each group is sorted according to the data sampling time; the difference between every two adjacent data in each sorted group is calculated using a window function, and data is filtered from each group based on the difference between every two adjacent data; the data filtered from multiple group data is inserted into the application layer to obtain the application layer data for the second business task.

[0046] For example, the second business task is the charging interval duration task. The data between two charging sessions can be regarded as a complete process (the data between two charging sessions can be regarded as a charge and discharge cycle). There is a difference between two adjacent data in the four states: slow charging state, slow charging gun connection state, fast charging state, or fast charging gun connection state. By filtering out the data between two charging sessions from the grouped data, we can filter out the data of a complete process, which is the business data of the charging interval duration task.

[0047] The task of tracking the temperature change of a single cell throughout the entire process is similar to the task of tracking the duration of charging intervals, except that the data for the task of tracking the temperature change of a single cell throughout the entire process includes temperature.

[0048] Furthermore, based on the application layer data of each first business task or each second business task, indicator analysis is performed on each first business task or each second business task, including: processing the application layer data of each first business task or each second business task using the Web backend; rendering the page using the Web frontend based on the processing results of the Web backend to obtain performance statistics charts; and performing multi-dimensional indicator analysis on the batteries of multiple vehicles based on the performance statistics charts.

[0049] Each business task corresponds to a single dimension of metric analysis. This application's embodiment utilizes a web backend and a web frontend to achieve multi-dimensional metric analysis of the battery using multiple business tasks. The web backend can perform data querying and aggregation operations. The web backend aggregates data from a single business task according to data categories to obtain processing results for that task. Then, the web frontend interacts with the web backend to render a page based on the processing results, generating performance statistics charts for that business task, thus achieving metric analysis in one dimension. Alternatively, the web backend aggregates data from multiple business tasks according to data categories to obtain processing results for multiple business tasks. Then, the web frontend interacts with the web backend to render a page based on the processing results, generating performance statistics charts for multiple business tasks, thus achieving multi-dimensional metric analysis.

[0050] Furthermore, based on the application layer data of each second business task, indicator analysis is performed on each second business task, including: for each second business task: determining one or more first business tasks corresponding to the second business task; and performing indicator analysis on the second business task based on the application layer data of the second business task and the application layer data of one or more first business tasks corresponding to the second business task.

[0051] This application embodiment uses application layer data of the second business task as the main component and application layer data of the first business task as a supplement to perform indicator analysis of the second business task.

[0052] For example, if the second business task is the task of changing the temperature of individual cells throughout the entire process, the corresponding first business task could be the task of changing the temperature of individual cells during the charging process and the task of changing the temperature of individual cells during the discharging process. Then, the application layer data of the tasks of changing the temperature of individual cells throughout the entire process, the task of changing the temperature of individual cells during the charging process, and the task of changing the temperature of individual cells during the discharging process can be used to perform index analysis on the second business task.

[0053] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0054] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0055] Figure 3 This is a schematic diagram of a vehicle battery multi-dimensional index analysis device provided in an embodiment of this application.

[0056] like Figure 3 As shown, the vehicle battery multi-dimensional index analysis device includes:

[0057] The receiving module 301 is configured to receive battery data of the corresponding vehicle of each vehicle cloud platform reported by each vehicle cloud platform at a first preset interval, and store the received battery data in the target data warehouse after parsing and processing until a second preset interval is reached, so as to obtain the operation layer data corresponding to the second preset interval.

[0058] The scheduling module 302 is configured to schedule various business tasks to process operational layer data using a query language to obtain business data for each business task.

[0059] The partitioning module 303 is configured to divide each business task into a first business task that is not part of the entire process and a second business task that is part of the entire process based on the business type of each business task.

[0060] The first analysis module 304 is configured to convert the business data of each first business task into application layer data, and perform indicator analysis of each first business task based on the application layer data of each first business task.

[0061] The second analysis module 305 is configured to convert the business data of each second business task into detailed layer data, wait for the next or subsequent multiple conversions of the business data of each second business task into detailed layer data, until all the detailed layer data of each second business task conforms to the whole process, convert all the detailed layer data of each second business task into application layer data, and perform indicator analysis on each second business task based on the application layer data of each second business task.

[0062] According to the technical solution provided in the embodiments of this application, the system receives battery data of vehicles corresponding to each vehicle cloud platform reported by each vehicle cloud platform at a first preset interval. Each received battery data is parsed and processed, then stored in a target data warehouse until a second preset interval is reached, obtaining the operational layer data corresponding to the second preset interval. The system schedules each business task, using a query language to process the operational layer data, obtaining the business data of each business task. Based on the business type of each business task, each business task is divided into a first business task (not part of the entire process) and a second business task (part of the entire process). The business data of each first business task is converted into application layer data, and based on the application layer data of each first business task, indicator analysis is performed on each first business task. The business data of each second business task is converted into detailed layer data, and this process continues until all the detailed layer data of each second business task conforms to the entire process. Finally, all the detailed layer data of each second business task is converted into application layer data, and based on the application layer data of each second business task, indicator analysis is performed on each second business task. Therefore, by adopting the above-mentioned technical means, the problem that the comprehensiveness and accuracy of the analysis of various indicators of new energy vehicle batteries in the existing technology need to be improved can be solved, thereby improving the comprehensiveness and accuracy of the analysis of various indicators of new energy vehicle batteries.

[0063] The business tasks include: single-cell voltage task during charging, single-cell current task during charging, single-cell temperature change task during discharging, single-cell temperature change task throughout the entire process, and charging interval duration task; business data for the single-cell voltage task during charging includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and single-cell voltage; business data for the single-cell current task during charging includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and single-cell current; business data for the single-cell temperature change task during discharging includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and temperature; business data for the single-cell temperature change task throughout the entire process includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and temperature; business data for the charging interval duration task includes: slow charging status, slow charging gun connection status, fast charging status, fast charging gun connection status, and mileage.

[0064] The full process refers to a complete charge and discharge cycle, while the non-full process refers to a process that is not a complete charge and discharge cycle. The first task includes: the individual cell voltage task during the charging process, the individual cell current task during the charging process, and the individual cell temperature change task during the discharging process. The second task includes: the charging interval duration task.

[0065] Optionally, the first analysis module 304 is further configured to, for each first business task: group the business data of the first business task according to the vehicle identification number to obtain group data for each vehicle; sort the data within each group according to the data sampling time; calculate the difference between every two adjacent data in each sorted group using a window function; filter data from each group based on the difference between every two adjacent data; and insert the data filtered from multiple group data into the application layer to obtain the application layer data of the first business task.

[0066] Optionally, the second analysis module 305 is also configured to use query language and UDTF functions to convert the business data of each second business task into detail-level data.

[0067] Optionally, the second analysis module 305 is further configured to, for each second business task: group all the detailed layer data of the first business task according to the vehicle identification number to obtain group data for each vehicle; sort the data within each group according to the data sampling time; calculate the difference between every two adjacent data in each sorted group using a window function; filter data from each group based on the difference between every two adjacent data; and insert the data filtered from multiple group data into the application layer to obtain the application layer data of the second business task.

[0068] Optionally, the first analysis module 304 is also configured to process the application layer data of each first business task using a web backend; based on the processing results of the web backend, render the page using a web frontend to obtain performance statistics charts; and perform multi-dimensional index analysis on the batteries of multiple vehicles based on the performance statistics charts.

[0069] Optionally, the second analysis module 305 is also configured to process the application layer data of each second business task using a web backend; based on the processing results of the web backend, render the page using a web frontend to obtain performance statistics charts; and perform multi-dimensional index analysis on the batteries of multiple vehicles based on the performance statistics charts.

[0070] Optionally, the second analysis module 305 is further configured to perform indicator analysis on each second business task based on the application layer data of each second business task, including: for each second business task: determining one or more first business tasks corresponding to the second business task; and performing indicator analysis on the second business task based on the application layer data of the second business task and the application layer data of one or more first business tasks corresponding to the second business task.

[0071] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0072] Figure 4 This is a schematic diagram of the vehicle networking server 4 provided in an embodiment of this disclosure. Figure 4 As shown, the vehicle networking server 4 in this embodiment includes: an offline processing engine 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the offline processing engine 401. When the offline processing engine 401 executes the computer program 403, it implements the steps in the various method embodiments described above. Alternatively, when the offline processing engine 401 executes the computer program 403, it implements the functions of each module / unit in the various device embodiments described above.

[0073] The vehicle networking server 4 may include, but is not limited to, an offline processing engine 401 and a memory 402. Those skilled in the art will understand that... Figure 4 This is merely an example of the vehicle-to-everything (V2X) server 4 and does not constitute a limitation on the V2X server 4. It may include more or fewer components than shown in the figure, or different components.

[0074] The memory 402 can be an internal storage unit of the vehicle networking server 4, such as a hard drive or memory of the vehicle networking server 4. The memory 402 can also be an external storage device of the vehicle networking server 4, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the vehicle networking server 4. The memory 402 can also include both internal and external storage units of the vehicle networking server 4. The memory 402 is used to store computer programs and other programs and data required by the vehicle networking server.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0076] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by an offline processing engine, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0077] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for multi-dimensional index analysis of vehicle batteries, characterized in that, include: Receive battery data of the corresponding vehicle from each vehicle cloud platform at a first preset interval, and store the received battery data in the target data warehouse after parsing and processing until the second preset interval is reached, and obtain the operation layer data corresponding to the second preset interval. Schedule various business tasks to process the operational layer data using a query language to obtain the business data for each business task; Based on the business type of each business task, each business task is divided into a first business task that is not part of the entire process and a second business task that is part of the entire process. The full process refers to a complete charge and discharge cycle, while the non-full process refers to a process that is not a complete charge and discharge cycle. The first business task includes: single-cell voltage task during charging, single-cell current task during charging, and single-cell temperature change task during discharging; the second business task includes: charging interval duration task and single-cell temperature change task throughout the entire process. The business data of each primary business task is converted into application layer data, and the indicator analysis of each primary business task is performed based on the application layer data of each primary business task. The business data of each second business task is converted into detailed layer data. This process is repeated multiple times until all the detailed layer data of each second business task conforms to the entire process. Then, all the detailed layer data of each second business task is converted into application layer data. Based on the application layer data of each second business task, the indicator analysis of each second business task is performed.

2. The method according to claim 1, characterized in that, The business data of each primary business task is converted into application layer data, including: For each primary business task: The business data of the first business task is grouped according to the vehicle identification number to obtain the group data of each vehicle, and the data of each group is sorted according to the data sampling time. Window functions are used to calculate the difference between any two adjacent data points in each sorted group, and data is then filtered from each group based on the difference between any two adjacent data points. The data selected from multiple grouped data is inserted into the application layer to obtain the application layer data for the first business task.

3. The method according to claim 1, characterized in that, The business data of each second business task is converted into detailed layer data, including: Use query language to filter data from the business data of each secondary business task; The filtered data is split into rows using UDTF functions, and the split data is used as the detail layer data.

4. The method according to claim 1, characterized in that, Transform all the detailed layer data of each second business task into application layer data, including: For each second business task: All the detailed layer data of the second business task are grouped according to the vehicle identification number to obtain the group data of each vehicle, and the data of each group is sorted according to the data sampling time. Window functions are used to calculate the difference between any two adjacent data points in each sorted group, and data is then filtered from each group based on the difference between any two adjacent data points. The data selected from multiple grouped data is inserted into the application layer to obtain the application layer data for this second business task.

5. The method according to claim 1, characterized in that, Based on the application layer data of each first business task or each second business task, conduct indicator analysis for each first business task or each second business task, including: The application layer data of each first business task or each second business task is processed using a web backend. Based on the processing results of the Web backend, the Web frontend is used to render the page and obtain performance statistics charts; Based on the aforementioned performance statistics charts, a multi-dimensional index analysis was conducted on the batteries of multiple vehicles.

6. The method according to claim 1, characterized in that, Based on the application layer data of each secondary business task, indicator analysis is performed on each secondary business task, including: For each second business task: Identify one or more first business tasks corresponding to the second business task; Based on the application layer data of the second business task and the application layer data of one or more first business tasks corresponding to the second business task, the indicator analysis of the second business task is carried out.

7. A multi-dimensional index analysis device for vehicle batteries, characterized in that, include: The receiving module is configured to receive battery data of the corresponding vehicle from each vehicle cloud platform at a first preset interval, and store the received battery data in the target data warehouse after parsing and processing until a second preset interval is reached, thereby obtaining the operation layer data corresponding to the second preset interval. The scheduling module is configured to schedule various business tasks to process the operational layer data using a query language to obtain the business data of each business task. The partitioning module is configured to divide each business task into a first business task that is not part of the entire process and a second business task that is part of the entire process, based on the business type of each business task. The full process refers to a complete charge and discharge cycle, while the non-full process refers to a process that is not a complete charge and discharge cycle. The first business task includes: single-cell voltage task during charging, single-cell current task during charging, and single-cell temperature change task during discharging; the second business task includes: charging interval duration task and single-cell temperature change task throughout the entire process. The first analysis module is configured to convert the business data of each first business task into application layer data, and perform indicator analysis on each first business task based on the application layer data of each first business task. The second analysis module is configured to convert the business data of each second business task into detailed layer data, wait for the next or subsequent multiple conversions of the business data of each second business task into detailed layer data, until all the detailed layer data of each second business task conforms to the entire process, convert all the detailed layer data of each second business task into application layer data, and perform indicator analysis on each second business task based on the application layer data of each second business task.

8. A vehicle networking server, characterized in that, The system includes a memory, an offline processing engine, and a computer program stored in the memory and executable on the offline processing engine, wherein the offline processing engine, when executing the computer program, implements the vehicle battery multi-dimensional index analysis method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle battery multi-dimensional index analysis method as described in any one of claims 1 to 6.

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