Battery test data processing method and device and electronic equipment

Abstract

The invention discloses a battery test data processing method and device and electronic equipment. The method comprises the following steps: acquiring a target item for executing a test on a target battery; determining a target processing condition corresponding to the target item based on a predetermined mapping relationship, the mapping relationship being a relationship between a historical processing condition and a historical item, the historical processing condition and the historical item being obtained by testing the reference battery, and the type of the reference battery being matched with the type of the target battery; and based on the target item, processing the target test data of the target battery to obtain a data processing result. According to the invention, the technical problem that the processing efficiency of the battery test data is not ideal in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a battery test data processing method, device and electronic equipment. Background Art

[0002] As a rechargeable battery with high energy density, long service life and no memory effect, lithium battery is widely used in new energy vehicles, energy storage and various power tools. In the process of lithium battery research and development, production, testing and decommissioning and recycling, the acquisition and analysis of data such as battery performance, safety and life cycle assessment are crucial. With the continuous improvement of the intelligence and automation of battery testing equipment, the amount of battery data generated is becoming increasingly large, the data types are diverse, and the proportion of non-standard data is high, which puts higher requirements on data processing and analysis. In the related technology, the processing and analysis of lithium battery test data mainly relies on manual collection of lithium battery test data, which is not only inefficient, but also takes a lot of time in the data entry and verification process of data collection, and the data quality is difficult to guarantee. Secondly, the data processing process cannot be standardized, resulting in invalid storage and invalid reading of data, resulting in low efficiency of data analysis. Finally, the lack of the ability to identify and eliminate abnormal data makes it difficult to guarantee the accuracy of the analysis results. In summary, the related technology has technical problems for the processing and analysis of battery test data, and the processing effect of battery test data is not ideal.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a battery test data processing method, device and electronic device to at least solve the technical problem of unsatisfactory battery test data processing efficiency existing in the related art.

[0005] According to one aspect of the embodiment of the present application, a battery test data processing method is provided, including: obtaining a target item for executing a test on a target battery; determining a target processing condition corresponding to the target item based on a predetermined mapping relationship, wherein the mapping relationship is the relationship between a historical processing condition and a historical item, and the historical processing condition and the historical item are obtained by testing a reference battery, and the type of the reference battery matches the type of the target battery; based on the target item, processing the target test data of the target battery to obtain a data processing result. By establishing an intelligent mapping between battery types and test items, automatically identifying and applying matching processing conditions, the accuracy and efficiency of data processing are significantly improved, ensuring the consistency analysis of test data across battery types, and providing a solid data foundation for battery performance evaluation.

[0006] Optionally, the requirements of the target project include electrical performance characteristics corresponding to multiple test items, and the target processing conditions corresponding to the target project are determined based on a predetermined mapping relationship, including: determining static indicators according to the type of target battery; using a mapping relationship, based on the electrical performance characteristics corresponding to multiple test items, respectively determining dynamic indicators corresponding to multiple test items; determining target processing conditions based on static indicators and dynamic indicators corresponding to multiple test items. By dynamically determining static and dynamic indicators of battery testing through intelligent mapping and automatically adapting processing conditions, the personalization and accuracy of data processing are greatly improved, the comprehensive evaluation of battery performance characteristics is accelerated, and efficient data support is provided for battery optimization design. Through automated indicator matching, human errors are avoided and the reliability and efficiency of the data processing process are enhanced.

[0007] Optionally, the method further includes: obtaining the model information of the battery test equipment; determining the conversion strategy based on the model information; and using the conversion strategy to process the initial test data output by the battery test equipment to obtain the target test data. By automatically identifying the battery test equipment model and applying a customized conversion strategy, the input of various data sources is effectively standardized, ensuring the compatibility and consistency of data from different devices, greatly improving the accuracy and efficiency of the conversion from initial test data to target test data, and laying a solid foundation for subsequent data analysis.

[0008] Optionally, based on the model information, the conversion strategy is determined, including: parsing the output files of multiple candidate devices with different model information to obtain the data extraction positions and data accuracy information corresponding to the multiple candidate devices; determining the predetermined strategies corresponding to the multiple candidate devices based on the data extraction positions and data accuracy information corresponding to the multiple candidate devices; according to the model information, among the predetermined strategies corresponding to the multiple candidate devices, determining the conversion strategy corresponding to the battery test device. Through the above processing, automatic model identification and customized strategy conversion are performed, the standardization of multi-source data is realized, the data compatibility and consistency are enhanced, the data conversion efficiency and accuracy are significantly improved, and a reliable guarantee is provided for efficient data analysis.

[0009] Optionally, the target test data includes multiple data points. Based on the target project, the target test data of the target battery is processed to obtain data processing results, including: aggregating multiple data points to obtain aggregation results; based on the aggregation results, performing abnormal elimination processing on multiple data points to obtain updated target test data; using target processing conditions to process the updated target test data to obtain data processing results. Through the above processing, the aggregation and abnormal elimination technology optimizes the data points, intelligently aggregates and eliminates abnormalities, enhances data quality, and combines customized processing conditions to achieve precise analysis, significantly improve the efficiency and accuracy of data analysis, and provide strong support for battery performance evaluation.

[0010] Optionally, the method further includes: determining the role information of the account in response to receiving a query request from the account; determining the operation level and access rights of the account according to the project type associated with the role information; and determining that the account is allowed to access the target test data based on the operation level and access rights. Through the above processing, the dynamic association between roles and permissions is realized, user access is accurately controlled, data security is guaranteed, management efficiency is improved, personalized data services are realized, user experience is optimized, and the overall security of the system and the flexibility of user operations are enhanced.

[0011] Optionally, before processing the target test data of the target battery based on the target project and obtaining the data processing result, the method further includes: determining the data volume of the target test data; when the data volume is greater than or equal to the predetermined storage threshold, stream processing is performed on the target test data according to the predetermined reading batch to obtain multiple batches of data; and the multiple batches of data are sequentially stored in the database. By dynamically evaluating the data volume, intelligently stream processing large files, and efficiently storing in batches, memory overflow is avoided, the stable operation of the system is ensured, the big data processing capability and storage efficiency are significantly improved, resource utilization is optimized, and the overall performance of the system is enhanced.

[0012] Optionally, the method further includes: detecting whether data increment is generated in the database; if data increment is generated in the database, triggering the execution of processing of the target test data. Through the incremental detection mechanism, data analysis is automatically triggered to achieve seamless connection between data update and processing, improve the timeliness of analysis, reduce resource consumption, ensure that decisions are based on the latest data, and enhance the system's responsiveness and intelligence level.

[0013] According to another aspect of an embodiment of the present application, a battery test data processing device is provided, including: a test item determination module, used to obtain a target item for executing a test on a target battery; a processing condition determination module, used to determine a target processing condition corresponding to the target item based on a predetermined mapping relationship, wherein the predetermined mapping relationship is a relationship between historical processing conditions and historical items, and the historical processing conditions and historical items are obtained based on testing a reference battery, and the type of the reference battery matches the type of the target battery; a processing result generation module, used to process the target test data of the target battery based on the target item to obtain a data processing result.

[0014] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement any one of the battery test data processing methods.

[0015] In the embodiment of the present application, the target project for executing the test on the target battery is obtained; based on the predetermined mapping relationship, the target processing condition corresponding to the target project is determined, wherein the mapping relationship is the relationship between the historical processing condition and the historical project, the historical processing condition and the historical project are obtained by testing the reference battery, and the type of the reference battery matches the type of the target battery; based on the target project, the target test data of the target battery is processed to obtain the data processing result. The purpose of using the standardized processing method to improve the efficiency of preprocessing the uploaded battery test data and using the mapping relationship to determine the processing conditions to improve the reliability of the target test data processing results is achieved, and the technical effect of improving the efficiency of battery test data processing is achieved, thereby solving the technical problem of unsatisfactory battery test data processing efficiency existing in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a flowchart of an optional battery test data processing method provided according to an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of an optional battery test data processing method provided according to an embodiment of the present application;

[0019] Figure 3 It is a schematic diagram of an optional battery test data processing device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0023] MPP (Massively Parallel Processing) database is a database system used to process large-scale data sets and high-concurrency queries. MPP database distributes data and processing loads across multiple nodes and uses the parallel computing capabilities of these nodes to accelerate data processing and query responses. MPP database has the characteristics of parallel processing, easy scalability, strong analytical capabilities and load balancing.

[0024] ETL is a term commonly used in data warehouse and big data processing, representing three key data processing steps: Extract, Transform, and Load. The ETL process is mainly used to integrate data from different sources into a centralized data warehouse or data lake for deeper data analysis and report generation.

[0025] IP (Internet Protocol) is a protocol used for communication on the Internet, which defines how data packets are transmitted on the network. In technical implementation, an IP address is a unique identifier assigned to each device on the network for data routing and addressing.

[0026] Clickhouse database is a database used for online analytical processing, capable of efficiently processing large-scale data sets and providing real-time analytical query capabilities. It has the characteristics of high query performance, real-time data processing, low maintenance cost and distributed processing.

[0027] Spark is an open source distributed computing framework. It is widely used in the field of big data due to its high-speed data processing capabilities and flexibility. It is suitable for application scenarios of large-scale data processing.

[0028] Stream processing is a real-time data processing method used to process continuous data streams. Stream processing systems can receive, process, and analyze data in real time without waiting for data collection to complete.

[0029] MySQL database is a relational database, that is, a database management system designed based on the relational model. In a relational database, data is stored in the form of a two-dimensional table (i.e., a relational table). Each row in the table represents an entity, and each column represents an attribute of the entity. This structured method makes the data have better atomicity, consistency, isolation, and persistence, which can ensure the integrity of the data and the correct execution of transactions. It is often used to store, manage, and retrieve structured data.

[0030] According to an embodiment of the present application, a method embodiment of a battery test data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] Figure 1 is a flowchart of a battery test data processing method according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0032] Step S102, obtaining target items for executing tests on a target battery;

[0033] It can be understood that according to the test requirements for the target battery, the target items for testing the target battery are obtained. Through the identification of the target items, the system can accurately locate the test data related to the target battery test items, avoiding errors and omissions in data analysis, and thus providing a data basis for battery electrical performance evaluation.

[0034] Optionally, the target items of the battery test should comprehensively evaluate the performance, safety and reliability of the battery. The target items include battery capacity test, battery energy efficiency test, battery power test, battery cycle life test, battery storage performance test, battery temperature impact test, battery safety performance test, battery consistency test, battery self-discharge rate test, battery internal resistance test, battery thermal management test and battery environmental adaptability test.

[0035] Battery capacity testing is the process of measuring the maximum amount of charge that a battery can store under specific conditions.

[0036] Battery energy efficiency testing is to evaluate the energy conversion efficiency of the battery during the charging and discharging process, that is, the ratio of discharge energy to charging energy.

[0037] Battery power testing includes pulse power and continuous power testing, which evaluate the battery's ability to supply power under different current loads. Pulse power testing is used to evaluate the battery's ability to provide high current in a short period of time; continuous power testing is used to evaluate the battery's ability to provide stable power over a long period of time. Battery cycle life testing evaluates the battery's cycle life through multiple charge and discharge cycles, that is, the number of times the battery can be charged and discharged before its performance drops to a certain threshold.

[0038] The battery storage performance test is to evaluate the performance recovery of the battery after long-term storage at different temperatures and conditions. The battery temperature impact test is to evaluate the performance of the battery under different temperature conditions, including charge and discharge efficiency, capacity retention rate and safety.

[0039] Battery safety performance tests, including overcharge test, over-discharge test, short circuit test, hot box test, needle puncture test, etc., are used to evaluate the safety performance of batteries under abnormal conditions. Battery consistency test, by conducting consistency tests on mass-produced batteries, ensures that battery performance parameters fluctuate within a small range and improves the stability and efficiency of the battery pack. Battery self-discharge rate test, by examining the self-discharge of the battery when not in use, evaluates the battery's energy retention capacity.

[0040] Battery internal resistance test, which measures the internal resistance of the battery to evaluate the health and efficiency of the battery. Battery thermal management test, which evaluates the thermal management performance of the battery under various operating conditions to ensure that the battery does not overheat during operation.

[0041] Battery environmental adaptability test evaluates the environmental adaptability and stability of the battery by testing the performance of the battery under different environmental conditions. Through comprehensive testing, the stability and safety of the battery in various application environments can be ensured.

[0042] Step S104, determining a target processing condition corresponding to the target project based on a predetermined mapping relationship, wherein the mapping relationship is a relationship between a historical processing condition and a historical project, the historical processing condition and the historical project are obtained by testing a reference battery, and the type of the reference battery matches the type of the target battery;

[0043] It can be understood that a reference battery that matches the type of the target battery is determined. Based on the mapping relationship between the historical processing conditions and historical items of the reference battery obtained during the battery test data processing of the reference battery, a predetermined mapping relationship between the target processing conditions and the target items of the target battery is determined. Based on the above predetermined mapping relationship, the target processing conditions corresponding to the target items of the target battery are determined. Automatically matching processing conditions through mapping relationships avoids manual search and setting of rules, thereby improving the efficiency of data standardization processing. At the same time, data processing based on historical data and preset mapping relationships can reduce data quality problems caused by manual configuration errors and improve data reliability.

[0044] Optionally, due to differences in test items and experimental equipment brands, the data files obtained by the experimenters differ in file type and layout, data accuracy and units, so the system will first standardize the uploaded files. The predetermined mapping relationship of the target battery can be determined by an intelligent algorithm. The algorithm can generate a corresponding mapping relationship based on the equipment brand, project type and the first line name of the file, convert the original experimental file into a standardized file format for storage, and determine the target processing conditions corresponding to the target project of the target battery, avoiding manual processing of large batches of data files and improving data reliability and analysis efficiency.

[0045] Optionally, the above intelligent algorithm can use a machine learning algorithm. For the data formats generated by battery test cabinets of different brands and models, a machine learning algorithm is used to automatically identify and learn the file structure, automatically generate a mapping relationship table, and reduce the complexity and error rate of manual configuration. For example, a deep learning model is used to analyze experimental data files, automatically extract key fields and data types, and thus achieve more efficient data standardization.

[0046] In an optional embodiment, the requirements of the target project include electrical performance characteristics corresponding to multiple test items, and the target processing conditions corresponding to the target project are determined based on a predetermined mapping relationship, including: determining static indicators according to the type of the target battery; using a mapping relationship to determine dynamic indicators corresponding to the multiple test items based on the electrical performance characteristics corresponding to the multiple test items; determining the target processing conditions based on the static indicators and the dynamic indicators corresponding to the multiple test items.

[0047] It can be understood that the target project requirements of the target battery include multiple test items, and each test item has its corresponding electrical performance characteristics. According to the type of the target battery, the static indicators of the target battery are determined. Static indicators usually refer to those basic and common characteristics in battery testing, such as battery voltage, current, temperature, charge and discharge rate, etc. According to the mapping relationship of the target battery and the electrical performance characteristics corresponding to the multiple test items, the dynamic indicators corresponding to the multiple test items are determined. Based on the static indicators and the dynamic indicators corresponding to the multiple test items, the target processing conditions are determined. The target processing conditions may include indicator calculation formulas, data processing procedures, threshold settings, etc. The introduction of dynamic indicators allows the system to flexibly adjust the analysis strategy according to different test requirements and electrical performance characteristics to meet the diverse and complex data analysis needs. At the same time, by combining static indicators with dynamic indicators, the system can more comprehensively and accurately evaluate the electrical performance of the battery and identify the differences in the performance of the battery under different test conditions.

[0048] Step S106, based on the target project, the target test data of the target battery is processed to obtain a data processing result.

[0049] It can be understood that according to the target items of the target battery test, the target test data of the target battery is processed and analyzed using a predetermined processing program, and the obtained processing results are stored in a database. Among them, the Clickhouse database can be used to store the processing results. By processing and analyzing the target test data using a predetermined processing program, abnormal conditions in the test data can be quickly identified, and efficient and accurate processing of the battery test data can be achieved.

[0050] Optionally, the Spark dynamic calculation program can be used as a predetermined processing program to process and analyze the target test data of the target battery. The Spark dynamic calculation program is a cluster computing system based on a distributed computing framework, which provides the ability to quickly process large-scale data while maintaining high ease of use and versatility. When the Spark dynamic calculation program detects that the database generates incremental data, it will start to execute. Since batteries have a variety of different target projects, and different target projects focus on different calculation indicators and error identification rules, the general indicators (i.e., static indicators) and dynamic indicators of each target project, as well as the calculation rules and thresholds corresponding to each indicator, can be determined through a configurable dynamic calculation program. Using the above calculation rules, the indicator calculation results of the target test data are obtained. According to the indicator calculation results and thresholds, abnormal data is determined and marked.

[0051] Optionally, for the common indicators (i.e., static indicators) and dynamic indicators of each target project, the calculation rules of each indicator can be dynamically configured through a dynamic calculation rule engine. The dynamic calculation rule engine allows experimenters to dynamically configure the calculation rules through a graphical interface or a simple scripting language without modifying the program code. The rule engine can automatically select or adjust the calculation method based on the properties of the test item (such as battery type, test conditions, etc.), improving the flexibility and adaptability of the system. For example, a rule-based engine can be designed to handle the specific calculation requirements of different test items (capacity, energy, pulse power, etc.), and even automatically adjust the calculation logic according to emerging test requirements.

[0052] In an optional embodiment, the method further includes: acquiring model information of the battery testing equipment; determining a conversion strategy based on the model information; and using the conversion strategy to process initial test data output by the battery testing equipment to obtain target test data.

[0053] It can be understood that the model of the battery testing equipment used to test the target battery is obtained. Based on the model of the battery testing equipment and the mapping relationship, the conversion strategy for the initial test data is determined. Using the conversion strategy, the initial test data output by the battery testing equipment is standardized to obtain the target test data of the target battery. The above-mentioned processing of the initial test data includes data cleaning, field conversion, unit unification, data format standardization, etc., to ensure that all initial test data have a unified format and standard before entering the subsequent data analysis process. Standardization of the initial test data enables the system to flexibly respond to battery testing equipment of different brands and models, ensuring that the data obtained from different testing equipment can be uniformly and accurately processed by the system, providing a high-quality data foundation for subsequent electrical performance evaluation, while also demonstrating the flexibility of the system, automated processing capabilities and data quality control technology, significantly improving the efficiency and effectiveness of battery performance analysis.

[0054] In an optional embodiment, a conversion strategy is determined based on model information, including: parsing output files of multiple candidate devices with different model information to obtain data extraction positions and data accuracy information corresponding to the multiple candidate devices; determining predetermined strategies corresponding to the multiple candidate devices based on the data extraction positions and data accuracy information corresponding to the multiple candidate devices; and determining a conversion strategy corresponding to the battery testing device from among the predetermined strategies corresponding to the multiple candidate devices according to the model information.

[0055] It can be understood that, according to the battery testing device, multiple candidate devices with the same test capability and different models as the battery testing device are determined. The output files of the above-mentioned multiple candidate devices with different models are parsed to determine the data extraction position and data accuracy information of the output files corresponding to the multiple candidate devices. Based on the data extraction position and data accuracy information corresponding to the multiple candidate devices, the predetermined strategies corresponding to the multiple candidate devices are determined, for example, how to extract data from the output file, how to adjust the data accuracy, and how to convert the data format. According to the model information of the battery testing device, the candidate device matching the model information is determined from the multiple candidate devices, and the predetermined strategy of the matching candidate device is used as the conversion strategy of the battery testing device. Using the conversion strategy, the initial test data output by the battery testing device is standardized to obtain the target test data of the target battery. The use of the predetermined strategy ensures that the system can not only intelligently identify and process the test data of different device types, but also automatically adjust the data conversion strategy according to the characteristics of the data file, avoiding repeated strategy exploration for each device type, shortening the time of data standardization processing, ensuring the accuracy and efficiency of data processing, and thus supporting the efficient evaluation and analysis of battery electrical performance.

[0056] In an optional embodiment, the target test data includes multiple data points, and based on the target project, the target test data of the target battery is processed to obtain a data processing result, including: aggregating the multiple data points to obtain an aggregate result; based on the aggregate result, performing abnormal elimination processing on the multiple data points to obtain updated target test data; using the target processing conditions to process the updated target test data to obtain a data processing result.

[0057] It can be understood that the target test data of the target battery includes multiple data points, and the multiple data points are aggregated to obtain the aggregation results of the data points. Based on the aggregation results, the abnormal data points in the target test data can be identified, and the identified abnormal data points can be eliminated. After eliminating the abnormal data points in the target test data, the updated target test data is obtained, and the updated target test data is processed using the target processing conditions to obtain the data processing results, and the processing results are stored in the database. Among them, the Clickhouse database can be used to store the processing results. Eliminating outliers can avoid misjudgments caused by data noise, ensure the consistency and validity of the data set, avoid the interference of invalid data, and improve the reliability of electrical performance evaluation; data aggregation reduces the amount of data for subsequent processing and accelerates the data processing speed. Through aggregation and abnormal elimination processing, the system improves the quality of target test data, ensures the accuracy of analysis results, and helps to formulate data-based battery design and verification strategies.

[0058] Optionally, in the process of processing the target test data, in addition to checking whether the data point is an abnormal data point through gathering processing, more comprehensive data quality checks such as data integrity, consistency, timeliness, etc. can be introduced to ensure the reliability of the analysis results. Statistical methods can be used to detect data distribution and identify outliers. Consistent hashing algorithms can also be used to ensure the integrity of data during processing. The hash algorithm maps input data of arbitrary length to an output of fixed length (i.e., a "hash value" or "digest") through a function. The hash algorithm not only ensures the integrity of the data and the efficiency of data processing, but also, through the hash algorithm, regardless of the size of the input data, the output of the hash algorithm is a fixed-length value, which helps to save space in data storage and transmission.

[0059] In an optional embodiment, the method further includes: determining role information of the account in response to receiving a query request from the account; determining the operation level and access rights of the account according to the project type associated with the role information; and determining that the account is allowed to access target test data based on the operation level and access rights.

[0060] It can be understood that in response to the account's query request, the system identifies the account of the request initiator and queries the role information of the account. Based on the account's role information, the type of project it can access is determined. Based on the project type, the system further determines the account's operation level and access rights. Based on the account's operation level and access rights, the system checks whether the requested operation is within the scope of the account's permissions. If the operation requested by the account exceeds the permissions, the system will refuse to execute and return an error message of insufficient permissions; if the operation requested by the account does not exceed the permissions, the system will provide access to the target test data based on its operation level and access rights. Through the role-based access control mechanism, the system can effectively limit data access, prevent unauthorized personnel from viewing sensitive data, and ensure data security and compliance.

[0061] In an optional embodiment, before processing the target test data of the target battery based on the target project to obtain the data processing results, the method also includes: determining the data volume of the target test data; when the data volume is greater than or equal to a predetermined storage threshold, stream processing the target test data according to predetermined reading batches to obtain multiple batches of data; and storing the multiple batches of data in the database in sequence.

[0062] It can be understood that determining the data volume of the target test data can be achieved by calculating the size or number of records of the data file. The data volume is compared with a predetermined storage threshold, wherein the predetermined storage threshold is used to determine whether it is necessary to use stream processing to process the target test data in batches. If the data volume is less than the predetermined storage threshold, the target test data is directly stored in a database, such as an MPP database; if the data volume is greater than or equal to the predetermined storage threshold, the target test data is stream processed according to the predetermined reading batch of the target test data to obtain multiple batches of data, and they are stored in the database in sequence. The stream processing mode can process data in parallel, avoiding the resource bottleneck caused by loading a large amount of data at one time, and improving the speed and efficiency of data processing. At the same time, through stream processing technology, the system can effectively process large-capacity battery test data, providing strong data support for battery performance evaluation.

[0063] Optionally, an ETL program can be used to read the target test data. The ETL program will periodically read the standardized file (i.e., the target test data), process the target test data into easy-to-analyze structured data, and store it in a database, such as an MPP database. At the same time, the ETL program retains the interface for interacting with external data sources to facilitate the widening of data dimensions. In order to improve the efficiency of cluster memory usage, a predetermined storage threshold can be set, for example, the predetermined storage threshold is set to 500MB (megabit). In the process of reading the target test data, if the file size of the target test data is less than 500MB, the target test data is directly stored in the database; if the file size of the target test data is greater than or equal to 500MB, the target test data is stored in batches using stream processing. After all batches of target test data are stored, the target test data scattered in different batches are aggregated and integrated to generate a complete target test data set, providing a data basis for subsequent data analysis and indicator calculation.

[0064] Optionally, for target test data with a large amount of data, real-time data stream processing can be used to read and store it. A stream processing framework such as Apache Flink or Kafka can be integrated to achieve real-time analysis of the battery test data being generated, and to provide timely feedback on abnormal situations, thereby reducing the lag of experimental data and improving the timeliness of decision-making. Among them, Apache Flink is a large-scale data stream processing and event processing framework that provides two data processing modes: stream processing and batch processing. Flink has high-performance, low-latency, and high-fault-tolerant data processing capabilities, and is suitable for processing real-time data streams, streaming analysis, and demanding real-time applications.

[0065] In an optional embodiment, the method further includes: detecting whether data increment is generated in the database; if data increment is generated in the database, triggering the execution of processing of the target test data.

[0066] It can be understood that the target test data in the database is detected to determine whether data increment is generated in the database. If data increment is detected in the database, the target test data in the database is processed and analyzed using a predetermined processing program, and the obtained processing results are stored in the database. The data increment detection and automatic triggering mechanism enable the system to automatically and efficiently process new data in the database, ensuring the real-time, accuracy and consistency of the data, while reducing manual operations and improving the system's automation level and exception handling capabilities.

[0067] Optionally, the system can record the data processing process by establishing supporting log generation rules and store it in a database, such as a Mysql database, so that errors in the data processing process can be tracked and debugged. If there are exceptions in the data reading and processing process, the system will first print the corresponding error logs to the database, and classify and display these logs on the front-end platform into program errors, data errors, and operation errors to facilitate subsequent exception handling. Secondly, the relevant experimental personnel and managers will be notified by email, SMS or corporate communication tools so that timely measures can be taken.

[0068] Optionally, in addition to the Clickhouse database for storing processing results, the MPP database for storing structured target test data, and the MySQL database for storing data processing logs, a data lake can also be built to store raw unstructured or semi-structured data. A data lake is an architecture for storing large amounts of raw data, which can be structured, semi-structured, or unstructured. The main feature of a data lake is that it can store data in its original format without having to define data schemas or perform data conversions in advance, which makes the data lake a flexible and scalable data storage platform. The design concept of a data lake is "store first, process later", which allows the original state of data to be retained when extracting data, and then converted and extracted according to specific needs during data analysis and processing. Based on the Clickhouse database for storing processing results, the MPP database for storing structured target test data, the MySQL database for storing data processing logs, and the data lake, a unified data management platform can be formed, which can better support the long-term storage, analysis, and mining of data, and at the same time, provide the system with higher flexibility and scalability.

[0069] Through the above step S102, the target project for executing the test on the target battery is obtained; step S104, based on the predetermined mapping relationship, the target processing condition corresponding to the target project is determined, wherein the mapping relationship is the relationship between the historical processing condition and the historical project, the historical processing condition and the historical project are obtained based on the test of the reference battery, and the type of the reference battery matches the type of the target battery; step S106, based on the target project, the target test data of the target battery is processed to obtain the data processing result. It can achieve the purpose of improving the efficiency of preprocessing the uploaded battery test data by using the standardized processing method, and improving the reliability of the target test data processing result by determining the processing conditions by using the mapping relationship, and achieve the technical effect of improving the processing effect of the battery test data, thereby solving the technical problem of the unsatisfactory efficiency of the battery test data processing in the related art.

[0070] Based on the above embodiments and optional embodiments, the present application proposes an optional implementation method for analyzing and processing large amounts of complex battery test data.

[0071] With the development of new energy vehicles, the requirements and functions for batteries are increasing. A large amount of relevant basic test data will be generated in the daily development process of lithium batteries, so the demand for the collection, storage and analysis of test data is also increasing. The existing data analysis methods cannot meet the requirements for processing and analyzing massive and complex battery test data, cannot produce effective retention and analysis results, and similar product experience and historical data cannot be used as reference, resulting in high costs for battery system development and verification. In addition, the existing data analysis system has defects such as single function, poor data accuracy, and high maintenance costs. In order to overcome these problems, a data storage and analysis system for lithium battery test analysis has been developed, which can support the standardized storage of battery verification data, generate dynamic analysis indicators, standardize electrical performance evaluation methods, form technical precipitation, and support the analysis and processing of massive data.

[0072] Figure 2 is a schematic diagram of an optional battery test data processing method provided according to an embodiment of the present application, such as Figure 2As shown in the figure, the battery test data processing method consists of five parts: intelligent standardization algorithm (S1), ETL program reading battery test data (S2), Spark dynamic calculation program (S3), log management system (S4), and user authority management (S5). First, it is necessary to build JDK (Java Development Kit, a software development kit for Java language), Spark, Mysql and Clickhouse environment on the server, and then deploy the intelligent standardization algorithm, ETL program and Spark calculation program, and configure the database address and the directory address for storing standardized files according to the set port and host IP. After uploading, the data file is first standardized and stored in the specified path; then, the ETL program will read the files on the directory regularly, and store the reading results and logs in the MPP database and Mysql respectively; then, according to the corresponding target processing conditions, such as calculation logic and judgment rules, the Spark program will aggregate the data, determine indicators (static indicators and dynamic indicators), and identify and eliminate abnormal data; finally, the generated calculation results (i.e., processing results) will be written to the Clickhouse database and displayed on the front-end page for experimental personnel to analyze and process. These five bits will be introduced below.

[0073] First is the intelligent standardization algorithm (S1) part. For large amounts of complex battery test data, the initial test data of the uploaded test files needs to be standardized. Due to differences in test items and brand differences in experimental equipment (i.e., battery test cabinets), the data files obtained differ in file type and layout, data accuracy and units, etc. Therefore, the system will first standardize the uploaded files. The predetermined mapping relationship of the target battery can be determined by an intelligent algorithm. The algorithm can generate corresponding mapping relationships based on the equipment brand, model, project type, and the first line name of the file, convert the original experimental file into a standardized file format for storage, and at the same time, determine the target processing conditions corresponding to the target project of the target battery, avoiding manual processing of large batches of data files and improving data reliability and analysis efficiency.

[0074] The above intelligent algorithm can adopt machine learning algorithm. For the data formats generated by battery test cabinets of different brands and models, machine learning algorithm is used to automatically identify and learn the file structure, automatically generate mapping relationship table, and reduce the complexity and error rate of manual configuration. For example, deep learning model is used to analyze experimental data files, automatically extract key fields and data types, so as to achieve more efficient data standardization.

[0075] Obtain the model of the battery testing equipment that tests the target battery. Based on the model of the battery testing equipment and the mapping relationship, determine the conversion strategy for the initial test data. Using the conversion strategy, standardize the initial test data output by the battery testing equipment to obtain the target test data of the target battery. The above-mentioned processing of the initial test data includes data cleaning, field conversion, unit unification, data format standardization, etc., to ensure that all initial test data have a unified format and standard before entering the subsequent data analysis process. Standardizing the initial test data enables the system to flexibly respond to battery testing equipment of different brands and models, ensuring that the data obtained from different testing equipment can be uniformly and accurately processed by the system, providing a high-quality data foundation for subsequent electrical performance evaluation. It also demonstrates the system's flexibility, automated processing capabilities and data quality control technology, significantly improving the efficiency and effectiveness of battery performance analysis.

[0076] For the determination of the conversion strategy, multiple candidate devices with the same test capability and different models as the battery testing device can be determined based on the battery testing device. The output files of the above multiple candidate devices with different models are parsed to determine the data extraction position and data accuracy information of the output files corresponding to the multiple candidate devices. Based on the data extraction position and data accuracy information corresponding to the multiple candidate devices, the predetermined strategies corresponding to the multiple candidate devices are determined, for example, how to extract data from the output file, how to adjust the data accuracy, and how to convert the data format. According to the model information of the battery testing device, the candidate device matching the model information is determined from the multiple candidate devices, and the predetermined strategy of the matching candidate device is used as the conversion strategy of the battery testing device. Using the conversion strategy, the initial test data output by the battery testing device is standardized to obtain the target test data of the target battery. The use of the predetermined strategy ensures that the system can not only intelligently identify and process the test data of different device types, but also automatically adjust the data conversion strategy according to the characteristics of the data file, avoiding repeated strategy exploration for each device type, shortening the time of data standardization processing, ensuring the accuracy and efficiency of data processing, and thus supporting the efficient evaluation and analysis of battery electrical performance.

[0077] For the part where the ETL program reads the battery test data (S2), the ETL program can be used to read the target test data. The ETL program will read the standardized file (i.e., the target test data) at regular intervals, process the target test data into easy-to-analyze structured data, and store it in a database, such as an MPP database. At the same time, the ETL program retains the interface for interacting with external data sources to facilitate the widening of data dimensions. In order to improve the efficiency of cluster memory usage, a predetermined storage threshold can be set, for example, the predetermined storage threshold is set to 500MB (megabit). In the process of reading the target test data, if the file size of the target test data is less than 500MB, the target test data is directly stored in the database; if the file size of the target test data is greater than or equal to 500MB, the target test data is stored in batches using stream processing. After the target test data of all batches is stored, the target test data scattered in different batches are aggregated and integrated to generate a complete target test data set, providing a data basis for subsequent data analysis and indicator calculation.

[0078] Determining the data volume of the target test data can be achieved by calculating the size or number of records of the data file. Compare the data volume with the predetermined storage threshold, where the predetermined storage threshold is used to determine whether it is necessary to use stream processing to process the target test data in batches. If the data volume is less than the predetermined storage threshold, the target test data is directly stored in a database, such as an MPP database; if the data volume is greater than or equal to the predetermined storage threshold, the target test data is stream processed according to the predetermined reading batch of the target test data to obtain multiple batches of data, and store them in the database in sequence. The stream processing mode can process data in parallel, avoiding the resource bottleneck caused by loading a large amount of data at one time, and improving the speed and efficiency of data processing. At the same time, through stream processing technology, the system can effectively process large-capacity battery test data, providing strong data support for battery performance evaluation.

[0079] For target test data with large data volumes, real-time data stream processing can also be used to read and store them. Stream processing frameworks such as Apache Flink or Kafka can be integrated to achieve real-time analysis of the battery test data being generated, and to provide timely feedback on abnormal situations, thereby reducing the lag of experimental data and improving the timeliness of decision-making. Among them, Apache Flink is a large-scale data stream processing and event processing framework that provides two data processing modes: stream processing and batch processing. Flink has high-performance, low-latency, and high-fault-tolerant data processing capabilities, and is suitable for processing real-time data streams, streaming analysis, and demanding real-time applications.

[0080] Then comes the Spark dynamic calculation program (S3) part. The Spark dynamic calculation program can be used as a predetermined processing program to process and analyze the target test data of the target battery. The Spark dynamic calculation program is a cluster computing system based on a distributed computing framework. It provides the ability to quickly process large-scale data while maintaining high ease of use and versatility. When the Spark dynamic calculation program detects that the database generates incremental data, it will start to execute. Since batteries have a variety of different target projects, and different target projects focus on different calculation indicators and error identification rules, the configurable dynamic calculation program can be used to determine the common indicators (i.e. static indicators) and dynamic indicators of each target project, as well as the corresponding target processing conditions, such as calculation rules and thresholds. Using the above calculation rules, the indicator calculation results of the target test data are obtained. According to the indicator calculation results and thresholds, abnormal data is determined and marked.

[0081] Before processing and analyzing the target test data of the Spark dynamic calculation program, it is necessary to obtain the target items for executing tests on the target battery based on the test requirements for the target battery. The target items for battery execution tests should comprehensively evaluate the performance, safety and reliability of the battery. The target items include battery capacity test, battery energy efficiency test, battery power test, battery cycle life test, battery storage performance test, battery temperature impact test, battery safety performance test, battery consistency test, battery self-discharge rate test, battery internal resistance test, battery thermal management test and battery environmental adaptability test. Through the identification of target items, the system can accurately locate the test data related to the target battery test items, avoid errors and omissions in data analysis, and provide a data basis for battery electrical performance evaluation.

[0082] In addition to determining the target items for testing the target battery, it is also necessary to identify and eliminate abnormal data in the target test data. The target test data of the target battery includes multiple data points, and the multiple data points are aggregated to obtain the aggregated results of the data points. Based on the aggregation results, the abnormal data points in the target test data can be identified, and the identified abnormal data points can be eliminated. After eliminating the abnormal data points in the target test data, the updated target test data is obtained. The updated target test data is processed using the target processing conditions to obtain the data processing results, and the processing results are stored in the database. Among them, the Clickhouse database can be used to store the processing results. Eliminating outliers can avoid misjudgments caused by data noise, ensure the consistency and validity of the data set, avoid interference from invalid data, and improve the reliability of electrical performance evaluation; data aggregation reduces the amount of data for subsequent processing and accelerates the data processing speed. Through aggregation and abnormal elimination processing, the system improves the quality of target test data, ensures the accuracy of analysis results, and helps to formulate data-based battery design and verification strategies.

[0083] In the process of processing the target test data, in addition to checking whether the data point is an abnormal data point through gathering processing, more comprehensive data quality checks can be introduced, such as data integrity, consistency, timeliness, etc., to ensure the reliability of the analysis results. Statistical methods can be used to detect data distribution and identify outliers. Consistent hashing algorithms can also be used to ensure the integrity of data during processing. The hash algorithm maps input data of any length to an output of a fixed length (i.e., a "hash value" or "summary") through a function. The hash algorithm not only ensures the integrity of the data and the efficiency of data processing, but also, through the hash algorithm, regardless of the size of the input data, the output of the hash algorithm is a fixed-length value, which helps to save space in data storage and transmission.

[0084] After determining the target project and eliminating abnormal data points, determine the predetermined mapping relationship between the target processing conditions and the target project of the target battery based on the reference battery that matches the type of the target battery and the mapping relationship between the historical processing conditions and historical projects of the reference battery obtained during the battery test data processing of the reference battery. Based on the above predetermined mapping relationship, determine the target processing conditions corresponding to the target project of the target battery. Automatically match the processing conditions through the mapping relationship, avoid manual search and setting of rules, and improve the efficiency of data standardization processing. At the same time, data processing based on historical data and preset mapping relationships can reduce data quality problems caused by manual configuration errors and improve data reliability.

[0085] The target project requirements of the target battery include multiple test items, each of which has its corresponding electrical performance characteristics. According to the target battery type, determine the static indicators of the target battery. Static indicators usually refer to those basic and common characteristics in battery testing, such as battery voltage, current, temperature, charge and discharge rate, etc. According to the mapping relationship of the target battery and the electrical performance characteristics corresponding to the multiple test items, determine the dynamic indicators corresponding to the multiple test items. Based on the static indicators and the dynamic indicators corresponding to the multiple test items, determine the target processing conditions. The target processing conditions may include indicator calculation formulas, data processing procedures, threshold settings, etc. The introduction of dynamic indicators allows the system to flexibly adjust the analysis strategy according to different test requirements and electrical performance characteristics to meet the diverse and complex data analysis needs. At the same time, by combining static indicators with dynamic indicators, the system can more comprehensively and accurately evaluate the electrical performance of the battery and identify the differences in the performance of the battery under different test conditions.

[0086] For the common indicators (i.e. static indicators) and dynamic indicators of each target project, the calculation rules of each indicator can be dynamically configured through the dynamic calculation rule engine. The dynamic calculation rule engine allows experimenters to dynamically configure the calculation rules through a graphical interface or a simple scripting language without modifying the program code. The rule engine can automatically select or adjust the calculation method based on the properties of the test item (such as battery type, test conditions, etc.), improving the flexibility and adaptability of the system. For example, a rule-based engine can be designed to handle the specific calculation requirements of different test items (capacity, energy, pulse power, etc.), and even automatically adjust the calculation logic according to emerging test requirements.

[0087] According to the target items of the target battery test, the target test data of the target battery is processed and analyzed using the Spark dynamic calculation program, and the obtained processing results are stored in the database. Among them, the Clickhouse database can be used to store the processing results. Using the Spark dynamic calculation program to process and analyze the target test data can quickly identify abnormal conditions in the test data and realize efficient and accurate processing of battery test data.

[0088] For the log management system (S4), the system can record the data processing process by establishing supporting log generation rules and store it in a database, such as a Mysql database, so that errors in the data processing process can be tracked and debugged. If there are exceptions in the data reading and processing process, the system will first print the corresponding error logs to the database, and classify and display these logs on the front-end platform into program errors, data errors, and operation errors to facilitate subsequent exception handling. Secondly, the relevant experimental personnel and managers will be notified by email, SMS or corporate communication tools so that timely measures can be taken.

[0089] For the user rights management (S5) part, in response to the query request of the account, the system identifies the account of the request initiator and queries the role information of the account. According to the role information of the account, the project type that it can access is determined. Based on the project type, the system further determines the operation level and access rights of the account. Based on the operation level and access rights of the account, the system checks whether the requested operation is within the scope of the account's authority. If the operation requested by the account exceeds the authority, the system will refuse to execute and return an error message of insufficient authority; if the operation requested by the account does not exceed the authority, the system will provide access to the target test data according to its operation level and access rights. First, the role is associated with the experiment type, and then the role is bound to the user. Then, for each user, their operation level and access rights are controlled, realizing a hierarchical management system for data access. Through the role-based access control mechanism, the system can effectively restrict data access, prevent unauthorized personnel from viewing sensitive data, and ensure data security and compliance.

[0090] In addition to the Clickhouse database for storing processing results, the MPP database for storing structured target test data, and the MySQL database for storing data processing logs, a data lake can also be built to store raw unstructured or semi-structured data. A data lake is an architecture for storing large amounts of raw data, which can be structured, semi-structured, or unstructured. The main feature of a data lake is that it can store data in its original format without having to define data schemas or perform data conversions in advance, which makes the data lake a flexible and scalable data storage platform. The design concept of a data lake is "store first, process later", which allows the original state of data to be retained when extracting data, and then converted and extracted according to specific needs during data analysis and processing. Based on the Clickhouse database for storing processing results, the MPP database for storing structured target test data, the MySQL database for storing data processing logs, and the data lake, a unified data management platform can be formed, which can better support the long-term storage, analysis, and mining of data, while providing the system with greater flexibility and scalability.

[0091] The embodiment of the present application realizes the analysis of electrical performance experimental data through Java (i.e., a programming language), Saprk, Clickhouse and other technologies. The above-mentioned programs and methods can be deployed on a server to save hardware resources.

[0092] The above optional implementation methods achieve at least the following effects: the standardized processing method of battery test data can be used to process non-standard data files generated by battery test cabinets of different brands and models. By determining the mapping relationship, the file is converted into a standardized format file, avoiding the instability of manual processing and improving the efficiency of file standardization; the dynamic calculation program can be configured to meet the general indicators (i.e. static indicators) and dynamic indicators of various test target items, as well as the calculation rules and thresholds corresponding to each indicator, and then adapt to the target test data of different test items, thereby improving the flexibility of the system and the data analysis efficiency; the log management system facilitates the tracing of problem data and operation records, and provides an effective basis for subsequent review and data correction; the hierarchical management control system can not only ensure data security, but also facilitate the issuance and recovery of user permissions, thereby improving the reliability of the system.

[0093] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0094] In this embodiment, a battery test data processing device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0095] According to an embodiment of the present application, a device embodiment for implementing a battery test data processing method is also provided. Figure 3 is a schematic diagram of a battery test data processing device according to an embodiment of the present application, such as Figure 3 As shown, the battery test data processing device comprises a test item determination module 302, a processing condition determination module 304, and a processing result generation module 306. The device is described below.

[0096] A test item determination module 302 is used to obtain a target item for executing a test on a target battery;

[0097] The processing condition determination module 304 is connected to the test item determination module 302, and is used to determine the target processing condition corresponding to the target item based on a predetermined mapping relationship, wherein the predetermined mapping relationship is a relationship between a historical processing condition and a historical item, and the historical processing condition and the historical item are obtained by testing a reference battery, and the type of the reference battery matches the type of the target battery;

[0098] The processing result generating module 306 is connected to the processing condition determining module 304 and is used to process the target test data of the target battery based on the target item to obtain a data processing result.

[0099] In a battery test data processing device provided by an embodiment of the present application, a test item determination module 302 is set to obtain a target item for executing a test on a target battery; a processing condition determination module 304 is connected to the test item determination module 302, and is used to determine the target processing condition corresponding to the target item based on a predetermined mapping relationship, wherein the predetermined mapping relationship is the relationship between the historical processing condition and the historical item, and the historical processing condition and the historical item are obtained by testing a reference battery, and the type of the reference battery matches the type of the target battery; a processing result generation module 306 is connected to the processing condition determination module 304, and is used to process the target test data of the target battery based on the target item to obtain a data processing result. The purpose of using a standardized processing method to improve the efficiency of preprocessing uploaded battery test data and using a mapping relationship to determine processing conditions to improve the reliability of target test data processing results is achieved, and the technical effect of improving the processing effect of battery test data is achieved, thereby solving the technical problem of unsatisfactory efficiency of battery test data processing in the related art.

[0100] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0101] It should be noted that the test item determination module 302, the processing condition determination module 304, and the processing result generation module 306 correspond to steps S102 to S106 in the embodiment, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the modules as part of the device can be run in a computer terminal.

[0102] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0103] The above-mentioned battery test data processing device may also include a processor and a memory. The test item determination module 302, the processing condition determination module 304, the processing result generation module 306, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0104] The processor includes a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0105] An embodiment of the present application provides a non-volatile storage medium on which a program is stored. When the program is executed by a processor, a battery test data processing method is implemented.

[0106] The embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and can be run on the processor. When the processor executes the program, the following steps are implemented: obtaining a target item for executing a test on a target battery; determining a target processing condition corresponding to the target item based on a predetermined mapping relationship, wherein the mapping relationship is a relationship between a historical processing condition and a historical item, and the historical processing condition and the historical item are obtained by testing a reference battery, and the type of the reference battery matches the type of the target battery; based on the target item, processing the target test data of the target battery to obtain a data processing result. The device in this article can be a server, a PC, etc.

[0107] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: obtaining a target item for executing a test on a target battery; determining a target processing condition corresponding to the target item based on a predetermined mapping relationship, wherein the mapping relationship is a relationship between historical processing conditions and historical items, and the historical processing conditions and historical items are obtained based on testing a reference battery, and the type of the reference battery matches the type of the target battery; based on the target item, processing the target test data of the target battery to obtain a data processing result.

[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0113] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0114] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0115] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0116] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0117] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A battery test data processing method, characterized in that: include: Acquire a target item for executing a test on a target battery; Determining a target processing condition corresponding to the target project based on a predetermined mapping relationship, wherein the mapping relationship is a relationship between a historical processing condition and a historical project, the historical processing condition and the historical project are obtained by testing a reference battery, and the type of the reference battery matches the type of the target battery; Based on the target project, target test data of the target battery is processed to obtain a data processing result.

2. The method according to claim 1, characterized in that: The requirements of the target project include electrical performance characteristics corresponding to a plurality of test items respectively. The determining of the target processing conditions corresponding to the target project based on a predetermined mapping relationship includes: Determining static indicators according to the type of the target battery; Adopting the mapping relationship, based on the electrical performance characteristics respectively corresponding to the multiple test items, respectively determine the dynamic indicators respectively corresponding to the multiple test items; The target processing condition is determined based on the static indicator and the dynamic indicators corresponding to the multiple test items.

3. The method according to claim 1, characterized in that: The method further comprises: Get the model information of the battery testing equipment; Based on the model information, determining a conversion strategy; The conversion strategy is adopted to process the initial test data output by the battery testing device to obtain the target test data.

4. The method according to claim 3, characterized in that: The step of determining a conversion strategy based on the model information includes: Parsing the output files of multiple candidate devices with different model information to obtain data extraction locations and data accuracy information respectively corresponding to the multiple candidate devices; Determine the predetermined strategies corresponding to the plurality of candidate devices respectively based on the data extraction positions and data accuracy information respectively corresponding to the plurality of candidate devices; According to the model information, a conversion strategy corresponding to the battery testing device is determined from the predetermined strategies respectively corresponding to the plurality of candidate devices.

5. The method according to claim 1, characterized in that The target test data includes a plurality of data points. The target test data of the target battery is processed based on the target item to obtain a data processing result, including: Aggregate multiple data points to obtain aggregate results; Based on the aggregation result, performing abnormal elimination processing on the multiple data points to obtain updated target test data; The updated target test data is processed using the target processing condition to obtain the data processing result.

6. The method according to claim 1, characterized in that The method further comprises: In response to receiving a query request for an account, determining role information of the account; Determining the operation level and access rights of the account according to the project type associated with the role information; Based on the operation level and the access permission, it is determined that the account is allowed to access the target test data.

7. The method according to any one of claims 1 to 6, characterized in that Before processing the target test data of the target battery based on the target project to obtain a data processing result, the method further includes: Determining the data volume of the target test data; When the data volume is greater than or equal to a predetermined storage threshold, stream processing is performed on the target test data according to predetermined read batches to obtain multiple batches of data; The multiple batches of data are stored in the database in sequence.

8. The method according to claim 7, characterized in that The method further comprises: Detecting whether data increment is generated in the database; If data increment is generated in the database, the processing of the target test data is triggered.

9. A battery test data processing device, characterized in that: include: A test item determination module, used to obtain a target item for executing a test on a target battery; a processing condition determination module, configured to determine a target processing condition corresponding to the target project based on a predetermined mapping relationship, wherein the predetermined mapping relationship is a relationship between a historical processing condition and a historical project, the historical processing condition and the historical project being obtained by testing a reference battery, the type of the reference battery matching the type of the target battery; The processing result generating module is used to process the target test data of the target battery based on the target project to obtain a data processing result.

10. An electronic device, characterized in that: include: One or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the battery test data processing method described in any one of claims 1 to 8.

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