Battery data benchmarking method and device, electronic equipment and storage medium

Abstract

The application provides a battery data benchmarking method and device, electronic equipment and storage medium. The method comprises: obtaining a benchmarking requirement; searching whether there is battery benchmarking data matching the benchmarking requirement in a database; wherein the database is established by obtaining an initial battery experience data set in an external data source; if there is battery benchmarking data, output the battery benchmarking data matching the benchmarking requirement. The method establishes a database by obtaining an initial battery experience data set in an external data source, queries the database according to the benchmarking requirement in the battery data benchmarking process, and outputs the queried battery benchmarking data to the user. Since the data in the data established by obtaining the external data source is more comprehensive, more different benchmarking requirements are met, the smoothness of the battery data benchmarking is ensured to a certain extent, and the efficiency of the battery data benchmarking method is improved.

Description

Technical Field

[0001] This application relates to the field of new energy technology, and more specifically, to a method, apparatus, electronic device, and storage medium for benchmarking battery data. Background Technology

[0002] The battery design process directly affects the overall performance of the power battery. In order to obtain design experience from batteries from different manufacturers, it is usually necessary to benchmark against the data of hot batteries on the market during the power battery development process.

[0003] Currently, most methods for benchmarking battery data lack sufficient empirical data on batteries. Without sufficient empirical data, benchmarking efforts progress slowly or even stagnate.

[0004] In other words, most current methods for benchmarking battery data are not efficient enough. Summary of the Invention

[0005] The purpose of this application is to provide a battery data benchmarking method, apparatus, electronic device, and storage medium. By acquiring an initial battery experience dataset from an external data source to establish a database, which is used for querying during the battery data benchmarking process, the efficiency of the battery data benchmarking method is improved.

[0006] In a first aspect, embodiments of this application provide a battery data benchmarking method, comprising: obtaining benchmarking requirements; searching a database for battery benchmarking data that matches the benchmarking requirements; wherein the database is established by obtaining an initial battery experience dataset from an external data source; if the battery benchmarking data exists, then outputting battery benchmarking data that matches the benchmarking requirements.

[0007] The aforementioned battery data benchmarking method establishes a database by acquiring an initial battery experience dataset from an external data source. During the battery data benchmarking process, this database is queried according to the benchmarking requirements, and the retrieved battery benchmarking data is output to the user. Because the data obtained from the external data source is richer and more comprehensive, it meets more diverse benchmarking needs, thus ensuring the smoothness of battery data benchmarking to a certain extent and improving the efficiency of the battery data benchmarking method.

[0008] In conjunction with the first aspect, optionally, the database has multiple primary categories, each primary category has multiple secondary categories, and the secondary categories include the battery benchmarking data; the search for whether battery benchmarking data matching the benchmarking requirement exists in the database includes: selecting one primary category and selecting one secondary category from the selected primary category; searching the selected secondary category for whether battery benchmarking data matching the benchmarking requirement exists; if no battery benchmarking data is found in the selected secondary category, then searching other secondary categories or other primary categories, until the pre-stored requirement is found or all primary and secondary categories are searched.

[0009] The aforementioned battery data benchmarking method improves the efficiency and coverage of finding target data in complex data structures by combining a depth-based and breadth-based retrieval approach within the database structure. Ultimately, this further enhances the efficiency of the battery data benchmarking method.

[0010] In conjunction with the first aspect, optionally, the specific method for establishing the database includes: obtaining the initial battery experience dataset; and organizing the initial battery inspection dataset using keyword matching based on the names of the primary and secondary categories and the keywords in the initial battery experience dataset, to obtain a database containing the battery benchmarking data.

[0011] The aforementioned battery data benchmarking method, by employing keyword matching to divide the initial battery test dataset according to the structure of primary and secondary categories in the database, effectively organizes the initial battery test dataset and improves the efficiency of the dataset organization. This, in turn, improves the efficiency of the battery data benchmarking method.

[0012] In conjunction with the first aspect, optionally, the search for whether there is battery benchmarking data in the database that matches the benchmarking requirement includes: calculating the similarity between the benchmarking requirement and the battery benchmarking data based on the parameter values ​​in the benchmarking requirement and the parameter values ​​in the battery benchmarking data; and selecting at least one with the highest similarity as the battery benchmarking data that matches the benchmarking requirement.

[0013] The aforementioned battery data benchmarking method calculates the similarity between each battery benchmarking data and the benchmarking requirements, and selects the at least one with the highest similarity as the battery benchmarking data that matches the benchmarking requirements. This comprehensively considers all parameters in the battery benchmarking data, thereby further improving the efficiency of the battery data benchmarking method.

[0014] In conjunction with the first aspect, the method may optionally further include: if the battery benchmarking data does not exist, determining the keywords in the benchmarking requirement and the external data source to be queried; using the keywords to query the external data source, filtering out a supplementary battery experience dataset that matches the benchmarking requirement; and storing the supplementary battery experience dataset in the database.

[0015] The aforementioned battery data benchmarking method, when the database lacks the required battery benchmarking data, obtains supplementary battery experience datasets from external data sources and stores them in the database, thus promptly filling the gaps in the database. This prevents delays in battery data benchmarking to some extent, thereby improving the efficiency of the battery data benchmarking method.

[0016] In conjunction with the first aspect, optionally, the step of outputting battery benchmarking data that matches the benchmarking requirement includes: searching the database to see if there is a benchmarking suggestion corresponding to the benchmarking requirement; if the benchmarking suggestion is found, then outputting the battery benchmarking data and the benchmarking suggestion; if the benchmarking suggestion is not found, then generating the benchmarking suggestion using production rules based on the benchmarking requirement and the battery benchmarking data that matches the benchmarking requirement.

[0017] The aforementioned battery data benchmarking method, even when no matching suggestions exist in the database, generates them using production rules, still providing users with relevant technical insights and design suggestions. This improves the convenience of the battery data benchmarking method and further enhances its efficiency.

[0018] In conjunction with the first aspect, optionally, the output of battery benchmarking data matching the benchmarking requirements further includes: if no benchmarking suggestion is found, obtaining an incremental battery experience dataset to update the benchmarking data in the database.

[0019] The aforementioned benchmarking method for battery data, by updating the benchmarking data in the database when no benchmarking suggestions are found, provides stronger data support for the subsequent generation of relevant benchmarking suggestions, thereby improving the breakthrough, cutting-edge nature, and pioneering nature of the generated benchmarking suggestions.

[0020] Secondly, embodiments of this application also provide a battery data benchmarking device, comprising: an acquisition module for acquiring benchmarking requirements; a retrieval module for retrieving whether battery benchmarking data matching the benchmarking requirements exists in a database; wherein the database is established by acquiring an initial battery experience dataset from an external data source; and an output module for outputting battery benchmarking data matching the benchmarking requirements if the battery benchmarking data exists.

[0021] The battery data matching device described above has the same beneficial effects as the battery data matching method provided by the first aspect or any optional embodiment of the first aspect, and will not be elaborated here.

[0022] Thirdly, embodiments of this application also provide an electronic device, including: a processor and a memory, the memory storing machine-readable instructions executable by the processor, which, when executed by the processor, perform the method described above.

[0023] The aforementioned electronic device has the same beneficial effects as the battery data benchmarking method provided by the first aspect or any alternative embodiment of the first aspect, which will not be elaborated here.

[0024] Fourthly, embodiments of this application also provide a storage medium, the storage medium including a computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor to perform the methods described above.

[0025] The aforementioned storage medium has the same beneficial effects as the battery data benchmarking method provided by the first aspect or any alternative embodiment of the first aspect, which will not be elaborated here.

[0026] In summary, this application provides a battery data benchmarking method, apparatus, electronic device, and storage medium. It establishes a database by acquiring an initial battery experience dataset from an external data source, queries this database according to benchmarking requirements during the battery data benchmarking process, and outputs the retrieved battery benchmarking data to the user. This satisfies a wider range of benchmarking needs, thus ensuring the smoothness of battery data benchmarking to a certain extent and improving the efficiency of the battery data benchmarking method. By employing a retrieval method combining the depth and breadth of the database structure, the efficiency of the battery data benchmarking method is further improved. By using keyword matching to divide the initial battery test dataset according to the structure of primary and secondary categories in the database, the initial battery test dataset is organized, improving the efficiency of the initial battery test dataset organization. By calculating the similarity between each battery benchmarking data and the benchmarking requirements, and selecting at least one with the highest similarity as the battery benchmarking data matching the benchmarking requirements, all parameters in the battery benchmarking data are comprehensively considered, thereby further improving the efficiency of the battery data benchmarking method. When no battery benchmarking data matching the benchmarking requirements exists in the database, supplementing the battery experience dataset by obtaining it from external data sources can, to some extent, prevent delays in battery data benchmarking, thereby improving the efficiency of the battery data benchmarking method. Similarly, if no benchmarking suggestions corresponding to the benchmarking requirements are found in the database, generating such suggestions through production rules can also provide users with relevant technical insights and design recommendations. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A first flowchart of a battery data benchmarking method provided in an embodiment of this application;

[0029] Figure 2 A schematic diagram of the classification structure in the database provided in the embodiments of this application;

[0030] Figure 3 A first detailed flowchart of step S120 in the battery data benchmarking method provided in the embodiments of this application;

[0031] Figure 4 A flowchart illustrating the database construction method provided in this application embodiment;

[0032] Figure 5A second detailed flowchart of step S120 in the battery data benchmarking method provided in the embodiments of this application;

[0033] Figure 6 A second flowchart of the battery data benchmarking method provided in the embodiments of this application;

[0034] Figure 7 A second detailed flowchart of step S130 in the battery data benchmarking method provided in the embodiments of this application;

[0035] Figure 8 Functional block diagram of the battery data benchmarking device provided in the embodiments of this application;

[0036] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0037] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application.

[0039] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0040] Please refer to Figure 1 , Figure 1 This is a flowchart of the first method for benchmarking battery data provided in this application. The method for benchmarking battery data provided in this application can be executed by electronic devices such as personal computers, servers, and smartphones, and may include:

[0041] Step S110: Obtain benchmarking requirements.

[0042] In step S110 above, the benchmarking requirements can be manually input by the user through an electronic device. Specifically, taking a power battery as an example, the obtained benchmarking requirements could be "find a power battery with an energy density ≥150Wh / kg, a cycle life ≥1000 cycles, good low-temperature performance (discharge capacity retention rate ≥80% at -20℃) and low cost".

[0043] Step S120: Search the database for battery benchmarking data that matches the benchmarking requirements.

[0044] In step S120 above, the electronic device can use the user's benchmarking requirements as search criteria to retrieve matching battery benchmarking data and corresponding batteries from the database. The database is established by acquiring an initial battery experience dataset from an external data source. This initial battery experience dataset refers to battery experience data acquired before the database was established. External data sources can be knowledge bases containing authoritative journals, professional knowledge, professional books, and expert experience. Examples include IEEE Xplore (Institute of Electrical and Electronics Engineers database), ScienceDirect, Google Scholar, Web of Science (Science Citation Index), and Scopus (Scopus Academic Database).

[0045] If battery benchmarking data exists, then execute step S130: output battery benchmarking data that matches the benchmarking requirements.

[0046] In step S130 above, if battery benchmarking data matching the benchmarking requirements is retrieved, the electronic device may display the battery benchmarking data to the user through its display screen.

[0047] In the above implementation process, a database is established by acquiring an initial battery experience dataset from an external data source. During battery data benchmarking, this database is queried according to the benchmarking requirements, and the retrieved battery benchmarking data is output to the user. Because the data acquired from the external data source is richer and more comprehensive, it meets more diverse benchmarking needs, thus ensuring the smoothness of battery data benchmarking to a certain extent and improving the efficiency of the battery data benchmarking method.

[0048] In some alternative implementations, the database has multiple primary categories, each with multiple secondary categories, and the secondary categories may include battery benchmarking data.

[0049] For example, such as Figure 2 As shown, Figure 2This is a schematic diagram of the classification structure in the database provided in this application embodiment. Secondary categories may include "battery type," "application scenario," and "technical route," etc. The primary category "battery type" may include secondary categories such as "lithium-ion battery" and "sodium-sulfur battery." The secondary category "lithium-ion battery" may include battery benchmarking data such as "single cell performance data" and "battery pack performance data"; the secondary category "sodium-sulfur battery" may also include corresponding battery benchmarking data such as "single cell performance data" and "battery pack performance data." The primary category "application scenario" may include secondary categories such as "electric vehicle" and "energy storage power station." The secondary category "electric vehicle" may include battery benchmarking data such as "model A benchmarking data" and "model B benchmarking data." The secondary category "energy storage power station" may include battery benchmarking data such as "project A benchmarking data" and "project B benchmarking data."

[0050] It should be understood that the levels at which data is classified in the database are not limited to the two levels (first-level category and second-level category) described in the embodiments of this application. Those skilled in the art can also divide the data into third-level categories, fourth-level categories, fifth-level categories, etc., according to actual needs, and these schemes should all be within the protection scope of this application.

[0051] Accordingly, please refer to Figure 3 , Figure 3 This is a first detailed flowchart of step S120 in the battery data benchmarking method provided in this application embodiment. Step S120 may include:

[0052] Step S121: Select a primary category, and then select a secondary category from the selected primary category.

[0053] Step S122: Search the selected secondary categories for battery benchmarking data that matches the benchmarking requirements.

[0054] If no battery benchmarking data is found in the selected secondary category, proceed to step S123: search other secondary categories or other primary categories until the pre-stored requirements are found or all primary or secondary categories have been searched.

[0055] In the above steps S121 to S123, with Figure 2For example, first select the "Battery Type" category to search, then enter the "Lithium-ion Battery" category, and look for battery benchmarking data that meets the benchmarking requirements in the "Individual Cell Performance Data" and "Battery Pack Performance Data" sections. If no perfectly matching battery benchmarking data is found in the current category (Lithium-ion Battery), continue to the next relevant category, such as the "Electric Vehicle" category within the "Application Scenarios" category. Under the "Electric Vehicle" category, check the "Model A Benchmarking Data," "Model B Benchmarking Data," etc., in sequence to find battery benchmarking data that meets the benchmarking requirements.

[0056] If no perfectly matching battery benchmark data is found, the search will then proceed to other secondary categories (e.g., from "Model A Benchmark Data" back to "Electric Vehicles," and then search for other secondary categories such as "Energy Storage Power Stations"), or to other primary categories (e.g., from "Model A Benchmark Data" back to "Application Scenarios," and then search for other primary categories such as "Technology Roadmap" and "Manufacturer").

[0057] In the above implementation process, by combining the depth and width directions of the database structure for retrieval, the efficiency and coverage of finding target data in complex data structures are improved. Ultimately, this further enhances the efficiency of the battery data benchmarking method.

[0058] Please refer to Figure 4 , Figure 4 This is a flowchart of a database construction method provided in an embodiment of this application. In some optional embodiments, the specific method of database creation can also be performed by an electronic device and may include:

[0059] Step S210: Obtain the initial battery experience dataset.

[0060] In step S210 above, the initial battery experience dataset can be obtained by the electronic device from an external data source.

[0061] Step S220: Based on the names of the primary and secondary categories, and combined with the keywords in the initial battery experience dataset, use keyword matching to organize the initial battery test dataset to obtain a database containing battery benchmarking data.

[0062] The above step S220 continues with Figure 2For example, the initial battery experience dataset contains data on "single lithium battery energy density = 150Wh / kg", and the keywords can include "single", "lithium", and "battery". Based on the keyword "battery", the data can first be classified into the primary category of "battery type". Then, based on the keyword "lithium", the data can be classified into the secondary category of "lithium-ion battery". Finally, based on the keyword "single", the data can be classified into "single battery performance data".

[0063] In the above implementation process, the initial battery test dataset is divided according to the structure of primary and secondary categories in the database by using keyword matching. This process organizes the initial battery test dataset and improves the efficiency of the dataset organization. Consequently, it also improves the efficiency of the battery data benchmarking method.

[0064] Please refer to Figure 5 , Figure 5 This is a second detailed flowchart of step S120 in the battery data benchmarking method provided in this application embodiment. In some optional implementations, step S120 may include:

[0065] Step S124: Calculate the similarity between the benchmarking requirements and the battery benchmarking data based on the parameter values ​​in the benchmarking requirements and the parameter values ​​in the battery benchmarking data.

[0066] Step S125: Select at least one of the most similar data as the battery benchmarking data that matches the benchmarking requirements.

[0067] In steps S124 and S125 above, for example, the benchmarking requirement is "to find a battery with an energy density ≥150Wh / kg, cycle life ≥1000 cycles, and low-temperature performance (discharge capacity retention ≥80% at -20℃)". The following battery benchmarking data has been retrieved:

[0068] Battery benchmark data A: Energy density = 160Wh / kg, Cycle life = 1200 cycles, Discharge capacity retention rate at -20℃ = 80%.

[0069] Battery benchmark data B: Energy density = 155Wh / kg, cycle life = 800 cycles, discharge capacity retention rate at -20℃ = 90%.

[0070] Battery benchmark data C: Energy density = 120Wh / kg, Cycle life = 1000 cycles, Discharge capacity retention rate at -20℃ = 85%.

[0071] One possible method for calculating similarity is as follows: since the benchmarking requirements specify three battery parameters, the highest similarity score is 1. Therefore, for each parameter in the battery benchmarking data that meets the benchmarking requirements, the similarity score of that battery benchmarking data is recorded as 1 / 3. For parameters that do not meet the benchmarking requirements, the highest similarity score of 1 / 3 for that individual parameter is discounted based on the percentage difference between that parameter and the corresponding parameter in the benchmarking requirements.

[0072] Battery benchmark data A: Energy density, cycle life, and discharge capacity retention all meet the benchmark requirements, and the similarity is 1 / 3×3=1.

[0073] Battery benchmark data B meets the benchmark requirements in both energy density and discharge capacity retention, thus achieving a similarity of "2 / 3". However, its cycle life does not meet the benchmark requirements. Therefore, the percentage difference between its cycle life and the benchmark requirements is used to discount the highest similarity of this single parameter, resulting in the similarity for the "cycle life" parameter. Specifically, the calculation is |800-1200| ÷ 1200 × 100% = 33.3%. Discounting 1 / 3 by 33.3% yields: 33.3% × 1 / 3 = 1 / 9. Therefore, the similarity of battery benchmark data B is 2 / 3 + 1 / 9 = 7 / 9.

[0074] Battery benchmark data C: Both cycle life and discharge capacity retention meet the benchmark requirements, thus achieving a similarity of "2 / 3". However, since its energy density does not meet the benchmark requirements, the percentage difference in cycle life compared to the benchmark requirements is used to discount the highest similarity of this single parameter, resulting in the similarity of the "energy density" parameter. The specific calculation method is |120-150| ÷ 150 × 100% = 20%, and discounting 1 / 3 by 20% yields: 20% × 1 / 3 = 1 / 15. Therefore, the similarity of battery benchmark data B is 2 / 3 + 1 / 15 = 11 / 15.

[0075] Therefore, the similarity from high to low is as follows: battery benchmark data A, battery benchmark data B, and battery benchmark data C.

[0076] Those skilled in the art can set corresponding similarity thresholds according to their needs, and use battery benchmarking data that meets the thresholds as battery benchmarking data that matches the benchmarking requirements.

[0077] In the above implementation process, by calculating the similarity between each battery benchmarking data and the benchmarking requirements, and taking at least one of the highest similarities as the battery benchmarking data that matches the benchmarking requirements, all parameters in the battery benchmarking data are comprehensively considered, thereby further improving the efficiency of the battery data benchmarking method.

[0078] Please refer to Figure 6 , Figure 6This is a second flowchart of the battery data benchmarking method provided in this application embodiment. In some optional embodiments, the battery data benchmarking method provided in this application embodiment may further include:

[0079] If no battery benchmarking data exists, proceed to step S140: determine the keywords in the benchmarking requirements and the external data source to be queried.

[0080] In step S140 above, the method for determining keywords can be consistent with the method described in step S220 above. The external data source to be queried can be determined based on its reliability (i.e., the credibility of the data source, including the author's authority, the reputation of the publishing institution, and the frequency of data citations), uptime (i.e., whether the information in the data source is the latest, ensuring that the data provided to users reflects the current technological level and market conditions), accessibility (i.e., whether the data source can be automatically accessed by the program, such as through API interfaces or web scraping technology), and data format (i.e., whether the data provided by the data source is structured data, facilitating automated processing and analysis).

[0081] Step S150: Use keywords to query external data sources and filter out supplementary battery experience datasets that match the benchmarking requirements.

[0082] Step S150 above, which queries and filters supplementary battery experience datasets that match the benchmarking requirements from external data sources, can also be done in a similar manner to step S220. The supplementary battery experience dataset is the dataset containing the missing battery benchmarking data in the database relative to the current benchmarking requirements.

[0083] Step S160: Store the supplementary battery experience dataset into the database.

[0084] In step S160 above, the method of storing the supplementary battery experience dataset into the database can be similar to that in steps S210 and S220 above.

[0085] In the above implementation process, when the database lacks battery benchmarking data matching the requirements, supplementary battery experience datasets are obtained from external data sources and stored in the database, thus immediately filling the gaps in the database. This prevents delays in battery data benchmarking to some extent, thereby improving the efficiency of the battery data benchmarking method.

[0086] Please refer to Figure 7 , Figure 7 This is a second detailed flowchart of step S130 in the battery data benchmarking method provided in this application embodiment. In some optional implementations, step S130 may include:

[0087] Step S131: Search the database for benchmarking suggestions that correspond to the benchmarking requirements.

[0088] In step S131 above, the benchmarking suggestions can be technical insights, design suggestions, etc.

[0089] If a benchmarking suggestion is found, proceed to step S132: output the battery benchmarking data and the benchmarking suggestion.

[0090] If no benchmarking recommendations are found, proceed to step S133: Generate benchmarking recommendations using production rules based on the benchmarking requirements and the battery benchmarking data that match the requirements.

[0091] In step S133 above, the production rule representation is also known as the forward reasoning strategy. Its expression formula can be: IF<condition>THEN<action>. This means that if the rule premise is true, then the reasoning conclusion is true. The rule premise can consist of multiple conditions, linked by the logical relations "AND" or "OR".

[0092] For example, the performance of an electric vehicle (EV) battery is currently being evaluated, and it is desirable to understand how the battery performs under high-temperature conditions. Those skilled in the art know that high temperatures can affect battery life and safety. Therefore, a user has entered a benchmarking requirement regarding "predicting and optimizing its performance under such conditions."

[0093] Based on the principles of battery science, the following rules can be defined:

[0094] If battery temperature > threshold A, then battery aging rate = high.

[0095] If the battery aging rate is high, then the expected battery lifespan is short.

[0096] If the battery temperature exceeds threshold B, then the risk of thermal runaway is high.

[0097] The collected data is then combined with these rules. For example, if a battery cell is observed to be operating at high temperatures (> the temperature threshold), its aging rate can be inferred to be "high." Based on this logical deduction, the following conclusion can be drawn: "This battery operates in a high-temperature environment and has a shorter expected lifespan; if the temperature continues to exceed the temperature threshold, there is a high risk of thermal runaway." This conclusion is then presented to users as a technological insight.

[0098] In the above implementation process, when no corresponding benchmarking suggestion is found in the database, the benchmarking suggestion is generated through production rules, which can still provide users with relevant technical insights and design suggestions. This improves the convenience of the battery data benchmarking method and further enhances its efficiency.

[0099] Please note that in some optional implementations, step S130 may further include:

[0100] If no benchmarking suggestions are found, then step S134: Obtain the incremental battery experience dataset to update the benchmarking data in the database.

[0101] In step S133 above, the absence of any benchmarking suggestions typically indicates that the benchmarking requirement is relatively niche, which in turn suggests that the database contains relatively little battery benchmarking data for that requirement. Therefore, the benchmarking data in the database can be updated using methods similar to those in steps S210 and S220 or step S160. The incremental battery experience dataset refers to the dataset containing the relatively scarce battery benchmarking data in the database when no benchmarking suggestions are currently found.

[0102] In the above implementation process, by updating the benchmarking data in the database when no benchmarking suggestions are found, stronger data support is provided for the subsequent generation of relevant benchmarking suggestions, thereby improving the breakthrough, cutting-edge and innovative nature of the generated benchmarking suggestions.

[0103] Please refer to Figure 8 , Figure 8 This is a functional block diagram of battery data benchmarking provided in this application embodiment. Based on the same concept, the battery data benchmarking device 800 provided in this application embodiment may include: an acquisition module 810, which can be used to acquire benchmarking requirements; a retrieval module 820, which can be used to retrieve whether battery benchmarking data matching the benchmarking requirements exists in the database; wherein, the database is established by acquiring an initial battery experience dataset from an external data source; and an output module 830, which can be used to output battery benchmarking data matching the benchmarking requirements when battery benchmarking data exists.

[0104] As some alternative implementations, the database has multiple primary categories, each primary category having multiple secondary categories, and the secondary categories may include battery benchmarking data.

[0105] Accordingly, in the process of searching the database for battery benchmarking data that matches the benchmarking requirements, the retrieval module 820 can specifically be used to: select a primary category and select a secondary category from the selected primary category; search the selected secondary category for battery benchmarking data that matches the benchmarking requirements; if no battery benchmarking data is found in the selected secondary category, then search other secondary categories or other primary categories until the pre-stored requirements are found or all primary and secondary categories are searched.

[0106] As some optional implementation methods, the specific way to establish the database may include: obtaining an initial battery experience dataset; and organizing the initial battery inspection dataset by combining the names of the primary and secondary categories with the keywords in the initial battery experience dataset using keyword matching to obtain a database containing battery benchmarking data.

[0107] As some optional implementation methods, in the process of searching the database for battery benchmarking data that matches the benchmarking requirements, the retrieval module 820 may specifically be used to: calculate the similarity between the benchmarking requirements and the battery benchmarking data based on the parameter values ​​in the benchmarking requirements and the parameter values ​​in the battery benchmarking data; and select at least one with the highest similarity as the battery benchmarking data that matches the benchmarking requirements.

[0108] As some optional implementations, the battery data benchmarking device 800 may also include a supplementary module, which can be used to determine the keywords in the benchmarking requirements and the external data source to be queried when no battery benchmarking data exists; to use the keywords to query the external data source and filter out the supplementary battery experience dataset that matches the benchmarking requirements; and to store the supplementary battery experience dataset in a database.

[0109] As some optional implementation methods, in the process of outputting battery benchmarking data that matches the benchmarking requirements, the output module 830 can be specifically used to: search the database to see if there is a benchmarking suggestion corresponding to the benchmarking requirements; if a benchmarking suggestion is found, output the battery benchmarking data and the benchmarking suggestion; if no benchmarking suggestion is found, generate benchmarking suggestions using production rules based on the benchmarking requirements and the battery benchmarking data that matches the benchmarking requirements.

[0110] As some alternative implementation methods, in the process of outputting battery benchmarking data that matches the benchmarking requirements, the output module 830 may also be used to: if no benchmarking suggestions are found, obtain an incremental battery experience dataset to update the benchmarking data in the database.

[0111] It should be understood that this device corresponds to the battery data benchmarking method embodiment described above and is capable of performing the various steps involved in the above method embodiment. The specific functions of this device can be found in the description above, and detailed descriptions are omitted here to avoid repetition. This device may include at least one software function module that can be stored in memory or embedded in the device's operating system (OS) in the form of software or firmware.

[0112] Based on the same inventive concept, please refer to Figure 9 , Figure 9 This is a schematic diagram of the structure of an electronic device 900 provided in an embodiment of this application. The electronic device 900 may include a memory 911, a memory controller 912, a processor 913, a peripheral interface 914, an input / output unit 918, and a display unit 916. Those skilled in the art will understand that... Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device 900. For example, the electronic device 900 may also include components that are more... Figure 9 The more or fewer components shown, or having the same Figure 9 The different configurations shown.

[0113] The aforementioned memory 911, memory controller 912, processor 913, peripheral interface 914, input / output unit 918, and display unit 916 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The aforementioned processor 913 is used to execute executable modules stored in the memory.

[0114] The memory 911 can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 911 stores programs, and the processor 913 executes these programs upon receiving execution instructions. The methods executed by the electronic device 900, as defined in any embodiment of this application, can be applied to or implemented by the processor 913.

[0115] The aforementioned processor 913 may be an integrated circuit chip with signal processing capabilities. The processor 913 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor.

[0116] The peripheral interface 914 described above couples various input / output devices to the processor 913 and the memory 911. In some embodiments, the peripheral interface 914, the processor 913, and the memory controller 912 can be implemented in a single chip. In other instances, they can be implemented by separate chips.

[0117] The aforementioned input / output unit 918 is used to provide user input data. The input / output unit 918 may be, but is not limited to, a mouse and keyboard, etc.

[0118] The aforementioned display unit 916 provides an interactive interface (e.g., a user interface) between the electronic device 900 and the user, or displays image data for the user's reference. In this embodiment, the display unit can be a liquid crystal display (LCD) or a touch display. If it is a touch display, it can be a capacitive touchscreen or a resistive touchscreen that supports single-point and multi-point touch operations. Supporting single-point and multi-point touch operations means that the touch display can sense touch operations generated simultaneously from one or more locations on the touch display and pass the sensed touch operations to the processor for calculation and processing.

[0119] The electronic device 900 in this embodiment can be used to perform the various steps in the various methods provided in the embodiments of this application.

[0120] This application also provides a storage medium, which includes a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the methods described above.

[0121] The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0122] It should be understood that the disclosed apparatus and methods can also be implemented in other ways, given the several embodiments provided in this application. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0123] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0124] The above description is only an optional implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application.

Claims

1. A method of benchmarking battery data, the method comprising: include: Obtain benchmarking requirements; The database is searched to determine if there is battery benchmarking data that matches the benchmarking requirements; wherein the database is established by acquiring an initial battery experience dataset from an external data source. If the battery benchmarking data exists, output the battery benchmarking data that matches the benchmarking requirements; The database has multiple primary categories, each primary category has multiple secondary categories, and the secondary categories include the battery benchmarking data. Whether the search database contains battery benchmarking data that matches the benchmarking requirements includes: Select one of the primary categories, and then select one of the secondary categories from the selected primary categories; Search within the selected secondary category for battery benchmarking data that matches the benchmarking requirements; If the battery benchmarking data is not found in the selected secondary category, the other secondary categories or other primary categories will be searched until the pre-stored requirement is found or all primary or secondary categories have been searched. The specific methods for establishing the database include: Obtain the initial battery experience dataset; Based on the names of the primary and secondary categories, and combined with the keywords in the initial battery experience dataset, the initial battery inspection dataset is organized using keyword matching to obtain a database containing the battery benchmarking data.

2. The method of claim 1, wherein, Whether the search database contains battery benchmarking data that matches the benchmarking requirements includes: Calculate the similarity between the benchmarking requirements and the battery benchmarking data based on the parameter values ​​in the benchmarking requirements and the parameter values ​​in the battery benchmarking data. The battery benchmarking data with the highest similarity is selected as the battery benchmarking data that matches the benchmarking requirements.

3. The method of claim 1, wherein, The method further includes: If the battery benchmarking data does not exist, then determine the keywords in the benchmarking requirements and the external data source to be queried; Using the keywords, a query is performed in the external data source to filter out a supplementary battery experience dataset that matches the benchmarking requirements; The supplementary battery experience dataset is stored in the database.

4. The method of claim 1, wherein, The output of battery benchmarking data that matches the benchmarking requirements includes: Search the database to see if there are any benchmarking suggestions corresponding to the benchmarking requirements; If the benchmarking suggestion is found, the battery benchmarking data and the benchmarking suggestion are output. If the benchmarking suggestion is not found, the benchmarking suggestion is generated using production rules based on the benchmarking requirements and the battery benchmarking data that matches the benchmarking requirements.

5. The method according to claim 4, characterized in that, The output of battery benchmarking data that matches the benchmarking requirements also includes: If the benchmarking suggestion is not found, an incremental battery experience dataset is obtained to update the benchmarking data in the database.

6. A battery data benchmarking device, characterized by, include: The acquisition module is used to acquire benchmarking requirements; The retrieval module is used to search the database for battery benchmarking data that matches the benchmarking requirements; wherein, the database is established by acquiring an initial battery experience dataset from an external data source; The output module is used to output battery benchmarking data that matches the benchmarking requirements when the battery benchmarking data exists. The database has multiple primary categories, each primary category has multiple secondary categories, and the secondary categories include the battery benchmarking data. In the process of searching the database for battery benchmarking data that matches the benchmarking requirement, the retrieval module is specifically used to: select one primary category, and then select one secondary category from the selected primary category; search within the selected secondary category for battery benchmarking data that matches the benchmarking requirement; if no battery benchmarking data is found in the selected secondary category, then search other secondary categories or other primary categories until the pre-stored requirement is found or all primary and secondary categories have been searched. The specific method for establishing the database includes: obtaining the initial battery experience dataset; and organizing the initial battery inspection dataset by using keyword matching based on the names of the primary and secondary categories and the keywords in the initial battery experience dataset, so as to obtain a database containing the battery benchmarking data.

7. An electronic device, comprising: include: A processor and a memory, the memory storing machine-readable instructions executable by the processor, which, when executed by the processor, perform the method as described in any one of claims 1 to 5.

8. A storage medium, characterized by The storage medium includes a computer-readable storage medium; the computer-readable storage medium stores a computer program that, when executed by a processor, performs the method as described in any one of claims 1 to 5.

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