Database construction method and vehicle parameter benchmarking method
By constructing a K&C experiment and inertia experiment database and establishing a search model, the problem that the manufacturer cannot benchmark the K&C experiment and inertia experiment indicators is solved, data security and intellectual property protection are achieved, and the output capability of horizontal data analysis results is provided, and the research and development and improvement of the manufacturer's products are promoted.
Patent Information
- Application Number
- CN202510161938.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-13
- Publication Date
- 2025-06-06
AI Technical Summary
The current lack of K&C experiment and inertia experiment databases has led to the inability of vehicle manufacturers to benchmark K&C experiment and inertia experiment indicators, and there is a lack of tools that can output horizontal data analysis results while ensuring data security and intellectual property security.
By building a database, using the report release model to calculate the original data to generate a report file of the tree structure, and parsing it into key-value pair data through the data analysis model and storing it to the database. At the same time, a search model is established for users to conduct global comparisons across experiments and models.
It solves the problem of benchmarking K&C experiment and inertia experimental indicators, realizes data security and intellectual property protection, and provides the output capability of horizontal data analysis results, helping car manufacturers to promote product research and development and improvement.
Smart Images
Figure CN120104616A_ABST
Abstract
Description
Technical Field
[0001] The present invention mainly relates to the field of motor vehicle detection, and in particular to a database construction method and a vehicle parameter calibration method. Background Art
[0002] Benchmarking is an important part of the vehicle development process. K&C characteristic data is obtained by conducting K&C tests on benchmark models. There are 11 types of vehicle suspension K&C tests and inertia tests. Each type of test corresponds to multiple working conditions, each working condition corresponds to multiple modes, and each mode corresponds to multiple indicators. Therefore, in order to store such a large amount of complex data, it is necessary not only to have a good understanding of vehicle suspension tests, but also to establish standardized indicator key-value mapping rules. At present, there is no public K&C test and inertia test database in China for us to achieve the benchmarking of K&C test and inertia test indicators. Secondly, there is currently no tool on the domestic market that can output lateral K&C test and inertia test data analysis results under the premise of ensuring customer data security and developer intellectual property security. Car manufacturers cannot obtain industry production benchmarks that are accurate to indicators to promote product development and improvement, improve quality, and meet customer requirements. Summary of the invention
[0003] The technical problem to be solved by the present invention is to provide a database construction method and a vehicle parameter benchmarking method to solve the problem that there is currently a lack of K&C test and inertia test databases for car manufacturers to achieve benchmarking of K&C test and inertia test indicators.
[0004] To solve the above technical problems, the present invention provides a database construction method, including: inputting raw data into a report issuance model, the report issuance model calculates the raw data through a built-in algorithm to generate a report file, and the data storage format in the report file is a tree structure; constructing a data parsing model, the data parsing model is used to parse the tree structure into key-value pair data, including: writing an iterative function for structure parsing, the running logic of the iterative function is for the tree structure, if a node is a divisible structure, then return the path to add the node name, and repeat the iterative function; if a node is not divisible, stop the iteration, store the node name as the Value of the key-value pair, and store the path of the parent node of the node as the Key of the key-value pair; generate a random and non-repetitive file id for the parsed key-value pair data, and store the key-value pair data in a database.
[0005] Optionally, the original data includes K&C experimental data or center of mass and moment of inertia experimental data.
[0006] Optionally, the report generation model includes a plurality of built-in algorithms, each experiment type corresponds to a built-in algorithm, and the report generation model is further used to determine the type of the raw data and calculate the raw data using the built-in algorithm corresponding to the type.
[0007] Optionally, the data parsing model includes a KC parsing model and an inertia parsing model, the KC parsing model generates one key-value pair data for one report file, and the inertia parsing model requires three report files to generate one key-value pair data.
[0008] Optionally, it also includes: using a packaging tool to encapsulate the data analysis model into a ctf file and deploy it to the computing layer of the cloud platform. After configuration, the user can run the data analysis model on the cloud platform.
[0009] Optionally, it also includes: returning the file ID to the application layer, and the application layer associates multiple tables according to the correspondence between the file ID and the experimental data and business data to provide data support for multi-dimensional horizontal data mining.
[0010] In order to solve the above technical problems, the present invention provides a vehicle parameter benchmarking method, comprising: establishing a database through the database construction method as described above; establishing a retrieval model, wherein the retrieval model obtains index values under different vehicle models, test types, working conditions, and mode restrictions by sending different retrieval commands to the database according to the hierarchical relationship of the user input fields, and performs a global comparison across experiments and vehicle models.
[0011] Optionally, the retrieval model includes a navigation area and a data area, the navigation area enumerates all input parameter combinations, and the data area displays the retrieved indicator values.
[0012] Optionally, the retrieval model also includes a selection area, a label area and a drawing area, the selection area is used to select the data required for drawing from the indicator values retrieved and generated in the data area, the label area is used to select the data in the selection area that needs to be labeled, and the drawing area is used to generate an image based on the data in the selection area and the label area to facilitate the intuitive display of horizontal comparison of indicators.
[0013] Optionally, the method further includes: selecting a plurality of key-value pair data according to the comparison result, and decomposing the key-value pair data into a tree-like structure; respectively drawing characteristic curves corresponding to the tree-like structure, and comparing the plurality of characteristic curves.
[0014] Compared with the prior art, the present invention has the following advantages:
[0015] The database construction method of the present invention generates a report file by calculating the original data, and then parses the tree structure in the report file into structured key-value pair data through a data analysis model, thereby constructing a K&C experiment and inertia experiment database, solving the problem of the current or domestic lack of K&C experiment and inertia experiment database; secondly, through the retrieval model of the present invention, authorized users can perform horizontal comparisons of the same indicators of different vehicle models without accessing specific algorithms, thereby protecting the intellectual property security of model developers. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The accompanying drawings are included to provide a further understanding of the present application. They are included and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and together with the present specification serve to explain the principles of the present application. In the accompanying drawings:
[0017] Figure 1 It is a schematic diagram of the process of using the report issuance model to construct an analytical model and finally realize data accumulation and storage.
[0018] Figure 2 It is a schematic diagram of parsing a tree structure into key-value pair data according to an embodiment of the present disclosure.
[0019] Figure 3 It is a system block diagram of a data parsing model according to an embodiment of the present disclosure.
[0020] Figure 4 It is a schematic diagram of a retrieval model according to an embodiment of the present disclosure.
[0021] Figure 5 It is a list of options corresponding to different input parameters of an embodiment of the present disclosure.
[0022] Figure 6 It is a schematic diagram of the input interface of the combination graph drawing model according to an embodiment of the present disclosure.
[0023] Figure 7 yes Figure 6 The corresponding combination diagram of multiple characteristic curves. DETAILED DESCRIPTION
[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.
[0025] Suspension design is a very complicated process from scratch. For domestic OEMs, the simplest way is to develop according to the benchmark. By conducting K&C tests and center of mass and moment of inertia tests on benchmark models, the index values are obtained as target values to promote product development and improvement, improve quality, and meet customer requirements.
[0026] There are 11 types of experiments for vehicle suspension K&C experiments and inertia experiments. Each type of experiment corresponds to multiple working conditions, each working condition corresponds to multiple modes, and each mode corresponds to multiple indicators. According to statistics, there are 2,157 types of table and drawing indicators. Therefore, in order to store such a large amount of complex data, it is necessary not only to have a good understanding of vehicle suspension experiments, but also to establish standardized indicator key-value mapping rules. At present, there is no public K&C experiment and inertia experiment database in China to achieve the benchmarking of K&C experiment and inertia experiment indicators. Secondly, there is currently no tool on the domestic market that can output lateral K&C experiment and inertia experiment data analysis results under the premise of ensuring customer data security and developer intellectual property security.
[0027] In view of the above problems, the present invention needs to establish a K&C experiment and inertia experiment database while establishing an indexing tool.
[0028] The present invention can be used in the field of motor vehicle inspection, and is applicable to the application scenario of batch-formatted vehicle suspension K&C test and inertia test data being parsed and stored into a non-fixed number of indicators, and horizontal comparison of the same indicators of different vehicle models.
[0029] Figure 1 It is a schematic diagram of the process of using the existing report to issue a model to construct a parsing model and finally realize data accumulation and storage. Figure 1 As shown, the database construction method 100 includes:
[0030] Step S1: Input the original data into the report issuance model.
[0031] In this embodiment, the original data is stored in the document management system in the form of Tab files.
[0032] Optionally, the original data includes but is not limited to K&C experimental data or center of mass and moment of inertia experimental data.
[0033] The K&C test is a decoupling test of the vehicle suspension and the stress conditions. The KC characteristic is the K characteristic and the C characteristic. The K characteristic is the suspension kinematic characteristic, which refers to the characteristics of the angular displacement and linear displacement changes of the wheel plane and the wheel center point due to the action of the suspension guide mechanism during the reciprocating motion of the wheel in the vertical direction. The C characteristic is the suspension elastic kinematic characteristic, which refers to the characteristics of the angular displacement and linear displacement changes of the wheel plane and the wheel center caused by the force and torque acting on the tire by the ground. The K&C experimental conditions include six types of conditions: vertical motion (VVT), roll motion (VRT), steering motion (SRT), lateral force loading (LAT), longitudinal force loading (BRK), and return torque loading (ATA). Each condition corresponds to multiple modes, and each mode corresponds to multiple indicators.
[0034] The center of mass and moment of inertia experiment (hereinafter referred to as "inertia experiment") refers to the test bench simulating the roll motion, pitch motion and yaw motion of the vehicle body and measuring the three-axial torque to obtain the inertia characteristics of the vehicle, including: vehicle mass M, center of mass position coordinates X, Y, Z, three moments of inertia around the center of mass Ixx, Iyy, Izz, and three products of inertia around the center of mass Ixy, Iyz, Izx.
[0035] Step S2: The report generation model calculates the original data through a built-in algorithm to generate a report file. The data storage format in the report file is a tree structure.
[0036] Optionally, the report issuance model is a MATLAB model. This model covers the calculation methods of the table indicators and curve data in the KC test experiment report. The report issuance model is mainly used to generate a test report based on the original data. The built-in algorithm of the report issuance model has a clear definition of the K&C indicators that need to be tested and the indicator calculation method. The original data can be processed according to the existing calculation method, and the original data can be parsed into the indicators required for the report and filled in the report. However, due to the large number of indicator categories, a large number of indicators in a single report are temporarily stored in the memory in the form of a "structure", which is a tree-like unstructured storage format that comes with MATLAB. After the report is generated, the structure data in the corresponding memory is cleared and not accumulated.
[0037] Optionally, the report generation model includes a variety of built-in algorithms, one for each experimental type. The report generation model is also used to determine the type of raw data and calculate the raw data using the built-in algorithm corresponding to the type. For K&C experimental data, each type of experimental condition has its own specific data analysis algorithm. Therefore, the first step in the experimental analysis is to identify the experimental type of the experimental tab file to determine which data analysis algorithm to use, and store it in the data analysis structure (TestVars) for this type of condition. Afterwards, in order to facilitate later unified calls, all experimental condition structures are integrated into a total data analysis structure (TestVarsMuti). For center of mass and moment of inertia experimental data, an independent data analysis algorithm is used and stored in the data analysis structure (VIMTestVars) for this type of condition.
[0038] Step S3: construct a data parsing model, which is used to parse the tree-like structure into key-value pair data.
[0039] If the commonly used parsing method is used to store the structure directly in JSON encoding, it will cause the problem that one field in the database has too much content and a lot of irrelevant data needs to be parsed when searching for a specific indicator, resulting in a waste of resources. Therefore, the idea of the present disclosure is to vertically disassemble each node of each structure, drill down to the bottom layer, obtain the most fine-grained node, and obtain its value.
[0040] To achieve this effect, building a data parsing model includes: writing an iterative function for structure parsing. The running logic of the iterative function is for the tree-like structure. If a node is a divisible structure, it returns the path to add the node name and repeats the iterative function. If a node is indivisible, stop the iteration, store the node name as the Value of the key-value pair, and store the path of the parent node of the node as the Key of the key-value pair.
[0041] Figure 2 FIG. 1 is a schematic diagram of parsing a tree structure into key-value pair data according to an embodiment of the present disclosure. Figure 2As shown, the root node of the tree structure is TestVarsMuti, and the judgment is iterated downward layer by layer from the root node. For example, first, if the TestVarsMuti node is divisible, the path is returned: TestVarsMuti, and the judgment is continued. If the ATA1 node is divisible, the path is returned: TestVarsMuti_ATA1, and the RfToeVars node is divisible, the path is returned: TestVarsMuti_ATA1_RfToeVars, and the midlinesSlope node is divisible, the path is returned: TestVarsMuti_ATA1_RfToeVars_midlinesSlope, and the judgment is continued. If the 8.43342 node is not divisible, 8.43342 is stored as the Value of the key-value pair, and the path of the parent node midlinesSlope of the 8.43342 node: TestVarsMuti_ATA1_RfToeVars_midlinesSlope is stored as the Key of the key-value pair. The iteration process of other nodes is similar and will not be described here.
[0042] There are differences in the definition of indicators between the K&C experiment and the inertia experiment. Each experimental file of the K&C experiment can form a set of indicators independently, while some combined indicators in the inertia experiment require the use of three types of files, Ixx, Iyy, and Izz, at the same time. Therefore, the K&C experiment and inertia experiment files are parsed using different models. Figure 3 is a system block diagram of a data parsing model according to an embodiment of the present disclosure. Figure 3 As shown, the data parsing model includes a KC parsing model 31 and an inertia parsing model 32. The KC parsing model 31 generates a key-value pair data for a report file, and the inertia parsing model 32 requires three report files to generate a key-value pair data. Optionally, the number of files uploaded each time is solidified during the process of configuring the platform "model administrator" file. If the requirements are not met, an error will be reported.
[0043] Step S4: Generate a random non-repetitive file ID for the parsed key-value pair data, and store the key-value pair data in a database.
[0044] Optionally, step S5 is also included: returning the file ID to the application layer, and the application layer associates multiple tables according to the correspondence between the file ID and the experimental data and business data to provide data support for multi-dimensional horizontal data mining.
[0045] Optionally, considering the "low interaction, high computation" characteristics of the data analysis model, the data analysis model is deployed as a RESTFUL API for application layer calls. The specific process is as follows: Use the encapsulation tool to encapsulate the data analysis model as a ctf file and deploy it to the computing layer of the cloud platform. After configuration, users can run the data analysis model on the cloud platform.
[0046] Optionally, it also includes version management of the key-value pairs in the database. For example, the parsed file can be unparsed (the records in the database become historical versions), directly deleted (all related records in the database are deleted), and parsed again (other related data except the newly parsed version become historical versions).
[0047] Optionally, considering the increase in the amount of data and the number of car models and brands, the key-value pair data in the database may be divided into separate tables and databases according to dimensions such as car model and brand to achieve permission isolation at the data layer.
[0048] At present, there is an urgent need to carry out vehicle parameter benchmarking work within the automotive industry. At present, there is no tool on the domestic market that can output the analysis results of lateral K&C test and inertia test data under the premise of ensuring the security of customer data and developer intellectual property rights. Car manufacturers cannot obtain industry production benchmarks that are accurate to indicators to promote product development and improvement, improve quality, and meet customer requirements.
[0049] The present disclosure also provides a vehicle parameter benchmarking method, including establishing a K&C experiment and an inertia experiment database, and then establishing a retrieval model. The retrieval model obtains index values under different vehicle models, test types, working conditions, and mode restrictions by sending different retrieval commands to the database according to the hierarchical relationship of the user input fields, and performs a global comparison across experiments and vehicle models.
[0050] Figure 4 FIG. 1 is a schematic diagram of a retrieval model according to an embodiment of the present disclosure. Figure 4 As shown, the retrieval model includes:
[0051] ① Navigation area: Input the screening conditions layer by layer to generate the search conditions in the search area;
[0052] ②Search area: manually adjust the conditions generated by the search area (fuzzy search can be added) to form the final search keyword group;
[0053] ③Data area: After clicking the Generate and Search buttons, the searched result data is displayed, and a check box marked with "car model + weight + file id" is generated;
[0054] ④Selection area: Select the data that needs to be used for drawing from the data generated by retrieval in the data area;
[0055] ⑤Label area: Select the data in the selection area that needs to be labeled;
[0056] ⑥ Drawing area: After clicking Paint and Trend, the area where the picture is generated. The user can check the target data to generate the indicator comparison curve;
[0057] ⑦ Prediction area: After clicking Trend, the generated axis distance corresponds to the trend line least squares fitting result of the indicator.
[0058] Since there are 11 test types for the vehicle KC parameter measurement test and the center of mass moment of inertia test, each test type corresponds to multiple working conditions, each working condition corresponds to multiple modes, and each mode corresponds to multiple indicators. According to statistics, there are 2157 possible input combinations of table and drawing indicators, which are difficult to remember accurately, and sometimes users only know the Chinese name of the indicator. Therefore, the retrieval model constructed in the present invention will enumerate all input parameter combinations, form an indicator index table, and import it into the database. The retrieval model returns the following information by sending different retrieval commands to the database according to the hierarchical relationship between the fields. Figure 5 A list of options corresponding to the different input parameters shown.
[0059] The retrieval model constructed by the present disclosure also supports fuzzy search. In addition to the method of drilling down layer by layer to lock the index, the user can also directly enter or add, delete or modify the keyword list generated after the drilling process is completed in the keyword box to search the database according to personalized needs. For uncertain keyword fields, the fuzzy character "%" can be used as a substitute.
[0060] The vehicle parameter benchmarking method disclosed in the present invention, in addition to the horizontal comparison of indicators, can also restore the curve in the report model based on the reverse analysis of the data in the database, and perform a horizontal comparison of the KC curve. Specifically, it includes:
[0061] 1) Select multiple key-value pairs according to the comparison result, and reverse-parse the key-value pairs into a tree structure.
[0062] By using the reverse parsing method to restore the key-value pairs of the database into a structure for curve drawing, you also need to write an iteration function as described above. The logic of this iteration function is opposite to that of the iteration function analyzed above.
[0063] 2) Draw the characteristic curves corresponding to the tree-like structures respectively and compare multiple characteristic curves.
[0064] Optionally, the vehicle parameter benchmarking method disclosed in the present invention also includes constructing a combination graph drawing model, in which the user uses the retrieval model to first obtain the file ID list and curve graph name required for model input, and fills them into the input column of the combination graph drawing model together with other input parameters, and runs the program to obtain the required combination graph of multiple characteristic curves.
[0065] Figure 6 Schematic diagram of the input interface of the combined graph drawing model of an embodiment of the present disclosure. Figure 6 As shown, in this embodiment, the file IDs of the key-value pair data to be compared are ATA1739212711 and ATA17392127. The name of the curve graph is: Aligning Torque Camber. Figure 7 yes Figure 6 The corresponding combination of multiple characteristic curves. Figure 7 As shown, the characteristic curve L1 and the characteristic curve L2 almost overlap, indicating that the performance of the Toe angle of the two vehicles in the flat jump condition is similar.
[0066] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may be removed from these processes.
[0067] The basic concepts have been described above. Obviously, for those skilled in the art, the above invention disclosure is only used as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.
[0068] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.
[0069] Some aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program codes. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes ...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs ...), smart cards, and flash memory devices (e.g., cards, sticks, key drives ...).
[0070] A computer-readable medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer-readable medium may be propagated via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above mediums.
[0071] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of the present application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the subject of the present application requires more features than the features mentioned. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above.
[0072] As shown in this application, unless the context clearly indicates an exception, the words "a", "an", "a kind" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.
[0073] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values of the parts and steps set forth in these embodiments do not limit the scope of the application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. The technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.
[0074] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values is as accurate as possible within the feasible range.
[0075] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions may be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the essential spirit of the present application, they will fall within the scope of the present application.
Claims
1. A database construction method, characterized in that: include: Inputting the original data into a report generation model, the report generation model calculates the original data through a built-in algorithm to generate a report file, wherein the data storage format in the report file is a tree-like structure; Constructing a data parsing model, the data parsing model is used to parse the tree-like structure into key-value pair data, including: writing an iterative function for structure parsing, the operation logic of the iterative function is for the tree-like structure, if a node is a divisible structure, then returning the path to add the node name, repeating the iterative function; if a node is not divisible, then stopping the iteration, storing the node name as the Value of the key-value pair, and storing the path of the parent node of the node as the Key of the key-value pair; A random non-repetitive file ID is generated for the parsed key-value pair data, and the key-value pair data is stored in a database.
2. The database construction method according to claim 1, characterized in that: The original data include K&C experimental data or center of mass and moment of inertia experimental data.
3. The database construction method according to claim 2, characterized in that: The report generation model includes a plurality of built-in algorithms, each experiment type corresponds to a built-in algorithm, and the report generation model is further used to determine the type of the raw data and calculate the raw data using the built-in algorithm corresponding to the type.
4. The database construction method according to claim 2, characterized in that: The data analysis model includes a KC analysis model and an inertia analysis model. The KC analysis model generates one key-value pair data for one report file, and the inertia analysis model requires three report files to generate one key-value pair data.
5. The database construction method according to claim 1, characterized in that: Also includes: The data analysis model is encapsulated as a ctf file using an encapsulation tool and deployed to the computing layer of the cloud platform. After configuration, the user can run the data analysis model on the cloud platform.
6. The database construction method according to claim 1, characterized in that: Also includes: The file ID is returned to the application layer, and the application layer associates multiple tables according to the correspondence between the file ID and the experimental data and business data, so as to provide data support for multi-dimensional horizontal data mining.
7. A vehicle parameter benchmarking method, characterized in that: include: Establishing a database by the database construction method according to any one of claims 1 to 6; A retrieval model is established. The retrieval model obtains the index values under different vehicle models, test types, working conditions, and mode restrictions by sending different retrieval commands to the database according to the hierarchical relationship of the user input fields, and performs a global comparison across experiments and vehicle models.
8. The vehicle parameter benchmarking method according to claim 7, characterized in that: The retrieval model includes a navigation area and a data area. The navigation area enumerates all input parameter combinations, and the data area displays the retrieved indicator values.
9. The vehicle parameter benchmarking method according to claim 8, characterized in that: The retrieval model also includes a selection area, a label area and a drawing area. The selection area is used to select the data required for drawing from the indicator values retrieved and generated in the data area, the label area is used to select the data in the selection area that needs to be labeled, and the drawing area is used to generate an image based on the data in the selection area and the label area to facilitate the intuitive display of horizontal comparison of indicators.
10. The vehicle parameter benchmarking method according to claim 7, characterized in that: Also includes: Select multiple key-value pair data according to the comparison result, and reverse-parse the key-value pair data into a tree-like structure; Characteristic curves corresponding to the tree-like structures are drawn respectively, and the plurality of characteristic curves are compared.