Model calling identification management method and system, electronic equipment and storage medium

Abstract

The invention discloses a model calling recognition management method and system, electronic equipment and a storage medium, and relates to the field of recognition management, and the method comprises the steps: extracting a simulation analysis task of a corresponding stage through a keyword according to each stage of a project plan; receiving core parameters of a simulation analysis task defined by a user; based on a preset model recognition system, according to the vehicle configuration, the vehicle model platform and the analysis hierarchy, performing model library preliminary screening, including recommending a model library with the matching degree meeting a threshold value and related instructions to a user, and correspondingly providing a target model library interaction port for the user to select; based on the first N models with the highest recommendation matching degree, displaying model applicable vehicle models, previous application conditions, results and interaction data of boundary parameter requirements; waiting and judging confirmation data of the user on the recommendation model; if the user confirms the recommendation model, collecting boundary parameter specific numerical values submitted by the user; and if the user neglects the recommendation model, collecting dissatisfaction reasons submitted by the user or refining demand data.

Description

Technical Field

[0001] This application relates to the field of recognition management, and in particular to model invocation recognition management methods, model invocation recognition management systems, electronic devices, storage media, and interactive platforms. Background Technology

[0002] With the deepening of global energy conservation and emission reduction efforts, my country's regulations on fuel consumption and emissions for commercial vehicles are becoming increasingly stringent. Users are also placing higher demands on the energy efficiency competitiveness of freight vehicles, making energy conservation and consumption reduction a crucial research direction for major manufacturers. To ensure targeted energy conservation and consumption reduction at every stage of product design, vehicle energy management simulation design has begun to occupy an important position in product development. How to quickly perform vehicle energy management simulation during product development is key to ensuring timely completion of design milestones. The FAW Jiefang vehicle energy management platform provides a solution. However, the model calling method within the platform is a crucial step in ensuring the smooth operation of the energy management platform. Accurately and quickly providing design engineers with a suitable simulation model is crucial for supporting the normal operation of the vehicle energy management platform and lowering the threshold for simulation analysis tasks, enabling design engineers to perform simple simulations.

[0003] Therefore, a model identification and invocation strategy based on an energy management platform is needed to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of this invention is to provide a model call recognition and management method, a model call recognition and management system, an electronic device, a storage medium, and an interactive platform, thereby solving at least one of a number of technical problems.

[0005] 1. How to quickly conduct vehicle energy management simulation to ensure timely completion of design milestones; 2. How to accurately and quickly provide design engineers with a suitable simulation model to support the normal operation of the vehicle energy management platform, reduce the threshold for simulation analysis tasks, and enable design engineers to perform simple simulations; 3. How to ensure the smooth operation of the energy management platform after freeing up the platform's in-platform model calls.

[0006] This invention provides the following solution:

[0007] According to a first aspect of the present invention, a model call identification management method is provided, comprising:

[0008] Based on the project plan, the simulation analysis tasks for the corresponding stages are extracted by keywords and pushed to the user interface.

[0009] Receive the core parameters of the simulation analysis task defined by the user in the user interface;

[0010] The core parameters include vehicle configuration, vehicle platform, analysis level, analysis purpose, analysis stage, and configuration of each system.

[0011] Based on the preset model recognition system, the model library is initially screened according to vehicle configuration, vehicle platform, and analysis level. This includes recommending model libraries that meet the matching threshold to users and related explanations, and providing an interactive port for users to select target model libraries.

[0012] Within the target model library, a weighted calculation of model matching degree is performed based on the analysis purpose, analysis stage, and system configuration, including recommending the top N models with the highest matching degree to the user;

[0013] Based on the top N models with the highest matching degree, interactive data is displayed showing the applicable vehicle models, past application situations, results, and boundary parameter requirements of the models; the system then waits for and judges the user's confirmation data on the recommended models.

[0014] If the user confirms the recommendation model, then the specific values ​​of the boundary parameters submitted by the user will be collected.

[0015] It also includes the system determining the model number based on the parameter range and properties, retrieving the corresponding model and updating the boundary parameters before performing simulation calculations;

[0016] If a user rejects the recommendation model, then collect the reasons for the user's dissatisfaction or refine the data on their needs.

[0017] This also includes the system re-executing the model screening.

[0018] Furthermore, including:

[0019] Keywords include performance, expertise, and goals;

[0020] Vehicle configurations include gasoline, gas, hybrid, gas-electric, and pure electric types;

[0021] The analysis levels are divided into vehicle level and system level.

[0022] Furthermore, including:

[0023] The formula for weighted calculation of model matching degree is: M=(C1×G1+C2×G2+…+CN×GN) / N;

[0024] Among them, C1 to CN are the model recognition conditions;

[0025] G1 to GN are the weight factors corresponding to each identification condition;

[0026] M represents the model matching degree.

[0027] Furthermore, including:

[0028] The value of N is set to 3, and the threshold for the initial screening of the model library is the preset minimum standard for model matching.

[0029] Furthermore, including:

[0030] The core parameters also include basic vehicle information and target parameters;

[0031] The analysis aims to select conceptual solutions, verify the capabilities of the solutions, and predict their performance.

[0032] According to a second aspect of the present invention, a model recall recognition management system is provided, comprising:

[0033] The task extraction module is used to extract simulation analysis tasks for the corresponding stage based on keywords in the project plan and push them to the user interface.

[0034] The user interaction module is provided on the user interaction terminal, allowing users to define the core parameters of the simulation analysis task, select the model library, confirm the recommended model, fill in the boundary parameters, or submit reasons for dissatisfaction and detailed requirements.

[0035] The model matching module is used to perform initial screening of the model library and weighted calculation of model matching degree based on core parameters, and generate model library recommendation results and the top N high matching degree model recommendation results.

[0036] The model retrieval module is used to collect user confirmation data for the recommended model, including determining the model number and retrieving the corresponding model from the model library based on the user's confirmed model and the boundary parameters filled in, and updating the boundary parameters into the model.

[0037] The simulation calculation module is used to perform simulation calculations on the model after updating the boundary parameters.

[0038] Furthermore, including:

[0039] The core parameters include vehicle configuration, vehicle platform, analysis level, analysis purpose, analysis stage, configuration of each system, basic vehicle information, and target parameters.

[0040] The user interaction module also includes a feature for providing and displaying the applicable vehicle models, past application scenarios and results, and boundary parameter requirements on the user interaction platform.

[0041] Furthermore, including:

[0042] The model matching module has a built-in weight factor configuration unit, which is used to store and call the weight factors corresponding to the recognition conditions of each model.

[0043] The weight factor is adjusted adaptively according to the actual application scenario.

[0044] Furthermore, including:

[0045] The model retrieval module also includes collecting user confirmation data for the recommendation model, which is used to verify whether the boundary parameters filled in by the user meet the model requirements.

[0046] If it does not meet the requirements, the user will be prompted to make corrections through the user interaction module on the user interaction terminal.

[0047] Furthermore, including:

[0048] The task extraction module also includes linking project plan phases with simulation analysis tasks to ensure that no task is missed and is pushed to the user interface of the corresponding person in charge.

[0049] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0050] The memory stores a computer program, which, when executed by the processor, causes the processor to perform steps such as model invocation recognition management methods.

[0051] According to a fourth aspect of the present invention, a computer-readable storage medium is provided storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform steps such as a model invocation recognition management method.

[0052] According to a fifth aspect of the present invention, an interactive platform is provided, comprising:

[0053] Electronic devices used to implement steps such as model invocation recognition management methods;

[0054] The processor runs programs, and when the programs are running, they execute steps such as model invocation of recognition and management methods based on data output from electronic devices.

[0055] Storage medium used to store programs that, when running, execute steps such as model invocation of recognition and management methods on data output from electronic devices.

[0056] The above solution achieves the following beneficial technical effects:

[0057] In this application, the system extracts simulation analysis requirements based on the project plan, uniformly extracts, plans, and manages simulation matters to avoid errors and omissions, and pushes them to the user task interface to facilitate efficient and accurate human-computer interaction.

[0058] In this application, users define their vehicle configuration, development stage, basic vehicle model information, analysis type (whole vehicle or system), target parameters, etc. for each analysis task based on actual conditions. The system calculates the matching degree of models in the model library according to user needs, automatically recommends relevant models, and displays the boundary parameters of model requirements. It quickly and accurately matches user data with product data to form product development direction.

[0059] In this application, users can select models based on their own boundary parameters. If the recommended model cannot meet their needs, users can further refine their requirements. For example, after clarifying the boundary conditions, they can re-select models, which enhances the dynamism and accuracy of user data collection and makes user data more precise and real-time. Attached Figure Description

[0060] Figure 1 This is a flowchart of a model call identification and management method provided by one or more embodiments of the present invention.

[0061] Figure 2 This is a structural diagram of a model call recognition management system provided by one or more embodiments of the present invention.

[0062] Figure 3 This is a schematic diagram of a model call recognition process provided in a specific embodiment of the present invention.

[0063] Figure 4 This is a block diagram of an electronic device structure for a model call recognition and management method provided in one or more embodiments of the present invention. Detailed Implementation

[0064] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Figure 1 This is a flowchart of a model call identification and management method provided by one or more embodiments of the present invention.

[0066] like Figure 1 The model call recognition management methods shown include:

[0067] Step S1: Based on the project plan, extract the simulation analysis tasks for the corresponding stages using keywords and push them to the user interaction terminal.

[0068] Step S2: Receive the core parameters of the simulation analysis task defined by the user on the user interface.

[0069] The core parameters include vehicle configuration, vehicle platform, analysis level, analysis purpose, analysis stage, and configuration of each system.

[0070] Step S3: Based on the preset model recognition system, the model library is initially screened according to vehicle configuration, vehicle platform, and analysis level. This includes recommending model libraries that meet the matching threshold to the user and related explanations, and providing an interactive port for the user to select the target model library.

[0071] Within the target model library, a weighted calculation of model matching degree is performed based on the analysis purpose, analysis stage, and system configuration, including recommending the top N models with the highest matching degree to the user;

[0072] Step S4: Based on the top N models with the highest matching degree, display interactive data on the applicable vehicle models, past application situations, results, and boundary parameter requirements of the models; wait for and judge the user's confirmation data on the recommended models;

[0073] If the user confirms the recommendation model, then the specific values ​​of the boundary parameters submitted by the user will be collected.

[0074] It also includes the system determining the model number based on the parameter range and properties, retrieving the corresponding model and updating the boundary parameters before performing simulation calculations;

[0075] If a user rejects the recommendation model, then collect the reasons for the user's dissatisfaction or refine the data on their needs.

[0076] This also includes the system re-executing the model screening.

[0077] In this embodiment, it includes:

[0078] Keywords include performance, expertise, and goals;

[0079] Vehicle configurations include gasoline, gas, hybrid, gas-electric, and pure electric types;

[0080] The analysis levels are divided into vehicle level and system level.

[0081] In this embodiment, it includes:

[0082] The formula for weighted calculation of model matching degree is: M=(C1×G1+C2×G2+…+CN×GN) / N;

[0083] Among them, C1 to CN are the model recognition conditions;

[0084] G1 to GN are the weight factors corresponding to each identification condition;

[0085] M represents the model matching degree.

[0086] In this embodiment, it includes:

[0087] The value of N is set to 3, and the threshold for the initial screening of the model library is the preset minimum standard for model matching.

[0088] In this embodiment, it includes:

[0089] The core parameters also include basic vehicle information and target parameters;

[0090] The analysis aims to select conceptual solutions, verify the capabilities of the solutions, and predict their performance.

[0091] Figure 2 This is a structural diagram of a model call recognition management system provided by one or more embodiments of the present invention.

[0092] like Figure 2 The model call recognition management system shown includes:

[0093] The task extraction module is used to extract simulation analysis tasks for the corresponding stage based on keywords in the project plan and push them to the user interface.

[0094] The user interaction module is provided on the user interaction terminal, allowing users to define the core parameters of the simulation analysis task, select the model library, confirm the recommended model, fill in the boundary parameters, or submit reasons for dissatisfaction and detailed requirements.

[0095] The model matching module is used to perform initial screening of the model library and weighted calculation of model matching degree based on core parameters, and generate model library recommendation results and the top N high matching degree model recommendation results.

[0096] The model retrieval module is used to collect user confirmation data for the recommended model, including determining the model number and retrieving the corresponding model from the model library based on the user's confirmed model and the boundary parameters filled in, and updating the boundary parameters into the model.

[0097] The simulation calculation module is used to perform simulation calculations on the model after updating the boundary parameters.

[0098] In this embodiment, it includes:

[0099] The core parameters include vehicle configuration, vehicle platform, analysis level, analysis purpose, analysis stage, configuration of each system, basic vehicle information, and target parameters.

[0100] The user interaction module also includes a feature for providing and displaying the applicable vehicle models, past application scenarios and results, and boundary parameter requirements on the user interaction platform.

[0101] In this embodiment, it includes:

[0102] The model matching module has a built-in weight factor configuration unit, which is used to store and call the weight factors corresponding to the recognition conditions of each model.

[0103] The weight factor is adjusted adaptively according to the actual application scenario.

[0104] In this embodiment, it includes:

[0105] The model retrieval module also includes collecting user confirmation data for the recommendation model, which is used to verify whether the boundary parameters filled in by the user meet the model requirements.

[0106] If it does not meet the requirements, the user will be prompted to make corrections through the user interaction module on the user interaction terminal.

[0107] In this embodiment, it includes:

[0108] The task extraction module also includes linking project plan phases with simulation analysis tasks to ensure that no task is missed and is pushed to the user interface of the corresponding person in charge.

[0109] It is worth noting that although this system / device only discloses the above-mentioned modules / units, it does not mean that this system / device is limited to the above-mentioned basic functional modules. On the contrary, what this invention intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can add one or more functional modules in combination with the prior art to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. It cannot be assumed that the scope of protection of the claims of this invention is limited to the above-disclosed basic functional modules just because this embodiment only discloses a few basic functional modules.

[0110] In one specific embodiment, a strategy for model call recognition is disclosed, which achieves the following: Figure 3 The model call recognition process is shown below:

[0111] After the project team releases the project plan, simulation analysis tasks corresponding to each stage are extracted based on keywords (e.g., performance, specialization, objectives) according to the various phases within the project plan. These tasks are then linked to the user interface. Users define the analysis tasks presented on the interface, primarily defining vehicle configuration (fuel, gas, hybrid, gas-electric, pure electric, etc.), vehicle platform (A, B, C, etc.), analysis level (vehicle-level, system-level), analysis purpose (e.g., concept scheme selection, scheme capability verification, performance prediction, etc.), analysis stage (e.g., concept scheme, engineering design stage, etc.), and the configuration of each system. After completing the definition, the task is submitted to the model recognition system. The model recognition system first matches the vehicle configuration, vehicle platform, and analysis level against a model library, recommending models with a matching degree greater than a certain threshold, along with a brief description of the model library and its internal model usage. Users then select the appropriate model library based on the description and brief description.

[0112] Within the user-selected model library, models are filtered based on their analysis purpose, analysis stage, and system configuration. The results are then weighted and the top three models with the highest matching degree are recommended to the user. Each model is then presented with information on applicable vehicle models, previously used vehicle models, results, and boundary parameter requirements for user selection.

[0113] If the designer selects "approved," they fill in the specific values ​​of the parameters required for the model on the user interface. The model library determines the model's serial number in the model library based on the user's boundary parameter range and properties. The model recognition system retrieves the relevant model from the model library based on the model serial number, updates the boundary parameters to the model, and performs simulation calculations.

[0114] If the recommended model designer does not pass the selection, they need to describe the reasons for dissatisfaction with the selected model in the user interface, or further refine their requirements, and submit them to the model recognition system for model selection.

[0115] Weighted calculation of model matching degree:

[0116] If the model identification conditions are C1, C2, C3...CN, and the weight factors for each condition are G1, G2, G3...GN, then the formula for calculating the model matching degree is:

[0117] M=( C1 G1+ C2 G2+ C3 G3+……CN GN) / N

[0118] Through the above embodiments, the model recognition and invocation method and system are based on the project plan. The simulation tasks are extracted according to the project plan and assigned to the user interfaces of the responsible persons to avoid omissions. Users define the vehicle configuration, development stage, basic vehicle model information, analysis type (whole vehicle or system), target parameters, etc. of each analysis task based on the actual situation. The system calculates the matching degree of models in the model library according to user needs, automatically recommends relevant models, and displays the boundary parameters required by the model. Users can submit the calculation after completing the simple boundary parameter filling, omitting the modeling steps.

[0119] In this embodiment, the process may include other finely detailed judgments to improve model accuracy; the model selection parameters are not limited to those listed in the text, and all parameters that can define model attributes or uses can be used as search keywords.

[0120] Figure 4 This is a block diagram of an electronic device structure for a model call recognition and management method provided in one or more embodiments of the present invention.

[0121] like Figure 4 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0122] The memory stores a computer program, which, when executed by the processor, causes the processor to perform steps of a model invocation recognition management method.

[0123] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a model invocation recognition management method.

[0124] This application also provides an interactive platform, including:

[0125] Electronic equipment used to implement the steps of model invocation recognition and management methods;

[0126] The processor runs programs, and when the program runs, it executes the steps of calling the recognition and management methods based on the data output from the electronic device.

[0127] Storage medium used to store programs that, when running, execute model calls to identify and manage methods based on data output from electronic devices.

[0128] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0129] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0130] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0131] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0132] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0133] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0134] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0135] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0136] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model call recognition management method characterized by comprising: The model calling identification management method comprises: According to the stages of the project plan, the simulation analysis task of the corresponding stage is extracted through the keyword, and is pushed to the user interaction end; Receive the user's definition of the core parameters of the simulation analysis task on the user interaction end; The core parameters include vehicle configuration, vehicle platform, analysis level, analysis purpose, analysis stage, and each system configuration; Based on the preset model identification system, the model library is preliminarily screened according to the vehicle configuration, the vehicle platform, and the analysis level, including recommending the model library and the related instructions to the user whose matching degree meets the threshold, and providing the target model library interaction port for the user to select; Among the target model library, based on the analysis purpose, the analysis stage, and each system configuration, the model matching degree is weighted and calculated, including recommending the top N models with the highest matching degree to the user; Based on the top N models with the highest matching degree, the interaction data of the model applicable vehicle type, previous application situation, results, and boundary parameter requirements are displayed; and the user's confirmation data for the recommended model is waited for and judged; If the user confirms the recommended model, the specific numerical value of the boundary parameter submitted by the user is collected; Further comprising: the system determines the model serial number according to the parameter range and nature, and executes simulation calculation after corresponding model is called and boundary parameter is updated; If the user denies the recommended model, the user's submitted dissatisfaction reason or detailed demand data is collected; Further comprising: the system re-executes the model screening. 2.The model call identification management method of claim 1, wherein, Comprise: The keywords include performance, profession, and target; The vehicle configuration includes fuel, gas, hybrid, fuel cell, and pure electric types; The analysis level includes whole vehicle level and system level. 3.The model call identification management method of claim 1, wherein, Comprise: The formula of the model matching degree weighted calculation is: M=(C1×G1+C2×G2+…+CN×GN) / N; Wherein, C1 to CN are model identification conditions; G1 to GN are weight factors corresponding to each identification condition; M is the model matching degree.

4. The model call identification management method of claim 1, wherein, Comprise: The value of N is set to 3, and the threshold of the model library preliminary screening is the preset minimum standard of the model matching degree.

5. The model invocation identification management method of claim 1, wherein, Comprise: The core parameters further include basic vehicle information and target parameters; The analysis purpose includes concept scheme selection, scheme capacity checking, and performance prediction.

6. A model invocation recognition management system, characterized by, The model calling identification management system comprises: A task extraction module for extracting simulation analysis tasks of corresponding stages according to keywords of project plans and pushing to a user interaction end; A user interaction module for providing, on the user interaction end, core parameters of simulation analysis tasks for the user to define, model libraries for the user to select, recommended models for the user to confirm, boundary parameters for the user to fill in, or dissatisfaction reasons and detailed demands for the user to submit; A model matching module for preliminarily screening a model library according to core parameters and performing model matching degree weighted calculation, to generate model library recommendation results and top N high-matching-degree model recommendation results; A model calling module for collecting the user's confirmation data for the recommended model, including determining the model serial number and calling the corresponding model from the model library according to the model confirmed by the user and the boundary parameters filled in by the user, and updating the boundary parameters in the model; A simulation calculation module for performing simulation calculation on the model with updated boundary parameters.

7. The model invocation identification management system of claim 6, wherein, Comprise: The core parameters include vehicle configuration, vehicle platform, analysis level, analysis purpose, analysis stage, each system configuration, basic vehicle information, and target parameters. The user interaction module further includes providing, at the user interaction end, a display model applicable vehicle type, previous application situation and results, and boundary parameter requirements.

8. The model invocation identification management system of claim 6, wherein, The model matching module includes: The model matching module includes a weight factor configuration unit for storing and calling weight factors corresponding to each model identification condition; The weight factors are adaptively adjusted according to actual application scenarios.

9. The model invocation identification management system of claim 6, wherein, The model matching module includes: The model matching module includes collecting user confirmation data for recommended models to verify whether the boundary parameters filled by the user meet the model requirements; If not, the user is prompted to correct through the user interaction module at the user interaction end. The task extraction module further includes associating each stage of the project plan with simulation analysis tasks to ensure that tasks are not missed and pushed to the user interaction end of the corresponding person in charge.

10. An electronic device comprising: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the model calling identification management method according to any one of claims 1 to 5.

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