Hardware configuration generation method and device, terminal equipment and readable storage medium

Abstract

The invention is suitable for the technical field of industrial automation control, and provides a hardware configuration generation method and device, terminal equipment and a readable storage medium, and the method comprises the steps: obtaining first configuration information inputted by a user; wherein the first configuration information is description information of to-be-configured hardware; generating a first hardware configuration list according to the first configuration information; acquiring hardware parameters corresponding to the first hardware configuration list; and performing hardware configuration according to the first hardware configuration list and the hardware parameters to obtain a hardware configuration result. According to the method, the hardware configuration efficiency and reliability can be effectively improved, and control item landing is accelerated.

Description

Technical Field

[0001] This application belongs to the field of industrial automation control technology, and in particular relates to a hardware configuration generation method, apparatus, terminal equipment and readable storage medium. Background Technology

[0002] Programmable Logic Controller (PLC) hardware configuration is the core prerequisite for realizing the functions of PLC systems in the field of industrial automation control. Its importance runs through the entire process of PLC hardware system from "hardware unit" to "usable control hub".

[0003] Existing technologies are based on a manual PLC hardware configuration process, which requires engineers to manually complete the entire process from equipment selection to parameter setting in the PLC host computer software based on their personal experience. This method is cumbersome due to the manual operation process, and the configuration deviation is easily caused by negligence during manual verification and input, resulting in low efficiency and accuracy in generating hardware configurations. Summary of the Invention

[0004] This application provides a hardware configuration generation method, apparatus, terminal device, and readable storage medium, which can effectively improve hardware configuration efficiency and reliability and accelerate the implementation of control projects.

[0005] In a first aspect, embodiments of this application provide a hardware configuration generation method, including: Obtain the first configuration information input by the user; wherein, the first configuration information is the description information of the hardware to be configured; A first hardware configuration list is generated based on the first configuration information; Obtain the first hardware parameters corresponding to the first hardware configuration list; The first hardware configuration is generated based on the first hardware configuration list and the first hardware parameters.

[0006] In this embodiment, the hardware description information (first configuration information) proposed by the user based on control requirements is first obtained and converted into a standardized first hardware configuration list. Then, the parameters of each hardware component in the list are automatically extracted from a preset database. Finally, the list and parameters are integrated to generate the hardware configuration. By directly converting the user's hardware description into a configuration list, the experience bias and model incompatibility issues of manual selection can be avoided. The parameter setting stage relies on the preset database to automatically extract parameters, eliminating the tediousness of manually consulting manual manuals and inputting data, and reducing input errors. The overall process, through the automated connection of each stage, ensures that the hardware configuration generation is accurate and effectively improves configuration efficiency and reliability, accelerating the implementation of control projects.

[0007] In one possible implementation of the first aspect, the first configuration information includes first description type information and / or second description type information; A first hardware configuration list is generated based on the first configuration information, including: If the first configuration information includes the first description type information, the first description type information is input into the first recognition model to obtain the first sub-configuration list; If the first configuration information includes the second description type information, the second description type information is input into the second recognition model to obtain the second sub-configuration list; A first hardware configuration list is generated based on the first sub-configuration list and / or the second sub-configuration list.

[0008] In this embodiment, by adapting different models to different types of information, it is possible to accurately extract hardware-related content from various types of information, avoid the bias of a single model processing multiple types of information, and improve the accuracy of list generation; it is also possible to complete the parsing and list generation of two types of information in parallel or in an orderly manner, reduce the steps of manually splitting information and processing it separately, and thus efficiently produce a complete hardware configuration list, laying a solid foundation for subsequent parameter extraction and configuration generation.

[0009] In one possible implementation of the first aspect, a first hardware configuration list is generated based on a first sub-configuration list and / or a second sub-configuration list, including: If the first configuration information only includes the first description type information, the first sub-configuration list will be determined as the first hardware configuration list; If the first configuration information only includes the second description type information, the second sub-configuration list will be determined as the second hardware configuration list; If the first configuration information includes first description type information and second description type information, the first sub-configuration list and the second sub-configuration list are compared to obtain the first comparison result; If there is a mismatch between the first sub-configuration information and the second sub-configuration information in the first comparison result, the first sub-configuration information and the second sub-configuration information are verified to obtain the first verification information; wherein, the first sub-configuration information is the configuration information corresponding to the first hardware module in the first sub-configuration list; the second sub-configuration information is the configuration information corresponding to the first hardware module in the second sub-configuration list; the first hardware module is any hardware module in the first sub-configuration list or the second sub-configuration list; The third sub-configuration information, the fourth sub-configuration information, and the first verification information are merged to obtain the first hardware configuration list; wherein, the third sub-configuration information is the configuration information in the first sub-configuration list other than the first sub-configuration information; and the fourth sub-configuration information is the configuration information in the second sub-configuration list other than the second sub-configuration information.

[0010] In this embodiment, the first and second sub-configuration lists are compared. If a conflict is found in the configuration information of the same hardware module, the conflict information is verified to obtain accurate verification information. Finally, the conflict-free configuration information and the verification information are integrated to form the final list. The comparison step can promptly identify information inconsistencies between the two sub-lists, the verification step can correct conflicts to ensure information accuracy, and the final fusion operation not only ensures the integrity of the first hardware configuration list but also eliminates errors caused by conflicting information. This provides a reliable core basis for subsequent hardware parameter extraction and configuration generation, reducing rework in subsequent processes due to list issues.

[0011] In one possible implementation of the first aspect, obtaining the hardware parameters corresponding to the first hardware configuration list includes: For each hardware module in the first hardware configuration list, extract the corresponding hardware identifier; Extract the core data corresponding to the hardware identifier from the preset database; The core data is converted into a preset format to obtain the first hardware parameters.

[0012] In this embodiment, for each hardware module in the list, its hardware identifier is first extracted, then core data is retrieved from the database based on this identifier, and finally, this data is converted into a preset format to form hardware parameters. Accurate matching of the hardware identifier with database information ensures the accuracy of parameter extraction; uniform conversion to a preset format ensures the standardization of the parameters, facilitating their direct use in subsequent hardware configuration generation methods. This avoids errors from manual parameter lookup and improves the efficiency of parameter retrieval, laying a data foundation for the rapid generation of reliable configurations.

[0013] In one possible implementation of the first aspect, the hardware parameters include multiple hardware sub-parameters; generating a first hardware configuration based on a first hardware configuration list and the hardware parameters includes: Create a corresponding control hub for each hardware module in the first hardware configuration list; The first hardware configuration is generated based on the hardware sub-parameters corresponding to the control hub and hardware modules.

[0014] In this embodiment, a corresponding control hub is created for each hardware module in the list, and the hardware sub-parameters of each module are imported into its control hub. By matching the corresponding hardware module to the control hub and accurately importing the sub-parameters, the automatic generation of hardware configuration is realized, ensuring the accuracy of the correspondence between configuration and hardware and parameters. At the same time, the manual configuration step is eliminated, improving configuration efficiency and reliability.

[0015] In one possible implementation of the first aspect, the method further includes: If a user input adjustment command is detected, the adjustment command is received; Execute the adjustment command to adjust the first hardware configuration and obtain the adjusted first hardware configuration.

[0016] In this embodiment, in response to user requests for hardware configuration adjustments, the hardware configuration is quickly corrected or optimized to ensure that the configuration meets actual control requirements, reduce overall process rework caused by previous configuration deviations, and improve the flexibility and adaptability of the configuration.

[0017] In one possible implementation of the first aspect, the method further includes: Obtain user feedback regarding hardware configuration; Feedback information is sent to the first recognition model and the second recognition model so that the first recognition model and the second recognition model can be optimized according to the feedback information to obtain the optimized first recognition model and the second recognition model. The second configuration information input by the user is identified based on the optimized first and second recognition models to obtain the second hardware configuration list; Obtain the second hardware parameters corresponding to the second hardware configuration list; Generate a second hardware configuration based on the second hardware configuration list and the second hardware parameters.

[0018] In the above method, user feedback is used to optimize the recognition model, making the model more accurate in recognizing subsequent configuration information, and the generated hardware configuration list is more in line with actual needs, thereby continuously improving the accuracy and adaptability of automated configuration.

[0019] Secondly, embodiments of this application provide a hardware configuration generation apparatus, including: The configuration information acquisition module is used to acquire the first configuration information input by the user; wherein, the first configuration information is the description information of the configuration hardware; The configuration list generation module is used to generate a first hardware configuration list based on the first configuration information. The hardware parameter extraction module is used to obtain the first hardware parameters corresponding to the first hardware configuration list; The hardware configuration generation module is used to generate the first hardware configuration based on the first hardware configuration list and hardware parameters.

[0020] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the hardware configuration generation method as described in any of the first aspects above.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the hardware configuration generation method as described in any of the first aspects above.

[0022] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the hardware configuration generation method of any one of the first aspects described above.

[0023] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the hardware configuration generation method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the process for generating the first hardware configuration list provided in the embodiments of this application. Figure 1 ; Figure 3 This is a schematic diagram of the recognition process of the first recognition model provided in the embodiments of this application; Figure 4 This is a schematic diagram of the recognition process of the second recognition model provided in the embodiments of this application; Figure 5 This is a schematic diagram of the process for generating the first hardware configuration list provided in the embodiments of this application. Figure 2 ; Figure 6 This is a schematic diagram of the hardware parameter extraction process provided in an embodiment of this application; Figure 7 This is a schematic diagram of the hardware configuration process provided in the embodiments of this application; Figure 8 This is a flowchart illustrating the optimization model provided in an embodiment of this application; Figure 9 This is a schematic diagram of the overall structure of the hardware configuration generation method provided in the embodiments of this application; Figure 10 This is a structural block diagram of the hardware configuration generation device provided in the embodiments of this application; Figure 11 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0027] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0028] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0030] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0032] Currently, the conventional practice for hardware configuration in PLC host computer software is still quite traditional: first, the device information needs to be manually verified, then the device model needs to be manually selected, then each parameter needs to be configured one by one, and the device name, communication address, etc. need to be manually assigned.

[0033] This approach has several obvious drawbacks: it is not only inefficient and prone to errors due to human negligence, but also highly dependent on the personal experience of engineers, resulting in high learning costs for programmers and high barriers to project implementation.

[0034] Today, with the rapid development of artificial intelligence (AI) vision technology and large language models, many scenarios that have traditionally relied on a lot of manual operation are ushering in an opportunity for transformation.

[0035] By introducing AI capabilities, automated preprocessing of some configuration tasks can be achieved. For example, through graphical representations (such as CAD system topology diagrams, Excel configuration tables, and high-resolution images of equipment inside cabinets), system configuration can be automatically identified, parameter configuration suggestions can be generated, and equipment names and addresses can be assigned. Engineers can then verify and optimize the AI ​​output, significantly reducing repetitive work and the probability of errors. This human-machine collaborative work mode not only improves configuration efficiency but also reduces over-reliance on human experience, providing a practical path for the intelligent upgrading of industrial automation engineering.

[0036] To address the problems in the aforementioned related technologies, this application provides a hardware configuration generation method, apparatus, terminal device, and readable storage medium. In particular, this application relates to a host computer software design technology for PLCs, specifically a method and system for automatically configuring PLC hardware systems using Natural Language Processing (NLP) and Computer Vision (CV) technologies. Through a process of receiving text / hardware image input, identifying and extracting information, fusing verification lists, querying a knowledge base, automatically generating configurations, and receiving user fine-tuning feedback, the configuration efficiency is reduced from hours to minutes, while simultaneously improving accuracy, lowering the implementation threshold, and possessing self-learning capabilities.

[0037] See Figure 1 This is a flowchart illustrating the hardware configuration generation method provided in this application embodiment. It is intended as an example and not a limitation. The method may include the following steps: S101, Obtain the first configuration information input by the user; wherein, the first configuration information is the description information of the hardware to be configured.

[0038] In this embodiment, the "first configuration information" is the first core configuration data acquired by the system, specifically referring to the "description information of the hardware to be configured," which is the user-provided description of the PLC hardware configuration requirements. This description information serves as the foundational input data for subsequent AI recognition, inventory generation, and automatic configuration. This description information includes basic hardware information such as the PLC module model (e.g., Siemens S7-1500 series), quantity (e.g., 1 CPU module + 3 digital input modules), and installation order (e.g., CPU module first, input modules arranged sequentially).

[0039] Specifically, the system provides clear prompts to the user through the host computer software interface, informing them of the type of information required, such as, "Please enter a description of the PLC hardware to be configured (including model, quantity, installation order, functional requirements, etc.), or upload a hardware image to assist in identification," guiding the user to provide a complete and detailed description. The system supports two flexible input methods to adapt to different user habits: Text input: Users can directly enter the above hardware description information through the software's text box (e.g., "One Siemens S7-1500 CPU module and three 16-channel digital input modules, installed in the order of CPU first, with the input modules connected in sequence, for automotive welding production line control"). Image-assisted input: If users find it difficult to fully describe the hardware in words, they can upload images of the actual hardware, CAD topology diagrams, or Excel configuration sheets. The system will receive the configuration information after detecting it.

[0040] S102, Generate a first hardware configuration list based on the first configuration information.

[0041] In this embodiment of the application, based on the first configuration information input by the user (description information of the hardware to be configured, such as text description or hardware image), the system automatically generates a first hardware configuration list, which is a standardized list that records key information of PLC hardware.

[0042] In one embodiment, the first configuration information includes first description type information and / or second description type information; see also Figure 2 This is a schematic diagram of the process for generating the first hardware configuration list provided in the embodiments of this application. Figure 1 ,like Figure 2 As shown, step S102 includes: S201, If ​​the first configuration information includes the first description type information, the first description type information is input into the first recognition model to obtain the first sub-configuration list.

[0043] In this embodiment, the first configuration information is the basic information input by the user for PLC hardware configuration. It corely includes two selectable (or combined) description types, not a single form. For example, "hardware image information" is the first description type (such as CAD system topology diagrams, high-definition images of the internal components, screenshots of Excel configuration tables, etc.), and is an important component of the first configuration information. The first recognition model is a "visual recognition model" (such as YOLO, CNN, etc.), specifically designed to process visualized hardware image information and extract key configuration data.

[0044] Specifically, when the first configuration information contains "first description type information (hardware image)", a dedicated processing flow is triggered, and the image information is passed to the "first recognition model (visual recognition model)". The model is used to extract valid data and finally generate the first sub-configuration list (the hardware list from which the image is sourced). This sub-configuration list is a structured list output by the first recognition model after processing the first description type information (the core of which includes key information such as the model, quantity, and installation order of the PLC module).

[0045] See Figure 3 This is a schematic diagram of the recognition process of the first recognition model provided in the embodiments of this application, such as... Figure 3 As shown, the system provides an image upload entry (e.g., a "Click to Upload Hardware Image" button) through the host computer software interface. It receives the first description type information uploaded by the user, automatically filters valid images (excluding blurry images, images without hardware content, and images with unsupported formats), and prompts the user to re-upload if the image is invalid, ensuring the input data is qualified. The system automatically calls a preset visual recognition model (e.g., YOLO, CNN), inputs the filtered hardware images into the model for processing, specifically: First, the image is preprocessed (e.g., noise reduction, cropping, highlighting hardware module areas) → Target detection (identifying each PLC module in the image, distinguishing CPU modules, input / output modules, etc.) → Model recognition (reading model identifiers, interface identifiers, etc. on the modules) → Installation order sorting (determining the installation order from left to right / from top to bottom based on the physical location of the modules in the image). After extracting key information such as "model, quantity, and installation order" from the model, the data is organized according to the system's preset structured format (e.g., table format), and the first sub-configuration list is output. The list content is clear and unambiguous (e.g., "Serial number 1: Module model Siemens S7-1500 CPU, quantity 1, installation order 1; Serial number 2: Module model SM 1214C, quantity 3, installation order 2-4"), providing a standardized data foundation for subsequent "information fusion (comparison and verification with the second sub-configuration list)".

[0046] S202, If the first configuration information includes the second description type information, input the second description type information into the second recognition model to obtain the second sub-configuration list.

[0047] In this embodiment, the second descriptive information can be "natural language text information" (such as a user's description of "1 Siemens S7-1500 CPU module + 3 digital input modules, installed from left to right"), which is another core input format. The second recognition type can be a large NLP model (such as LLM), specifically used to parse natural language text and extract key information related to hardware configuration.

[0048] Specifically, when the first configuration information provided by the user contains "natural language text" (second description type information), a dedicated processing flow is triggered. The text is parsed through an NLP model, and the unstructured text description is transformed into a structured hardware list, namely the second sub-configuration list. This sub-configuration list is a structured list generated after the NLP model processes the text information. Its core contains key configuration data such as the model, quantity, function, and installation order of the PLC module.

[0049] See Figure 4 This is a schematic diagram of the recognition process of the second recognition model provided in the embodiments of this application, as shown below. Figure 4 As shown, the system receives natural language text input from the user through a text input box in the host computer software (such as "Please enter a text description of the hardware configuration"). It automatically performs basic validity checks, excluding blank text and invalid inputs without hardware-related content. If the input is invalid, the user is prompted to supplement or modify it, ensuring that the text information is relevant to the PLC hardware configuration. The system automatically calls the NLP large model to perform layered parsing on the filtered text, specifically: First, upon receiving the text input (second description information), entity recognition is performed, extracting core hardware elements from the text (such as module model, quantity, function, installation order, communication protocol, etc.). Then, relationship extraction is performed, clarifying the associations between entities (such as the correspondence between "16-channel digital input module" and "quantity 2", and the association between "CPU module" and "installation order first"). Finally, the NLP model organizes the parsed entities and relationships according to the system's preset structured format (such as fields such as serial number, module type, model, quantity, installation order, and functional remarks), outputting the second sub-configuration list. This ensures that the list content is clear and unambiguous (e.g., "Serial number 1: Module type CPU, model Siemens S7-1500, quantity 1, installation order 1, functional remarks: welding production line main control; Serial number 2: Module type digital input module, model SM1221, quantity 2, installation order 2-3, functional remarks: 16-channel input, connected to sensors"), providing standardized data for subsequent integration with the first sub-configuration list.

[0050] S203, Generate a first hardware configuration list based on the first sub-configuration list and / or the second sub-configuration list.

[0051] In this embodiment of the application, the first configuration information may contain only the first description type information, only the second description type information, or both types. The "first sub-configuration list" is a list (including module model, quantity, etc.) obtained after the visual recognition model parses the hardware image, and the "second sub-configuration list" is a list (including hardware entities, relationships, etc.) obtained after the NLP model parses the text description. Therefore, a comprehensive hardware configuration list and a first hardware configuration list can be obtained according to the input type of the configuration information.

[0052] The above method adapts different models to different types of information, which can accurately extract hardware-related content from various types of information, avoid the bias of a single model processing multiple types of information, and improve the accuracy of list generation. It can also complete the parsing and list generation of two types of information in parallel or in an orderly manner, reduce the steps of manually splitting information and processing it separately, and thus efficiently produce a complete hardware configuration list, laying a solid foundation for subsequent parameter extraction and configuration generation.

[0053] In one embodiment, see Figure 5 This is a schematic diagram of the process for generating the first hardware configuration list provided in the embodiments of this application. Figure 2 ,like Figure 5 As shown, step S203 includes: S301, if the first configuration information only includes the first description type information, the first sub-configuration list is determined as the first hardware configuration list.

[0054] In this application, when the user only provides "first description type information" (i.e., hardware images, such as CAD topology diagrams, cabinet interior images, etc.), the system does not need to integrate other data. Instead, it directly uses the image to generate a "first sub-configuration list" (including module model, quantity, installation order, etc.) after parsing the image through a visual recognition model (i.e., the first recognition model). This list is then used as the final usable "first hardware configuration list" and serves as the basic data for subsequent knowledge base queries and automatic configuration.

[0055] S302, if the first configuration information only includes the second description type information, the second sub-configuration list is determined as the second hardware configuration list.

[0056] In this embodiment, when the user only provides "second description type information" (i.e., natural language text description, such as "1 Siemens S7-1500 CPU module + 2 16-channel digital input modules for welding production line") and does not upload any hardware images, the system does not need to perform information fusion. Instead, it directly uses the "second sub-configuration list" (containing structured information such as hardware model, quantity, and functional association) generated by parsing the text through the NLP large model (i.e., the second recognition model) to determine the final usable "second hardware configuration list" as the core basic data for subsequent knowledge base query and automatic configuration.

[0057] S303, if the first configuration information includes first description type information and second description type information, the first sub-configuration list and the second sub-configuration list are compared to obtain the first comparison result.

[0058] In this embodiment, when a user provides both hardware images and text descriptions for configuration, the system first generates a first sub-configuration list (image parsing result) using a visual recognition model and a second sub-configuration list (text parsing result) using an NLP model. Then, the system performs a targeted comparison between these two lists and finally outputs the first comparison result—the core of which is to verify the consistency of the two lists and locate any contradictions or gaps. The comparison focuses on key information related to the PLC hardware configuration to ensure no omissions or contradictions, specifically: Consistency of basic information: module model (e.g., whether they are all "Siemens S7-1500"), quantity (e.g., whether they are all "1 CPU + 3 input modules"), installation order (e.g., whether they are all "CPU first, input modules arranged in sequence"). Key attribute matching degree: Module function description (e.g., if the text mentions "welding production line control", does the image recognition module support this function?), number of interfaces / IOs (e.g., if the text mentions "16 inputs", does the image recognition module meet this requirement?). Information integrity complementarity: If a list is missing a certain type of information (e.g., the communication protocol is not identified in the image, but the text explicitly says "Profinet"), it is marked as a "supplementary item"; if the information is conflicting (e.g., the number of images identified is 2, but the text description is 3), it is marked as a "contradictory item".

[0059] Specifically, the system has generated structured first and second sub-configuration lists with unified fields (such as "module model, quantity, installation order, function, communication protocol"). The system compares the two lists one by one according to the unified fields, automatically marking "consistent items", "contradictory items" and "supplementary items" to form a clear first comparison result (such as "consistent model, contradictory quantity (2 images / 3 texts), supplementary communication protocol (text provided by Profinet)").

[0060] S304, if there is a mismatch between the first sub-configuration information and the second sub-configuration information in the first comparison result, then the first sub-configuration information and the second sub-configuration information are verified to obtain the first verification information; wherein, the first sub-configuration information is the configuration information corresponding to the first hardware module in the first sub-configuration list; the second sub-configuration information is the configuration information corresponding to the first hardware module in the second sub-configuration list; the first hardware module is any hardware module in the first sub-configuration list or the second sub-configuration list.

[0061] In this embodiment, the user provides both a hardware image (first description type information) and a text description (second description type information). When the configuration information of the same hardware module (first hardware module) is inconsistent after comparing the first and second sub-configuration lists generated by parsing the two, the system will extract the specific configuration data (first sub-configuration information and second sub-configuration information) corresponding to the conflicting module from the two lists, verify the accuracy of the information through specific verification logic, and finally output the first verification information that clearly indicates the cause of the conflict and the tendency of the correct information, so as to provide a decision basis for subsequent information fusion.

[0062] Specifically, from the first comparison results, hardware modules with "mismatches" are selected (first hardware modules). The configuration fields corresponding to the conflicts (such as quantity and model) are identified. The configuration information for these modules is extracted from the first sub-configuration list (first sub-configuration information), and the corresponding configuration information is extracted from the second sub-configuration list (second sub-configuration information). Both are then validated. The validation process can be as follows: Rule verification: Compare with the general specifications of PLC hardware (e.g., a certain model of module only supports a specific number of IOs) to determine which information conforms to the specifications (e.g., if the text says "8 inputs", it conforms to the standard of this model, but the image recognition misidentifies it as 16 inputs, then the text information is more likely to be correct). Source credibility weighting: For fields that are "visually verifiable" such as model number and installation order, image analysis results are given priority; for fields that are "user-subjectively set" such as functional requirements and custom names, text descriptions are given priority.

[0063] Finally, output the results in a structured format, for example: "First hardware module: Digital input module; Conflict field: Quantity; First sub-configuration information: 2; Second sub-configuration information: 3; Verification conclusion: The physical identifier of the image recognition module is 2, which is consistent with the normal configuration of this model of module in the knowledge base. The first sub-configuration information is preferred. Users are advised to check whether the text description is correct."

[0064] S305, the third sub-configuration information, the fourth sub-configuration information and the first verification information are merged to obtain the first hardware configuration list; wherein, the third sub-configuration information is the configuration information in the first sub-configuration list other than the first sub-configuration information; the fourth sub-configuration information is the configuration information in the second sub-configuration list other than the second sub-configuration information.

[0065] In this embodiment, the third sub-configuration information is all the non-conflicting configuration information (such as the model, quantity, and installation order of other modules) in the first sub-configuration list (generated from hardware images through visual recognition), except for the "first sub-configuration information" (configuration of a hardware module that conflicts with the second sub-configuration list); the fourth sub-configuration information is all the non-conflicting configuration information (such as the functions and communication protocols of other modules) in the second sub-configuration list (generated from text through NLP parsing), except for the "second sub-configuration information" (configuration of the same hardware module that conflicts with the first sub-configuration list); the first verification information is the accurate configuration information determined after verification for the conflicting module (first hardware module) (such as clarifying the correct value of the conflicting field and supplementing missing information). The valid information without conflict in the two lists is retained, and the conflicting part is replaced with the accurate information after verification. Finally, they are integrated into a first hardware configuration list without contradictions and complete.

[0066] By employing a fusion logic of "retaining conflict-free information and replacing conflicting correct information," the first hardware configuration list ensures that it not only fully covers the valid information of both sub-lists but also resolves conflict issues, providing reliable basic data for subsequent knowledge base queries and automatic configuration generation.

[0067] In the above method, the first and second sub-configuration lists are compared. If a conflict is found in the configuration information of the same hardware module, the conflict information is verified to obtain accurate verification information. Finally, the conflict-free configuration information and the verification information are integrated to form the final list. The comparison step can promptly identify information inconsistencies between the two sub-lists, the verification step can correct conflicts to ensure information accuracy, and the final fusion operation not only ensures the integrity of the first hardware configuration list but also eliminates errors caused by conflicting information. This provides a reliable core basis for subsequent hardware parameter extraction and configuration generation, reducing rework in subsequent processes due to list issues.

[0068] S103, obtain the first hardware parameters corresponding to the first hardware configuration list.

[0069] In this embodiment, based on the generated first hardware configuration list (including core information such as PLC module model, quantity, and installation order), the detailed technical parameters corresponding to each hardware module in the list are queried and obtained from the local or cloud-based PLC hardware knowledge base of the system, providing complete data support for subsequent automatic configuration.

[0070] In one embodiment, see Figure 6 This is a schematic diagram of the hardware parameter extraction process provided in the embodiments of this application, such as... Figure 6 As shown, step S203 includes: S401, for each hardware module in the first hardware configuration list, extract the corresponding hardware identifier.

[0071] In this embodiment, "first hardware configuration list" refers to the specific hardware configuration list that appears first; "each hardware module" refers to each individual hardware component included in the list; and "hardware identifier" typically refers to unique information that distinguishes different hardware components, such as model number (e.g., i7-13700K CPU), serial number (e.g., hard drive serial number), or specification combination. These identifiers allow for accurate identification of the corresponding hardware module. The entire process involves processing each hardware module in the list one by one, extracting their respective unique identifier information, i.e., the hardware identifier.

[0072] S402 extracts the core data corresponding to the hardware identifier from the preset database.

[0073] In this embodiment of the application, based on the final determined PLC (Programmable Logic Controller) hardware list, i.e. the first hardware list, the PLC hardware knowledge base stored locally or in the cloud is queried to obtain detailed information of each module in the list.

[0074] Specifically, using the hardware identifier as the query keyword, a database query statement (such as a SQL SELECT statement or a NoSQL query command) is constructed and a query request is sent to the database. For example, in a relational database, records are filtered out using the condition "hardware identifier = target identifier". After the database returns the query results, core data such as detailed parameters of each hardware module (such as functional characteristics and technical specifications), GSD files (or other types of hardware description files, which are used to describe the characteristics of hardware devices, facilitating identification and configuration of devices in programming software), and default input / output (I / O) addresses of the modules are extracted from the results.

[0075] S403 converts the core data into a preset format to obtain the first hardware parameters.

[0076] In this embodiment, based on the final hardware list, the original module information (such as scattered technical specifications, hardware description files in different formats, and non-standardized IO addresses) obtained from the knowledge base is the core data. The core data is transformed into specifications that conform to the automatic configuration of the host computer software, including unified naming of parameter fields (such as "number of input points" and "power supply voltage"), unified data types (such as numerical and string formats), compatible versions of GSD files, and encoding formats of IO addresses (such as "I0.0-I0.7" and "Q0.0-Q0.7").

[0077] Transforming raw core data into structured and standardized primary hardware parameters ensures that the host computer software can directly read, recognize, and apply it, laying the foundation for subsequent automatic creation of the master station, addition of modules, and configuration of parameters, and avoiding configuration failures due to inconsistent data formats.

[0078] In the above method, for each hardware module in the list, its hardware identifier is first extracted, then core data is retrieved from the database based on this identifier, and finally, this data is converted into a preset format to form hardware parameters. Accurate matching of the hardware identifier with database information ensures the accuracy of parameter extraction; the unified conversion to a preset format guarantees the standardization of the parameters, facilitating their direct use in subsequent hardware configuration generation methods. This avoids errors from manual parameter lookup and improves the efficiency of parameter retrieval, laying a data foundation for the rapid generation of reliable configurations.

[0079] S104, Generate the first hardware configuration based on the first hardware configuration list and hardware parameters.

[0080] In this embodiment of the application, the host computer software automatically completes the creation of the master station, module addition, parameter configuration and address allocation by combining the determined first hardware configuration list (including core information such as module model and quantity) and the hardware parameters obtained from the knowledge base (such as detailed specifications, GSD file, default IO address, etc.), and finally generates a hardware configuration result that can be directly put into use.

[0081] The above method first obtains the hardware description information (first configuration information) proposed by the user based on control requirements, transforms it into a standardized first hardware configuration list, then automatically extracts the parameters of each hardware component in the list from a preset database, and finally integrates the list and parameters to generate the hardware configuration. By directly converting the user's hardware description into a configuration list, the method avoids the experience bias and model incompatibility issues associated with manual selection; the parameter setting stage automatically extracts parameters from the preset database, eliminating the tedious process of manually consulting manual manuals and inputting data, thus reducing input errors; the overall process, through the automated connection of each step, ensures that the generated hardware configuration is accurate and effectively improves configuration efficiency and reliability, accelerating the implementation of control projects.

[0082] In one embodiment, see Figure 7 This is a schematic diagram of the hardware configuration process provided in the embodiments of this application, such as... Figure 7 As shown, step S104 includes: S501 creates a corresponding control hub for each hardware module in the first hardware configuration list.

[0083] In this embodiment, for each hardware module listed in the first hardware configuration list, a corresponding control hub is created for each. This control hub can be understood as a master station, which is a core node or unit within a system (such as a communication system or control system) responsible for managing, monitoring, controlling, or interacting with a specific hardware module. Each hardware module has its corresponding master station, which specifically performs management and interaction functions for its associated hardware modules to ensure that all hardware modules in the entire system can operate normally and orderly.

[0084] It should be noted that the host computer software can automatically create each hardware module based on the first hardware configuration list.

[0085] S502 generates the first hardware configuration based on the hardware sub-parameters corresponding to the control hub and hardware modules.

[0086] In this embodiment, for each hardware module, parameter configuration is performed in the control hub's configuration software based on its hardware sub-parameters. This includes module address allocation (ensuring unique addresses for each module and avoiding conflicts), signal type settings (such as range selection for analog modules), and communication parameter configuration (such as baud rate and verification method). The configuration software defines the logical connections between the control hub and each hardware module, clarifying the data interaction paths and methods, such as which input module signals need to be transmitted to the control hub and how the control hub's output signals are distributed to the corresponding output modules.

[0087] After completing the configuration, perform offline verification to check whether the parameter settings are complete and the logical connections are correct. Then connect to the actual hardware for online debugging to test whether the control hub can correctly recognize all modules and whether the input and output signals of the modules can be accurately transmitted and responded to, ensuring that the entire hardware system operates normally.

[0088] In the above method, a corresponding control hub is created for each hardware module in the list, and the hardware sub-parameters of each module are imported into its control hub. By matching the control hub with the corresponding hardware module and accurately importing the sub-parameters, the automatic generation of hardware configuration is realized, ensuring the accuracy of the correspondence between configuration and hardware and parameters. At the same time, the manual configuration step is eliminated, improving configuration efficiency and reliability.

[0089] In one embodiment, the method further includes: If a user input adjustment command is detected, the adjustment command is received; the adjustment command is executed to adjust the first hardware configuration and obtain the adjusted first hardware configuration.

[0090] In this embodiment, the system provides a user confirmation and manual fine-tuning interface. When the system receives an adjustment command from the user regarding the hardware configuration, it will operate according to the specific content of the command. This step is the core process, meaning that the system will actually adjust the existing hardware configuration according to the command requirements. This may involve enabling / disabling hardware components, modifying parameters, changing connection relationships, etc. After executing the adjustment command, the original hardware configuration will change, ultimately forming the "adjusted hardware configuration," which is a new hardware configuration state that meets the user's command requirements.

[0091] The above method responds to users' needs for adjusting hardware configuration, quickly corrects or optimizes the hardware configuration, ensures that the configuration fits the actual control requirements, reduces overall process rework caused by previous configuration deviations, and improves the flexibility and adaptability of the configuration.

[0092] In one embodiment, see Figure 8 This is a flowchart illustrating the optimization model provided in the embodiments of this application, as shown below. Figure 8 As shown, the method also includes: S601 obtains user feedback information regarding hardware configuration.

[0093] In this application embodiment, the user's feedback information on hardware configuration refers to the confirmation results, modification instructions, or optimization suggestions given by the user through the confirmation and manual fine-tuning interface after the system generates a hardware configuration that can be used directly. For example, optimization feedback includes pointing out identification errors, parameter matching problems, etc., and giving improvement suggestions.

[0094] S602, the feedback information is sent to the first recognition model and the second recognition model so that the first recognition model and the second recognition model can be optimized according to the feedback information to obtain the optimized first recognition model and the second recognition model.

[0095] In this embodiment, user feedback on hardware configuration (such as optimization suggestions) is synchronously sent to the first recognition model (visual recognition model, used to parse hardware images) and the second recognition model (NLP large model, used to parse natural language text), allowing the two models to iteratively optimize recognition accuracy and parsing ability based on feedback, ultimately resulting in a version with better performance.

[0096] Specifically, after the user provides feedback through the confirmation / fine-tuning interface, the system extracts key information from the feedback (such as module models that are incorrectly identified in images or configuration requirements that are missing in text parsing) and passes them to the corresponding visual recognition model and NLP model respectively. After receiving the feedback, the model will use it as a learning sample to correct its own recognition logic (such as optimizing the recognition algorithm for module models in images) and parsing rules (such as improving the extraction logic of hardware relationships in text), thereby improving the accuracy of subsequent processing of multimodal configuration information and making the generated hardware list more in line with the user's actual needs.

[0097] S603 identifies the second configuration information input by the user based on the optimized first and second recognition models to obtain the second hardware configuration list.

[0098] In this embodiment, a visual recognition model (first recognition model) optimized based on user feedback and an NLP large model (second recognition model) are used to process the second configuration information (including hardware images and / or natural language text) newly input by the user. After extracting key hardware information, a second hardware configuration list is finally generated.

[0099] S604, obtain the second hardware parameters corresponding to the second hardware configuration.

[0100] In this application, the second hardware parameters corresponding to the second hardware configuration are, in essence, standardized configuration data obtained by matching from the local / cloud PLC hardware knowledge base based on the second hardware configuration list. The method of obtaining them is the same as that of obtaining the first hardware parameters mentioned above, and will not be repeated here.

[0101] S605 generates a second hardware configuration based on the second hardware configuration list and the second hardware parameters.

[0102] In this embodiment, based on the second hardware configuration list (which specifies the module model, quantity, etc. to be configured) and the second hardware parameters (detailed technical specifications of the module, GSD file, default IO address, etc.), the system automatically completes the creation of the main station, the addition of modules, and the configuration of parameters and addresses, and finally generates a second hardware configuration that can be used directly.

[0103] In the above method, user feedback is used to optimize the recognition model, making the model more accurate in recognizing subsequent configuration information, and the generated hardware configuration list is more in line with actual needs, thereby continuously improving the accuracy and adaptability of automated configuration.

[0104] In one embodiment, see Figure 9 This is a schematic diagram of the overall structure of the hardware configuration generation method provided in the embodiments of this application, as shown below. Figure 9 As shown, it includes: The top layer is the user, who serves as the input source for multimodal configuration information (text / images) and the feedback point for configuration results; The middle layer includes cloud-based AI and local AI, which respectively handle the multimodal information parsing and processing of visual recognition models and large NLP models; The data support layer consists of a cloud-based knowledge base and a local knowledge base, storing core data such as PLC module parameters and GSD files for AI models to query and access. The underlying layer is the host computer software, which receives the configuration data processed by AI and ultimately completes the automatic generation of hardware configuration.

[0105] The overall architecture clearly reflects the entire process of "user input - AI parsing - knowledge base support - software implementation", realizing the configuration logic of multimodal input, AI-driven, and human-machine collaboration. It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0106] Corresponding to the hardware configuration generation method in the above embodiments, Figure 9This is a structural block diagram of the hardware configuration generation device provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0107] Reference Figure 10 The device includes: The configuration information acquisition module 101 is used to acquire the first configuration information input by the user; wherein, the first configuration information is the description information of the configuration hardware; The configuration list generation module 102 is used to generate a first hardware configuration list based on the first configuration information. The hardware parameter extraction module 103 is used to obtain the first hardware parameters corresponding to the first hardware configuration list; The hardware configuration generation module 84 is used to generate the first hardware configuration based on the first hardware configuration list and hardware parameters.

[0108] Optionally, the first configuration information includes first description type information and / or second description type information; the configuration manifest generation module 102 is also used for: If the first configuration information includes the first description type information, the first description type information is input into the first recognition model to obtain the first sub-configuration list; If the first configuration information includes the second description type information, the second description type information is input into the second recognition model to obtain the second sub-configuration list; A first hardware configuration list is generated based on the first sub-configuration list and / or the second sub-configuration list.

[0109] Optionally, the configuration manifest generation module 102 is also used for: If the first configuration information only includes the first description type information, the first sub-configuration list will be determined as the first hardware configuration list; If the first configuration information only includes the second description type information, the second sub-configuration list will be determined as the second hardware configuration list; If the first configuration information includes first description type information and second description type information, the first sub-configuration list and the second sub-configuration list are compared to obtain the first comparison result; If there is a mismatch between the first sub-configuration information and the second sub-configuration information in the first comparison result, the first sub-configuration information and the second sub-configuration information are verified to obtain the first verification information; wherein, the first sub-configuration information is the configuration information corresponding to the first hardware module in the first sub-configuration list; the second sub-configuration information is the configuration information corresponding to the first hardware module in the second sub-configuration list; the first hardware module is any hardware module in the first sub-configuration list or the second sub-configuration list; The third sub-configuration information, the fourth sub-configuration information, and the first verification information are merged to obtain the first hardware configuration list; wherein, the third sub-configuration information is the configuration information in the first sub-configuration list other than the first sub-configuration information; and the fourth sub-configuration information is the configuration information in the second sub-configuration list other than the second sub-configuration information.

[0110] Optionally, the hardware parameter extraction module 103 is also used for: For each hardware module in the first hardware configuration list, extract the corresponding hardware identifier; Extract the core data corresponding to the hardware identifier from the preset database; Convert the core data into a preset format to obtain the first hardware parameters. Optionally, the hardware configuration module 104 is also used for: Create a corresponding control hub for each hardware module in the first hardware configuration list; The first hardware configuration is generated based on the hardware sub-parameters corresponding to the control hub and hardware modules.

[0111] The hardware configuration generation device 10 also includes a configuration adjustment module 105, used for: If a user input adjustment command is detected, the adjustment command is received; Execute the adjustment command to adjust the hardware configuration and obtain the adjusted first hardware configuration.

[0112] The hardware configuration generation device 10 also includes a model optimization module 106, used for: Obtain user feedback regarding hardware configuration; Feedback information is sent to the first recognition model and the second recognition model so that the first recognition model and the second recognition model can be optimized according to the feedback information to obtain the optimized first recognition model and the second recognition model. The second configuration information input by the user is identified based on the optimized first and second recognition models to obtain the second hardware configuration list; Obtain the second hardware parameters corresponding to the second hardware configuration list; The second hardware configuration is generated based on the second hardware configuration list and the second hardware parameters.

[0113] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0114] in addition, Figure 10The hardware configuration generation device shown can be a software unit, a hardware unit, or a combination of software and hardware built into an existing terminal device. It can also be integrated into the terminal device as an independent component, or exist as an independent terminal device.

[0115] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0116] Figure 11 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. For example... Figure 11 As shown, the terminal device 11 of this embodiment includes: at least one processor 110 ( Figure 11 (Only one is shown in the diagram) a processor, a memory 111, and a computer program 112 stored in the memory 111 and executable on at least one processor 110. When the processor 110 executes the computer program 112, it implements the steps in any of the above-described hardware configuration generation method embodiments.

[0117] The terminal device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 11 This is merely an example of terminal device 11 and does not constitute a limitation on terminal device 11. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0118] The processor 110 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0119] In some embodiments, memory 111 may be an internal storage unit of terminal device 11, such as a hard disk or memory of terminal device 11. In other embodiments, memory 111 may be an external storage device of terminal device 11, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on terminal device 11. Furthermore, memory 111 may include both internal and external storage units of terminal device 11. Memory 111 is used to store operating system, applications, boot loader, data, and other programs, such as program code of computer programs. Memory 111 can also be used to temporarily store data that has been output or will be output.

[0120] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.

[0121] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0122] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0123] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0124] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] In the embodiments provided in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A hardware configuration generation method, characterized in that, The method includes: Obtain first configuration information input by the user; wherein, the first configuration information is description information of the hardware to be configured; A first hardware configuration list is generated based on the first configuration information; Obtain the first hardware parameters corresponding to the first hardware configuration list; The first hardware configuration is generated based on the first hardware configuration list and the hardware parameters.

2. The hardware configuration generation method as described in claim 1, characterized in that, The first configuration information includes first description type information and / or second description type information; The step of generating the first hardware configuration list based on the first configuration information includes: If the first configuration information includes the first description type information, the first description type information is input into the first recognition model to obtain the first sub-configuration list; If the first configuration information includes the second description type information, the second description type information is input into the second recognition model to obtain the second sub-configuration list; The first hardware configuration list is generated based on the first sub-configuration list and / or the second sub-configuration list.

3. The hardware configuration generation method as described in claim 2, characterized in that, The step of generating the first hardware configuration list based on the first sub-configuration list and / or the second sub-configuration list includes: If the first configuration information only includes the first description type information, then the first sub-configuration list is determined as the first hardware configuration list; If the first configuration information only includes the second description type information, then the second sub-configuration list is determined as the second hardware configuration list; If the first configuration information includes the first description type information and the second description type information, then the first sub-configuration list and the second sub-configuration list are compared to obtain a first comparison result; If there is a mismatch between the first sub-configuration information and the second sub-configuration information in the first comparison result, then the first sub-configuration information and the second sub-configuration information are verified to obtain the first verification information; wherein, the first sub-configuration information is the configuration information corresponding to the first hardware module in the first sub-configuration list; the second sub-configuration information is the configuration information corresponding to the first hardware module in the second sub-configuration list; the first hardware module is any one of the hardware modules in the first sub-configuration list or the second sub-configuration list; The third sub-configuration information, the fourth sub-configuration information, and the first verification information are merged to obtain the first hardware configuration list; wherein, the third sub-configuration information is the configuration information in the first sub-configuration list other than the first sub-configuration information; and the fourth sub-configuration information is the configuration information in the second sub-configuration list other than the second sub-configuration information.

4. The hardware configuration generation method as described in claim 3, characterized in that, The step of obtaining the first hardware parameters corresponding to the first hardware configuration list includes: For each hardware module in the first hardware configuration list, extract the corresponding hardware identifier; Extract the core data corresponding to the hardware identifier from the preset database; The core data is converted into a preset format to obtain the first hardware parameters.

5. The hardware configuration generation method as described in claim 4, characterized in that, The hardware parameters include multiple hardware sub-parameters; the step of generating the first hardware configuration based on the first hardware configuration list and the hardware parameters includes: Create a corresponding control hub for each hardware module in the first hardware configuration list; The first hardware configuration is generated based on the hardware sub-parameters corresponding to the control hub and the hardware module.

6. The hardware configuration generation method as described in claim 2, characterized in that, The method further includes: If a user input adjustment command is detected, the adjustment command is received; The adjustment instruction is executed to adjust the first hardware configuration, resulting in the adjusted first hardware configuration.

7. The hardware configuration generation method as described in claim 6, characterized in that, The method further includes: Obtain user feedback information regarding the first hardware configuration; The feedback information is sent to the first recognition model and the second recognition model so that the first recognition model and the second recognition model can be optimized according to the feedback information to obtain the optimized first recognition model and the second recognition model. The second configuration information input by the user is identified based on the optimized first recognition model and the second recognition model to obtain the second hardware configuration list; Obtain the second hardware parameters corresponding to the second hardware configuration list; The second hardware configuration is generated based on the second hardware configuration list and the second hardware parameters.

8. A hardware configuration generation device, characterized in that, include: The configuration information acquisition module is used to acquire first configuration information input by the user; wherein, the first configuration information is description information of the configuration hardware; The configuration list generation module is used to generate a first hardware configuration list based on the first configuration information. The hardware parameter extraction module is used to extract the hardware parameters corresponding to the first hardware configuration list; The hardware configuration generation module is used to perform hardware configuration based on the first hardware configuration list and the hardware parameters to obtain the hardware configuration result.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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