An industrial software data analysis system and method based on large language model AI technology

Abstract

The present invention proposes an industrial software data parsing system and method based on large language model AI technology, introduces large language model AI technology in the industrial Internet control process, identifies industrial software data parsing large language instructions through the AI ​​model of LLM, extracts classification keywords and performs intelligent analysis, and clusters the instructions, so that the system can better understand the industrial software data parsing instructions. Secondly, the present invention pushes the instructions to different industrial software data extraction parties through natural language processing of large language model AI technology, and obtains industrial software data of various types from different locations of the system such as native digital factories, industrial process control modules, and industrial product output modules for centralized analysis, and then optimizes and outputs natural language processing through large language model AI technology, so that the system's control platform interaction is humanized and the convenience and readability of data analysis are improved.

Description

Technical Field

[0001] The present invention belongs to the field of new generation information technology, and in particular relates to an industrial software data analysis system and method based on large language model AI technology. Background Art

[0002] Industrial control processes often use industrial software to perform management. Industrial software needs to input a variety of data, information and instructions so that the entire industrial control system can operate stably. Similarly, in the process of industrial software control, a variety of industrial control information will be output or interacted, including production information, product information, process information, etc. These industrial software information data generally need to go through the encapsulation and decapsulation process to ensure the stability and security of data transmission. Performing industrial software or industrial system data analysis in this process is a necessary means to analyze system stability and optimize system management.

[0003] As a representative achievement of technologies such as deep learning and machine learning, large language models are increasingly used in natural language interaction processes. In the field of natural language processing (NLP), large language models can often understand and generate human language based on language analysis, and have friendly conversations with users through human language. Generally speaking, for large language AI models or large language AI tools, large sample language training is necessary. Under the limitations of the engine's data processing capabilities, it is a conventional model training method to improve the natural language processing capabilities of large language AI models by repeatedly inputting natural language understanding and processing logic into the model. ChatGPT is a typical example of a large language model.

[0004] The present invention proposes an industrial software data parsing method and system based on large language model AI technology, introduces large language model AI technology in the industrial Internet control process, identifies industrial software data parsing large language instructions through the AI ​​model of LLM, extracts classification keywords and performs intelligent analysis, and clusters the instructions, so that the system can better understand the industrial software data parsing instructions. Secondly, the present invention pushes the instructions to different industrial software data extraction parties through natural language processing of large language model AI technology, and obtains industrial software data of various types from different locations of the system such as native digital factories, industrial process control modules, and industrial product output modules for centralized analysis, and then optimizes and outputs natural language processing through large language model AI technology, so that the system's control platform interaction is humanized and the convenience and readability of data analysis are improved. Summary of the invention

[0005] The present invention aims to provide an industrial software data parsing system based on large language model AI technology that is superior to the prior art.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

[0007] An industrial software data parsing system based on large language model AI technology, the system comprising:

[0008] An industrial Internet data parsing instruction issuing platform, which receives industrial software data parsing big language instructions from a system administrator and sends them to a big language AI model;

[0009] Big language AI model, native digital factory, industrial process control module, industrial product output module, production middleware database, and industrial Internet optimization cluster.

[0010] Preferably, the big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing and obtain instruction keyword clustering results;

[0011] Generate a clustering mark comdK according to the instruction keyword clustering result, and push the instruction to one of the native digital factory, the industrial process control module or the industrial product output module according to the clustering mark comdK, where K is an integer of 1-3;

[0012] The native digital factory receives the industrial software data parsing language instruction with the clustering mark comd1, and inputs the digital factory related information into the production middleware database, wherein the digital factory related information is a packaged data packet;

[0013] The industrial process control module receives the industrial software data parsing language instruction with the clustering mark comd2, and inputs the production process related information into the production middleware database, wherein the production process related information is an encapsulated data packet;

[0014] The industrial product output module receives the industrial software data parsing language instruction with the clustering mark comd3, and inputs the product related information into the production middleware database, wherein the product related information is a packaged data packet;

[0015] The production middleware database parses the encapsulated data packet according to a preset protocol, obtains the relevant information industrialINF of the digital factory or production process or product in the encapsulated data packet, and sends it to the industrial Internet optimization cluster;

[0016] The relevant information industrialINF is stored using the industrial Internet data storage protocol preset by the system; the preset protocol is an industrial control protocol, or an industrial Ethernet protocol, or a data packet encapsulation protocol predefined by the system;

[0017] The industrial Internet optimization cluster receives the relevant information industrialINF, performs natural language processing optimization based on the large language model AI technology, obtains the optimized natural language output, submits it to the output end of the industrial software data analysis system based on the large language model AI technology, and feeds back the relevant information industrialINF in the form of natural language to the industrial Internet data analysis instruction publishing platform that issued the instruction.

[0018] Preferably, the industrial software data parsing large language instructions are in natural language form and contain predefined type keywords.

[0019] Preferably, the instruction keyword clustering result is one of the following:

[0020] Production information data analysis class, which is used to characterize relevant information of production participants, corresponding to the clustering mark comd1;

[0021] Production process data analysis class, which is used to characterize production process related information, corresponding to the clustering label comd2;

[0022] Product information data analysis class, which is used to characterize product related information and corresponds to the clustering label comd3.

[0023] Preferably, the big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology, based on machine learning, or based on deep learning;

[0024] The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing, and obtains instruction keyword clustering results, including at least:

[0025] The big language AI model first performs natural language processing and word segmentation on the industrial software data parsing big language instructions based on natural language processing;

[0026] Based on the natural language processing results and word segmentation results, K-means clustering is performed;

[0027] Perform homogeneous clustering on pre-set, standard industrial software data parsing big language instructions for querying production information;

[0028] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production process information;

[0029] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying product information;

[0030] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production information, a clustering mark comd1 is generated;

[0031] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production process information, a clustering mark comd2 is generated;

[0032] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying product information, a clustering mark comd3 is generated.

[0033] At the same time, the present invention also proposes an industrial software data parsing method based on a large language model AI technology, the method is applied to the industrial software data parsing system based on the large language model AI technology as described above, and the method comprises the following steps:

[0034] Step 1: Operate the Industrial Internet Data Parsing Instruction Publishing Platform to receive the industrial software data parsing big language instructions from the system administrator and send them to the big language AI model;

[0035] Step 2: Operate the big language AI model to parse the industrial software data. Perform instruction parsing based on the big language model AI technology to obtain instruction keyword clustering results;

[0036] Generate a clustering mark comdK according to the instruction keyword clustering result, and push the instruction to one of the native digital factory, the industrial process control module or the industrial product output module according to the clustering mark comdK, where K is an integer of 1-3;

[0037] Step 3: Control the native digital factory to receive the industrial software data parsing language instruction with the clustering mark comd1, and input the digital factory related information into the production middleware database, wherein the digital factory related information is a packaged data packet;

[0038] Step 4: operate the industrial process control module to receive the industrial software data parsing language instruction with the clustering mark comd2, and input the production process related information into the production middleware database, wherein the production process related information is a packaged data packet;

[0039] Step 5: operate the industrial product output module to receive the industrial software data parsing language instruction with the clustering mark comd3, and input the product related information into the production middleware database, wherein the product related information is a packaged data packet;

[0040] Step 6: Operate the production middleware database to parse the encapsulated data packet according to the preset protocol, obtain the relevant information industrialINF of the digital factory or production process or product in the encapsulated data packet, and send it to the industrial Internet optimization cluster;

[0041] The relevant information industrialINF is stored using the industrial Internet data storage protocol preset by the system; the preset protocol is an industrial control protocol, or an industrial Ethernet protocol, or a data packet encapsulation protocol predefined by the system;

[0042] Step 7: Operate the industrial Internet optimization cluster to receive the relevant information industrialINF, perform natural language processing optimization based on the large language model AI technology, obtain the optimized natural language output, submit it to the output end of the industrial software data analysis system based on the large language model AI technology, and feed back the relevant information industrialINF in the form of natural language to the industrial Internet data analysis instruction publishing platform that issued the instruction.

[0043] Preferably, the industrial software data parsing large language instructions are in natural language form and contain predefined type keywords.

[0044] Preferably, the instruction keyword clustering result is one of the following:

[0045] Production information data analysis class, which is used to characterize relevant information of production participants, corresponding to the clustering mark comd1;

[0046] Production process data analysis class, which is used to characterize production process related information, corresponding to the clustering label comd2;

[0047] Product information data analysis class, which is used to characterize product related information and corresponds to the clustering label comd3.

[0048] Preferably, the big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology, based on machine learning, or based on deep learning;

[0049] The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing, and obtains instruction keyword clustering results, including at least:

[0050] The big language AI model first performs natural language processing and word segmentation on the industrial software data parsing big language instructions based on natural language processing;

[0051] Based on the natural language processing results and word segmentation results, K-means clustering is performed;

[0052] Perform homogeneous clustering on pre-set, standard industrial software data parsing big language instructions for querying production information;

[0053] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production process information;

[0054] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying product information;

[0055] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production information, a clustering mark comd1 is generated;

[0056] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production process information, a clustering mark comd2 is generated;

[0057] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying product information, a clustering mark comd3 is generated.

[0058] At the same time, the present invention also proposes a computer-readable storage medium, in which a program executable by a processor is stored, characterized in that the program executable by the processor is used to execute the method described above when executed by the processor.

[0059] The present invention proposes an industrial software data parsing method and system based on large language model AI technology, introduces large language model AI technology in the industrial Internet control process, identifies industrial software data parsing large language instructions through the AI ​​model of LLM, extracts classification keywords and performs intelligent analysis, and clusters the instructions, so that the system can better understand the industrial software data parsing instructions. Secondly, the present invention pushes the instructions to different industrial software data extraction parties through natural language processing of large language model AI technology, and obtains industrial software data of various types from different locations of the system such as native digital factories, industrial process control modules, and industrial product output modules for centralized analysis, and then optimizes and outputs natural language processing through large language model AI technology, so that the system's control platform interaction is humanized and the convenience and readability of data analysis are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a basic system structure diagram of an industrial software data analysis system based on a large language model AI technology shown in the present invention;

[0061] Figure 2It is a schematic diagram of a preferred embodiment of a system structure of a large language AI model parsing process in an industrial software data parsing system based on large language model AI technology shown in the present invention;

[0062] Figure 3 It is a schematic diagram of a preferred embodiment of a system structure for data transmission in an industrial software data analysis system based on a large language model AI technology shown in the present invention;

[0063] Figure 4 It is a schematic diagram of a preferred embodiment of the industrial software data parsing method process based on the large language model AI technology shown in the present invention;

[0064] Figure 5 It is a schematic diagram of a preferred embodiment of the instruction flow part steps in the industrial software data parsing method based on the large language model AI technology shown in the present invention. DETAILED DESCRIPTION

[0065] The following describes in detail several embodiments and beneficial effects of the industrial software data parsing system and method based on large language model AI technology claimed in the present invention, so as to facilitate a more detailed review and decomposition of the present invention.

[0066] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0067] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0069] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0070] It should be understood that although the terms first, second, etc. may be used to describe the method and the corresponding device in the embodiments of the present invention, these keywords should not be limited to these terms. These terms are only used to distinguish the keywords from each other. For example, without departing from the scope of the embodiments of the present invention, the clustering label comdK, etc. may also be called the clustering label comdN.

[0071] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0072] As the instruction manual Figure 1-3 As shown, it is one of the industrial software data parsing system based on large language model AI technology and one of the specific embodiments of the modules contained therein claimed in the present invention, and the industrial software data parsing system based on large language model AI technology includes:

[0073] An industrial Internet data parsing instruction issuing platform, which receives industrial software data parsing big language instructions from a system administrator and sends them to a big language AI model;

[0074] Big language AI model, native digital factory, industrial process control module, industrial product output module, production middleware database, and industrial Internet optimization cluster.

[0075] As a superimposable preferred embodiment, the big language AI model performs instruction parsing on the industrial software data parsing big language instructions based on the big language model AI technology to obtain instruction keyword clustering results;

[0076] As a superimposable preferred embodiment, the industrial Internet data parsing instruction issuing platform receives the industrial software data parsing big language instruction from the system administrator and sends it to the big language AI model, specifically: the industrial Internet data parsing instruction issuing platform receives instructions from the administrator, the instructions are in natural language form, and the instructions are used to perform industrial software data parsing, for example, for performing feedback and parsing of production information related data; or, when the production process needs to be controlled, the extraction and parsing of production process information is performed; and, when product information needs to be sampled, the extraction and parsing of product-related information is performed. As another superimposable preferred embodiment, the instruction is an industrial software data parsing big language instruction. As another superimposable preferred embodiment, the instruction can be specifically: I want to know the output and yield rate of a certain product; or, I want to know the production party information of a certain product; or, I want to know which workshops / links / assembly lines the production process of a certain product passes through, so as to improve and optimize it. As another superimposable preferred embodiment, the above information is dispersedly managed remotely by native digital factories, industrial process control modules, industrial product output modules, etc.

[0077] Generate a clustering mark comdK according to the instruction keyword clustering result, and push the instruction to one of the native digital factory, the industrial process control module or the industrial product output module according to the clustering mark comdK, where K is an integer of 1-3;

[0078] The native digital factory receives the industrial software data parsing language instruction with the clustering mark comd1, and inputs the digital factory related information into the production middleware database, wherein the digital factory related information is a packaged data packet;

[0079] The industrial process control module receives the industrial software data parsing language instruction with the clustering mark comd2, and inputs the production process related information into the production middleware database, wherein the production process related information is an encapsulated data packet;

[0080] The industrial product output module receives the industrial software data parsing language instruction with the clustering mark comd3, and inputs the product related information into the production middleware database, wherein the product related information is a packaged data packet;

[0081] The production middleware database parses the encapsulated data packet according to a preset protocol, obtains the relevant information industrialINF of the digital factory or production process or product in the encapsulated data packet, and sends it to the industrial Internet optimization cluster;

[0082] The relevant information industrialINF is stored using the industrial Internet data storage protocol preset by the system; the preset protocol is an industrial control protocol, or an industrial Ethernet protocol, or a data packet encapsulation protocol predefined by the system;

[0083] As a superimposable preferred embodiment, the encapsulated data packet may adopt any suitable encapsulation protocol in the prior art, such as IP data packet encapsulation, or adopt a system predefined encapsulation protocol.

[0084] The industrial Internet optimization cluster receives the relevant information industrialINF, performs natural language processing optimization based on the large language model AI technology, obtains the optimized natural language output, submits it to the output end of the industrial software data analysis system based on the large language model AI technology, and feeds back the relevant information industrialINF in the form of natural language to the industrial Internet data analysis instruction publishing platform that issued the instruction.

[0085] As a superimposable preferred embodiment, the industrial software data parsing large language instruction is in natural language form and contains predefined type keywords.

[0086] As a superimposable preferred embodiment, the instruction keyword clustering result is one of the following:

[0087] Production information data analysis class, which is used to characterize relevant information of production participants, corresponding to the clustering mark comd1;

[0088] Production process data analysis class, which is used to characterize production process related information, corresponding to the clustering label comd2;

[0089] Product information data analysis class, which is used to characterize product related information and corresponds to the clustering label comd3.

[0090] As a superimposable preferred embodiment, the big language AI model performs instruction parsing on the industrial software data parsing big language instructions based on big language model AI technology, based on machine learning, or based on deep learning;

[0091] The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing, and obtains instruction keyword clustering results, including at least:

[0092] The big language AI model first performs natural language processing and word segmentation on the industrial software data parsing big language instructions based on natural language processing;

[0093] Based on the natural language processing results and word segmentation results, K-means clustering is performed;

[0094] Perform homogeneous clustering on pre-set, standard industrial software data parsing big language instructions for querying production information;

[0095] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production process information;

[0096] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying product information;

[0097] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production information, a clustering mark comd1 is generated;

[0098] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production process information, a clustering mark comd2 is generated;

[0099] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying product information, a clustering mark comd3 is generated.

[0100] At the same time, the present invention also proposes an industrial software data parsing method based on a large language model AI technology, the method is applied to the industrial software data parsing system based on the large language model AI technology as described above, and the method comprises the following steps:

[0101] Step S102: operate the industrial Internet data parsing instruction issuing platform to receive the industrial software data parsing big language instruction from the system administrator, and send it to the big language AI model;

[0102] Step S104: operating the big language AI model to parse the industrial software data into big language instructions, and performing instruction parsing based on the big language model AI technology to obtain instruction keyword clustering results;

[0103] Generate a clustering mark comdK according to the instruction keyword clustering result, and push the instruction to one of the native digital factory, the industrial process control module or the industrial product output module according to the clustering mark comdK, where K is an integer of 1-3;

[0104] As a superimposable preferred embodiment, the industrial Internet data parsing instruction issuing platform receives the industrial software data parsing big language instruction from the system administrator and sends it to the big language AI model, specifically: the industrial Internet data parsing instruction issuing platform receives instructions from the administrator, the instructions are in natural language form, and the instructions are used to perform industrial software data parsing, for example, for performing feedback and parsing of production information related data; or, when the production process needs to be controlled, the extraction and parsing of production process information is performed; and, when product information needs to be sampled, the extraction and parsing of product-related information is performed. As another superimposable preferred embodiment, the instruction is an industrial software data parsing big language instruction. As another superimposable preferred embodiment, the instruction can be specifically: I want to know the output and yield rate of a certain product; or, I want to know the production party information of a certain product; or, I want to know which workshops / links / assembly lines the production process of a certain product passes through, so as to improve and optimize it. As another superimposable preferred embodiment, the above information is dispersedly managed remotely by native digital factories, industrial process control modules, industrial product output modules, etc.

[0105] Step S106: Control the native digital factory to receive the industrial software data parsing language instruction with the clustering mark comd1, and input the digital factory related information into the production middleware database, wherein the digital factory related information is a packaged data packet;

[0106] Step S108: operate the industrial process control module to receive the industrial software data parsing language instruction with the clustering mark comd2, and input the production process related information into the production middleware database, wherein the production process related information is a packaged data packet;

[0107] Step S110: operate the industrial product output module to receive the industrial software data parsing language instruction with the clustering mark comd3, and input the product related information into the production middleware database, wherein the product related information is a packaged data packet;

[0108] Step S112: operate the production middleware database to parse the encapsulated data packet according to the preset protocol, obtain the relevant information industrialINF of the digital factory or production process or product in the encapsulated data packet, and send it to the industrial Internet optimization cluster;

[0109] The relevant information industrialINF is stored using the industrial Internet data storage protocol preset by the system; the preset protocol is an industrial control protocol, or an industrial Ethernet protocol, or a data packet encapsulation protocol predefined by the system;

[0110] Step S114: operate the industrial Internet optimization cluster to receive the relevant information industrialINF, perform natural language processing optimization based on the large language model AI technology, obtain the optimized natural language output, submit it to the output end of the industrial software data analysis system based on the large language model AI technology, and feed back the relevant information industrialINF in the form of natural language to the industrial Internet data analysis instruction publishing platform that issued the instruction.

[0111] As a superimposable preferred embodiment, the encapsulated data packet may adopt any suitable encapsulation protocol in the prior art, such as IP data packet encapsulation, or adopt a system predefined encapsulation protocol.

[0112] As a superimposable preferred embodiment, the industrial software data parsing large language instruction is in natural language form and contains predefined type keywords.

[0113] As a superimposable preferred embodiment, the instruction keyword clustering result is one of the following:

[0114] Production information data analysis class, which is used to characterize relevant information of production participants, corresponding to the clustering mark comd1;

[0115] Production process data analysis class, which is used to characterize production process related information, corresponding to the clustering label comd2;

[0116] Product information data analysis class, which is used to characterize product related information and corresponds to the clustering label comd3.

[0117] As a superimposable preferred embodiment, the big language AI model performs instruction parsing on the industrial software data parsing big language instructions based on big language model AI technology, based on machine learning, or based on deep learning;

[0118] The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing, and obtains instruction keyword clustering results, including at least:

[0119] The big language AI model first performs natural language processing and word segmentation on the industrial software data parsing big language instructions based on natural language processing;

[0120] Based on the natural language processing results and word segmentation results, K-means clustering is performed;

[0121] Perform homogeneous clustering on pre-set, standard industrial software data parsing big language instructions for querying production information;

[0122] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production process information;

[0123] Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying product information;

[0124] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production information, a clustering mark comd1 is generated;

[0125] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production process information, a clustering mark comd2 is generated;

[0126] If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying product information, a clustering mark comd3 is generated.

[0127] At the same time, the present invention also proposes a computer-readable storage medium, in which a program executable by a processor is stored, characterized in that the program executable by the processor is used to execute the method described above when executed by the processor.

[0128] The present invention proposes an industrial software data parsing method and system based on large language model AI technology, introduces large language model AI technology in the industrial Internet control process, identifies industrial software data parsing large language instructions through the AI ​​model of LLM, extracts classification keywords and performs intelligent analysis, and clusters the instructions, so that the system can better understand the industrial software data parsing instructions. Secondly, the present invention pushes the instructions to different industrial software data extraction parties through natural language processing of large language model AI technology, and obtains industrial software data of various types from different locations of the system such as native digital factories, industrial process control modules, and industrial product output modules for centralized analysis, and then optimizes and outputs natural language processing through large language model AI technology, so that the system's control platform interaction is humanized and the convenience and readability of data analysis are improved.

[0129] In all the above embodiments, in order to realize some special data transmission and read / write function requirements, the above method operation process and its corresponding device can add devices, modules, components, hardware, pin connections or memory, processor differences to expand functions.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the methods, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the method steps is only a logical or functional division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as the various steps of the method and the separated components of the device may or may not be logically or physically separated, and may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, the method steps and their implementations and functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0134] The above method and device can be implemented as an integrated unit in the form of a software functional unit, which can be stored in a computer-readable storage medium. The above software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (Processor) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), NVRAM, disk or optical disk and other media that can store program codes.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0136] It should be noted that the above embodiments are only used to more clearly explain and illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An industrial software data parsing system based on large language model AI technology, the system comprising: An industrial Internet data parsing instruction issuing platform, which receives industrial software data parsing big language instructions from a system administrator and sends them to a big language AI model; Big language AI model, native digital factory, industrial process control module, industrial product output module, production middleware database, and industrial Internet optimization cluster; The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing and obtain instruction keyword clustering results; Generate a clustering mark comdK according to the instruction keyword clustering result, and push the instruction to one of the native digital factory, the industrial process control module or the industrial product output module according to the clustering mark comdK, where K is an integer of 1-3; The native digital factory receives the industrial software data parsing language instruction with the clustering mark comd1, and inputs the digital factory related information into the production middleware database, wherein the digital factory related information is a packaged data packet; The industrial process control module receives the industrial software data parsing language instruction with the clustering mark comd2, and inputs the production process related information into the production middleware database, wherein the production process related information is an encapsulated data packet; The industrial product output module receives the industrial software data parsing language instruction with the clustering mark comd3, and inputs the product related information into the production middleware database, wherein the product related information is a packaged data packet; The production middleware database parses the encapsulated data packet according to a preset protocol, obtains the relevant information industrialINF of the digital factory or production process or product in the encapsulated data packet, and sends it to the industrial Internet optimization cluster; The relevant information industrialINF is stored using the industrial Internet data storage protocol preset by the system; the preset protocol is an industrial control protocol, or an industrial Ethernet protocol, or a data packet encapsulation protocol predefined by the system; The industrial Internet optimization cluster receives the relevant information industrialINF, performs natural language processing optimization based on the large language model AI technology, obtains the optimized natural language output, submits it to the output end of the industrial software data analysis system based on the large language model AI technology, and feeds back the relevant information industrialINF in the form of natural language to the industrial Internet data analysis instruction issuing platform that issued the instruction; The large language instruction is in natural language form and contains predefined type keywords; The big language AI model performs instruction parsing on the big language instructions of the industrial software data based on the big language model AI technology to obtain instruction keyword clustering results, which at least includes: the big language AI model first performs natural language processing and word segmentation on the big language instructions of the industrial software data based on natural language processing; based on the natural language processing results and word segmentation results, obtains instruction keyword clustering results.

2. The industrial software data parsing system based on large language model AI technology as claimed in claim 1, characterized in that: The command keyword clustering result is one of the following: Production information data analysis class, which is used to characterize relevant information of production participants, corresponding to the clustering mark comd1; Production process data analysis class, which is used to characterize production process related information, corresponding to the clustering label comd2; Product information data analysis class, which is used to characterize product related information and corresponds to the clustering label comd3.

3. The industrial software data analysis system based on large language model AI technology as claimed in claim 2, characterized in that: The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology, based on machine learning, or based on deep learning; The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing, and obtains instruction keyword clustering results, including at least: The big language AI model first performs natural language processing and word segmentation on the industrial software data parsing big language instructions based on natural language processing; Based on the natural language processing results and word segmentation results, K-means clustering is performed; Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production information; Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production process information; Perform homogeneous clustering on pre-set, standard industrial software data parsing big language instructions for querying product information; If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production information, a clustering mark comd1 is generated; If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production process information, a clustering mark comd2 is generated; If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying product information, a clustering mark comd3 is generated.

4. A method for industrial software data analysis based on large language model AI technology, the method being applied to the industrial software data analysis system based on large language model AI technology as claimed in claim 2, the method comprising the following steps: Step 1: Operate the Industrial Internet Data Parsing Instruction Publishing Platform to receive the industrial software data parsing big language instructions from the system administrator and send them to the big language AI model; Step 2: Operate the big language AI model to parse the industrial software data. Perform instruction parsing based on the big language model AI technology to obtain instruction keyword clustering results; Generate a clustering mark comdK according to the instruction keyword clustering result, and push the instruction to one of the native digital factory, the industrial process control module or the industrial product output module according to the clustering mark comdK, where K is an integer of 1-3; Step 3: Control the native digital factory to receive the industrial software data parsing language instruction with the clustering mark comd1, and input the digital factory related information into the production middleware database, wherein the digital factory related information is a packaged data packet; Step 4: operate the industrial process control module to receive the industrial software data parsing language instruction with the clustering mark comd2, and input the production process related information into the production middleware database, wherein the production process related information is a packaged data packet; Step 5: operate the industrial product output module to receive the industrial software data parsing language instruction with the clustering mark comd3, and input the product related information into the production middleware database, wherein the product related information is a packaged data packet; Step 6: Operate the production middleware database to parse the encapsulated data packet according to the preset protocol, obtain the relevant information industrialINF of the digital factory or production process or product in the encapsulated data packet, and send it to the industrial Internet optimization cluster; The relevant information industrialINF is stored using the industrial Internet data storage protocol preset by the system; the preset protocol is an industrial control protocol, or an industrial Ethernet protocol, or a data packet encapsulation protocol predefined by the system; Step 7: Operate the industrial Internet optimization cluster to receive the relevant information industrialINF, perform natural language processing optimization based on the large language model AI technology, obtain the optimized natural language output, submit it to the output end of the industrial software data analysis system based on the large language model AI technology, and feed back the relevant information industrialINF in the form of natural language to the industrial Internet data analysis instruction issuing platform that issued the instruction; the large language instruction is in the form of natural language and contains predefined type keywords; The big language AI model performs instruction parsing on the industrial software data parsing big language instruction based on the big language model AI technology to obtain instruction keyword clustering results, which at least includes: the big language AI model first performs natural language processing and word segmentation on the industrial software data parsing big language instruction based on natural language processing; Based on the natural language processing results and word segmentation results, obtain the command keyword clustering results.

5. The industrial software data parsing method based on large language model AI technology as claimed in claim 4, characterized in that: The command keyword clustering result is one of the following: Production information data analysis class, which is used to characterize relevant information of production participants, corresponding to the clustering mark comd1; Production process data analysis class, which is used to characterize production process related information, corresponding to the clustering label comd2; Product information data analysis class, which is used to characterize product related information and corresponds to the clustering label comd3.

6. The industrial software data parsing method based on large language model AI technology as claimed in claim 5, characterized in that: The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology, based on machine learning, or based on deep learning; The big language AI model parses the big language instructions of the industrial software data based on the big language model AI technology to perform instruction parsing, and obtains instruction keyword clustering results, including at least: The big language AI model first performs natural language processing and word segmentation on the industrial software data parsing big language instructions based on natural language processing; Based on the natural language processing results and word segmentation results, K-means clustering is performed; Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production information; Perform homogeneous clustering on pre-set, standard industrial software data parsing language instructions for querying production process information; Perform homogeneous clustering on pre-set, standard industrial software data parsing big language instructions for querying product information; If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production information, a clustering mark comd1 is generated; If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying production process information, a clustering mark comd2 is generated; If the clustering result has the highest similarity with the preset standard industrial software data parsing language instruction for querying product information, a clustering mark comd3 is generated.

7. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 4 to 6 when executed by the processor.

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