GPT-based digital twin mechanism model construction method and system for texturing machine

Abstract

The present invention discloses a method and system for constructing a digital twin mechanism model of a texturing machine based on GPT. The method comprises: S1. Acquiring several historical digital twin mechanism models of texturing machines and converting them into natural language text using a "model-program-text" forward multimodal mapping mechanism; S2. Constructing a GPT deep learning model and training the GPT deep learning model using the natural language text acquired in S1 and the corresponding computer program as samples; S3. Acquiring requirements for a target digital twin mechanism model of a texturing machine, converting these requirements into natural language text in a preset format, and inputting them into the trained GPT deep learning model. Finally, the output of the GPT deep learning model is converted into a model to obtain the target digital twin mechanism model of the texturing machine. The present invention achieves high construction efficiency and low cost.

Description

Technical Field

[0001] The present invention relates to the field of fault prediction of complex textile equipment, and in particular to a method and system for constructing a digital twin mechanism model of a texturing machine based on GPT. Background Art

[0002] The textile industry is vital to people's livelihoods and a national foundational industry. High-end, complex textile equipment is a key metric for measuring a country's textile production capabilities. Texturing machines are highly integrated, advanced engineering machinery, and this type of equipment is key to the textile industry's intelligent transformation. Texturing machines typically operate 24 / 7 and are subject to prolonged overload and high-intensity conditions. Damage or performance degradation of key components can not only reduce production quality but can also cause unexpected equipment downtime, resulting in significant financial losses for the company. Therefore, the use of digital methods to predict texturing machine faults is crucial. Fault prediction for texturing machines requires not only the collection of real-time, comprehensive operating data but also, more importantly, the development of digital models that accurately reflect the underlying mechanisms.

[0003] When constructing a mechanistic model, it is necessary to fully consider the physical coupling relationships between components within a complex system and their corresponding degradation mechanisms to form a digital model that accurately maps the physically degraded entities. However, currently, the construction of such mechanistic models relies heavily on manual labor, which is not only costly but also inefficient. In particular, changes in design, process, or operating conditions require repeated manual modeling, which makes it difficult to meet the needs of practical applications. Summary of the Invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide an efficient method and system for constructing a digital twin mechanism model of a texturing machine based on GPT.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] A method for constructing a digital twin mechanism model of a texturing machine based on GPT includes the following steps:

[0007] S1. Obtaining several historical texturing machine digital twin mechanism models and converting them into natural language texts through a "model-program-text" forward multimodal mapping mechanism, wherein the forward multimodal mapping mechanism is used to first convert the historical texturing machine digital twin mechanism models into computer programs, and then convert the computer programs into natural language texts in a preset format;

[0008] S2. Build a GPT deep learning model and use the natural language text and corresponding computer program generated in step S1 as samples to train the GPT deep learning model, wherein the GPT deep learning model is used to generate a corresponding computer program based on the natural language text of the digital twin mechanism model of the texturizing machine;

[0009] S3. Obtain the requirements of the digital twin mechanism model of the target texturing machine, convert the requirements into natural language text in a preset format, input it into the trained GPT deep learning model, and convert the output of the GPT deep learning model into a model to obtain the digital twin mechanism model of the target texturing machine.

[0010] Furthermore, step S1 specifically includes the following steps:

[0011] S11. Obtain several historical texturing machine digital twin mechanism models, convert the historical texturing machine digital twin mechanism models into computer programs in a preset programming language using a model conversion tool, perform tests, and delete or modify computer programs that fail the tests;

[0012] S12. Extract key textual knowledge of the texturing machine based on the historical digital twin mechanism model of the texturing machine, construct a textual knowledge database of the texturing machine, and map the textual knowledge in the texturing machine textual knowledge database with key information of the texturing machine in the computer program to form a textual knowledge-computer program mapping relationship;

[0013] S13, parsing the computer program obtained in step S11, extracting key information of the texturing machine therein, and using natural language generation technology, combined with a text knowledge-computer program mapping relationship, converting the key information of the texturing machine in the computer program into a natural language text in a preset format;

[0014] S14: Verify the natural language text obtained in step S13, and modify and adjust any inconsistencies or errors therein.

[0015] Furthermore, the natural language generation technology is used in combination with the text knowledge-computer program mapping relationship to convert the key information of the texturing machine in the computer program into a natural language text in a preset format, specifically including:

[0016] Through a pre-written text template, using rules or machine learning algorithms, based on the text knowledge-computer program mapping relationship, the key information of the texturing machine in the computer program is matched and filled with the text template, and a natural language text in a preset format is generated. After filling in, the text template is checked; wherein, the text template is a template that describes the different components and working states of the texturing machine.

[0017] Furthermore, step S2 specifically includes the following steps:

[0018] S21. Taking the natural language text and computer program of each historical texturing machine digital twin mechanism model as sample pairs, forming a sample set, and dividing it into a training set and a test set;

[0019] S22. Construct a GPT deep learning model, wherein the GPT deep learning model includes a text encoder and a program parser connected to each other, wherein the text encoder is used to encode the input natural language text of the digital twin mechanism model of the springing machine into a series of vector representations, wherein the vector representations capture the semantic and syntactic information in the natural language text; and the program parser is used to generate a corresponding computer program based on the vector representations;

[0020] S23. Use the training set of step S21 to pre-train the GPT deep learning model. After the pre-training is completed, use the test set to test the GPT deep learning model. Finally, fine-tune the GPT deep learning model according to the test results until the GPT deep learning model meets the preset requirements.

[0021] Furthermore, step S3 specifically includes the following steps:

[0022] S31. Obtaining requirements for a target texturing machine digital twin mechanism model, and filling the requirements for the target texturing machine digital twin mechanism model into a pre-written text template to obtain a natural language text in a preset format;

[0023] S32. Input the natural language text obtained in step S31 into the trained GPT deep learning model, and the output is a computer program of the digital twin mechanism model of the target texturing machine;

[0024] S33. Convert the computer program of the digital twin mechanism model of the target texturing machine into the digital twin mechanism model of the target texturing machine using a model conversion tool.

[0025] A GPT-based digital twin mechanism model construction system for a texturing machine, including:

[0026] A forward mapping module is used to obtain several historical texturing machine digital twin mechanism models and convert them into natural language text through a "model-program-text" forward multimodal mapping mechanism, wherein the forward multimodal mapping mechanism is used to first convert the historical texturing machine digital twin mechanism models into computer programs, and then convert the computer programs into natural language text in a preset format;

[0027] A deep learning model acquisition module is used to build a GPT deep learning model and train the GPT deep learning model using the natural language text and corresponding computer program generated in step S1 as samples, wherein the GPT deep learning model is used to generate a corresponding computer program based on the natural language text of the digital twin mechanism model of the texturizing machine;

[0028] The target model construction module is used to obtain the requirements of the digital twin mechanism model of the target texturing machine, convert the requirements into natural language text in a preset format, input it into the trained GPT deep learning model, and convert the output of the GPT deep learning model into a model to obtain the digital twin mechanism model of the target texturing machine.

[0029] Furthermore, the forward mapping module specifically includes:

[0030] A model conversion unit is used to obtain a number of historical texturing machine digital twin mechanism models, convert the historical texturing machine digital twin mechanism models into computer programs in a preset programming language using a model conversion tool, and perform tests, and delete or modify computer programs that fail the test;

[0031] A text mapping relationship establishment unit is used to extract key text knowledge of the texturing machine based on the historical digital twin mechanism model of the texturing machine, build a text knowledge database of the texturing machine, and map the text knowledge in the text knowledge database with the key information of the texturing machine in the computer program to form a text knowledge-computer program mapping relationship;

[0032] a text conversion unit, configured to parse the computer program acquired by the model conversion unit, extract key information of the texturing machine therein, and convert the key information of the texturing machine in the computer program into a natural language text in a preset format by using natural language generation technology and combining a text knowledge-computer program mapping relationship;

[0033] The verification unit is used to verify the natural language text obtained by the text conversion unit and to modify and adjust the inconsistencies or errors therein.

[0034] Furthermore, the text conversion unit specifically includes:

[0035] A program parsing subunit is used to parse the computer program obtained by the model conversion unit and extract key information of the texturing machine;

[0036] The text generation subunit is used to match and fill in the key information of the texturing machine in the computer program with the text template based on the text knowledge-computer program mapping relationship through a pre-written text template, using rules or machine learning algorithms, to generate natural language text in a preset format, and check it after filling in; wherein the text template is a template that describes the different components and working states of the texturing machine.

[0037] Furthermore, the deep learning model acquisition module specifically includes:

[0038] A sample set generation unit is used to take the natural language text and computer program of each historical texturing machine digital twin mechanism model as sample pairs to form a sample set, and divide it into a training set and a test set;

[0039] A deep learning model construction unit is used to construct a GPT deep learning model. The GPT deep learning model includes a text encoder and a program parser connected to each other. The text encoder is used to encode the input natural language text of the digital twin mechanism model of the springing machine into a series of vector representations. The vector representation captures the semantic and syntactic information in the natural language text; the program parser is used to generate a corresponding computer program based on the vector representation;

[0040] The model training unit is used to pre-train the GPT deep learning model using the training set generated by the sample set generation unit, and after the pre-training is completed, use the test set to test the GPT deep learning model, and finally fine-tune the GPT deep learning model according to the test results until the GPT deep learning model meets the preset requirements.

[0041] Furthermore, the target model construction module specifically includes:

[0042] A target model text acquisition unit is used to obtain the requirements of the digital twin mechanism model of the target texturing machine, fill in the requirements of the digital twin mechanism model of the target texturing machine in a pre-written text template, and obtain a natural language text in a preset format;

[0043] The target program acquisition unit is used to input the natural language text acquired by the target model text acquisition unit into the trained GPT deep learning model, and the output is the computer program of the digital twin mechanism model of the target texturing machine;

[0044] The target model acquisition unit is used to convert the computer program of the digital twin mechanism model of the target texturing machine into the digital twin mechanism model of the target texturing machine using a model conversion tool.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] (1) The present invention uses the GPT deep learning model to solve the rapid conversion of the digital twin mechanism model from text to the corresponding computer program;

[0047] (2) The present invention realizes the automatic generation of digital twin mechanism models. Compared with traditional manual modeling, it improves the construction efficiency of the mechanism model and reduces the construction cost of the mechanism model. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the method for constructing a digital twin mechanism model of a texturing machine based on GPT provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0050] Example 1

[0051] The embodiment of the present invention provides a method for constructing a digital twin mechanism model of a texturing machine based on GPT, such as Figure 1 As shown, the following steps are included:

[0052] S1. Obtain several historical digital twin mechanism models of texturing machines and convert them into natural language text through the "model-program-text" forward multimodal mapping mechanism.

[0053] Among them, the forward multimodal mapping mechanism is used to first convert the digital twin mechanism model of the historical texturing machine into a computer program, and then convert the computer program into a natural language text in a preset format.

[0054] Specifically, step S1 includes the following steps:

[0055] S11. Obtain several historical digital twin mechanism models of texturing machines, convert these historical digital twin mechanism models into computer programs in a preset programming language (e.g., C / C++) using a model conversion tool, and test them. Delete or modify any computer programs that fail the test. The model conversion tool can be a tool such as Simulink Coder or Embedded Coder. The model is converted into a program, and the generated program is verified to ensure that it accurately reflects the behavior of the digital twin mechanism model of the texturing machine.

[0056] S12. Based on the historical digital twin mechanism model of the texturing machine, extract key textual knowledge about the texturing machine and construct a textual knowledge database for the texturing machine. The textual knowledge in the texturing machine textual knowledge database is then mapped to key information about the texturing machine in the computer program, forming a textual knowledge-computer program mapping relationship. Key textual knowledge about the texturing machine can include the structure, operating principle, key parameters, and their impacts of the texturing machine. Key information about the texturing machine in the computer program can include variables, functions, and expressions within the program.

[0057] S13. Parse the computer program obtained in step S11 to extract key information about the texturing machine. Using natural language generation (NLG) technology and a text knowledge-computer program mapping relationship, convert the key information about the texturing machine in the computer program into a pre-formatted natural language text. Specifically, a pre-written text template can be used, using rules or machine learning algorithms, to match and populate the key information about the texturing machine in the computer program with the text template based on the text knowledge-computer program mapping relationship, generating a pre-formatted natural language text. After completion, the text template is checked; the text template describes the various components and operating states of the texturing machine. The check primarily involves grammatical and semantic checks on the generated text to ensure its accuracy and readability.

[0058] S14: Verify the natural language text obtained in step S13, and modify and adjust any inconsistencies or errors. Verification mainly involves comparing the original model, the generated computer program, and the converted text to ensure that the mapping relationship between them is accurate.

[0059] S2. Build a GPT deep learning model and use the natural language text and corresponding computer program generated in step S1 as samples to train the GPT deep learning model.

[0060] This step specifically includes:

[0061] S21. The natural language text and computer program of each historical texturing machine digital twin mechanism model are used as sample pairs to form a sample set, which is divided into a training set and a test set.

[0062] S22. Construct a GPT deep learning model for generating a corresponding computer program based on the natural language text of the digital twin mechanism model of the texturizing machine. The GPT deep learning model is a Transformer structure, specifically comprising a connected text encoder and a program parser. The text encoder is used to encode the input natural language text of the digital twin mechanism model of the texturizing machine into a series of vector representations that capture the semantic and syntactic information in the natural language text; the program parser is used to generate a corresponding computer program based on the vector representations.

[0063] S23. Use the training set of step S21 to pre-train the GPT deep learning model. After the pre-training is completed, use the test set to test the GPT deep learning model. Finally, fine-tune the GPT deep learning model according to the test results until the GPT deep learning model meets the preset requirements.

[0064] S3. Obtain the requirements of the digital twin mechanism model of the target texturing machine, convert the requirements into natural language text in a preset format, input it into the trained GPT deep learning model, and convert the output of the GPT deep learning model into a model to obtain the digital twin mechanism model of the target texturing machine.

[0065] This step specifically includes:

[0066] S31: Obtain the requirements of the digital twin mechanism model of the target texturing machine, and fill the requirements of the digital twin mechanism model of the target texturing machine into a pre-written text template to obtain a natural language text in a preset format. The text template is the template in step S13.

[0067] S32. Input the natural language text obtained in step S31 into the trained GPT deep learning model, and the output is a computer program of the digital twin mechanism model of the target texturing machine.

[0068] S33. Convert the computer program of the digital twin mechanism model of the target texturing machine into the digital twin mechanism model of the target texturing machine using a model conversion tool. The model conversion tool is a tool such as Simulink.

[0069] In addition, after obtaining the digital twin mechanism model of the target texturing machine, it is also necessary to evaluate the accuracy of the digital twin mechanism model of the texturing machine, including the common index evaluation of the texturing machine mechanism and the characteristic index evaluation of the texturing machine mechanism.

[0070] Example 2

[0071] The embodiment of the present invention provides a GPT-based digital twin mechanism model construction system for a texturing machine, comprising:

[0072] A forward mapping module is used to obtain several historical texturing machine digital twin mechanism models and convert them into natural language text through a "model-program-text" forward multimodal mapping mechanism, wherein the forward multimodal mapping mechanism is used to first convert the historical texturing machine digital twin mechanism models into computer programs, and then convert the computer programs into natural language text in a preset format;

[0073] A deep learning model acquisition module is used to build a GPT deep learning model and train the GPT deep learning model using the natural language text and corresponding computer program generated in step S1 as samples, wherein the GPT deep learning model is used to generate a corresponding computer program based on the natural language text of the digital twin mechanism model of the texturizing machine;

[0074] The target model construction module is used to obtain the requirements of the digital twin mechanism model of the target texturing machine, convert the requirements into natural language text in a preset format, input it into the trained GPT deep learning model, and convert the output of the GPT deep learning model into a model to obtain the digital twin mechanism model of the target texturing machine.

[0075] The forward mapping module specifically includes:

[0076] A model conversion unit is used to obtain a number of historical texturing machine digital twin mechanism models, convert the historical texturing machine digital twin mechanism models into computer programs in a preset programming language using a model conversion tool, and perform tests, and delete or modify computer programs that fail the test;

[0077] A text mapping relationship establishment unit is used to extract key text knowledge of the texturing machine based on the historical digital twin mechanism model of the texturing machine, build a text knowledge database of the texturing machine, and map the text knowledge in the text knowledge database with the key information of the texturing machine in the computer program to form a text knowledge-computer program mapping relationship;

[0078] a text conversion unit, configured to parse the computer program acquired by the model conversion unit, extract key information of the texturing machine therein, and convert the key information of the texturing machine in the computer program into a natural language text in a preset format by using natural language generation technology and combining a text knowledge-computer program mapping relationship;

[0079] The verification unit is used to verify the natural language text obtained by the text conversion unit and to modify and adjust the inconsistencies or errors therein.

[0080] The text conversion unit specifically includes:

[0081] A program parsing subunit is used to parse the computer program obtained by the model conversion unit and extract key information of the texturing machine;

[0082] The text generation subunit is used to match and fill in the key information of the texturing machine in the computer program with the text template based on the text knowledge-computer program mapping relationship through a pre-written text template, using rules or machine learning algorithms, to generate natural language text in a preset format, and check it after filling in; wherein the text template is a template that describes the different components and working states of the texturing machine.

[0083] The deep learning model acquisition module specifically includes:

[0084] A sample set generation unit is used to take the natural language text and computer program of each historical texturing machine digital twin mechanism model as sample pairs to form a sample set, and divide it into a training set and a test set;

[0085] A deep learning model construction unit is used to construct a GPT deep learning model. The GPT deep learning model includes a text encoder and a program parser connected to each other. The text encoder is used to encode the input natural language text of the digital twin mechanism model of the springing machine into a series of vector representations. The vector representation captures the semantic and syntactic information in the natural language text; the program parser is used to generate a corresponding computer program based on the vector representation;

[0086] The model training unit is used to pre-train the GPT deep learning model using the training set generated by the sample set generation unit, and after the pre-training is completed, use the test set to test the GPT deep learning model, and finally fine-tune the GPT deep learning model according to the test results until the GPT deep learning model meets the preset requirements.

[0087] The target model building module specifically includes:

[0088] A target model text acquisition unit is used to obtain the requirements of the digital twin mechanism model of the target texturing machine, fill in the requirements of the digital twin mechanism model of the target texturing machine in a pre-written text template, and obtain a natural language text in a preset format;

[0089] The target program acquisition unit is used to input the natural language text acquired by the target model text acquisition unit into the trained GPT deep learning model, and the output is the computer program of the digital twin mechanism model of the target texturing machine;

[0090] The target model acquisition unit is used to convert the computer program of the digital twin mechanism model of the target texturing machine into the digital twin mechanism model of the target texturing machine using a model conversion tool.

[0091] The system provided in the embodiment of the present invention can be used to execute the method provided in the first embodiment of the present invention, and has the corresponding functions and beneficial effects of executing the method.

[0092] It is worth noting that in the embodiment of the above system, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0093] The embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Those skilled in the art will readily appreciate that each embodiment may be implemented using software plus a necessary general-purpose hardware platform, or may be implemented solely through hardware, as long as the functionality or effect can be achieved.

Claims

1. A method for constructing a digital twin mechanism model of a texturing machine based on GPT, characterized in that: The steps include: S1. Obtaining several historical texturing machine digital twin mechanism models and converting them into natural language text using a "model-program-text" forward multimodal mapping mechanism, wherein the forward multimodal mapping mechanism is used to first convert the historical texturing machine digital twin mechanism models into computer programs, and then convert the computer programs into natural language text in a preset format; S2. Build a GPT deep learning model and use the natural language text and corresponding computer program generated in step S1 as samples to train the GPT deep learning model, wherein the GPT deep learning model is used to generate a corresponding computer program based on the natural language text of the digital twin mechanism model of the texturizing machine; S3. Obtain the requirements of the digital twin mechanism model of the target texturing machine, convert the requirements into natural language text in a preset format, input it into the trained GPT deep learning model, and convert the output of the GPT deep learning model into a model to obtain the digital twin mechanism model of the target texturing machine. Step S1 specifically includes the following steps: S11. Obtain several historical texturing machine digital twin mechanism models, convert the historical texturing machine digital twin mechanism models into computer programs in a preset programming language using a model conversion tool, perform tests, and delete or modify computer programs that fail the tests; S12. Extract key textual knowledge of the texturing machine based on the historical digital twin mechanism model of the texturing machine, construct a textual knowledge database of the texturing machine, and map the textual knowledge in the texturing machine textual knowledge database with key information of the texturing machine in the computer program to form a textual knowledge-computer program mapping relationship; S13, parsing the computer program obtained in step S11, extracting key information of the texturing machine therein, and using natural language generation technology, combined with a text knowledge-computer program mapping relationship, converting the key information of the texturing machine in the computer program into a natural language text in a preset format; S14: Verify the natural language text obtained in step S13, and modify and adjust any inconsistencies or errors therein.

2. The method for constructing a digital twin mechanism model of a texturing machine based on GPT according to claim 1, characterized in that: The method of converting the key information of the texturing machine in the computer program into a natural language text in a preset format by using natural language generation technology and combining the text knowledge-computer program mapping relationship specifically includes: Through a pre-written text template, using rules or machine learning algorithms, based on the text knowledge-computer program mapping relationship, the key information of the texturing machine in the computer program is matched and filled with the text template, and a natural language text in a preset format is generated. After filling in, the text template is checked; wherein, the text template is a template that describes the different components and working states of the texturing machine.

3. The method for constructing a digital twin mechanism model of a texturizing machine based on GPT according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21. Taking the natural language text and computer program of each historical texturing machine digital twin mechanism model as sample pairs, forming a sample set, and dividing it into a training set and a test set; S22. Construct a GPT deep learning model, wherein the GPT deep learning model includes a text encoder and a program parser connected to each other, wherein the text encoder is used to encode the input natural language text of the digital twin mechanism model of the springing machine into a series of vector representations, wherein the vector representations capture the semantic and syntactic information in the natural language text; and the program parser is used to generate a corresponding computer program based on the vector representations; S23. Use the training set of step S21 to pre-train the GPT deep learning model. After the pre-training is completed, use the test set to test the GPT deep learning model. Finally, fine-tune the GPT deep learning model according to the test results until the GPT deep learning model meets the preset requirements.

4. The method for constructing a digital twin mechanism model of a texturizing machine based on GPT according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31. Obtaining requirements for a target texturing machine digital twin mechanism model, and filling the requirements for the target texturing machine digital twin mechanism model into a pre-written text template to obtain a natural language text in a preset format; S32. Input the natural language text obtained in step S31 into the trained GPT deep learning model, and the output is a computer program of the digital twin mechanism model of the target texturing machine; S33. Convert the computer program of the digital twin mechanism model of the target texturing machine into the digital twin mechanism model of the target texturing machine using a model conversion tool.

5. A digital twin mechanism model construction system for a texturing machine based on GPT, characterized in that: include: A forward mapping module is used to obtain several historical texturing machine digital twin mechanism models and convert them into natural language text through a "model-program-text" forward multimodal mapping mechanism. The forward multimodal mapping mechanism is used to first convert the historical texturing machine digital twin mechanism models into computer programs, and then convert the computer programs into natural language text in a preset format; A deep learning model acquisition module is used to build a GPT deep learning model and train the GPT deep learning model using the natural language text and corresponding computer program generated in step S1 as samples, wherein the GPT deep learning model is used to generate a corresponding computer program based on the natural language text of the digital twin mechanism model of the texturizing machine; The target model construction module is used to obtain the requirements of the digital twin mechanism model of the target texturing machine, convert the requirements into natural language text in a preset format, input it into the trained GPT deep learning model, and convert the output of the GPT deep learning model into a model to obtain the digital twin mechanism model of the target texturing machine; The forward mapping module specifically includes: A model conversion unit is used to obtain a number of historical texturing machine digital twin mechanism models, convert the historical texturing machine digital twin mechanism models into computer programs in a preset programming language using a model conversion tool, and perform tests, and delete or modify computer programs that fail the test; A text mapping relationship establishment unit is used to extract key text knowledge of the texturing machine based on the historical digital twin mechanism model of the texturing machine, build a text knowledge database of the texturing machine, and map the text knowledge in the text knowledge database with the key information of the texturing machine in the computer program to form a text knowledge-computer program mapping relationship; a text conversion unit, configured to parse the computer program acquired by the model conversion unit, extract key information of the texturing machine therein, and convert the key information of the texturing machine in the computer program into a natural language text in a preset format by using natural language generation technology and combining a text knowledge-computer program mapping relationship; The verification unit is used to verify the natural language text obtained by the text conversion unit and to modify and adjust the inconsistencies or errors therein.

6. The GPT-based digital twin mechanism model construction system for a texturizing machine according to claim 5 is characterized in that: The text conversion unit specifically includes: A program parsing subunit is used to parse the computer program obtained by the model conversion unit and extract key information of the texturing machine; The text generation subunit is used to match and fill in the key information of the texturing machine in the computer program with the text template based on the text knowledge-computer program mapping relationship through a pre-written text template, using rules or machine learning algorithms, to generate natural language text in a preset format, and check it after filling in; wherein the text template is a template that describes the different components and working states of the texturing machine.

7. The GPT-based digital twin mechanism model construction system for texturizing machines according to claim 5 is characterized in that: The deep learning model acquisition module specifically includes: A sample set generation unit is used to take the natural language text and computer program of each historical texturing machine digital twin mechanism model as sample pairs to form a sample set, and divide it into a training set and a test set; A deep learning model construction unit is used to construct a GPT deep learning model. The GPT deep learning model includes a text encoder and a program parser connected to each other. The text encoder is used to encode the input natural language text of the digital twin mechanism model of the springing machine into a series of vector representations. The vector representation captures the semantic and syntactic information in the natural language text; the program parser is used to generate a corresponding computer program based on the vector representation; The model training unit is used to pre-train the GPT deep learning model using the training set generated by the sample set generation unit, and after the pre-training is completed, use the test set to test the GPT deep learning model, and finally fine-tune the GPT deep learning model according to the test results until the GPT deep learning model meets the preset requirements.

8. The GPT-based digital twin mechanism model construction system for a texturizing machine according to claim 5 is characterized in that: The target model building module specifically includes: A target model text acquisition unit is used to obtain the requirements of the digital twin mechanism model of the target texturing machine, fill in the requirements of the digital twin mechanism model of the target texturing machine in a pre-written text template, and obtain a natural language text in a preset format; The target program acquisition unit is used to input the natural language text acquired by the target model text acquisition unit into the trained GPT deep learning model, and the output is the computer program of the target texturing machine digital twin mechanism model; the target model acquisition unit is used to convert the computer program of the target texturing machine digital twin mechanism model into the target texturing machine digital twin mechanism model using a model conversion tool.

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