Intelligent overall allocation method based on large model for steel industry

Abstract

The present invention provides a large-scale model-based intelligent overall allocation method for the steel industry, which relates to the field of artificial intelligence technology. The large-scale model-based intelligent overall allocation method for the steel industry is implemented by a user end, a task requirement input module, a large-scale model semantic understanding module, a sub-model scheduling module, and an execution and result analysis module. The method includes: the user end inputs the task requirements of steel industry production; the task requirement input module obtains standardized text data by standardizing the task requirements; the large-scale model semantic understanding module processes the text data to obtain task objectives, task model requirements, and task model parameters; the sub-model scheduling module performs dynamic scheduling to determine the sub-model; the sub-model executes the task to obtain the execution result of the sub-model; the execution and result analysis module processes the execution result and outputs a comprehensive analysis result that meets the task requirements. The present invention can improve task processing efficiency.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an intelligent overall allocation method based on a large model for the steel industry. Background Art

[0002] With the rapid development of modern manufacturing and intelligent production, the number of tasks in production has increased dramatically, especially in the key steel and metallurgical industries. Traditional production management models often rely on manual task setting and manual scheduling of different models for corresponding calculations and analysis. However, with the increasing complexity and diversified demands of industrial production, existing single-task processing methods are clearly unable to meet the real-time, accurate, and efficient task execution requirements of industrial sites.

[0003] During the production of rolled steel, managers often need to monitor and analyze mechanical performance indicators, including yield strength and tensile strength, in real time to make multi-dimensional decisions and schedules. However, traditional mechanical performance prediction models can only predict a single indicator, and whenever the task requirements change, the model must be reconfigured and adjusted, which is cumbersome and inefficient. More importantly, production management needs are often complex and dynamic. For example, a user may want to query "the proportion of a batch of rolled steel whose yield strength meets the standard." This requirement is usually posed without a clear task goal and requires obtaining results from different data sources and multiple models.

[0004] In existing steel industry production management systems, single-task learning (STL) methods are often used to process production data. Single-task models are often trained and predicted based on specific production goals. For example, mechanical property prediction models can usually only focus on a single mechanical property indicator, or require manual selection of different subtask models and manual scheduling. The above processing methods have the following problems when facing diverse production tasks: (1) Limitations of the single-task processing mode: Traditional systems need to manually split tasks when facing complex tasks, resulting in slow system response and low efficiency; (2) Inflexible model scheduling: In existing technologies, model calls rely on pre-set hard-coded rules and cannot automatically select and call appropriate sub-models based on the user's actual needs; (3) Inaccurate task requirement identification: Existing systems cannot intelligently understand the complex task requirements proposed by users and require manual intervention and decomposition and processing through traditional rule engines. Complex task objectives cannot be directly input through natural language. The above shortcomings limit the flexibility and adaptability of traditional industrial management systems, making it difficult to meet rapidly changing production needs and diverse task objectives. Summary of the Invention

[0005] In order to solve the technical problems of the traditional steel industry production management system existing in the prior art, such as slow response speed and low efficiency; model calling relies on pre-set hard-coded rules, cannot automatically select and call appropriate sub-models according to the actual needs of users, and cannot intelligently understand the complex task requirements proposed by users, requiring manual intervention and disassembly and processing through traditional rule engines, and cannot directly input complex task objectives through natural language, the embodiment of the present invention provides a large-scale model-based intelligent overall allocation method and system for the steel industry. The technical solution is as follows:

[0006] In one aspect, a large-scale model-based intelligent overall allocation method for the steel industry is provided. The method is implemented by a large-scale model-based intelligent overall allocation device for the steel industry. The method includes:

[0007] S1. The user terminal inputs the task requirements for steel industry production; the task requirement input module receives the task requirements and obtains standardized text data by standardizing the task requirements;

[0008] S2. The task requirement input module uploads the standardized text data to the large model semantic understanding module, and processes the large model semantic understanding module to obtain task objectives, task model requirements, and task model parameters;

[0009] S3, the task requirement input module uploads the task goal, task model requirements and task model parameters to the sub-model scheduling module, and the sub-model scheduling module performs dynamic scheduling to determine the sub-model; according to the task goal, the sub-model executes the task and obtains the execution result of the sub-model;

[0010] S4. The sub-model scheduling module uploads the execution result of the sub-model to the execution and result analysis module, processes it through the execution and result analysis module, and outputs a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end.

[0011] Optionally, the task requirements for steel industry production input by the user terminal in S1 are natural language text.

[0012] Optionally, the step S1 of obtaining standardized text data by standardizing the task requirements includes:

[0013] S11, formatting the input task requirements to obtain formatted text data;

[0014] S12. Based on the formatted text data, use the word segmenter of the GPT pre-trained model to perform word segmentation on the formatted text data to obtain a text sequence after word segmentation;

[0015] S13. According to the text sequence after word segmentation, use the NLTK stop word list to remove the stop words from the text sequence after word segmentation to obtain the text after the stop words are removed;

[0016] S14. Use the BERT-based dependency analysis model to perform dependency analysis on the text after removing stop words to obtain the grammatical relationship between words in the text data;

[0017] S15. Based on the text after removing stop words and the grammatical relationship between words in the text data, the GPT pre-training model is used to perform feature encoding to obtain an encoding vector.

[0018] Optionally, the step S2 is processed by the large model semantic understanding module to obtain task objectives, task model requirements, and task model parameters, including:

[0019] S21. Input the encoded vector into the intent classification network to generate a task type label;

[0020] S22. Generate task tag information based on the task type tag;

[0021] S23, using an information extraction function to convert the task requirements input by the user into structured data based on the encoding vector and task label information; decomposing the structured data to generate multiple subtask objectives;

[0022] S24. According to the structured data and the multiple subtask objectives, a model selection function is used to determine the task model of each subtask and the parameters of each subtask model.

[0023] Optionally, the step S3 of performing dynamic scheduling by the sub-model scheduling module, determining the sub-model to execute the task, and obtaining the execution result of the sub-model includes:

[0024] Dynamically schedule each subtask based on its objectives and the parameters of each subtask model to determine the submodel that will execute each subtask.

[0025] According to each subtask goal, each sub-model executes the corresponding sub-task and obtains the execution result of each sub-model.

[0026] Optionally, the step S4 integrates and analyzes the results through the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal, including:

[0027] S41. Based on the execution result of each sub-model, perform multimodal preprocessing on the execution result of each sub-model using a multimodal data processing technology to obtain a preprocessed execution result;

[0028] S42. Based on the preprocessed results, a long short-term memory network is used to perform feature extraction to obtain key features of each modality.

[0029] S43. Based on the key features of each mode, a weighted average fusion method is used to perform fusion processing, and a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end is output.

[0030] Optionally, after the step of integrating and analyzing the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal in S4, the step further includes:

[0031] The intelligent coordination system based on large models for the steel industry receives user feedback on the output comprehensive analysis results;

[0032] Based on user feedback, the large-scale model-based intelligent coordination system for the steel industry dynamically adjusts and optimizes model scheduling strategies, task processing procedures, and prediction accuracy.

[0033] In another aspect, a large-scale model-based intelligent overall allocation system for the steel industry is provided. The system is applied to a large-scale model-based intelligent overall allocation method for the steel industry. The system includes:

[0034] The user terminal is used for inputting task requirements of steel industry production;

[0035] The task requirement input module is used for receiving the task requirement and obtaining standardized text data by performing standardization processing on the task requirement;

[0036] The large model semantic understanding module is used for the task requirement input module to upload the standardized text data to the large model semantic understanding module, and the large model semantic understanding module processes the data to obtain the task objectives, task model requirements and task model parameters;

[0037] The sub-model scheduling module is used for the task requirement input module to upload the task objectives, task model requirements and task model parameters to the sub-model scheduling module, and dynamically schedule the sub-model through the sub-model scheduling module to determine the sub-model; according to the task objectives, the sub-model executes the task and obtains the execution result of the sub-model;

[0038] The execution and result analysis module is used for the sub-model scheduling module to upload the execution results of the sub-model to the execution and result analysis module, and integrate and analyze them through the execution and result analysis module to output comprehensive analysis results that meet the task requirements of steel industry production input by the user end.

[0039] Optionally, the task requirements for steel industry production input by the user terminal are natural language text.

[0040] Optionally, the step of obtaining standardized text data by standardizing the task requirements includes:

[0041] Format the input task requirements to obtain formatted text data;

[0042] According to the formatted text data, the word segmenter of the GPT pre-trained model is used to segment the formatted text data to obtain the text sequence after word segmentation;

[0043] According to the text sequence after word segmentation, the NLTK stop word list is used to remove the stop words from the text sequence after word segmentation to obtain the text after the stop words are removed;

[0044] The BERT-based dependency analysis model is used to perform dependency analysis on the text after removing stop words to obtain the grammatical relationship between words in the text data;

[0045] According to the text after removing stop words and the grammatical relationship between words in the text data, the GPT pre-training model is used to perform feature encoding to obtain the encoding vector.

[0046] Optionally, the processing by the large model semantic understanding module to obtain task objectives, task model requirements, and task model parameters includes:

[0047] Input the encoded vector into the intent classification network to generate the task type label;

[0048] Generate task tag information based on task type tag;

[0049] Based on the encoding vector and task label information, an information extraction function is used to convert the task requirements input by the user into structured data; the structured data is decomposed to generate multiple subtask objectives;

[0050] According to the structured data and multiple subtask objectives, a model selection function is used to determine the task model of each subtask and the parameters of each subtask model.

[0051] Optionally, the performing dynamic scheduling by the sub-model scheduling module, determining the sub-model to execute the task, and obtaining the execution result of the sub-model includes:

[0052] Dynamically schedule each subtask based on its objectives and the parameters of each subtask model to determine the submodel that will execute each subtask.

[0053] According to each subtask goal, each sub-model executes the corresponding sub-task and obtains the execution result of each sub-model.

[0054] Optionally, the integration and analysis processing by the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal includes:

[0055] According to the execution result of each sub-model, multimodal preprocessing is performed on the execution result of each sub-model using multimodal data processing technology to obtain the preprocessed execution result;

[0056] According to the execution results after preprocessing, long short-term memory network is used for feature extraction to obtain the key features of each modality;

[0057] According to the key features of each mode, a weighted average fusion method is used for fusion processing to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end.

[0058] Optionally, after the step of integrating and analyzing the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal, the step further includes:

[0059] The intelligent coordination system based on large models for the steel industry receives user feedback on the output comprehensive analysis results;

[0060] Based on user feedback, the large-scale model-based intelligent coordination system for the steel industry dynamically adjusts and optimizes model scheduling strategies, task processing procedures, and prediction accuracy.

[0061] On the other hand, a large-scale model-based intelligent coordinated allocation device for the steel industry is provided, and the large-scale model-based intelligent coordinated allocation device for the steel industry includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned large-scale model-based intelligent coordinated allocation methods for the steel industry is implemented.

[0062] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned large-model-based intelligent coordinated allocation methods for the steel industry.

[0063] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0064] In the embodiment of the present invention, the user terminal inputs the task requirements of the steel industry production; the task requirement input module receives the task requirements, and obtains standardized text data by standardizing the task requirements; the task requirement input module uploads the standardized text data to the large model semantic understanding module, and processes it through the large model semantic understanding module to obtain the task objectives, task model requirements and task model parameters; secondly, the task requirement input module uploads the task objectives, task model requirements and task model parameters to the sub-model scheduling module, and dynamically schedules the sub-model through the sub-model scheduling module to determine the sub-model; according to the task objectives, the sub-model executes the task and obtains the execution result of the sub-model; finally, the sub-model scheduling module uploads the execution result of the sub-model to the execution and result analysis module, and processes it through the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal.

[0065] The embodiment of the present invention can automatically understand the task objectives when the user proposes a complex task, and intelligently schedule related models to process the task without manual intervention, which greatly improves the efficiency of task processing; through the combination of large models and sub-models, the system can handle multiple task requirements at the same time, and integrate the output results of each task to ensure the accuracy and consistency of the comprehensive analysis results; through natural language processing technology, the system can understand the user's unstructured needs and convert them into structured task instructions, thereby realizing intelligent task allocation and predictive analysis; the feedback and optimization of the system enable the model scheduling strategy and task execution process to be continuously improved, optimizing the system's resource utilization and task response speed. The system architecture designed by the present invention is highly flexible and scalable, can adapt to various complex steel industry production management tasks, and can be widely used in different industrial fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0067] Figure 1This is a flow chart of a large-scale model-based intelligent overall allocation method for the steel industry provided by an embodiment of the present invention;

[0068] Figure 2 This is a schematic diagram of the overall process of a large-scale model-based intelligent overall allocation method for the steel industry provided by an embodiment of the present invention;

[0069] Figure 3 This is a block diagram of a large-scale model-based intelligent overall allocation system for the steel industry provided by an embodiment of the present invention;

[0070] Figure 4 This is a structural diagram of a large-model-based intelligent coordinated allocation device for the steel industry provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0072] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0073] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0074] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0075] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0076] The embodiment of the present invention provides a large-scale model-based intelligent overall allocation method for the steel industry. The method can be implemented by a large-scale model-based intelligent overall allocation device for the steel industry. The large-scale model-based intelligent overall allocation device for the steel industry can be a terminal or a server. Figure 1The flowchart of the intelligent overall allocation method based on a large model for the steel industry is shown. The processing flow of the method may include the following steps:

[0077] S1. The user inputs the task requirements of steel industry production; the task requirement input module receives the task requirements and obtains standardized text data by standardizing the task requirements.

[0078] Among them, the task requirement input module is used for users to input task requirements through the system interface; the input form supports complex natural language descriptions without the need for specific instructions or formats, which can improve the system's flexibility and user experience.

[0079] Optionally, the user terminal of S1 inputs the task requirements of steel industry production in the form of natural language text.

[0080] In a feasible implementation, users can input task requests through natural language. The system does not need a fixed input format, and users can flexibly express complex requirements. The input task requests include descriptive requirements, such as "count the number of rolled products whose yield strength meets the standard."

[0081] Optionally, the specific implementation process of S1 may include S11-S15:

[0082] S11, formatting the input task requirements to obtain formatted text data;

[0083] In a feasible implementation, the text of the input task requirement is formatted, including converting to lowercase, removing special characters, and deleting spaces; the consistency of word segmentation is ensured through the formatting process.

[0084] S12. Based on the formatted text data, use the word segmenter of the GPT pre-trained model to perform word segmentation on the formatted text data to obtain a text sequence after word segmentation;

[0085] In a feasible implementation, the process of using the word segmenter of the GPT pre-trained model to segment the formatted text data is expressed by the following formula (1):

[0086] (1)

[0087] Among them, Tokenized_text represents the text sequence after word segmentation; Normalized_text represents the input text.

[0088] Among them, the GPT pre-training model is a model with powerful natural language processing capabilities that can generate text through autoregression.

[0089] In a feasible implementation, the tokenizer of the GPT pre-trained model is used to break down the text into subunits. For example, "user requirement input" is split into ["user", "requirement", "input"]. If there are words not in the dictionary, the tokenizer of the GPT pre-trained model will further break down the subunits into subwords. The tokenized result not only retains the original sentence structure but also retains semantic information and is mapped to specific word vectors in the encoding of the GPT pre-trained model for convenient subsequent processing.

[0090] S13. According to the tokenized text sequence, use the NLTK stopword list to perform stopword removal on the tokenized text sequence to obtain the text after stopword removal.

[0091] Among them, NLTK (Natural Language Toolkit) is a natural language processing library that can provide stopword lists for multiple languages. Among them, stopwords refer to words that frequently appear in the text but usually have little semantic content, such as stopwords like "de", "shi", and "he".

[0092] In a feasible implementation, stopword removal can reduce unnecessary corpus interference and highlight the important content in the task requirements input by the user. Stopwords include conjunctions and articles.

[0093] Among them, using the NLTK stopword list can ensure that the removed words will not affect the overall semantics of the text. The process of using the NLTK stopword list to traverse the tokenized text sequence and remove the words in the stopword library to obtain the text after stopword removal can be represented by the following formula (2):

[0094] (2)

[0095] Among them, Filtered_Tokens represents the text after stopword removal; Stopwords represents stopwords.

[0096] Among them, the text after stopword processing can greatly reduce the model's computational burden and highlight the key information in the task request.

[0097] S14. Use a BERT-based dependency analysis model to perform dependency analysis on the text after stopword removal to obtain the grammatical relationships between words in the text data.

[0098] Among them, the BERT-based dependency analysis model is a method for performing dependency syntactic analysis using the BERT model. Dependency syntactic analysis is an important task in natural language processing that can identify the dependency relationships between words in a sentence and construct a dependency tree to better understand the structure and meaning of the sentence.

[0099] In a feasible implementation, dependency analysis is used to analyze the grammatical structure of a text and identify the subject, predicate, and object components in a sentence.

[0100] In one feasible implementation, a BERT-based dependency parser model is used to analyze the dependency relationships of formatted text data, annotating the grammatical relationships between words in the text. For example, a BERT-based dependency parser analyzes the sentence "The number of statistical yield strengths that meet the standard" and obtains a dependency structure of: statistics (predicate), quantity (object), and strength (modifier). Based on this dependency structure, the text is identified as consisting of noun phrases and verb phrases, and a corresponding grammatical label is assigned to each word.

[0101] Among them, the dependency analysis process can accurately extract task objectives and requirement content.

[0102] S15. Based on the text after removing stop words and the grammatical relationship between words in the text data, the GPT pre-training model is used to perform feature encoding to obtain an encoding vector.

[0103] In one feasible implementation, each word after segmentation is converted into a corresponding word vector to obtain a multi-dimensional embedding representation; wherein, the word vector retains semantic relationships and contextual information, which helps the system understand complex requirements.

[0104] The specific process of obtaining the encoding vector can be expressed by the following formulas (3) and (4):

[0105] (3)

[0106] (4)

[0107] Among them, Encoded_Text represents the encoded vector; h[CLS] represents the output vector of the [CLS] tag generated by the GPT pre-training model; [CLS] Token represents the [CLS] tag at the first position in the sequence preprocessed by GPT input.

[0108] In a feasible implementation, according to the text sequence, the encoding vector is generated using formula (3), and the semantic features of the entire input can be represented by the [CLS] bit vector of GPT, or the overall vector can be extracted through the pooling operation.

[0109] S2. The task requirement input module uploads the standardized text data to the large model semantic understanding module, which processes the data to obtain the task objectives, task model requirements, and task model parameters.

[0110] Among them, the large model is a GPT large language pre-training model; the large model semantic understanding module is used to call the large-scale pre-trained language model for semantic analysis and intent recognition after the system receives the user's task requirements. Through natural language processing technology, the system converts the user's natural language task requirements into structured task goals, and identifies the main task goals by extracting key features of the task requirements input by the user; decomposes the subtasks in complex tasks to ensure that each task goal is clear and unambiguous. For requirements that include multiple tasks, the system automatically refines the requirement content and outputs the task structure and key parameters of each task model.

[0111] Optionally, the specific implementation process of S2 may include S21-S24:

[0112] S21. Input the encoded vector into the intent classification network to generate a task type label;

[0113] Among them, intent classification networks are a conventional technical means of natural language processing; they are used to identify and understand the intentions expressed by users in text, language or other forms of input, and can be applied to fields such as chatbots, voice assistants, search engines, and customer service systems; intent classification networks can accurately respond to user needs.

[0114] Among them, the task type labels include classification, regression and data statistics; the task type label is used to indicate the nature of the task, which is expressed by the following formula (5):

[0115] (5)

[0116] Where W represents the weight of the intent classifier; b represents the bias parameter of the intent classifier; Intent_Type represents the task type label; and softmax() represents the activation function of the output layer.

[0117] S22. Generate task tag information based on the task type tag;

[0118] The task label information includes: operation keywords and constraints; the task label information is expressed by the following formula (6):

[0119] (6)

[0120] Among them, Generated_Tags represents the generated task tag information; Keywords represents the operation keywords; Constraints represents the constraint conditions.

[0121] S23, using an information extraction function to convert the task requirements input by the user into structured data based on the encoding vector and task label information; decomposing the structured data to generate multiple subtask objectives;

[0122] In a feasible implementation, the process of obtaining structured data can be expressed by the following formula (7):

[0123] (7)

[0124] Among them, Structured_Data represents the structured data format; f() represents the information extraction function.

[0125] Among them, the structured data is further decomposed into multiple subtasks, and the operations and goals of each subtask are clearly marked.

[0126] S24. According to the structured data and the multiple subtask objectives, a model selection function is used to determine the task model of each subtask and the parameters of each subtask model.

[0127] In a feasible implementation, based on structured data and multiple subtask objectives, a model selection function is used to extract the model type and key parameters required for each subtask. Through model selection, the objectives and operational requirements of each subtask are analyzed, and the corresponding model for each subtask is determined. The model mapping can be expressed by the following formula (8):

[0128] (8)

[0129] Among them, Model represents the selected model; g() represents the model selection function.

[0130] Among them, the methods of model selection function include: cross-validation method, model evaluation index and model selection criteria; among them, the model selection method used in this application is the BIC Bayesian Information Criterion.

[0131] In a feasible implementation, the corresponding model parameters are extracted according to the specific requirements of each subtask, and the key parameter set of the model is expressed by the following formula (9):

[0132] (9)

[0133] Among them, Key_Params represents the key parameter set of the model; h() represents the extraction function of the model parameters.

[0134] S3. The task requirement input module uploads the task objectives, task model requirements and task model parameters to the sub-model scheduling module, and dynamically schedules the sub-model through the sub-model scheduling module to determine the sub-model; according to the task objectives, the sub-model executes the task and obtains the execution result of the sub-model.

[0135] Among them, the sub-model scheduling module is used by the system to automatically select the most suitable model to perform a specific task based on the requirements of each sub-task; the sub-model scheduling module will optimize and allocate models based on conditions such as task complexity, time requirements, and computing resources to ensure efficient completion of tasks; for tasks with complex requirements, the system can simultaneously call multiple sub-models for collaborative processing to improve prediction results.

[0136] Optionally, the specific implementation process of S3 may include:

[0137] Dynamically schedule each subtask based on its objectives and the parameters of each subtask model to determine the submodel that will execute each subtask.

[0138] According to each subtask goal, each sub-model executes the corresponding sub-task and obtains the execution result of each sub-model.

[0139] S4. The sub-model scheduling module uploads the execution results of the sub-model to the execution and result analysis module, which processes the sub-model and outputs a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end.

[0140] Among them, the execution and result analysis module is used by the system to summarize and analyze the results of all subtasks; through multimodal data fusion technology, the system can integrate the results of different sub-models and conduct in-depth analysis, providing more accurate and targeted prediction results; the final output results will be presented as clear reports or visual data for users to view and understand.

[0141] In one feasible implementation, each sub-model completes its own task based on task requirements and executes it according to the parameters of the task model. After completing its task, each sub-model returns the execution results to the larger model's semantic understanding module for preliminary verification, including checking the data format and the validity and rationality of the execution results. This preliminary verification of the execution results ensures that all sub-tasks are executed as expected.

[0142] For key tasks, the system can verify the accuracy of execution results based on rule-based verification or through cross-validation of the main model, thereby improving the reliability of the execution results. For example, whether the generated text and annotated image targets match or support specific user needs.

[0143] Optionally, the specific implementation process of S4 may include S41-S43:

[0144] S41. Based on the execution result of each sub-model, perform multimodal preprocessing on the execution result of each sub-model using a multimodal data processing technology to obtain a preprocessed execution result;

[0145] In a feasible implementation, the data of each modality is cleaned, a median filter or a Gaussian filter is used to remove noise in the image, regular expressions are used to process special characters and punctuation marks in the text, and missing values ​​in the numerical data are filled through interpolation methods including linear interpolation or K-nearest neighbor interpolation. At the same time, a hash table or set is used to remove duplicates to obtain the preprocessed execution result.

[0146] In a feasible implementation, multimodal preprocessing is performed on the execution results so that multimodal features can be compared and fused on the same dimension.

[0147] S42. Based on the preprocessed results, a long short-term memory network is used to perform feature extraction to obtain key features of each modality.

[0148] Among them, the long short-term memory network is a conventional technical means, and the present invention will not be further elaborated here.

[0149] S43. Based on the key features of each mode, the weighted average fusion method is used for fusion processing to output a comprehensive analysis result that meets the task requirements of steel industry production input by the user end.

[0150] Among them, the weighted average fusion method is a commonly used image fusion method that assigns a weight to each image and calculates the weighted average of the corresponding pixel values ​​of all images at each pixel point to obtain the final fused image.

[0151] In a feasible implementation method, in order to ensure the accuracy of the fusion results, the system dynamically adjusts the weight of each feature according to the task requirements and the priority of the modality; adopts weighted averaging method, attention mechanism or joint embedding method to fuse multimodal data to obtain the final fusion result; and uses the final fusion result as a comprehensive analysis result that meets the task requirements of steel industry production input by the user end.

[0152] In a feasible implementation, Figure 2 This is a schematic diagram of the overall process of an intelligent coordinated allocation method based on a large model for the steel industry provided by an embodiment of the present invention; in a feasible implementation method, flexible task requirements are input, and according to the task requirements, the system performs semantic understanding of the large model, performs large model analysis by calling the interface, and obtains comprehensive analysis results; wherein, the calling interface includes: data model, image model and text model.

[0153] Optionally, after the step of integrating and analyzing the execution with the result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end in S4, the step further includes:

[0154] The intelligent coordination system based on large models for the steel industry receives user feedback on the output comprehensive analysis results;

[0155] In a feasible implementation, the system receives user feedback on output results, records task execution results and user feedback, and facilitates continuous optimization of system performance.

[0156] Based on user feedback, the large-scale model-based intelligent coordination system for the steel industry dynamically adjusts and optimizes model scheduling strategies, task processing procedures, and prediction accuracy.

[0157] In one feasible implementation, by analyzing feedback data, the system can dynamically adjust and optimize the model scheduling strategy, task processing flow, and prediction accuracy, thereby improving task execution results. The system can monitor the model's performance indicators in real time, including response time and resource utilization, dynamically adjust computing resource allocation based on actual load, and use multi-model integration technology to improve system stability and prediction accuracy. At the same time, the task processing flow is optimized, workflow management and anomaly detection mechanisms are introduced to automate task processing and respond quickly to abnormal situations. By establishing a continuous learning mechanism, user feedback and new data are regularly collected to retrain the model, and feature engineering and hyperparameter settings are continuously optimized to ensure that the model is always in the best state and improve prediction accuracy. Through the above steps, the system can maintain efficient and accurate task execution capabilities in a changing environment.

[0158] In the embodiment of the present invention, the user terminal inputs the task requirements of the steel industry production; the task requirement input module receives the task requirements, and obtains standardized text data by standardizing the task requirements; the task requirement input module uploads the standardized text data to the large model semantic understanding module, and processes it through the large model semantic understanding module to obtain the task objectives, task model requirements and task model parameters; secondly, the task requirement input module uploads the task objectives, task model requirements and task model parameters to the sub-model scheduling module, and dynamically schedules the sub-model through the sub-model scheduling module to determine the sub-model; according to the task objectives, the sub-model executes the task and obtains the execution result of the sub-model; finally, the sub-model scheduling module uploads the execution result of the sub-model to the execution and result analysis module, and processes it through the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal.

[0159] The embodiment of the present invention can automatically understand the task objectives when the user proposes a complex task, and intelligently schedule related models to process the task without manual intervention, which greatly improves the efficiency of task processing; through the combination of large models and sub-models, the system can handle multiple task requirements at the same time, and integrate the output results of each task to ensure the accuracy and consistency of the comprehensive analysis results; through natural language processing technology, the system can understand the user's unstructured needs and convert them into structured task instructions, thereby realizing intelligent task allocation and predictive analysis; the feedback and optimization of the system enable the model scheduling strategy and task execution process to be continuously improved, optimizing the system's resource utilization and task response speed. The system architecture designed by the present invention is highly flexible and scalable, can adapt to various complex steel industry production management tasks, and can be widely used in different industrial fields.

[0160] Figure 3 This is a block diagram of a large-scale model-based intelligent overall allocation system for the steel industry according to an exemplary embodiment. The system is used for a large-scale model-based intelligent overall allocation method for the steel industry. Figure 3 The system includes a user terminal 310, a task requirement input module 320, a large model semantic understanding module 330, a sub-model scheduling module 340, and an execution and result analysis module 350. Among them:

[0161] The user terminal 310 is used for inputting task requirements of steel industry production;

[0162] The task requirement input module 320 is configured to receive the task requirement and obtain standardized text data by performing standardization processing on the task requirement;

[0163] The large model semantic understanding module 330 is used for the task requirement input module to upload the standardized text data to the large model semantic understanding module, and the large model semantic understanding module processes the data to obtain the task objectives, task model requirements and task model parameters;

[0164] The sub-model scheduling module 340 is used for the task requirement input module to upload the task objectives, task model requirements and task model parameters to the sub-model scheduling module, and the sub-model scheduling module performs dynamic scheduling to determine the sub-model to execute the task and obtain the execution result of the sub-model;

[0165] The execution and result analysis module 350 is used for the sub-model scheduling module to upload the execution results of the sub-model to the execution and result analysis module, and integrate and analyze them through the execution and result analysis module to output comprehensive analysis results that meet the task requirements of the steel industry production input by the user end.

[0166] Optionally, the task requirements for steel industry production input by the user terminal are natural language text.

[0167] Optionally, the step of obtaining standardized text data by standardizing the task requirements includes:

[0168] Format the input task requirements to obtain formatted text data;

[0169] According to the formatted text data, the word segmenter of the GPT pre-trained model is used to segment the formatted text data to obtain the text sequence after word segmentation;

[0170] According to the text sequence after word segmentation, the NLTK stop word list is used to remove the stop words from the text sequence after word segmentation to obtain the text after the stop words are removed;

[0171] The BERT-based dependency analysis model is used to perform dependency analysis on the text after removing stop words to obtain the grammatical relationship between words in the text data;

[0172] According to the text after removing stop words and the grammatical relationship between words in the text data, the GPT pre-training model is used to perform feature encoding to obtain the encoding vector.

[0173] Optionally, the processing by the large model semantic understanding module to obtain task objectives, task model requirements, and task model parameters includes:

[0174] Input the encoded vector into the intent classification network to generate the task type label;

[0175] Generate task tag information based on task type tag;

[0176] Based on the encoding vector and task label information, an information extraction function is used to convert the task requirements input by the user into structured data; the structured data is decomposed to generate multiple subtask objectives;

[0177] According to the structured data and multiple subtask objectives, a model selection function is used to determine the task model of each subtask and the parameters of each subtask model.

[0178] Optionally, the performing dynamic scheduling by the sub-model scheduling module, determining the sub-model to execute the task, and obtaining the execution result of the sub-model includes:

[0179] Dynamically schedule each subtask based on its objectives and the parameters of each subtask model to determine the submodel that will execute each subtask.

[0180] According to each subtask goal, each sub-model executes the corresponding sub-task and obtains the execution result of each sub-model.

[0181] Optionally, the integration and analysis processing by the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal includes:

[0182] According to the execution result of each sub-model, multimodal preprocessing is performed on the execution result of each sub-model using multimodal data processing technology to obtain the preprocessed execution result;

[0183] According to the execution results after preprocessing, long short-term memory network is used for feature extraction to obtain the key features of each modality;

[0184] According to the key features of each mode, a weighted average fusion method is used for fusion processing to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end.

[0185] Optionally, after the step of integrating and analyzing the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal, the step further includes:

[0186] The intelligent coordination system based on large models for the steel industry receives user feedback on the output comprehensive analysis results;

[0187] Based on user feedback, the large-scale model-based intelligent coordination system for the steel industry dynamically adjusts and optimizes model scheduling strategies, task processing procedures, and prediction accuracy.

[0188] In the embodiment of the present invention, the user terminal inputs the task requirements of the steel industry production; the task requirement input module receives the task requirements, and obtains standardized text data by standardizing the task requirements; the task requirement input module uploads the standardized text data to the large model semantic understanding module, and processes it through the large model semantic understanding module to obtain the task objectives, task model requirements and task model parameters; secondly, the task requirement input module uploads the task objectives, task model requirements and task model parameters to the sub-model scheduling module, and dynamically schedules the sub-model through the sub-model scheduling module to determine the sub-model; according to the task objectives, the sub-model executes the task and obtains the execution result of the sub-model; finally, the sub-model scheduling module uploads the execution result of the sub-model to the execution and result analysis module, and processes it through the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal.

[0189] The embodiment of the present invention can automatically understand the task objectives when the user proposes a complex task, and intelligently schedule related models to process the task without manual intervention, which greatly improves the efficiency of task processing; through the combination of large models and sub-models, the system can handle multiple task requirements at the same time, and integrate the output results of each task to ensure the accuracy and consistency of the comprehensive analysis results; through natural language processing technology, the system can understand the user's unstructured needs and convert them into structured task instructions, thereby realizing intelligent task allocation and predictive analysis; the feedback and optimization of the system enable the model scheduling strategy and task execution process to be continuously improved, optimizing the system's resource utilization and task response speed. The system architecture designed by the present invention is highly flexible and scalable, can adapt to various complex steel industry production management tasks, and can be widely used in different industrial fields.

[0190] Figure 4 This is a schematic diagram of the structure of a large-scale model-based intelligent overall allocation device for the steel industry provided by an embodiment of the present invention. Figure 4 As shown, the intelligent overall allocation equipment based on large models for the steel industry can include the above Figure 3 The shown intelligent coordinated allocation system based on a large model for the steel industry. Optionally, the intelligent coordinated allocation device 410 based on a large model for the steel industry may include a first processor 2001 .

[0191] Optionally, the large model-based intelligent coordinated allocation device 410 for the steel industry may further include a memory 2002 and a transceiver 2003 .

[0192] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0193] The following combination Figure 4 The following describes in detail the components of the large-scale model-based intelligent coordinated allocation device 410 for the steel industry:

[0194] The first processor 2001 is the control center of the large-scale model-based intelligent coordinated allocation device 410 for the steel industry. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0195] Optionally, the first processor 2001 can perform various functions of the large model-based intelligent coordinated allocation device 410 for the steel industry by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0196] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0197] In a specific implementation, as an embodiment, the intelligent coordinated allocation device 410 based on a large model for the steel industry may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0198] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0199] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and be accessed through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0200] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0201] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0202] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and be connected to the first processor 2001 through the interface circuit of the intelligent overall allocation device 410 based on a large model for the steel industry ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0203] It should be noted that Figure 4 The structure of the large-scale model-based intelligent coordinated allocation device 410 for the steel industry shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0204] In addition, the technical effects of the large-scale model-based intelligent coordinated allocation device 410 for the steel industry can refer to the technical effects of the large-scale model-based intelligent coordinated allocation method for the steel industry described in the above method embodiment, and will not be repeated here.

[0205] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0206] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0207] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0208] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0209] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0210] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0211] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0212] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0213] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of the system or unit, which can be electrical, mechanical or other forms.

[0214] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0215] In addition, each functional unit in each embodiment 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.

[0216] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0217] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An intelligent overall allocation method based on a large model for the steel industry, characterized by: The large-scale model-based intelligent coordination and allocation method for the steel industry is implemented by a large-scale model-based intelligent coordination system for the steel industry. The large-scale model-based intelligent coordination system for the steel industry includes: a user end, a task requirement input module, a large-scale model semantic understanding module, a sub-model scheduling module, and an execution and result analysis module; the method includes: S1. The user terminal inputs the task requirements for steel industry production; the task requirement input module receives the task requirements and obtains standardized text data by standardizing the task requirements; The step S1 of obtaining standardized text data by standardizing the task requirements includes: S11, formatting the input task requirements to obtain formatted text data; S12. Based on the formatted text data, use the word segmenter of the GPT pre-trained model to perform word segmentation on the formatted text data to obtain a text sequence after word segmentation; S13. According to the text sequence after word segmentation, use the NLTK stop word list to remove the stop words from the text sequence after word segmentation to obtain the text after the stop words are removed; S14. Use the BERT-based dependency analysis model to perform dependency analysis on the text after removing stop words to obtain the grammatical relationship between words in the text data; S15. Based on the text after removing stop words and the grammatical relationship between words in the text data, a GPT pre-trained model is used to perform feature encoding to obtain an encoding vector; S2. The task requirement input module uploads the standardized text data to the large model semantic understanding module, and processes the large model semantic understanding module to obtain task objectives, task model requirements, and task model parameters; The S2 is processed by the large model semantic understanding module to obtain the task objectives, task model requirements and task model parameters, including: S21. Input the encoded vector into the intent classification network to generate a task type label; S22. Generate task tag information based on the task type tag; S23, using an information extraction function to convert the task requirements input by the user into structured data based on the encoding vector and task label information; decomposing the structured data to generate multiple subtask objectives; S24. Determine a task model for each subtask and parameters of each subtask model using a model selection function based on the structured data and the multiple subtask objectives; S3, the task requirement input module uploads the task goal, task model requirements and task model parameters to the sub-model scheduling module, and the sub-model scheduling module performs dynamic scheduling to determine the sub-model; according to the task goal, the sub-model executes the task and obtains the execution result of the sub-model; S4. The sub-model scheduling module uploads the execution result of the sub-model to the execution and result analysis module, processes it through the execution and result analysis module, and outputs a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end.

2. The intelligent overall allocation method based on a large model for the steel industry according to claim 1 is characterized in that: The user terminal in S1 inputs the task requirements for steel industry production in the form of natural language text.

3. The intelligent overall allocation method based on a large model for the steel industry according to claim 1 is characterized in that: The step S3 performs dynamic scheduling through the sub-model scheduling module, determines the sub-model to execute the task, and obtains the execution result of the sub-model, including: Dynamically schedule each subtask based on its objectives and the parameters of each subtask model to determine the submodel that will execute each subtask. According to each subtask goal, each sub-model executes the corresponding sub-task and obtains the execution result of each sub-model.

4. The intelligent overall allocation method based on a large model for the steel industry according to claim 3 is characterized in that: The step S4 integrates and analyzes the execution and result analysis module to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end, including: S41. Based on the execution result of each sub-model, perform multimodal preprocessing on the execution result of each sub-model using a multimodal data processing technology to obtain a preprocessed execution result; S42. Based on the preprocessed results, a long short-term memory network is used to perform feature extraction to obtain key features of each modality. S43. Based on the key features of each mode, a weighted average fusion method is used to perform fusion processing, and a comprehensive analysis result that meets the task requirements of the steel industry production input by the user end is output.

5. The intelligent overall allocation method based on a large model for the steel industry according to claim 1 is characterized in that: After the step of integrating and analyzing the execution and result analysis module in step S4 to output a comprehensive analysis result that meets the task requirements of the steel industry production input by the user terminal, the method further includes: The intelligent coordination system based on large models for the steel industry receives user feedback on the output comprehensive analysis results; Based on user feedback, the large-scale model-based intelligent coordination system for the steel industry dynamically adjusts and optimizes model scheduling strategies, task processing procedures, and prediction accuracy.

6. An intelligent overall allocation system based on a large model for the steel industry, wherein the intelligent overall allocation system based on a large model for the steel industry is used to implement the intelligent overall allocation method based on a large model for the steel industry as claimed in any one of claims 1 to 5, characterized in that: The system comprises: The user terminal is used for inputting task requirements of steel industry production; The task requirement input module is used for receiving the task requirement and obtaining standardized text data by performing standardization processing on the task requirement; The large model semantic understanding module is used for the task requirement input module to upload the standardized text data to the large model semantic understanding module, and the large model semantic understanding module processes the data to obtain the task objectives, task model requirements and task model parameters; The sub-model scheduling module is used for the task requirement input module to upload the task objectives, task model requirements and task model parameters to the sub-model scheduling module, and dynamically schedule the sub-model through the sub-model scheduling module to determine the sub-model; according to the task objectives, the sub-model executes the task and obtains the execution result of the sub-model; The execution and result analysis module is used for the sub-model scheduling module to upload the execution results of the sub-model to the execution and result analysis module, and integrate and analyze them through the execution and result analysis module to output comprehensive analysis results that meet the task requirements of steel industry production input by the user end.

7. An intelligent overall allocation device based on a large model for the steel industry, characterized by: The large-scale model-based intelligent coordinated allocation equipment for the steel industry includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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