96 results about "Decision aiding" patented technology

The invention provides a legal affair intelligent auxiliary processing method and a multi-agent system. The method comprises the following steps: constructing a dynamically updated legal affair knowledge system; according to the dynamically updated law knowledge system, constructing a case shunting agent Agent, a law application Agent, a class case retrieval Agent and a risk early warning Agent, and realizing a multi-Agent collaboration mechanism through a collaboration structure based on message transmission and state sharing; according to a multi-Agent cooperation mechanism, a preference mapping relation model is constructed through case complexity evaluation and quality evaluation, and multi-case relation perception preference mapping is realized; constructing a planning tree PLANTOR framework by combining a large language model and a knowledge management technology based on Prolog based on multi-case relationship perception preference mapping, generating a robot-oriented knowledge base and a planning program, and converting the robot-oriented knowledge base and the planning program into a behavior tree; according to the PLANTOR framework, multi-Agent inference chain alignment is achieved through joint optimization of transmission optimization and embedded optimization, and law decision assistance is completed. According to the scheme, the legal affair processing accuracy and efficiency are effectively improved.

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention provides an underwater robot intelligent navigation and environment sensing system based on a multi-sensor fusion collaborative technology. The system integrates a plurality of sensors such as sonar, optical, electromagnetic and the like, and realizes comprehensive data acquisition and real-time perception of an underwater environment. By applying advanced algorithms such as Kalman filtering and a neural network, the system performs synchronization, preprocessing and deep fusion on multi-source data, and the consistency and accuracy of information are remarkably improved. In addition, the system also has adaptive navigation and environment perception capabilities, can dynamically adjust a navigation path and a perception strategy according to fused data, and adapts to a complex and changeable underwater environment. And the intelligent decision support system provides decision assistance for an operator in combination with an environment perception result and a task target. And due to the design of a user interaction and monitoring interface, the usability and real-time monitoring capability of the system are ensured. According to the implementation of the invention, the precision and efficiency of the underwater robot in tasks such as monitoring and exploration are greatly improved.

The invention discloses an AI data scheduling distribution system and method based on cloud computing, and relates to the technical field of intelligent cloud computing, and the system comprises an AI data collection module, a feature extraction module, a decision parameter generation module, a scheduling distribution module and a visual feedback module. The AI data acquisition module acquires equipment video data and temperature data, the feature extraction module extracts video temperature features, the decision parameter generation module outputs equipment vibration intensity and temperature rise rate, and the scheduling distribution module calculates equipment shutdown probability and node load score and triggers a shutdown instruction and a task migration decision. And the visual feedback module maps the equipment health state and node load distribution, and optimizes the feature weight. Through multi-modal data fusion and a closed-loop feedback mechanism, accurate early warning of equipment abnormity and dynamic optimal allocation of resources are realized, visual decision assistance and adaptive weight adjustment are combined, equipment safety and cluster load balancing efficiency in a cloud computing environment are improved, and manual intervention requirements and operation and maintenance cost are reduced.

The invention discloses an intelligent fusion analysis and decision support system and method for multi-source heterogeneous data of a spacecraft. The intelligent fusion analysis and decision support system for the multi-source heterogeneous data of the spacecraft and the intelligent fusion analysis and decision support method for the multi-source heterogeneous data of the spacecraft are included. The system comprises a dynamic adaptation access layer, a unified semantic modeling layer, a space-time semantic fusion engine, a digital twin collaborative decision-making center, a knowledge enhancement decision-making auxiliary subsystem, a cross-department collaborative interaction platform and a side cloud collaborative intelligent processing framework. The method comprises the steps of multi-source heterogeneous information dynamic access and semantic modeling, space-time and semantic deep fusion, digital twin driven decision deduction, knowledge enhanced decision assistance, cross-department collaborative decision and interaction, edge cloud collaborative intelligent processing and the like. According to the method, the safety, reliability and decision-making efficiency of spacecraft tasks can be remarkably improved, and the method is suitable for development, testing and on-orbit task stages of various spacecrafts.

The invention relates to the technical field of data processing, and discloses a decision-making auxiliary method, device and equipment based on portrait data and a medium, which can be applied to financial and medical scenarios, and the method comprises the following steps: processing an original data set to obtain standard data; respectively constructing a basic attribute portrait, a service behavior portrait and a consumption behavior portrait based on the standard data to extract multi-dimensional feature information, and converting the multi-dimensional feature information into a customer portrait vector; identifying customer behaviors according to the customer portrait vector, and constructing a portrait dynamic updating mechanism to obtain dynamic portrait data; and constructing a decision-making auxiliary model by using the dynamic portrait data, and operating the decision-making auxiliary model to output a decision-making auxiliary scheme. In the invention, aiming at the problem of unreasonable resource allocation caused by incomplete client portrait construction, the decision-making auxiliary model can be constructed and operated by utilizing the calculated dynamic portrait data, so as to output the decision-making auxiliary scheme. Therefore, resources can be reasonably distributed, and the working efficiency of personnel is improved.

The invention relates to the technical field of big data, and discloses a talent recommendation method and system based on a person and post adaptation model, and the method comprises the steps: collecting talent data, carrying out the classification and summarization of the talent data, and generating a talent portrait; internal post data of the enterprise is analyzed, and a post demand portrait is generated; establishing a person and post adaptation model, and performing quantitative scoring to obtain a person and post adaptation matching degree score of the talent portrait and the post demand portrait; integrating the person and post adaptation model into a decision-making auxiliary information module, and generating and displaying a talent lifting list according to the matching degree score; and analyzing the internal personnel structure of the enterprise in combination with the talent lifting list. The system comprises a data collection module, a man-post adaptation model establishment module, a decision-making auxiliary information module and an enterprise personnel analysis module. The invention further discloses electronic equipment and a computer readable storage medium. The method is used for providing the talent extraction recommendation list from multiple dimensions and assisting in organizing personnel work decisions.

The invention provides an emergency decision-making auxiliary system based on a knowledge graph. Comprising a data acquisition and processing module, a knowledge graph construction and reasoning module, an information retrieval and generation module, a multi-level reasoning and thinking chain prompting module, a multi-agent cooperation and game optimization module, an incremental learning and dynamic knowledge updating module and a block chain trust guarantee module. The emergency decision-making efficiency, accuracy, cooperation capability and safety are broken through, dynamic and complex emergency scenes can be effectively dealt with, and efficient, reliable and intelligent technical support is provided for decision makers.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention belongs to the technical field of decision-making assistance, and provides a medical decision-making assistance method and system based on a large reasoning model. The method comprises the following steps: recording voice interaction content between a doctor and a patient, and extracting first key medical information from the voice interaction content; extracting second key medical information from an electronic medical record written by a doctor, and carrying out consistency analysis and conflict analysis on the first key medical information and the second key medical information by using the large reasoning model; and outputting corresponding abnormity prompt information to a doctor according to an analysis result. The method can achieve the automatic verification of the medical information, guarantees that the electronic medical record is consistent with the oral medical advice, reduces the drug use and re-visit risks of a patient, and improves the accuracy and safety of a medical decision.

The invention discloses an electric power system data anomaly prediction method based on deep learning, and relates to the field of data anomaly prediction.The method comprises the steps that firstly, a graph convolutional network and LSTM are used for deeply mining time series data of an electric power system and spatial-temporal characteristics of a topological structure, and high-precision anomaly detection and positioning are achieved; and when an exception is detected, an exception attribution mechanism is introduced, and exception signal features (such as exception fragments and positioning information) at a mathematical level are converted into a structured query object at a semantic level. And then, a barrier between numerical data and the unstructured operation and maintenance knowledge base is broken through a vector retrieval technology, and related maintenance regulations and historical cases are accurately recalled. And finally, an executable recommended disposal scheme is automatically generated based on recall knowledge in combination with the generation capability of a large language model, so that an intelligent closed loop from anomaly perception and knowledge matching to decision assistance is constructed, and the accuracy and efficiency of power system fault handling are remarkably improved.

The invention provides an AI decision-assisted sudden traffic event scheduling decision-making method and system, and relates to the technical field of traffic decision-making, and the method comprises the steps: connecting a data collection system, and collecting traffic data and environment data as real-time data; analyzing and monitoring abnormal data in the real-time data by using a time sequence, and identifying a traffic event; using the influence range prediction model to predict the influence range of the traffic event; resource scheduling and traffic control are carried out based on the influence range, and a scheduling strategy is generated; and sending the scheduling strategy to an execution unit for strategy execution. According to the invention, the technical problems in the prior art that the response speed of the scheduling decision is slow and the flexibility is insufficient when the emergency traffic event occurs, the timeliness of traffic event processing and the scientificity of the scheduling strategy are influenced, and the low efficiency of urban traffic management and the traffic jam problem are aggravated can be solved; the technical targets of quick response and intelligent scheduling decision making are achieved, and the technical effects of improving urban traffic management efficiency and relieving traffic jam are achieved.

The invention provides a disaster site-oriented multi-unmanned aerial vehicle rapid three-dimensional reconstruction and decision-making auxiliary method and related equipment, and belongs to the technical field of emergency surveying and mapping and three-dimensional reconstruction. The method comprises the steps that multiple unmanned aerial vehicles are used for cooperatively collecting image data of a disaster site, and uncertainty indexes used for representing the data missing degree are constructed; taking the uncertainty index as input, selecting an optimal viewpoint to generate a supplementary collection task and executing the supplementary collection task based on the contribution degree of candidate viewpoints to reduction of uncertainty and the comprehensive cost of executing the viewpoints, and obtaining an image data set with coverage integrity; screening a key frame set for three-dimensional reconstruction from the image data set; performing motion recovery structure and space partitioning based on the key frame set, and fusing depth priori and semantic information to obtain an initialized point cloud; and based on a three-dimensional Gaussian sputtering model, extracting structured elements, constructing a risk cost map, and obtaining a rescue route and a disaster situation decision report. According to the invention, emergency command and rescue actions can be supported.

Disclosed in the present invention is a point-cloud-based operation and maintenance decision aid method for a distribution network corridor. The method comprises: a data collection module acquiring point cloud data of a distribution network corridor by means of a laser scanning device scanning the distribution network corridor, and preprocessing the collected point cloud data; a defect detection module using a constructed point cloud model to perform defect detection and classification on the distribution network corridor; a three-dimensional visualization module performing integration and visual display on the point cloud data and defect information, in order to realize the three-dimensional visualization of the distribution network corridor; and an operation and maintenance module formulating a dynamic operation, maintenance and overhaul strategy on the basis of device defect information. In the present invention, point cloud data and a three-dimensional model are used to perform device state monitoring and defect detection, so as to realize the comprehensive and rapid detection and evaluation on distribution network corridors, thereby improving the operation and maintenance efficiency.

The invention discloses a grid fault intelligent management decision-making system based on a large model, and relates to the technical field of grid fault decision-making, and the system comprises a closing data detection module which is used for obtaining an electrical value, obtaining an equipment value, obtaining a hardware safety coefficient through the electrical value and the equipment value, obtaining a personnel risk value, and obtaining an execution value; obtaining a personnel safety coefficient through the personnel risk value and the execution value; the guarantee coefficient is obtained by combining the state of the electrical equipment and the condition of the maintenance personnel, and whether closing is carried out is judged through the guarantee coefficient and the preset threshold value of the guarantee coefficient, so that the safety risk after closing is comprehensively judged, and then risk assessment is conveniently given to management personnel before closing execution; therefore, decision-making assistance can be conveniently carried out on the closing operation of the management personnel, the data of each closing decision can be conveniently recorded by establishing the risk closing database, and the decision-making system can conveniently carry out decision-making optimization and adjustment according to historical data.

The invention discloses a knowledge graph-based spacecraft overall design large model data acquisition method and equipment, belongs to the technical field of spacecraft overall design, and solves the problems of difficulty in knowledge acquisition, low knowledge reuse rate and weak knowledge intelligent application capability in the existing spaceflight field. The method comprises the following steps: acquiring a data source in the field of overall design of the spacecraft, wherein the data source comprises unstructured data; marking the data source in the step 1; a Bi-LSTM + CRF network is used for named entity recognition, and a Bi-LSTM + CRF model comprises an embedded layer, a Bi-LSTM and a conditional random field CRF; establishing a top layer structure of the knowledge graph, wherein the top layer structure comprises an entity type, a relation type and an attribute value; a database is selected according to the data volume and the query requirement for triple storage; the application of intelligent questioning and answering, a recommendation system and decision-making assistance is realized based on the knowledge graph. The method is suitable for obtaining the fine adjustment data of the spacecraft overall design large model.

The invention belongs to the technical field of man-machine interaction equipment, and particularly relates to an intelligent man-machine interaction method and system based on a multi-modal large model, and the system comprises a multi-modal input processing unit, an intelligent analysis decision unit, an interface protocol management unit and a visual man-machine interaction interface unit. Through modular design and tight data flow direction control, efficient and accurate man-machine interaction is achieved, all the modules cooperate with one another to jointly complete multi-modal data acquisition, processing, fusion, decision support, instruction execution and feedback tasks, and the system is high in practicability and easy to popularize. Decision-making auxiliary supports such as autonomous task planning, dynamic environment perception and real-time intelligent control are provided for underwater operation equipment, the safety and execution efficiency of the underwater operation equipment are improved, and the problems that a traditional man-machine interaction system is insufficient in perception, rigid in planning and delayed in control in complex scenes such as underwater operation are solved.

The invention provides a power distribution network voltage quality online evaluation method and system supporting new energy group access, and belongs to the field of power system automation. Comprising the following steps: generating a node dynamic feature vector through multi-source data fusion and feature extraction; constructing a dynamic causal association diagram, and synchronously outputting a voltage quality multi-dimensional evaluation index and disturbance source contribution degree analysis by using a gating space-time diffusion mechanism; and generating graded early warning based on evaluation and traceability results, and generating auxiliary decision-making information of recommended control measures through strategy matching, effect simulation and multi-target optimization. The system comprises a data acquisition and fusion module, a dynamic causal association diagram construction module, a causal time sequence diagram diffusion evaluation module, an endogenous traceability analysis module, a grading early warning and decision auxiliary module and the like. According to the invention, real-time evaluation, accurate traceability and intelligent decision support of the voltage quality problem of the new energy power distribution network are realized.

The invention provides an intelligent examination and approval auxiliary system and method driven by relation chain analysis. The system comprises: a data input and processing module supporting a plurality of data input modes and preprocessing input data; the relation chain analysis module performs deep analysis on data in the approval process through a relation chain analysis algorithm to generate a relation chain graph; the intelligent decision support module performs intelligent analysis on data in the approval process through an artificial intelligence algorithm and provides decision auxiliary suggestions; the risk identification and management module monitors data in the approval process in real time through an intelligent algorithm and relation chain analysis, and discovers business exceptions and potential risks; and the data visualization module provides a visual data analysis graph and supports an approver to comprehensively understand business conditions and decision-making backgrounds. The invention provides an intelligent examination and approval auxiliary system and method driven by relation chain analysis, and aims to improve the efficiency and accuracy of an examination and approval process and enhance the capability of identifying and managing enterprise risks.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention relates to a trade bill multi-dimensional processing method, device and system, and the method comprises the steps: obtaining a plurality of pieces of trade bill data, and carrying out the intelligent recognition and structural processing, and obtaining bill field information; performing service association integration on the bill field information based on a preset service identifier to generate bill service data; performing multiple verification and analysis on the bill business data to obtain verification information; and performing multi-dimensional trade process analysis according to the verification information and the bill business data, and outputting decision-making auxiliary information. According to the invention, the automatic conversion from the original bill to the decision auxiliary information can be realized, and the transparency, controllability and operation efficiency of the whole trade process are improved.

The present application provides a method for intelligent legal assistance and a multi-agent system, the method including: constructing a dynamically updated legal knowledge system; constructing a case diversion agent, a legal application agent, a similar case retrieval agent and a risk warning agent based on the dynamically updated legal knowledge system, and realizing a multi-agent collaborative mechanism through a collaborative structure based on message passing and state sharing; constructing a preference mapping relationship model through case complexity assessment and quality assessment based on the multi-agent collaborative mechanism, and realizing multi-case relationship perception preference mapping; based on multi-case relationship perception preference mapping, combining a large language model with Prolog-based knowledge management technology to construct a planning tree PLANTOR framework, generate a robot-oriented knowledge base and planning program, and convert them into a behavior tree; according to the PLANTOR framework, realize multi-agent reasoning chain alignment through the joint optimization of transmission optimization and embedding optimization, and complete legal decision-making assistance. The above scheme effectively improves the accuracy and efficiency of legal processing.

The invention provides an enterprise complex decision auxiliary method and system based on a multi-modal cognitive intelligent large model, and belongs to the technical field of artificial intelligence. The method comprises the steps of collecting multi-modal enterprise complex decision auxiliary data, and performing data cleaning and data labeling to obtain a training data set; constructing an enterprise complex decision-making auxiliary large model; inputting the training data set into the enterprise complex decision auxiliary large model, and optimizing large model training to obtain a trained enterprise complex decision auxiliary large model; and deploying the trained large model to a server, and generating decision suggestions in combination with an input data enhancement strategy. According to the method, through joint processing of the bimodal data, the reasonability and the intelligent degree of model decision can be remarkably improved, and the model prediction accuracy in different scenes is ensured.

The invention discloses an end-stage kidney disease patient dialysis mode decision-making auxiliary system, and relates to the technical field of data processing, and the system comprises a multi-dimensional data collection module which is used for carrying out the data collection of a patient, and building a patient data set; the integrated geographic information module is used for generating a dialysis center reachability index, and the reachability index comprises real-time traffic time cost and public traffic coverage density; the feature coupling module is used for mapping the patient data set and the reachability index to a unified vector space and then executing adaptive weight distribution; the fitness analysis unit is used for configuring a balance fitness function based on the self-adaptive weight distribution result, performing balance optimization by using the balance fitness function and the vector data mapped to the unified vector space, and establishing a balance optimization result; and the visual terminal is used for interactively displaying the balance optimization result. Therefore, the technical effect of providing visual data support and accurate decision-making assistance is achieved.

The application provides a liver cancer risk stratification and treatment recommendation information generation method and device, the method comprises the following steps: obtaining liver cancer related structured clinical data of a target object; based on a pre-set electronic medical record narrative template, converting the structured clinical data into an electronic medical record style narrative text containing clinical semantics, embedding key clinical variable markers corresponding to the structured clinical data into the electronic medical record style narrative text, and constructing a model input sequence; inputting the model input sequence into a pre-trained large language model for inference operation, outputting a structured text stream, and the large language model is obtained through decision tree constraint based on a liver cancer diagnosis and treatment guideline and multi-objective reinforcement learning strategy training; and extracting comprehensive decision assistance information from the structured text stream by using a pre-set analysis rule, realizing high-credibility clinical assistance decision, and significantly improving the accuracy and logical consistency of the treatment recommendation information.

Genome-wide data is obtained, and data cleansing, data sparsity processing and bioinformatics feature extraction are performed on the obtained genome-wide data; model construction is performed based on the sparsity-processed genome-wide data and the bioinformatics features to obtain a preliminary hybrid model; model training, regularization, and interpretability enhancement are performed on the preliminary hybrid model to obtain a trained model weight and an interpretability analysis corresponding to the trained model weight; learning and uncertainty estimation are performed based on the trained model weight and to-be-predicted genome-wide data on the hybrid model to obtain an integrated prediction result and uncertainties corresponding to the integrated prediction result; and personalized medical advice and decision assistance are performed based on the integrated prediction result, the interpretability analysis, and the uncertainties corresponding to the integrated prediction result.

The invention discloses a non-invasive hemodynamic detection and decision-making assistance method and device. The method comprises the steps of obtaining a first data set; preprocessing the first data set to obtain a second data set; an intracardiac prediction model based on a Transform architecture is constructed and trained; inputting the second data set to the trained intracardiac prediction model, and outputting the second data set as a third data set; collecting expert experience knowledge; constructing a relational expert knowledge base by adopting a knowledge representation method of a semantic network; performing trend analysis and correlation analysis on the third data set, the real-time electrocardiogram monitor physiological parameters and the electronic medical record to obtain an analysis result; performing matching reasoning on the analysis result and a treatment scheme of a corresponding case in an expert knowledge base to obtain a reasoning result; and generating personalized decision suggestions based on the reasoning result. According to the method, the treatment scheme decision is generated only based on the electrocardiograph monitor, and an efficient, intelligent and non-invasive diagnosis and treatment assistant is provided for clinical medical staff.

The present invention proposes a decision-making system and analysis method for rail transit engineering data analysis, the method comprising: constructing a first virtual digital twin model according to first data related to the rail transit engineering; analyzing the first data to obtain first construction risk data, first key node data and first countermeasures; obtaining multiple first construction plans according to the first virtual digital twin model, the first construction risk data, the first key node data and the first countermeasures; obtaining a first construction model by combining the first data and the first construction plan; installing multiple IoT smart sensors for collecting second data according to the first construction model; developing a construction decision support system based on a rule engine and artificial intelligence according to the first construction model and the second data, and providing decision suggestions. The scheme of the present invention can not only intelligently make flexible construction management decisions based on engineering data, but also reduce manual participation, and improve the efficiency and accuracy of construction management decisions.

The invention discloses a power-economic mapping matching method and system based on an improved hierarchical neural network model, and the method comprises the steps: inputting power data and economic data into a feature coding layer for feature extraction, and obtaining context semantic features of the power-economic data; inputting the context semantic features into an event relation modeling layer, constructing a multi-type graph structure by taking time nodes as event nodes in the graph structure, and modeling the multi-type graph structure to obtain a power-economic state sequence; performing feature matching on the electric power-economic state sequence through a preset electric power-economic feature matching mechanism of a feature matching layer to obtain a semantic similarity between the current electric power economic state and a target economic structure; and the output prediction layer constructs a task output structure according to different task requirements on the basis of semantic similarity, and obtains a final conclusion of power-economy mapping in combination with structural features and a matching result. According to the invention, the expression ability and decision-making assistance ability of the model to a complex coupling system are significantly improved.

The invention discloses a cotton textile industry decision-making auxiliary management system in the technical field of textile, which comprises a management system, a basic model, a vertical enhancement module and a mechanism model fusion module are integrated on the management system, and a technical training module, a core function module and a data set construction and computing power system are further loaded on the management system. The technical training module is loaded with a basic training concept, data stream fusion and an operation engine. According to the method, a domestic general large model and deep industry vertical enhancement are combined, and digital and intelligent application of knowledge is realized. The international advanced OODA design concept is introduced, an enterprise model with decision as the center is constructed, and instant perception and response are achieved. A physical transformation rule of textile material science and a yarn forming mechanism model are integrated into large model training, and the illusion problem of a general large model in the professional industrial field is solved. Three intelligent agents of AI cotton assorting, AI production scheduling and order whole process are constructed, and deep mapping and automatic closed loop of a business scene are realized.