153 results about "Case base" patented technology

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention relates to a bidding document total-factor blind-state desensitization method fusing multi-modal artificial intelligence, and the method comprises the following steps: S1, obtaining an original bidding document package, carrying out the data preprocessing, and obtaining a standardized document set; s2, according to the standardized document set, performing multi-modal content identification and alignment to obtain a multi-modal content library; s3, constructing a sensitive information classification system based on domain expert knowledge and a historical case library; s4, constructing a dynamic rule base according to the sensitive information classification system; s5, according to the multi-modal content library and the dynamic rule library, in combination with deep learning, multi-modal sensitive information intelligent detection is carried out, and a sensitive area set is confirmed; and S6, based on the dynamic rule base, performing intelligent desensitization on the sensitive area set to obtain a desensitized document. According to the method, the risk of sensitive information leakage is effectively reduced while the bidding file expression effect is improved.

The invention discloses an intelligent class case retrieval method and system based on reinforcement learning self-feedback, and the method comprises the following steps: S1, constructing a multi-level semantic understanding framework, carrying out the semantic understanding of a query case through the multi-level semantic understanding framework, and generating a complete semantic representation; s2, carrying out multi-dimensional similarity calculation and sorting optimization on the query case and historical discriminants in the candidate case library; s3, constructing a dynamic user portrait and optimizing a recommendation strategy; and S4, establishing multiple rounds of dialogues and context awareness, and carrying out visual display and feedback mechanism optimization. According to the intelligent class case retrieval method and class case retrieval system based on reinforcement learning self-feedback, a more accurate, efficient and reliable law intelligent retrieval system is finally achieved, judicial practice requirements are met, and quantitative and measurable technical progress is achieved in the aspects of retrieval accuracy, sorting quality, personalized service, law adaptability and the like.

The invention discloses an LLM central multi-agent tool arrangement and virtual-real closed-loop evolution scheduling system. The system comprises an intelligent sensing module, a knowledge module, an intelligent scheduling decision module and a closed-loop execution module. The intelligent sensing module collects real-time and multi-modal data of a physical environment and outputs structured sensing data, the knowledge module is used for constructing a knowledge graph and a historical task case library, and the intelligent scheduling decision-making module takes a large language model (LLM) as a core scheduling center, receives and analyzes task instructions, fuses the structured sensing data and graph and case information, and performs task scheduling on the knowledge graph and the historical task case library. Generating a decision scheme according with a constraint condition by using knowledge enhanced reasoning, and continuously optimizing in multiple iterations to generate a decision instruction; and the closed-loop execution module receives and drives the execution end to execute the decision instruction. According to the method, an intelligent closed-loop system of perception-decision-execution-evolution is constructed, and global optimization and high-reliability self-adaptive scheduling of multi-machine collaborative welding are realized.

The invention discloses a power grid fault intelligent diagnosis method and system based on multi-mode cooperation, and belongs to the technical field of power system fault diagnosis. The method comprises the following steps: collecting and off-line importing multi-modal data of primary equipment and secondary equipment of a multi-space power system in a power grid fault range in real time and standardizing the multi-modal data, and sequentially inputting the standardized multi-modal data into a local edge professional model, the method comprises the following steps: preliminarily reasoning fault properties in combination with a local knowledge base, inputting a knowledge-embedded causal reasoning large model deployed at a cloud end, generating an edge-cloud fault collaborative diagnosis model, iteratively updating the edge-cloud fault collaborative diagnosis model in combination with a multi-modal fault case library, and converting newest collected and imported power grid fault data into a multi-modal fault case library; and inputting the updated edge-cloud fault collaborative diagnosis model, generating a latest fault diagnosis report, and realizing the intelligent diagnosis of the power grid fault based on multi-modal collaboration. According to the invention, the power grid fault reason analysis performance and the identification accuracy are improved.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

The invention provides an intelligent contract risk identification method and device based on a big language model, and the method comprises the steps: constructing a legal knowledge graph and a risk case library, carrying out the structural processing of a contract text, carrying out the preliminary risk assessment through combining with a big language model, and carrying out the deep reasoning and case verification through combining with the knowledge graph. And generating a final report containing the risk type, the confidence coefficient, the reasoning chain and the modification suggestion, and continuously optimizing the model through user feedback data, so that efficient, accurate, explainable and adaptive optimization of contract risk identification is realized.

The embodiment of the invention discloses a method for intelligently assisting in reviewing a water conservation scheme of a water and soil conservation production and construction project, and the method achieves the intelligent feature matching of a new scheme and similar cases through the construction of a tagged knowledge base integrating historical cases of multiple industries, so as to recognize the generality compliance. A double-engine review mechanism is adopted, a form review module automatically checks document structure integrity and format specifications, and a logic review module executes cross-chapter data consistency check and business logic compliance analysis and highlights and positions error positions in a document. And recording decision logic in real time to form a feedback closed loop. And dynamically optimizing case library matching weight and recognition rules based on high-frequency error statistics and expert correction records. The modes of positioning review key points to chapters and sections, describing specific requirements of each review key point in a language, adding a check formula among data to review content needing to be calculated, determining a check type to which each review key point belongs, and the like are adopted, and the model is trained more clearly, so that the model has relatively professional review ability.

The invention discloses an intelligent voice question-answering system and a knowledge reasoning method for operation and maintenance of power equipment. The system comprises a knowledge base construction module and a core question and answer engine module, the knowledge base construction module is used for constructing a power equipment knowledge graph and a fault case library, and the core question and answer engine module comprises a natural language understanding unit, a multistage semantic retrieval unit and a voice interaction ambiguity resolution unit. A natural language understanding unit identifies intent categories and power entities. The multi-stage semantic retrieval unit executes the following steps: matching in an ontology layer of the knowledge graph; performing extended query to obtain detailed information; and similar case matching is carried out in the fault case library. The voice interaction ambiguity resolution unit is used for executing disambiguation processing; analyzing the confidence and clarifying the dialogue; and triggering knowledge retrieval and reasoning and generating a sorting answer. The operation and maintenance efficiency is improved, the dependence on experts is reduced, the operation and maintenance quality and safety are improved, knowledge precipitation and sharing are promoted, the field work experience is improved, and scientific decision making is assisted.

The invention relates to the technical field of tunnel engineering monitoring and construction, and discloses a shield construction tunnel full deformation prediction method based on artificial intelligence. The method comprises the following steps: acquiring a geological sensing data stream, a construction operation data stream and a historical deformation case library; performing multi-modal data fusion on the geological sensing data flow and the construction operation data flow to generate a construction environment map with consistent time and space; searching a case sequence matched with the current construction environment map from a historical deformation case library, performing feature significance evaluation through the case sequence, and identifying a dominant feature group of full deformation prediction; constructing an algorithm preferential engine based on the dominant feature group; activating an algorithm preferential engine to perform parallel processing on the construction environment map, and generating an adaptability measurement set of each algorithm; integrating the adaptability measurement set and real-time construction limiting conditions, and selecting an optimal prediction algorithm by adopting a trade-off decision-making mechanism; and generating a full deformation prediction model, and integrating the model to a shield construction monitoring platform.

The invention provides an external plug-in system based on retrieval enhancement. The external plug-in system comprises an indexing module, a retrieval module and a generation module, the index module is used for performing cleaning, segmentation, organization, semantic coding and storage tasks of text data; the index module comprises a dynamic data management sub-module, a strategy decision case library management sub-module, a Chinese character recursive segmentation sub-module, a tree index structure construction sub-module and a vectorization storage sub-module; the retrieval module is used for completing semantic matching and sorting on the basis of the structured index; the retrieval module comprises a double-tower rough arrangement sub-module, a cross fine arrangement sub-module, a real-time update sensing sub-module and a cache management sub-module; the generation module is used for carrying out integration and semantic modeling on the high-quality intelligence information provided by the retrieval module; the generation module comprises an information integration and reasoning sub-module and a credibility evaluation and feedback sub-module; the method supports fact alignment, real-time updating, logic interpretability and output evaluability.

The invention provides an intelligent auxiliary workflow system based on a localized large model and a dynamic knowledge base. The intelligent auxiliary workflow system comprises a localized large language model, a dynamic knowledge base module and an intelligent interaction engine. The dynamic knowledge base module comprises a policy and regulation base, a historical case base and a dynamic version control module; the intelligent interaction engine comprises an intention analysis module, a multi-stage retrieval strategy engine and a compliance verification module. The localized large language model is deployed in a private server. The policy and regulation library stores policies and regulations, and adopts a block vectorization technology to process long texts; the historical case library stores case information in a structured manner, and a correlation graph is constructed to support similar scene recommendation; and the dynamic version control module monitors a policy and regulation release channel API, automatically updates and marks an influence range, and carries out version management. According to the method, the problems of knowledge base updating lag, low retrieval efficiency, case base experience reuse and privacy security in a traditional office scene are solved.

The invention provides a data analysis method and system based on case self-learning. The method comprises the steps of obtaining a target task input by a user; retrieving a historical case based on the target task; if the historical case is retrieved, extracting context parameters according to the target task to adapt a solution of the historical case, generating a customized solution and obtaining a first analysis result; if not, performing real-time planning through a large language model to generate a new solution and obtain a second analysis result; performing quality evaluation on the newly generated solution and the second analysis result; and when the evaluation result meets a preset storage condition, storing the new solution as a new historical case in the case library. Through double-path processing and quality evaluation feedback closed loop, the problems that in the prior art, a case library is prone to being polluted due to lack of quality evaluation, and multiplexing of historical cases is rigid and low in efficiency are solved, the self-learning quality can be guaranteed, and the analysis efficiency can be improved.

The invention belongs to the technical field of large models and data agents, and particularly relates to a question answering method based on a large model and a data agent, and the method comprises the steps: receiving and preprocessing a user question, and generating a question data set; judging whether external data support is needed or not by utilizing a large model, and if so, dynamically generating an agent calling strategy; real-time data acquisition, domain knowledge analysis, multi-source data fusion and logical reasoning verification intelligent agent cooperative execution tasks are scheduled according to a strategy; fusing the traceability information returned by the intelligent agent and the knowledge of the large model to generate a preliminary answer, and outputting the preliminary answer after interpretable optimization; and finally, optimizing the case library according to user feedback and iterating an agent calling strategy. All data agent processing results are attached with full-link traceability information, data sources and reasoning processes are presented in an explanatory optimization mode, and a user can verify answer basis.

The invention discloses an artificial intelligence-based intelligent question and answer method in the human society field, and relates to the technical field of artificial intelligence and government affair services. Comprising the following steps: collecting and cleaning laws and regulations, files, common questions and case library contents related to human society through a knowledge graph construction module; constructing a structured human social knowledge graph, and performing intention recognition and slot extraction on the content input by the user by using a pre-training language model through a natural language understanding module; context memory and state tracking are carried out through a dialogue management module, and multi-round dialogue logic is controlled for an interaction mode of inquiry, clarification and guidance; providing a voice and character dual-channel input and output interface; an answer generation module generates a natural language answer based on a human society knowledge graph query result, the natural language answer is used for visualized display of structured data, and an API interface can be called to obtain real-time data; user interaction behaviors and identity information are recorded through a user portrait and personalized recommendation module, personalized policy interpretation and service recommendation are provided in combination with a user portrait, and historical question and answer record retrieval and reuse are provided; and obtaining question and answer effect feedback through a feedback learning and adaptive optimization module, carrying out question and answer strategy optimization by utilizing a reinforcement learning algorithm, automatically discovering uncovered questions and triggering a knowledge base updating process.

The invention relates to the technical field of enterprise management, in particular to an enterprise process custom configuration method and system based on a code-free technology, and the method comprises the steps: constructing a multi-layer directed hypergraph containing data, control and condition dependence, calculating an edge weight, and converting the dependence into a first-order logic constraint set; performing incremental satisfiability detection, and outputting a conflict core set containing conflict types and severity in combination with minimum unsatisfiability subset mining and strong connected component analysis; mapping the conflicts into modifiable elements, and generating parameterized candidate repair schemes based on a repair operation library and historical cases; converting the candidate scheme into a weighted constraint optimization problem, and solving an optimal scheme by adopting a branch and bound algorithm; influence propagation verification is completed in the copy, a differential patch and a rollback identifier are generated, transactional submission is carried out after passing, and the weight and the case library are updated. According to the method, automatic diagnosis and low-risk repair of implicit dependency conflicts are realized.

The invention discloses an atmospheric pollution case system, a case analysis method and a medium, and the system comprises a basic information layer which is used for storing the whole-process feature data and treatment measure data of a pollution event; the logic analysis layer is used for carrying out quality control on the data, carrying out source analysis and cause analysis on the whole-process feature data of the pollution event after the quality control, and carrying out effect evaluation on the treatment measures after the quality control; and the comprehensive decision-making layer is used for comparing historical cases according to the retrieval conditions and matching similar historical cases, and finding out treatment measures corresponding to the similar historical cases according to the similar historical cases, or formulating targeted new treatment measures according to the corresponding treatment measures. The invention provides a'basic information-logical analysis-comprehensive decision 'three-layer progressive architecture case library, so that other modules can be conveniently expanded on the architecture of the case library subsequently; and multi-source data are systematically integrated, so that the problem of fragmentation of existing case data is solved.

The invention relates to a judicial field case similar matching retrieval system and method, an element decomposition module is characterized in that original case materials are subjected to OCR recognition, text denoising and format unification to form a standardized file, and a standardized element set is output through a legal field named entity recognition model; the initial weight assignment module is used for constructing an element matrix for the standardized element set and carrying out weight assignment on each element in the element matrix; the retrieval intention analysis module is used for carrying out semantic understanding on the input application form, analyzing the retrieval intention of the input application form, dynamically adjusting element weights according to the retrieval intention and generating an element matrix after the weights are adjusted; and the vector matching module converts the dynamically adjusted element matrix into case vector representation, performs similarity matching with case vectors in a case library in combination with vector search, and outputs a plurality of historical cases closest to the current case in the aspects of legal structure and semantic key points and matching degree scores of the historical cases. And the response capability of similar case retrieval to the real intention of the user is effectively improved.

The invention discloses a vehicle load identification method based on a multi-modal large model, and the method comprises the following steps: selecting a reference bridge in a region for testing, and obtaining a reference bridge case library; analyzing the new bridge through a multi-modal large model, and matching a plurality of reference bridges most similar to the core characteristics of the new bridge from a reference bridge case library; the multi-modal large model migrates a load response model and model parameters of the reference bridge case to a new bridge through a case reasoning mechanism; and according to the structural difference between the new bridge and the matched reference bridge and the actual working condition of the new bridge, the multi-modal large model carries out architecture reconstruction on the migrated load response model. The method has the beneficial effects that the new bridge can directly reuse the model parameters and the load response rule of the reference bridge through the constructed reference bridge case library in combination with the retrieval enhancement generation and bridge-crossing knowledge migration mechanism of the multi-modal large model, so that the time cost and the economic cost of bridge-crossing deployment can be greatly reduced.

The invention relates to the technical field of intelligent exercise training, and particularly discloses a causal knowledge-driven man-machine collaborative personalized exercise training case reasoning method, which comprises the following steps of: constructing a case library containing a plurality of cases, and performing causal knowledge reconstruction at different levels; searching a case most similar to the target case from a case library based on the weight of each description variable; the difference of training scheme variables between the target case and the retrieved similar cases is recognized, anti-fact intervention and inference of any training scheme variable are conducted on the target case, and one or more new cases are screened out; when an actual result generated after the new case is executed does not conform to expectation, executing an attribution process, and generating different processing suggestions according to attribution types; new cases which do not conform to expectation are stored in a case library, and causal knowledge reconstruction is periodically carried out. According to the method, causal science and human-computer interaction can be deeply fused to carry out personalized training scheme reasoning.

The invention discloses a power industry cue word optimization method and system, and the method comprises the steps: carrying out the static structured optimization of a non-standardized cue word inputted by a user, obtaining a structured cue word which accords with the AI model understanding logic and the power industry specification, and storing a data record generated by the static structured optimization into a static optimization result temporary storage library; constructing a reinforcement learning model, carrying out offline training and online iteration, carrying out secondary optimization on the structured cue word by utilizing the trained reinforcement learning model, and inputting an optimization result into an AI model; and storing an output result of the AI model, user feedback, a data record generated by static structured optimization of the cue word and data generated by secondary optimization structured cue word of the reinforcement learning model into a cue word optimization case library. According to the method, the accuracy, the reliability and the adaptivity of the cue word generation result are improved, the application of a large model in the power industry is further promoted, and powerful technical support is provided for intelligent transformation of the power industry.

The invention provides a similar case retrieval method and device, electronic equipment, a storage medium and a product, and relates to the technical field of artificial intelligence. The method comprises the steps that a to-be-retrieved case input by a user is received; extracting a first key element in the to-be-retrieved case, and performing semantic vectorization processing on the to-be-retrieved case to obtain a first semantic vector set; wherein the first semantic vector set comprises at least two of a first key fact vector, a first sentence vector and a first chapter vector; matching the first key element with a second key element corresponding to a case library, and matching the first semantic vector set with a second semantic vector set corresponding to the case library to obtain a multi-path recall matching result; wherein the second semantic vector set comprises at least two of a second key fact vector, a second sentence vector and a second chapter vector; and according to a multi-path recall matching result, determining a similar case corresponding to the to-be-retrieved case. According to the method, the accuracy of judicial similar case retrieval results can be improved.

The invention relates to the technical field of computers, and discloses a health degree monitoring method for a server storage system, and the method comprises the steps: collecting a multi-source heterogeneous operation index; constructing a dynamic causal inference graph based on system topology and driving specifications; an anti-factual reasoning algorithm is adopted to trace an abnormal propagation path so as to identify a root cause; performing mode matching in combination with a historical fault case library, and calculating a comprehensive health degree score of the storage unit; and triggering grading early warning according to the score. According to the invention, through explicit modeling of the physical and logic dependency relationship and fusion of causal reasoning and historical experience, the fault positioning accuracy and early warning effectiveness are significantly improved, and the operation and maintenance noise is reduced.

The invention provides a power grid first-aid repair scheme generation method and system based on an expert model and enhanced scheduling, and belongs to the field of power distribution network emergency disposal. The method comprises the following steps: receiving fault information of a power distribution network and retrieving similar historical emergency repair cases from a historical case library; performing fusion reasoning on the fault information and the historical cases by using a retrieval enhancement generation and hybrid expert model to generate a preliminary first-aid repair scheme; performing rationality verification and resource completion on the preliminary first-aid repair scheme by using the operation and maintenance knowledge graph of the power distribution network; a reinforcement learning algorithm is adopted to carry out scheduling strategy optimization on the first-aid repair scheme after verification and completion; and establishing and solving a mathematical programming model, carrying out mathematical optimization on the optimized scheduling strategy, and generating and outputting a final first-aid repair scheme. Through deep integration of multiple technologies, automatic and intelligent generation of the first-aid repair scheme is realized, the problems of low manual decision making efficiency, module splitting and the like are effectively solved, and the scheme generation efficiency is remarkably improved.

The application discloses a multi-source data-based judicial case quality evaluation system management system and method, and belongs to the technical field of data processing. Through source discussion on multi-source data factors influencing judicial case quality, labeling on entity parts, and division into two categories of reaching standards and exceeding standards for procedure parts, a coded case label set under unified rules is generated, a reaching standard association set and an exceeding standard association set are generated, and further in-depth mining and analysis are carried out from social scales, the possibility of reaching standards in non-reached standards, and the possibility of non-reached standards in reached standards. Whether the case can be stored in a judicial case database as a high-quality judicial case is judged and analyzed in multiple aspects of static and dynamic, internal and external, and subjective and objective, so that the systematization and accuracy of the construction of the judicial case database are ensured. Meanwhile, in the process of judgment and analysis, the error and inaccuracy caused by human operation are greatly reduced, and the auditing of the quality of the judicial case has a comprehensive auxiliary guiding effect.

The invention discloses a case reasoning-based tower top injection agent optimization method for an oil refining atmospheric and vacuum distillation unit, which comprises the following steps of: acquiring historical data of a DCS (Distributed Control System) of the oil refining atmospheric and vacuum distillation unit for preprocessing, and extracting a steady-state working condition data set; dimensionality reduction is carried out on the data, and a production process database is established; collecting LIMS crude oil feeding analysis historical data of the atmospheric and vacuum distillation unit, and classifying the LIMS crude oil feeding analysis historical data; establishing a production case library based on index rule standardized data; calculating the similarity between the current to-be-optimized working condition, the physical property and the injection amount and each variable attribute in the case library according to the classification condition of the same feeding physical property in the production case library; in combination with the similarity weights of the case library attribute variables, the overall similarity is adopted to calculate the similarity of the injection agent optimization overall process; selecting L cases with the maximum similarity with the current case as a similar case set; and taking the injection agent corresponding to the minimum corrosion rate in the similar case set as the optimal injection agent. The problem that the tower top of an existing atmospheric and vacuum distillation unit is prone to corrosion is solved.

The invention provides a block chain case research and judgment method and system, a program product, equipment and a storage medium, and the method comprises the steps: obtaining a case semantic context corresponding to a block chain case based on the case input information of the block chain case through a large language model; the case semantic context is used for capital flow tracking; the case semantic context comprises on-chain traceable information and / or under-chain semantic information; based on the case semantic context, determining a target block chain address associated with the case-involved block chain address, and constructing a fund network graph; and analyzing the fund network atlas to obtain a case research and judgment result. According to the method, a large language model is utilized to perform semantic analysis on case input information, a fund network graph is constructed based on case-related block chain addresses in the case input information, and then global and multi-dimensional comprehensive analysis is performed on the constructed fund network graph, so that the case research and judgment efficiency is improved, and the problem of low manual analysis and judgment efficiency is solved.

The application provides a large model-based automatic code generation and optimization method and system. Among them, the application generates an initial logical framework by a large model, forms a symbolic structure through symbolic processing, and generates a code intermediate representation that retains key semantics, and finally outputs source code. An index set is constructed using a vulnerability knowledge graph, a source code instruction sequence is scanned through parallel computing, and risk instruction positions and vulnerability details are marked by combining semantic similarity and path pattern matching. Based on vulnerability information, a historical case database is retrieved, a matching relationship is established, and an optimization strategy containing repair operations and constraints is generated. A semantic analysis model is loaded at the risk position, a difference graph of execution mode is constructed to generate features, a large model parameter is fine-tuned based on the constructed adversarial sample, and the newly generated code is rechecked for vulnerabilities. The application realizes intelligent generation from requirements to code, and completes the loop of vulnerability identification, repair strategy generation, large model dynamic optimization, and post-repair verification.

The invention discloses a fair competition review case library construction and expansion method based on knowledge modeling and large language model generation, and the method comprises a case library construction stage and an intelligent expansion stage. Collecting data from the heterogeneous data source, analyzing the data into structured case data through an information extraction technology, and finally constructing case knowledge elements for describing case types, subjects and law article application relationships; in the expansion stage, based on existing case knowledge elements, a'case-legal provision 'relation model with variable scene parameters is constructed, diversified candidate case texts are generated by utilizing a large language model, and the candidate case texts are stored after being subjected to validity and logic consistency rechecking, so that efficient and high-quality automatic expansion of a case library is realized.

The invention discloses an auxiliary decision-making method and device for a wind power plant, electronic equipment and a medium, and relates to the technical field of artificial intelligence and wind power operation and maintaining.According to the scheme, multi-source heterogeneous information of the wind power plant is obtained in real time and converted into a structured Markdown format document, so that corresponding knowledge fragments are extracted from a knowledge base and a case base in the wind power field; and finally, splicing the knowledge fragments into a prompt template and inputting the prompt template into the pre-trained large language model to obtain the auxiliary decision content output by the pre-trained large language model, which is more accurate compared with the traditional manual experience, and is more accurate by depending on the wind power field knowledge base and case base and the pre-trained large language model. The finally output auxiliary decision content is more suitable for the actual problem of the wind power plant, the error rate is lower, and the efficiency is higher.