31 results about "Case base" patented technology

This invention discloses a fault feedback and planning strategy iterative repair method for high-orbit satellite collaborative mission execution. This method determines whether a fault warning is triggered based on fused features and dynamic thresholds. A closed-loop framework of "fault diagnosis - impact assessment - planning correction - verification iteration" is constructed. The diagnosis stage employs a hybrid diagnostic method combining deep learning and rule-based reasoning. The impact assessment stage establishes a quantitative assessment model based on task priority and resource reserves to determine the degree of fault impact. The planning correction stage introduces a dynamic resource reallocation algorithm to adjust the satellite payload operating mode and inter-satellite link topology based on the assessment results. The verification iteration stage uses digital twin technology to construct a simulation environment to verify the effectiveness of the correction strategy. Furthermore, an iterative optimization mechanism for the strategy library based on case-based reasoning and reinforcement learning is established. The strategy library is iteratively optimized using reinforcement learning algorithms to form an adaptive fault response strategy library, thereby improving the efficiency of the system's anti-interference capability evolution.

This invention relates to the field of intelligent document processing technology, specifically to a method and system for document processing in power transmission and transformation scenarios based on a brain-like memory mechanism. The method includes the following steps: generating a query plan based on query intent; recalling relevant documents from a hierarchically modeled power transmission and transformation document knowledge base; and extracting and reasoning information from the recalled documents using a dual-path parallel processing mechanism and a hierarchical abstraction processing mechanism; filling in a structured form according to a predefined template to generate preliminary structured output; simultaneously transforming the extracted and reasoned information into interpretable reasoning chain records; generating memory cases based on the reasoning chain records and confidence assessment results; and storing and optimizing the memory cases using a brain-like memory mechanism. This method possesses the ability to handle complex multi-step reasoning tasks, significantly improves reasoning accuracy, automates document information extraction, and greatly reduces manual processing time.

This invention discloses an industrial agent reasoning framework and method based on physical first-principle constraints, belonging to the field of industrial intelligence technology. The framework includes a data access layer, a task parsing layer, a candidate solution generation layer, a physical constraint verification layer, a conflict rollback and replanning layer, an execution decision layer, and a knowledge accumulation layer. The physical constraint verification layer uses at least one physical first-principle model as a pre-constraint hard constraint gating before the candidate decision sequence enters the execution decision layer. It performs parameter mapping, boundary condition solving, consistency calculation, residual determination, and executability determination on the physical computation variables obtained from the structured reasoning task mapping, and outputs structured conflict information when the verification fails. The conflict rollback and replanning layer performs targeted rollback and replanning based on the violation type, violation node level, and source of missing boundary conditions in the structured conflict information. The execution decision layer only outputs execution-type output results for candidate decision sequences that have obtained the pass flag. The knowledge accumulation layer writes the structured conflict information and the corrected pass results into a rule base, template base, or historical case base to serve as filtering conditions, initial parameter values, tool sorting criteria, or threshold setting criteria during subsequent candidate generation. This invention can improve the physical feasibility, reliability, interpretability, and auditability of industrial agent decision-making results.

This invention discloses a self-evolving multi-agent collaborative method and system based on a dynamic cognitive graph. The method includes constructing a dynamic cognitive graph and modeling the reasoning process as a directed acyclic graph; initializing a multi-agent collaborative network; executing sub-tasks and dynamically reconstructing the cognitive graph using execution feedback; evaluating the execution process using a case-based reasoning evolutionary mechanism, generating structured cases, and encoding and storing the structured cases in a case library for experience reuse in subsequent tasks; the multi-agent collaborative network adopts a hybrid collaborative architecture, communicating between agents through a blackboard system; monitoring metacognition by real-time monitoring of the confidence level of the reasoning process, and using an arbitration mechanism to arbitrate conflicts when multiple agents reach contradictory conclusions; outputting the execution and evaluating the system performance. This invention significantly improves the processing capability of complex long-link reasoning tasks and the system's continuous learning capability.

This invention discloses a method, apparatus, equipment, and storage medium for assisting in the review of disciplinary cases based on RAG technology, belonging to the field of artificial intelligence technology. The method involves: extracting various disciplinary elements from the disciplinary case to be analyzed and generating several semantic vectors for these elements; calculating the semantic distance between the disciplinary case to be analyzed and various legal provisions in a preset RAG knowledge base based on these semantic vectors, and selecting candidate disciplinary provisions; when a preset exception provision exists among the candidate disciplinary provisions, selecting reference cases from several historical cases corresponding to the preset exception provision whose case similarity to the disciplinary case to be analyzed is greater than a preset similarity threshold, and generating disciplinary features based on each disciplinary element and each reference case; inputting the disciplinary features into a preset disciplinary prediction model so that the preset disciplinary prediction model outputs disciplinary assistance results for the disciplinary case to be analyzed. By implementing this invention, the problem of low accuracy in disciplinary assistance results can be solved.

The application relates to the technical field of artificial intelligence and industrial simulation, in particular to an evolvable industrial simulation question and answer intelligent agent system and a simulation question and answer method thereof. The evolvable industrial simulation question and answer intelligent agent system comprises a knowledge base module, a tool calling module, an intelligent agent core module, a user interaction module and a log and audit module; the knowledge base module comprises a multi-element knowledge collection unit, a document loading and cutting unit, a vectorization embedding and storage unit, a semantic retrieval unit, a knowledge evolution unit, a rule base and a case base; the application has the beneficial effects that (1) knowledge is dynamically evolved and updated autonomously and timely; (2) the system has the capability of autonomously refining knowledge from simulation results; (3) tool calling is intelligentized, and the system realizes "question and answer as calculation"; (4) the system has strong capabilities of complex problem disassembly and iterative solution; and (5) the system has excellent adaptability and expandability.

The present disclosure relates to the field of machine learning, and provides a medical question and answer method, device, storage medium, electronic equipment and product. The method comprises: obtaining a medical description question, identifying a question type corresponding to the medical description question; in the case of a non-concept type, determining historical case information matched with the medical description question based on a pre-constructed case library; and determining knowledge information matched with the medical description question based on a pre-constructed knowledge graph and knowledge base; and determining reply information corresponding to the medical description question based on the historical case information and the knowledge information through a question and answer model. By matching the historical case information in the case library, the matched historical case information and the recalled knowledge information are input into the question and answer model at the same time, the historical case information is used as prior information, the hit rate of the knowledge information is improved, and the accuracy of the reply information is improved.

The application relates to the technical field of green construction scheme evaluation, and discloses a green construction scheme evaluation method and system based on case reasoning and a fuzzy analytic hierarchy process, which comprises the following steps: constructing an evaluation index system, structuring historical cases to form a case library, obtaining subjective weights based on the fuzzy analytic hierarchy process and performing consistency checking, obtaining objective weights based on an entropy weight method, fusing the subjective weights and the objective weights to obtain comprehensive weights of each index based on the minimum discrimination information principle, converting index values into cloud model parameters based on a reverse cloud model, respectively generating index cloud model parameters of a target engineering scheme and each historical case, obtaining a weighted comprehensive similarity according to position similarity and shape similarity, selecting cases with a weighted comprehensive similarity not lower than a threshold value as similar cases, determining an evaluation grade of the target engineering, and optimizing the target engineering scheme; and the application solves the problems of one-sided index system, low case matching accuracy and disconnection between evaluation and optimization measures in evaluation.

The present application relates to the technical field of fault diagnosis, and more particularly to an IT system fault automatic diagnosis method and system based on a large language model, which comprises an IT diagnosis center, a heterogeneous data acquisition module, a diagnosis analysis module, a repair scheme matching module, a repair execution and verification module, and a diagnosis evaluation and attribution module; the present application realizes automatic collection and standardized processing of multi-source heterogeneous operation and maintenance data, solves the problem of traditional scattered data, provides comprehensive and accurate data support for diagnosis, and improves root cause positioning efficiency and accuracy by combining a large language model and a text similarity algorithm, thereby eliminating the dependence on artificial experience; through three-level repair verification and full-process closed-loop evaluation of "alarm-indicator-root cause", the effectiveness of repair is ensured, the failure root cause is accurately located, the blindness of optimization is avoided, the scheme library and case library are dynamically updated, the IT environment changes are adapted, the diagnosis and repair capability is continuously improved, and the operation and maintenance cost is reduced.

This invention discloses a method for optimizing digital human cognitive decision-making in the procurement domain based on graph neural networks, comprising the following steps: collecting multi-source data to construct a procurement knowledge graph and rule base; inputting the graph into a hyperbolic graph convolutional network and applying logical constraints to the hyperbolic embeddings of nodes; constructing a case library; for new tasks, retrieving similar cases based on the current subgraph and embeddings to generate a preliminary strategy; performing uncertainty detection on the preliminary strategy to generate a question list, selectively retrieving supplementary information, and revising the strategy; submitting the final strategy to an expert system for rule base verification, and if it fails, generating revision suggestions and outputting the revised strategy; feeding back the reviewed and revised cases to the network for online incremental learning, and adding successful cases to the case library for updates. This invention achieves a deep integration of procurement domain knowledge and data-driven models, improving the accuracy, interpretability, and adaptability of digital human decision-making.

This invention relates to the field of data processing technology and discloses a method for constructing infectious disease transmission chains based on mechanistic process coupling. The method includes: representing the process with case data; calculating the initial transmission probability between pairs of cases based on the degree of overlap of their spatiotemporal trajectories; constructing an infectious disease knowledge graph to represent the infectious disease mechanism, and fusing case data into a Bayesian network model to perform mechanism-guided probability correction on the initial transmission probability to obtain the posterior transmission probability between pairs of cases; constructing a weighted directed propagation graph based on the posterior transmission probability, and reconstructing the infectious disease transmission chain through graph structure optimization. This method introduces infectious disease mechanisms into the transmission probability calculation process to constrain the determination of transmission relationships between cases, avoiding false transmission edges generated solely based on spatiotemporal trajectory overlap, and improving the reliability and interpretability of transmission chain inference.

This invention provides a method and system for fire emergency response plan simulation and verification based on case-based reasoning. The method includes establishing a structured historical fire case database, calculating the mixed similarity between the current scenario and historical cases based on rough set theory and cloud models, and selecting similar source cases; adapting the handling strategies of the source cases to the model by constraining the model to generate an initial plan to be verified; establishing a generalized stochastic Petri net model representing the evolution of the disaster-affected state and the interaction of rescue resources, mapping the initial plan to be verified to the transition rate and initial identifier in the database within the model, performing concurrent conflict detection and temporal logic deduction, and evaluating transient performance indicators; if the target rescue success indicator does not reach a preset threshold, iteratively correcting the resource scheduling parameters of the plan based on the sensitivity analysis results of key transitions, and repeating the deduction steps until the termination condition is met, outputting the target fire emergency response plan.

The wisdom heating full-dimensional perception and decision assistance system and method based on a large model of the present application comprise the following steps: constructing a multi-modal heterogeneous data perception and preprocessing network; performing semantic alignment and unified representation of multi-modal features, extracting feature vectors of heterogeneous data through respective modal dedicated encoders; performing retrieval enhancement generation based on space-time constraints, retrieving matching disposal plans and mechanism knowledge in the pre-constructed heating field knowledge base and historical case base according to the fusion context vector corresponding to the current working condition; performing causal reasoning and decision generation based on thought chains, comprehensively analyzing the fusion context vector and the enhanced prompt context by using a large-scale pre-trained language model; and performing decision closed-loop optimization based on human feedback. The present application realizes the continuous accumulation of knowledge assets; and the special cold start strategy and online evolution mechanism ensure that the system can be quickly put into operation and produce actual benefits in old pipe networks or newly built areas with missing data.

The application discloses a teaching evaluation method and system based on multi-agent collaborative evaluation closed loop, and belongs to the technical field of education informatization and artificial intelligence. The method comprises the following steps: collecting multi-source evaluation data of school teachers, enterprise tutors and student self-evaluation and mutual evaluation; constructing a multi-dimensional index system comprising theoretical knowledge, industrial practical ability, team cooperation ability and innovative thinking; adopting a fuzzy comprehensive evaluation method and a D-S evidence theory cascade two-stage fusion algorithm to fuse data, and dynamically adjusting the weights of each evaluation subject; establishing a correlation matrix of knowledge points and evaluation indexes, triggering an optimization suggestion generation engine when the evaluation result is lower than a threshold value, retrieving a course content optimization scheme from a knowledge graph / case library and pushing the scheme; iteratively executing the above steps to drive continuous optimization of course content, practical projects and training modes; and adopting a reinforcement learning algorithm to self-learn and optimize strategies. The application realizes the connection of evaluation and industrial demand and the self-adaptive evolution of the teaching system, and improves the talent training quality.

This invention discloses a project compliance audit method and system based on collaborative reasoning using an expert knowledge base and a large model. First, a large model is used to analyze a collection of laws and regulations and audit practice documents, automatically extracting compliance audit-related knowledge. Then, this compliance audit-related knowledge is used to construct an expert knowledge base comprising a legal and regulatory knowledge base, an audit case library, an audit experience library, an audit practice library, and a compliance audit rule library. This invention achieves the function of automatically constructing and dynamically expanding the expert knowledge base by extracting legal and regulatory knowledge and audit knowledge using a large model. Furthermore, the reliability of the expert knowledge base and factual data is ensured through a confidence level mechanism and human-machine collaborative verification. Simultaneously, the use of audit evidence traceability chain information enables full-link traceability from audit conclusions to original evidence, greatly improving knowledge conversion efficiency and enhancing the interpretability and reliability of compliance audits, making it suitable for widespread promotion and use.

The application relates to the technical field of intelligent sports training, and particularly discloses a case reasoning method for personalized sports training driven by cause-effect knowledge and based on man-machine cooperation, which comprises the following steps: constructing a case library containing multiple cases and reconstructing cause-effect knowledge at different levels; retrieving a case most similar to a target case from the case library based on the weight of each description variable; identifying the difference between the training scheme variables of the target case and the retrieved similar case, performing counterfactual intervention and inference on any training scheme variable of the target case, and screening one or more new cases; when the actual result generated after the execution of the new case does not meet the expectation, executing an attribution process and generating different processing suggestions according to the attribution type; and storing the new case that does not meet the expectation in the case library and periodically reconstructing the cause-effect knowledge. The application can deeply integrate cause-effect science and man-machine interaction to perform personalized training scheme reasoning.

The application discloses a device troubleshooting method, device, equipment and program product, solves the problems of insufficient information utilization, fixed diagnosis process and inability to automatically execute long-chain reasoning in the existing troubleshooting scheme. The scheme includes: performing device entity recognition on the multi-modal description information of the user input device for reporting a fault, obtaining the device attribute information of the device for reporting a fault. Retrieve the standard working state information corresponding to the device attribute information from the device knowledge base, and compare the multi-modal description information with the standard working state information to obtain the abnormal description information of the device for reporting a fault. Retrieve the diagnosis knowledge corresponding to the abnormal description information from the fault diagnosis knowledge base and / or the fault diagnosis historical case base to generate the diagnosis process of the abnormal description information. Configure the diagnosis process as a diagnosis chain cascaded by at least two diagnosis task nodes, execute the diagnosis task nodes in the diagnosis chain in path order, obtain all diagnosis results, generate and output a fault diagnosis report for the device for reporting a fault.

A small space carbon neutralization strategy generation system based on deep reinforcement learning reasoning comprises a feature analysis module, a digital carbon space initialization module, a carbon neutralization strategy optimization module, a carbon neutralization strategy derivation module, a model training support module and a carbon concentration prediction module. The present application drives the construction of a digital carbon space based on a fluid simulation engine based on physical formulas, generates a focus concentration monitoring sequence based on a massive space case library, constitutes a training set, extracts rich space-time semantics in the training set through a carbon concentration prediction model, forms an efficient reasoning method for space carbon concentration, uses a deep reinforcement learning model to reason and optimize for any given small space, and thus can directly reason the carbon concentration of a given space-time. The carbon concentration prediction model is used as an interactive environment to construct and train a space carbon neutralization strategy optimization module based on deep reinforcement learning. For the space environment point cloud model, carbon sink point cloud model and carbon source configuration input by the user, an efficient space carbon neutralization strategy is reasoned and generated, and the complete point cloud model and related configuration of the corresponding strategy are output.

The application provides a method and system for matching and pushing judicial views of a large model based on internal and external case fusion, comprising: obtaining internal case data and external case data, extracting case characteristics and corresponding judicial views of each case; cleaning and fusing the judicial views, including: grouping the cases based on the case characteristics, and for the judicial views with conflicts in the same group, screening according to the evaluation results of the authority dimension and the evaluation results of the fitting degree dimension, and retaining the judicial views; constructing a structured judicial view library indexed by the case characteristics, the judicial view library being used for storing the cleaned and governed judicial views; receiving description information of a target case, and extracting case characteristics of the target case by using a large model; matching the case characteristics of the target case with the index characteristics in the judicial view library, and outputting the matched judicial views. The judicial views that meet the actual needs of users are intelligently and accurately matched and pushed from a large number of cases.

The invention discloses a bridge report generation method and system based on multi-modal causal and reinforcement learning. The method comprises the following steps: acquiring a disease image and text description, extracting structured disease information from the text description, generating multi-modal feature data, and performing image-text consistency verification; retrieving standard articles and historical cases based on the verified disease information, and positioning disease nodes in the causal knowledge graph and reversely tracing root cause nodes to generate a reasoning chain; fusing the multi-modal feature data, the retrieval result and the reasoning chain, generating an evaluation report draft through a large language model, and performing logic verification; and obtaining an expert correction text, inputting the expert correction text into the reward model, calculating a feedback score, and optimizing a knowledge graph edge weight and a report generation strategy. The invention also provides a system for realizing the method. According to the method, through image-text consistency verification, causal reasoning and reinforcement learning, the report accuracy, interpretability and compliance are effectively improved.

The application discloses a kind of knowledge graph construction methods based on fine-grained retrieval and reverse restoration self-error correction, specifically: first, standard reference case library is constructed, and standard reference vector is calculated.Then, the long text S to be measured is disassembled into sentences to be processed;Each sentence is vectorized, and the similarity score of its standard reference vector is calculated, and the first standard reference case of each sentence is screened;Through large language model, the non-standard logical relationship contained in each sentence is extracted and vectorized, the cosine similarity is calculated, and the candidate mapping is obtained by descending arrangement, after summarizing, double-feature reordering is carried out using multi-round greedy reordering algorithm, and the first standard reference case is screened out, and the single sentence is spliced into large language model, and triple extraction is carried out;Finally, the relationship triple set of all sentences constitutes knowledge graph.The application improves the precision and recall rate of triple extraction under the premise of avoiding redundancy.

Embodiments of the present disclosure provide a human-machine collaboration-based behavior plan generation method and device, which comprises: receiving project information, performing semantic reasoning on the received project information based on an ontology library, and decomposing the project into multiple executable sub-projects; evaluating the current project environment, preconditions and constraints based on historical data in a case library, and recommending appropriate strategies and behavior arrangements for each sub-project; allocating resources required by each sub-project in the project execution process based on a resource library, and outputting a resource allocation scheme; generating an execution sequence for each sub-project according to the front-back dependency relationship of the project, and dynamically adjusting the execution sequence according to the state change; and adjusting the priority, resource allocation and execution strategy of the project through a feedback mechanism based on a dynamic artificial intervention threshold, and generating a behavior plan. The present scheme can effectively improve the efficiency and accuracy of project planning and execution through the mode of machine autonomous planning and artificial auxiliary behavior plan formulation.

The application discloses a kind of based on big language model and the intelligent generation method of multi-source knowledge fusion grass-roots emergency command plan, it is related to artificial intelligence technical field;Including: step 1: build data access layer, and real-time data is converted into uniform format by standardized interface;Step 2: construct knowledge fusion layer, establish causal association index;Step 3: build LLM inference engine layer, adopt big model fine-tuning adaptation grass-roots emergency field;Step 4: build intelligent interaction layer and provide natural language interface, plan visual rendering, artificial disposal result is input knowledge fusion layer reversely, realize the incremental update of case base and atlas, form use-feedback-optimization positive cycle.

The application relates to the technical field of safety management, and provides a violent attack event intelligent disposal decision method and system based on deep learning for key places. A disposal step of a current violent attack event is determined from a pre-constructed disposal case library through attribute similarity, so that an initial disposal process of the current violent attack event is generated, the initial disposal process is corrected based on a rule library, and a structured disposal process of the current violent attack event is obtained. Finally, an improved table-to-text generation model is used to convert the structured disposal process into natural language in stages, and a disposal scheme of the current violent attack event is generated. Through the disposal case library of the violent attack event, case reasoning, rule reasoning and deep learning are combined to generate the disposal process of the current violent attack event, so that the problem that a single reasoning mechanism has poor adaptability and low precision is effectively solved.

The application relates to the technical field of artificial intelligence and industrial simulation, in particular to an evolvable industrial simulation question and answer intelligent agent system and a simulation question and answer method thereof. The evolvable industrial simulation question and answer intelligent agent system comprises a knowledge base module, a tool calling module, an intelligent agent core module, a user interaction module and a log and audit module; the knowledge base module comprises a multi-element knowledge collection unit, a document loading and cutting unit, a vectorization embedding and storage unit, a semantic retrieval unit, a knowledge evolution unit, a rule base and a case base; the application has the beneficial effects that (1) knowledge is dynamically evolved and updated autonomously and timely; (2) the system has the capability of autonomously refining knowledge from simulation results; (3) tool calling is intelligentized, and the system realizes "question and answer as calculation"; (4) the system has strong capabilities of complex problem disassembly and iterative solution; and (5) the system has excellent adaptability and expandability.

An artificial-intelligence-assisted (AI-assisted) certification system includes an argumentation processor and an assurance case processor. The argumentation processor is configured to generate an argumentation pattern. The assurance case processor is configured to obtain the argumentation pattern from the argumentation processor, to automatically generate an assurance case based on one or more argumentation patterns, to determine evidence indicative of premises in the argumentation pattern, and to automatically assess the assurance case based on the evidence.

This invention relates to the field of ship navigation and maneuvering automation technology. It discloses a method and system for supporting the berthing and maneuvering of large ships based on case-based reasoning and hybrid intelligence, comprising the following steps: S1, constructing a large ship berthing case library, wherein each case includes characteristic attributes and a corresponding decision-making scheme. The characteristic attributes include at least multi-dimensional information on ship characteristics, meteorological and hydrological conditions, and port conditions; S2, responding to the input characteristic attributes of the current berthing task. This invention organically combines expert experience with artificial intelligence technologies such as case-based reasoning, cloud models, BP neural networks, and digital twins, providing safe, efficient, and explainable intelligent decision support for large ship operations. This significantly reduces maneuvering risks and effectively solves the pain point of traditional berthing relying on personal experience. The case library enables explicit inheritance of experience, shortening the talent training cycle; intelligently generating standardized maneuvering instructions reduces decision-making subjectivity and improves operational standardization.

The application relates to a power supply repair material reservation recommendation method and system based on a large model intelligent agent. The method comprises the following steps: a task analysis intelligent agent calls a large language model to perform semantic analysis on a repair task description, and finds similar cases from a historical repair case library; a recommendation decision intelligent agent constructs a multi-round reasoning process for interaction with the large language model through Few-shot prompt words, and outputs a repair material recommendation list through the large language model; the recommendation decision intelligent agent and a material inventory system establish an interface linkage mechanism to generate a repair material replacement list; a feedback optimization intelligent agent generates a recommendation explanation and collects user feedback data, and optimizes the large language model according to the user feedback data. Through semantic reasoning and historical case analogy by calling the large language model through the task analysis intelligent agent, a specific repair scene can be more accurately matched, the first reservation hit rate is improved, the repeated scheduling rate is reduced, and the material preparation efficiency and the decision quality are improved.

This invention provides an automated code optimization method and system for low-code platforms, relating to the field of low-code platform technology. First, it obtains the set of code to be optimized and a full description of the business scenario from the user's visual configuration within the low-code platform. Next, it constructs a dynamic adaptation model for the code scenario, generating quantified results of code unit scenario adaptation. Then, based on the quantified results and a historical case library, it generates a set of dynamically self-adjusting optimization rules. Next, it optimizes the code units according to the set of dynamically self-adjusting optimization rules and coordinates the invocation of related logic, generating a preliminary optimized code set. Finally, it deploys the preliminary optimized code to a simulation environment for verification and generates deployment instructions to be sent to the deployment module. This invention closely integrates with business scenarios, possesses dynamic adjustment capabilities, and can improve the efficiency and quality of code optimization on low-code platforms.

This invention discloses a vectorless intelligent retrieval method for agricultural planning based on a state machine. First, it acquires a set of original case texts and a set of professional terms for agricultural planning, deconstructs the unstructured text into business dimensions, generates structured knowledge data, and builds a standardized case library, a structured dimensional model for agricultural planning, and supporting dictionaries, rule systems, and toolsets. Then, it configures a built-in state machine and task judgment rules for the intelligent agent, completing the deployment of autonomous execution logic. The intelligent agent transforms user needs into standardized dimensional conditions for the adaptation model, conducts multi-dimensional logical matching retrieval, dynamically optimizes the retrieval based on the state machine, obtains a qualified case dataset, and finally extracts common experiences from the cases, labels differentiated features, and generates an agricultural planning implementation plan adapted to the needs. This invention completely abandons the vector matching approach, avoiding semantic drift at its source through precise logical matching of business dimensions, and realizing autonomous need completion and retrieval iteration for the intelligent agent.