420 results about "Rule matching" patented technology

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention provides a cement equipment maintenance decision-making method and device based on a knowledge graph and large model reasoning, relates to the field of cement industry intelligent operation and maintenance, and solves the technical problem of decision-making response delay caused by knowledge fragmentation. The method comprises the following steps: extracting real-time characteristics from vibration spectrum signals, temperature curves and torque waveform data collected by an edge gateway, and extracting a work order entity triple from a natural language work order text of an EAM system; based on an equipment BOM list, a historical maintenance record and an FMEA analysis table, physical assembly constraint conditions are defined through ontology modeling to generate a cement equipment topological relation and a fault rule chain, and a knowledge graph is created to output a fault rule base with confidence coefficient weights. And inputting the real-time feature vector and the work order entity triple into a multi-modal collaborative inference engine, triggering a matched fault rule chain by combining real-time features and semantic features, outputting a fault root cause and an associated maintenance strategy ID, and labeling a logic chain. And activating the associated maintenance strategy ID and obtaining the real-time characteristic deviation degree of the maintenance strategy ID, quantifying the decision credibility through a tracing rule matching path, obtaining an executable maintenance instruction packet with a logic chain, executing the maintenance instruction packet and dynamically updating the knowledge graph based on a maintenance result. The method is used in the maintenance decision-making process of the cement equipment.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention discloses a semantic judicial contract compliance automatic generation and examination system based on a DIKWP artificial consciousness model. The system is constructed according to five layers of data, information, knowledge, wisdom and intention, wherein the data layer integrates multi-source information; the information layer enables terms to be semantized and disambiguated; the knowledge layer performs rule matching and compliance judgment by using a legal knowledge graph; the wisdom layer automatically corrects the risk terms based on a fair principle and performs scene simulation; and the intention layer is embedded into targets of all parties to ensure that the contract is consistent with the compliance intention. The system process covers the whole process from intention analysis, knowledge reasoning to intelligent generation and intention negotiation, and finally a compliance contract and an audit report are output. The system is provided with a multi-language semantic adaptation and intelligent negotiation interface, and convenient access can be realized through an API (Application Program Interface). The system realizes integration of contract generation and review, intention driving and decision interpretability, can effectively reduce compliance cost and dispute risks, and is suitable for multiple scenes such as finance, e-commerce, government purchase and the like.

The invention discloses a business data quality treatment method supporting system hidden danger identification, and relates to the technical field of computer information processing, and the method comprises the steps: S1, collecting a real-time batch tracking data stream from an enterprise inventory system through a multi-source data interface, and obtaining an initial data set containing inconsistent formats and missing batch numbers; s2, scanning a data stream according to the initial data set by applying a preset automatic detection rule, judging positions and types of abnormal points by utilizing a rule matching mode and a threshold comparison mechanism to obtain an abnormal point set, and activating a real-time stream analyzer to process field complete verification to generate an abnormal log record; according to the business data quality treatment method supporting system hidden danger identification, real-time and accurate anomaly detection and repair are realized, the consistency and reliability of inventory data are improved, and the enterprise inventory management efficiency is remarkably optimized.

The invention belongs to the technical field of artificial intelligence task planning, and provides a task planning PDDL file automatic generation method based on natural language input, and the method comprises the steps: (10) constructing task environment description words: analyzing environment elements and participants, and designing a diversified scene framework; (20) building a knowledge enhancement fine tuning model: fusing a vector RAG knowledge base retrieval result and pre-training model parameters by the model, and inhibiting logic illusion; (30) PDDL file generation and knowledge constraint: driving the fine tuning model to output a correct PDDL file in combination with scene semantics and knowledge base rules; (40) multi-stage dynamic evaluation: executing a feasibility index verification instruction through a knowledge base rule matching degree; and (50) model closed-loop iterative optimization: updating RAG knowledge base content and model parameters based on execution feedback, and constructing a closed-loop iterative optimization model. The method has the beneficial effects that the instruction logic deviation rate is reduced through RAG knowledge base constraint, the complex scene instruction generation accuracy is improved, and the dynamic decision response time is shortened.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention relates to the technical field of engineering technology research and development, in particular to a risk assessment method for personalized marketing content for generative AI output. According to the method, firstly, feature extraction is carried out on marketing content data to obtain structured representation, and then preliminary screening is carried out through a preset rule to obtain a rule matching condition; inputting the feature data and the rule matching result into a trained neural network model for deep evaluation, and outputting a risk score and a risk type label; and calculating a model parameter update value based on a manual auditing result and a business key index. According to the method, the interpretability and the risk identification capability of evaluation are ensured through the combination of the rule and the model, and the dynamic adjustment of the evaluation standard is realized by introducing the business index and the online learning mechanism, so that the risk evaluation better meets the business requirements.

The invention provides an intelligent agent-based automatic verification and rule matching system for insurance-waiting evaluation reports. The intelligent agent-based automatic verification and rule matching system comprises a report acquisition module which pulls to-be-verified and historical insurance-waiting reports; the auditing processing module calls an attention Bi-LSTM model which is subjected to reinforcement learning fine adjustment and is fused with knowledge graph embedding, and scores the report sentence by sentence; the rule management module is used for storing and analyzing an equal-guarantee 2.0 structured machine rule and carrying out secondary accurate matching on low-confidence sentences; the knowledge graph module is used for maintaining a multi-dimensional association graph of a standard family, a control measure and an evaluation item and providing graph reasoning; the agent decision-making module generates actions of passing, returning, manual rechecking or automatic correction according to the auditing state; the feedback learning module collects artificial recheck differences, an incremental training model, a strategy network and atlas vectors; and the storage module stores reports, models, rules, maps and logs in a multi-modal manner through a relation library, a map library and a vector library, and implements data governance and access control. According to the invention, the audit efficiency, accuracy and standard consistency of the evaluation report can be improved.

The invention provides a regional wind field multi-source heterogeneous work order data fast structuring system fused with a large language model, which comprises a plurality of modules, and is characterized in that a data input module receives various work order images; the OCR layout recognition module comprehensively recognizes the data, extracts key information, determines a spatial position and outputs text fragments with coordinates and layout anchor point information, and the rule field extraction module recognizes format category fields from the text fragments and calculates rule matching confidence; the semantic field recognition module recognizes descriptive fields from unstructured content and outputs related confidence information, the fusion decision module calculates final confidence of field candidates according to a predetermined function and judges output or complementation according to a threshold value, and the data consistency verification module is connected with an asset database to verify key information. The data complementation module generates complementation candidates and confidence coefficients, the man-machine collaborative closed-loop module pushes suggestions and receives feedback, and the result output module outputs standardized data. According to the method, the multi-source heterogeneous work order data can be effectively processed.

The invention relates to the field of electric power monitoring, in particular to an electric power monitoring system intrusion detection method and system based on flow analysis. The method comprises the following steps: collecting network traffic, analyzing and recombining to obtain structured session data; time sequence behavior features and function code distribution features are extracted to construct a multi-dimensional feature set; inputting the feature set into a compliance rule base and a behavior baseline model in parallel, and respectively outputting a rule matching result and an abnormal deviation degree score; generating a comprehensive threat index by adopting a weighted decision fusion strategy; and when the index exceeds a dynamic threshold value, intrusion is determined and an alarm is given. According to the invention, the problem of insufficient precision and adaptability caused by single feature dimension and isolated detection mechanism is solved.

The invention belongs to the technical field of code-free development, and particularly relates to a Web3D zero-code interactive design platform and method based on a unified model, and the platform comprises a unified business logic model UBLM and a platform-independent, self-described and structured data container which is used for completely defining all behavior logics in 3D interactive application, including events, conditions, actions, parameters, variables and flow relations; the dual-mode editor comprises a form-type editor and a blueprint-type editor, and the form-type editor and the blueprint-type editor can be used for completely creating, editing and visualizing the same UBLM and realizing real-time state synchronization; and the interpreted rule execution engine IREE is a lightweight engine running in a JavaScript environment of a browser and is responsible for analyzing the UBLM during running and converting the declarative logic into an executable instruction in a 3D scene through a rule matching and function registration mechanism. According to the invention, the development and cooperation efficiency can be improved, and the iterative maintenance process is simplified.

The invention discloses a multi-rule template quintuple rule matching method and device based on an FPGA, and the method comprises the steps: storing a plurality of rule templates through a high-bandwidth memory, carrying out the cascading of the rule templates, and carrying out the rule matching of quintuple information in a to-be-processed message, carrying out the matching in a non-cascading / cascading manner, and carrying out the rule matching of the quintuple information in a to-be-processed message. And obtaining an action strategy of the to-be-processed message. The matching information of each template is flexible and configurable, a complex matching function is realized through cascading of the templates, the number of the templates is effectively reduced, and system resources are reduced.

The invention discloses a method for monitoring and identifying dangerous behaviors of a smart park, and relates to the field of intelligent security and protection. The method comprises the following steps: acquiring an environment data stream through a sensing device deployed at a key point location of a park and carrying out standardization processing on the environment data stream; the data stream is sent to an edge computing node, a target is recognized through a lightweight target detection model, the detection confidence coefficient is obtained, a motion track is generated through a multi-target tracking algorithm, the rule matching degree is calculated according to a predefined rule base, and dangerous behaviors are preliminarily recognized; calculating a comprehensive model prediction credibility based on the detection credibility and the rule matching degree, and generating a final comprehensive credibility through a multi-dimensional credibility fusion model in combination with the time sequence consistency credibility and the scene matching credibility; and dangerous behavior trend prediction is carried out according to the comprehensive confidence, and an alarm instruction is generated to trigger disposal linkage, so that advanced prevention and quick response of dangerous behaviors are realized, and the intelligent level and operation and maintenance efficiency of park security and protection are greatly improved.

The invention discloses a clinical decision interaction method and system based on structured evidence reasoning, and relates to the technical field of computer technology and medical informatization. The method obtains a user input query; the method comprises the following steps: performing mixed entity identification from a query input by a user based on an entity identification model and a rule matching algorithm to obtain a candidate entity set, and performing conflict resolution and entity standardization processing on the candidate entity set to obtain a core entity; performing multi-granularity intention recognition based on confidence calculation between the classification systems of the core entity and the medical exclusive intention to obtain a target intention; performing screening query in a pre-constructed structured knowledge graph according to the core entity and the target intention to obtain a corresponding structured knowledge fragment; generating a cue word based on the structured knowledge fragment, user input query, instruction constraint and multiple verification; and inputting the cue word into the large language model, and outputting an answer. According to the invention, more accurate and more reliable dialogue service can be provided for the medical field.

The invention relates to a knowledge base construction method and system for multi-source heterogeneous files, and the method comprises the steps: receiving Word, PDF, Excel and other multi-source heterogeneous files, and converting the multi-source heterogeneous files into standardized representation through a file analysis function; an event boundary recognition technology fusing rule matching, entity recognition, semantic similarity and large language model verification is adopted, and a cross-paragraph complete business event is accurately extracted; generating structured information through an information extraction function; the information is converted into vectors, full-text indexes and a graph database to be stored by means of a multi-dimensional storage conversion function; and verifying and correcting an extraction result through a quality auditing function, and finally outputting a knowledge base containing an event set, an entity relationship and vector representation. According to the method, the problem of knowledge fragmentation in traditional document processing is effectively solved, and the consistency, the searchability and the reasonability of knowledge are remarkably improved.

The invention provides a vehicle-mounted test abnormal log automatic lading method based on rule matching, and the method comprises the steps: firstly collecting log data from multiple data sources of a vehicle-mounted test, converting the log data into structured log entries through preprocessing, and forming a log event pool; comparing the structured log entries with the standardized rule base in real time, and generating abnormal candidate events if matching succeeds; performing context correlation analysis on the abnormal candidate events through a log time sequence and multi-source cross information, eliminating noise and false alarms, and merging correlation events to obtain a confirmed abnormal event set; extracting abnormal event key information, automatically filling a defect list template, generating a defect list containing a log attachment, and submitting the defect list to a defect management platform; and finally, recording the whole process information of the lading bill, and establishing traceability association of the defect bill, the original log and the matching rule to realize the version management of the lading bill. The processing efficiency of the test log can be improved.

The invention provides a consumer right protection intelligent review method and system based on a large language model, and relates to the technical field of artificial intelligence and natural language process.The method comprises the steps that a to-be-reviewed text is preprocessed, and structured text feature data is obtained; performing semantic understanding on the structured text feature data by using a pre-trained large language model to obtain a text semantic vector containing field semantic information; performing joint retrieval and matching on the text semantic vector in combination with a knowledge graph and a rule base, and identifying and outputting a risk element set; and performing compliance determination based on the risk factor set to obtain a preliminary auditing conclusion including a risk level, evidence sufficiency, a rule matching degree and a reference confidence coefficient. Through full-process intelligent processing, the original text is automatically converted into a high-credibility auditing conclusion with interpretability.

The invention relates to a wharf safety intelligent monitoring method, device and equipment based on digital twinning and a medium. The method comprises the following steps: firstly, carrying out space-time alignment fusion on multi-source heterogeneous original data of a wharf site to generate a dynamic digital twinborn scene; extracting and matching entity relationships and events based on the dynamic digital twinborn scene and a pre-constructed wharf operation knowledge graph, and generating a dynamic knowledge situation sub-graph; performing graph neural network coding processing on the dynamic knowledge situation sub-graph to obtain coding features of nodes in the graph; risk and conflict analysis is carried out based on the coding features, and a current risk list and a potential conflict prediction list are generated; and generating security alarm information based on the list. By adopting the method, safety monitoring can be improved from a perception level based on simple rule matching to a cognition level fusing semantic understanding and context association reasoning, so that false alarm and missing alarm are remarkably reduced, and accurate early warning of potential risks in a complex working environment is realized.

The invention provides a tunnel mechanical fault shutdown influence prediction method and system based on a CART decision tree, and belongs to the technical field of tunnel engineering. The method comprises the steps that equipment operation data and construction environment parameters in drilling and blasting method tunnel construction are collected, and a multi-dimensional feature set is constructed; performing preprocessing and feature screening on the data in the multi-dimensional feature set, and establishing a fault-shutdown associated feature matrix; based on a CART decision tree algorithm, constructing a fault shutdown influence prediction model by using the fault-shutdown association feature matrix; inputting equipment state data collected in real time and construction parameters into the prediction model, and dynamically outputting a prediction result through decision tree rule matching; and adaptively adjusting and optimizing the parameters of the prediction model according to the dynamic change of the construction stage. According to the method, multi-dimensional, high-precision and interpretable prediction on the mechanical fault shutdown influence can be realized, and intelligent support is provided for construction decision making.

The invention discloses a permission matching method and device, and relates to the technical field of computers. The method comprises the following steps: acquiring a to-be-matched path; performing permission rule matching on the to-be-matched path based on a pre-constructed permission rule tree; when the permission rule in the permission rule tree is matched, determining a corresponding rule control permission based on the permission rule, and obtaining a permission matching result of the to-be-matched path; and when the permission rule in the permission rule tree is not matched, determining a preset default control permission as a permission matching result of the to-be-matched path. According to the method, the accurate child nodes, the single-wildcard child nodes and the multi-wildcard child nodes are integrated through the permission rule tree of the single tree structure, common permission matching scenes such as accurate matching, single-segment wildcard matching and cross-multi-segment wildcard matching can be covered without introducing a regular expression, the integrity of the expression ability is guaranteed, the implementation complexity is reduced, and the implementation efficiency is improved. And the system stability is improved. And meanwhile, the problem of matching logic confusion caused by traditional multi-rule scattered storage is avoided.

The invention discloses a sensitive data identification method, system and device based on rule matching and LLM deep semantic analysis, a medium and a product, and relates to the field of artificial intelligence, the method comprises the following steps: preprocessing data in an information data set; the information data set comprises multi-industry corpora, personal information and safety knowledge; the multi-industry corpora cover structured data format features and unstructured text semantic scenes; according to the preprocessed data, a LoRA technology is adopted to carry out field adaptation fine tuning on the large language model; the large language model comprises a rule matching layer and an LLM depth recognition layer; the rule matching layer screens and identifies the structured data; the LLM depth recognition layer recognizes unstructured data and implicit sensitive data; performing semantic recognition on the to-be-tested data according to the fine-tuned large language model, and outputting sensitive data; the sensitive data comprises information such as an identity card number, a home address and a mobile phone number, and the sensitive data can be accurately recognized.

The application provides a consumer rights protection intelligent review method and system based on a large language model, and relates to the technical field of artificial intelligence and natural language processing. The method comprises: preprocessing a text to be reviewed to obtain structured text feature data; using a pre-trained large language model to perform semantic understanding on the structured text feature data to obtain a text semantic vector containing domain semantic information; performing joint retrieval and matching on the text semantic vector in combination with a knowledge graph and a rule base to identify and output a risk element set; and performing compliance determination based on the risk element set to obtain a preliminary review conclusion containing a risk level, evidence sufficiency, rule matching degree and benchmark confidence. The application automatically converts an original text into a high-confidence review conclusion with explainability through intelligent processing of the whole process.

The invention provides a cross-file semantic association analysis system based on a hierarchical rule engine. The cross-file semantic association analysis system comprises a plurality of modules, wherein a code compilation analysis module is used for converting an input code file into an AST object and executing cross-file dependency and reference analysis; the semantic fact construction module generates an initial fact set according to the data output by the code compiling and analyzing module; the rule interpreter receives the rule set of the hierarchical rule base and constructs a corresponding hierarchical rule network; the rule matcher injects the initial fact set into the hierarchical rule network in a streaming mode to execute rule matching; the condition verification module verifies a rule matching condition according to the input fact data and returns a result; when rule matching succeeds, the rule executor executes corresponding processing logic and updates the fact base; and the result analyzing and processing module analyzes and integrates the final reasoning fact data and writes a result into a conclusion library. The method can be used for constructing a universal cross-file code context analysis system, and the code static analysis performance is improved.

The invention relates to the technical field of computer software, provides a visual flow arrangement method for e-commerce marketing, and aims to solve the problem of insufficient system processing performance and expansibility when an existing flow arrangement technology is applied to an e-commerce marketing scene. Generating and storing a state transition rule set; obtaining a user behavior event, obtaining a historical state transition event sequence according to the user behavior event, and then dynamically calculating a current user journey state; acquiring a corresponding state transition rule set, and performing matching query by taking the current user journey state and the user behavior event as input; if matching succeeds, a corresponding marketing action command is executed, and a new state transition event is generated and persisted to an event log so as to update a user journey state, so that efficient and accurate state-driven rule matching and automatic execution are realized, and the processing efficiency, expandability and state traceability of a system in a high-concurrency scene are improved.

The invention relates to the technical field of supervision information processing, in particular to an intelligent supervision method and system based on new energy charging infrastructure service. The method comprises the steps of obtaining supervision target domain data and strategy version records, and generating multi-source standardized data through term dictionary registration and protocol session configuration; performing incremental compensation parameter registration, data hash comparison and missing marking, time anchor point alignment and time window index generation to obtain an alarm event packet; based on the alarm event packet, performing alarm duplication removal, rule matching, work order assembly and operation and maintenance resource loading, and generating a work order distribution record; and carrying out evidence storage load packaging, on-chain verification rule matching, dynamic gridding arrangement and strategy version updating, and finally generating a supervision report. According to the invention, the problems of difficult data fusion, weak transmission integrity guarantee and imperfect supervision closed loop of the multi-source heterogeneous charging equipment are effectively solved, and the accuracy, reliability and automation level of supervision are improved.

The invention relates to the technical field of electronic document content review methods, particularly discloses an electronic document content review method, and aims to solve the problems that traditional manual review is low in efficiency and inconsistent in standard and implicit compliance risks are difficult to recognize. The method comprises the following steps of: performing unified analysis and OCR (Optical Character Recognition) conversion on a PDF, DOCX or TXT format document, extracting a full-amount text, and structurally dividing the full-amount text into semantic units such as a title, a term and an attachment; in combination with large-model deep semantic analysis and double-layer regular rule matching, hidden risks such as logic conflicts and fuzzy expressions and dominant violations such as terms, units and sensitive words are recognized respectively; multi-source detection results are fused, after deduplication, sorting is carried out according to risk levels, and a structured report containing problem positioning, basis and suggestion is generated; according to the method, high-precision, high-efficiency and interpretable automatic review is realized through a'large model + rule 'dual mechanism, and compliance consistency and processing efficiency are remarkably improved.

The invention relates to the field of radio interference intelligent identification and positioning, in particular to a multi-source data fusion water radio interference identification and positioning method and system, and the method comprises the following steps: collecting multi-source data; performing multi-source data fusion processing; constructing a rule base, and screening out suspected abnormal signals from the frequency spectrum situation map through threshold judgment and rule matching; a support vector machine model is called, multi-dimensional static signal features are used as input, a classification hyperplane is constructed through a radial basis kernel function, and normal signals and abnormal signals are distinguished from suspected abnormal signals; calling a decision tree model, and performing scene judgment on the suspected abnormal signal to identify the abnormal signal; calling a convolutional neural network model, converting the time domain signal into a spectrogram through short-time Fourier transform, and extracting texture features through multilayer convolution to identify an abnormal signal; through the method and the system, real-time identification and high-precision positioning of interference signals can be realized.

The invention discloses an order issuing control method and device based on a rule engine and electronic equipment, and the method comprises the steps: extracting effective order information through automatic recognition of order features; then, the orders are classified according to a preset rule, that is, the orders pushed by an upstream system are stored in different data pools based on rule matching through three data pools of rejection issuing, instant issuing and planned issuing; finally, the orders can be issued in order according to the pushing rules of different data pools. Therefore, compared with the traditional method of directly issuing the orders to the downstream system, the method has the advantages that the classified pushing of the orders is realized, so that the flexibility of issuing the orders is improved, the ordered issuing of the orders is realized, the operation pressure of the downstream system is reduced, and the operation stability of the system is ensured; meanwhile, the whole process does not need manual intervention, the efficiency is improved, and the error rate is reduced; therefore, the method is very suitable for large-scale application and popularization.

The invention relates to the technical field of purchase matching, in particular to an online purchase processing method and system oriented to multiple subjects and configurable rules, and the method comprises the following steps: obtaining parameter contents, verifying semantic relationships, field formats and numerical boundaries, embedding state paths, synchronously updating parameter positions, constructing a rule configuration set, and establishing a rule matching set; dividing a role permission supplement operation path, mapping a multi-subject behavior structure, screening a same-value field pushing round sequence, marking time, and matching a response sequence to judge a path inheritance relationship. According to the method, through a parameter checking and semantic analysis mechanism, the consistency of content embedding paths is kept, a multi-role action sequence is constructed based on permission mapping, an operation path is divided and cooperatively controlled, a field aggregation and timestamp extension rule is identified, a quotation round sequence is constructed, and an action path is inherited and promoted by depending on a response matching judgment mechanism. The state link dynamic state is updated, the interface is synchronized, and the overall linkage of parameter analysis, path control, behavior recognition and process scheduling is enhanced.