1204 results about "Decision taking" patented technology

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches. The invention uses hierarchical and fuzzy deontic logic implementations alongside connectionist AI / ML to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration. In at least one embodiment, the invention operates through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining coherence, consistency and supporting compound workflows. The invention provides a framework for AI systems to make logically consistent, ethically-aware decisions by combining deontic reasoning with multi-agent coordination, token space communications and knowledge, including on intermediate results, enabling automated decision-making for a variety of applications.

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches alongside quantum-inspired token management. The invention uses hierarchical and fuzzy deontic logic implementations and quantum-inspired state representations that combine complex amplitudes and phase information to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve complex goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration and information-theoretic metrics. The platform is capable of operating through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining system coherence and logical consistency using quantum-inspired token operations and phase alignment transformations for optimizing information transfer between states.

The invention relates to the technical field of knowledge maps, and discloses a semantic association and logic rule inference method based on a knowledge map, and the method comprises the steps: obtaining a cross-functional department first data set, and constructing a cross-department knowledge map; extracting index nodes from the cross-department knowledge graph, identifying semantic conflict indexes, generating an index semantic conflict list, and constructing the cross-department knowledge graph after semantic mapping based on the index semantic conflict list; obtaining a second data set of a cross-functional department, and obtaining a fused cross-department knowledge graph and a semantic association degree matrix based on the second data set and the cross-department knowledge graph after semantic mapping; dynamically adjusting the weight of each node and edge of the fused cross-department knowledge graph according to the semantic association degree matrix, and generating a cross-department collaborative knowledge graph; generating a cross-department collaborative decision suggestion based on the cross-department collaborative knowledge graph; according to the invention, the efficiency and quality of cross-department collaborative decision-making are significantly improved.

The method is suitable for the technical field of automatic driving, an automatic driving large model framework based on 3D space-time perception and human-like decision reasoning is provided, through deep integration of cross-modal dynamic semantic patch embedding and multi-scale chain reasoning, the three-dimensional space-time semantic reasoning ability of an automatic driving system in a complex scene is effectively improved, and the automatic driving efficiency is improved. The efficient cooperative processing of the full-view scene video stream and the global aerial view is realized; meanwhile, through a multi-level space-time semantic self-adaptive data extraction normal form and semantic action representation generation based on rule driving and semantic action interaction, and in combination with a cross-modal task-oriented multi-level training matrix architecture, the model shows excellent performance in the aspects of scene understanding, decision transparency and end-to-end driving task optimization. The accuracy of the system in dynamic behavior deduction is effectively improved, the decision interpretability is improved, and powerful technical support is provided for global path planning and human-like decision making of automatic driving.

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention discloses a causal atlas-driven enterprise intelligent decision-making method and system, and belongs to the technical field of enterprise management. The method comprises the following steps: constructing an enterprise-level causal atlas by obtaining enterprise business processes, operating indexes and unstructured text data; executing path reasoning based on the to-be-decided data, and determining a multi-hop causal path chain; determining a current association rule and a decision suggestion in combination with an expert rule base; causal maps and rule tags of different industries are introduced, and a migratable causal path and an association rule are identified based on structural similarity and a semantic mapping rule; and finally, current and migration knowledge is fused to generate an enterprise decision execution scheme and a causal reasoning result. According to the scheme, based on comprehensive application of current and migrated knowledge, scientificity, accuracy and interpretability of decision making are effectively enhanced, meanwhile, dynamic updating and inter-industry knowledge sharing are supported, enterprise decision making efficiency and response speed are remarkably improved, and enterprises are assisted to achieve intelligent and fine management.

The invention provides an industrial agent decision-making method and device, electronic equipment and a storage medium. The method comprises the following steps: loading multi-source industrial data to a knowledge graph, and constructing a domain ontology and an MCP ontology; performing parameter fine tuning on a preset base large language model; receiving an industrial process planning demand input by a user, and performing semantic analysis on the industrial process planning demand; processing the query result by utilizing an MCP context strategy to generate a prompt context; inputting the prompt context and the industrial process planning demand into a fine tuning large language model to generate a first process planning scheme; performing rule verification on the first process planning scheme based on the knowledge graph, and judging whether the first process planning scheme meets a preset rule or not according to a verification result; and converting the final process planning scheme into a control script or an interface calling sequence which can be analyzed by an industrial execution system, and outputting the control script or the interface calling sequence. According to the method, the cross-software system compliance process planning with unified knowledge, automatic generation and rule verification can be realized.

The invention discloses a city updating intelligent expert system architecture and method based on a large language model, and relates to the technical field of city planning and construction. The knowledge base management module supports efficient retrieval and application of professional domain knowledge; the expert agent group comprises a user-defined core expert and a system dynamic supplement expert; the expert team collaborative decision-making module is used for executing multiple rounds of hierarchical collaborative decision-making processes; the meta-agent module is responsible for project feature analysis and expert team dynamic configuration; the auxiliary agent module comprises a host agent which is responsible for guiding the decision making process; the recorder agent generates a decision summary report; the verification agent is responsible for verifying and evaluating the professional ability of the expert agent; and the scoring agent evaluates the decision quality. According to the method, the problems of dispersed professional knowledge, low cross-domain expert cooperation efficiency, high decision-making cost and the like in the existing city updating decision-making process are solved, and the decision-making efficiency and quality of city updating early-stage planning are comprehensively improved.

The invention provides an agent construction method and system based on a large language model, a medium and equipment, and relates to the technical field of decision agent construction. According to the method, an appropriate large language model is selected, a series of prompt project texts are established for the large language model, target codes are generated as required by utilizing the large language model and prompt projects, and class library functions of a network model library, a reinforcement learning algorithm and a simulation training environment can be correspondingly called by running the codes. Finally, auxiliary construction and training of the decision-making agent are realized, and the problems in the existing decision-making agent construction and training process are solved.

The invention relates to a large model agent interactive question and answer task decision-making method, device and equipment and a medium. The method comprises the following steps: constructing an agent strategy model based on a partially observable Markov decision-making process; acquiring an instruction input by a user, and generating an information search track by adopting the agent strategy model; constructing a decision tree of an information search trajectory by adopting a Monte Carlo tree search algorithm to obtain a preference data set labeled by a Q value; setting a large language model, and training the large language model by adopting the step-level optimization objective function according to the preference data set marked by the Q value to obtain a trained large language model; constructing a Q value model according to the trained large language model, and then outputting Q value estimation according to the Q value model; and according to Q value estimation and information search trajectory selection actions, generating a question and answer process. According to the method, the expression and generalization ability of the large model agent in the autonomous reasoning and decision-making process can be remarkably enhanced.

The invention discloses a structured decision-making method based on multi-agent collaborative decision-making and reinforcement learning, and relates to the technical field of natural language processing, knowledge engineering and agent collaboration, and the method comprises the steps: receiving an original rule document, analyzing the document type, complexity and constraint conditions, and defining a task target and a success standard; and according to the task target, matching and scheduling the intelligent agent from the registered intelligent agent library, and further analyzing the capacity configuration of the intelligent agent for standby. Through the multi-agent cooperation and reinforcement learning technology, full-process automation of rule documents from input to structured analysis is realized, document types, complexity evaluation and constraint condition analysis can be automatically identified, and a clear task target and a success standard are generated; and the large language model generates a structured workflow according to task requirements and agent capabilities, so that the performability is ensured through logic verification, manual intervention is greatly reduced, and the processing efficiency and the system intelligence degree are improved.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a decision making method, device, equipment and medium based on multi-modal data, comprising: collecting and cleaning multi-modal original data, executing format conversion and desensitization processing, and generating standardized data; multi-modal features are extracted and fused based on the standardized data, and multi-modal feature vectors are generated; performing reasoning on the multi-modal feature vector through a reasoning model to generate reasoning result data; pushing the reasoning result data to a review terminal, and receiving correction feedback data; and updating parameters of the reasoning model based on the corrected feedback data, and generating an updated reasoning model. According to the method, after data cleaning, format conversion and desensitization processing, multi-modal feature extraction and reasoning model reasoning processing are executed, effective fusion and accurate reasoning of data multi-modal features are achieved, and decision accuracy and data safety are improved.

The invention relates to the technical field of intelligent assistant systems, and discloses an intelligent assistant system and method based on an AI technology, and the system comprises a user interaction module, a knowledge graph module, a natural language processing module, and a reasoning and decision module. The user interaction module realizes input and output interaction with a user; the knowledge graph module constructs a multi-domain knowledge system; the natural language processing module uses a BERT model to understand user intentions; and the reasoning and decision-making module fuses rule reasoning and machine learning prediction to realize accurate response. Particularly, an index construction unit is arranged in the reasoning and decision-making module to improve the retrieval efficiency, a reasoning engine adopts a Markov logic network to carry out logical reasoning, and a decision-making optimization unit continuously optimizes a decision-making strategy through reinforcement learning. The AI technology is comprehensively applied, so that the knowledge coverage, the response accuracy, the logical reasoning ability and the decision intelligent level of the intelligent assistant system are remarkably improved, and more efficient and convenient intelligent assistant services are provided for users.

The invention discloses a report generation system and method based on a legal knowledge graph, and relates to the technical field of artificial intelligence and law, and the method comprises the steps: obtaining a legal appeal of a user through a non-standardized domain corpus collection engine, carrying out the multi-dimensional intention deconstruction through a semantic slicing model, and obtaining a judicial element topology network; inputting the judicial element topology network into a law deduction engine of dynamic value perception ability, calculating a decision vector weight through a dynamic value evaluation network, and generating a judicial decision path by using a time sequence feedback mechanism; based on a judicial decision path, through an association rule mining engine of a multi-modal knowledge graph, a dynamic weighting law retrieval confidence matrix is generated in combination with semantic coupling degree analysis and logic path influence evaluation. According to the method, qualitative change of legal consultation from information retrieval to intelligent decision is realized through collaborative innovation of a legal deduction engine with dynamic value perception capability and a legal semantic constraint decoder.

The invention relates to the technical field of artificial intelligence and insurance, and discloses an intelligent insurance decision core system based on a dynamic dialogue strategy. The system comprises a user interaction module which collects user risk attribute data through voice and text multi-mode input; the insurance product database is used for storing multi-source insurance product structured data; and the intelligent decision-making module is used for generating personalized insurance suggestions by applying a random forest, a gradient boosting tree and other multi-model fusion technologies. A dynamic dialogue strategy engine is further arranged, and a dynamic interaction sequence is generated based on context-aware reinforcement learning. The data synchronization method of the system can obtain latest insurance product data, the multi-model fusion technology improves the decision accuracy, the dynamic dialogue strategy realizes intelligent interaction, the problems of incomplete information collection, inaccurate decision, inflexible interaction and the like in the traditional insurance decision process can be effectively solved, and more efficient and personalized insurance decision service is provided for users.

The present application provides a method and apparatus for multi-drone round-up of hierarchical collaborative learning, an electronic device and a medium. The method includes: determining, according to the current agent joint state and the current escape target state, a present target stage task based on a top-layer decision-making network of a hierarchical decision-making network, inputting a task parameter corresponding to the target stage task into a bottom-layer decision-making network of the hierarchical decision-making network, and obtaining an action decision-making result according to an agent state and received communication data by a strategy network of each agent after obtaining the task parameter; controlling, according to the action decision-making result obtained by the strategy network of each agent, each agent to perform a corresponding maneuver action to execute a multi-drone collaborative pursuit task under the target stage task.

The invention provides a multi-agent-based decision-making method and device, equipment and a medium, and the method comprises the steps: collecting data of a plurality of data sources, and constructing a plurality of knowledge bases in different knowledge fields; aiming at different task requirements contained in the target social measure, adopting a corresponding decision model and a knowledge base of a corresponding knowledge field to construct a plurality of sub-agents; and controlling the plurality of sub-agents to formulate respective preliminary social measure implementation schemes for the target social measure, and performing debate aiming at the plurality of preliminary social measure implementation schemes to obtain a final social measure implementation scheme. According to the method, decision making is carried out by integrating the multiple sub-agents representing different perspectives, multi-field and multi-dimensional insights can be formed, then social measure implementation schemes of the multiple sub-agents are fused through a debate mechanism of the multiple sub-agents, the agents can generate more comprehensive and diversified decision making results, and the decision making efficiency is improved. And complex and changeable decision problems can be effectively solved.

The invention provides a consensus decision question answering system based on multi-AI agent game, and relates to the technical field of artificial intelligence. The system comprises a multi-domain information aggregation module, an interaction effect deduction module, a strategy fusion calibration unit, a distributed behavior adaptive mechanism and an aggregation strategy discrimination module. The multi-domain information gathering module is used for unifying multi-source strategy information and environment situation data, the interaction effect deduction module is used for analyzing and quantifying the mutual influence relation of strategies between intelligent agents, and the strategy fusion calibration unit generates correction suggestions based on a game deduction and optimization method. The distributed behavior self-adaptive mechanism is used for locally and progressively executing a correction path in an intelligent agent; and the aggregation strategy judgment module dynamically evaluates the overall strategy state. The multi-agent consensus decision-making question-answering method realizes consensus decision-making question-answering of multiple agents in a complex environment, can effectively identify and correct non-collaborative strategy deviation, and improves the coordination, stability and immunity of a system.

The embodiment of the invention discloses a community governance decision generation method and device based on a knowledge graph. The method comprises the following steps: collecting community governance data; performing information extraction and text processing on the community governance data by adopting a data extraction model to generate structured data; constructing a causal analysis atlas, a decision treatment atlas and a space-time correlation atlas by using the structured data; fusing the causal analysis graph, the decision governance graph and the space-time association graph to obtain a community governance knowledge graph, wherein the community governance knowledge graph supports semantic-based query and reasoning; acquiring resident appeal information, and performing risk reasoning based on a preset reasoning rule to obtain a risk reasoning result; and based on a risk reasoning result, in combination with spatio-temporal information defined by a spatio-temporal association graph, querying in the community governance knowledge graph, and generating a community governance scheme according to a query result. According to the invention, the dynamism, the accuracy and the intelligent level of community management can be improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent decision-making method, device, equipment and medium based on security boundary constraint. Limiting a security boundary according to a macroscopic decision direction, retrieving updated knowledge from the dynamic knowledge graph and generating a multi-modal decision priority analysis result fused with the updated knowledge, determining a target decision in the security boundary based on the multi-modal decision priority analysis result, and performing security verification by comparing the target decision with the security boundary. And generating a target decision that the security verification is passed. According to the method, multi-modal decision priority analysis is carried out by combining unified feature representation and updated knowledge, and target decisions are screened and verified in a security boundary, so that effective fusion and dynamic knowledge enhancement of multi-source information are realized, and the accuracy and security of decisions in a complex environment are improved.

The invention relates to the technical field of auxiliary decision making, and discloses a graph-enhanced intelligent game and operation planning optimization auxiliary decision making system and method, and the method comprises the steps: inputting multi-source heterogeneous data into a space-time semantic mapping frame, and generating a unified space-time semantic data structure; generating a cognitive-spatio-temporal manifold model through a cognitive spatio-temporal manifold generation algorithm; obtaining user interaction data, and generating an adaptive view expression through an interaction response manifold deformation algorithm; obtaining user interaction behavior data, and generating a cognitive balance view through a cognitive load perception projection algorithm; generating an interactive decision-making scene through an immersive space-time scene rendering technology; according to the method, a complex game and operation planning optimization problem is mapped into a visual space-time scene, so that a decision maker can foresee and control a decision result in a virtual space-time space, and the understandability and controllability of game and operation planning optimization are greatly enhanced.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The invention relates to the technical field of man-machine collaborative interaction, and discloses a resource allocation method and system in a man-machine collaborative decision-making scene, and the method comprises the steps: generating corresponding task features based on the three dimension indexes of task complexity, knowledge density and intelligent adaptation degree of a to-be-processed task; initializing initial weight coefficients of the three-dimensional indexes, and dynamically optimizing the weight coefficients corresponding to the three-dimensional indexes by using a preset strategy network according to the task characteristics and the real-time system resource state; performing weighted normalization on the three-dimensional indexes according to the weight coefficient to generate a task comprehensive score; task priorities are divided according to the task comprehensive scores, and the tasks are dynamically allocated to manual and / or intelligent systems to execute the tasks according to the priorities, so that the flexibility of artificial experience and the high efficiency of the intelligent systems can be brought into full play, man-machine labor division is flexibly adjusted according to task characteristics, and the universality of cross-task scenes is improved. And the dynamic adaptability, decision reliability and task execution efficiency of the man-machine cooperation system are improved.

The invention provides an intelligent decision-making method and device for cross-modal data, equipment and a storage medium, and the method comprises the steps: carrying out the blockchain storage of collected multi-modal process data, and generating an on-chain hash certificate; performing cross-modal space-time alignment and feature fusion on the multi-modal process data according to the space-time metadata analyzed by the Hash certificate on the chain to generate a process knowledge graph; constructing an interactive teaching model according to the process knowledge graph; determining a multi-dimensional contribution measurement value according to the use data of the interactive teaching model; and based on the multi-dimensional contribution magnitude and the hierarchical smart contract architecture, determining the data access permission through a community voting mechanism. Through implementation of the scheme, the process knowledge graph and the interactive teaching model, the inheritor operation data and the material response data form a causal association model, a traceable decision basis is provided for accurate reproduction of an endangered process, and meanwhile, the intelligent contract can automatically execute a decision to ensure high efficiency and transparency of the decision process.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention provides a method and a system for improving intelligent decision-making ability based on multi-modal learning fusion. The method comprises the following steps: acquiring chessboard image data, strategy text data and historical match chess manual data; processing the chessboard image data by using a convolutional neural network, and extracting spatial features; processing the Go related strategy text by using a natural language processing model, and extracting semantic features of the text; extracting historical chess manual characteristics, namely common strategies and decision modes, from the history game chess manual data; extracting features of different modes by using a multi-mode neural network model, and introducing an attention mechanism to fuse and weight the features of the different modes to generate comprehensive feature representation; based on the fused comprehensive feature representation, a comprehensive decision network model is used for strategy selection and optimization; the method can generate a more accurate and comprehensive Go strategy, not only improves the ability of Go AI to understand and cope with complex situations, but also has the application potential of future multi-modal information fusion in other strategic game fields.

The invention discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The invention provides a reservation and resource scheduling system based on cognitive intelligence and a self-evolution rule engine, and the system comprises a rule cognition and self-evolution engine, an enhanced visual configuration, deduction and auditing engine, and a dynamic loading, real-time decision and predictive optimization module. A rule cognition and self-evolution engine of the multi-target intelligent conflict resolution and collaborative scheduling module is used for constructing a knowledge graph and generating a reservation rule in a current state by adopting a deep learning algorithm; the enhanced visual configuration, deduction and auditing engine is used for providing a rule editing function for a user; the rule demand edited by the user is transmitted to the rule cognition and self-evolution engine for detection; the dynamic loading, real-time decision-making and predictive optimization module is used for enabling the changed rule to take effect immediately; and the multi-target intelligent conflict resolution and cooperative scheduling module is used for processing the conflict problem of the reservation requests or the conflict problem between the reservation requests and available resources.

The invention discloses an agent inference system based on multiple maps, and relates to the technical field of artificial intelligence, and the system comprises the steps: based on industry report, academic literature and business manual multi-source data, extracting entity-relationship-attribute, and constructing a knowledge map; dynamically capturing cooperation, report and task allocation relationships among entities, and constructing a production relationship graph; constructing a decision graph based on a field expert heuristic rule; based on the thinking engineering theory, human thinking modes and emotional states are analyzed, and a thinking map is constructed; integrating a knowledge graph, a production relation graph, a decision graph and a thinking graph, performing entity and mode alignment, mapping multi-source nodes and edges into the same vector space, introducing conflict resolution, and constructing a unified multi-mode heterogeneous knowledge graph; and guiding a large language model to generate a reasoning direction through path cue word injection, querying a constraint reasoning boundary, outputting an optimal reasoning result, and realizing agent reasoning of multiple maps. The method has the beneficial effect that the reasoning accuracy is improved.

The invention discloses a security reinforcement learning-driven large language model security decision agent, and the decision agent comprises a high-level semantic planner which is used for receiving a target and constraint instruction in a text form, receiving a language or visual observation signal of an environment at the same time, and outputting text formatted security risk information and suggested action planning; the low-layer action actuator is used for receiving low-dimensional observation and semantic codes of the environment, and the semantic codes are output by the high-layer semantic planner after text embedding conversion; the strategy network of the low-layer action actuator outputs a final safety action; the training alignment module is used for optimizing the strategy network and the value network; a high-level semantic planner is fed back and prompted through reward and cost signals collected through environment interaction, and parameters of a strategy network and a value network are trained through a security reinforcement learning algorithm. According to the method, the decision cannot violate the given text security constraint while the decision of the given text target is completed.