873 results about "Decision-making" patented technology

The invention provides a decision model construction method based on a big data environment. The method belongs to the economic data decision field. The method comprises the following steps: firstly, acquiring multi-source heterogeneous data including sensor time sequence data, an expert rule base and equipment causal priori knowledge, and constructing a standardized training data set; then, extracting an explicit causal relationship from the historical data by utilizing a causal discovery algorithm, and generating an interpretable knowledge graph in combination with priori knowledge of an equipment manual; thirdly, constructing a neural symbol joint model; neural network parameters and rule confidence coefficients are synchronously adjusted, and collaborative learning of data driving and knowledge driving is realized. And then, carrying out multi-dimensional logic verification on the trained model, including rule conflict detection, anti-factual reasoning and decision path traceability verification. The method can effectively improve the accuracy and interpretability of the decision model, and is suitable for processing decision tasks in a big data environment.

The invention relates to a cross-domain intelligent reasoning method based on knowledge graph enhancement, and the method achieves the precise reasoning in a complex scene through the construction of a hierarchical knowledge expression framework and a dynamic optimization mechanism. A multi-source heterogeneous data fusion technology is adopted, subject fine-grained knowledge units are generated through multi-modal feature extraction, and a three-dimensional knowledge graph structure comprising a core common concept layer, a subject feature ontology layer and a dynamic semantic mapping layer is established; based on a path exploration algorithm driven by reinforcement learning, cross-domain implicit association is mined while subject independence is reserved, and controllability and interpretability of the reasoning process are achieved in combination with an attention fusion mechanism of a large language model. According to the method, the limitation of traditional unified ontology modeling is broken through, the problems of concept drift and path deviation existing in reasoning in the cross fields of medicine-finance, engineering-law and the like are effectively solved, and the accuracy and knowledge traceability of complex decision tasks are remarkably improved.

The invention provides a digital human interaction control method and device fusing emotion semantics and logical reasoning and a storage medium. The method comprises the following steps: analyzing multi-modal input data of a user, constructing emotion-semantics joint representation, and generating a logic decision path; and through a cognitive fusion module, emotion-semantic representation and a logic decision path are fused, and an interaction response adapting to emotion and logic consistency is generated. The system optimizes an emotion semantic model and a logical reasoning rule on line according to user feedback and interaction history, and real-time interaction of emotion dynamic and logical rules is achieved. The system can dynamically adjust the logic decision path based on the multi-mode emotional state of the user, and improves the naturalness and situation adaptability of interaction. A dynamic time warping algorithm and a factorization machine are introduced to process a multi-modal feature fusion problem, and the accuracy and robustness of emotional state recognition are improved. The online optimization mechanism enables the model and the rule to be evolved continuously, and reasoning errors are corrected automatically through user feedback, so that error circulation is avoided.

The invention discloses a dynamic optimization and real-time decision-making method and device for multi-agent collaborative target search, and the method comprises the steps: constructing a global model by a global optimization layer, obtaining an initial path plan and an initial task distribution scheme of multiple agents, and transmitting the initial path plan and the initial task distribution scheme to each agent; meanwhile, the global optimization layer updates the global model at a first preset frequency; each agent adjusts the initial path plan according to the initial path plan and the initial task allocation scheme in combination with the real-time sensing data to obtain an execution result, and sends the execution result to the global optimization layer; meanwhile, each agent updates the execution result at a second preset frequency; the global optimization layer judges whether a global model needs to be updated or not according to the execution result of each agent, if yes, the global model is updated and sent to each agent, and if not, the execution result of each agent is stored; and obtaining a path planning and task allocation scheme in real time. The task execution efficiency and completeness can be improved.

The invention provides a knowledge base question-answering system optimization method and device based on hybrid fine tuning and multi-dimensional evaluation and a readable storage medium thereof, and provides the following scheme: constructing a hybrid progressive fine tuning framework, fusing low rank adaptation (LoRA) and direct preference optimization (DPO), and realizing domain knowledge migration through hierarchical dynamic parameter configuration; establishing a logic-semantic-knowledge three-dimensional quantitative evaluation system, and forming closed-loop optimization by using dynamic weight fusion and a visual decision system; and designing a multi-domain prompt template library with layered parameter freezing, sparse constraint and attention driving, and realizing model lightweight and cross-domain logic constraint. According to the method, the adaptive bottleneck of a general model and domain characteristics is broken through, the small sample training efficiency and the generated content compliance are improved, the computing resource consumption is reduced, an efficient and reliable knowledge service base is provided for professional scenes such as laws, medical treatment and finance, and the technical advantages of specialization, light weight and interpretability are achieved.

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 enhanced decision method, device, equipment and medium based on thinking chain annotation, which comprises the following steps: extracting core information features to generate a structured data set, loading a basic language model and executing supervision fine tuning to generate a fine-tuned model, fusing multi-modal input to generate fusion features, constructing a state observation space to receive the fusion features as input, generating reward signals based on a double reward mechanism and optimizing model parameters to generate an optimized model, deploying a monitoring module to dynamically adjust parameter configuration to generate an adaptive decision model, and outputting a decision response result. According to the method, multi-source data extraction, structured expression, multi-modal fusion, reinforcement learning optimization and dynamic adaptive mechanism fusion are carried out, so that the understanding ability of the model to complex data, reasoning transparency and the adaptive ability of the model to coping with environmental changes are remarkably improved.

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

The invention discloses a multi-modal data fused large model Agent intelligent decision-making method and system, belongs to the technical field of artificial intelligence, multi-modal data processing, deep learning, reinforcement learning and intelligent decision-making, and aims to solve the technical problem of how to improve the performance and adaptability of intelligent decision-making in processing complex tasks and dynamic environments. According to the technical scheme, the method comprises the steps of multi-modal data fusion, wherein text, image and audio data from different modals are integrated, and unified feature representation is generated through feature extraction and feature fusion technologies; intelligent decision-making: decision-making reasoning is carried out based on the fused feature representation, and a final decision-making result is generated by adopting a deep learning model and a reinforcement learning algorithm; adaptive learning: monitoring data changes and decision-making effects in real time, and dynamically adjusting deep learning model parameters and strategies; and feedback optimization: further optimizing the performance of the deep learning model by collecting the feedback information of the decision result.

The invention relates to the technical field of multi-mode interpretable decision scheme design, in particular to a multi-mode interpretable decision method and system and electronic equipment. According to the method, the intelligent decision-making level in the industrial manufacturing environment can be effectively improved through dynamic knowledge path optimization, multi-modal data fusion, symbol reasoning and self-adaptive feedback. A reinforcement learning driven dynamic knowledge retrieval technology is introduced, so that efficient organization and retrieval of multi-source heterogeneous data are realized; reasoning is enhanced based on the knowledge graph, the relevance between cross-modal data is improved, and the reasoning logic between the data is clearer and more reliable; and in combination with an ontological reasoning mechanism, the interpretability and transparency of the system are enhanced, so that the system conforms to causal derivation rules in industrial production. According to the technical scheme, dynamic knowledge path optimization, ontology reasoning and multi-modal data fusion are combined, and an efficient, accurate and explainable industrial manufacturing decision-making scheme is provided.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal hierarchical feature fusion and decision making method, device, equipment and medium, and the method comprises the steps: obtaining vision, language and motion data, and carrying out the hierarchical feature extraction to generate a multi-modal initial feature set; feature importance is analyzed, screening and dimension reduction are carried out, and screened multi-modal features are obtained; performing semantic enhancement on the screened multi-modal features to generate multi-modal semantic enhancement features; executing cross-modal attention fusion on the multi-modal semantic enhancement features to obtain cross-modal fusion features; and inputting the cross-modal fusion features into a semantic reasoning network to generate a decision result. According to the method, through multi-level screening dimension reduction, semantic enhancement and cross-modal attention fusion, the model can accurately utilize key feature relationships among multi-modal data, redundant interference is reduced, and semantic reasoning accuracy and decision-making efficiency are improved.

The invention relates to a remote-control system of a stratospheric airship and a control method thereof. The control system is divided to four levels: a decision-making level for executing the selection and switch of a remote-control flight mode according to the actual working state data, and sending a corresponding mode command; a planning level for dynamically planning and generating an airship motor drive command according to the mode command sent by the decision-making level; an executing level for realizing the drive control to an airship motor according to the motor drive command of the planning level; and a perceiving level for finishing the real-time monitoring and data collection of the airship working state, and providing the original data of the airship working state to the decision-making level. The four-level control system architecture design of the control system is capable of simplifying the control device, improving the control efficiency, and fundamentally solving the problems of the function, efficiency and reliability of the near space airship ground and airborne control system.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

The invention relates to the technical field of intelligent decision making of production equipment, and discloses a decision making method and system based on a knowledge graph, and the decision making method based on the knowledge graph comprises the following steps: processing input data through a multi-granularity knowledge graph construction system, and obtaining a multi-scale knowledge graph; processing the real-time sensor data and the multi-scale knowledge graph through a time sequence knowledge dual representation learning framework to obtain a dynamic representation model; processing the dynamic representation model and the multi-scale knowledge graph through a time-varying causal propagation network decision system to obtain a decision analysis result; processing the decision analysis result and the multi-scale knowledge graph through a multi-hypothesis reasoning algorithm to obtain a root cause analysis report; a multi-scale knowledge graph is constructed by fusing multi-source heterogeneous data, and intelligent decision with high accuracy and strong interpretation is realized by combining time sequence knowledge dual representation learning and time-varying causal propagation network analysis.

The invention discloses a technology development situation awareness system and method, and belongs to the field of information processing, and the system comprises a data input module, a field overview module, an intelligence task question and answer module and a data output module. The method comprises the steps of 1, receiving a user question, and performing deep semantic analysis; 2, constructing a thinking chain and cue word engineering system based on metadata; 3, executing thinking chain reasoning based on large language model driving and generating a first draft of a report; step 4, executing metadata consistency checking of the first draft of the report, executing report content updating, and presenting a research report and evidence; and 5, archiving high-quality scientific data in the research report. According to the method, key feature rules in technology development can be effectively mined, comprehensive, convenient, efficient and intelligent perception of the technology development situation is realized, automatic reasoning of a technology situation perception result is provided, and efficient and credible intelligence reference is provided for decision making.

The invention discloses a gravity dam safety risk management and control method and system based on a knowledge graph and a large model. The method comprises the following steps: collecting accident case data of a water conservancy project; performing entity and relationship extraction on all case data in the case library by using the fine-tuned model with domain knowledge to obtain a case data knowledge graph; the obtained case data knowledge graph is converted into natural language description, a text is used for fine tuning of a large language model, and finally a large language model with domain knowledge is obtained; performing information extraction on the engineering general situation information by using an engineering model, forming a natural language description problem by using an extraction result, and inputting the natural language description problem into a large language model with domain knowledge; the big language model answers the questions, and the expert judgment module provides expert opinions according to replies of the big language model. The intelligent system integrating artificial intelligence, reasoning and decision making is formed by utilizing the powerful language and reasoning ability of a large model.

The invention relates to an intelligent delivery decision-making method and system based on multi-dimensional index association, and the method comprises the following steps: S1, collecting user, advertisement and context multi-dimensional data, and carrying out the preprocessing of the data to generate standardized features; s2, performing fusion calculation on the multi-dimensional standardized features, extracting key features, and constructing and generating a user-advertisement-context joint feature set; s3, constructing a multi-task prediction model, and training according to a user-advertisement-context joint feature set; s4, according to a prediction result and real-time features of the trained multi-task prediction model, rapidly matching an optimal advertisement for a given user in a real-time bidding process; and S5, performing causal analysis according to the exposure / click log of the optimal advertisement, verifying the real effect of the advertisement, correcting the index, and feeding back to the feature engineering in the S2 and the model training step in the S3 according to the corrected index. According to the invention, the advertisement putting efficiency and effect are effectively improved.

The invention provides a work ticket auxiliary decision-making method and system, and relates to the technical field of electric power operation, and the method comprises the steps: collecting and fusing the multi-source data of an electric power operation system, and obtaining the multi-modal data of the electric power operation system; obtaining deep semantic features of the multi-modal data according to the multi-modal data through a large-scale language model, a data feature extraction model and an image feature extraction model; matching according to the deep semantic features through a preset knowledge graph to obtain knowledge graph nodes and relationships related to the multi-modal data; determining an initial auxiliary decision of the work ticket of the power operation by combining a preset deep reinforcement learning network; inputting the data into a power operation system model for simulation to obtain an execution effect; and optimizing the initial auxiliary decision according to the execution effect and the expected execution effect to obtain a final auxiliary decision of the work ticket. According to the invention, the accuracy and consistency of decision making are improved, and the intelligent degree of auxiliary decision making of the work ticket is improved.

The invention belongs to the field of language processing, particularly relates to a multi-causal relationship extraction method and system for intelligent evaluation, and aims to solve the problems of performance bottleneck, remarkable wooden barrel effect and insufficient adaptability and integration caused by dependence on multiple independent models. The method comprises the steps that a causal analysis prompt template used for generating step-by-step reasoning is constructed to serve as a thinking chain prompt word, a fine adjustment corpus is generated in combination with an original corpus and input into a large language model for training, a low-rank adaptation technology is adopted in the training process to conduct fine adjustment training on the large language model, and a causal relationship extraction model is obtained; inputting corpora of a to-be-extracted causal relationship into the causal relationship extraction model, and outputting a causal relationship extraction result; and generating a comprehensive evaluation result and a decision suggestion. According to the method, the efficiency and the quality of the multi-causal relationship extraction task are improved, the stability and the adaptability of the system are enhanced, meanwhile, the labor cost is reduced, and scientific and reliable evaluation results and decision suggestions are provided.

The invention relates to the field of bill auditing, and particularly provides a financial bill auditing decision-making method and system, a terminal and a medium, and the method comprises the steps: collecting multi-mode finance and tax data including texts, images and structured data, and carrying out the preprocessing and alignment; carrying out feature extraction on the preprocessed data by using a multi-modal large model, carrying out feature fusion by using a dynamic weight distribution algorithm based on the credibility of each modal, the service priority and historical feedback, and obtaining a risk probability through a risk identification neural network; the risk probability is compared with a preset threshold value and rule in an auditing rule base, automatic passing is triggered, after risk abnormity is recorded, passing is conducted, and auditing actions such as manual auditing or starting of a high-risk emergency plan are pushed; and finally, adjusting model weight parameters according to manual feedback information to realize system self-optimization. The auditing decision-making efficiency is improved, and the accuracy and the service adaptability are improved.

The invention discloses a customs-used domestic large language model retrieval enhancement generation method, which comprises the following steps of S1, constructing a GraphRAG, and implementing dynamic relationship deconstruction on multi-modal data such as customs announcements, enterprise customs declarations and international provisions based on the deep semantic understanding capability of a large language model; s2, dividing laws and regulations sub-communities based on a Leiden algorithm, combining a Leiden community discovery algorithm with laws and regulations effectiveness analysis, and constructing a triad effectiveness map of laws and regulations clauses-revision events-effective areas; s3, hierarchically retrieving a framework, and decomposing a retrieval process into three-level probability decisions; and S4, establishing a knowledge graph full-stack system architecture of the multi-source heterogeneous data. According to the method, a GraphRAG technology is taken as a core carrier, and breakthrough of a customs complex knowledge scene is realized through double innovation paths: firstly, a graph structure retrieval enhancement model adaptive to customs business characteristics is constructed, and secondly, a dynamic governance mechanism of a Leiden community discovery algorithm optimization regulation system is introduced, so that the decision reliability of a customs intelligent supervision system is improved.

The invention discloses an employment and entrepreneurship support system based on artificial intelligence, relates to the technical field of occupational planning, and aims to solve the problem that a traditional support mode is insufficient in individuation and accuracy. The system comprises a multi-modal data acquisition module used for acquiring facial expressions, whole body dynamics and voice data of a user in at least 30 minutes of video dialogue in real time; a dialogue text is processed through a large language model based on a Transform architecture, continuous time sequence modeling is carried out on multi-modal dynamic behaviors by applying a liquid time constant network, and deep fusion reasoning is carried out in combination with multiple groups of multi-head Transform attention mechanisms. The core analyzes the real thought, psychological state, behavior pattern and core ability of the user through consistency verification, generates a structured dynamic user insight abstract, and customizes personalized vocational development or entrepreneurship planning according to the structured dynamic user insight abstract. According to the method, the potential of the user can be deeply informed, high-precision personalized planning is provided, the decision-making quality and success rate are improved, and the method has dynamic adaptation and continuous learning capabilities.

The invention discloses a large model construction method for intelligent collaboration and decision-making of a power time sequence, and aims to solve the problems of complex time sequence analysis task, high technical threshold and lack of transparency of a prediction result in a power scene. Comprising the following steps: S1, constructing a diversified power time sequence data set and enhancing characteristics thereof; s2, generating a data set with context description; s3, designing a time sequence thinking chain, and finishing alignment of the power time sequence data and the natural language mode; s4, constructing a flexible cue word template, and generating a diversified question and answer training set; and S5, carrying out model adaptation and supervision fine tuning by adopting a low-rank adaptation technology, and constructing a power time sequence large model with intelligent collaboration and decision-making capabilities. According to the method, the technical threshold of power time sequence analysis is remarkably reduced, the accuracy and transparency of a prediction result are improved, the method can be widely applied to the fields of power load prediction, anomaly detection, energy optimization scheduling and the like, and technical support is provided for intelligent transformation of the power industry.

The invention provides a multi-modal information fusion feeding decision-making system and method for chicken breeding behavior recognition, and the method comprises the steps: collecting a multi-source heterogeneous data set, and extracting a primary fusion feature vector; constructing a triple knowledge graph; mining implicit association rules of the ingestion frequency and the body temperature; performing secondary fusion on the primary fusion feature vector and an implicit association rule to generate an intermediate decision feature; and generating a feeding decision instruction through the pre-training decision model and the expert rule base. According to the method, the knowledge graph is constructed, GNN reasoning is utilized, and a manual preset rule static mode is replaced; performing secondary feature fusion, generating intermediate decision features by combining primary fusion features and implicit rules, and then combining a pre-training model and an expert rule base, ensuring decision real-time performance, integrating domain knowledge, outputting accurate adjustment parameters, realizing full-link intelligence, improving the accuracy and adaptability of breeding chicken feeding decisions, and improving the accuracy and adaptability of chicken feeding decisions. Therefore, dynamic requirements of complex breeding scenes are met, and chicken flock health and breeding efficiency improvement are promoted.

The invention discloses an emergency situation intelligent decision-making method, device and system based on deep learning, and the method comprises the steps: firstly collecting multi-modal data of an affected area, carrying out the fusion processing of the multi-modal data into a unified embedded vector, carrying out the time and space dimension feature extraction of the unified embedded vector through a space-time perception neural network, and constructing a state sequence containing a space-time evolution characteristic; constructing a dynamic causal graph based on the sequence to identify a potential causal relationship, and predicting a future emergency situation development trend according to the potential causal relationship; and finally, generating an emergency decision strategy accurately adaptive to the actual condition of the affected area in combination with historical disposal experience and the current resource constraint condition. According to the invention, accurate perception of disaster situations, evolution prediction and intelligent decision generation under resource constraint can be realized.

The invention relates to the technical field of semantic decision management and control, in particular to a multi-modal semantic alignment intelligent decision and risk management and control system, which comprehensively and accurately captures cross-modal semantic association through multi-modal semantic alignment processing so as to generate a plurality of possible reasoning chains with reliability and interpretability. And combining dimensions such as knowledge conflicts and historical risks, calculating reasoning overlapping values to evaluate inter-chain association, realizing multi-dimensional and multi-angle analysis of potential risks, bringing risk fingerprint values and the reasoning overlapping values into a quantitative calculation framework of decision response values, dynamically setting a decision response threshold value, and realizing quantitative calculation of the risk fingerprint values and the reasoning overlapping values. The system can flexibly trigger emergency, early warning or monitoring response according to different risk levels, and refinement and differentiation of risk management and control are realized. The mechanism not only ensures timely disposal in a high-risk scene, but also avoids resource waste in a low-risk scene, and effectively balances risk prevention and control and execution efficiency.

The invention discloses a water conservancy multi-modal intelligent decision-making method based on a model association protocol, and the method comprises the following steps: obtaining hydrological, meteorological and remote sensing data, and carrying out the preprocessing and feature extraction respectively; performing structured coding on the extracted multi-modal features by using a data serialization protocol to generate corresponding serialization codes, and performing packaging and transmission by using an MCP protocol; at a receiving end, de-encapsulation is carried out by using a corresponding MCP protocol, the data is restored to original multi-modal features, and each modal feature is fused through a dynamic weight distribution mechanism and a cross-modal attention mechanism; inputting the fused features into a big decision model driven by a knowledge graph, and generating a flood risk level and a corresponding decision through mixed decision reasoning by means of the knowledge graph and a rule engine; according to the method, the problem of data islands is solved, efficient fusion of multi-source data is realized, the decision process is more intelligent and interpretable, and the flood risk level and the prediction accuracy of the corresponding decision are improved.

The invention provides a large model thinking chain knowledge distillation method and system based on anti-factual reasoning, and belongs to the technical field of artificial intelligence and natural language processing. Comprising the steps that minimum semantic disturbance is applied to a key position of an original reasoning task data set, and an anti-fact problem is constructed; based on the original problem and the anti-fact problem, calling a large language model to generate a multi-view reasoning chain; a high-loyalty distillation data set is screened out through causal intermediary analysis, and thinking chain consistency optimization training is carried out on the large language model; and deploying the large language model after optimization training to a downstream task. According to the method, by analyzing the anti-fact space of the input problem and quantifying the causal contribution of the inference chain to the final decision, the causal reliability and logic consistency of the large model thinking chain can be improved, and then effective data support is provided for downstream inference application.

The invention discloses a marketing prediction intelligent analysis method and system based on machine learning, and the method comprises the steps: generating a competition relation matrix between commodity categories through an ecological niche competition model, and achieving the strategy diversity maintenance through combining the strategy feature coding of a dynamic strategy pool with a red queen adjustment mechanism; and multi-strategy game simulation is carried out by using an ecological game engine, an optimal strategy combination is calculated and output through Nash equilibrium, and finally parameters are optimized based on a real-time feedback closed-loop link. According to the technical scheme of the invention, through a competition-game-feedback dynamic balance mechanism, autonomous emergence of a cross-category co-evolution strategy is realized, the environmental adaptability and evolution ability of a marketing strategy are significantly improved, the problem of strategy stiffness caused by manual rule dependence is overcome, and an intelligent decision-making system with an ecological self-organization characteristic is formed.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention discloses a flexible job shop scheduling method based on deep reinforcement learning, and belongs to the field of intelligent manufacturing and intelligent scheduling, and the method comprises the steps: firstly, taking the minimization of the maximum job completion time and the maximization of the average machine utilization rate as optimization targets, and obtaining a target model; then representing a flexible job shop scheduling problem by using a disjunction graph model to obtain a scheduling state; according to the scheduling state and the target model, an end-to-end learning framework is constructed, and the framework is combined with a multi-attention network for deep feature extraction and a decision network for extensible decision, and a Markov decision process is utilized to obtain a job scheduling model; and finally, the multiple attention network constructs a production adaptability operation-AGV-machine scheme by using the complex relationship among the operation, the AGV and the machine to support the decision of the decision network, and obtains a scheduling schedule by using the job scheduling model to complete flexible job shop scheduling. According to the method, complex modes and relations in data can be better captured, and the performance and generalization ability of the model are improved.