695 results about "Decision taking" patented technology

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 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 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 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 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 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 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.

The invention provides a multi-modal decision-making auxiliary method and system for law compliance examination. The method comprises the steps of S1, obtaining multi-source data of a to-be-examined object; s2, preprocessing the multi-source data; s3, extracting modal feature vectors of the text data, the voice data, the image data and the video data; s4, inputting each modal feature vector into a trained first convolutional neural network model, and outputting a unified feature representation matrix; s5, calling a law and regulation knowledge graph retrieval interface, and generating a law and regulation constraint vector corresponding to the associated law and regulation terms; and S6, inputting the unified feature representation matrix and the regulation constraint vector into a trained second convolutional neural network model, and outputting an illegal clause matching result and interpretable information. According to the method, the latest regulation terms are automatically matched, the visual risk evidence chain is output, and the review coverage, accuracy and timeliness are greatly improved.

The invention relates to the technical field of material management, and discloses a dynamic rule generation and self-adaptive auditing system and method for material management, and the system comprises a rule intelligent extraction module, a rule management knowledge base, an enhanced auditing engine, a man-machine cooperation calibration module and a self-adaptive execution module. The method comprises the steps of automatic rule extraction, rule storage and management, enhanced auditing and reasoning, man-machine collaborative calibration, knowledge base real-time optimization and adaptive routing execution. According to the method, the rule is automatically extracted from the unstructured document, the problem that a traditional system rule depends on manpower and is lagged in updating is solved, dynamic optimization of the rule and confidence is achieved by introducing a man-machine collaborative feedback closed loop, the accuracy and transparency of an audit decision are improved by enhancing reasoning and explainable decision technologies, and the audit efficiency is improved. And the optimal balance between auditing efficiency and risk control is realized through self-adaptive routing execution based on credibility.

The invention relates to the technical field of artificial intelligence, in particular to a large model and multi-agent collaborative decision-making method based on dynamic knowledge flow, which comprises the following steps of: analyzing a static knowledge and dynamic information fusion relationship through joint modeling, extracting a hierarchical structure of equipment constraints and environment variables, identifying constraint conflicts and deviations in task decomposition, and obtaining a multi-agent collaborative decision-making result; screening a consistency decomposition direction, extracting a task constraint parallel optimization theory, correcting constraint conflicts, evaluating consistency changes, and outputting a collaborative task convergence robust state identifier. According to the method, by integrating static knowledge and dynamic information and optimizing understanding of task decomposition and equipment constraints, the collaborative decision-making capacity of multiple agents in a complex environment is enhanced, the conflict and deviation processing capacity in the task execution process is improved, the accuracy and consistency of tasks are enhanced, and the convergence and stability of task targets are improved; the robustness of multi-agent cooperative work is promoted, and finally more efficient resource utilization and task completion effects are achieved.

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

The invention provides a large model agent interactive question and answer task decision-making method and system, and relates to the technical field of natural language process.The method comprises the steps that 1, user input is received through multiple rounds of dialogues, key information is extracted to form an initial memory node set, meanwhile, fuzziness and information integrity of a query intention are analyzed, and an initial memory node set is obtained; generating an uncertainty evaluation result; and step 2, based on the uncertainty evaluation result, evaluating the timeliness and importance degree of each node, generating an enhanced memory node set, and constructing a dynamic memory structure according to a semantic and sequential relationship to form a structured memory network. According to the method, the understanding accuracy of the intelligent agent on multi-round dialogue contexts, the logic continuity of decision response and the continuous adaptability of task processing capacity are improved.

The invention relates to the technical field of intelligent decision making, in particular to a digital twin command and decision feedback system for war game deduction based on multi-agent game confrontation. Comprising a digital twin modeling unit; a multi-agent game confrontation unit; a command instruction generation unit; a decision feedback evaluation unit; and a data interaction unit. According to the design of the invention, the precision of the model is adaptively adjusted through the digital twinborn modeling unit according to the criticality of the deduction scene, and the digital twinborn model can adapt to different criticality deduction requirements of a strategic layer, a tactical layer, a key confrontation scene and the like in combination with multi-scale division and cross-scale parameter coupling transmission; realizing cross-level dynamic association between the physical entity and the digital model; a real-time perception-decision closed-loop mechanism constructed based on a multi-agent game confrontation unit enables the agents to realize collaborative confrontation based on a real-time state autonomous evolution strategy of a digital twinborn scene, and solves the problem that the agent decision is disjointed from a physical scene state.

The invention discloses a major decision legitimacy compliance review system based on a large language model, which comprises a knowledge base creation module, a content recall module and an answer generation module, and realizes reasoning and content memory in the legal field by constructing a basic structure in which the large language model is combined with a vector database. Therefore, query and dialogue of the natural language of the user are realized. Various legal documents need to be input into the system, and the documents are vectorized and stored in a vector database. When the system is used by staff of the department of law, after the system receives a question, the question is converted into a semantic vector through the large model, the question vector is matched with a content vector in a knowledge base, after a corresponding result is obtained, the question vector is summarized into a natural language form through the large language model and fed back to a user, and meanwhile, a matched corresponding unstructured file is listed as a reference. The method can be used for legal compliance examination of major decisions, searching in existing precipitated legal documents and giving corresponding general answers and instructive suggestions.

The invention discloses an intelligent transaction decision-making method and system based on hierarchical multi-round confrontation debate, and is suitable for stock and other financial asset transaction scenes. According to the method, a hierarchical multi-round adversarial debate mechanism is constructed, so that efficient, low-delay and interpretable intelligent transaction decision is realized, and the limitation of the prior art on deep reasoning, delay control and decision transparency is solved. The core of the method is to construct a complete process of multi-agent confrontation debate, dynamic reputation evaluation and transparent decision making, divide multi-role agents into an argument generation group and a reputation group, and execute R rounds of deep debate. In the debate process, the agent reputation is updated by adopting Bayesian reputation, and a transaction signal is generated within millisecond-level delay in combination with a Soft-Borda dynamic voting mechanism. And meanwhile, through a transparent decision-making link of multiple rounds of debate logs and interpretation vectors, the requirements of financial supervision on model interpretability and real-time auditing are met.

The invention discloses a multi-agent collaborative decision-making system and method based on knowledge Token, and the method comprises the following steps: S1, constructing a knowledge base architecture, executing the content Hash calculation, and generating an original fingerprint in an original fidelity layer; s2, extracting content, packaging the content into knowledge Token, and writing identification, semantics, source, authority, value, contract, association, traceability and health information; s3, activating a knowledge layer, constructing a vector index and mapping knowledge graph nodes; s4, unifying an index layer, matching task requirements, and screening and verifying candidate Tokens to obtain an authorization set; s5, the multiple agents generate local decisions and fuse values and semantics to form a collaborative result; and S6, binding a Token identifier after consistency verification, generating a collaborative decision and recording data consanguinity information. According to the method, multi-agent collaborative decision-making is realized through the knowledge Token and the knowledge graph, and the decision-making accuracy and traceability are improved.

The invention discloses a large language model auxiliary optimization strategic decision-making system and a large language model auxiliary optimization strategic decision-making method. The system comprises six core modules. The dynamic knowledge fusion module constructs a three-layer distributed knowledge network, constructs an entity association weight matrix through a bidirectional Transform model based on an attention mechanism, and realizes knowledge dynamic association in combination with a time attenuation factor and a hybrid coding technology. The large language model module performs field fine tuning by adopting incremental pre-training and low-rank adaptation technologies, and introduces an exclusive word segmentation list to improve professional analysis precision. The full-process intelligent writing module covers submodules for report generation, revision and the like, and supports full-life-cycle management of reports. The strategic decision intelligent deduction module integrates scene impact factors, and realizes multi-scene deduction through reinforcement learning and Monte Carlo tree search. The interaction display module provides a visual interface, and the multi-mode interaction module realizes full task chain management. According to the invention, real-time knowledge support and intelligent deduction capability are provided for strategic decision making, and decision making efficiency and accuracy are improved.

The invention discloses an intelligent optimization system for enterprise resource planning, and relates to the technical field of enterprise resource management, and the system comprises a data collection and preprocessing layer, a knowledge construction and reasoning layer, a risk early warning and decision support layer, and a resource scheduling and execution layer. According to the invention, a knowledge graph covering various resources of an enterprise and association relationships thereof is constructed by using preprocessed data through a knowledge construction and reasoning layer, a causal relationship network among the data is established, and meanwhile, an intervention effect evaluation unit combines an anti-factual reasoning result of a causal reasoning unit to evaluate the intervention effect of the enterprise. Quantitative evaluation is carried out on different resource pre-allocation intervention schemes, and a detailed evaluation report is output, so that an enterprise can perceive and take corresponding measures in advance before a risk occurs, and can select an optimal scheme according to a scientific evaluation result when a resource planning decision is made, thereby effectively ensuring stable operation of enterprise resources, and improving the resource planning efficiency. And the operation risk caused by the resource problem is reduced.

The invention provides a retrieval enhanced large language model system and generation method based on knowledge fusion, and the system comprises a retrieval module which is used for receiving user query and retrieving related corpora from an external knowledge base; the generation module is realized by a pre-trained large language model and is used for generating candidate answers and fact bases thereof; and the knowledge fusion decision-making module is realized by the large language model and is used for receiving the user query and the multiple groups of candidate answers and fact bases generated by the generation module, performing analysis and decision-making and outputting final answers and final fact bases. By introducing a double-stage generation and decision-making mechanism based on an inverse normal form and an adjustment and optimization method based on direct preference optimization, the problems of insufficient knowledge fusion and rough decision in the existing retrieval enhancement generation technology are effectively solved, the accuracy and factuality of generated answers are improved, and the user experience is improved. And the generalization ability and robustness of the method in cross-domain and complex contexts are enhanced.

Multimodal large language models (MLLMs) have excellent reasoning capabilities and are used for autonomous driving applications. For real-world applications, understanding and navigating in three-dimensional (3D) space is necessary, particularly for autonomous vehicles (AVs) to make informed decisions, anticipate future states, and interact safely with the environment. An MLLM agent system includes a 3D projector model and an adapted LLM that extends understanding and reasoning capability from 2D to 3D. Another component of the MLLM agent system is development of a benchmark visual question-answering (VQA) training dataset for training the MLLM agent. The VQA tasks include scene description, traffic regulation, 3D grounding, counterfactual reasoning, decision making, and planning.

The invention discloses a distributed collaborative decision-making system based on multi-modal data driving and an implementation method thereof, and relates to the technical field of group intelligence and distributed decision-making, and the system comprises a user end interaction module which provides a multi-modal interaction and decision-making scheme visual interface; the distributed node management module comprises a main node and an edge node, and the main node manages node registration, state monitoring and task distribution; the information fusion and preprocessing module is used for processing multi-source heterogeneous data; the decision analysis module is used for carrying out clustering analysis on the opinions and generating candidate schemes in combination with domain knowledge; the domain knowledge graph module is used for constructing a domain entity relationship network; the consensus mechanism and credit evaluation module determines multiple rounds of interaction rules, calculates a user credit value and influences an opinion weight; and the decision result output and feedback module is used for collecting user feedback for system optimization. According to the method, the stability, the response speed and the load balancing capacity are improved, the multi-source information processing and opinion aggregation quality is optimized, and efficient and reliable support is provided for distributed collaborative decision making.

The invention discloses an intelligent decision support system and method for grassroots social governance based on multi-modal fusion. The method comprises the following steps: S1, acquiring and preprocessing multi-modal data in the field of grassroots social governance; s2, constructing a heterogeneous graph structure according to the data type and the treatment subject category; s3, extracting single-modal features respectively and generating cross-modal dynamic association features; s4, performing collaborative optimization on the heterogeneous graph structure and the cross-modal fusion parameters by adopting a flying fox optimization algorithm; s5, analyzing data in real time according to the optimized heterogeneous graph, and generating potential risk early warning, event trend prediction and event association deduction information; s6, pushing an auxiliary decision-making scheme and a disposal strategy in real time; and S7, continuously optimizing the heterogeneous graph structure by using a feedback result. The decision-making efficiency and accuracy of grassroots social governance are effectively improved.

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 provides a layered multi-agent-based full-process intelligent decision-making method and system for mineral development. The method comprises the following steps: constructing a three-level intelligent decision-making main body-based full-process layered collaborative decision-making architecture for mineral development; designing a communication protocol; and establishing a quantitative coupling relationship between the cross-link coupling constraint and the process parameters. And constructing a state space and an action space of the three-level intelligent decision-making main body. Designing a global objective function, a link objective function and a key parameter collaborative optimization model; and establishing an adaptive weight optimization mechanism. And designing a distributed collaborative optimization mechanism based on an alternating direction multiplier method. And implementing an agent collaborative decision-making process of the evolutionary game. The selected decision scheme is converted into a specific production instruction to be issued and executed, the execution effect and feedback data are monitored in real time, and the strategy is dynamically adjusted and optimized. According to the invention, the multi-objective collaborative optimization of the full life cycle of mineral development is realized, and the intelligent, green and sustainable development of mineral resource development is promoted.

The invention relates to the technical field of artificial intelligence knowledge maps, and discloses a customs emergency decision-making method and system based on a multi-agent large language model, and the method comprises the steps: constructing a knowledge map from an unstructured customs emergency procedure document through a multi-agent cooperation framework; the multi-agent cooperation framework at least comprises a regulation analysis agent, a key point screening agent, an emergency element extraction agent and a logic construction agent; and providing support for customs emergency decision-making problems of the user and giving customs emergency decision-making answers through a map navigation agent driven by a large language model on the basis of the constructed knowledge map. According to the application, high-fidelity automatic construction of the customs emergency knowledge graph can be realized, and the accuracy and integrity of knowledge are ensured; a static knowledge graph can be converted into an interactive dynamic decision support tool, and a full link from an unstructured regulation document to intelligent decision support is really opened.

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 decision making method and device based on multi-modal information, equipment and a medium. Comprising the steps of obtaining visual data, language instructions and action historical data, processing the visual data, the language instructions and the action historical data into visual features, language features and action historical features, fusing the features to generate preprocessed multi-modal features, generating action sequence features by using a layered action decoder, mapping the action sequence features into control parameters to generate action decisions, and outputting the action decisions. Environment feedback information is collected, new action sequence features are generated based on the environment feedback information, and action decisions are updated. Through multi-modal information processing and hierarchical action decoding, vision, language and action historical information are dynamically fused, action sequence generation and decision updating are optimized in combination with environment feedback, the adaptability and accuracy of action decision in a complex environment are effectively improved, and the response ability of a model to variable scenes is enhanced.

The invention discloses a sewage plant multi-process-section collaborative intelligent decision fusion system, which comprises a sensing and rule fusion layer used for collecting inlet and outlet water quality parameters, process control parameters and operation state parameters of multiple process sections in real time, preprocessing data and fusing an expert rule base; the mechanism and data driving joint modeling layer is used for establishing a mechanism model and a data driving model under the constraint condition of the expert rule base and predicting control quantities respectively; and the collaborative optimization and fusion decision-making layer is used for executing cross-process-section multi-target collaborative optimization and fusion decision-making based on an output result of the mechanism and data driving joint modeling layer under the constraint condition of an expert rule base, calculating a dynamic fusion weight, generating a final control quantity, and issuing the final control quantity to execution equipment. And in combination with real-time feedback self-adaptive adjustment, closed-loop optimization is realized. According to the system, the sewage treatment stability, decision precision and resource utilization efficiency are improved, and the environmental risk is reduced.