154 results about "Decision quality" patented technology

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 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 an intelligent emergency decision support method and device based on multi-Agent cooperation. The method comprises a task planning module, an information acquisition module, a data fusion module and an execution monitoring module. The task planning module adopts a hierarchical decision-making mechanism, performs task decomposition in a plan making stage, generates a plurality of candidate execution paths by using thinking tree reasoning in a plan execution stage, and selects an optimal scheme. The information acquisition module acquires multi-source information such as network search, knowledge graph and geographic data through a plurality of professional Agents. And the data fusion module adopts a blackboard mode to manage heterogeneous information, and realizes intelligent abstract and correlation analysis through a large language model. And the execution monitoring module realizes dynamic optimization and fault self-recovery of the system through a multi-dimensional progress evaluation and cooperative monitoring mechanism. According to the invention, the problems of insufficient information processing capability, low decision-making efficiency and poor system stability of a traditional emergency decision-making system are solved. The information collection and processing efficiency is improved through large language model multi-Agent cooperation, the decision quality and accuracy are improved through a hierarchical decision mechanism, and long-term stable operation of the system is guaranteed through self-adaptive monitoring. The method is suitable for complex emergency decision-making scenes such as natural disasters, safety accidents and public health events.

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The invention discloses a power distribution network fault self-healing time sequence decision-making method and system based on new energy fluctuation, and relates to the technical field of intelligent power distribution networks, and the method comprises the steps: detecting a power distribution network fault in real time, executing an isolation operation, collecting new energy output data, load data and network topology information, constructing a dynamic evolution model, and calculating the power distribution network fault self-healing time sequence decision-making. And generating a self-healing operation sequence based on a decision framework associated with a time sequence, optimizing load recovery and operation cost, adaptively adjusting the operation sequence according to real-time state fluctuation, executing the adjusted self-healing operation, and updating decision parameters. According to the method, the new energy fluctuation model fusing the space-time characteristics is constructed, accurate composite state representation is established, and a method of combining multi-stage optimization and adaptive robust decision is adopted, so that the decision quality in a high-uncertainty environment is effectively improved, and the problem of strategy failure in an extreme fluctuation scene of a traditional method is solved; and a more reliable fault self-healing solution is provided for the high-proportion new energy power distribution network.

The invention relates to the technical field of enterprise project management, and discloses an enterprise project management method and system based on multi-source heterogeneous data integration, and the method comprises the steps: receiving heterogeneous data from a plurality of data sources; classifying the heterogeneous data into different-speed data streams of a high-speed stream, a medium-speed stream and a low-speed stream according to the updating frequency; allocating a logic timestamp for the different-speed data stream; the different-speed data flow is processed based on the logic timestamp, and the logic consistency of data processing is guaranteed; converting, aggregating and analyzing the processed data according to a preset data stream processing logic; the analysis result is applied to enterprise project management decision support; real-time performance, consistency and integrity of data in enterprise project management are achieved, efficiency and decision quality of enterprise project management are improved, and the problems of data islands, data inconsistency and data processing lag in traditional enterprise project management are solved.

The invention discloses a carton size automatic generation method under multi-target constraint and a packaging decision-making system. The method comprises the steps of obtaining attribute information of a to-be-packaged commodity and a plurality of optimization targets; establishing a multi-objective optimization model; solving the model by adopting an evolutionary algorithm based on Pareto sorting to obtain a Pareto optimal solution set, performing multi-stage decision processing on the solution set, making a primary decision based on user preference, automatically identifying an abnormal product and starting an additional verification process, constructing a digital twin model and performing a virtual simulation test to intelligently decide a final scheme, and according to the weight of the user preference, determining the final scheme according to the weight of the user preference. The technical problems that traditional packaging design depends on artificial experience, efficiency is low, and a globally optimal solution is difficult to obtain among multiple conflict targets are solved, particularly, automatic and high-reliability verification of high-risk commodity packaging is achieved, automation and intelligentization of packaging design are achieved, and the method is suitable for large-scale popularization and application. And the decision-making quality can be improved by means of self-learning of historical data.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

The invention relates to the technical field of glass hot working control, and discloses a rapid prototyping control system and a rapid prototyping control method for tempered glass production. Comprising a thermal coupling module, a temperature control decision module, a flow field solving module, a photoelastic stress analysis module, a quantum annealing optimization module and a time domain synchronous control module. According to the system, a three-dimensional thermal-stress field is constructed on the basis of physical properties and thermal boundaries of glass, heating power is predicted through reinforcement learning, flow field simulation and stress image analysis are combined, control parameter self-adaptive adjustment is achieved through quantum annealing optimization, beats of all subsystems are coordinated through a synchronization module, and an efficient closed-loop control structure is formed. By introducing the quantum annealing optimization module, parameter adjustment in the control system is optimized, the technical effect of improving the precision of complex control decisions is achieved, and the optimization speed and the decision quality of the system are improved.

The invention relates to the technical field of artificial intelligence multi-agents, in particular to a proposal review method and system based on evidence verification and multi-agent cooperation. A cross validation mechanism based on a review information pool is introduced. The cross validation mechanism is a multi-agent round table cooperation mechanism, and a preliminary review result generated by an agent is not directly output, but passes multiple rounds of mutual verification, supplementation and correction until a preset convergence condition is met. According to the evidence verification process, cognitive deviation and knowledge blind areas possibly existing in a single agent are effectively eliminated, and the omission ratio and the misjudgment rate of the review result are remarkably reduced, so that the accuracy, the integrity and the reliability of a final review report are ensured; the technical problems of low efficiency and low accuracy caused by insufficient review depth, single angle and lack of verification in the prior art are effectively solved, and the review efficiency and decision quality of enterprise project proposals are remarkably improved.

The invention provides a civil aircraft flight control system dynamic reconstruction method fusing uncertainty quantification and toughness decision, and belongs to the field of aviation electromechanical product reliability engineering, and the method comprises the steps: S1, multi-source uncertainty modeling and optimization; s2, running state monitoring and risk prediction; s3, evaluating the toughness margin of the flight control system; S4, generating a candidate toughness response strategy; s5, multi-attribute utility evaluation based on online model prediction; s6, selecting an optimal robust toughness decision; s7, executing an optimal robust toughness decision; and S8, empirical learning and model adaptive optimization are carried out. The invention provides a novel operation safety guarantee theory and method which deeply integrates uncertainty quantification, a toughness engineering principle and a multi-criterion robust decision-making aiming at severe uncertainty faced by a dynamic reconfigurable civil aircraft flight control system in a complex operation environment and challenge on flight safety. And the safety decision quality and the overall operation toughness of the system under uncertainty can be obviously improved.

The invention relates to a trading platform that shifts focus from market prediction to disciplined execution, capital management, and trading psychology. By disclosing the general market trend in advance, users are empowered to train and operate within a known directional framework. The system generates synthetic charts using directional random walks anchored at algorithmically or randomly selected convergence points from historical data. This recreates real trends while introducing stochastic variability between anchors. A simulated order book models supply-demand dynamics using Poisson-distributed flows, capturing sentiment, liquidity shifts, and large trades. Features include Accelerated Time Compression for fast-forwarded trade simulation and a Rewind Trade mechanism allowing entry at the start of validated candlesticks. The rewind logic applies to both futures and spot markets, including staking actions. Safeguards such as rewind limits, volume caps, and LP prioritization preserve fairness. The platform supports multi-timeframe execution and bridges strategic training with interactive trading, enhancing decision quality and behavioral discipline.

The invention relates to the related technical field of clinical data analysis, in particular to a clinical medical examination data classification system and method based on big data analysis, a man-machine interaction module is used for extracting and displaying a data classification result of a user, providing a Web / mobile terminal interface and supporting examination data uploading, classification result visualization and report exporting; the data processing module is used for realizing data cleaning (denoising and missing value filling), standardization (LOINC coding mapping) and feature extraction; the intelligent classification module integrates a random forest and an SVM algorithm model and supports automatic classification of inspection items; the database module is used for storing user information, clinical medical examination data of the user, historical clinical medical examination data, a clinical medical examination knowledge graph, clinical medical examination keywords, model metadata and a data classification result; the intelligent classification system for clinical medical examination data can significantly improve medical efficiency and decision quality through standardized integration and AI analysis.

The invention relates to the technical field of digital blackboard lightweight acquisition, and particularly discloses a digital blackboard lightweight acquisition method and system based on multi-modal data. Comprising the steps of multi-modal data acquisition, behavior recognition result generation, teaching scene mode judgment, adaptive acquisition strategy generation, key multi-modal data acquisition, core semantic feature generation and core semantic feature transmission. Through infrared, audio and video data fusion, a behavior identification model and an attention mechanism, intelligent understanding of a teaching process and adaptive focusing of data acquisition are realized, lightweight processing is executed at an acquisition end, original data are converted into core semantic features, and storage, processing and transmission loads are reduced; it is guaranteed that the system can still operate efficiently in an unstable network environment or on edge equipment, the recognition accuracy and robustness of a complex teaching scene are remarkably improved, and meanwhile the intelligent level and decision quality of the whole system are ensured.

The invention discloses a state compression and unlabeled reward-based reinforcement learning method and system and electronic equipment, and the method comprises the steps: receiving a user prompt outputted by a model, and converting the user prompt into a structured state vector which comprises a task target, a function call sequence and an environment feedback result; mapping the function call sequence into a complex plane vector, and automatically generating an unlabeled reward value based on the complex plane vector; based on the unmarked reward value, a low-rank adaptive network is adopted in a model training engine to carry out parameter fine tuning on the model, and batch training is carried out through a token gradient optimization strategy; and identifying and extracting a function call instruction from a text generated by the model, converting the function call instruction into tool API call, and injecting an execution result of the tool API call into a next round of model input in real time. According to the scheme, the decision quality is improved, the resource efficiency is optimized, the implementation cost is reduced, and the application scene can be expanded.

The invention relates to the technical field of service function chain migration, in particular to a service function chain migration method and system based on a user movement scenario, and the method comprises the steps: obtaining migration request information of a to-be-migrated service function chain in response to a service function chain migration demand caused by user movement, a migration decision task is determined for each virtual network function in each service function chain to be migrated, and on the premise of meeting resource requirements and performance indexes of the service function chain, comprehensive minimization of migration cost, migration delay and end-to-end delay of the service function chain after migration is defined as an optimization target. And performing optimization calculation on the determined migration decision-making task, and outputting an optimal service function chain migration decision-making scheme. According to the method, the migration cost, the migration delay and the subsequent end-to-end time delay are comprehensively minimized, and the decision-making quality, the resource efficiency and the user experience of SFC migration are remarkably improved.

The invention discloses a space-time cooperative scheduling method for an intelligent sky rail and an AGV in an automatic container terminal, and the method comprises the steps: determining the scheduling constraint conditions of an SMV and the AGV based on an SMV and AGV dual-cycle strategy, and constructing a model and constraint conditions which take the minimization of the completion time of all tasks as a target; using an LBBD algorithm to decompose the mixed integer linear programming model into a main problem and a sub-problem, constructing three acceleration strategies based on SMV and AGV dual-cycle strategies, embedding acceleration cut into the main problem as a constraint condition, solving the main problem and the sub-problem under the constraint condition, and generating Benders cut; embedding the Benders into the main problem, and solving again to obtain a scheduling optimization result; the scheduling decision quality is fundamentally improved, a set of scientific and efficient SMV-AGV collaborative operation method is provided for an intelligent air rail system, the equipment utilization rate can be remarkably improved, the operation completion time can be shortened, the optimal collaborative scheduling scheme can be rapidly and accurately obtained in a large-scale task scene, and the unloaded driving cost, the energy consumption cost and the operation cost are synchronously reduced.

The invention discloses a task planning-based material information processing method and device. The method comprises the steps of obtaining a multi-modal material information file; dividing the material information file according to the target content of the material information file to generate a plurality of analysis sub-tasks; according to a processing cluster resource state, dynamically distributing the analysis subtasks to cluster nodes, and giving structured information corresponding to the analysis subtasks; substituting the structured information into the information processing model to obtain processing result data corresponding to the structured information, the processing result data including a processing score, a risk probability and a supplier order; and carrying out AB test weight adjustment voting fusion on the processing result data to generate a material information report comprising multi-modal evidence. By dividing the multi-modal material information file, synchronous processing of multi-type data of the bid invitation file is realized, the data processing efficiency and accuracy are improved, a reliable data basis is provided for bid evaluation, and the intelligent level and decision quality of a bid invitation process are comprehensively improved.

The invention relates to the technical field of artificial intelligence, and discloses a crossing scheduling method and system based on reinforcement learning, and the method comprises the steps: obtaining crossing scheduling scene data, and generating a scene complexity evaluation result; processing historical operation data of dispatchers, and generating personnel ability and specialty portraits; acquiring reinforcement learning decision process data, and generating decision interpretation information; generating a man-machine task allocation scheme based on scene complexity and personnel speciality; processing reinforcement learning decision data, and quantifying decision uncertainty indexes; obtaining feedback data of dispatchers, and updating the reinforcement learning model; processing man-machine cooperation process data, and optimizing a man-machine division strategy; according to the invention, a decision situation which is difficult to determine by an AI system can be accurately identified, and human intervention is introduced in time; and by continuously integrating human professional knowledge, the decision-making quality and robustness of the system in a complex scene are improved.

The invention discloses an internet-based ship port entering and leaving scheduling system and method, and relates to the technical field of ship management, and the system comprises an initial ship scheduling unit; and the optimal ship scheduling unit is used for constructing a multi-target reward tensor in combination with a multi-target scheduling constraint condition, processing the multi-target reward tensor by using a sequence based on Monte Carlo tree search, constructing a strategy to generate an initial scheduling set, obtaining a candidate scheduling set through a denoising path optimization mechanism dynamically guided by a target condition, and performing ship scheduling. Selecting an optimal ship scheduling sequence; the multi-ship collaborative network unit is used for constructing a multi-dimensional space-time cascade atlas to identify implicit resource conflicts among ships, and generating a collaborative track by using a multi-ship collaborative track generation algorithm to form a multi-ship collaborative network; and a dispatching instruction generating and pushing unit. According to the invention, balance and accuracy of ship scheduling among multiple targets are ensured, scheduling errors and delay are reduced, and scheduling efficiency and decision quality are improved.

The invention discloses a large-model multi-agent task scheduling method with memory and retrieval capabilities, and the method comprises the steps: carrying out the cognitive analysis of a complex task based on task scheduling agents, and carrying out the distribution of subtasks through combining the functional attributes of all task execution agents and an integrated tool; each task execution agent completes execution work of a specific task through an integration tool, a task result is returned to the task scheduling agent for integration, and execution of a next link subtask or completion of the task is determined according to a task condition; maintaining task context information and a task entity information base by adopting a dynamic memory pool; and cross-agent and cross-task node multi-dimensional information sharing is realized by using cross-agent search. According to the method, the problem of state loss of a multi-agent system is solved by introducing a memory enhancement mechanism, and the context sensing capability in the task execution process is remarkably improved; cross-task and cross-agent knowledge reuse is realized through a cross-agent search mechanism, and the decision quality of the system is improved; the method is compatible with multi-source heterogeneous task requirements, breaks through the limitation of a traditional multi-agent system on task complexity, cross-agent collaboration and knowledge reuse capability, and can be widely applied to the field of intelligent scheduling of complex scenes such as emergency rescue, industrial automation and urban public management.

The invention relates to the technical field of artificial intelligence, multi-agent systems and natural language processing, in particular to a multi-agent collaborative decision-making and communication system with a body based on a large language model, and the system comprises a sensing module which is used for obtaining environment and self state information; the large language model core processing module is integrated to each agent, comprises a multi-modal perceptual representation and semantic mapping system, a semantic-structured information bidirectional conversion network and a self-adaptive multi-path decision generation system, and is used for generating individual action decision suggestions and natural language communication contents oriented to other agents; the cooperative communication network is used for supporting efficient and semantic-rich interaction between intelligent agents based on natural language communication content; the environment coordination module is used for analyzing communication content and generating a global or local coordination instruction, a large language model is integrated into the multi-agent system, efficient communication and collaborative decision-making between agents based on natural languages are achieved, and the adaptability and decision-making quality of the system in a complex and dynamic environment are improved.

The invention relates to the technical field of machine learning, in particular to an unmanned surface vehicle path planning method based on psychological expectation reinforcement learning. Comprising a multi-head D3QN module, an RND3QN module, a psychological expectation strategy module, a risk avoiding strategy module and a priority experience playback pool module, the multi-head D3QN module is used for respectively processing different types of external reward streams, and each reward stream independently uses different discount factors to carry out value iteration. An external reward flow is effectively processed through a multi-head D3QN module, a RND3QN module evaluates novelty and generates an integrated reward signal, a psychological expectation strategy module balances exploration and utilization, a risk avoiding strategy module filters dangerous actions, and a priority experience playback pool module optimizes training samples, so that the decision-making quality and efficiency of an intelligent agent are comprehensively improved; and a more reasonable decision can be made in a complex environment.

The invention discloses an intelligent switch fault diagnosis method based on multilayer data reasoning, which relates to the field of switches, collects and fuses multi-source data of a switch, converts entities, attributes and relationships in a multi-source fusion data set into an RDF triple based on a predefined network operation and maintenance ontology model, and performs fault diagnosis on the RDF triple. The method comprises the steps of constructing a switch fault diagnosis knowledge graph, dynamically generating an SPARQL query statement based on real-time monitoring data, performing graph traversal and logical reasoning on the switch fault diagnosis knowledge graph, and outputting a switch fault diagnosis conclusion. According to the method, comprehensive upgrading of switch fault diagnosis from passive response to active prevention is achieved, the network operation and maintenance efficiency and decision quality are remarkably improved, equipment monitoring, topological relations and service data are integrated through a unified semantic framework, a structured knowledge graph is constructed, and fault diagnosis has semantic interpretability.

The invention relates to the technical field of electronic data processing, and discloses a micro-service concurrent scheduling method and system based on DAG (Directed Acyclic Graphics) drive, which comprises the following steps: acquiring a directed acyclic graph, identifying a strong dependency side and a weak dependency side to carry out topological layering on the directed acyclic graph, and calculating a global parallelism factor; determining a prediction range, analyzing nodes in a subsequent main service layer in the prediction range, and generating a predictive resource demand vector; determining a target number of various service instances in the preheating pool; when the health degree attenuation value is lower than an activity threshold value, marking the idle instance as a to-be-refreshed state; calculating the scheduling affinity of each matching pair, and selecting the matching pair with the highest affinity to execute the task; if the execution time consumption exceeds the statistical standard, recording the type of the service node and the timeout amplitude as a path blocking event; the historical path blocking degree is updated and used for feeding back and adjusting the target number of the follow-up preheating pools. According to the method, the task can be matched to the optimal execution instance, and the scheduling decision quality is improved.

The invention relates to the technical field of equipment asset management and operation and maintenance decision, and discloses a fluid measurement and control equipment full-life-cycle operation and maintenance management system based on multi-source data, and the system comprises the steps: distributing an operation and maintenance depreciation right initial limit for equipment; the method comprises the following steps of: acquiring the multi-source data reflecting the physical loss, the business value, the real-time operation efficiency and the operation stability of the equipment, determining the product of a plurality of factors based on the multi-source data, dynamically calculating the consumption rate of the ODR, and further driving the operation and maintenance decision according to the residual limit. The consumption cost of the value budget management process is adjusted by the business value, the operation efficiency and the stability risk in real time, and a quantitative bridge for connecting the physical world and the business decision is established, so that the operation and maintenance decision can be carried out based on a clear economic basis instead of a single physical threshold value; therefore, the asset management level and the decision-making quality are improved.

The invention relates to a discrete industrial agent-based production management method and system, and relates to the field of production management, and the method comprises the steps: collecting a production prediction sample data set and a quality inspection decision sample data set, carrying out the data weight division of the two data sets, and obtaining two sample weight sets; obtaining a production prediction and quality inspection decision path array, a first prediction accuracy rate set and a decision accuracy rate set after integrated training; combining the two path arrays to obtain a discrete industrial agent array, carrying out joint optimization training, and testing to obtain a second prediction and decision accuracy set; obtaining current production basic data, inputting the current production basic data into the agent array, outputting a predicted production yield and a decision quality inspection parameter, performing compensation according to an error between the second prediction and decision accuracy set and the first prediction and decision accuracy set, and obtaining a predicted production yield and decision quality inspection parameter interval for production management. The technical problem that data interaction and business collaboration of a plurality of complex and independent scenes in the discrete manufacturing industry are difficult to realize in production management is solved.

The application discloses a kind of robot autonomous exploration mapping system and method, system includes perception module, decision module, planning module and control module, the output of perception module is connected with the input of decision module, and perception module transmits front point candidate set to decision module, the output of decision module is connected with the input of planning module, and decision module transmits optimal target point to planning module, the output of planning module is connected with the input of control module, and planning module transmits motion instruction to control module, and the output of control module is connected with the input of perception module, and control module state information is fed back to perception module, and forms closed loop control system. It can be adapted to diversified industrial scene, with the advantages of efficient completion of autonomous exploration, balancing decision quality and real-time, self-adapting different structure environment, improving decision stability and the like.

The application discloses a power-hydrogen-doped gas network combined scheduling method and relates to the field of energy scheduling. The application proposes a power and hydrogen-doped gas network combined scheduling framework based on graph neural network embedding optimization. Firstly, the hydrogen doping ratio in the pipe network is accurately modeled based on the graph neural network, and then the graph neural network inference process is equivalently embedded in the optimization problem, so that the energy scheduling decision can accurately and efficiently consider the hydrogen doping ratio. The application can improve the decision quality of the combined scheduling operation of the power grid-hydrogen-doped gas pipe network system, thereby improving the safety and economy of the operation, and can be deployed and migrated in batches at low cost.

The application discloses a multi-knowledge base fusion deduction method based on collaborative decision-making, comprising: performing semantic analysis on a deduction request input by a user to extract key semantic features; activating a target knowledge base in a multi-knowledge base cluster based on the features and retrieving preliminary knowledge fragments; performing semantic alignment and conflict resolution on the preliminary knowledge fragments to generate a fusion knowledge graph; performing cross-library joint reasoning on the fusion knowledge graph, and performing a multi-step deduction cycle with the preliminary reasoning conclusion as a starting point, wherein a hypothesis generation and verification mechanism is introduced; and finally performing confidence evaluation, sorting and synthesis on each conclusion in the final deduction conclusion chain to output an optimal deduction result. The application realizes a leap from "information retrieval" to "knowledge deduction", has advantages such as cross-library joint reasoning, multi-step deduction cycle, hypothesis verification and interpretability, and improves reasoning capability, dynamic adaptability and decision quality in a complex decision-making scenario.