814 results about "Decision maker" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention discloses a factor multiple optimization-based flood risk dynamic zoning method and system, and the method comprises the steps: constructing an initial flood risk assessment index set, carrying out the screening of the initial flood risk assessment index set, and calculating the frequency value of each index; constructing a comprehensive discrimination index value, and further screening to obtain a key flood risk assessment index set; constructing a risk assessment model and a key flood risk assessment index weight optimization model, and adopting a genetic-local search hybrid algorithm of simulated annealing optimization to calculate and obtain an optimal weight combination scheme; and calculating the similarity between the scene of each time point of the research area and a historical typical flood event, selecting an optimal weight combination scheme, and inputting the optimal weight combination scheme into the risk assessment model for calculation to obtain a flood risk dynamic division of the research area. According to the method, scientific and reasonable dynamic risk assessment can be provided, effective decision support can be provided for related decision makers, and the flood control toughness and the extreme environment coping capacity of the drainage basin area can be improved.

The invention discloses a reservoir group scheduling decision-making method considering multi-source uncertainty propagation and evolution tracking, relates to the technical field of reservoir scheduling, and is used for solving the problem of reservoir scheduling multi-target risk decision-making in a random environment. According to the method, a space-time two-dimensional risk deduction model is constructed, refined risk diagnosis and dynamic deduction are performed on a reservoir group scheduling process, an uncertainty propagation and evolution tracking mechanism is obtained, and the robustness and reliability of a reservoir scheduling decision in an uncertainty environment are effectively improved; and a preference demodulator FAHP-OWA which comprehensively considers subjective weight and preference conflicts is adopted to obtain a high-dimensional weight vector in the aspect of obtaining group preference information. Meanwhile, multiple rounds of interactive optimization and a weight distribution mechanism are combined, preference information of decision makers can be introduced step by step, the scheduling process is more flexible, decision groups are more harmonious, the decision efficiency and accuracy of the decision groups in an uncertain environment are improved, and accurate judgment of the advantages and disadvantages of the scheme is achieved.

The invention provides a deep learning training and reasoning task dynamic cooperation system based on GPU space-time resource sharing, and the system comprises a GPU resource state perceptron which is used for monitoring a GPU kernel function call sequence and a video memory distribution state of a distributed training task in real time, dynamically capturing a calculation gap and video memory fragments generated by the training task, and transmitting the calculation gap and the video memory fragments to the GPU resource state perceptron; generating a two-dimensional resource spatial-temporal characteristic spectrum, and predicting a GPU calculation idle period caused by communication synchronization based on an LSTM model; the kernel function dynamic scheduling decision maker is used for performing priority division and dynamic resource quota allocation on an online reasoning task and an offline reasoning task by adopting a self-adaptive allocation strategy on the basis of a resource spatial-temporal characteristic spectrum so as to realize spatial-temporal resource decoupling of the training task and the reasoning task; and the kernel function execution arbiter is used for dynamically controlling submission and blockage of the reasoning task kernel function according to the scheduling decision through video memory space multiplexing and a calculation instruction arbitration mechanism. According to the method, the GPU resource utilization rate is remarkably improved, on the premise that stable training task performance is guaranteed, fragmented resources are effectively utilized to support parallel execution of multiple types of reasoning tasks, and efficient resource collaboration of a deep learning task cluster is achieved.

The invention discloses a cross-platform authority unified management method and system based on a multi-WEB system, and the key point of the technical scheme is that the method comprises the steps: obtaining organization structure data of an enterprise system, analyzing the organization structure data, constructing a multi-level tree topology network, forming organization entity topology parameters, analyzing the organization entity topology parameters, and storing the analyzed organization entity topology parameters into a database; according to the scheme, post authority baseline parameters are generated, dynamic optimization of channel selection is achieved by constructing a dual-channel transmission decision maker and combining real-time network state data and an inter-system interaction characteristic score table, and compared with a traditional static configuration mode, the mechanism can achieve dynamic optimization of channel selection according to parameters such as department communication strength, system transmission performance and safety scores. According to the method, the optimal transmission path is automatically selected, network congestion or high-delay nodes are avoided, cross-system adaptation rules are generated through mapping calculation of an authority interface strategy and post authority baseline parameters, the problem of authority granularity difference between different systems is solved, and the accuracy and consistency of authority distribution are ensured.

The invention discloses a scene granularity interface test case generation method based on multiple agents, and the method comprises a plurality of large language model agents and related components, namely context memory, an observer, a decision maker, an executor, a supervisor, a recorder and a to-be-tested application. An observer identifies the interface control, detection and attribute identification of the control are effectively focused, and it is ensured that accurate data input is provided for subsequent testing steps. Based on the output of an observer, a decision maker generates a detailed plan of the next operation according to the current page state, and an operation strategy can be independently formulated, so that test generation is supported by business scene logic. And an executor analyzes and executes the generated plan operation, carries out test operation strictly according to the plan, and ensures the traceability and repeatability of the test process. Furthermore, the supervisor verifies whether the operation is executed according to the plan and meets the requirements of the target scene, and makes corresponding feedback according to the check result, thereby ensuring that the wrong operation is corrected in time, avoiding the continuous accumulation of errors, and ensuring the effectiveness of each test step. The recorder is responsible for storing the execution instruction and the context information of the test process to the context memory, and the operation history and result in the test process can be traced through the test context information, so that a better decision can be made in the subsequent iteration, and the result export after the test is completed can be effectively supported. And after the recording is finished, the test generation enters the next iteration until the coverage of the test scene is finished.

The invention relates to the technical field of informatization war, and particularly provides a big data analysis-based combat decision auxiliary system and method, and the system comprises a data collection module, a data preprocessing module, a multi-source intelligence fusion module, a situation prediction depth mining and analysis module, a military strength deployment module, and an auxiliary optimization module. The method comprises the following steps: S101, data acquisition, connection and preprocessing; s102, fusing multi-source intelligence to judge a target; s103, accurately analyzing and predicting the attempt of the enemy; s104, carrying out multi-dimensional association recommendation on the available strength; s105, assisting in optimizing a combat scheme; according to the scheme, how to apply a big data analysis technology is introduced in detail, a set of efficient and intelligent combat scheme generation system is constructed, and scientific and accurate tactical and strategic guidance is provided for military decision makers. According to the system, key technical links such as data acquisition, fusion, analysis, modeling and visualization are integrated so as to cope with a complex and dynamic battlefield environment, and maximization of combat effectiveness is achieved.

The invention relates to a multi-source data fusion-based urban physical examination evaluation index calculation method and system. The method comprises the steps of extracting a multi-source data sequence; identifying a data source of the urban physical examination index set, and extracting an independent time sequence data sequence; calculating the information entropy of the independent time sequence data sequence, and distributing a basic fusion weight; calculating a dynamic state evaluation value of the independent time sequence data sequence, and performing weighted fusion on the basic fusion weight and the dynamic state evaluation value to obtain a comprehensive state evaluation value; obtaining a distribution variance of the basic fusion weight, inputting the distribution variance into the uncertainty quantification model, and obtaining an index calculation result containing uncertainty measurement; the real-time performance of the evaluation result is enhanced through an aging attenuation mechanism, and the latest state of the city system is accurately reflected; the output uncertainty measurement index provides a quantitative basis of result credibility for a decision maker, and the decision risk caused by a data fusion error is reduced.

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

This disclosure describes techniques in which an artificially intelligent system includes a progressive machine learning model. In some examples, a system includes both a static and progressive model, each trained to make predictions or other assessments through machine learning techniques. The progressive model may be progressively updated, modified, and / or improved through additional training data or training examples, which may be derived from a local source and / or from one or more remote sources that may be chosen by a qualifier. The qualifier may be a human decision-maker that chooses, selects, and / or evaluates potential data or new training examples for use in updating the progressive model. The qualifier may, in other examples, be an artificially intelligent system trained to perform the functions of the qualifier.

The invention discloses a reservoir group scheduling interval multi-target optimization method under an uncertain condition, and belongs to the technical field of reservoir optimization scheduling. The method comprises the following steps: firstly, establishing an innovative cascade reservoir group interval multi-target scheduling model; secondly, providing a double-population dynamic coordination interval optimization method for solving the interval multi-target scheduling model, and further obtaining an optimal scheduling scheme of the reservoir group; and finally, outputting an optimal scheduling scheme set of the reservoir group. According to the optimization method, through organic combination of a chaos-order mapping initialization strategy, a dynamic adaptive mutual promotion updating strategy and an elite guidance optimization strategy, optimization is rapidly and efficiently carried out to obtain a group of reliable and accurate scheduling scheme sets considering runoff uncertainty; an effective decision-making technical means for solving the cascade reservoir group optimization multi-target complex engineering problem is provided for a scheduling decision maker, the intelligent level and comprehensive benefits of water resource management are improved, and the capacity of a water resource system for coping with complex environments and uncertain factors is further enhanced.

The invention discloses a real-time and efficient basin flood inundation simulation method and system, and relates to the technical field of flood simulation. Multi-source data such as hydrology, remote sensing, weather and terrain are collected in real time, and cleaning, interpolation processing and format standardization are carried out; a data fusion technology and a dynamic weight distribution algorithm are utilized, the system integrates multi-source data to generate a high-precision data set, and the weight of each data source is dynamically adjusted according to the real-time performance, the precision and the space coverage degree of the data; factors such as gradient, elevation and land utilization types are considered, correlation coefficients are quantified, the inundation range and water depth distribution in the drainage basin are estimated, and visual display and early warning release are carried out through the GIS technology. In addition, the system assists in decision making, submerging risk index calculation, emergency evacuation path planning and resource scheduling scheme generation; the real-time performance and accuracy of flood simulation are effectively improved, the flood early warning and emergency response capabilities are enhanced, and powerful support is provided for decision makers.

The invention relates to a large-model-driven KPI (Key Performance Indicator) management and service analysis system and method, and the method comprises the steps: S1, carrying out the definition of an enterprise KPI through a low-code configuration tool, and building an index value network; s2, semantic analysis is carried out on the input natural language instruction of the user, key entities and query intentions in the natural language instruction of the user are recognized, and key indexes comprise a target index and a time range; s3, index data matching is carried out according to the key entities, and correlation analysis and root cause positioning based on an index value network are carried out according to the query intention so as to identify related indexes and potential root causes; and S4, outputting an analysis report and a decision suggestion according to index data matching and correlation analysis and root cause positioning results. According to the invention, the business context can be understood and the demand can be analyzed through natural language dialogues, and more intelligent, flexible and efficient index management and analysis support can be provided for enterprise decision makers.

The invention provides an urban carbon sink construction evaluation method and system. The method belongs to the technical field of urban construction evaluation, and comprises the steps of determining an evaluation target and an evaluation range of urban carbon sink construction, and constructing a multi-dimensional evaluation index system based on the determined evaluation target and evaluation range; a dynamic evaluation model is designed through the constructed multi-dimensional evaluation index system, and various index data in the urban carbon sink construction process are monitored in real time through the evaluation system. Based on the actual demand of urban carbon sink construction, a multi-dimensional evaluation index system is constructed, a plurality of key dimensions such as natural ecological system carbon sink capacity, urban planning and management level and social and economic development conditions are covered, comprehensiveness and scientificity of evaluation are ensured, and the method is suitable for popularization and application. And public institutions and decision makers are helped to deeply understand the current situation and potential of urban carbon sink construction from multiple angles.

The invention relates to the technical field of ecological protection, and discloses a method for evaluating the stability of an ecological system in an arid region, which comprises the following steps: S1, constructing an index system, S2, performing multi-source remote sensing fusion, S3, performing response modeling on the ecological system, and S4, constructing a stability evaluation matrix in a sensitivity dimension; the method comprises the following steps: S1, constructing four-quadrant stability classification to carry out index weighting, S5, intelligently collecting ground sample points, and then combining an ecological change hot area graph, S6, carrying out model dynamic feedback optimization, and S7, outputting a result and carrying out zoning application, namely dividing a key protection and restoration area and supporting regional ecological safety pattern construction and continuous management. Through fusion of remote sensing data and intelligent acquisition and analysis of ground samples, dynamic updating and real-time monitoring of stability evaluation of the ecological system in the arid region are realized, and the effects of accurately reflecting ecological changes and providing efficient and timely data support for decision makers are obtained.

The invention discloses a building facade optimization method and system based on sustainable analysis, and relates to the technical field of data processing. According to the method, parametric modeling is carried out on the external facade of a target building, and a design space is defined; training a machine learning model integrating energy consumption, carbon emission, cost and lighting prediction functions by using historical data; building an optimization problem by taking external facade parameters as decision variables and taking minimization of energy consumption, carbon emission and cost and maximization of lighting as targets, and automatically generating an optimal scheme set by adopting a multi-target optimization algorithm; a decision maker is assisted to select a final scheme through a visual interface, and a corresponding BIM model is output; the method effectively solves the problems that a traditional design method is difficult to coordinate multi-target conflicts, depends on experience and is low in efficiency, and can automatically and intelligently generate an optimal design scheme which is energy-saving, low-carbon, low-cost and good in lighting performance.

The embodiment of the invention provides a heterogeneous aircraft low-altitude management method and device, and the method achieves the dynamic generation of a global avoidance decision through the innovative building of an aircraft dynamics model library, and the combination of a distributed cooperative avoidance network and a multi-objective optimization algorithm. And designing a flight rule identification model, fusing visual, instrument and digital flight rules, and realizing dynamic grouping and airspace distribution based on task similarity through an airspace management decision maker. And a task priority evaluator is constructed, a conflict resolution optimization model is trained based on historical data features, and avoidance instruction optimization under multi-target constraint is realized. According to the method, the defects of the traditional technology in the aspects of cooperative avoidance, rule recognition, conflict resolution and the like are effectively overcome, and the safety and efficiency of heterogeneous aircraft low-altitude management are remarkably improved.

The invention discloses a deep learning algorithm comprehensive evaluation method and system based on game theory combination weighting-TOPSIS, and the method comprises the steps: constructing a deep learning algorithm evaluation index system, calculating different evaluation index values of each candidate algorithm, constructing an evaluation matrix, and carrying out the range standardization processing according to a positive or negative index type; obtaining importance weights of four groups of indexes based on an analytic hierarchy process, an optimal sequence diagram method, an entropy weight method and a CRITIC weight method; on the basis of the four groups of index importance weights, a final index importance weight is obtained based on a game theory combination weighting method; and according to the final index importance weight, in combination with a weighted TOPSIS method, obtaining a comprehensive evaluation and selection sorting result of all candidate algorithms. According to the method, the comprehensiveness and accuracy of evaluation are improved, and a decision maker can be better supported to make scientific judgment in the deep learning algorithm selection process.

The invention discloses a Token level cache matching method and system of a large language model and a storage medium. The method comprises the following steps: constructing a local context fragment; generating a context embedding vector of the current Token, and calculating a context entropy value, a semantic consistency index and a semantic change gradient; for the target Token, determining a semantic category of the target Token; inputting the semantic category to which the target Token belongs into a Hash decision maker to obtain a Hash granularity level; if the Hash is the first-level Hash, executing fixed-length Hash and mapping the Hash to a semantic cache bucket based on a semantic theme or a context range of the first-level Hash; if the first-level hash is the second-level hash, dynamically adjusting the hash length according to the semantic similarity with the adjacent Token; if the Hash granularity level of the target Token is a third-level Hash, constructing a high-dimension context representation and executing a fine Hash operation; and matching the hash result with the KV in the pre-stored cache, and executing corresponding operation according to the matching result.

The invention relates to the technical field of digital twinning, in particular to a digital twinning task scheduling system and a digital twinning task scheduling method oriented to multi-machine cooperation, which realize intelligent mapping between resources and tasks by introducing a topological resource space model. Comprising a topological resource space builder, a task analysis and modeling device, a task resource mapping decision-making device, a global monitoring and optimizing unit and a data fusion processor, the topological resource space builder builds a topological space model of a resource and task mapping relation, and the task analysis and modeling device extracts feature information and a dependency relation of a digital twin task; a task resource mapping decision maker generates a task scheduling decision based on a topological space model and feature information, a global monitoring and optimizing unit dynamically adjusts connectivity parameters of the topological space model according to execution information, a scheduling strategy is optimized, the resource utilization rate is increased, and a data fusion processor merges execution result data and improves the task scheduling efficiency. And through multi-dimensional task feature analysis and intelligent decomposition combination, complex tasks are flexibly dealt with, and the execution efficiency is improved.

The embodiment of the invention discloses a human and AI collaborative large-scale group decision-making method and equipment based on a trusted network. The method comprises the following steps: constructing a decision-making problem; performing initial evaluation on the alternative scheme through a human decision maker set and an AI decision maker set to obtain initial evaluation information; constructing a human and AI trusted network; establishing a continuously learned human and AI consensus reaching algorithm, and dynamically updating the human and AI trust network and the initial evaluation information through the human and AI consensus reaching algorithm to obtain final evaluation information; calculating the weight of each attribute in the alternative scheme and the weight of each decision maker according to the final evaluation information; the final score of each alternative scheme is calculated, and a final decision is generated; the decision-making response speed, precision and decision-making consistency can be effectively improved, and the method is suitable for the complex decision-making field.

The invention discloses a geological disaster risk assessment method and system, and particularly relates to the field of natural disaster risk management, and the method comprises the steps: S101, collecting geological disaster data through a remote sensing technology, ground exploration, meteorological monitoring and a public database, and carrying out the feature extraction and fusion of the collected geological disaster data; s102, correlating different types of geological disaster information with geographical environment and climate factors, and constructing a simulation model to simulate geological disasters; according to the method, the risk of data leakage is avoided, the generalization ability of the whole model is improved, consumption of computing resources is remarkably reduced, the operation efficiency of the model on edge equipment is improved, the reasoning process of the model is accelerated, and a geological disaster department can be helped to efficiently and timely make a rescue plan; the method can improve the decision-making efficiency and reduce the decision-making cost, can provide flexible and effective disaster prevention and reduction measures for decision makers, maximizes the resource utilization rate, and guarantees the continuous effectiveness of the disaster prevention and reduction measures.

The invention discloses a multi-objective optimization-based multi-commodity order fulfillment method and device, equipment and a medium, and relates to the technical field of supply chain management. The multi-commodity order fulfillment method comprises the following steps: acquiring order and inventory information including a current order set, a commodity set and a warehouse inventory set; and modeling an order fulfillment problem based on an actual demand of fulfillment of a multi-commodity order in the order and inventory information. The established model is a multi-objective optimization problem containing three objectives. Generating an initial population by adopting a heuristic method based on priority and inventory feasibility; and carrying out iterative optimization on the initial population by adopting a rapid non-dominated sorting genetic algorithm with elitism based on a double-layer elitism strategy to obtain a Pareto non-dominated solution set. And selecting a final order fulfillment scheme from the Pareto non-dominated solution set according to the preference of a decision maker. According to the method, three core operation indexes are synchronously improved under the condition of meeting feasibility constraints such as inventory, processing capability and demand.

The invention discloses a multi-objective optimization water resource allocation scheduling method, which combines an LSTM-Prophet hybrid prediction model with an attention mechanism, synchronously captures climate periodicity, policy regulation sensitivity and industrial development tendency characteristics in water demand prediction, and improves water demand prediction precision through a genetic algorithm. Through an improved NSGA-III algorithm and a PSO collaborative optimization mechanism, the convergence problem of a high-dimensional target space is effectively solved in combination with a chaotic mapping technology, and the multi-target optimization efficiency is improved by 40% in cooperation with a dynamic weight adjustment strategy of deep reinforcement learning. And multi-scenario simulation verification of a scheduling scheme is realized by utilizing deep coupling of a digital twin system and a three-dimensional GIS, and a real-time feedback correction mechanism of a Markov decision process is realized. A finally constructed entropy weight-TOPSIS multi-dimensional evaluation system is combined with AR visualization and intelligent contract traceability technologies, and a Pareto optimal solution giving consideration to social fair, economic benefits and ecological integrity is provided for decision makers.

The invention provides a bridge group multi-target maintenance decision-making method fusing an evolutionary algorithm and artificial intelligence, and relates to the technical field of civil engineering and artificial intelligence crossing. The method comprises the steps of defining bridge group maintenance cost and structure failure risks, representing preferences of decision makers for different decision targets by weight combinations, and constructing a multi-target maintenance decision optimization model; encoding the weight combination into an individual of a multi-objective evolutionary algorithm, and randomly generating an initial population; aiming at each generation of weight combination, constructing a bridge group Markov decision-making environment; learning an optimal maintenance strategy by adopting an A2C training reinforcement learning agent; the optimal maintenance strategy is evaluated, and an evaluation result is used as individual fitness to be fed back to the multi-objective evolutionary algorithm; using a multi-objective evolutionary algorithm to perform evolutionary search on the multi-objective weight combination; and through a closed-loop feedback mechanism, outputting a Pareto optimal solution set containing an optimal maintenance strategy under various weight combinations, thereby realizing collaborative optimization of weight optimization and strategy learning.

The invention provides an intelligent recommendation method, system and device for MySQL dynamic parameters and a medium, and belongs to the technical field of databases. The method comprises the following steps: collecting structured performance indexes in real time, extracting unstructured text knowledge, and storing the unstructured text knowledge into a data lake; performing cleaning, fusion and feature extraction processing on the acquired data, constructing a time sequence data set, and generating a multi-modal feature vector; constructing a parameter optimization model fusing the text encoder, the parameter dependency graph encoder and the reinforcement learning decision maker, and training the parameter optimization model; according to the real-time system state vector and the user optimization target, using the parameter optimization model to output parameter adjustment suggestions, generating a parameter adjustment suggestion list, and predicting potential risks; according to the parameter adjustment suggestion, a progressive adjustment strategy is adopted to execute parameter adjustment operation, and the adjusted performance index is monitored in real time; and feeding back a parameter adjustment implementation result to a parameter optimization model training process, updating a model weight, generating a parameter adjustment case and storing the parameter adjustment case in a knowledge base.

The invention provides an emergency decision-making auxiliary system based on a knowledge graph. Comprising a data acquisition and processing module, a knowledge graph construction and reasoning module, an information retrieval and generation module, a multi-level reasoning and thinking chain prompting module, a multi-agent cooperation and game optimization module, an incremental learning and dynamic knowledge updating module and a block chain trust guarantee module. The emergency decision-making efficiency, accuracy, cooperation capability and safety are broken through, dynamic and complex emergency scenes can be effectively dealt with, and efficient, reliable and intelligent technical support is provided for decision makers.

The invention discloses a multi-agent collaborative visual navigation reasoning enhancement method and system, and the method comprises the steps: an inference agent analyzes input data, and generates structured output containing a reasoning process and an initial answer; independently analyzing the input data by the evaluator agent, generating an evaluation answer and reviewing the output of the inference agent; comparing whether the initial answer is consistent with the evaluation answer, and if yes, outputting a final answer; if not, calling a bifurcation feedback agent, and generating a core bifurcation point abstract and a visual evidence verification plan; and calling a final decision maker agent to directionally retrieve the video evidence according to the visual evidence verification plan, and outputting a final answer. Four roles of an inference person, an evaluator, a divergence feedback person and a final decision maker are introduced, a complete'generation-evaluation-feedback-optimization 'decision link is constructed, and the cognition and inference accuracy in a complex scene is remarkably improved.

The invention provides an FaaS edge-center deployment decision-making system based on a large language model. The FaaS edge-center deployment decision-making system is used for solving the intelligent deployment problem of an FaaS module in Serverless application. The system comprises a Serverless application FaaS module feature extractor and an edge-center deployment decision maker based on a large language model. The feature extractor extracts key features of the FaaS module from the YAML configuration file, wherein the key features include function runtime, memory requirements, timeout setting, trigger types and the like. And the deployment decision maker generates edge or center deployment decisions through a large language model by using the extracted features. The system further comprises a context-aware decision framework which integrates environmental factors such as a current system state and network conditions and adopts a multi-objective decision method to balance technology and service constraints. In addition, the system is equipped with a feedback learning module which records an actual deployment effect and is used for optimizing future decisions. The method has the beneficial effects that the accuracy and flexibility of FaaS deployment are improved, the resource utilization efficiency is optimized, the decision quality can be continuously improved, and an intelligent deployment solution is provided for Serverless application.