26 results about "Decision making analysis" patented technology

The application provides an intelligent unmanned system decision reliability evaluation and optimization method based on reinforcement learning, and belongs to the technical field of intelligent unmanned systems. The application adopts a multi-layer operation network model based on a coloring graph theory to represent individual autonomy and functional diversity, overcomes the limitation of a single-layer network, and provides a basis for decision analysis. An intelligent unmanned system distributed decision model based on an enhanced actor-critic architecture is adopted to improve the strategy performance and learning stability of individual decision. An intelligent unmanned system decision reliability evaluation model oriented to an operation loop is adopted. The model integrates the proposed reliability indexes into the individual learning process to improve the effectiveness of evaluation. The model successfully quantifies the influence of individual decision on system reliability. A decision reliability optimization model based on a cooperative multi-agent deep deterministic policy gradient algorithm is adopted. The proposed cooperative reward is integrated into the policy gradient optimization process to optimize the decision reliability.

The embodiment of the application relates to the field of data processing, and provides a financial institution decision analysis method based on data driving and related devices, which comprises the following steps: obtaining endogenous business data set and exogenous correlation data set of a financial market; performing business expansion analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining business expansion analysis data set; performing risk management analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining risk management analysis data set; performing resource allocation analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining resource allocation analysis data set; performing conflict checking processing according to the business expansion analysis data set, the risk management analysis data set and the resource allocation analysis data set, and performing decision suggestion quantization output, obtaining a financial institution decision analysis result, and more accurate financial institution decision analysis can be realized.

The sewage plant multi-energy utilization robust decision method, device, computer equipment and medium belong to the field of urban sewage plant comprehensive energy utilization and multi-energy complementary optimization. Among them, the method comprises the following steps: constructing a practical application-oriented sewage plant comprehensive energy utilization and multi-energy complementary optimization stochastic multi-criteria decision analysis framework for the robust decision of sewage plant comprehensive energy utilization and multi-energy complementary optimization under uncertain conditions; using game theory to aggregate and resolve the weight of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization, and estimating the uncertainty of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization; constructing a sewage plant comprehensive energy utilization and multi-energy complementary optimization stochastic multi-criteria decision acceptability analysis-gray correlation analysis model to quantitatively evaluate the uncertainty and decision-making risk of sewage plant comprehensive energy utilization and multi-energy complementary optimization multi-criteria decision; using significance analysis method to determine the influence of input parameter uncertainty of each decision standard of sewage plant comprehensive energy utilization and multi-energy complementary optimization on the decision scheme of sewage plant comprehensive energy utilization and multi-energy complementary optimization.

This paper presents a sparse grid polynomial-based orbital game situation analysis method, belonging to the field of aerospace orbital adversarial and intelligent decision analysis technology. Addressing the problems of lack of systematic representation of multi-source uncertainties, low computational efficiency of traditional quantification methods, and difficulty in achieving multi-objective equilibrium optimization in existing high-orbit multi-to-multi dynamic adversarial scenarios, this paper constructs a POMDP multi-to-multi dynamic game model incorporating uncertainties in high-orbit dynamics and control execution, strategy behavior, and communication delay. A surrogate model is established using sparse grid sampling and polynomial chaotic expansion, and global sensitivity analysis is performed. Based on this, a multi-objective optimization model is constructed, and the Pareto optimal policy set is solved. This achieves game strategy generation that balances performance, robustness, and economy, and is applicable to spacecraft strategy optimization and space situation analysis in high-orbit multi-to-multi dynamic adversarial missions.

The application provides a comprehensive decision-making and evaluation method for halogenated hydrocarbon contaminated soil and groundwater remediation strategy, comprising: establishing a site baseline and conducting technical preliminary screening based on health and ecological risk assessment (HERA) and a three-level evidence system; conducting rapid resource efficiency assessment based on environmental footprint analysis (EFA); conducting global long-term assessment based on life cycle assessment (LCA) and cost-benefit analysis (CBA); and dynamic evaluation and closed-loop control based on digital twinning and multi-criteria decision analysis (MCDA) coupling. In the dynamic evaluation stage, an adaptive hybrid dynamic weighting mechanism is constructed, the subjective benchmark weight and the objective fluctuation weight calculated based on real-time data information entropy are integrated, and the comprehensive utility score is calculated; at the same time, the apparent degradation rate constant of the pollutant is solved in real time, and when it is lower than the target rate and the strategy is replaced, the intervention instruction is automatically output. The application realizes the deep integration of multi-dimensional indicators, dynamic adaptive evaluation and closed-loop intelligent regulation and control, and solves the problems of single, static and lack of feedback of the existing evaluation method.

This invention relates to the field of aviation emergency rescue auxiliary decision analysis, and particularly to a classification and grading method for aviation emergency rescue scenarios. The method first acquires historical case information and designs prompt templates, then uses a large language model to parse unstructured text to obtain structured scenario element data. Next, it constructs a hierarchical structure model for classifying and grading forest fire scenarios, and uses the analytic hierarchy process (AHP) to calculate element weights. Subsequently, it constructs a scoring mapping model, sets monotonicity constraints, and aims to maximize the Pearson correlation between scenario scores and the scale of rescue force deployment, using a trust region algorithm to iteratively solve the model parameters. Finally, it calculates the total scenario score, performs clustering operations based on the deployment scale, and determines the grading threshold based on the cluster centers and dispersion. This invention achieves in-depth utilization of unstructured rescue information, enabling scientific quantification and accurate grading of complex scenarios such as forest fires, effectively improving the scientific nature and response efficiency of aviation emergency rescue decision-making.

This invention discloses a data-driven method for quality management of formwork construction in high-speed railway bridge-building machines, belonging to the field of engineering construction management technology. It includes acquiring multi-source real-time status data, environmental monitoring data, and historical adjustment records; calculating based on an initial quality sensitivity model to generate a dynamic quality sensitivity graph and perform resource scheduling to obtain refined monitoring data; then performing digital twin simulation of the construction process to generate a simulation report and conduct multi-objective optimization decision analysis; generating adjustment instructions; controlling the execution mechanism to execute the adjustment instructions; collecting actual formwork response data and comparing it with the simulation report; generating model calibration data; and updating the initial quality sensitivity model. This invention employs a collaborative working mode of multi-source data perception, digital twin simulation, closed-loop feedback control, and model self-optimization, enabling predictive management of construction quality, scientific optimization of decisions, and continuous improvement of the process.

The technology described herein is directed towards workload prioritization (ranking) in a cloud (e.g., private / hybrid) environment based on multi-criteria decision analysis using Mahalanobis distance-based variance-covariance matrix that takes into account the pair-wise correlation between criteria attributes from a dataset. The technology described herein calculates relative closeness values for alternatives (e.g., virtual machines), and can perform a final ranking of a group of virtual machines based on their associated relative closeness values. The workload prioritization process can be incorporated into a decision engine. The decision engine can be used in various use cases, including backup prioritization, shutdown prioritization during power failures, workflow scheduling prioritization, and the like.

The application discloses a kind of season freezing area existing bridge and tunnel concrete anti-freezing durability evaluation and promotion method, its method is: first step, service period anti-freezing durability damage information acquisition and the establishment of characterization model;Second step, the variable model of comprehensive anti-freezing durability damage is constructed;Third step, the establishment of damage evolution and decision analysis model;Fourth step, the establishment of durability promotion strategy recommendation model in stages.There are beneficial effects: realize the quantitative characterization of existing bridge and tunnel concrete internal hidden, gradual freeze-thaw damage, overcome the problem of large dispersion of single index evaluation result.Make durability promotion measure from experience judgment to quantitative analysis based on comprehensive freeze-thaw damage variable, improve the scientificity and economy of existing bridge and tunnel anti-freezing maintenance decision.The scientificity of long-term freeze-thaw durability evaluation and decision is improved.Effectively delay freeze-thaw deterioration process, improve the anti-freezing service reliability and engineering economy of existing bridge and tunnel structure, with good engineering popularization value.

This application discloses an intelligent generation method and apparatus for decision analysis reports in the insurance field. The method includes: constructing a thematic data mart containing insurance business data, and vectorizing and semantically slicing insurance field rules and indicator texts to form a knowledge unit index; responding to a report generation request, constructing a computational agent based on a matched report template; using this agent, generating analytical text by using retrieved data and knowledge units as a context-driven generative model, and synthesizing a decision analysis report; collecting interactive operation data related to the report, and adjusting the configuration parameters of the report template accordingly. This application achieves full-process automation from multi-source data integration and professional knowledge structuring to intelligent analysis, report generation, and continuous optimization, solving the problems of low efficiency, poor accuracy, and difficulty in adapting to large-scale data and complex business logic in traditional manual analysis in the insurance industry, significantly improving decision support efficiency and intelligence.

This invention provides a massive data processing method for DSGE analysis and decision-making related to marine industries and ecology. Belonging to the technical field of massive data processing for DSGE models, this invention collects multi-source heterogeneous data streams and performs spatiotemporal alignment preprocessing. It then performs threshold cointegration tests on the preprocessed data to identify nonlinear threshold points and establish a state transition matrix. The data is input into a heterogeneous computing scheduler for resource allocation and grid partitioning. A multi-objective decomposition framework combined with particle swarm optimization and simulated annealing algorithms is used to complete parameter calibration. A sliding window mechanism is established, and a recursive least squares algorithm is used for incremental updates. A low-rank approximate decomposition is performed on the high-dimensional weight matrix, and the optimized low-rank submatrix is ​​used as the weight parameters of the DSGE model for decision analysis. This solves the technical problem that massive heterogeneous data is difficult to efficiently support DSGE model parameter calibration and decision optimization under real-time processing conditions.

This invention provides a method and equipment for park operation decision analysis, relating to the field of smart park operation management technology. The invention constructs a candidate solution scenario library through structured definitions of covariates and intervention variables, covering various operational intervention scenarios to be evaluated; relying on generative causal Bayesian networks to integrate causal inference and generative modeling capabilities, it achieves reliable causal prediction of counterfactual solutions without historical observation data; it outputs predicted effect values ​​and uncertainty indicators to quantify prediction credibility and decision risk; in high-uncertainty hypothetical scenarios, through proactive surveys and incremental updates, it achieves effect prediction of counterfactual and hypothetical solutions to assist decision analysis, reduce investment risk and decision costs, and improve the robustness and efficiency of operational decisions. This invention addresses the problem of unreliable prediction and difficulty in proactive decision-making for counterfactual and hypothetical solutions without historical data in park operations, achieving a closed-loop, end-to-end park operation decision analysis.

This invention discloses a data processing method and system based on digital twins, belonging to the field of information technology. It includes: multi-source heterogeneous data acquisition and preprocessing, constructing a static spatial database for water plants and real-time access to dynamically sensed data; multi-precision hierarchical modeling, establishing a digital twin model covering geometric topology and semantic attributes; high-fidelity virtual scene construction and optimization, coupling water quality parameters into a water body rendering model to achieve physically driven visual representation; virtual-real fusion interaction and dynamic mapping, achieving real-time binding of sensor data and 3D model attributes through a data platform; and multi-dimensional business logic processing and decision analysis, constructing business modules such as energy efficiency optimization and pipe burst location. This invention solves the data silo problem through semantic fusion and achieves a balance between high-fidelity rendering and real-time interaction through hierarchical modeling and dynamic scheduling, significantly improving the operational efficiency and decision support capabilities of the water affairs digital twin system.

The application discloses a cross-department decision visualization method for water resource management and related equipment, which can be applied to the technical field of water resource management and intelligent decision support. After obtaining the local water resource management data of participating departments, the application constructs and encrypts the local causal subgraph corresponding to each participating department without leaving the local network domain. Based on the vertical federated learning mechanism, the application performs secure alignment on all encrypted local causal subgraphs, and then performs constraint-based or model-based fusion operation to obtain the global causal structure diagram of cross-department water resource management. Thus, the application can provide quantifiable reconciliation basis for conflicting conclusions while meeting privacy protection and compliance requirements. Based on the global causal structure diagram, the application performs causal reasoning and decision analysis on the water resource management process and then performs visual display, thereby effectively improving the reliability, interpretability and generation efficiency of the decision, and enhancing the transparency of the decision process.

The present application relates to the technical field of artificial intelligence decision analysis and behavior modeling, in particular to a kind of situation-reasoning one figure-based character decision mode analysis method and equipment, including the construction decision mode situation-reasoning one figure, the extraction and quantization of decision influencing factors and the extraction and quantization steps of decision influencing factors, in the execution above-mentioned steps, first, the structure of situation-reasoning one figure is used, and massive unstructured open source data is converted into computable, situation-reasoning one figure that can be deduced;Then, according to the decision theory, the internal psychological cognitive characteristics of the character are quantified, and they are used as the key constraint condition of reasoning network;Finally, the decision mode neural network is constructed, and the decision process of "perception stimulation-cognitive screening-logical reasoning" of the character under a certain situation is simulated.The present application realizes the deep integration and accurate analysis from external objective situation to internal subjective cognition.

This invention discloses a big data-based enterprise digital intelligent operation decision analysis platform, relating to the field of enterprise operation technology. The platform includes: generating a standardized decision dataset; simultaneously building a dynamic operation topology map with business priority weights and a virtual-real linkage decision simulation link; merging the dynamic operation topology map, the virtual-real linkage decision simulation link, and time-series operation logs into a hybrid intelligent decision model combining graph neural networks and reinforcement learning; performing feature extraction and iterative optimization through decision simulation to output four core decision evaluation values: operational efficiency, cost optimization rate, risk control accuracy, and business synergy; based on the matching degree analysis between the four core decision evaluation values ​​and the enterprise's strategic goals, generating a digital intelligent operation decision package containing scenario-based implementation paths, dynamic resource allocation schemes, intelligent business process refactoring, and strategy iteration cycles; embedding an effect prediction model and a risk hedging mechanism to form a complete decision output. This invention improves the efficiency of enterprise digital intelligent operation decision analysis.

The hospital operation methodology, system, and storage media are based on value chain and data-driven approaches. The methodology includes: constructing a value chain system comprising multiple value chains: front-end settlement chain, procurement payment chain, cost control chain, performance-based compensation chain, and decision analysis chain. Each value chain includes multiple modules. The front-end settlement chain outputs settlement details to the cost control chain based on billing information and budget adjustment instructions. The procurement payment chain outputs procurement expenditures to the cost control chain based on procurement information and procurement optimization instructions. The cost control chain outputs cost accounting results to the performance-based compensation chain based on settlement details, procurement expenditures, and cost control instructions. The performance-based compensation chain outputs performance results to the decision analysis chain based on cost accounting results and performance correction instructions. The decision analysis chain outputs budget adjustment instructions, procurement optimization instructions, cost control instructions, and performance correction instructions to the remaining value chains based on performance results, and outputs analysis and decision-making results.

The application discloses an intelligent power distribution room AI decision analysis system based on edge computing, which comprises a data acquisition preprocessing module, a friction state modeling module, a comprehensive state construction module, a fast and slow state evolution module, an operation decision determination module and a control instruction generation module.The data acquisition preprocessing module forms standardized multi-dimensional operation state data.The friction state modeling module constructs an improved GMS friction model and forms a friction state variable set.The comprehensive state construction module constructs a comprehensive operation state vector.The fast and slow state evolution module forms fast variable state evolution sequences and slow variable state evolution sequences.The operation decision determination module executes transient operation state determination and operation degradation trend determination, and generates edge side operation decision analysis information.The control instruction generation module generates operation control instructions.The application realizes stable operation decision analysis of the intelligent power distribution room on the nonlinear behavior of the actuator and the fast and slow evolution state by introducing the improved GMS friction model and the singular perturbation theory.

This application relates to the field of agricultural irrigation control technology, and in particular to a method, system, equipment, and medium for paddy field irrigation and drainage decision-making. The method includes: acquiring historical environmental parameters of the target paddy field; constructing a paddy field growth simulation environment based on the historical environmental parameters; transforming the paddy field irrigation and drainage decision-making process concept into a Markov decision process within the paddy field growth simulation environment, and constructing a reinforcement learning environment through the Markov decision process; training the agent's policy network and value network within the paddy field growth simulation environment; updating the network parameters of the value network through the reinforcement learning environment during training, and updating the network parameters of the policy network through the updated value network; and executing the target paddy field irrigation and drainage decisions using the trained policy network. This solves the problems of related technologies that focus on static or short-term decision analysis at local time scales, model only single irrigation actions, have limited action space, and can only achieve short-term benefit optimization.

The application discloses a production-study-research transformation intelligent decision support system and method based on multi-source data fusion, comprising a multi-source heterogeneous data acquisition module, a data preprocessing and standardization module, a multi-dimensional data fusion module, a production-study-research feature extraction and quantification module, a production-study-research transformation knowledge graph construction module, an intelligent decision analysis and scheme generation module, a decision execution feedback and model optimization module; the application realizes efficient processing and deep fusion of multi-source heterogeneous production-study-research data, breaks the data silos among colleges and universities, enterprises, governments and industries, eliminates the format and measurement differences of heterogeneous data through standardized processing, mines the correlation value among different data sources based on a scientific weighted fusion algorithm, and improves the overall utilization efficiency of production-study-research data.

The present application relates to the technical field of knowledge graph and retrieval, in particular to an e-commerce decision analysis method based on knowledge graph. The method collects a commodity image feature vector, a commodity text description feature vector and a user historical operation log, constructs a heterogeneous knowledge graph containing an image node, a text node, a user node and a commodity node; sets a time sequence sliding window on an interaction edge according to a timestamp, and eliminates interaction edges exceeding a preset time threshold; inputs the updated graph into a heterogeneous graph attention network, allocates attention weights according to the type of the interaction edge in the message passing stage, and aggregates to generate a commodity decision representation vector; inputs the commodity decision representation vector into a decision classifier to output a decision result. The present application overcomes the problem of incomplete feature representation caused by ignoring the difference between multi-modal attributes and interaction time decay in the existing homogeneous graph, eliminates the data lag defect of static graph in the time sequence decision scenario, and reduces the interference of invalid historical features on decision analysis.

This invention discloses a method and terminal for quantifying the value of structural health monitoring information for helicopter rotor blades. The steps are as follows: establishing a rotor blade damage probability model based on Weibull failure distribution and Paris crack propagation formula; obtaining maintenance costs based on a damage cost model; and performing minimum cost decision analysis using a decision tree based on Bayesian update theory. This invention quantifies the value of deteriorated structural monitoring information by modeling structural performance and utility using a damage probability model, a Bayesian-based decision tree method, and a system cost and benefit analysis model. This solves the problem of difficulty in quantifying the economics of structural health monitoring, and is of great significance for operators to assess the impact on helicopter operations before investment and to promote the application of structural health monitoring.

The application discloses a task decision generation method and device suitable for an atmospheric environment, a medium and a terminal, relates to the technical field of artificial intelligence, and mainly aims to improve the problems that the existing method relies on artificial experience as a reward signal, the subjectivity of the labeling result is strong, different expert standards are not the same, the task decision for the atmospheric environment field cannot truly meet the field requirements, and the labeling cost is high due to a large amount of manual labeling work. The method comprises the following steps: receiving target task input by a target user about the atmospheric environment field; performing task decision analysis operation according to the target task based on an atmospheric environment field decision model that has completed model parameter fine tuning, to generate a task decision corresponding to the target task; and identifying a pollution responsible party, outputting a pollutant over-standard alarm and screening an optimal control measure based on the task decision.

This invention provides a spare parts procurement management method for nuclear power plants, comprising the following steps: Step S1: Obtaining procurement project initiation information, including material information and procurement measurement data; Step S2: Determining whether the corresponding spare parts are procurable based on the material information; if procurable, determining the procurement amount based on the procurement measurement data; Step S3: Receiving a project initiation submission instruction, performing decision analysis on the procurement project initiation based on the material information and the procurement amount, and obtaining the decision analysis result; Step S4: Matching and triggering the corresponding procurement approval process based on the decision analysis result. This invention achieves automatic identification of procurable status and automatic matching of approval processes during the spare parts procurement process by automatically determining whether spare parts are procurable based on material information after obtaining procurement project initiation information, and performing decision analysis to match the corresponding approval process after receiving the project initiation submission instruction.

The application discloses a knowledge graph construction method and device and electronic equipment, and belongs to the technical field of data processing. The method comprises the following steps: acquiring a plurality of decision factors and a plurality of blood relationships, wherein the decision factors represent elements affecting data decision, and one blood relationship describes the dependency information between two decision factors in the plurality of decision factors; acquiring attribute texts of each decision factor; for any two decision factors, determining the semantic relationship between the two decision factors according to the attribute texts of the two decision factors; and constructing a first knowledge graph according to the plurality of decision factors, the plurality of blood relationships and the plurality of semantic relationships, wherein the first knowledge graph is used for describing the blood relationships and the semantic relationships between the decision factors. The blood relationship is an explicit relationship, and the semantic relationship is an implicit relationship. The explicit relationship and the implicit relationship between the decision factors are described through the first knowledge graph, which is beneficial to data management, decision analysis and decision optimization based on the first knowledge graph.