235 results about "Decision support system" patented technology

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

A system and method for AI-based dynamic pricing, mapping, and emissions control to optimize inner-city mobility. The system acquires real-time nitrogen dioxide data across a geographic area from sources including satellites, feeds this data into a decision support system along with road-specific weight matrices from authorities, and updates a dynamic penalty matrix for roads in the area. Automatic number plate recognition tracks vehicle movement, queries vehicle databases for emissions profiles, and updates the platform. Users input journey parameters and the system, using AI tools including a genetic algorithm economic and environmental dispatch (GA-EED) optimizer, generates an optimized route based on user-selected criteria such as time, emissions, penalty, and cost. The system enables dynamic, environmentally-aware route optimization for inner-city mobility.

The invention discloses a reservoir scheduling decision support system for multi-objective optimization, and relates to the technical field of hydraulic engineering scheduling. According to the system, comprehensive optimization of flood control, power generation and ecological targets is realized through cooperative work of the multi-source dynamic constraint analysis module, the collaborative optimization module, the scheduling module and the correction module. A dynamic constraint boundary table is generated by collecting meteorological, hydrological and ecological data in real time. And matching a multi-target coordination rule through a physical constraint double-depth network algorithm, generating a Pareto feasible solution set, and mapping the Pareto feasible solution set into a coordination strategy of flood discharge flow and power generation output. In combination with a hydraulic model, flood control and power generation weights are adjusted, an executable dispatching instruction is generated, it is ensured that the ecological flow reaches the standard, and the influence of a downstream submerged area is optimized. And correcting the dynamic constraint boundary table according to actual monitoring data, and continuously optimizing a scheduling decision. Accurate management and multi-objective optimization of reservoir dispatching are realized, and the utilization efficiency of water resources is improved.

The invention provides a learning performance evaluation-driven teaching management method constructed based on a capability graph. The method comprises the following steps: S1, constructing a multi-dimensional capability graph; s2, a step of operating a multi-modal data acquisition system; s3, performing a dynamic capability value calculation model; s4, a step of constructing a real-time capability early warning system; s5, generating a self-adaptive teaching strategy; s6, a step of carrying out interdisciplinary ability association analysis; s7, generating a personalized learning path; s8, dynamically evaluating the teaching effect; and S9, dynamically optimizing the teaching resources. According to the method, the core problems of data lag, inaccuracy in intervention and one-sided evaluation in traditional education management are solved by constructing a dynamic capability evaluation model, and a quantifiable decision support system is provided for precise teaching. The innovation of the method is that differential equation modeling and reinforcement learning are combined, and continuous optimization and adaptive adjustment of the education process are realized.

The invention relates to the technical field of cultivated land resource management, and discloses a data optimization and intelligent decision support system in cultivated land resource early warning, which comprises a cultivated land data acquisition module, a soil index analysis module, a degradation identification module, a land force deduction module, a threshold adaptation module and a decision generation module. The cultivated land data acquisition module acquires cultivated land multi-dimensional attribute data in real time and partitions the data; the soil index analysis module divides soil fertility units and fuses soil indexes to generate resource state vectors; the degradation identification module extracts a core degradation factor, establishes a risk layering model and obtains a data correction coefficient; the land force deduction module identifies the association rule and the dynamic correction factor and calculates the land force attenuation gradient; the threshold adaptation module derives an optimal early warning threshold and generates a risk difference sequence; and the decision generation module analyzes the sequence and integrates the sequence into a regulation and control decision scheme. According to the system, intelligent analysis of cultivated land data, dynamic early warning of degradation risks and accurate decision making are realized, and scientificity and efficiency of cultivated land resource management are improved.

The invention relates to the field of building settlement monitoring, and discloses a multi-source data-artificial intelligence fused building deviation rectification settlement prediction method and system, and the method comprises the following steps: multi-source data collection: obtaining multi-class sensor data related to building settlement, the multiple types of sensors comprise a level gauge, a total station, an underground water level sensor and a soil body pressure sensor, and multi-modal data input is formed; the invention also discloses a multi-source data-artificial intelligence fused building deviation rectification settlement prediction system. The system comprises a data acquisition module, a data processing module, a deep learning prediction module, a model optimization module, an intelligent early warning module and a user interaction module. According to the invention, by dynamically adjusting the settlement risk threshold and introducing a multi-stage early warning signal and an intelligent decision support system, accurate early warning and real-time decision support of the settlement risk are realized, and the adaptability, accuracy and response efficiency of the system are significantly improved.

The invention discloses a big data risk control decision support system based on multi-modal data, and the system comprises a data access module which is used for accessing and analyzing multi-source entity information, building a grouping calibration rule base, and generating a compliance multi-modal data set; the preprocessing and characterization module is used for preprocessing the data, generating basic characterization of each modal and establishing a micro verifier library; the causal hypergraph module is used for constructing a causal hypergraph and outputting a stable causal structure and a gating mask; the fusion and anti-fact module is used for executing cross-modal fusion to generate a risk representation, generating and verifying a minimum change anti-fact sample, and outputting a reachable identifier and an adjustment representation; the evidence chain module is used for calculating a shortest evidence overpass, generating a structured evidence chain and outputting an evidence index; and the strategy module is used for generating and correcting scores, triggering strategy actions and recording execution information. Through multi-modal data fusion and causal reasoning, high-precision, low-delay and interpretable big data risk control decision support is realized.

A water pollution risk early warning and tracing method based on multi-source data fusion belongs to the technical field of water pollution monitoring and early warning, and comprises the following steps: step 1, constructing a multi-source heterogeneous data acquisition network and realizing real-time data transmission; 2, performing multi-source data fusion and feature enhancement processing based on space-time reference; 3, constructing a water pollution risk dynamic early warning system based on a WOA-LSSVM model; fourthly, reverse positioning of the pollution source is completed on the basis of a CNN-GRU-SE Attention model; and 5, carrying out development and emergency response on a multi-dimensional visual decision support system, and positioning a pollution source. Multi-source information such as water quality sensor data, unmanned aerial vehicle image data and geographic information data is fused, and an intelligent monitoring network is constructed; through multi-source data fusion and an intelligent algorithm, water pollution risk early warning accuracy and traceability efficiency are effectively improved, and the method has the advantages of high response speed, wide monitoring range, accurate positioning and the like, can be widely applied to the fields of urban water supply, drainage basin management and the like, and meets the requirements of water environment safety guarantee.

The invention provides an intelligent question number and index management engine and system based on dynamic reward optimization, relates to the technical field of data learning, and is used for enterprise index analysis, attribution diagnosis and decision support. The system is provided with an index governance layer, index caliber, computational logic, blood relationship, credibility score and version information are managed in a unified mode through a dynamic knowledge graph, unified semantic constraint is carried out on a multi-agent analysis process, and index consistency and traceability are guaranteed. The system also establishes a causal cognition module, based on time sequence data and in combination with expert priori, generates and corrects a business index causal directed acyclic graph, realizes root cause positioning and anti-fact simulation, and answers what change is and what intervention is. The multi-agent collaborative analysis core is responsible for natural language intention analysis, index compliance verification, automatic access, causal inference, narrative generation and chart presentation, calculates a multi-target composite reward value based on user feedback and interaction behaviors, and adaptively adjusts output; and the user corrects and writes back to form closed-loop learning.

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 relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention relates to the technical field of building informatization and intelligent construction, and discloses a building engineering digital twinning progress management and control method and system.The building engineering digital twinning progress management and control method comprises the steps that multi-source heterogeneous data in the building engineering construction process is collected; carrying out fusion processing on the original data set by adopting a space-time alignment network; updating the digital twin body by adopting an incremental state estimation method; predicting a future progress by adopting a model fusing a graph neural network and a time sequence attention mechanism; a multi-level detection mechanism is adopted to identify progress deviation and analyze a root cause; generating an optimized resource scheduling scheme by adopting a reinforcement learning method; an intelligent decision support system is constructed, and visual display and interaction functions are provided; according to the method, the graph neural network and the time sequence attention mechanism are fused, the progress prediction model capable of capturing the task topology dependency relationship and the time sequence evolution characteristics at the same time is constructed, and the accuracy of progress prediction is improved.

The invention discloses a medical risk intelligent prediction system based on time series data mining. The system comprises a multi-dimensional time sequence data acquisition and preprocessing module, a time sequence mode deep mining engine, a multi-dimensional risk assessment engine, an intelligent intervention decision support system and a real-time monitoring feedback module. A time sequence mode mining engine adopts a layered architecture, and short, medium and long-term time sequence modes are respectively analyzed through a bidirectional LSTM-attention network, a wavelet transform-convolutional network and a seasonal decomposition-gating circulation network. The risk assessment engine integrates an isolated forest, an auto-encoder, a Transform multi-task network and knowledge graph reasoning, and realizes all-around risk quantification. The decision support system generates a personalized intervention strategy based on deep Q network reinforcement learning and case reasoning. According to the system, early prediction and accurate intervention of medical risks are realized, and the prediction accuracy and the medical safety level are remarkably improved.

The invention relates to the technical field of safety management, and discloses a hydraulic engineering construction potential safety hazard monitoring method based on Internet of Things, which comprises the following steps: S1, real-time data acquisition: arranging a plurality of sensors at a construction site to acquire data and uploading the data in a wireless communication mode, the method comprises the steps of S1, data acquisition and analysis, S2, data pre-processing and analysis: pre-processing acquired data at a cloud or an edge computing platform, S3, adaptive adjustment: automatically adjusting sensor layout and monitoring frequency according to construction stages and environment changes, S4, multi-source data fusion, S5, alarm and emergency: triggering alarm when monitoring data is abnormal, providing emergency measures by means of an intelligent decision support system, and carrying out emergency management. S6, visualization and decision support; and S7, intelligent feedback and optimization. By adopting a dynamic threshold adjustment mechanism and a multi-modal data fusion technology, a more accurate hidden danger identification effect is achieved, the phenomena of missing report and false report are remarkably reduced, and the effectiveness of early warning is improved.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

The invention relates to the field of finance, and discloses an AI-based investment project feasibility intelligent analysis and decision support system, which comprises a multi-modal data fusion engine used for extracting a feature vector of an unstructured text through a BERT variant model, carrying out joint coding on the feature vector and a structured data feature, and outputting fused multi-modal data; the dynamic knowledge graph construction module is connected with the multi-modal data fusion engine; the deep reinforcement learning decision module comprises a feasibility prediction network and a risk assessment network which are cooperatively trained; an interpretable AI module; and a closed loop feedback module. By means of a multi-modal data fusion engine and a deep reinforcement learning decision module, automatic processing and intelligent analysis of data are achieved, and the analysis time of a single project is greatly shortened to 8 minutes. The high-efficiency processing speed enables the investment institution to quickly respond to the market change, timely grasp the investment opportunity, and occupy the decision-making precedence in the financial market which changes instantaneously.

The invention discloses a building decoration carbon emission optimization decision support system. The data acquisition unit acquires real-time data through the multi-source sensing device, and the data preprocessing unit performs abnormal value filtering, format unification and standardization operation on the real-time data. And the decision optimization unit integrates an intelligent dynamic strategy adjustment mechanism and a time sequence prediction model, and generates an optimization scheme through efficiency comparison incremental learning and feedback correction. And the visual output unit converts the scheme into a graphical display interface. In addition, the system is provided with a performance evaluation unit, the carbon emission reduction rate can be counted, the decision accuracy can be analyzed, the comprehensive efficiency score can be generated, and the data acquisition frequency and algorithm parameters can be dynamically adjusted. The system provides comprehensive support for carbon emission optimization of building decoration and assists green development of the building industry.

The invention discloses an intelligent fusion analysis and decision support system and method for multi-source heterogeneous data of a spacecraft. The intelligent fusion analysis and decision support system for the multi-source heterogeneous data of the spacecraft and the intelligent fusion analysis and decision support method for the multi-source heterogeneous data of the spacecraft are included. The system comprises a dynamic adaptation access layer, a unified semantic modeling layer, a space-time semantic fusion engine, a digital twin collaborative decision-making center, a knowledge enhancement decision-making auxiliary subsystem, a cross-department collaborative interaction platform and a side cloud collaborative intelligent processing framework. The method comprises the steps of multi-source heterogeneous information dynamic access and semantic modeling, space-time and semantic deep fusion, digital twin driven decision deduction, knowledge enhanced decision assistance, cross-department collaborative decision and interaction, edge cloud collaborative intelligent processing and the like. According to the method, the safety, reliability and decision-making efficiency of spacecraft tasks can be remarkably improved, and the method is suitable for development, testing and on-orbit task stages of various spacecrafts.

The invention relates to the technical field of Chinese trumpet creeper breeding, and discloses a Chinese trumpet creeper-based efficient breeding decision support system and method. The method comprises the following steps: acquiring multi-dimensional phenotypic data in a Chinese trumpet creeper breeding test, wherein the multi-dimensional phenotypic data comprises plant height, leaf area, flowering period and yield indexes; performing normalization processing on the multi-dimensional phenotypic data, eliminating influences of different dimensions, and calculating a variable coefficient of each phenotypic index; then screening out phenotypic indexes of which the stability is higher than a preset threshold value based on the variable coefficient, and constructing a Chinese trumpet creeper phenotypic feature matrix; carrying out dimensionality reduction on the Chinese trumpet creeper phenotypic feature matrix by adopting an adaptive weighting algorithm, and extracting key phenotypic features; inputting the key phenotypic features into a genetic algorithm optimization module, and calculating the contribution degree of each genetic locus in combination with the Chinese trumpet creeper genotype data; and constructing a Chinese trumpet creeper breeding decision model according to the contribution degree of the gene locus, and outputting an optimal breeding combination scheme. The system can optimize the Chinese trumpet creeper breeding process and provide effective support for Chinese trumpet creeper breeding work.

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 relates to the technical field of block chains and data processing, in particular to an industrial digital real-time risk control and decision support system based on a block chain, which comprises a causal entropy chain construction module, a commercial plan simulation module, a multi-party credible cooperation module and a commercial credit assessment module. A causal entropy chain construction module is used for inferring a causal relationship of enterprise operation data by using a dynamic graph neural network, quantifying an entropy value of the enterprise operation data, packaging a result into a block, and recording the block on a block chain to form a causal relationship account book; based on the risk links with entropy values exceeding a preset threshold value in the causal relationship account book, the commercial plan simulation module performs adversarial risk simulation to generate a commercial decision plan; the multi-party credible cooperation module allows enterprises to cooperatively train a global risk prediction model through federated learning, zero-knowledge proof and other modes under the condition that private data is not exposed; and the commercial credit assessment module generates a dynamic credible two-dimensional code capable of representing the enterprise operation capability and the risk level, and the dynamic credible two-dimensional code is used for various commercial scenes.

The invention discloses a postpartum rehabilitation intelligent evaluation and early warning system, and the system comprises a postpartum rehabilitation knowledge base which is used for carrying out the knowledge extraction and structuring of postpartum rehabilitation data based on a medical coding marker technology, obtaining a medical vector representation, constructing a postpartum rehabilitation knowledge graph based on the medical vector representation, and obtaining a postpartum rehabilitation knowledge graph; the postpartum rehabilitation knowledge graph is stored and updated based on the graph database and knowledge representation learning; and the intelligent evaluation and personalized recommendation system is used for performing fusion and depth feature extraction on the multi-modal data based on the trans-attention auto-regression Transform multi-modal electronic health record to obtain multi-modal depth features, performing risk prediction on the multi-modal depth features by using a deep learning model, and performing recommendation on the multi-modal depth features. And generating a personalized rehabilitation scheme based on case reasoning and expert rules. The early warning decision support system is used for carrying out risk early warning by using a multi-dimensional early warning model in combination with the risk prediction result; and the remote expert consultation and cooperation platform is used for remote cooperation guidance. In this way, the problems of experience dependence, insufficient knowledge utilization and the like of traditional postpartum rehabilitation can be solved.

The invention relates to an irrigation decision-making method and system based on a crop model and deep reinforcement learning. The method comprises the following steps: creating a virtual environment for simulating crop growth through a decision support system crop growth model after parameter calibration; then constructing a time sequence state matrix, and encoding the time sequence state matrix into a context vector concentrated with historical dynamic information; outputting irrigation actions of each decision-making day through a strategy network of a deep reinforcement learning agent based on a soft strategy-value algorithm; therefore, a decision support system crop model and a deep reinforcement learning technology are deeply fused, an intelligent irrigation decision system with biological rationality is constructed, and the problems of insufficient training data and environment distortion of an existing deep reinforcement learning model in agricultural application are solved.

The invention discloses a market intelligence-oriented automatic data acquisition and decision support system, particularly relates to the technical field of artificial intelligence and information processing, and comprises a structure rendering module, a semantic extraction module, and a credible evaluation and execution feedback module. The system extracts structural anchor points through image rendering, generates semantic vectors in combination with context modeling, calculates a structural consistency index and a semantic validity index, outputs a five-level credibility level result through a fuzzy logic device, refreshes a collection task scheduling priority, and updates a pre-training language model based on semantic offset feedback. Self-iteration semantic generalization and efficient decision support under a cross-platform context are realized; according to the method, stable recognition of the webpage structure is achieved through structure rendering, multi-dimensional semantic feature generation is achieved by means of semantic extraction, the credibility level is output in combination with double-index evaluation and fuzzy logic, then collection scheduling and model self-adaptive updating are optimized, and the obtaining precision and decision reliability of market information data are effectively improved.

The invention belongs to the field of airborne auxiliary decision making, and particularly relates to a functional structure of an air and space platform driver auxiliary decision making system. The model database is used for storing and managing model data; the mathematical model layer is used for constructing a mathematical model according to the model data; the decision knowledge base is used for storing and managing decision data; the comprehensive balance layer is used for acquiring flight data and performing situation assessment according to current situation information in the flight data to obtain a situation assessment result; the auxiliary decision-making layer is used for selecting a corresponding mathematical model and decision-making data according to the situation assessment result to carry out auxiliary decision-making to obtain an auxiliary decision-making result; the man-machine interaction layer is used for acquiring a driver instruction, displaying a result and converting an auxiliary decision result into an aircraft control instruction; and the efficiency evaluation layer is used for performing efficiency evaluation on the auxiliary decision-making result according to the execution result of the aircraft control instruction to obtain an efficiency evaluation result, and updating the corresponding mathematical model and the decision-making data according to the efficiency evaluation result.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The invention provides a natural resource investigation monitoring method and device and electronic equipment, and relates to the technical field of resource investigation monitoring, based on a natural resource investigation monitoring system, the natural resource investigation monitoring system comprises a master control vehicle and cloud equipment; the natural resource investigation monitoring method comprises the following steps: acquiring a task planning scheme through a decision support system by using cloud equipment; controlling a master control vehicle to collect original multi-modal data through the task planning scheme; performing space-time calibration processing on the original multi-modal data to obtain target multi-modal data; performing target detection on the target multi-modal data to obtain target detection image data; performing data compression on the target detection image data to obtain bit stream data, and sending the bit stream data to a cloud device; and decoding the bit stream data by using the cloud equipment to obtain a natural resource survey monitoring report. According to the invention, the accuracy of natural resource investigation and monitoring is improved.

The invention discloses a multi-dimensional analysis house building health monitoring and early warning system and method, and relates to the technical field of monitoring and early warning. During operation of the system, various sensors are deployed to monitor the physical states of different materials in a building, collect surrounding environment data and transmit the data to a central processing unit in real time, and through a data fusion algorithm, real-time monitoring and early warning are achieved; the method comprises the following steps: integrating sensor data from different materials into a unified data set, preprocessing and normalizing the data, constructing a multi-material interaction mathematical model, calculating an effective stress sigma eff of a building structure, constructing a building overall health assessment model, and calculating a building health comprehensive index HI; according to the building health comprehensive index HI and the comparison result of the health index of each material and the safety threshold value, the health change of the building is detected in real time, an intelligent decision support system is provided, and building management personnel are helped to carry out long-term maintenance planning and resource optimization distribution.

The invention belongs to the technical field of underwater production system information processing and intelligent operation and maintenance, and discloses an underwater manifold leakage emergency response decision support system based on heterogeneous information, which comprises a data acquisition and integration module, a leakage consequence evaluation module and an emergency response scheme generation module. Firstly, a data acquisition and integration module acquires multi-source heterogeneous data in real time, including underwater sensor data, environmental data and historical leakage accident data, and cleans, formats and stores the data; secondly, the leakage consequence evaluation module predicts the leakage diffusion range, evaluates the accident influence degree and quantifies the accident level based on computational fluid mechanics leakage diffusion simulation and in combination with an SVM model; and finally, an emergency response scheme generation module calculates an index weight by adopting an entropy weight method based on a leakage evaluation result and historical emergency plan data, performs priority ranking on emergency schemes in combination with CoCoSo, selects an optimal emergency response scheme, and supports real-time adjustment and optimization.

The invention relates to a photovoltaic low-carbon park smart energy management system and method based on digital twinning. Accurate mapping and simulation analysis of a park energy system are realized by constructing a digital twinborn model, dynamic optimization scheduling of energy equipment is realized by adopting a multi-agent cooperative control algorithm, an energy optimization strategy is formulated by taking low carbon as a target, the health state of the equipment is predicted by utilizing machine learning, and an intelligent operation and maintenance plan is generated. And decision support is provided through energy big data analysis. The system integrates multi-source data, and efficient utilization and low-carbon operation of park energy are realized through the steps of digital twin modeling, intelligent agent collaboration, low-carbon scheduling, equipment prediction, big data analysis and the like. Practical application shows that the system can improve the photovoltaic efficiency by 8%, reduce the energy cost by 18%, reduce the carbon emission intensity by 30%, and significantly improve the park energy management level and the low-carbon degree.