295 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.

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

The invention discloses a smart road driving time prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: constructing a multi-source data collection and fusion platform, developing a spatial-temporal feature deep learning model, deploying a real-time data processing and anomaly detection system, building a self-adaptive prediction optimization mechanism, and constructing a smart traffic application service platform. Comprehensive acquisition, deep analysis, real-time processing, accurate prediction and intelligent service of traffic data are realized. The method specifically comprises the following steps: deploying an Internet of Things sensor network to collect multi-source data, and designing an adaptive weighted fusion algorithm to construct a multi-dimensional dynamic traffic database; constructing an LSTM-GNN hybrid neural network architecture to extract spatio-temporal features; real-time data processing and anomaly detection are carried out by adopting a streaming computing framework and various technologies; designing a multi-objective optimization algorithm and a reinforcement learning model to realize adaptive prediction optimization; and developing a visual decision support system and a personalized navigation service. The system effectively improves the traffic management efficiency and the public travel experience.

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 present invention relates to the technical field of agricultural information, and specifically to a plant factory information automatic acquisition method and system based on unmanned aerial vehicle machine vision. The method comprises the following steps: using a camera mounted on an unmanned aerial vehicle to capture an image, and using a deep learning and image segmentation technology to generate a plant region segmentation image. According to the present invention, the unmanned aerial vehicle is used to carry the camera to capture a plant image, and efficient and accurate plant region segmentation is realized by using the deep learning technology. A plant health assessment report is generated by means of a computer vision technology, and a health problem is early warned. A plant health analysis report comprehensively considering environmental factors is generated on the basis of ground sensor data and time series analysis, and comprehensive and accurate health assessment is provided. A multi-dimensional plant health monitoring model provides support for all-round health monitoring. A plant disease nutrition analysis report is generated on the basis of a machine learning classification algorithm, a problem is accurately recognized, and a fertilization and pesticide-spraying strategy is formulated on the basis of a decision support system, thereby optimizing resource utilization, and improving the yield and quality.

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 discloses a rock tunnel intelligent supporting system and method adaptive to geological conditions, and relates to the technical field of tunnel construction automation and intellectualization. The system comprises an intelligent advanced geological forecast module, an intelligent surrounding rock quality evaluation system, a self-adaptive support design optimization module, an intelligent construction equipment monitoring system, an environment monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system and an integrated control system. Through real-time monitoring and analysis of the intelligent advanced geological forecast module and the safety early warning system, potential geological risks and safety threats in the tunnel construction process can be found in time. And through an adaptive support design optimization algorithm, support parameters and a construction scheme can be dynamically adjusted according to real-time geological data and a surrounding rock quality evaluation result. The integrated control system serves as the core of the whole intelligent supporting system, and unified management and coordination of all the sub-modules are achieved.

The invention relates to the technical field of road maintenance and management, in particular to a pavement internal disease analysis method and system based on artificial intelligence. Five core processes of multi-layer sensor network real-time data acquisition, multi-modal data fusion and deep learning feature extraction, dynamic environment and use mode combined disease prediction and analysis, intelligent prediction and decision support system, and real-time disease condition evaluation and report generation are carried out. Through the technical means, comprehensiveness and timeliness of data acquisition, accuracy of disease identification and prediction, scientificity of a maintenance strategy and optimization of resource allocation are realized; the finally generated disease current situation report provides a detailed disease distribution diagram, disease type analysis and disease severity evaluation, comprehensive, accurate and timely disease information is provided for maintenance personnel, and the efficiency and effect of pavement maintenance and management are remarkably 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 drainage pipe network pollution traceability and alarm system based on invariant analysis, which belongs to the field of data communication of a data center and comprises a data collection module, a cloud platform processing module, an invariant analysis module, an abnormal emission identification and alarm module and a decision support system. The supervision efficiency of the drainage pipe network is improved, environmental pollution is reduced, and sustainable utilization of water resources is guaranteed; according to the system, various sensors are installed at key nodes of a drainage pipe network, data such as water quality, water level, water volume and flow velocity are collected in real time, and the data are transmitted to a cloud platform through a wireless communication technology; by utilizing the data processing capability of the cloud computing platform and combining with an invariant analysis algorithm, the system can quickly position a pollution source, track a pollution path and give an alarm to related personnel in time; the monitoring, management and pollution prevention and control capabilities of the drainage pipe network are effectively improved, and powerful technical support is provided for urban water resource management and environmental protection.

The invention relates to a conference data intelligent analysis and decision support system. Audio data in a conference process are collected in real time; extracting conference information of the audio data by adopting a natural language processing technology, wherein the conference information comprises conference participant positions, conference themes, conference contents and voting results; and adopting a big data analysis model to predict an expected effect of meeting decision execution according to the meeting information. According to the invention, the problem of low working efficiency caused by the fact that the prior art depends on manual recording and summarization, effective information of the conference is difficult to extract and the execution effect of the conference decision is predicted according to the conference information is solved. The conference information variable set having significant influence on the execution effect is selected through the random forest model, and the efficiency and accuracy of conference information analysis are 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.

The invention provides an underwater robot intelligent navigation and environment sensing system based on a multi-sensor fusion collaborative technology. The system integrates a plurality of sensors such as sonar, optical, electromagnetic and the like, and realizes comprehensive data acquisition and real-time perception of an underwater environment. By applying advanced algorithms such as Kalman filtering and a neural network, the system performs synchronization, preprocessing and deep fusion on multi-source data, and the consistency and accuracy of information are remarkably improved. In addition, the system also has adaptive navigation and environment perception capabilities, can dynamically adjust a navigation path and a perception strategy according to fused data, and adapts to a complex and changeable underwater environment. And the intelligent decision support system provides decision assistance for an operator in combination with an environment perception result and a task target. And due to the design of a user interaction and monitoring interface, the usability and real-time monitoring capability of the system are ensured. According to the implementation of the invention, the precision and efficiency of the underwater robot in tasks such as monitoring and exploration are greatly improved.

The invention provides an equipment operation and maintenance intelligent decision support system based on large model reasoning, and aims to improve the accuracy of equipment fault prediction and the intelligent level of operation and maintenance decision. The system obtains equipment operation data in real time through a data acquisition module, performs reasoning analysis on equipment health conditions by using a large-scale pre-trained deep learning model, and predicts the residual service life of equipment and various fault types. The system combines multi-task learning and a multi-objective optimization algorithm, intelligently optimizes operation and maintenance decisions, generates personalized maintenance suggestions, helps operation and maintenance personnel to effectively reduce the downtime of equipment, reduces the maintenance cost, and prolongs the service life of the equipment. The system can be widely applied to the fields of manufacturing industry, energy, traffic and the like, and the accuracy and efficiency of equipment management are remarkably improved.

The invention relates to the technical field of road maintenance, and discloses a road maintenance decision support system based on digital twinning, which comprises a data acquisition module, a digital twinning module, a maintenance decision module, an intelligent monitoring module and a user interface module, wherein the data acquisition module is used for acquiring various data of a road in real time, including road surface conditions, traffic flow and environmental factors; wherein the digital twin module is used for establishing a road virtual model, performing simulation analysis and providing accurate data support for maintenance decision making; wherein the maintenance decision module is used for formulating a maintenance plan and strategy by utilizing an optimization algorithm and a data analysis technology based on the output of the digital twin module. According to the road maintenance decision support system based on the digital twinning, the digital twinning model of the road is constructed, and the real-time monitoring data and the advanced algorithm are combined, so that the intelligent support of the road maintenance decision is realized.

The present invention is an auxiliary hospital space design method and device based on a building energy consumption model, including: obtaining a natural language description of the initial hospital space layout diagram and functional requirements; extracting features from the space layout diagram and performing semantic analysis on the functional requirements description; inputting the spatial structure feature matrix into the energy consumption analysis model, combining the demand semantic vector, predicting the energy consumption performance of each functional area, and generating an energy consumption assessment diagram; obtaining an energy-optimized space design scheme based on the energy consumption assessment diagram and functional requirements; and simulating and verifying the energy-optimized space design scheme. The present invention realizes the intelligentization, optimization, and dynamic adjustment of hospital space design by integrating advanced technologies such as multimodal deep learning, generative adversarial networks, building energy consumption simulation, adaptive optimization algorithms, and multi-objective decision support systems.

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 technical field of pollution analysis, in particular to a construction method and device of an agricultural watershed non-point source pollution decision support system.The construction method of the agricultural watershed non-point source pollution decision support system comprises the steps that firstly, the nitrogen and phosphorus loss amount of each pollution source is calculated; acquiring digital elevation data; determining an intermediate water body between each pollution source and the target water body according to the digital elevation data, and obtaining a migration path corresponding to each pollution source; when the absorption amount of each migration path is determined, the water body type is considered, and the influence of the difference between the hydraulic characteristics and the physicochemical properties of the water body on the absorption amount can be reflected through the water body type, so that the calculated absorption amount of each migration path is more accurate; furthermore, the target water body considers the complex communication relation and topological relation among the ditch, the pond, the reservoir and the river, and the simulated water body load capacity is more accurate.

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.