304 results about "Perception risk" patented technology

The invention discloses an electric power operation risk early warning method and system based on knowledge enhancement and multi-modal fusion. The method comprises the steps that video monitoring data, sensor monitoring data and service system data are collected in real time through multi-source sensing equipment deployed on an electric power operation site; the method comprises the following steps of: extracting entities and relationships from unstructured texts such as regulation documents and job logs by utilizing a natural language processing technology based on deep learning, extracting behavior characteristics from video streams by adopting a computer vision algorithm, and constructing an electric power security knowledge graph with dynamic updating capability; designing a multi-modal feature fusion algorithm based on an attention mechanism, and effectively integrating visual features, text features and sensor data; a graph neural network is adopted to train a dynamic risk prediction model to carry out risk prediction, intelligent research and judgment of electric power operation risks are realized, accurate management and control of the risks are realized through a grading early warning mechanism, and closed-loop management from risk perception to early warning treatment is formed.

The invention discloses a real-time multi-dimensional sensitivity evaluation and self-adaptive safety prevention and control method and system for natural resource geographic information. The method comprises the following steps: acquiring real-time natural resource geographic information data; obtaining a preset behavior graph model; updating a preset behavior graph model according to the real-time natural resource geographic information data so as to obtain an updated behavior graph model; acquiring an updated node risk vector according to the updated behavior graph model; obtaining a trained Bayesian risk prediction model; inputting the updated node risk vector into a trained Bayesian risk prediction model so as to obtain a real-time abnormal behavior identification result; and generating a personalized prevention and control strategy scheme according to the real-time abnormal behavior recognition result. According to the method, intelligent identification, dynamic evaluation and active protection of natural resource geographic information in a full life cycle are realized by constructing a multi-dimensional sensitivity quantitative model, a dynamic risk perception mechanism based on a graph structure and a safety prevention and control strategy capable of being updated in real time.

The invention relates to the technical field of intelligent building AI construction monitoring, and discloses a high-rise building construction monitoring method and system based on intelligent AI. The method comprises the following steps: acquiring construction physical signals, and fusing to generate multi-dimensional state sensing information; constructing a digital twinborn body comprising a structure topology and a material parameter evolution path; deducing a construction process by using an AI agent, and outputting a risk situation map and a behavior intervention instruction set; and acquiring a field real-time resource state, carrying out matching verification on the instruction, and starting AI reconstruction on an unmatched instruction to generate a final executable instruction set. Through dynamic evolution modeling of material parameters, the structural state simulation and risk prediction precision is improved; through the real-time verification and reconstruction mechanism of the instruction, the performability of intervention measures is ensured, and closed-loop intelligent control from risk perception to accurate intervention is realized.

The invention relates to the technical field of supply chain risk management, and particularly discloses a comprehensive risk early warning method and system fusing a supply chain graph and machine learning, and the method comprises the steps: collecting supply chain multi-modal heterogeneous data and prior risk knowledge, dynamically constructing and updating a sequential supply chain knowledge graph, and carrying out the early warning of the risk of a supply chain. Performing node-level, edge-level and system-level multi-level anomaly detection, generating an anomaly score and a network vulnerability index, when the anomaly score exceeds a dynamic threshold value, deducing risk propagation and node sweep probability by improving an SEIR model, calculating a dynamic fusion weight in combination with data source authority and the like, and performing weighted fusion on multi-dimensional indexes to generate a comprehensive risk score, and dynamically adjusting a threshold value to generate graded early warning, an optimization model, a map and a priori knowledge base. According to the method, the defects of lag, isolation and stiffness of a traditional method can be overcome, risk perception in advance, comprehensive coverage and accurate early warning are achieved, the method has the self-evolution capacity, the method adapts to dynamic changes of a supply chain, and powerful support is provided for safety and stability of an industrial chain.

The invention discloses an intelligent fishing point dynamic prediction method and system based on multi-source marine environment data fusion. The method comprises the following steps: step 1, access, space-time alignment and pre-screening of multi-source heterogeneous marine environment data; step 2, priori knowledge base construction and suitability modeling based on target fish ecological habits; 3, constructing a fishing point prediction model fusing the multi-time-sequence environmental characteristics and deep learning; step 4, fusing two-channel prediction results under the Bayesian framework and quantifying uncertainty; 5, generating a dynamic mask of a real-time sea condition safety threshold value and fishery regulation space constraint; 6.1, constructing a comprehensive scoring function of the risk perception function. According to the method, multi-source heterogeneous data is constructed, a target fish ecological suitability model and a depth time sequence prediction model are combined, the fishing point posterior probability is generated through a Bayesian fusion mechanism, the prediction uncertainty is quantified, and dynamic fishing point recommendation with risk perception and compliance safety is realized.

The invention discloses an integrated foundation pit support monitoring and early warning system and method. A multi-source sensor array, a distributed data processing unit and an intelligent early warning module form a cooperative work framework; the multi-source sensor array is deployed in a stress concentration node area of a foundation pit enclosure wall body and an internal steel support system by adopting a multi-level nested structure, and the data processing unit carries out space-time registration and feature fusion processing on a multi-source heterogeneous monitoring data flow, and constructs a dynamic safety margin evaluation model containing a steel support axial force-bending moment coupling effect; and the intelligent early warning module generates a multi-level risk early warning signal based on the output result of the dynamic safety margin evaluation model. According to the method, the axial force / bending moment / temperature parameters are synchronously captured through asymmetrically arranged multi-source sensor arrays, cooperative warning of threshold triggering and deep learning trend prediction is achieved in combination with a two-stage early warning mechanism, continuous monitoring during communication interruption is guaranteed by deploying edge computing nodes, and the risk sensing precision and the advanced early warning capacity of a deep foundation pit supporting system are improved.

The invention discloses a reinforcement learning unmanned ship path control method based on double-track regulation and control random network distillation. The method comprises the operation steps that an unmanned ship builds a path tracking simulation environment and a kinetic model; the unmanned ship builds a core algorithm flexible action evaluation algorithm framework; the unmanned ship deploys a priority experience playback pool based on quality and success guidance; an uncertainty perception and risk perception mechanism is introduced into the unmanned ship; the unmanned ship builds a success rate-based reward attenuation and cold start module, and the unmanned ship calculates a total reward and designs a reward softening mechanism to smooth the total reward; the unmanned ship imports hyper-parameters of all the modules, starts training circulation in a simulation environment, and dynamically adjusts exploration intensity and the like; according to the method, uncertainty and risk indexes are introduced, the exploration intensity of the intelligent agent is controlled, the intelligent agent is prevented from making dangerous actions, and the robustness is improved; a priority experience playback pool based on quality and success guidance is introduced, high-quality samples are better played back, and strategy convergence is accelerated.

The invention discloses a real-time positioning method, system and device supporting multi-frequency Beidou signals and a medium. The method comprises the steps that multi-frequency observation is collected in parallel, and quality features are generated; performing distribution alignment of risk perception based on the quality features to obtain equivalent observation; generating a causal weight by adopting anti-fact evaluation based on influence approximation; in the cross-frequency fusion model, taking multiple paths as potential disturbance quantities, only implementing group stripping on suspicious stars, frequencies or time periods, and solving and outputting a positioning result in combination with robust constraint and increment; a confidence radius is generated on line, the confidence radius is used as a main control signal to execute risk-driven self-adaptive control, and the risk-driven self-adaptive control comprises weight adjustment, measurement subset selection and front-end loop and frequency chain working state adjustment; and implementing topological consistency goalkeeping on a multi-frequency phase level, and triggering time domain virtual array suppression and feeding back to the fusion model when abnormity occurs. The system can significantly improve the precision, robustness and integrity of Beidou multi-frequency real-time positioning.

The invention relates to the technical field of meteorological early warning, and discloses a meteorological AI abnormity early warning method and system for extreme weather risk perception, and the method comprises the steps: collecting and preprocessing multi-source meteorological data, and obtaining standardized multi-source meteorological data; feature extraction and anomaly recognition are carried out on the standardized multi-source meteorological data in the continuous time period, and preliminary risk information and a first confidence value are obtained; and constructing a space-time diagram model and applying consistency constraint in the training and reasoning process of the space-time diagram model to obtain a risk identification result. And according to the risk identification result, determining a dynamic alarm threshold value to generate graded meteorological anomaly alarm information, and adjusting the sampling frequency. And sending the meteorological abnormity alarm information and the sampling adjustment instruction to a cloud platform and a terminal display device, and carrying out risk perception and early warning release of extreme weather. According to the invention, high-credibility, low-time-delay and full-period dynamic perception and hierarchical response of extreme weather risks are realized.

The invention belongs to the field of cloud computing, and provides a risk management real-time monitoring method and system based on cloud computing, and the method comprises the steps: carrying out the semantic annotation and context fusion processing of data collected by a computing power center, and then forming a risk perception feature set; the cloud risk analysis center generates a dynamic business dependency graph according to the risk perception feature set reported by each computing power center; when the cloud system detects that the risk state of the father node changes, conduction is carried out among different nodes in a non-linear time sequence mode; when the cloud system detects that a father node is continuously in a high-risk state, a risk buffer layer is generated, and decoupling and filtering are executed before a risk data stream enters a child node; and identifying high-risk areas with similar contexts, and generating predictive early warning information when detecting that a semantic-related risk resonance phenomenon exists between different areas. According to the invention, the efficiency and accuracy of risk management of cloud computing can be improved.

The invention discloses a food safety risk assessment method and system based on model analysis, and relates to the technical field of food safety production monitoring, a production line virtual model synchronized with a physical production line in real time is constructed based on the structural parameters of a target production line, and the deviation of production line data is corrected through a model fusion algorithm, so that the risk assessment accuracy is improved. And identifying abnormal physical parameter inflection points strongly related to microbial pollution. According to the method, a production line virtual model is constructed through digital twinning, a model fusion algorithm is introduced, time-domain and frequency-domain multi-dimensional comparison and correction are performed on real-time misalignment data and historical steady-state data, instantaneous interference and systematic drifting are effectively filtered out, the corrected production line data are output, and the real-time misalignment data and the historical steady-state data are compared and corrected. And on the basis, abnormal physical parameter inflection points strongly related to microbial pollution are accurately identified, so that weak abnormal signals which are difficult to perceive by a traditional method can be captured in the initial stage of continuous production, and the early pollution risk perception capability for pathogenic bacteria is remarkably improved.

The invention discloses an automatic driving vehicle risk assessment method and device, electronic equipment, a readable storage medium and a computer program product. The method comprises the following steps: collecting historical motion state information and road structure information of a vehicle and surrounding vehicles; fusing the historical motion state information to obtain a fused context feature vector; performing trajectory prediction according to a pre-constructed vehicle trajectory prediction module and the context feature vector to obtain a surrounding vehicle prediction trajectory and a self-vehicle prediction trajectory; according to the surrounding vehicle prediction trajectory, the own vehicle prediction trajectory and the road structure information, calculating a quantitative perception risk time sequence of preset look-ahead time of the own vehicle in the prediction interval; and performing fusion perception risk calculation according to the quantitative perception risk time sequence to obtain a potential risk assessment result of the vehicle in the driving scene. According to the method, the problem of inaccurate evaluation result caused by difficult parameter adjustment in a multi-vehicle interaction scene of an existing risk evaluation method can be solved.

The invention relates to the technical field of electric power system risk perception, in particular to an electric power asset risk perception method and device based on a multi-dimensional time sequence and a large model and a medium, and the method comprises the steps: collecting asset fingerprints of an electric power information system, matching the asset fingerprints with a standard component knowledge base, and standardizing the asset fingerprints; obtaining a standardized asset fingerprint ledger library; extracting vulnerability feature information from the multi-source unstructured risk early warning information to obtain a structured risk intelligence information base; performing vectorization representation on asset features in the standardized asset fingerprint machine account library and vulnerability influence component features in the risk information library, calculating vector similarity between the asset features and the vulnerability influence component features, performing feature matching according to the vector similarity, positioning affected assets, and generating an asset risk list; and driving a risk disposal process through a process engine based on the asset risk list. The asset risk perception efficiency is improved, and the response period is shortened.

The invention provides a power business anti-quantum cryptography migration progressive control method based on risk awareness, and belongs to the technical field of migration control, and the method comprises the steps: collecting key parameters of all to-be-migrated businesses in a power system, carrying out the standardization preprocessing of the collected key parameters, and constructing a business feature matrix; acquiring relevant parameters of quantum attacks in real time, and calculating a real-time quantum attack risk value of each service to be migrated; based on the service feature matrix and the real-time quantum attack risk value, constructing a dynamic migration rhythm adaptation model, and calculating a migration priority, a migration rate and a migration interval of each service to be migrated; and constructing a migration fault-tolerant and recovery model, monitoring the migration process in real time, calculating a fault influence range and recovery cost based on fault information, and executing corresponding adjustment operation. Accurate, dynamic and high-reliability control of anti-quantum password migration of the power business is realized, safe and stable operation of the power business in the migration process is guaranteed, and the migration efficiency and the anti-quantum attack capability are improved.

The invention belongs to the technical field of power system power transmission line operation and maintenance, and particularly relates to a power transmission line dynamic risk assessment and grading early warning method based on multi-modal fusion perception. According to the method, multi-modal sensing data of a power transmission line are obtained through multiple channels; aiming at different modal data characteristics, a differential preprocessing method is adopted to eliminate noise and redundancy, and key feature vectors are extracted; then generating a comprehensive risk characterization vector based on the multi-modal feature vector after space-time alignment; inputting the comprehensive risk representation vector into a dynamic risk assessment model, and outputting a real-time risk score; according to a preset dynamic grading threshold value, the risk score is mapped into four-level early warning, and early warning content is generated; and finally, early warning information is pushed through multiple channels, feedback is received, and closed-loop optimization is formed. According to the method, deep fusion of multi-source data is realized, the risk perception comprehensiveness and real-time performance are improved, the false report and missing report rate is reduced, the operation and maintenance decision efficiency is optimized, and safe and stable operation of a power grid is guaranteed.

The invention relates to the technical field of intelligent medical treatment, in particular to a basic nursing risk intelligent identification and early warning system and method. The system comprises a multi-source data perception and fusion module which is used for acquiring and fusing dominant risk data and implicit risk data of a patient in real time and outputting a structured patient state feature set; the risk intelligent identification core module simulates physiological state changes through a patient personalized digital twinborn model and is coupled with a risk conduction knowledge graph to deduce a risk evolution trajectory so as to realize intelligent identification of potential risks and intervention measure simulation; the intelligent early warning and intervention module is used for executing hierarchical early warning and active intervention linkage; and the man-machine collaborative feedback and self-evolution module continuously optimizes the system model and strategy through clinical feedback data. The problems that in the prior art, the risk perception dimension is single, early warning lags behind and intervention is disjointed are solved, and prospective and precise recognition, prevention and control of the nursing risk are achieved.

The invention discloses a power grid big data multi-dimensional risk perception method and system, and belongs to the technical field of power grid big data risk prediction.The power grid big data multi-dimensional risk perception method comprises the steps that multi-source perception data is collected, time-space synchronization and normalization processing are conducted, and a multi-dimensional feature representation matrix is constructed; constructing a feature coordinate system based on the matrix, mapping the feature coordinate system to a risk topology network, and generating a key node connection weight map; a multi-dimensional disturbance response entropy model is further constructed, and nonlinear situation modeling of power grid state disturbance is achieved; generating a risk energy distribution diagram by using the prediction model, extracting a high-risk path, and generating a microcosmic situation compensation curve in combination with a historical trajectory for adjusting an abnormal threshold; and finally, outputting a risk level evaluation result, and triggering a safety control and early warning response mechanism in a linkage manner. The method has the advantages of being high in multi-source data fusion degree, high in risk prediction accuracy and intelligent in response control, and the risk perception and active defense capability of a power grid in a complex environment is effectively improved.

The invention relates to the technical field of tunnel engineering safety monitoring, in particular to a karst rock stratum tunnel health diagnosis method, device and equipment and a storage medium. Comprising the steps that whether catastrophe nodes matched with tunnel multi-source sensing data obtained in real time exist or not is judged by searching a karst-lining-surrounding rock coupling knowledge graph; if yes, diagnosis is carried out according to a node embedded karst water-rock coupling mechanism; and if not, calling a preset karst catastrophe fusion model to carry out online learning and dynamic diagnosis, and adding catastrophe nodes in the knowledge graph. According to the fusion model, characteristics such as karst water chemical ion concentration change rate, surrounding rock effective porosity mutation gradient and lining back cavity volume growth rate are extracted, karst water inrush and mud inrush risk probability distribution is output based on multiple neural network model parallel coupling architectures, and high-risk perception data are automatically screened out or marked according to a threshold value. The karst water inrush and mud inrush risk of the karst stratum tunnel can be accurately identified, and real-time early warning can be carried out.

The invention discloses a fire alarm inspection system and method based on big data, and relates to the technical field of intelligent fire protection and public safety. The problems that a traditional fire fighting system is serious in data island phenomenon, single in risk perception dimension, static and rigid in early warning model, high in false alarm rate and low in inspection efficiency are solved. According to the method, a fire-fighting theme database is constructed, and based on a multi-dimensional dynamic analysis model, comprehensive analysis of space aggregation risks, time sequence evolution trends and equipment linkage logic is realized, and dynamic fire danger hidden danger indexes are calculated; a key monitoring object is positioned through a visual thermodynamic diagram, grading alarm is triggered, differentiated inspection tasks and an optimal route are generated based on the intelligence, and emergency disposal linkage is supported; closed-loop management from global risk perception, intelligent analysis and early warning to precise inspection treatment is realized, and early recognition and early warning accuracy and resource scheduling efficiency of fire hazards are remarkably improved.

The invention provides a non-tampering operation and maintenance auditing system based on a block chain, and the system comprises a multi-source auditing data collection module, a block chain evidence storage engine, an intelligent contract auditing module, a cross-scene cooperative auditing module and a visual auditing platform, and all the modules cooperate according to a preset logic to form a closed-loop auditing system. According to the method, the problems of uncredible data, incomplete coverage, poor compliance adaptation, early warning lagging, insufficient cross-scene consistency and the like of a traditional operation and maintenance auditing system are solved, and through deep fusion of a block chain technology and an intelligent algorithm, auditing data full-link credible evidence storage, risk real-time perception, compliance automatic adaptation and cross-scene consistent tracing are realized; on the premise of guaranteeing the security and compliance of an information system, the auditing efficiency is improved, the operation and maintenance interference and resource consumption are reduced, and the operation and maintenance auditing requirements under complex IT architectures in multiple industries such as finance and government affairs are met.

The invention relates to a multi-disaster coupling city comprehensive risk analysis method based on reinforcement learning, in particular to the field of multi-disaster coupling city comprehensive risk analysis. According to the scheme, through multi-source heterogeneous data fusion and spatial-temporal feature extraction, a dynamic risk sensing environment is constructed, and a multi-agent reinforcement learning framework is utilized; an emergency resource allocation strategy is collaboratively optimized in a complex scene of multi-disaster coupling, and a coupling item and a hidden Markov look-ahead model are introduced, so that the system not only can analyze interaction among disasters, but also can predict a risk evolution trend, thereby guiding the strategy to avoid a high-risk mode in advance, and finally realizing conversion from passive response to active adaptation. And the accuracy, the collaboration and the robustness of emergency decision making are obviously improved.

The invention discloses an agricultural robot trajectory planning method and system with optimal energy consumption under collision constraint. The method comprises the following steps: acquiring historical state sequence data and environmental map information of a target machine and peripheral machines in real time by a self-machine; identifying the operation intention of the target machine, and outputting a multi-modal prediction trajectory; the method comprises the following steps of: generating a global coarse reference track covering an operation area by taking completion of an own operation task as a main target; generating a space-time safety corridor by adopting a space-time inheritance strategy, and reserving a safety space for the dynamic obstacle; constructing an optimization problem cost function, and comprehensively considering reference trajectory cost, collision risk cost and energy consumption cost; and under the boundary constraint of the safety corridor and the kinematics constraint of the robot, solving the optimization problem by using a numerical optimization solver to obtain an optimized local trajectory. According to the method, safe, efficient and energy-saving collaborative operation can be realized by fusing multi-modal trajectory prediction of the dynamic obstacle, refining energy consumption modeling and risk perception planning.

The invention discloses a financial flow situation awareness system and method based on a multi-modal large model, and relates to the technical field of supply chain financial risk control. Aiming at the problems of data isolation, high processing delay and risk perception lagging in the existing storage pledge financing scene, the method comprises the following steps: obtaining a storage digital planar graph, dividing logic sub-regions, collecting and processing multi-source data of each sub-region in parallel, and extracting pledge stock time sequence data and warehouse receipt information by utilizing a visual and text model respectively; constructing a sub-region association map by taking the cargo identifier as a node and fusing the fund flow; and fusing all the sub-maps to construct a global association map, performing cross-regional consistency verification and anomaly recognition, generating a dynamic risk judgment result, and outputting hierarchical situation awareness information. According to the invention, accurate association and panoramic risk perception of the "object-bill-money" state are realized, and the real-time performance and accuracy of risk identification and the expandability of the system are improved.

The invention provides a computing power scheduling system and method based on reinforcement learning and thermodynamic diagram.The system constructs an intelligent closed loop of perception, decision, evaluation and optimization, and the method comprises the steps that a multi-dimensional state vector is constructed by fusing a server thermodynamic diagram, resource demand prediction and a fragment risk quantification result through a state perception and feature engineering module; the deep reinforcement learning decision engine receives the vector and outputs a scheduling action; the multi-target reward value calculation module calculates a composite reward value according to the scheduling result; and the online learning and model updating module drives a decision engine to continuously evolve by using the reward value. According to the method, the risk perception of the dynamic thermodynamic diagram is deeply coupled with the decision-making capability of reinforcement learning, so that the crossing of the computing power scheduling from a static rule to dynamic self-adaption is realized, and the comprehensive performance of a large-scale cluster under multiple targets such as resource utilization rate, task completion time, fairness and energy efficiency is remarkably improved.

The invention relates to a vehicle control method and a vehicle, and is applied to the field of vehicle driving technology application. The vehicle control method comprises the steps of obtaining multi-dimensional perception data and time sequence change data, conducting prediction based on the multi-dimensional perception data and the time sequence change data to obtain a current risk index of a vehicle, and triggering early warning operation corresponding to the risk level based on the risk level to which the current risk index belongs. And meanwhile, the vehicle is controlled to execute emergency operation corresponding to the risk level. Therefore, risk prediction is carried out by integrating the multi-dimensional data of the environment, the vehicle and the driver and the time sequence change data, risk perception can be carried out in a cross-dimension manner, the false alarm rate is reduced, risk pre-identification is realized by predicting the current risk index, and cooperation of early warning and execution is realized by triggering early warning operation and controlling the vehicle to execute emergency operation. And the overall response efficiency of the system is improved.

The embodiment of the invention discloses a risk perception method and device for a global supply chain enterprise and a medium, and relates to the technical field of data analysis, and the method comprises the steps: receiving a risk perception request of a target enterprise for an enterprise perception object, the enterprise perception object comprises a target product and a target shipping port, the risk perception request comprises real-time perception and simulation interruption perception; based on the risk perception request, performing entity relationship extraction from a pre-constructed dynamic knowledge graph to form an evaluation sub-graph, and calculating a multi-dimensional quantitative toughness index through the evaluation sub-graph; and fusing the multi-dimensional quantitative toughness indexes to generate a comprehensive toughness index corresponding to the enterprise perception object, and carrying out risk perception on the enterprise perception object based on the comprehensive toughness index.

The invention belongs to the technical field of safety prevention and control of flammable and explosive environments, and particularly relates to an explosion suppression grading protection system and method, and the system comprises a monitoring module which monitors the environment information in a to-be-monitored area; the signal processing module is used for generating a grading early warning instruction according to the environment information; the explosion suppression execution module is used for executing an explosion suppression action according to the grading early warning instruction; and the reset module is used for generating reset information so as to reset the explosion suppression execution module. On the basis of cooperation of the monitoring module, the signal processing module and the explosion suppression execution module, corresponding explosion suppression actions can be automatically triggered, manual intervention is not needed, a complete and rapid safety protection closed loop from risk perception to active suppression is achieved, the speed and reliability from risk discovery to active intervention are remarkably improved, and the safety protection effect is improved. And the safety risk of the to-be-monitored area is greatly reduced. Based on the cooperation of the reset module, the signal processing module and the explosion suppression execution module, the standby state can be quickly recovered after explosion suppression is completed, the maintenance steps are simplified, and the use cost is reduced.

The invention relates to the technical field of path planning, in particular to a risk awareness path planning method and system based on time sequence prediction, and the method comprises the steps: predicting the passability probability of a dynamic node at a future moment, calculating the predictive risk cost of the node, and adding the predictive risk cost into a cost function of an A * algorithm, thereby enabling a robot to actively avoid a high-risk region, and improving the path planning efficiency. According to the method, the path planning is prospective, the path which is failed during arrival is prevented from being planned, the path planning can be quantitatively balanced between the path length and the blocking risk, the optimal path selection is realized, and the efficiency and the quality of the path planning are improved.

The invention relates to the technical field of real estate data management, and particularly discloses a real estate data exchange system based on ownership survey data, which comprises the following steps of: establishing an association relationship among a data entity, a processing interface and an operation main body by constructing an ownership data blood relationship network; receiving a data exchange request and extracting context information; extracting a multi-hop association sub-graph from the blood relationship network based on the context information to form a dynamic risk association view; by analyzing a view topological structure, evaluating path sensitivity and connectivity, and quantitatively calculating a single risk value representing a data association leakage risk; and finally, according to a matching result of the risk value and the threshold interval, selecting and executing a corresponding data exchange response action from the security policy library. According to the method, the problem of permission leakage caused by legal operation sequence combination in the prior art is solved, the conversion from static permission control to dynamic risk perception is realized, and the security of real estate data exchange is improved.

The invention relates to the technical field of equipment state monitoring, and particularly discloses a combustor fault prediction and health management system. Comprising nine core modules including a multi-modal sensor fusion module, a sensor verification and recovery module, an intelligent feature extraction module, a digital twinborn simulation module, a health state evaluation module, a remaining useful life prediction module, a risk perception decision module, an uncertainty quantification and management module and an adaptive control module. Comprehensive monitoring and accurate prediction of the state of the combustor are realized through a method of combining multi-source data fusion, a physical model and data driving; and carrying out risk perception decision-making by adopting distributed reinforcement learning, verifying a maintenance strategy through a digital twin platform, and realizing closed-loop optimization in combination with adaptive control. According to the invention, through the adaptive weighting algorithm of the multi-modal sensor fusion module, the state perception comprehensiveness is improved; through a dual-path network of the intelligent feature extraction module, collaborative analysis can be carried out on multi-dimensional features.