580 results about "Perception risk" patented technology

The invention relates to the technical field of power systems and automation thereof, in particular to a distribution network auxiliary decision-making method and system considering source load fluctuation relevance and a medium. The method comprises the following steps: firstly, collecting related information of a distribution network area, quantifying a synchronization and hysteresis association rule of multi-source heterogeneous data fluctuation, and constructing a composite feature vector and a standardized risk perception data set; defining a state space and an action space of a reinforcement learning algorithm based on the composite feature vector, and realizing auxiliary decision-making optimization of the distribution network; constructing a scene feature library, calculating the fluctuation relevance similarity between a new scene and a historical scene, and multiplexing a deep reinforcement learning model architecture and carrying out transfer learning; building a power grid digital twinborn simulation platform, designing evaluation indexes, generating candidate schemes, deducing the candidate schemes, selecting recommendation strategies and storing the recommendation strategies in a strategy knowledge base.

The invention discloses an oil and gas pipe network operation state risk perception early warning method and system, and the method comprises the steps: carrying out the fusion of the multi-dimensional features of the operation state of a pipe network according to the real-time operation data of the oil and gas pipe network, and generating a spatial-temporal feature matrix; inputting the spatial-temporal feature matrix into a deep Gaussian process model, performing probabilistic modeling on abnormal features in the pipe network operation state, and outputting a risk feature vector with confidence; constructing a three-dimensional risk concentration distribution field according to the risk feature vector, and predicting a propagation path and a diffusion trend of the risk concentration distribution field; and for the prediction result, constructing a self-adaptive early warning threshold generation model based on the generative adversarial network, generating a multi-stage early warning threshold changing along with the operation state of the pipe network, and realizing risk perception early warning of the operation state of the oil and gas pipe network according to the comparison result of the real-time risk characteristics and the early warning threshold. According to the embodiment of the invention, dynamic risk assessment and self-adaptive early warning can be realized, and the safety and emergency response capability of the pipe network are enhanced.

The invention relates to the technical field of water traffic risk assessment, particularly provides a traffic risk dynamic assessment model construction system, and solves the problem that the intelligent degree and dynamic performance of traffic risk assessment need to be further improved. According to the system, a first modeling assembly constructs a risk propagation model adopting meta-learning; the second modeling component constructs a multi-subject collaborative risk assessment model; an active intervention component; and generating an active intervention strategy. The traffic safety is improved, and the system can effectively reduce the occurrence rate of traffic accidents through real-time risk perception, accurate evaluation and active intervention; a traffic management dynamic risk map and an intervention strategy are optimized, a scientific decision basis is provided for a traffic management department, and the management efficiency is improved; through multi-channel early warning and path induction, safer and more convenient travel experience can be provided for a driver; the development of intelligent traffic is promoted, and technical support is provided for the realization of an intelligent traffic system.

The invention relates to the field of intelligent driving, in particular to a driver dangerous behavior intervention system and method based on a space-time diagram neural network, and the method comprises the steps: obtaining a physiological signal, a driving posture and control behavior data through a multi-modal data collection module; constructing a dynamic graph structure and evaluating a driver risk state by using a space-time diagram neural network risk perception engine; the intelligent intervention execution module adopts a tactile, visual or vehicle control intervention strategy according to the evaluation result; the closed-loop optimization module monitors an intervention effect and updates a risk model and a strategy; according to the system, millimeter wave radar and multi-channel visual analysis are fused, the recognition accuracy is improved to 98.6%, dangerous events are predicted 15-30 seconds in advance through physiological signals and micro-expression analysis, and sufficient response time is provided for a driver.

The invention relates to the field of enterprise risk early warning analysis, in particular to an intelligent risk early warning method and system based on multi-dimensional data analysis. The method comprises the following steps: acquiring a multi-dimensional enterprise data stream, performing heterogeneous index information analysis and logic hierarchy reconstruction, and constructing an enterprise running state sensing map; performing multi-index local fluctuation amplitude calculation on the enterprise operation state sensing map, and performing dynamic disturbance feature mining to construct a risk disturbance spectrogram; performing deep semantic analysis and node state abrupt change feature analysis on the risk disturbance spectrogram to generate a dynamic transition type of an abrupt change node; and performing full-period time sequence tracing on the risk disturbance spectrogram, performing potential risk node prediction based on the dynamic transition type, and identifying other potential risk propagation ports. Through accurate and efficient enterprise risk perception, the risk intervention decision is made in advance, and the anti-risk capability and the operation stability of the enterprise are improved.

The invention discloses an industrial network risk perception and collaborative early warning method based on a dynamic risk map, and relates to the technical field of industrial internet security, and the method comprises the steps: S1, multi-source perception deployment; s2, heterogeneous data fusion acquisition; s3, constructing a knowledge graph engine; s4, analyzing depth data; s5, performing dynamic risk assessment; and S6, intelligent early warning decision making. According to the industrial network risk perception and collaborative early warning method based on the dynamic risk map, through fusion perception of OT layer data such as equipment states and process parameters, the problems of single perception dimension, evaluation lagging and disjunction in the prior art are solved, the false alarm rate is extremely low, and the method is suitable for popularization and application. Particularly, a dynamic adjustment mechanism of a time-varying risk weight matrix is improved, novel attacks can be dynamically responded, meanwhile, cross-domain risk conduction analysis is achieved, the accuracy and response speed of industrial network security early warning are improved, and meanwhile a closed-loop mechanism of attack path prediction and disposal suggestions is constructed.

The invention discloses a ship typical channel auxiliary navigation method and system based on unmanned aerial vehicle accompanying, and belongs to the technical field of intelligent shipping auxiliary control. A channel area multi-dimensional risk perception model is constructed, and historical accident, hydrology and obstacle information is extracted to generate a risk density matrix; forming a high-risk accumulation area based on risk spectrum dynamic clustering, and endowing an adjustable intervention weight; scheduling an unmanned aerial vehicle with self-positioning and differential recognition capabilities to carry out accompanying flight, and obtaining track deviation, a velocity vector and attitude information through multi-modal sensing and a low-delay link; calculating an intervention coefficient by using a risk decision engine and generating course, speed and steering point control parameters; and finally, local path dynamic optimization and feedback closed-loop control are realized. According to the invention, the course precision and risk avoiding capability of the ship in a complex typical channel are improved, and the method is suitable for intelligent shipping application in a high-density navigation environment.

The invention discloses a security risk cognitive training system and method based on behavior trajectory analysis. The system comprises a multi-source data acquisition module, a behavior trajectory analysis module, a dynamic training module and a real-time feedback and evaluation module. The multi-source data acquisition module comprises a sensor network unit and an environment data unit. Physiological, behavior and environmental data are integrated through the multi-source data acquisition module, multi-modal fusion analysis is realized in combination with a Transform architecture, and the one-sidedness problem of traditional single data is solved; an LSTM network and a Prophet model are used to identify an abnormal behavior mode in real time and predict a risk trend, and real-time capture and dynamic early warning of risks are realized; a customized training task is generated by means of PPO reinforcement learning, a dynamic virtual scene and self-adaptive difficulty adjustment are combined, and the problems of fixed training content and lack of personalization are solved; through a multi-modal feedback and quantitative evaluation system, the risk perception capability and the training effect are improved, and closed-loop optimization of data acquisition-analysis-training-evaluation is formed.

The invention discloses a multi-aircraft 4D trajectory prediction method based on a graph attention hybrid neural network, and belongs to the technical field of aviation. Historical trajectory implicit information of different aircrafts in an airspace is extracted by utilizing Transform and a long-short-term memory network, the aircrafts in the airspace are regarded as nodes, an aircraft connection network graph is constructed, and modeling is performed on the aircraft implicit information and the aircraft connection network graph through a graph attention network so as to capture interaction information between the aircrafts. The graph attention network integrates the track implicit information of all aircrafts in the airspace and shares and updates the track implicit information, finally decodes the track implicit feature information of the aircrafts through two decoders based on the long and short term memory network, fuses the output of the two decoders, and outputs a prediction track through a full connection layer. Compared with a method which does not consider aircraft space interaction information, the accuracy of flight trajectory prediction is improved, and an accurate trajectory prediction technology provides support for a subsequent risk perception technology and a conflict resolution technology.

The invention discloses an intelligent building safety management system based on big data, and belongs to the technical field of intelligent buildings. Comprising the following modules: a multi-source data fusion and preprocessing module, which adopts intelligent data standardization and feature extraction technologies to realize efficient integration and semantic conversion of cross-source heterogeneous data and ensure data quality and consistency; the security risk dynamic knowledge graph construction module is used for constructing a semantic association network of building security elements through a graph neural network so as to continuously self-learn and dynamically update a risk feature relationship; the intelligent risk assessment and early warning module is used for carrying out comprehensive modeling, accurate positioning, layered assessment and intelligent early warning on building safety risks; the safety decision support module is used for providing an executable risk assessment report and an emergency decision suggestion; and the safety management visualization module adopts an interactive multi-dimensional visualization technology to intuitively present the dynamic evolution process of the building safety risk, so that the risk perception and management efficiency is remarkably improved.

The invention provides a risk behavior perception method and device based on deep learning, and the method comprises the steps: firstly collecting multi-source behavior data containing a network access record, a terminal operation log and an application program interface calling sequence in a preset time window of a target user, carrying out the spatial-temporal feature extraction of the multi-source behavior data, generating a behavior track feature vector, and carrying out the feature extraction of the behavior track feature vector; the method comprises the following steps: acquiring a network access record, analyzing the network access record through a protocol to obtain a network behavior feature vector, inputting the network behavior feature vector and the network behavior feature vector into a pre-trained risk assessment neural network for joint coding to obtain behavior risk probability distribution, determining a risk level according to a risk category exceeding a preset threshold, generating an early warning signal, and finally triggering a real-time alarm module according to the early warning signal. The risk description text and the risk relief strategy are sent to the safety management terminal, multi-source data comprehensive analysis is achieved, the risk is accurately evaluated, early warning is timely and effective, and the risk perception ability is improved.

The invention discloses an intelligent ship situation awareness prediction method and system, and relates to the technical field of intelligent ship situation awareness and trajectory prediction, and the method comprises the steps: carrying out the time-space synchronization of collected operation environment data of a ship through a timestamp alignment method, and generating a ship situation data set; based on the ship situation data set, the course angle change rate and the Euclidean distance of the obstacle relative to the ship are extracted, and weighted fusion is carried out on the dynamic collision probability and the approaching time of the obstacle to obtain an obstacle threat degree index; inputting the obstacle threat degree index into a ship trajectory prediction model, and outputting the minimum meeting distance between the ship and each obstacle; and carrying out risk grade division on the minimum encounter distance according to an international maritime collision avoidance rule, outputting a graded early warning signal, and formulating a navigation strategy to execute obstacle avoidance and route adjustment. According to the method, the technical problems of risk perception lag, single and rough avoidance action and insufficient trajectory prediction accuracy in the prior art are effectively solved.

The invention belongs to the technical field of water conservancy project monitoring, and discloses a flood storage and detention area risk intelligent sensing and multi-stage progressive early warning system, which comprises a flood storage and detention area sensing module for collecting multi-source heterogeneous data covering a flood storage and detention area, constructing a unified space-time coding framework, mapping the multi-source heterogeneous data to a standardized geographic information grid, and performing early warning on the risk of the flood storage and detention area. A confidence regulation and control mechanism is adopted to form a dynamic storage and stagnation pressure map; the linkage instability propagation module is used for constructing a flood diversion driving situation flow field based on the dynamic storage stagnation pressure map, continuously tracking topological disturbance characteristics in the flood diversion driving situation flow field and identifying a linkage instability propagation chain and an instability propagation path; the risk phase change identification module dynamically tracks the derivative of the instability propagation tension gradient along with time according to the instability propagation chain and the instability propagation path, and combines the gravity center migration trend and the edge activation frequency in the flood diversion driving situation flow field; and a powerful support is provided for scientific decision-making in a complex flood environment.

The invention provides an operation risk early warning method and system based on a power grid information system, and relates to the technical field of power grid operation risk assessment. According to the method, equipment risk characterization and future risk score prediction are realized by constructing a risk perception graph fusing node and edge features; and identifying a high-risk region and a diffusion path by combining a clustering algorithm, and triggering a hierarchical response strategy based on a rule base. According to the invention, real-time early warning and intelligent management and control of the operation risk of the power grid are realized.

The invention relates to a visual safety risk early warning system for pseudo-classic architecture mixed structure construction, in particular to the field of safety risk early warning of pseudo-classic architecture construction, through multi-source data fusion and intelligent algorithm cooperation, the construction safety risk management and control capability is remarkably improved, the system collects mechanical and environmental data of key nodes of a high-support formwork body in real time, and the construction safety risk early warning capability is improved. Load distribution is dynamically updated in combination with the building information model, and a high-precision space-time label data stream is generated; predicting a short-term instability risk based on a two-channel neural network, constructing a complex network model to quantify a node failure linkage effect, and generating a visual risk propagation thermodynamic diagram; finally, a grading alarm strategy is triggered through a dynamic threshold value, augmented reality visual warning and equipment linkage control is achieved, the system breaks through the limitation of traditional manual monitoring, full-process closed-loop management of risk perception, prediction, positioning and response is achieved, the occurrence rate of safety accidents is effectively reduced, and the safety and reliability of construction of the complex structure of the pseudo-classic architecture are guaranteed.

The invention discloses a thermal power plant intelligent inspection path dynamic planning and anomaly recognition method, and particularly relates to the technical field of thermal power plants, and the method comprises the following steps: S1, constructing a dynamic three-dimensional risk thermodynamic diagram and an equipment health gene code; s2, multi-target dynamic path planning; s3, multi-modal data collaborative acquisition and anomaly detection are carried out; s4, performing abnormal root cause association and risk level judgment; s5, re-planning a closed-loop path of abnormal driving; and S6, executing and dynamically updating. According to the invention, through a core technology architecture of dynamic risk thermodynamic diagram-equipment health gene code-multi-modal anomaly detection-closed-loop path planning, full-process optimization of intelligent inspection of the thermal power plant from risk perception, path decision-making to anomaly handling is realized, and the inspection efficiency, the equipment reliability and the emergency response capability are significantly improved; meanwhile, risks of man-made misjudgment and system failure are reduced, and core technical support is provided for intelligent transformation of the thermal power plant.

The invention discloses an electricity larceny risk threshold dynamic optimization and multistage early warning method based on reinforcement learning, and the method achieves the dynamic adjustment of an electricity larceny risk threshold through constructing a multi-dimensional state space containing a risk perception percentage, a line loss rate deviation, a cost budget and a load feature vector, and employing a reinforcement learning algorithm. Safety constraints are introduced to ensure the rationality of threshold adjustment, a TD3 algorithm is adopted to train and update a strategy network and a value network, the learning efficiency is improved through priority experience playback, a multi-target weighted reward function is designed to balance the multi-target optimization requirements of detection precision, line loss control, inspection cost and the like, and a red, orange and yellow three-color early warning mechanism is constructed. Early warning actions of different levels are triggered according to the ratio of the dynamically updated reference threshold value to the risk perception percentage, and early warning visualization and strategy effect evaluation are achieved through a geographic information graph dynamic rendering module, a natural language report generation module and an anti-fact analysis module.

The invention discloses a regional orderly power utilization dynamic optimization monitoring method based on a self-adaptive threshold value, and particularly relates to the technical field of power utilization optimization. The method comprises the following steps: continuously monitoring real-time load and voltage data of a power utilization area, constructing an initial threshold by combining a historical behavior model and an extreme value theory, extracting key characteristic parameters after a continuous deviation event is triggered, quantifying a threshold adjustment trend and abnormal frequency change, and dividing a drift degree into a high level, a middle level and a low level; and under the medium level, further estimating the future identification performance of the system based on a dynamic identification accuracy prediction model, and dynamically limiting the threshold adjustment amplitude, thereby effectively preventing the system from mistakenly regarding the abnormal trend as a new normal state, avoiding the generation of a monitoring blind area, and improving the stability, intelligence and risk perception ability of the regional power utilization regulation and control system.

The invention relates to the technical field of sewage treatment, in particular to an intelligent municipal sewage resource utilization decision support method and system, and provides a method for realizing real-time monitoring and historical data analysis of a sewage treatment system by constructing a multi-domain knowledge coupled heterogeneous decision model; the method comprises construction of a knowledge graph in the sewage treatment field and coupling with real-time data, and a hierarchical decision scheme is formed. On this basis, a collaborative mechanism of microcosmic, mesoscopic and macroscopic layer decisions is established, and risk awareness decision optimization is executed; dynamically dividing risk levels through a Bayesian probability decision framework, and realizing closed-loop self-evolution of data-model-decision; meanwhile, a resource value quantitative model is constructed, and decision evaluation of multi-target balance is carried out; according to the invention, the problem of'knowledge isolated island 'of a traditional system is effectively solved, the decision accuracy is improved, and collaborative decision-making of expert knowledge and data driving is realized.

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 relates to the technical field of electric power detection, and discloses a wind turbine generator operation performance detection method and system based on a power generation working condition, and the method comprises the following steps: dynamically adjusting the data collection frequency and range according to the risk level; real-time operation data and real-time image data of the wind turbine generator are obtained, and working condition division is carried out; fusing the real-time operation data and the real-time image features to obtain real-time fusion features; based on a working condition division result, combining a dynamic weight distribution strategy to adjust the weights of the operation data and the image features in the real-time fusion features; and predicting a real-time power generation performance index of the wind turbine generator by using a pre-constructed multi-modal deep learning model, and analyzing the operation performance of the wind turbine generator based on the predicted real-time power generation performance index. The method overcomes the defects that a traditional method is single in data, insufficient in feature fusion and lagged in risk perception, and provides important technical support for efficient operation and maintenance and intelligent development of the wind turbine generator.

The invention discloses a non-power-grid sporadic material emergency purchase response system considering risk priority. The system comprises a sporadic material demand trend pre-judgment module, a risk perception purchase adaptation module, a risk income balance optimization module, an emergency purchase scheduling module, a non-power-grid material response module and a module cooperative control module. According to the system, data such as historical purchase and consumption rate are integrated through a demand trend pre-judgment module to output a demand prediction result, risk and performance parameters are coupled through a risk perception purchase adaptation module to generate an adaptation scheme, and an optimal purchase strategy is calculated through a risk income balance optimization module. A scheduling instruction is generated through an emergency purchase scheduling module according to the risk priority, a non-power-grid material response module executes inventory checking, transportation planning and other operations, and a module cooperation control module achieves whole-process data synchronization and parameter dynamic adjustment. According to the system, the decision-making scientificity and response efficiency of non-power-grid sporadic material emergency purchase are improved, and emergency purchase burstiness and complexity requirements are met.

The invention discloses a dynamic privacy protection closed-loop control method and system for mobile edge crowd sensing. According to the method, information related to a communication channel and data content is obtained through risk perception, and a multi-dimensional comprehensive privacy risk score is calculated; then, dynamically determining a value of a differential privacy budget epsilon according to the risk score by utilizing a reinforcement learning algorithm in a strategy decision-making stage; then, performing noise addition disturbance on crowd sensing data according to the epsilon in a privacy protection execution stage so as to realize a preset privacy protection level; then, the privacy leakage risk and the data validity of the disturbed data are evaluated in the effect verification stage, and an evaluation result is obtained; and finally, privacy control strategy parameters are adjusted according to an evaluation result in a feedback optimization stage, and feedback is applied to a risk perception and decision process of a next cycle, so that closed-loop control is formed, and a privacy protection effect is continuously optimized. According to the method, the user data privacy security can be improved, and the data availability and the energy consumption efficiency are both considered.

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 discloses a sponge city high-terrain rainwater management method and system, particularly relates to the technical field of rainwater prediction management, and is used for solving the problem of poor high-terrain rainwater prediction management scheduling. For a high terrain small watershed, data such as elevation, permeability coefficient, soil layer thickness and vegetation coverage are collected and preprocessed, a continuous physical field is generated, a facility pipe network is mapped, and then grid units are divided; a multivariate predictor is constructed based on grid cell center attributes, edge node increment fine adjustment is issued after cloud training, and a model is optimized through scene matching and federal learning; after rainstorm, automatically extracting observation and prediction residual increment to train a neural network and generating a confidence interval; and the edge nodes divide risks according to the confidence interval, the flood control water depth and the minimum infiltration threshold value and output gate opening and pump set power suggestions, so that the high-terrain small-watershed extreme rainstorm peak prediction precision and response time efficiency are remarkably improved, and refined risk perception scheduling and rainwater resource utilization are realized.

The invention provides a chemical potential safety hazard intelligent identification and early warning method. Relates to the field of chemical safety, and discloses a chemical potential safety hazard intelligent identification and early warning method comprising the following steps: S1, preprocessing multi-modal data through a multi-modal feature self-calibration fusion algorithm; s2, constructing a dynamic Bayesian network time-varying coupling evaluation model based on a protection layer theory; s3, related knowledge documents are retrieved by adopting an enhanced RAG technology; s4, carrying out hidden danger identification based on the knowledge-enhanced large model; s5, four-level intelligent early warning is generated based on the risk value calculation model; and S6, optimizing model parameters through deep reinforcement learning. The chemical potential safety hazard intelligent identification and early warning method based on the protective layer theory and the large model technology has the advantages that dynamic risk perception can be realized, cross-modal potential hazards can be accurately identified, an early warning scheme can be quickly generated, and the initiative and scientificity of chemical safety management can be improved.

The invention discloses a self-adaptive time sequence prediction method based on risk perception, which dynamically adjusts a prediction step length by perceiving the change of a prediction error, avoids the problem of high error caused by multi-step prediction, and ensures the accuracy of a prediction result. The method comprises three key modules: a feature extraction and fusion module based on multi-scale fragments, used for capturing periodic, seasonal and trending features with intricate service demands, and extracting long-term dependency and local semantic information therein; the time sequence probability prediction module based on the diffusion model is used for carrying out probability modeling on the prediction result and describing a credible value of the prediction result by probability distribution of a prediction moment; and the prediction optimization module for adaptive risk management is used for evaluating and predicting the risk and carrying out adaptive truncation. According to the invention, a new solution is provided in the aspects of accuracy and reliability of time sequence prediction.

The invention relates to the technical field of hazardous chemical substance production, and provides a hazardous chemical substance safety production management and control system, in the aspect of data acquisition, all-around acquisition of equipment operation, environment and personnel operation data is realized, a high-precision sensor ensures data accuracy and far exceeds traditional finite dimension and precision acquisition, and during data analysis and processing, edge calculation is combined with deep learning, so that the safety production management and control of hazardous chemical substances is realized. A risk assessment model is dynamically adjusted, the limitation of a traditional fixed model is broken through, production change and risk early warning can be adapted in real time, risk levels are dynamically adjusted, multi-element early warning and visual display are achieved, compared with single early warning and simple display, risk perception is more timely and accurate, accurate reasons are found through a unique accident analysis model, and the risk assessment efficiency is improved. According to the method, the production management and operation improvement can be promoted, each module of the system can be optimized, a closed-loop lifting mechanism is formed, the safety production management and control level of the hazardous chemical substances is comprehensively improved from data acquisition, analysis, early warning and optimization, the safety risk is greatly reduced, and the production safety and stability are guaranteed.

The invention discloses a block chain risk address identification method for a dual-structure time perception graph neural network, and the method comprises the steps: carrying out the analysis and cleaning of original data, generating a transaction pair and an account pair based on an effective transaction record, and finally carrying out the standardization processing of a timestamp. Carrying out model training on the marked training data and dividing a data set; and then a double-structure graph model of an account graph and a transaction graph is constructed, heterogeneous characteristics of accounts and transactions in the block chain are distinguished for the first time, an interaction relationship and behavior evolution are modeled respectively, and the comprehensiveness and accuracy of risk identification are improved. Relative and absolute time coding is introduced, short-term behavior modes and long-term trends are captured in a differentiated mode, and the perception ability of dynamic risks is enhanced. Compared with a traditional single graph model, the scheme has the advantages that multi-dimensional information is effectively fused, the model generalization performance is remarkably improved, overfitting is reduced, and the method is suitable for cross-scene and cross-cycle risk detection.

The invention is suitable for the technical field of unmanned aerial vehicle communication, and particularly relates to an unmanned aerial vehicle environment monitoring and data processing method supported by edge computing, and the method comprises the steps: obtaining a time sequence data stream collected by a multi-source environment sensing device, and carrying out the compression and processing of the time sequence data stream based on a linear projection compression model, and obtaining a compressed feature vector; calculating a communication cost based on the current heterogeneous network condition and the energy constraint through a preset communication cost function; and executing different data processing strategies based on different communication costs. According to the method, efficient edge compression is realized in an embedded resource limited scene; resource-aware data uploading control is realized through a communication cost function; a Bayesian state fusion method based on a compressed feature space reconstructs a Kalman filtering matrix structure, effectively supports local state estimation and uncertainty control, instantly triggers early warning when an environment index crosses a boundary, and effectively enhances the risk perception and response capability of a system.