455 results about "Perception risk" patented technology

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 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 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 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 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.

The invention provides an unmanned aerial vehicle intelligent decision-making method and system based on deep reinforcement learning, and relates to the technical field of intelligent decision-making, and the method comprises the steps: obtaining a landing region image and depth information through a visual sensor, processing feature representation and three-dimensional environment description through a risk perception type strategy gradient algorithm, and adaptively constructing an information association relationship. According to the method, feature fusion, optimal landing position determination, dynamic obstacle trajectory prediction and track point sequence generation are realized, a control strategy is optimized when the environment is suddenly changed, safe and efficient landing of the unmanned aerial vehicle is realized, and the safety and adaptability of landing of the unmanned aerial vehicle are improved.

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 belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and system with dynamic risk perception and attention focusing and a vehicle, and the method comprises the steps: predicting the track of a surrounding vehicle in real time through a multi-feature Gaussian weighted particle filtering algorithm, and improving the prediction precision through combining a vehicle kinematic model and resampling optimization; constructing a comprehensive evaluation model fusing transverse and longitudinal risks, and dynamically quantifying the collision risk of the vehicle and surrounding vehicles; and inputting the risk value as a key state feature into a double-depth Q network based on attention mechanism enhancement, focusing key information through a feature attention distribution mechanism, and generating an optimal driving decision in combination with a multi-target reward function. Compared with the prior art, the method solves the problems of insufficient quantification of uncertainty factors, incomplete risk assessment and low decision-making efficiency of automatic driving in a complex dynamic environment, and significantly improves the risk perception capability and decision-making safety of the automatic driving vehicle.

The invention provides a stiffening plate production risk perception and identification system based on big data analysis, and relates to the technical field of stiffening plate risk perception, and the system comprises a data collection module which is used for an industrial Internet of Things terminal to collect and preprocess multi-source data in a stiffening plate production process; the feature extraction module is used for extracting features related to the production risk of the stiffening plate; the classification module is used for dividing equipment fault sub-classifications; the risk model construction module is used for training a stiffening plate production risk analysis model under the sub-classification through the first feature set under each sub-classification; the correlation model construction module is used for training a correlation analysis model of the stiffening plate production risk; the risk prediction module is used for inputting real-time data into each trained risk analysis model and obtaining a final risk probability according to the association analysis model; the early warning module is used for triggering the early warning module when the final risk probability exceeds an early warning threshold value; and an accurate risk identification solution is provided for intelligent manufacturing of the stiffening plate.

The invention relates to a tractor test data acquisition and fault diagnosis system, which belongs to the technical field of agricultural machinery, and comprises a multi-source heterogeneous data acquisition module for performing real-time data acquisition on the working state of the whole tractor through a sensor group deployed at a key part of the tractor; collecting a vibration signal measured by an engine body acceleration sensor; acquiring a real-time rotating speed measured by an engine rotating speed sensor; load torque measured by a torque sensor of the power output shaft is collected; the current instruction rotating speed obtained by the vehicle-mounted control unit is collected; the collected original data are transmitted to the dynamic state evaluation module in real time; the dynamic state evaluation module is used for receiving the multi-dimensional data; carrying out fusion analysis on the multi-dimensional data based on an instantaneous impact index model; according to the method, the system obtains the comprehensive and robust perception capability for the risk, the early-stage and composite impact risk can be accurately recognized, and the misjudgment rate caused by single index fluctuation is greatly reduced.

The invention discloses a cross-border e-commerce information risk analysis method combined with cloud computing, and relates to the technical field of e-commerce information security. The method comprises the following steps: accessing multi-source data by adopting a cloud edge collaborative architecture, and carrying out data fingerprint identification to generate a cross-border unique feature code; performing dynamic time warping on the transaction behavior, and capturing a time sequence abnormal mode of the cross-border transaction; constructing a transaction space-time diagram, and capturing an abnormal space-time mode in the cross-border transaction by the space-time feature code; constructing a risk assessment screening model to carry out anomaly detection screening; constructing a risk assessment analysis model, and scoring the credit of the cross-border commercial tenants; and establishing a grading response mechanism, and automatically generating a qualified report. Through distributed data acquisition, real-time feature engineering and a self-adaptive deep learning model, a cloud-edge-end three-level risk perception system is utilized, a cross-border feature cross validation algorithm is provided, the problem that data standards of multiple countries are not uniform is solved, and cross-border e-commerce information processing efficiency is improved.

The invention relates to the field of agricultural data analysis, in particular to a straw returning effect analysis method combining soil nutrients and crop yield, and the method comprises the steps: collecting a multi-source feature data matrix for risk assessment; performing conjoint analysis on the crop yield data and the soil attribute data to obtain a static nitrogen deficiency risk amplification factor; performing difference analysis on the vegetation index data, the yield data and the cluster centroid to obtain a dynamic growth response asymmetric regulatory factor; carrying out fusion evaluation on the static nitrogen deficiency risk amplification factor and the dynamic growth response asymmetric regulation factor to obtain risk perception asymmetric distance measurement based on yield data; the clustering process of risk perception asymmetric distance measurement based on yield data is subjected to zoning optimization, and an optimal management zoning result for formulating a variable fertilization strategy is obtained, so that the nitrogen deficiency risk of crops in the seedling stage is reduced, and the nutrient utilization efficiency and the yield stability are improved.

The invention belongs to the technical field of software management, and particularly relates to a cross-platform repair method and system based on a large language model.The method comprises the steps that a cross-platform package manager interface is used for conducting static analysis on a dependency list of a target software project, a cross-platform dependency tree is obtained, and the target software project is repaired based on the cross-platform dependency tree; respectively injecting lightweight monitoring probes into at least two operating system platforms, and collecting dynamic tracking data of each dependency component in the dependency tree; fusing the dependency tree and the dynamic tracking data, and establishing a cross-platform security map comprising a dependency calling edge, a version conflict edge and a platform vulnerability association edge; according to the method, the lightweight monitoring probe is injected into the multi-operating system platform, the loading sequence, the function call sequence and the memory layout difference of the dependent components are dynamically collected, and the cross-platform security map is constructed in combination with the static dependency tree, so that the specific potential safety hazard of the platform can be accurately identified, and the comprehensiveness and the accuracy of cross-platform risk perception are remarkably improved.

The invention discloses a wind power plant intelligent simulation acquisition test method and system, and relates to the technical field of wind power plant intelligent acquisition and state prediction control, and the method comprises the steps: obtaining the operation state parameters of a wind turbine generator, and constructing a twinborn prediction model based on a time sequence. And comparing a model prediction value with a measured value to calculate an operation state error, and estimating prediction uncertainty. And dynamically adjusting the sampling frequency of the sampling channel according to the prediction uncertainty and controlling test execution. According to the method disclosed by the invention, a smart wind power plant simulation acquisition test system from data driving to state modeling to risk perception to dynamic regulation and control to multi-node collaborative integration is realized. According to the technical scheme, independent technical contributions are provided, link-by-link logic closed loops are constructed, a breakthrough is made from a traditional static acquisition mode, a digital wind field acquisition test system with predictability, self-adaptability and linkage response capability is formed, and the monitoring efficiency, the risk control granularity and the system resource utilization rate are improved.

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 low-voltage uninterruptible operation site visual monitoring optimization method based on an edge computing architecture, and the method comprises the following steps: constructing an operation scene graph analysis module, obtaining the distribution of operators, the operation type and the equipment voltage class information of each monitoring region, generating a risk class mapping graph, and carrying out the operation scene graph analysis module; positioning and initial identification of a high-risk area are realized; generating a risk perception matrix; constructing a priority queue of computing resources and risk levels; acquiring identification precision, processing delay and behavior capture integrity indexes, and establishing a task feedback mechanism; constructing a scheduling logic credibility evaluation module, analyzing a scheduling instruction, marking and recording an abnormal path, generating a scheduling behavior record library, and predicting and early warning a priority inversion risk; and iteratively optimizing scheduling model parameters, and dynamically updating risk scores and scheduling logic. According to the method, high-risk task resources are guaranteed preferentially, priority reversal is avoided, and the stability, robustness and safety response timeliness of the system in a complex operation environment are remarkably improved.

The invention provides a software monitoring method, system and device based on byte code replacement and risk awareness and a medium, and belongs to the technical field of software security. The method comprises the steps that a special annotation mark in a source code is scanned, a test code entry point is recognized, and metadata information is extracted; performing encryption processing on the metadata and generating a registry file with a digital signature; environment safety data are collected through four monitoring dimensions, and a comprehensive risk score is calculated; generating a grading fusing instruction according to the score; and finally executing the fusing operation of byte code replacement or thread sandbox isolation. According to the invention, accurate identification and real-time protection of the leakage risk of the test code are realized; through a multi-dimensional monitoring and hierarchical fusing mechanism, the system security is ensured, and the influence on the service performance is minimized; and byte code replacement and thread isolation technologies are adopted, so that security protection can be realized without restarting the application, and the availability and the operation and maintenance efficiency of the 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 urban safety monitoring, and discloses an unmanned aerial vehicle and bee colony coordinated urban safety monitoring and early warning system, which comprises a bee colony initialization module used for acquiring historical risk heat data, dividing honeycomb units and determining the number of unmanned aerial vehicles in each layer; the route planning module is used for generating a whistle ring layer grid route and an inspection layer clustering route and managing the standby state of a near inspection layer; the risk modeling module is used for constructing a risk projection matrix, generating a spatial risk level and calculating a preliminary risk index; the bee gathering response module is used for activating the micro unmanned aerial vehicle based on the initial risk index, forming a collaborative bee gathering unit and calculating a secondary risk index; the early warning level judgment module is used for fusing the risk index, the target density and the speed information and determining an early warning level; and the scheduling processing module is used for calculating ground resource priorities and completing guidance processing. According to the invention, an efficient risk perception and linkage disposal system of urban air-ground cooperation is realized.

The invention provides a low-altitude safety risk early warning method and system based on target airspace time-space characteristics, and relates to the technical field of risk identification, and the method comprises the steps: obtaining low-altitude target scene video data, carrying out the initial target detection to obtain a candidate region, and calculating a risk early warning level based on a historical risk event library; extracting space and time sequence features to generate an attention weight map for feature enhancement; a self-adaptive Kalman filtering algorithm is adopted to realize risk perception tracking; and outputting a motion state parameter and a risk prediction result. According to the method, the risk point of the low-altitude dynamic target can be accurately identified, the risk early warning precision is improved, and real-time risk perception of a complex scene is realized.

The invention belongs to the technical field of stage equipment boundary safety protection control, and particularly relates to a variable constraint control method for stage equipment based on risk perception and a dynamic safety domain. The characteristic that stage equipment is usually in a fixed application scene in the performance process is utilized, the inherent safety level and active protection capacity of a stage equipment system are greatly improved through tight combination of real-time collection and variable constraint, and therefore safer, more accurate and more smooth control is achieved. According to the method, a construction algorithm is embedded in software, a dynamic region division and segmentation threshold adjustment strategy is utilized, and a multi-stage braking redundancy cooperation mechanism is triggered, so that the anti-interference capability and fault tolerance performance of the control system are effectively enhanced, the implementation cost is low, a large amount of manpower and material debugging can be saved, and the system is suitable for large-scale popularization and application. And a high-precision and high-robustness safety control scheme is provided for large stage machinery such as a seat vehicle platform and a rotating stage.