189 results about "Risk matrix" patented technology

The invention relates to an urban underground passage fire risk dynamic monitoring and early warning method and system, and solves the problems that single index evaluation is difficult to reflect real risks and a fixed threshold cannot adapt to complex working conditions, and the method comprises the steps: building a four-level threshold library according to risk values, and dynamically adjusting a threshold boundary in combination with a scene; when the risk value is lower than a preset threshold value, the monitoring state is continued; when the risk value exceeds a threshold value in the current scene, triggering a corresponding early warning and pushing the early warning to a command center, coupling historical fire data and dynamic attribute matrix data, and calculating the fire probability of each grid; the smoke diffusion path and the structural fire endurance are simulated by calculating fluid mechanics, the fire consequence severity is quantified, the fire probability of each grid and the fire consequence severity are fused by adopting a fuzzy comprehensive evaluation method to generate a risk matrix, and a high-level risk area is marked. The method has the following technical effects that dynamic monitoring and early warning of the urban underground passage fire risk are realized, and the evaluation accuracy and the early warning reliability are improved.

The invention relates to the technical field of data processing, and discloses an anti-oxidation barrier layer performance degradation prediction method and system based on multi-source data. The method comprises the following steps: collecting and preprocessing multivariate data of a barrier layer environment, and constructing three-dimensional tensor data; indexes such as oxygen blocking efficiency and anti-permeability performance are calculated, and performance time sequence data are obtained; extracting multi-dimensional features and fusing the multi-dimensional features through an automatic encoder; applying a hybrid deep learning model to predict performance degradation; analyzing degradation curve characteristics, and executing mode clustering to obtain a risk matrix; and optimizing a maintenance decision scheme based on risk assessment. According to the invention, the hybrid deep learning model is utilized to capture the dependency relationship of time and space dimensions at the same time, and the prediction precision is significantly improved; based on degradation mode identification and risk level evaluation, accurate multi-scene maintenance decision is realized, environmental risk is reduced, and maintenance cost is optimized.

The invention relates to the technical field of construction risk identification, and discloses a bridge construction hazard source identification and risk analysis method and system. The method comprises the following steps: collecting geological, adjacent-channel and adjacent-river and support load parameters to establish a danger information base; performing three-dimensional mapping to generate an associated risk matrix; extracting key node data to form a stress distribution diagram; fusing the process progress to construct a safety margin curve; calculating a critical point to generate an early warning threshold table; and quantitatively verifying the prevention and control effect to form a closed-loop optimization strategy chain. According to the method, the bracket-load-environment three-dimensional association risk matrix and the dynamic safety margin index curve are established, and the risk dynamic identification, accurate early warning and closed-loop optimization of the whole construction process are realized, so that the safety management level of bridge bracket cast-in-place construction is improved, and the probability of construction safety accidents is reduced.

The invention discloses an intelligent induction and early warning system for poisonous gas in a limited space, and relates to the technical field of gas induction and early warning. A three-dimensional structure model of the limited space is constructed, and non-uniform grid division is carried out based on barrier characteristics; multiple types of toxic gas sensing units are arranged in the grids, and gas concentration data are collected in real time; constructing a toxic gas three-dimensional cloud picture, and generating a dynamic weight field based on the concentration gradient; fusing operator positioning and trajectory prediction information, and establishing an individual risk exposure model; calculating a space-time intersection of the toxic gas cloud picture and the high-risk track area to form a fusion risk matrix; realizing linkage early warning control of a region level and an individual level through multi-level dynamic threshold judgment and thermal block clustering identification; the system can realize accurate perception and active response of the toxic gas gathering trend and personnel exposure risk in the complex limited space, has the technical advantages of high sensitivity, high intelligence and low false alarm, and is suitable for various limited space scenes such as chemical engineering, tunnels and mines.

The invention discloses a multi-unmanned aerial vehicle path planning method for power transmission line inspection, and belongs to the field of path planning. According to the method, mobile edge computing (MEC) and unmanned aerial vehicle airborne intelligent decision making are combined, and the method comprises the steps that firstly, an MEC server synchronizes a cluster state and issues a global demand matrix; the unmanned aerial vehicle dynamically updates the global risk matrix through real-time environment perception; and fusing the real-time state, the risk and the demand matrix of each unmanned aerial vehicle airborne unit, and autonomously making a decision to generate an optimal short-term planning path. Before the path is executed officially, the path is reported to an MEC server for cooperative conflict detection, and the unmanned aerial vehicle can execute flight only after it is confirmed that no conflict exists. According to the method, the unmanned aerial vehicle cluster is endowed with autonomous perception and rapid avoidance capability for dynamic unknown risks, path planning becomes a real-time evolution intelligent decision-making process, and the safety of cluster collaborative operation is ensured through centralized conflict detection, so that the efficiency, robustness and reliability of a power transmission line inspection task are remarkably improved.

The invention relates to the technical field of chemical enterprise production management, in particular to a chemical enterprise safety production informatization management system which comprises a sensing network layer architecture, a digital twin management platform, a risk management and control and decision center, a process closed-loop management center and an emergency cooperative processing platform. According to the chemical enterprise safety production informatization management system and method, through construction of a chemical knowledge graph, on the basis of a dynamic risk matrix and an AI prediction model library, equipment remaining service life prediction, process tiny anomaly capture and accident consequence simulation processing are realized; meanwhile, a root cause analysis module is used for associating low-level alarm and traversing a knowledge graph to automatically infer a fault root, physical equipment and a virtual model are bound through an entity-model mapping engine, the model state is dynamically updated in combination with a real-time rendering technology, and technological parameter optimization, accident deduction and scheme verification can be completed in an analogue simulation engine; and the management intuition and the decision-making scientificity are effectively improved.

The invention discloses an automatic processing device and method for PCB lamination, and the method comprises the steps: depositing an erodable film on a lamination material layer and arranging a cavity concentric with a through hole array before the lamination of a PCB, and collecting acoustic resonance frequency data to obtain a reference matrix; dynamic dot frequency tracking processing is carried out on the multi-point acoustic time sequence signals in the pressing heating stage, and a through hole instantaneous acoustic track library is obtained; performing coupling judgment on time-temperature-pressure ternary working condition data according to the trajectory library to obtain a blockage risk matrix; carrying out time delay and gradient adjustment on a pressing temperature and pressure curve based on the risk matrix; and carrying out three-dimensional inversion processing on the whole board broadband acoustic spectrum collected before cooling, calculating through hole filling rate distribution and judging the quality of the board. According to the scheme, the resin filling state of the micro through hole can be monitored in real time in the PCB pressing process, an acoustic detection channel is constructed through the erodable film and the cavity structure, early warning and active intervention of the blocking risk are achieved, and the pressing quality is ensured.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

The invention relates to the technical field of charging pile intelligent control, and particularly discloses a charging pile intelligent control system with multiple safety protections, which is provided with a charging pile multi-modal data acquisition module, a monitoring risk matrix construction module, a game strengthening control strategy generation module and a charging pile defense network diagram output module. The problems that traditional safety monitoring depends on various sensors, only specific parameter detection can be carried out, complex risk scenes faced by the charging piles are difficult to fully cover, the capacity of monitoring some comprehensive faults or potential hidden dangers is limited, and along with the increase of the number of the charging piles, a traditional management mode is difficult to meet operation and maintenance requirements are solved. The method is beneficial for optimizing resource allocation, enhancing the capability of the charging pile system for resisting external attacks and internal faults, and can dynamically adjust and adapt to complex and variable operation scenes according to real-time change conditions, so that stable and efficient operation of the charging pile is guaranteed, and the friendliness to a power grid is improved.

The invention discloses an information comprehensive security protection method, system and device for data security, and relates to the technical field related to data security protection.The method comprises the steps that multi-source assets are comprehensively detected, and a dynamic asset ledger is established; traversal is carried out by using the industry security baseline, asset network vulnerabilities are identified, and a difference vulnerability data list is established; constructing a double-risk matrix including compliance weight, attack and defense weight and comprehensive risk value; and analyzing a network behavior map through a situation awareness engine, identifying an abnormal behavior and an attack path, determining a risk event type and an event level, matching a collaborative response path, and sending collaborative response information according to the collaborative response path for connecting a multi-role collaborative department to perform security collaborative isolation processing. The technical problems of insufficient asset visibility, compliance and attack and defense separation and low response efficiency in the prior art are solved, and the technical effects of comprehensive dynamic asset management and control, accurate quantification of security risks and intelligent collaborative quick response are achieved.

The invention discloses an enterprise digital operation real-time data analysis method based on multi-source data fusion, relates to the technical field of data processing, and solves the problems that firstly, an enterprise comprehensive benefit index is difficult to analyze and predict; secondly, it is difficult to embed the knowledge graph into a vector space to construct a dynamic graph neural network; then, the equipment abnormal offset, the supply chain abnormal offset and the customer activeness are difficult to analyze, a risk matrix is constructed, and a comprehensive benefit index decline propagation path is identified; service link resources are difficult to dynamically allocate according to an analysis result of the risk matrix; and finally, a data analysis result is difficult to display visually. According to the method, enterprise multi-source data is collected and preprocessed, a three-dimensional vector space and a dynamic graph neural network are constructed, enterprise benefits and risks are analyzed and predicted in combination with multiple technologies, resources of business links are dynamically allocated, and a data analysis result is visually displayed.

The invention relates to the technical field of data security, in particular to a quantum cryptography migration resisting method and system for a power system, and the method comprises the steps: carrying out the quantum threat risk assessment of a communication link based on the layering and partitioning architecture features of the power system; obtaining attribute data of the encryption component, and performing parallel migration risk analysis on the encryption component in combination with a quantum threat risk assessment result to generate an anti-quantum migration risk matrix; carrying out compatibility evaluation on a traditional public key algorithm and an anti-quantum cryptography algorithm based on algorithm features, and constructing a double-track encryption migration strategy; dynamically adjusting a double-track weight ratio, and constructing a double-track encryption hierarchical migration strategy based on a control hierarchy to which a communication link belongs; and executing the double-track encryption layered migration strategy and monitoring the migration effect, and performing self-adaptive correction on the double-track encryption layered migration strategy based on the migration effect. According to the method, a quantum threat assessment and double-track encryption layered migration mechanism is constructed, so that safe and smooth migration of the power system under the threat of quantum computing is realized.

The invention discloses an automatic quality monitoring system based on a nickel hydrazine nitrate manufacturing technology, and relates to the technical field of nickel hydrazine nitrate manufacturing. The system has the functions of integrating a multi-wavelength filter wheel module, a process stability monitoring module and the like, and full-parameter real-time monitoring of the reaction process is realized. A Savitzky-Golay filtering algorithm is adopted to calculate a reaction rate, an XGBoost model is combined to predict a quality grade, and a hierarchical control strategy is designed to cope with different quality risks. And the risk assessment module simulates a crystal growth process by using a reaction kinetic model, identifies key risk factors, constructs a risk matrix to divide production areas, and generates an assessment report. Spectral data are corrected through a three-dimensional compensation matrix, the type of a quality problem is judged by adopting a double-layer fusion algorithm, and closed-loop control is realized in combination with a PID controller and an emergency emission program. A monitoring interface displays key parameters and early warning information in real time, dynamic adjustment of process parameters is supported, the production process is optimized, and the product quality and the production efficiency are improved.

The invention discloses a multi-dimensional safety fusion risk assessment method and related equipment, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining a first data set and a second data set of a target system; performing functional security risk analysis, expected functional security risk analysis and information security risk analysis on the first data set to identify a hazardous event of the target system; based on the second data set, establishing a dynamic scene model, and obtaining a threat level of the hazard event through the dynamic scene model; constructing a multi-dimensional risk matrix according to the hazard event, the dynamic scene model and the threat level; and obtaining a risk value of the hazard event based on the multi-dimensional risk matrix, and performing risk grade division according to the risk value to obtain a multi-dimensional security risk assessment result of the target system.

The invention relates to the technical field of new energy power system instability risk assessment, in particular to a new energy power system frequency instability risk assessment method based on a heterogeneous graph attention network. The method comprises the following steps: constructing a wind power-photovoltaic heterogeneous graph model according to a power grid topology; performing combined sampling on different operation modes and anticipated disturbances, and determining corresponding input feature vectors; calculating a frequency stability index value label of the sample set; expanding the training set through an active learning iteration process, and carrying out model training; and inputting the collected operation data into the trained heterogeneous graph attention model, outputting a frequency stability index value, and evaluating the system frequency instability risk in combination with the risk matrix. By adopting the frequency instability risk assessment method for the new energy power system based on the heterogeneous graph attention network, the problem of low efficiency of risk assessment in a high-dimensional uncertain scene is solved, and the heterogeneous graph attention network can reflect the influence of different types of devices at different positions and disturbance types on the dynamic frequency of the system; and the accuracy of frequency instability risk assessment is improved.

The invention discloses an anti-misoperation intelligent control method and system based on centralized control integration, and relates to the technical field of electric power automation, and the method comprises the following steps: obtaining first data of a three-party centralized control system, and constructing a dynamic instruction pool and an operation sequence diagram; based on the dynamic instruction pool and the operation sequence diagram, checking operation consistency through a space-time coupling protocol, and constructing a power flow constraint network in combination with a compliance checking rule and power grid operation data; generating risk early warning by using a mixed integer programming method and an N-1 risk matrix according to the power flow constraint network; and generating an anti-error decision according to the risk early warning, and feeding back the anti-error decision to the three-party centralized control system. The method is used for solving the problems that operation instruction control of a traditional anti-misoperation system lacks dynamics and consistency check is weak.

The invention provides a disaster evolution path deduction method and system based on a multi-modal space-time atlas, and the method comprises the steps: obtaining the attribute data of a shopping mall, and constructing a deduction static atlas based on the attribute data of the shopping mall; the deduction static map initializes a state based on a set instruction, updates based on physical simulation, and outputs a deduction dynamic map; generating a comprehensive risk matrix based on the deduction static atlas and the deduction dynamic atlas, and clustering the comprehensive risk matrix to obtain a classification region cluster; performing physical prediction and digital prediction according to the classification region cluster and the deduction dynamic map, respectively obtaining physical determination and judgment data and digital determination and judgment data, and obtaining spreading probability data through the physical determination and judgment data and the digital determination and judgment data; according to the method, re-deduction is carried out through an intervention instruction, a deduction method capable of carrying out step-by-step deduction and backtracking deduction is provided, a deduction dynamic map is fused, and the deduction accuracy is improved in a physical and digital cooperation mode, so that making of a better emergency plan is assisted.

The invention relates to the technical field of automatic driving risk analysis, in particular to a traffic risk analysis method and analysis device based on a graph neural network, and the method comprises the steps: sequentially carrying out the coding of a graph encoder and the coding of an attention mechanism based on the obtained historical track information of an intelligent agent and the lane line information of a map, and obtaining an attention mechanism; obtaining an attention mechanism coding result; decoding by using a cross attention mechanism based on an attention mechanism coding result; and performing prediction trajectory decoding by using a multi-layer perceptron based on a cross attention mechanism decoding result, performing decoding by using a deconvolution network based on a decoding result, calculating risk matrix loss by using a probability loss function, and performing model training and verification to obtain a risk analysis model based on a graph neural network so as to predict traffic risks. Therefore, the problems that the risk analysis depth of traffic participants is limited and future intentions and potential risks of the traffic participants are difficult to accurately describe by traffic risk analysis methods in related technologies are solved.

The invention discloses a mountain tourism route planning method based on weather and natural disasters, and the method comprises the steps: 1, building a Markov game model based on POMDP, combining Mama multi-modal feature compression and RAG semantic enhancement, designing a dynamic risk matrix integrating geological disaster probability, weather trend and ecological sensitivity, and providing partial observable environment state perception; 2, establishing a Nash equilibrium constraint optimization model, sharing an extreme weather strategy through federal learning, introducing an NSGA-II-Actor-Critic architecture to optimize unmanned aerial vehicle inspection and path adjustment, and triggering dynamic communication when rainfall exceeds a threshold value; 3, fusing a security and experience income design reward function, processing continuous action planning by using improved DDPG, and accelerating multi-target Pareto frontier convergence in combination with NSGA-II; 4, constructing a user weather sensitivity portrait, and dynamically adjusting weight balance income and AR simulation preview preference; and 5, based on a GIS closed-loop decision engine, performing agent and environment feature complementation, recording an emergency log by a block chain, and constructing a disaster case library by meta-learning.

The invention relates to the technical field of artificial intelligence, and provides an intelligent insurance matching method and system combining a user portrait and a clause knowledge base. The method comprises the following steps: constructing a first portrait of a user based on a user data set and optimizing to generate a second portrait; extracting features from the insurance clause knowledge base in a multi-dimensional manner to construct a product portrait; performing demand coverage risk evaluation on the product portrait based on the second portrait of the user to generate a demand coverage risk matrix; performing guarantee conflict risk evaluation to generate a guarantee conflict risk matrix; introducing an underwriting risk analysis channel, and generating an underwriting risk matrix in combination with the second portrait of the user; and fusing the three types of risk matrixes to perform multi-level optimization matching, and generating an insurance optimization matching graph, so as to solve the technical problem of significant deviation between the recommendation result and the actual risk preference and guarantee demand of the user, realize dynamic and accurate matching of the insurance product and the user demand, reduce the guarantee conflict risk, improve the correlation of the recommendation result, and improve the user experience. And the technical effect of personalized insurance configuration requirements is met.

The invention relates to the technical field of gas turbines, in particular to a heavy gas turbine safety protection monitoring method and device fused with fuzzy evaluation. According to the heavy gas turbine safety protection monitoring method, on the basis of establishing a heavy gas turbine risk matrix, a multi-factor evaluation model is constructed by fusing with fuzzy comprehensive evaluation, and a heavy gas turbine multi-safety protection strategy is formed by quantifying multi-factor coupling influence and uncertainty and determining risk factor levels; through the heavy gas turbine control platform and the heavy gas turbine protection system platform, strategy-to-logic conversion is achieved, through debugging and iteration, protection and monitoring of the whole gas turbine system are achieved, and operation safety is guaranteed.

The invention provides a drought and flood sudden change prediction method fusing a soil moisture content and rainfall coupling model, and relates to the technical field of drought and flood sudden change. Comprising the steps that layered soil moisture content, high-frequency rainfall and auxiliary data are collected and preprocessed, and a soil moisture content-rainfall coupling coordination initial value is constructed and corrected; traversing drought and flood windows by using the standardized soil moisture content-rainfall coupling coordination index sequence, and determining an optimal window; carrying out nonlinear correction, judging a sudden turning event according to a corrected threshold value, and outputting a drought and flood sudden turning prediction result; and calculating a drought and flood sudden change duration prediction value Tint and a soil moisture content rainfall mutation index SPIef according to a drought and flood sudden change prediction result, a soil texture coefficient and the like, constructing a risk matrix, outputting an early warning level and a response suggestion in combination with the vulnerability of the disaster-bearing body, and outputting early warning information. According to the method, the optimal window is determined and optimized, drought and flood sudden turning is predicted, the scientificity and accuracy of prediction are guaranteed, and the practical application value is enhanced.

The invention provides an electric power project data processing method and system, and relates to the technical field of computers.The method comprises the steps that firstly, structured measurement data and unstructured knowledge data are obtained; secondly, performing feature extraction on the unstructured data, generating a text coding vector and an image feature vector, and calculating a sliding window time sequence correlation coefficient of the structured data; and then, fusing the features to construct a cross-modal dynamic alignment factor, inputting the cross-modal dynamic alignment factor, a dynamic line loss rate and an equipment health degree vector into a space-time knowledge fusion model, and outputting an equipment risk matrix and a line loss anomaly probability. And then, inputting the technical document into the electric power cognition large model, analyzing a model result in combination with a professional prompt template, and retrieving associated data to generate a governance scheme. According to the scheme, after finite element simulation verification of the digital twin module, if the line loss variable quantity is lower than a dynamic threshold value, the line loss variable quantity is pushed to a power management system to be executed. According to the method, accurate risk assessment and intelligent decision making of the power system can be realized.

The invention discloses a network security risk assessment method and system based on a data model. The method comprises the following steps of: firstly, collecting equipment logs, operation records, medical data access records and alarm information from key nodes of a hospital network, such as network equipment, a server, safety equipment and a medical information system in multiple channels, cleaning, de-weighting and normalizing, and then analyzing network topology, equipment safety, data sensitivity and user behaviors; building a model by using K-means clustering and a support vector machine, and training and optimizing by using historical data; and combining an analytic hierarchy process, vulnerability scanning and time sequence analysis to calculate asset value, vulnerability score and threat possibility, and obtaining a risk level according to a risk matrix method. The system comprises multiple layers of architectures such as data acquisition, processing, model construction, risk assessment decision, display interaction and the like, and the layers cooperate with each other. The system accurately adapts to a hospital network, comprehensively considers medical data characteristics, builds a foundation for hospital network safety management, and protects medical services.

The invention discloses an intelligent early warning system for an industrial safety production situation, particularly relates to the field of industrial network safety, and is used for solving the problems of real-time splicing of hidden abnormal sequences and quantification of cross-subnet dangerous potential energy in an industrial network in which variable topology and hidden channels coexist. Tracking the whole network dynamic state; the transient waveform is decomposed by a multi-scale wavelet packet to form a micro-impact fingerprint index, and abnormity is accurately detected; calculating a time slot compression potential and an energy jump amplitude in combination with gap virtual node interpolation and energy vector interpolation, and identifying hidden threats in a time domain and an energy domain respectively; extracting an intersection criticality through double logarithmic spiral mapping, driving a multi-body energy diffusion model, and drawing a danger potential value propagation gradient in real time; through comparison of the risk matrix and the dynamic inflection point criterion, the early warning advance and the coverage subnet range are significantly improved, the linkage trigger chain outbreak is avoided, the anomaly recognition sensitivity is improved, the link repair period is shortened, and the false alarm rate is reduced.

The embodiment of the invention discloses a mixed driving risk identification method and system for a road interleaving area and a computer storage medium, and the method comprises the steps: respectively quantifying the contribution value of each index to a traffic accident and a traffic conflict for each index in a multi-dimensional risk identification index system; sorting the indexes according to the contribution values to obtain a traffic accident contribution value sorting result and a traffic conflict contribution value sorting result; according to the traffic accident contribution value sorting result and the traffic conflict contribution value sorting result, indexes of which the contribution values to the traffic accidents and the traffic conflicts exceed a preset threshold value are selected and combined to form a high-contribution index system; screening the indexes of which the correlation among the indexes is smaller than a threshold value from the high-contribution index system; training a double-risk identification model based on the screened high-contribution index system; and constructing an accident conflict two-dimensional risk matrix so as to detect real-time traffic flow data by using the trained double-risk identification model and the accident conflict two-dimensional risk matrix.

The invention provides a mixing pile online monitoring method and device and electronic equipment, and relates to the technical field of mixing pile monitoring, and the mixing pile online monitoring method comprises the steps that drill rod inclination angle data, slurry flow data, cement consumption quality data and drill rod displacement data are acquired; coordinate transformation is conducted on the drill rod dip angle data and the drill rod displacement data, and the actual pile depth of the drill rod is calculated; performing double-source verification on the slurry flow data and the cement consumption quality data to generate effective cement consumption; constructing a pile body quality risk matrix model based on a preset pile depth threshold value, a cement consumption safety standard and a drill rod speed specification; inputting the actual pile depth of the drill rod, the effective cement dosage and the real-time drilling speed into the risk matrix model, and outputting a risk grade index; and when the risk grade index exceeds a preset threshold value, a regulation and control signal containing a drilling speed correction instruction, a grouting amount compensation instruction or an equipment locking instruction is generated. Accurate control over the pile depth is achieved, the cement consumption deviation is obviously lower than the industrial standard, and the risk of jerry work and material reduction is reduced.

The invention relates to the field of emergency response, in particular to an emergency lighting evacuation path dynamic planning method and device based on multi-source event triggering, and the method comprises the steps: obtaining multi-source event data in real time; according to the multi-source event data, an information fusion algorithm is adopted for analysis and fusion, and a comprehensive event level comprehensively representing disaster situation development and personnel conditions in the building is generated; dynamically distributing risk weight values for each path node in the building by combining the comprehensive event level and pre-stored building structure topological information, and generating a dynamic risk matrix changing along with time; according to the dynamic risk matrix, performing real-time calculation on personnel at different positions in the building to generate personalized dynamic evacuation paths; and converting the calculated dynamic evacuation path into a control instruction, driving the intelligent emergency lighting lamp to change the indication direction, and guiding the personnel to evacuate along the planned path in a dynamic light and shadow mode. The dynamic risk matrix generates personalized optimal paths for persons at different positions, and accurate evacuation is performed from the perspective of global optimization.

The invention discloses a power network security defense method and device based on digital twinning, and belongs to the technical field of network security, and the method comprises the steps: obtaining the power data of a power network in real time; constructing a digital twinborn simulation environment of the power network according to the power data; extracting electric power characteristic data in a digital twinborn simulation environment; performing layer-by-layer risk analysis on the power feature data according to each layer of hyperplane function of a preset decision tree, and determining a risk type and a risk matrix of the power network; in a digital twin simulation environment, reconstructing a propagation path of the current attack according to the risk matrix; and generating a security defense strategy of the power network according to the risk type and the propagation path. Therefore, by implementing the method and the device, the problem of low accuracy of power network risk detection in the prior art can be solved.

The invention relates to the technical field of risk management, in particular to a BP neural network-based special equipment operation fault prediction method and system, and the method comprises the steps: collecting the operation state data of a sensor of special equipment at a fixed position, and forming an operation record of an equipment assembly in combination with the identity information of a moving part on the equipment; key operation data, environment parameters and equipment structure attributes are collected, and dynamic stress parameters are output; state correlation analysis is carried out on the operation records of the equipment components, and equipment health trend indexes are extracted; performing data fusion on the health indexes and the stress parameters to form feature vectors for operation and maintenance analysis; inputting the feature vector into a preset risk prediction model, and outputting an operation and maintenance index of the equipment component; and in combination with a model output result and a preset risk matrix and management rule base, a maintenance scheduling suggestion and an operation intervention instruction of the special equipment are automatically generated and are used for assisting a management system in implementing equipment life cycle management and risk control.