1571 results about "Risk rating" patented technology

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

The invention relates to a power construction monitoring method, system and device and a medium, and the method comprises the steps: carrying out the space-time calibration and anti-interference processing through collected construction images, temperature and dust data, and carrying out the fusion to generate a multi-source fusion data set containing environment information; performing feature extraction on the multi-source fusion data set based on a neural network model, synchronously identifying an action track of a constructor and equipment operation parameters, and constructing a feature map set associated with behaviors and equipment states; performing action and equipment anomaly conjoint analysis on the feature map set, and generating a construction risk event set by analyzing human body joints and temperature feature detection; performing spatial distance calculation and time monitoring on the risk event, and quantifying the coupling risk level; and based on the dynamic risk threshold, a grading early warning instruction is triggered, and precise response of field personnel warning, equipment power failure and evacuation guidance is realized. According to the method, the technical problems of low construction risk identification precision, equipment anomaly detection lagging and risk assessment deficiency in a complex environment are solved.

The invention relates to the technical field of building construction, in particular to a building construction safety intelligent early warning system based on multi-sensor fusion and deep learning. Comprising a multi-source sensing unit; an intelligent fusion unit; a depth analysis unit; and a dynamic response unit. According to the method, through a mixed deep learning model, personnel-equipment-environment space association in a 1m * 1m * 0.5 m space grid is extracted through an improved U-Net network, and a space risk association map is output; modeling data of 10 sampling periods by using a bidirectional LSTM network, and outputting a short-term prediction value; and carrying out weighted fusion through an attention mechanism to form a risk feature vector, and removing invalid anomalies in cooperation with parameter anomaly judgment and cross validation. And then a risk grade evaluation module introduces multiple coefficients to calculate a risk grade index, and a grid diffusion range is delimited according to grades, so that real-time identification, quantitative evaluation and range pre-judgment of construction safety risks are realized, and the problem that risk identification evaluation lacks scenarized accuracy and comprehensiveness is solved.

The invention discloses a deep learning-combined power transformer partial discharge signal extraction and diagnosis method. The method comprises the following steps of S1, setting a multi-channel synchronous acquisition system in a power transformer body area to acquire a multi-dimensional original partial discharge data set; s2, preprocessing the acquired multi-dimensional original partial discharge data set; s3, performing time alignment and amplitude matching on the processed signal, and dividing the processed signal into a sliding time window to construct a standard input tensor; s4, constructing an attention enhancement model fused by the convolutional neural network and the bidirectional gating circulation unit; s5, performing supervised training on the attention enhancement model by using the labeled sample; s6, inputting the real-time signal into the training model, and outputting a discharge type label; s7, risk grade evaluation is carried out in combination with statistical characteristics; and S8, generating a structured diagnosis report and uploading the structured diagnosis report to a monitoring platform. According to the invention, multi-source signals and a depth model are fused, and intelligent diagnosis and risk assessment of transformer partial discharge are realized.

The invention provides an intelligent customer risk assessment system and method based on a large language model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining multi-modal data of a customer, carrying out the preprocessing, and extracting structured and unstructured features; constructing a hierarchical risk knowledge system, and realizing adaptive evolution of the knowledge system through a graph neural network and a generative model; constructing an initial negative sample library, and constructing a negative sample database in combination with a non-risk mode labeled by an expert and derivative layer analysis; optimizing the large language model by adopting a strong supervision, weak supervision and reinforcement learning cooperative training mechanism under each classification according to the customer type; mining risk features in a text by using the optimized large language model, processing multi-modal data through a multi-level attention network, and generating a positioning report including contradiction type coding, service influence dimension evaluation and risk level quantification; the accuracy, efficiency and flexibility of customer risk assessment are improved, and the risk management strategy is optimized.

The invention relates to the technical field of risk analysis, in particular to a dangerous chemical transportation risk prediction system and method based on big data analysis, and the system comprises a multi-dimensional data acquisition module which is used for collecting human-machine-ring-pipe four-dimensional data in the liquid ammonia water transportation process, and carrying out the standardization processing and time-space synchronization, and obtaining a multi-source heterogeneous data set; the risk level acquisition module is used for constructing a man-machine-ring-management collaborative risk assessment model and analyzing the multi-source heterogeneous data set to obtain a risk assessment result; the human-machine-environment-management collaborative risk assessment model comprises a human-machine interaction key node identification layer, a management behavior-equipment response association analysis layer, an environment-material interaction dynamic risk assessment layer and a multi-dimensional factor risk cascade assessment layer; and the emergency disposal scheme acquisition module constructs an emergency disposal scheme intelligent recommendation model to perform grading and classification analysis on the risk assessment result, generates a multi-level risk early warning and emergency disposal scheme, and optimizes the emergency disposal scheme.

The invention relates to the technical field of industrial chain monitoring and treatment, in particular to a monitoring method and system for industrial chain breakpoint treatment. The method comprises the steps that production, logistics, finance, policy and environment dynamic information is acquired through multi-source data, industrial chain comprehensive characteristics are generated through standardization, fractal dimension embedding expression and cross-dimension fusion, and historical trend dependency is introduced to enhance prospective prediction; a dynamic coupling network is constructed, and risk propagation intensity between nodes is quantified by using a dynamic edge weight, so that cross-level breakpoint propagation analysis is realized; risk indexes are calculated by fusing node features and a network structure, breakpoint candidate nodes are screened in combination with an adaptive threshold value, and a multi-step evolution trend is predicted by adopting a nonlinear propagation function and mapped into a multi-level early warning level. And generating a governance strategy according to the risk level, and evaluating the effect in real time and dynamically adjusting parameters through a closed-loop optimization mechanism. According to the invention, closed-loop management of risk identification, prediction and adaptive treatment is realized.

The invention relates to the technical field of risk management, and provides a risk management and control system and method based on a knowledge graph, and the method comprises the steps: configuring a risk grading management and control mechanism in advance, and determining a management and control channel to which multi-source heterogeneous data belongs; acquiring propagation path characteristics through the control channel, and determining a target dynamic knowledge graph based on the propagation path characteristics; and according to the target dynamic knowledge graph, calculating a multi-dimensional risk entropy value based on propagation path characteristics, comparing the multi-dimensional risk entropy value with a reference risk entropy value of each level in a risk hierarchical management and control mechanism, and determining a risk level and a risk management and control strategy corresponding to the risk level. A dynamic knowledge graph is combined with a management and control channel, so that a conventional knowledge graph also has an effect of reflecting dynamic risk evolution. In risk management and control, through combination of a multi-dimensional risk entropy value and a dynamic knowledge graph, strong association between risk grading and a grading mechanism is realized, a management and control strategy is directly matched with dynamic evolution characteristics, and risk data and diffusion are prevented and controlled.

The invention provides a production data monitoring method and system based on artificial intelligence, and belongs to the technical field of data processing, and the method comprises the steps: generating an initial monitoring strategy according to a quality influence factor evaluation model; based on the multi-modal data acquisition parameters, controlling multi-modal data acquisition equipment to acquire multi-source data in the charging pile assembling process; performing fusion processing on the multi-source data to generate a quality feature vector representing an assembly state; comparing the quality feature vector with a quality judgment threshold value, and outputting a state monitoring result; inputting the quality feature vector into a preset quality prediction model to obtain an assembly quality prediction result; determining a quality defect type and a risk level of the charging pile through a predefined quality defect mapping rule based on the state monitoring result and the assembly quality prediction result; according to the quality defect type and the risk level, at least one of the initial monitoring strategies is adjusted, and an updated monitoring strategy is generated; and the production quality and the production efficiency of the charging pile are improved.

The invention relates to the technical field of hazardous chemical substance safety management and control, and particularly provides a hazardous chemical substance storage accident risk analysis method, which comprises the following steps: collecting and preprocessing time sequence data and spatial topological data of a storage area; inputting the time sequence data into a time dynamic risk prediction model, and extracting time sensitive features to output a risk level time sequence curve with a risk storage unit label; labeling a high-risk period window in the curve; constructing a spatial diffusion risk assessment model based on the spatial topological data, extracting risk diffusion features of a target storage unit, associating adjacent units, and outputting a dynamic risk spatial distribution result; performing space-time coupling analysis on the high-risk time period and the spatial distribution, and outputting early warning information after correction; and finally, calling the management and control rule base to generate a risk management and control decision instruction. According to the method, time-space two dimensions are covered, dynamic risk assessment, early warning and management and control are realized, the comprehensiveness and management and control accuracy of hazardous chemical substance storage risk analysis are effectively improved, and a scientific support is provided for safety management of a storage area.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention discloses a multi-disaster disaster risk grade coupling evaluation method. The method comprises the following steps: establishing a multi-dimensional database covering multiple disaster types; key features related to the disaster risk are extracted from the multi-dimensional database; establishing a risk assessment model according to the key features, and determining a plurality of single-disaster risk levels; identifying association rules, causal relationships or space-time coupling modes among different disasters by using a big data mining algorithm; coupling weights among different disasters are determined, and a coupling model is constructed to convert a coupling relation into a quantitative risk superposition effect; based on the coupling model, a single-disaster risk and a coupling effect are integrated, a multi-disaster comprehensive risk level is generated, through multi-dimensional data integration, quantitative coupling modeling and a dynamic adjustment mechanism, advantages are formed in evaluation precision, social and economic adaptability and model adaptability, and the method is more suitable for scenes needing accurate risk grading and decision support.

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention relates to the technical field of control systems, in particular to a melt mixing parameter real-time feedback control system for color master batch production. The method comprises the following steps: acquiring a corresponding process parameter reference through a parameter reference setting module; carrying out risk assessment through a grading module to obtain risk grades and carrying out grading processing; constructing a global optimization model through a control sequence module; a target evolutionary algorithm is started in combination with the technological parameter benchmark, and the technological parameter adjustment amount is calculated and a regulation and control sequence is generated by analyzing the dynamic coupling correlation among the technological parameters and the comprehensive influence on the whole; and finally, the process parameters are adjusted through a closed-loop control module, and real-time closed-loop feedback control is formed. The defects exist in real-time feedback and accurate control links of parameters in a color master batch melting and mixing process. Therefore, the invention provides a melt mixing parameter real-time feedback control system for color master batch production.

The invention relates to the technical field of risk assessment, in particular to an AI enterprise financial risk dynamic assessment method and system. The method comprises the following steps: obtaining enterprise multi-dimensional financial data, operation activity data, industry environment data and macroeconomic index data, carrying out cleaning, standardization and time sequence alignment processing, and integrating the data into a financial risk assessment multi-dimensional feature matrix; constructing a dynamic weight distribution model based on the financial risk assessment multi-dimensional feature matrix, endowing differentiated weights, and generating a time sequence weighted feature vector; risk level mapping and comparative analysis are carried out on the time sequence weighted feature vectors, risk points exceeding a threshold value are identified, and feature sources of the risk points are traced; and constructing a risk conduction path map based on the risk points and feature sources thereof, simulating risk evolution trends under different intervention measures, and evaluating and generating a dynamic evaluation report containing risk early warning levels, key influence factors and intervention measure suggestions. According to the invention, the enterprise financial risk assessment efficiency can be improved.

The invention provides a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis, and relates to the technical field of financial risk control and big data analysis, and the method comprises the steps: obtaining multi-dimensional data from borrower basic information, financial statements, credit records and an external market environment, carrying out cleaning, standardization processing and storage on the multi-dimensional data; based on the multi-dimensional data, through a credit scoring model, cash flow analysis, liability ratio analysis and market environment risk assessment, generating a financial risk level of the borrower; the financial and market data of the borrowers are updated in real time, the dynamic change of the risk state of the borrowers is monitored in combination with a time sequence prediction model and risk correlation analysis, and a dynamic monitoring result is generated; and according to the risk level and the dynamic monitoring result, triggering a grading early warning mechanism, and generating a corresponding control measure suggestion so as to adjust a credit strategy or adopt a risk slow release means.

The invention discloses a coal mine rock burst risk assessment system which comprises a data acquisition module, an energy accumulation rate calculation module, a roadway network modeling module, a risk assessment module and an early warning regulation and control module. The method comprises the following steps of: firstly, calculating an energy accumulation rate of a roadway, then constructing a roadway network diagram, identifying a high stress conduction path, calculating a dynamic risk index and delimiting a risk area by integrating factors such as a historical impact event and an energy index, and automatically triggering corresponding early warning and regulation measures by a system according to a risk level so as to realize accurate early warning and dynamic regulation. And mine operation decision making and accident prevention are assisted.

The invention provides an electric power technology service guarantee supervision method, which comprises the steps of determining a matching degree evaluation criterion of an operation tool specification and a protection measure grade according to a real-time operation data set, and meanwhile, integrating a compliance deviation reason according to an out-of-plan repair judgment result to determine an operation rationality evaluation criterion; performing electric power risk grading analysis on the cascading failure prediction set, judging the existence condition of a high risk grade, and extracting transformer abnormal sign data to obtain a risk grade classification result; according to the flow difference degree of the optimized repair path and the initial repair operation step, the optimized path is integrated back to the real-time operation data set, and meanwhile, in combination with transformer abnormal sign data, an update operation guidance set is obtained; and generating a power operation visualization report according to the updated operation guidance set, verifying the applicable condition that the alarm is not directly started when the service standard process is deviated, and obtaining a final compliance judgment conclusion.

The invention provides a data privacy protection method for a data governance system, and belongs to the technical field of data governance, and the method comprises the steps: carrying out the cross verification of multi-source feature data and the unique identification information of an object collected on site, generating an original data set, carrying out the sensitive information recognition and grading, and generating the preprocessing data with a sensitive grade label; core identification information in the preprocessed data is disassembled to generate standardized desensitized data conforming to privacy protection, a bidirectional encryption mapping relation is established between object unique identification information collected on site and the standardized desensitized data, an encryption index is formed, the encryption index and preset multi-dimensional compliance data are fused, and a data fusion result is obtained; generating standardized fusion data; and based on the access token, generating a differential authorization data set divided according to permission granularity, performing privacy disclosure risk assessment, generating a risk level, performing privacy processing on the risk level, and outputting the risk level to a risk control system. And the data management efficiency is improved.

The invention discloses a vehicle-road cloud cooperative traffic abnormity early warning method, which comprises the following steps that: a road side unit receives abnormal event data reported by a vehicle side, checks the abnormal event data in combination with road side data, and uploads the abnormal event data to a cloud side; the cloud receives the data reported by the road side unit, performs fusion in combination with the cloud data, and evaluates the risk level after the occurrence of the abnormal event: constructs a space-time diagram neural network, and calculates the vulnerability index of the road network based on the traffic road network diagram structure; constructing a Bayesian network, updating probability distribution of the network by using detected data corresponding to the abnormal event as an observation value, and predicting a conduction path of the abnormal event; carrying out preliminary risk grade assessment according to the vulnerability index and the predicted conduction path; and performing traffic intervention simulation based on the abnormal event, calculating an intervention index improvement rate according to a simulation result, and correcting a risk level. According to the method, deep fusion is carried out on the vehicle and road cloud data, the traffic abnormal event identification capability is improved, and the dynamic risk level evaluation of the whole road network is realized.

The invention relates to the field of oil-immersed transformers, in particular to an oil-immersed transformer operation risk assessment method and system, and the method comprises the steps: obtaining real-time operation parameters and external influence factors of an oil-immersed transformer; the real-time operation parameters and the external influence factors are preprocessed; based on the preprocessed real-time operation parameters and the external influence factors, outputting a comprehensive risk assessment score; calculating a transformer health index based on the preprocessed real-time operation parameters and the external influence factors, and calculating a risk occurrence probability according to the transformer health index and the comprehensive risk assessment score; and outputting a risk level report based on the comprehensive risk assessment score, the transformer health index and the risk occurrence probability, and generating an early warning signal. According to the method, the aging factor is introduced, and the calculation weights of the comprehensive risk score, the health index and the risk probability are adjusted in combination with the service life of the transformer, so that the evaluation model can dynamically adapt along with the aging degree of the equipment, and the evaluation precision of the transformer is improved.

The invention discloses an enterprise data security management system and method. The system comprises a data acquisition module which acquires a dynamic context signal of a user access request in real time; the risk calculation module receives the dynamic context signal and generates a real-time risk level signal through a preset risk evaluation model, and the risk evaluation model outputs a risk quantification result based on weighted calculation of a multi-dimensional context factor; the strategy execution module receives the real-time risk level signal and generates a dynamic access control signal; the unified strategy library module is respectively connected with the risk calculation module and the strategy execution module, and provides a strategy configuration signal of the risk assessment model for the risk calculation module; and the auditing tracking module is connected with the strategy execution module and the data acquisition module, receives the dynamic context signal and the dynamic access control signal, and generates an auditing log signal containing a risk decision chain. According to the enterprise data security management system and method, the problem of fine-grained access control failure caused by static strategy hysteresis can be solved.

The invention provides a blast suppression method and system for an underground cavity of a pumped storage power station. The method comprises the following steps: configuring and deploying a multispectral gas sensor array; carrying out gas explosion limit calculation and risk assessment; calculating the risk level of explosion by using the prediction model; performing graded explosion suppression operation by using a graded explosion suppression execution module; combustible gas is guided to enter an inerting area by utilizing a space flow guide optimization module; and the ignition source blocking module is used for cooperative control to block an ignition source. The technical problems of low mixed gas explosion risk prediction and monitoring precision, low explosion suppression efficiency, slow response speed and relatively high operation and maintenance cost are solved.

The invention provides a multi-dimensional data fusion system for operating truck risk rating, and relates to the technical field of data processing, and the system comprises a data collection module which is used for obtaining the dynamic operation data and historical static information of a target operating truck; the data preprocessing module is used for performing field alignment, abnormal value elimination and format standardization; the feature analysis module is used for extracting dynamic behavior features of vehicle operation and performing statistical calculation in a preset time window; the event identification and risk assignment module is used for identifying a single event or a combined event, assigning an event risk weight to the single event or the combined event, and determining a corresponding road condition risk level in combination with the vehicle position information; the multi-dimensional fusion module is used for fusing features and generating time serialized multi-dimensional risk feature vectors; the risk rating module is used for scoring the multi-dimensional risk feature vector and outputting a corresponding risk rating result; according to the invention, the accuracy of the multi-dimensional data fusion system is improved.

The invention discloses a method and system for driving SOAR automatic disposal based on system dynamic risk assessment. According to the invention, a closed-loop protection system including data acquisition and preprocessing, dynamic risk assessment, disposal decision triggering, SOAR automatic disposal execution, system total risk value calculation, system-level risk determination, asset risk weighted upgrading, manual intervention triggering, manual disposal feedback and alarm level overall improvement is constructed; and accurate identification and efficient response of network security threats are realized. According to the method, the total risk value of the system is calculated through a multi-dimensional dynamic risk assessment model in combination with an asset value weight algorithm, and real-time upgrading of the asset risk level is realized through a risk threshold dynamic adjustment mechanism, so that a two-level response mechanism (automatic disposal-manual takeover) is realized, and the automation efficiency and the manual reliability are balanced. According to the invention, precise and efficient disposal of network security threats and intelligence of security protection are realized, and the method can be widely applied to core service systems of various industries.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention relates to a quality risk assessment method for a highway bridge engineering construction stage and a medium, and the method comprises the steps: determining the quality influence factor indexes of the highway bridge engineering construction stage, carrying out the dimension reduction through employing a PCA method, and constructing a quality risk index system, wherein the quality risk index system comprises a risk factor layer and a corresponding index layer; based on the quality risk index system, adopting a DEMATEL method to calculate the subjective influence weight of each index; based on the quality risk index system, an improved CRITIC method is adopted to calculate the objective weight of each index; based on the subjective influence weight and the objective weight, determining a comprehensive weight of each index by adopting a minimum identification information theory; and based on the comprehensive weight, combining the determined grade standard interval, and based on a variable fuzzy set theory, carrying out quality risk assessment, and outputting a quality risk assessment result. Compared with the prior art, the method has the advantages of improving the reliability of risk grade evaluation and the like.

The invention relates to a food safety risk analysis method and system based on big data. The method comprises the steps of collecting food full-life-cycle key data through Internet of Things equipment and a data platform, and completing data format standardization to form a structured data set; key risk factors are extracted through the structured data set, a risk factor weight matrix is constructed, and a primary risk value of the food sample is calculated; establishing a time sequence prediction model by using the primary risk value and historical time sequence data, calculating a risk change trend, and predicting a future risk fluctuation condition; calculating a food risk grade according to the primary risk value and the risk change trend, and generating a corresponding risk classification label; performing comparative analysis on the risk classification label and an actual supervision result, calculating a model deviation, and optimizing a risk calculation and trend prediction model based on a deviation result; the optimized analysis results are integrated, the risk level and the change trend are displayed through a graphical interface, and intelligent early warning prompts and intervention decision suggestions are provided.

The invention provides a factory energy safety dynamic monitoring method based on the Internet of Things and multi-mode perception, and belongs to the technical field of industrial Internet of Things. Comprising the following steps: acquiring electrical parameters, temperature data, combustible gas leakage concentration and vibration signals of equipment in real time; carrying out aggregation and protocol conversion on the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals through a multi-protocol intelligent gateway, and uploading the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals to a locally deployed edge computing node in a preset period; preprocessing the received data at the edge computing node, and generating a quantitative risk index based on a dynamic risk assessment model fusing the real-time state of the equipment, the historical aging trend and the environmental parameters; and carrying out risk grade judgment according to the quantitative risk index, and executing a corresponding grading response. Through fusion of multi-modal sensing data and edge intelligence, comprehensive sensing, real-time quantitative evaluation and hierarchical intelligent response of factory energy safety risks are realized.