916 results about "Risk indicator" patented technology

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention discloses an urban inland inundation risk multi-level prediction method and device based on time-space diagram learning, a storage medium and a computer program product, and relates to the technical field of natural disaster risk prediction, and the method comprises the steps: collecting multi-modal urban hydrological data; performing hierarchical time modeling on the multi-modal urban hydrological data, and extracting a time embedding vector; constructing a heterogeneous graph based on the time embedding vector, and performing spatial feature aggregation calculation on the heterogeneous graph to obtain spatial embedding representation; and performing multi-level prediction according to the spatial embedding representation to obtain a multi-granularity waterlogging risk index. Through multi-modal data acquisition and preprocessing, layered time modeling, heterogeneous graph construction, spatial feature aggregation calculation and multi-level prediction, multi-modal urban hydrological data are effectively fused, and comprehensive and accurate urban inland inundation risk prediction is realized.

The invention discloses an index anomaly detection and adaptive optimization method and system based on multi-model fusion, and relates to the technical field of intelligent operation and maintenance of a power system. According to the method, a dynamic causal network diagram is constructed based on an operation data stream, wavelet coherence analysis and a Bayesian-space-time diagram structure are fused, an edge weight is updated in real time, and a propagation probability is calculated; calling a plurality of anomaly detection models in parallel, dynamically adjusting fusion weight according to the confidence score and the propagation risk coefficient, and generating a fusion anomaly score result; and for a high-risk index section, extracting time frequency characteristics and topological structure characteristics, inputting a lightweight model to generate a confidence coefficient correction factor, calculating an abnormal influence value, and driving monitoring resource adaptive allocation. According to the method and the system, the model adaptability, the anomaly detection precision and the response efficiency in a complex power scene are improved.

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 field of medical informatization, in particular to a nursing risk assessment system and method based on multi-modal data. According to the nursing risk assessment system, data acquisition, preprocessing, multi-modal fusion, risk assessment and early warning are integrated, and comprehensive, accurate, timely and personalized nursing assessment is realized. The system collects physiological signals, nursing behaviors and environment data, the data are preprocessed and fused to generate feature vectors, a risk assessment matrix is constructed, and risk indexes are calculated. The risk early warning module sends early warning information to the nurse work station in real time, and nursing safety is ensured. Innovative technologies such as multi-modal data fusion, real-time dynamic evaluation, personalized adjustment and the like greatly improve the nursing quality, reduce the occurrence rate of adverse events, relieve the burden of medical staff and improve the utilization efficiency of medical resources. Long-term application is hopeful to significantly improve patient prognosis and reduce medical cost, and makes an important contribution to development of intelligent medical treatment and precise nursing.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention provides an intelligent security comprehensive management and control system, and relates to the field of intelligent security management and control. Comprising a monitoring layer, a regional decision-making layer, a cloud analysis layer and an execution control layer, the monitoring layer collects data through fixed-point monitoring equipment, mobile monitoring equipment and a simulation management node; the regional decision-making layer calculates a local risk index based on the data and generates a local service topology sub-graph, and triggers a simulation management node to generate an abnormal operation record; the cloud analysis layer integrates the local service topology sub-graphs, corrects risk index errors, constructs a dynamic priority service topology network and generates a risk gradient; and the execution control layer schedules the mobile monitoring equipment according to the risk gradient, closes a redundancy process, deploys an interception strategy and an optimization means, and verifies the running state of the equipment through a full-stack self-check protocol.

The invention relates to the technical field of medical health big data analysis, in particular to a diabetic nephropathy risk identification method and system based on big data analysis, and the method comprises the following steps: screening key index features of risk patients through big data analysis based on health risk data of diabetic nephropathy patients; association of key indexes of the risk patients is analyzed, risk index interaction features are extracted, risk feature constraint conditions are determined, virtual sample parameters are identified through multi-dimensional data association, and a virtual sample set of the risk patients is established. According to the method, interaction characteristic values are extracted by analyzing key indexes of risk patients, high-risk index association is accurately captured, coverage and balance are enhanced based on multi-dimensional data association, non-stationary influence is solved by combining time sequence key change rate and shear point identification, and accurate matching is realized by analyzing offset rate and change trend. And the overall risk level is quantitatively evaluated by integrating interval data weighting, so that the risk identification comprehensiveness and the result reliability are improved.

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

The invention discloses a gate opening adaptive control method and system based on resonance risk dynamic assessment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a current time point, obtaining a processing time period, and obtaining a historical water flow frequency and a historical gate frequency; obtaining a deviation degree corresponding to each to-be-processed time point, obtaining a gradient correction value corresponding to the current time point based on the prediction model and the deviation degrees corresponding to the plurality of to-be-processed time points, and obtaining a trend correction value corresponding to the current time point based on the trend model and the deviation degrees corresponding to the plurality of to-be-processed time points; obtaining the current water flow frequency and the current gate frequency at the current time point, and obtaining a risk index according to the current water flow frequency and the current gate frequency; and a target opening degree is obtained according to the risk index, the gradient correction value and the trend correction value, and the gate is controlled to be opened according to the target opening degree. The method has the advantages of dynamic perception, trend prediction and adaptive control.

The invention discloses a social risk early warning method and system based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a risk index library; identifying key risk indexes from the evaluation values by using a clustering algorithm; finding an alternative index for an unquantized key risk index, and establishing a quantitative model; updating the risk index library, performing hierarchical division, and calculating the weight of each risk index; constructing an index comprehensive weight matrix; quantized values of the key risk indexes are obtained through a quantitative model, and a comprehensive score is obtained in combination with the weight matrix and used for social risk early warning. According to the method, for social risk indexes which are difficult to quantify, index substitutes and a measurement and calculation model are constructed through expert experience and data analysis, and index quantification is achieved. Meanwhile, an analytic hierarchy process is adopted to construct a risk index system and give a weight, and a risk comprehensive scoring mechanism is formed. According to the method, the index system is dynamically adjusted by continuously collecting risk data and applying a time sequence analysis method and the like, so that the effectiveness of early warning is ensured.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention relates to the technical field of oil field geological prediction, in particular to an oil field geological parameter intelligent prediction system based on machine learning, which acquires static geological data and environmental data in real time through a data acquisition module, and performs noise reduction, normalization and cleaning on the acquired data through a data preprocessing module, so as to obtain an oil field geological parameter prediction result. The feature extraction module adopts an XGBoost model and a long-short-term memory network model to process static features and time sequence data respectively. And the model optimization and self-adaption module dynamically adjusts model parameters according to real-time feedback data by using a reinforcement learning algorithm so as to ensure the accuracy and adaptability of a prediction result. The visualization module provides visual display of a prediction trend, a geological data prediction value and a risk indicator, and helps an operator to make a decision based on data driving. According to the method, the prediction precision of the oil field geological data can be effectively improved, the adaptability to environmental changes is enhanced, and a scientific basis is provided for oil field management and production optimization.

The invention discloses a method and system for predicting leakage of a water supply pipe network, and the method comprises the steps: modeling nodes and pipe sections of the pipe network into a graph topological structure, and endowing the nodes and the pipe sections with static attributes; collecting operation data of the water supply network, and constructing time-varying graph data corresponding to the graph topology; combining the time-varying graph data with the static attributes to form space-time input features; constructing a graph time sequence prediction model based on a deep learning framework, performing graph structure feature extraction on node graph features and pipe section graph features of each time step to obtain node space features and pipe section space features, and outputting a node and pipe section space-time representation set; evaluating and analyzing the leakage level of each DMA or pressure partition; generating a pipe section leakage risk space distribution set; constructing a joint loss function, and training and updating the graph time sequence prediction model; and inputting operation data acquired in real time into the trained graph time sequence prediction model, and generating a leakage rate prediction value of each partition and a leakage risk index of each pipe section on line for leakage prediction and operation and maintenance decision.

The invention relates to the related technical field of risk indicator construction management, in particular to an evidence-based migraine risk indicator construction system, which comprises a feature extraction module for acquiring physiological data and coupling abnormal features, an error correction module for mapping features and correcting errors, a dynamic adjustment module for adjusting a probability confidence interval, and an evidence-based migraine risk indicator management module for establishing an evidence-based migraine risk indicator. The technical problems that generation of the risk report is fixed to a preset migraine risk index, the risk report cannot adapt to individual differences, and the accuracy of risk assessment is limited are solved, physiological feature data are extracted, coupling abnormal features are analyzed, and the accuracy of risk assessment is improved. The method has the advantages that physiological changes before migraine attack are reflected more comprehensively, reliability of risk assessment is improved, historical intervention cases are introduced, confidence intervals of risk probability values are dynamically adjusted, dynamic adjustment is performed according to individual differences of patients and intervention response conditions, and adaptability of migraine risk reports is improved.

HyperText Markup Language (HTML) content analysis (HCA) using machine learning is described. A feature vector schema may be generated based on domain names corresponding to HTML webpages and corresponding indications of a status of the HTML webpage. The schema may map each position in a feature vector of a given HTML webpage to a resource identifier. Information may be processed using the schema to generate respective feature vectors. The feature vectors may be used to train a model to generate risk indicators for HTML webpages. A potentially parked domain webpage or a potentially malicious domain webpage may be received. A feature vector for the webpage may be generated and inputted to the model. The model may generate a risk indicator for the webpage. The risk indicator may be output and may cause responsive actions. The model may be updated based on a determination indicating whether the webpage was a parked domain webpage or a malicious domain webpage.

The invention discloses a bird-related fault identification and early warning method based on a transmission tower gap model, and relates to the technical field of power system safety monitoring. The method comprises the following steps: firstly, constructing a three-dimensional transmission tower gap model containing a local insulated armor, and obtaining an electric field distortion feature library under various working conditions through finite element simulation; further simulating interference behaviors of the bird body, the bird nest and the excrement in the safety gap, calculating the discharge probability, and synthesizing and labeling a bird image for training a risk decision model. And in combination with a target detection algorithm integrating an attention mechanism and an SPPCSPC module, space coordinates of bird targets are extracted in real time and mapped to a high-risk area, and a discharge risk score and a breakdown trend index are output by using an LSTM network. And when the risk index exceeds a dynamically adjusted early warning threshold, generating an early warning signal and a fault coordinate. According to the invention, the precision and foresight of bird-related fault identification are improved, and the method has good engineering adaptability.

The invention relates to the technical field of traffic safety, in particular to a highway risk map construction method and device fusing multi-source data, and the method comprises the following steps: S1, selecting a traffic conflict risk, a driving behavior risk and a vehicle safety risk as risk dimensions, selecting risk indexes according to the traffic conflict risk, the driving behavior risk and the vehicle safety risk, and calculating risk index data and the weight of the risk indexes; s2, calculating the total risk of the road section according to the weight of the risk dimension, the weight of the risk index and the risk index data; and S3, obtaining a risk map according to the total risk of the road section and the weight of the risk dimension. According to the method, by constructing the risk map and identifying the high-risk area, the application of multi-source data fusion in the aspect of highway risk assessment is enhanced, a scientific basis and technical support can be provided for traffic safety management, and the safety of the highway is improved.

The invention relates to the technical field of distributed energy storage and intelligent power grid control, in particular to an intelligent distributed energy storage cluster collaborative management system. Comprising a state acquisition module used for acquiring local power grid state parameters of an edge computing node in real time; the power grid state parameters comprise power grid frequency, power grid voltage and a heartbeat communication state with the cloud management platform; the risk calculation module is used for calculating quantified island risk indexes; the mode judgment module is used for determining a current operation mode decision; the mode switching module is used for switching the system from a vertical cooperative mode to a horizontal autonomous mode when the island risk index is greater than an island switching threshold value; and when the island risk index is smaller than or equal to the island switching threshold value, the system is maintained to operate in the vertical cooperation mode. According to the system, misjudgment and missed judgment of a traditional single threshold value mode are overcome, it is ensured that the mode switching module can be switched to the horizontal autonomous mode from the vertical cooperative mode in time under the real risk, and decision making is accurate and reliable.

The invention discloses a charging pile intelligent distribution system based on dynamic load balancing and a charging pile, and relates to the technical field of charging piles, a distribution module is used for receiving an operation feature group of each charging pile at a first response moment, generating a feature weight set, and simulating importance judgment logic in the feature weight set according to a feature priority, dynamic load distribution of the charging piles is realized; the process module is used for receiving the data of each source in real time in the dynamic load distribution process, performing multi-source analysis, checking the fault burstiness of the charging pile, analyzing the communication interruption, predicting the charging demand performance and the power supply performance based on the data of each source at a second response moment, and constructing a comprehensive risk index in combination with an evidence theory; and the updating module is used for generating a judgment signal according to the comprehensive risk index value, and the judgment signal comprises an updating instruction and an iteration result.

The embodiment of the invention provides a method and device for adjusting configuration of a storage system, a computer readable storage medium and electronic equipment. The method comprises the steps that the current configuration condition of the storage system is obtained; determining working performance parameters of the storage system when the storage system processes the target business model according to the current configuration condition; according to the current configuration condition of the storage system and the working performance parameters, determining risk indexes of the storage system when the storage system is in the working performance parameters; and when the risk index is greater than a preset threshold value, adjusting the current configuration condition of the storage system, so that the risk index corresponding to the adjusted current configuration condition is less than or equal to the preset threshold value. Through a closed-loop feedback mechanism and configuration adjustment, the system can dynamically and adaptively optimize self-configuration so as to cope with variable service models and potential fault risks. Therefore, in different scenes, the storage system can maintain stable performance and high availability, and the reliability and service continuity of the storage system are improved.

The invention relates to a safety production equipment risk monitoring method and system, and belongs to the technical field of risk monitoring, and the method comprises the steps: obtaining historical operation data and real-time operation data of to-be-monitored safety production equipment; setting a dynamic threshold according to the historical operation data, predicting the range of the dynamic threshold according to preset target process operation data and the historical operation data through a bidirectional long-short-term memory network, and updating the range of the dynamic threshold in combination with the real-time operation data to obtain an updated dynamic threshold; the comprehensive risk index is calculated according to the updated dynamic threshold value and the real-time operation data through the analytic hierarchy process, the risk of the safety production equipment to be monitored is evaluated according to the comprehensive risk index, an evaluation result is obtained, dynamic monitoring and evaluation of the risk of the safety production equipment are achieved, and the safety production equipment risk evaluation efficiency is improved. The actual operation state of the equipment can be reflected more accurately, potential risks can be found in time, corresponding measures can be taken, and the operation safety and reliability of the equipment are improved.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The invention discloses a hydraulic engineering data intelligent monitoring method and system based on digital twinning, and the method comprises the steps: comprehensively considering the actual physical structure, operation parameters and surrounding environment information of a hydraulic engineering when a hydraulic engineering digital twinning model is constructed, and breaking through the limitation that a conventional monitoring means only pays attention to a single parameter or a few parameters; model association data is utilized and visualized display is carried out, management personnel can visually check states and parameter changes of all parts, the monitoring efficiency and comprehensiveness are improved, and the limitation that a traditional monitoring means lacks effective analysis and mining is solved. A risk assessment formula is adopted to convert data into risk indexes, so that managers can visually know the safety condition, when a risk assessment value exceeds a threshold value, the risk development trend and consequences are simulated and analyzed in combination with a model, problems and risks are found in advance, early warning is triggered, and related personnel can formulate response measures such as flood discharge flow adjustment and structure reinforcement according to simulation results. Disaster loss is reduced, and the safety and reliability of hydraulic engineering are improved.

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 discloses an urban safety risk assessment method and system based on a digital standard, and the method comprises the steps: obtaining urban safety risk monitoring data and historical data, constructing a risk index event library according to the historical data, carrying out the abnormal screening and classification of the monitoring data, and obtaining risk indexes, matching the risk indicator with the risk indicator event library to determine a first risk event, predicting an uncertain risk event according to the first risk event, and constructing a city safety risk assessment model according to the first risk event, the uncertain risk event and the risk event weight, and inputting to-be-assessed city safety risk monitoring data and historical data into the city safety risk assessment model to obtain a city safety risk assessment result. The method not only can improve the efficiency and accuracy of urban safety risk assessment, but also has good interpretability, and can be directly applied to an urban safety risk assessment system.

The invention provides a construction state monitoring and risk assessment method and device based on BIM multi-mode conversion, and relates to the technical field of building information models. According to the method, a standardized image mode is generated by analyzing and extracting component information of a BIM model, and a BIM text mode is generated by using natural language description; constructing a graph structure mode based on space and construction logic, and realizing unified alignment and deep fusion of multi-modal data through multi-level modal alignment and a cross-modal attention mechanism to obtain a cross-modal fusion representation which is used for inputting a state recognition model and automatically detecting an execution deviation so as to monitor a construction state; and then introducing a deviation conduction mechanism to quantitatively calculate a comprehensive risk index of the component so as to carry out risk assessment. According to the method, the fusion representation which not only keeps semantic consistency but also conforms to construction logic can be obtained, the abstract cross-modal semantic features are converted into quantifiable and interpretable construction states and risk indexes, and powerful support is provided for intelligent analysis and application in a construction scene.