58 results about "Risk research" patented technology

The invention provides a remote alarm risk studying and judging method and device based on underground coal mine multi-mode data, and relates to the technical field of coal mine safety. Obtaining time sequence data of abnormal sensors in an underground coal mine area, associated time sequence data of a plurality of associated sensors influencing the data of the abnormal sensors, states and behaviors of feature objects influencing underground coal mine safety in image videos, and working tracks of personnel; and judging the alarm reason of the sensor data abnormity, and performing multi-angle alarm risk research and judgment through a retrieval enhancement generation large model to obtain the risk level of the alarm reason and the corresponding alarm reason coping strategy. Therefore, multi-modal data fusion analysis is performed through the sensor data, the image video and the working track in the underground coal mine area, intelligent risk research and judgment of coal mine safety alarm are realized, the alarm risk research and judgment time is shortened, and the alarm risk research and judgment precision is improved.

The invention discloses an intelligent studying, judging and early warning system and method for external risks of a power grid transmission line, and relates to the technical field of power grid risk early warning. Compared with a previous power transmission line external risk monitoring and management mode, the power transmission line external risk monitoring and management method solves the problems of low efficiency, low accuracy and untimely early warning due to the fact that a traditional mode mainly depends on manual inspection and experience judgment; multi-source information is integrated, various risk factors can be comprehensively considered, and the limitation of a single data source is avoided, so that the accuracy of risk research and judgment is greatly improved; a risk deduction prediction algorithm is adopted to train and learn a large amount of historical data, and risk features and rules are automatically extracted to perform risk deduction prediction; performing real-time early warning on the risk, and performing dynamic adjustment and updating according to the risk change condition to ensure that the early warning information is always matched with the actual risk condition; and auxiliary decision-making suggestions are further provided for the operation and maintenance personnel, the operation and maintenance personnel are helped to quickly formulate reasonable countermeasures, and the working efficiency is improved.

The invention discloses a construction method and system for volatile organic compound release behavior characterization, and belongs to the technical field of air analysis and risk assessment. According to the method, feature analysis is carried out based on multi-source information such as a molecular structure, a release carrier and environmental conditions, modeling is carried out on a release process by utilizing a mapping operator, and a three-dimensional representation vector V = [I, S, E] reflecting release intensity, environmental sensitivity and time evolution is constructed. The representation is output in a behavior vector or digital label mode, and unified quantitative benchmarking of different substances in a cross-scene mode is achieved. According to the method, depth characteristics such as molecular polarity and desorption activation energy are introduced, so that the dependence on long-period environmental chamber experiments is reduced, the generalization prediction capability on unknown pollutants is improved, the logic connection problem between a bottom-layer physical mechanism and an upper-layer intelligent management and control system is solved, and the reliability of the system is improved. And standardized and explainable behavior input is provided for prospective risk research and judgment.

The invention discloses an electric power emerging business risk keyword identification and early warning method, system and device based on an NLP technology and a medium, and belongs to the technical field of electric power risk, and the method comprises the steps: obtaining multi-source customer service text data, and extracting risk keywords and corresponding voice spectrum data from the multi-source customer service text data; inputting the risk keyword into a power service knowledge graph, and generating a first risk research and judgment result through knowledge reasoning, the first risk research and judgment result comprising a risk traceability path; performing acoustic feature analysis on the speech spectrum data, extracting acoustic feature parameters, calculating an emotional excitation index, and generating a second risk identification result; performing weighted fusion on the first risk research and judgment result and the second risk identification result to generate a comprehensive risk score; and when the comprehensive risk score reaches a preset threshold value, generating a structured early warning work order, and carrying out data binding on the structured early warning work order, the original customer service work order and the call record. According to the invention, the efficiency of risk management and control is improved.

The invention relates to the technical field of safety operation and maintenance of power equipment, in particular to an active anti-condensation control method, system and equipment for a switch cabinet and a medium, which are applied to an active anti-condensation control system for the switch cabinet and used for preprocessing operation environment data of the switch cabinet acquired by a sensing layer based on a data acquisition unit; cabinet body surface temperature fusion and dew point temperature calculation are carried out based on the risk research and judgment unit, a dynamic condensation risk index and a global risk index are obtained, and a risk level is judged through the obtained dynamic condensation risk index and a preset threshold value; and according to the risk level and the global risk index, based on a cooperative control unit and according to a preset multi-mode control strategy, generating a control instruction to perform active condensation prevention of the switch cabinet. Therefore, the early condensation trend is pre-judged, and faults such as insulation reduction and metal corrosion caused by condensation formation are effectively avoided.

The invention discloses a social governance risk early warning large model, which comprises a data acquisition unit, an event comparison unit, an early warning analysis table generation unit, an early warning research and judgment unit, an early warning disposal unit and an interaction unit, and is characterized in that the data acquisition unit is used for generating a detailed data set of key personnel; the event comparison unit is used for generating a comparison event data set; the early warning analysis table generation unit is used for generating a key personnel comparison early warning analysis table; the early warning research and judgment unit is used for constructing an early warning research and judgment agent; the early warning processing unit is used for building an early warning processing agent; and the interaction unit is used for displaying the key personnel risk research and judgment situation overview map and the risk disposal strategy in real time, and building an intelligent interaction question and answer window through the early warning research and judgment agent and the early warning disposal agent. According to the invention, by introducing big data analysis and artificial intelligence technologies, intelligent early warning, research and judgment and disposal management of key personnel are realized, and the efficiency and level of social governance are improved.

The invention discloses a hydro-junction construction period structure safety assessment method and system based on digital twinning, and relates to the technical field of hydro-junction safety assessment, and the method comprises the steps: carrying out the safety correlation analysis of a hydro-junction construction period structure; constructing a multi-level digital twinborn space; the method comprises the following steps: collecting construction monitoring data of a hydro-junction during a construction period, synchronously mapping the construction monitoring data to a multi-level digital twin space for level simulation, and identifying a first type of safety events; and when the first type of security events exist, carrying out influence range tracing and risk conduction analysis, and generating a structure security assessment conclusion. According to the invention, the technical problem of insufficient evaluation comprehensiveness and accuracy caused by the fact that dominant safety event identification and hidden risk mining are difficult to consider in the construction period structure safety evaluation of the hydro-junction in the prior art is solved, and accurate monitoring and targeted risk research and judgment of the construction period structure safety are realized; and the technical effect of improving the comprehensiveness and accuracy of structure safety evaluation in the construction period is achieved.

The invention discloses a rapid risk research and judgment method in a complex task execution environment of an unmanned aerial vehicle, and the method can rapidly recognize and quantify risk factors by comprehensively considering the characteristics of the unmanned aerial vehicle, the flight task attributes, the environmental conditions and the stability of a control system. The method specifically comprises the steps of collecting related data of unmanned aerial vehicle hardware, a flight task, an environment and a control system; calculating respective risk indexes, such as hardware reliability and performance parameters of the unmanned aerial vehicle; types and complexity of flight tasks; climate and geographic characteristics of the environment; the stability and decision support efficiency of the system are controlled; and finally, integrating the risk indexes through weight factors, and calculating an overall risk assessment index. The method provides powerful support for flight safety management of the unmanned aerial vehicle, so that an unmanned aerial vehicle operator can make a more reasonable and safer decision in a complex environment.

The invention discloses a product oil storage tank foundation settlement and deformation monitoring and early warning system and method, and relates to the technical field of storage tank safety monitoring, the product oil storage tank foundation settlement and deformation monitoring and early warning system comprises a settlement sensing layer, a reservoir level application layer and a platform application layer; the settlement sensing layer is provided with multiple types of anti-explosion sensors, and is used for collecting storage tank data in real time and processing storage tank foundation settlement and deformation related data; the reservoir-level application layer receives the data transmitted by the settlement sensing layer, and realizes the prediction of the settlement trend of the storage tank and the quantitative evaluation of the risk level; and the platform application layer realizes monitoring data visual display, early warning release and data sharing by building an enterprise security risk intelligent management and control platform or docking with a third-party platform. The invention further discloses a product oil storage tank foundation settlement and deformation monitoring and early warning method. According to the invention, real-time data acquisition, deep analysis, accurate processing and risk research and judgment can be realized, the compatibility is high, data sharing and collaborative management and control are realized, and the security and intelligent management and control level is high.

The invention discloses a user electricity arrearage risk assessment method and device, equipment and a storage medium. Obtaining an area type of an area to which a target user belongs, a user type of the target user, a current arrearage length of the target user and an event problem element of the target user, and determining an object attribute risk coefficient of the target user based on the area type of the area to which the target user belongs and the user type of the target user, determining an arrearage length risk coefficient from an arrearage length and arrearage length risk coefficient relation table based on the current arrearage length of the target user, and determining an event problem element risk coefficient from an event problem element and event problem element risk coefficient relation table based on the event problem element, the arrearage risk value of the target user is calculated based on the object attribute risk coefficient, the arrearage long risk coefficient and the event problem element risk coefficient, errors caused by risk research and judgment depending on manual working experience are solved, and the risk assessment accuracy is improved.

The invention discloses a network attack risk research and judgment system and method, and belongs to the technical field of network security, the network attack risk research and judgment system comprises a data information extraction module, a data feature fusion processing module and a risk research and judgment analysis module; the data information extraction module is used for performing feature extraction from the network message and the user behavior features to obtain a message feature Vd and a user behavior feature Vu; the data feature fusion processing module is used for connecting the message feature Vd with the user behavior feature Vu, and obtaining a joint feature vector V by combining deep network fusion; and the risk research and judgment analysis module is used for training normal sample features and calculating risk scores. According to the method, the manual intervention process can be reduced, excessive dependence on manual feature engineering is avoided, the labeling cost can be reduced, the model deviation can be reduced, the feature extraction capability can be further enhanced, sufficient data sources are ensured, and the comprehensiveness of risk evaluation is ensured.

The invention relates to the technical field of data processing, and discloses a commercial tenant risk rating method and system based on multi-source data fusion, and the method comprises the steps: obtaining the multi-dimensional data of a commercial tenant, and carrying out the heterogeneous data alignment, and obtaining the standard data of the commercial tenant; performing data credibility evaluation on the standard data to obtain a quality score of the standard data; based on the quality score, performing multi-dimensional feature fusion on the standard data to obtain merchant associated feature data of the merchant; based on a preset risk research and judgment standard, carrying out risk quantitative analysis on the merchant associated feature data to obtain a risk index of the merchant; performing comprehensive evaluation on the risk index to obtain risk evaluation information of the merchant; performing level threshold comparison on the risk assessment information to obtain a risk level of the merchant; according to the invention, the accuracy of merchant risk rating based on multi-source data fusion can be improved.

The application discloses a kind of digital risk research management methods and systems for high-risk scene, it is related to industrial manufacturing technical field, the method includes: in each target equipment in high-risk place, real-time monitoring device is installed, and based on the functional attribute of each target equipment, the corresponding data type label is configured to the real-time monitoring data obtained by the real-time monitoring device corresponding to each target equipment;When the data type label of real-time monitoring data is state class signal, then based on the preset determination rule and the state corresponding to real-time monitoring data, fault determination is carried out;When the data type label of real-time monitoring data is numerical value class signal, then based on the preset multiple different alarm levels and corresponding alarm trigger threshold, fault determination is carried out.The application can realize the collection of the key parameters of high-risk equipment, improve equipment fault, environmental anomaly prediction accuracy, convert sudden failure repair to planned maintenance, realize safety management front.

The present application relates to a kind of methods for realizing network content risk analysis based on key points driving, comprising the following steps: depth semantic analysis is carried out to original text, and risk key points are identified and extracted;According to the preset confidence threshold filtering low reliability key points, calling big model intelligent screening risk key points;Multi-source evidence online retrieval is carried out, and multi-source search results are integrated and risk tendency determination;Multi-dimensional risk research and judgment is carried out.The method for realizing network content risk analysis based on key points driving, device, processor and computer readable storage medium thereof of the present application are adopted, by reconstructing analysis process with risk key points as core, and comprehensively deciding by depth fusion local semantic knowledge and external real-time information, surpassing existing technology in precision, explainability and flexibility etc.Dimensions, provide solid technical foundation for constructing next-generation intelligent, efficient, reliable network content risk prevention and control system, and have important practical value and innovation.

The invention discloses a bid procurement decision-making method based on multi-data analysis processing. Comprising the following steps: S1, acquiring electronic bidding files submitted by a plurality of bidders participating in the same bidding item target packet; s2, automatically extracting and structuring preset key information in the electronic bidding file to generate structured information; s3, acquiring and fusing multi-dimensional associated data for a plurality of bidders; s4, performing quantitative analysis on the multi-dimensional associated data to generate a comprehensive risk assessment result; s5, generating a control instruction based on the comprehensive risk assessment result; according to the invention, a structured information processing module verified by an external authoritative data source, a cross-domain risk fusion model adopting a nonlinear collaborative amplification algorithm, and a self-adaptive human-computer interaction interface directly driven by a risk assessment result are integrated; therefore, a set of complete closed-loop processing flow from high-credibility data input to intelligent risk research and judgment and then to guide-type decision support is established.

The invention discloses an intelligent water conservancy multi-modal perception and decision optimization system, and relates to the technical field of urban water network security, and the system comprises a multi-modal perception and fusion module which is used for generating a comprehensive perception data flow; the intelligent diagnosis and early warning module is used for carrying out abnormal feature extraction and event positioning on the comprehensive perception data flow and generating a preliminary diagnosis result; carrying out risk research and judgment to obtain an early warning risk result; the physical constraint risk deduction module is used for deducing by utilizing the physical constraint solving model to generate a risk evolution field; the multi-objective cooperative control module is used for constructing a multi-objective optimization function, reinforcing a learning framework by utilizing a meta-strategy gradient, and generating a cooperative scheduling strategy; the digital twinning and strategy rehearsal module is used for constructing a water network twinning body; and performing verification and screening by using a bidirectional state synchronization mechanism, and outputting an optimal decision set. According to the invention, the core problems of difficult positioning of pipe network aging, low multi-source monitoring precision, rainfall flood regulation lag and the like in urban water network management can be solved.

The invention discloses a garlic whole-process monitoring decision-making platform based on AI and big data, relates to the technical field of artificial intelligence, and solves the problems of data aperture heterogeneity and sample deviation in garlic price prediction in the prior art and the problem that a result deviates from the actual market due to the lack of inventory-behavior feedback modeling in the prediction process. According to the scheme, a multi-level linkage architecture comprising a data protocol layer, a causal deduction layer, an interactive analysis layer, a feedback simulation layer and a parameter calibration layer is constructed, and a consistent data stream is formed by performing time sequence protocol and causal correction on multi-source price data; performing interactive analysis on the price chain and the inventory sequence, introducing a multi-subject response model to simulate market feedback, and finally forming a closed-loop prediction sequence through a dynamic calibration mechanism; according to the method, the prediction stability of garlic whole-process monitoring decision and the reliability of risk research and judgment are remarkably improved.

The invention discloses a hydropower station operation risk level dynamic research and judgment method and system, and the method comprises the steps: obtaining risk work task data of hydropower station operation from an external system through setting a timed task, and building a risk task form; traversing the acquired hydropower station operation risk work task data, judging whether the data are in an operation risk historical library, and if so, recording and assigning the risk level and the disposal measure of the corresponding risk work task in the operation risk historical library to the risk level and the disposal measure of the current risk work task; otherwise, the names of the risk work tasks are subjected to word splitting, matching is conducted in a work risk historical library, and the names with the high matching rate serve as risk levels and disposal measures recommended preferentially; and forming a hydropower station operation risk research and judgment report based on the risk level and the disposal measure of the risk work task. According to the invention, a uniform safety risk research and judgment mechanism and rule can be formed, and non-uniform research and judgment results and wrong results caused by human factors are avoided.

The application discloses a power grid real-time operation risk automatic identification and grading method based on deep learning, and belongs to the technical field of power system automation, which comprises the following steps: obtaining and fusing power grid real-time and historical data to generate a data set; performing double-flow feature extraction based on causal inference to generate a deep feature vector; using a power grid digital twin model to perform forward deduction to obtain a risk deduction consequence set; developing dynamic risk research and judgment to generate a risk grade and a disposal plan; generating an interpretable early warning report and updating network weights through reinforcement learning according to dispatcher feedback data. The application adopts the double-flow feature extraction based on causal inference and the digital twin deduction technology, can effectively handle the nonlinear relationship between risks and working conditions, realizes automatic identification, quantitative calculation and dynamic grading of risks, and improves the timeliness and accuracy of dispatchers in dealing with real-time operation risks and cascading failures.

The application relates to a fund whole-process digital management and control and on-chain evidence storage method, which comprises the following steps: receiving an account adjustment operation request and analyzing the fund secret level; dynamically calling a corresponding set of security protection parameters based on the secret level; constructing a group behavior baseline model based on a privacy protection mechanism; collecting operation behavior characteristics in real time, and calculating the double-baseline deviation of the operation behavior characteristics from the individual and group baselines; fusing and judging the deviation according to a preset threshold to determine a risk level; executing a graded disposal strategy corresponding to the risk level, and synchronizing whole-process key data to a block chain node for evidence storage. The application realizes the online of the whole process of secret fund account adjustment, the intelligentization of risk research and judgment, and the trustworthiness of operation traces, and achieves the comprehensive effects of improving disposal efficiency, strengthening active risk control, ensuring the consistency of accounts and facts, and meeting the requirements of judicial audit level traceability.

The invention discloses a client interaction data analysis method and device, equipment and a medium. By automatically extracting semantic features and emotional features in customer interaction data, accurately analyzing customer appeals and emotional states, intelligently associating a target business entity with historical cases in combination with a preset domain business knowledge base, and generating associated features with business interpretability, risk analysis is deeply rooted to a professional knowledge system. And performing comprehensive intelligent research and judgment by fusing the multi-dimensional features, and automatically outputting a structured decision result containing a risk type, a risk level and a processing priority. Traditional low-efficiency and lagged risk research and judgment depending on manpower are converted into a real-time and accurate intelligent decision-making process. According to the invention, intelligent discovery and early warning of service risks are realized, and service personnel are assisted to make efficient responses according to clear priorities, so that the service efficiency and the service quality of online customer services are comprehensively improved, and the customer satisfaction is effectively improved.

The invention relates to the technical field of greenbelt management and maintenance, in particular to a greenbelt intelligent management and maintenance system based on cloud computing, which comprises a cloud multi-source integration module, a time sequence disturbance analysis module, a disturbance chain association module, a disease trend identification module and a management and maintenance regulation and control linkage module. And judging the online state of the sensing equipment and the plot numbering accuracy. According to the method, a cloud-oriented data integration system is constructed, multi-source environment parameters and spatial attributes are collected into a dynamic data structure, global association and time sequence connection of information among land parcels are achieved, data are automatically sorted through a cloud platform to form a parameter evolution link, multi-level feature criteria such as coverage, time sequence and trend are established for monitoring objects, and the monitoring efficiency is improved. Abnormal evolution performance of a disease state is directly fed back to a management and maintenance regulation and control process through linkage judgment, and system output regulation and control configuration is generated according to a real-time state, so that the consistency of risk research and judgment and the pertinence of a regulation and control decision are improved.

The invention belongs to the technical field of customs risk monitoring, and provides a customs risk assessment rule mining method based on interpretable deep learning, and the method comprises the steps: cleaning a specification and model bar, and introducing Internet public information from a customs risk knowledge base to expand customs declaration feature items; according to the method, key feature items are positioned from multiple expanded customs declaration feature items by utilizing the interpretability of a tabnet model, and a global risk rule and a local risk rule are respectively generated by using a feature attribution method in combination with a decision tree model, so that a reference is provided for customs risk assessment rule design. According to the method, the efficiency of customs risk assessment rule design can be improved, the comprehensiveness of feature selection is ensured, the research and judgment result is more accurate, and by generating the global risk rule and the local risk rule, the problems faced by customs business experts when the rules are designed for a customs risk research and judgment rule engine are comprehensively solved, and the efficiency of customs risk assessment rule design is improved. The problems that numerous feature items of the customs declaration cannot be fully considered, and public information on the Internet is difficult to use are solved.

The application discloses a kind of expressway multi-modal space-time risk situation awareness and research methods and system, it is related to expressway risk research technical field, including: parallel acquisition multi-modal sensing parameter and mapping to geographical grid;By double space-time mask traffic state perception algorithm extraction traffic state tensor;By risk factor gradient coupling analytical model output initial risk potential value;By dynamic road network topology iterative reasoning function update risk potential space distribution;By multilevel risk threshold determination generates structured situation message;By global road network risk situation research platform rendering generates visual view.The application realizes the accurate perception and dynamic deduction of global risk situation of expressway by speed density gradient coupling analysis and dynamic topology iteration propagation, improves the accuracy and timeliness of risk research.

The application discloses a kind of frozen disaster dynamic comprehensive risk research and early warning method, it is related to highway traffic disaster prevention and control technical field, including obtaining the real-time monitoring data of frozen disaster in 12 hours in monitoring area;Real-time monitoring data is matched with early warning grade division standard, and the risk grade of frozen disaster in monitoring area is researched and judged;Based on the risk grade research result, the highway network characteristics of monitoring area, the reserve base number of deicing and snow-removing equipment and materials are combined to generate the deicing and snow-removing material distribution basic scheme of frozen disaster;Combined with the preset path optimization algorithm, the material allocation and optimal transportation route of provincial and county-level reserve center are determined;The optimized material distribution scheme is issued to the corresponding provincial and county-level reserve center, and the reserve center executes material allocation and transportation according to the scheme.The application realizes the dynamic and accurate research of frozen disaster risk, and improves the scientificity and timeliness of material distribution.

The application relates to a kind of job safety risk identification methods based on multi-modal knowledge graph, comprising the following steps: obtaining multiple modal power field operation data and pre-processing;Schema of multi-modal knowledge graph is constructed;The knowledge extraction of different modal power field operation data is carried out respectively, and the label of power field operation data is obtained;The label of different modal power field operation data is fused;Knowledge storage is carried out through the label after fusion and Schema, and the multi-modal power field operation risk cognitive graph is obtained;The multi-modal power field operation risk cognitive graph is inferred;Through the multi-modal power field operation risk cognitive graph, auxiliary decision is carried out.The application solves the problem that multi-source data of power field operation is difficult to be efficiently utilized by constructing multi-modal power field operation risk cognitive graph, improves the comprehensiveness, timeliness and accuracy of operation risk research and judgment, and provides support for on-site operation safety control decision.

The invention discloses a nuclear power plant hydrogen risk analysis method and system, electronic equipment and a storage medium, and relates to the technical field of nuclear power plant safety analysis. Comprising the steps of establishing a key parameter probability distribution model of a nuclear power plant hydrogen originating accident; generating an initial condition combination sample set of the key parameters based on a probability sampling method, and constructing a thermal hydraulic calculation working condition matrix; executing high-fidelity simulation of a full-sample working condition, and generating a containment multi-compartment spatio-temporal evolution data set; training a time sequence neural network model by using the data set to realize mapping of an accident process and multi-physical field parameters; and inputting a real-time accident type and a monitoring initial value, driving the model to output hydrogen concentration prediction, and synchronously completing dynamic evaluation of the AICC risk and the DDT risk. According to the invention, through efficient calculation and data analysis means, rapid probability evaluation of diffusion, aggregation and explosion consequences of hydrogen in the containment is realized, and the risk research and judgment speed and decision reliability under severe accidents of the nuclear power plant are significantly improved.

The invention discloses a coal sample bag real-time detection and violation replacement early warning method. The method comprises the steps of sample initialization and monitoring starting, sample circulation monitoring and violation early warning and authorized unsealing and monitoring termination. The coal sample bag is provided with a flexible electronic tag and an anti-counterfeiting texture; the radio frequency radar and the camera are deployed at key nodes of a circulation path; the reasoning server is in signal connection with the radio frequency radar and the camera and is used for operating a reasoning algorithm; the personnel identity verification device and the anti-counterfeiting texture recognition device are arranged at the unsealing use node; according to the invention, through three core modules of anti-counterfeiting mark enhancement, multi-dimensional state perception and intelligent risk research and judgment, the tamper-proofing and replacement-preventing capabilities in the sample circulation process are significantly improved.

The invention relates to an information monitoring and tracing method and system based on voiceprint and semantic analysis, which effectively solves the problems of privacy invasion and computing power waste caused by a full-time full-amount analysis mode by introducing a condition triggering mechanism that non-content characteristics such as voiceprint and emotion are analyzed in advance and deep semantic understanding is triggered after the risk confidence reaches the standard. Only when the comprehensive risk assessment shows that the real risk exists, directional analysis is carried out on the voice content, so that the balance between privacy protection and effective monitoring is realized. Meanwhile, risk assessment is carried out by fusing multi-modal features such as voiceprints, emotions and environmental sounds, and an anonymous voiceprint behavior database is established for historical tracing, so that the accuracy and depth of risk identification are remarkably improved, the system can give an alarm in real time, behavior tracks and mode analysis of involved subjects can be provided, and the risk identification efficiency is improved. According to the invention, the spanning from single event alarm to continuous risk research, judgment and traceability is realized, and more comprehensive and more intelligent decision support is provided for managers.