563 results about "Risk prevention" patented technology

The invention provides a livestock breeding risk intelligent assessment method and system based on multi-source data fusion, and the method comprises the steps: collecting livestock individual vital sign data, breeding environment parameters, management behavior data and risk-related historical data through Internet of Things equipment, and carrying out the data preprocessing to form a standardized multi-source data set; extracting risk features of individual, group and environment levels based on the data set, and fusing the risk features to form a multi-dimensional risk feature library; utilizing machine learning to construct a differentiated risk assessment model; analyzing the incidence relation between the risk factor and the actual event through the Bayesian network to calibrate the model; realizing livestock risk grade dynamic division and early warning based on the calibrated risk scoring system; and finally, generating intervention suggestions for risk quantitative evaluation, risk prevention and control decision and loss evaluation. According to the method, accurate evaluation of livestock breeding risks is realized, decision support is provided for breeding safety management, and the method has relatively high application value.

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention provides an enterprise data risk processing method and system based on a dynamic knowledge graph, and the method comprises the steps: collecting a real-time data flow generated in the operation process of an enterprise, extracting an entity object and an incidence relation in the real-time data flow, and forming an initial node and an initial edge; and receiving a risk processing request, positioning an initial node corresponding to the target entity from the dynamic knowledge graph according to the risk attribute identifier, and determining an association propagation path generation rule of the target entity based on the risk influence range description. And calling a pre-trained risk propagation model, traversing adjacent nodes in the dynamic knowledge graph according to a correlation propagation path generation rule by taking the initial node as a starting point, and generating a risk propagation path set corresponding to the target entity. And calculating a comprehensive risk value corresponding to the target entity according to the node connection density and the risk attribute superposition intensity of each risk propagation path, and triggering a risk early warning signal when the comprehensive risk value exceeds a preset threshold. The enterprise risk prevention and control initiative and decision-making accuracy can be improved.

The invention discloses an intelligent planning method for space monitoring of an unmanned aerial vehicle. The method comprises the steps that intelligent path allocation is realized by constructing a task demand priority matrix; the method comprises the following steps: firstly, collecting geographical, climate and environmental parameters of a monitoring area, quantifying regional complexity and color features of a monitoring target by combining high-resolution image data with a neural network model, and generating a priority matrix according to task importance, change frequency and risk level; a Dijkstra algorithm is adopted to plan an initial flight path giving consideration to priority and flight limitation, a path complexity index is calculated, and the index comprehensively considers a target priority weight, a task detouring coefficient and a path relaxation degree; and finally, dividing a monitoring area into height layers according to a path complexity index threshold value, performing height layer adjustment on the initial path, and generating a dynamic flight path containing height layer switching, thereby realizing efficient resource allocation and accurate risk prevention and control in a complex monitoring scene.

The invention discloses a planning and scheduling system for optimizing road and bridge construction progress by using artificial intelligence, and relates to the technical field of road and bridge construction. The system comprises a data interaction system module, and the data interaction system module is bidirectionally and electrically connected with a model collaboration mechanism module. Through multi-source data fusion and model collaboration, the construction progress prediction precision is improved by 30%, resource waste is reduced by 20%, in the aspect of risk prevention and control, the geological disaster early warning advance rate reaches 85% through a space-time risk model, the social risk occurrence rate is reduced by 89% through public opinion monitoring, resource scheduling can be adjusted in real time through reinforcement learning and an evolutionary strategy, and the construction progress prediction efficiency is improved. The emergency risk response time is shortened by 50%, the block chain storage ensures that data cannot be tampered, the intelligent contract improves the multi-party collaboration transparency, and the construction management level is comprehensively improved. The problem that a road and bridge construction progress planning and scheduling system in the prior art does not dynamically adjust resource allocation in combination with risk factors and does not cover collaborative analysis of multi-source data is solved.

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention discloses an information management method and system based on an electronic bidding transaction platform, and relates to the technical field of information management. By constructing a multi-dimensional and multi-level information management system, deep mining and intelligent identification of potential association relationships and behavior patterns among bidding units are realized, so that the capability of the electronic bidding platform in the aspects of risk prevention and control and abnormal behavior identification is effectively improved. By fusing multi-source heterogeneous data such as industry and commerce registration information, bidding history, project contracts and the like and introducing atlas modeling and semantic analysis technologies, key risk factors such as legal person penetration, historical cooperation, high bidding document similarity and the like can be accurately identified, then the risk association strength between bidding units is quantitatively evaluated, and the risk assessment efficiency is improved. And a visual map and an auxiliary auditing mechanism are formed, so that the judgment efficiency of supervisors is improved.

The invention provides an intelligent risk identification and analysis method based on multi-modal heterogeneous data fusion, which relates to the technical field of risk management, and comprises the following steps of: acquiring multi-source data, constructing symbol-nerve double-space processing, generating semantic vectors, extracting features through orthogonal matrix decomposition and a bidirectional long-short-term memory network, and obtaining an intelligent risk identification and analysis result; according to the method, multi-modal information is fused by using an adaptive weight mechanism, and a risk propagation topology network is constructed by applying a graph embedding algorithm, so that accurate identification and early warning of risks are realized, and the risk prevention and control capability and prediction accuracy are effectively improved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention relates to the technical field of big data processing and analysis, in particular to a cross-border payment path optimization system and method based on multi-dimensional data analysis. Payment transaction data and exchange rate data are collected, a multi-dimensional payment path evaluation model is constructed from the angles of payment cost, payment timeliness, payment risk, payment availability and the like, and each payment path is scored in real time; constructing a payment path optimization model, dynamically selecting an optimal path and performing intelligent scheduling; a machine learning algorithm is introduced, an abnormal transaction mode is automatically identified from mass payment data, and a real-time anomaly detection and risk prevention and control strategy is established; according to the method provided by the invention, the success rate of cross-border payment can be improved, the cost is reduced, the payment time is shortened, and the payment risk is prevented, so that the cross-border payment service quality and the user experience are comprehensively improved.

The invention discloses an engineering construction quality safety risk management system, and belongs to the technical field of safety risks. Comprising a project management module, a quality safety risk management and control module, a compliance review and data analysis module, an integrated project management module, a risk identification and evaluation module, a risk prevention and control strategy module, a dynamic monitoring and early warning module, an accident handling and tracing module, a compliance review auditing module and a data analysis report module. According to the invention, basic information and design parameters of construction projects are collected through the project management module, a BIM model is established, construction simulation, conflict detection and resource optimization are carried out, an actual construction plan and schedule are formulated, multivariate data in the construction process are collected in real time, early recognition, evaluation and positioning of construction safety quality risks are realized by using the model, and the construction safety quality risk is evaluated and positioned. A risk prevention and control strategy is formulated, key parameters are monitored in real time, early warning is given out immediately when data exceed a threshold value, a risk model and prevention and control measures are improved, and efficiency and safety are improved.

The invention relates to the technical field of semantic decision management and control, in particular to a multi-modal semantic alignment intelligent decision and risk management and control system, which comprehensively and accurately captures cross-modal semantic association through multi-modal semantic alignment processing so as to generate a plurality of possible reasoning chains with reliability and interpretability. And combining dimensions such as knowledge conflicts and historical risks, calculating reasoning overlapping values to evaluate inter-chain association, realizing multi-dimensional and multi-angle analysis of potential risks, bringing risk fingerprint values and the reasoning overlapping values into a quantitative calculation framework of decision response values, dynamically setting a decision response threshold value, and realizing quantitative calculation of the risk fingerprint values and the reasoning overlapping values. The system can flexibly trigger emergency, early warning or monitoring response according to different risk levels, and refinement and differentiation of risk management and control are realized. The mechanism not only ensures timely disposal in a high-risk scene, but also avoids resource waste in a low-risk scene, and effectively balances risk prevention and control and execution efficiency.

The invention discloses an AI-based tendering and bidding full-process intelligent generation and review method and system. The method comprises the steps of tendering full-process intelligent generation, bidding full-process intelligent generation and bidding review. An AI scoring model is adopted for review, the technical scheme of each bidding document is deeply analyzed through a natural language processing technology, and real-time cross validation is carried out with a knowledge graph, so that the score of each sub-item is provided with detailed comments and a definite evidence chain, the objectivity, transparency and credibility of review are greatly improved, and the review efficiency is improved. The review quality is improved from experience judgment to a data and knowledge driven level; secondly, potential violation behaviors are identified through a surrounding bidding risk report generated through multi-dimensional correlation analysis, and the risk prevention and control capability is effectively enhanced; and finally, based on comprehensive data analysis, automatically generating a bid-winning candidate sorting and comprehensive review report, and ensuring that the calibration result is optimal.

The invention discloses an intelligent financial risk early warning method based on multi-modal data fusion, particularly relates to the field of financial risk management and control, and is used for solving the problem of existing enterprise transaction authenticity risk prevention and control. According to the method, a cross-modal transaction behavior chain of supervision enterprises is constructed through merging of transaction data in continuous account periods and modal information aggregation; generating an initial transaction relation graph based on the behavior chain, identifying business associated enterprises, and screening pseudo independent enterprises with control path crossing; performing modal semantic consistency check on the transaction path of the pseudo independent enterprise, identifying a semantic mismatch node, tracing a fund return path by taking the semantic mismatch node as a starting point, identifying a transaction closed-loop structure, and constructing a pseudo compliance risk chain; finally, the control relation and modal data are integrated, a structured evidence chain is generated, risk early warning output of involved enterprises is rapidly completed in a high-risk marking mode, and the automation degree, interpretability and accuracy of abnormal financial risk discrimination are improved.

The invention discloses a big data abnormal behavior risk detection method and device, equipment and a medium, and the method comprises the steps: constructing a time-space behavior tuple containing a timestamp, a geographic position and behavior characteristics through user behavior data, environment sensor data and external event data collected by terminal equipment; inputting the time-space behavior tuple into a neural network model, and generating a portrait feature vector representing a user behavior pattern; performing time sequence conversion on the portrait feature vector, and outputting an abnormal probability value through a multi-scale Transform model; based on the abnormal probability value, clustering behavior data of the same region and the same time period, and combining the external event data to construct a causal association map; and calculating contribution weights of environmental factors according to the causal association map, and determining risk tags according to the contribution weights. By analyzing the time-space behavior tuple of the user, the abnormal behavior detection and risk prevention and control capabilities are improved.

The invention discloses a supply chain data-oriented enterprise upstream and downstream collaborative risk control prediction method and system, and relates to the technical field of supply chain financial risk management, and the method comprises the steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating a supply chain collaborative credit rating based on a fluctuation amplitude and a historical default record, and recognizing a risk source enterprise; adopting a recursive partition time window to extract a period index and a sudden change index of the payment behavior, and constructing a payment behavior portrait; identifying associated enterprises through the phase overlapping degree of the payment behavior track to form a risk conduction sequence; and based on the evolution law and the risk conduction sequence of the payment behavior portrait, predicting a risk triggering node, and generating a risk prevention and control scheme including a credit line regulation and control instruction and a behavior monitoring strategy. The method can effectively identify the supply chain risk source, predict the risk conduction path, and improve the financial risk control capability of the supply chain.

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

The invention relates to the technical field of financial risk management, in particular to a big data-driven financial risk early warning system, which comprises a data detection module, a behavior risk rating module, a dynamic risk monitoring module, a behavior pattern clustering module and a financial risk early warning module. According to the method, the sensitivity and accuracy of risk detection are improved by analyzing small deviation between transaction behaviors and historical data, the dynamic risk monitoring function can realize quick response to abnormal transactions by continuously tracking the fund flow direction and the transaction frequency, and the risk detection accuracy is improved by analyzing the relationship between the fund flow direction and past risk events. According to the method, normal financial activities and potential risk behaviors can be effectively distinguished, an enterprise can take prevention measures before the risk actually occurs, and the clustering analysis of the risk behaviors can help to more accurately position a risk source, so that the measures are taken in a targeted manner, financial resources are optimized, and the risk prevention and control capability and decision-making efficiency are enhanced.

The invention discloses a data sharing system and method based on a smart contract, relates to the technical field of computer application, and realizes dynamic risk prevention and control and self-optimization closed loop of data sharing through deep integration of the smart contract and real-time monitoring. A three-dimensional feature vector is constructed by collecting the calling frequency, the response duration, the number of failures and the user address in real time, and the low / medium / high risk level is dynamically judged in combination with a preset rule base; user authorization parameters are automatically adjusted according to the risk level, a current limiting / fusing instruction is generated, and a data gateway limits a high-risk user request or switches a standby node in real time; historical data are periodically analyzed, a risk judgment reference value and a proportionality coefficient are dynamically updated, a rule base is optimized and is subjected to uplink storage, and a strategy is ensured to continuously adapt to system load changes. According to the scheme, the data sharing security, the system stability and the resource utilization rate are remarkably improved, and an efficient and intelligent risk prevention and control solution is provided for distributed data sharing.

The invention discloses a three-dimensional digital twin modeling method, system, equipment and medium taking a reservoir as an object, and relates to the technical field of reservoir monitoring, and the method comprises the steps: constructing a three-dimensional space model facing a reservoir entity, and forming a visual simulation environment; accessing multi-source data related to reservoir operation, and establishing a unified data aggregation and management mechanism; integrating a real-time operation state in the three-dimensional space model, and dynamically mapping perception information and business activities of the reservoir; and based on a set analysis model and a determination rule, carrying out intelligent processing on the reservoir operation data, identifying potential risks and generating an early warning result. According to the invention, by establishing a multi-type analysis model and a judgment rule system, real-time calculation and risk identification are carried out on key indexes, intelligent early warning of risks such as emergencies, trend anomalies and historical extreme value overrun is realized, and the risk prevention and control capability of the reservoir is effectively improved.

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

The invention discloses a digital twinning progress tracking and risk pre-control method for hydraulic engineering construction, and relates to the field of hydraulic engineering construction, and the method comprises the steps: obtaining mechanical positioning, material entering and environment monitoring data through a multi-source sensing terminal, and generating a time-space aligned construction situation data set; a progress weight template is activated based on the construction stage identifier, and a dynamic weight vector is generated in combination with the environment data; comparing the construction situation data with the reference model to calculate a comprehensive progress deviation index; when the index exceeds the limit, constructing a resource topological relation graph to generate an elastic scheduling instruction; outputting a derivative risk probability through the risk conduction model and generating an optimized scheduling instruction set; triggering a calibration instruction to feed back a correction parameter according to the execution data; by establishing a three-level calibration mechanism linked with the construction progress, the risk change and the environmental disturbance, layered accurate adjustment of data acquisition, environmental factors and model parameters is realized.

The invention discloses a mucky soil foundation intelligent monitoring method based on multi-source data fusion, and particularly relates to the technical field of foundation monitoring and early warning. The method comprises the following steps: constructing an original soil body state data set of a mucky soil foundation, and carrying out anomaly elimination and time calibration based on a rheological constitutive model to generate a foundation state characteristic matrix; performing dynamic causal association tracking analysis on the feature matrix in a sliding window mode to generate a water-soil coupling drift parameter set; extracting a stress historical dependency coefficient set by using a nonlinear scale analysis method; and according to the water-soil coupling drift parameter set and the stress historical dependency coefficient set, the stability risk level of the mucky soil foundation is judged, and corresponding early warning information is selected from a preset graded early warning instruction database and output, so that the accuracy and adaptability of anomaly recognition and risk early warning are improved, and the accuracy and the adaptability of abnormal recognition and risk early warning are improved. The method is suitable for long-period stability monitoring and intelligent risk prevention and control of high-soft foundations or silt stratums.

The invention provides a sedimentation inversion prediction method based on water-soil coupling and well reservoir dynamic feedback, which comprises the following steps: by collecting geological, hydrological, deformation monitoring and engineering construction data, constructing a three-dimensional geologic model containing a shallow unconsolidated formation and an underlying bedrock layer, and setting physical boundary constraints; establishing a water-soil bidirectional coupling model, integrating a seepage model and a deformation model, and realizing bidirectional coupling through a dynamic feedback mechanism; calculating the leakage / extraction amount based on the dynamic water level in the well and the hydraulic long pipe model, dividing the layer leakage amount and feeding back the layer leakage amount to the seepage model; model parameters are inversed and calibrated in combination with actually measured data; and finally, predicting a settlement spatio-temporal evolution process based on the calibration model, and generating a risk prevention and control scheme in combination with building settlement indexes. The method has the advantages that comprehensive information is obtained through multi-source data fusion, the deformation mechanism model considering the water-soil bidirectional coupling and the well storage effect is established, and the defects in multi-factor coupling, boundary condition processing and parameter inversion in the prior art are overcome.

The invention discloses a bank intelligent risk control system based on a block chain and multi-modal artificial intelligence. A data acquisition module acquires and integrates bank internal data, third-party data and cross-chain data; the edge computing module is provided with a plurality of edge computing nodes, and high-frequency transaction data is processed in real time by using a lightweight model; the block chain network module adopts a relay chain to connect a bank chain and a supply chain, and performs verification based on zk-SNARK zero knowledge proof; the artificial intelligence module constructs a multi-modal risk assessment model, and performs risk assessment in combination with an XGBoost integrated learning algorithm, GNN deep learning and federated learning; and the application decision module performs risk early warning, dynamic processing, visual monitoring and on-chain auditing according to the evaluation result of the artificial intelligence module. According to the scheme, the problems of information isolated island, difficulty in guaranteeing data authenticity, risk identification lag and the like in the existing bank risk control system are effectively solved, and the bank risk prevention and control capability and the market competitiveness are improved.

The invention discloses an active safety monitoring method for an energy storage power station, which relates to the field of safety detection of the energy storage power station, and comprises the following steps: setting multi-level acquisition frequency, acquiring energy storage safety data such as cell temperature, module internal resistance, hydrogen concentration and the like in stages according to time intervals from large to small, and establishing a dynamic model based on an Arrhenius equation to predict theoretical internal resistance; the residual error rate of the actually measured internal resistance and the theoretical internal resistance is calculated to evaluate internal resistance abnormity, a risk index is generated by combining the hydrogen diffusion coefficient, the concentration change rate and internal resistance abnormity weighting, and then dynamic early warning measures are executed in a graded mode according to the risk threshold value corresponding to the cell temperature interval. According to the method, through a multi-parameter fusion analysis and dynamic response mechanism, early abnormality of the battery can be accurately captured, monitoring precision can be flexibly adjusted, risk levels can be quantified, differentiated disposal strategies can be matched, timeliness, comprehensiveness and pertinence of safety monitoring of the energy storage power station can be improved, the misjudgment rate can be effectively reduced, and the safety of the energy storage power station can be improved. And a scientific basis is provided for battery fault early warning and risk prevention and control.

The invention relates to the technical field of electric power systems, in particular to an electric power spot market risk prevention and control method and system considering market subject behaviors. Acquiring operation data of generating capacity, electricity demand, market price and power grid state of the electric power spot market; on the basis of the acquired operation data, a coupling degree evaluation index and a stability evaluation index are introduced to carry out prediction and anomaly detection on a market trend, and influence factors of connection of a large-scale generator set and a small-scale power grid are particularly concerned; analyzing a market subject behavior pattern through transaction frequency, price volatility and market participation degree characteristic quantitative indexes, and identifying market manipulation and violation operation risks; market dynamics are monitored in real time, a risk threshold value is set, and an alarm is triggered when a risk index exceeds a set standard; early warning information of different levels is automatically sent out according to the risk degree, and corresponding countermeasures and suggestions are provided; and a supervision mechanism is helped to make a quick response through a visual interface.

The invention discloses a water quality monitoring method and system based on large model data analysis, and the method comprises the steps: collecting multi-dimensional environment index real-time data through a remote sensing device and an underwater mobile sensor, completing the noise filtering and format standardization through an edge calculation node, and inputting the data into a machine learning model; the method comprises the following steps: automatically extracting space-time distribution characteristics and intelligently identifying potential abnormal modes, immediately starting multi-parameter correlation analysis when an abnormal degree exceeds a threshold value, constructing a deviation correlation map to accurately invert pollution source space coordinates, further performing time sequence prediction modeling in combination with a historical data sequence, and deducing a future diffusion path track of pollutants. And finally, deeply overlapping and fusing the predicted trajectory and the ecological sensitive area map, quantitatively calculating the comprehensive risk score distribution of each intersection area by using a risk assessment algorithm, automatically generating a visual environment monitoring report, and updating a historical sequence to form closed-loop learning at the same time. According to the invention, the emergency response speed, the traceability accuracy and the risk prevention and control capability of the sudden pollution event of the water environment are greatly improved.

The invention discloses an escalator operation environment monitoring and risk assessment method, and relates to the related field of escalator safety monitoring, and the method comprises the following steps: cooperatively carrying out escalator monitoring data acquisition; real-time receiving and transmission are carried out based on the Internet of Things; performing data association mining on the time sequence environment parameters and the time sequence operation parameters, and outputting environment risk association data; loading to an environment risk assessment model for operation risk assessment, and constructing an integrated assessment system from the aspects of structural stability, durability, safety protection and the like; and escalator operation and maintenance decision making is carried out according to the risk assessment result, multi-dimensional risk prevention and control and response are achieved, performance assessment, risk grade division and early warning are included, and preventive maintenance, environmental adaptability transformation and emergency response mechanisms are established. The technical problems that existing escalator risk assessment is incomplete in monitoring, not real-time, inaccurate in risk identification and insufficient in assessment result reliability are solved, and the technical effect of improving the risk identification accuracy and the assessment result reliability is achieved.

The invention discloses a thermoelectric enterprise safety management system and method based on integration, and relates to the technical field of thermoelectric production safety control. The system performs quantification processing on the collected real-time disturbance data set through a preset coupling algorithm to generate a risk pre-coupling factor representing a multi-source disturbance coupling risk; generating a predicted operation state of a future time node in combination with a current operation state set representing the operation condition of the equipment; the prediction operation state is compared with a preset operation safety threshold value, the safety and stability margin is calculated, an original control target of the unit is dynamically adjusted, and the system generates a prediction error by comparing the prediction operation state with a subsequently collected actual operation state; and the prediction model correction parameters of the prediction model are corrected by using the prediction error, and online adaptive adjustment of the prediction model is realized, so that quantitative evaluation of multiple disturbance coupling effects is realized, and a decision basis is provided for predictive risk prevention and control.