726 results about "Risk evaluation" patented technology

The invention provides an urban water affair situation awareness management method and system, and relates to the technical field of urban water affair management, and the method comprises the steps: obtaining multi-source heterogeneous data; performing preprocessing, feature extraction and fusion on the multi-source heterogeneous data to obtain a comprehensive analysis feature set; inputting the comprehensive analysis feature set into a situation assessment model to determine the situation level of the urban water affair; inputting the comprehensive analysis feature set into a time sequence prediction model, and predicting to obtain a situation change trend of a future time period; based on the comprehensive analysis feature set, the situation level and the situation change trend, performing comprehensive assessment by using a preset risk assessment index system, and generating a risk assessment result; based on the situation level, the situation change trend and the risk assessment result, a preset expert knowledge base is called for matching and reasoning, and a candidate disposal strategy set is generated; and evaluating strategies in the candidate disposal strategy set, and outputting an optimal disposal strategy. According to the invention, the management level of urban water affairs is improved.

The invention specifically relates to the technical field of intelligent control, and discloses an intelligent control-based mold forming optimization system, which comprises a defective finished product image acquisition module, a defect intelligent identification module, a defect score quantification module, a process parameter synchronous acquisition module, a process risk dynamic analysis module and a process association closed-loop optimization module, the method comprises the following steps of: calculating a single-parameter risk index and a comprehensive risk index based on a process risk dynamic analysis module through a process parameter synchronous acquisition module in real time of full-flow process parameters of an injection molding process, identifying abnormity when the parameters deviate from an optimal target value, immediately triggering early warning, quickly positioning core cause parameters through a defect-process incidence matrix, and quickly positioning the core cause parameters through the defect-process incidence matrix. The influence of adjustment of different parameters on comprehensive defect scores is simulated within an equipment constraint range through a reverse optimization algorithm, a reverse optimization scheme is generated, a full-link traceable data link is formed through timestamps, stop loss time and production cost are greatly reduced, and accurate assessment and prediction of risks are achieved.

The invention relates to the field of surrounding rock supporting, and provides an aging cracking and multi-source dynamic response self-adaptive supporting system and method in order to improve the supporting reliability, and the state of surrounding rock is sensed in real time through a multi-source sensing module; the aging damage model can comprehensively and dynamically evaluate the damage state of the surrounding rock of the mixed rock, and progressive instability accidents such as hysteresis rock burst can be effectively prevented; the dynamic disturbance model can analyze the damage effect of dynamic loads such as TBM tunneling and blasting on mixed rock surrounding rock in real time, a millisecond-level dynamic risk assessment result is provided for a self-adaptive support system, and instant instability accidents are effectively restrained. The self-adaptive support of the dynamic execution module is realized by combining the parallel decision of the aging damage model and the dynamic disturbance model, the problem that the traditional support depends on static geological data and is difficult to adapt to the dynamic damage change of the surrounding rock is solved, the stability and reliability of the support system are remarkably improved, and the engineering cost and the safety risk are reduced.

The invention relates to the technical field of industrial data management, in particular to an unmanned aerial vehicle task scheduling intelligent method for low-altitude economy. Obtaining business demand data, equipment state data and environment monitoring data; establishing a business priority dynamic adjustment scheme, evaluating the urgency, potential hazard and environmental complexity of the tasks according to the business risk evaluation matrix, dynamically adjusting the corresponding priorities, and constructing a multi-task cooperation sequence; a multi-target business decision optimization method is adopted to analyze the multi-task cooperation sequence and the equipment state data, load matching clustering is utilized to carry out task preliminary grouping, and a multi-target business decision optimization function is solved to carry out task distribution and business decision management; and carrying out predictive analysis on the river and lake environment risk by adopting a prospective service scheduling scheme, and taking a prediction result as the prospective input of the multi-target service decision optimization method to realize service pre-distribution and decision management. According to the invention, adaptive task scheduling can be realized.

The invention relates to the technical field of safety monitoring, and discloses a multistage safety early warning and linkage processing system for an electricity-hydrogen complementary energy station, and the system comprises a monitoring collection module which is used for deploying a multi-source sensor network in the electricity-hydrogen complementary energy station, collecting key parameter data in real time, and generating a real-time data set comprising a timestamp, a sensor ID and a parameter value; the anomaly detection module is used for performing multi-dimensional analysis by adopting an anomaly detection algorithm according to the real-time data set, identifying an anomaly mode and outputting a graded early warning signal; the evaluation grading module is used for carrying out dynamic risk evaluation in combination with the early warning signal and the system state, and outputting a quantitative risk grade and a disposal suggestion; the linkage triggering module is used for automatically matching and triggering a corresponding plan according to the risk level and outputting a specific linkage control instruction; an execution feedback module; according to the invention, the emergency response speed is improved, the false alarm rate caused by normal working condition fluctuation is reduced, and fundamental conversion from passive response to active early warning is realized.

The invention relates to the technical field of construction safety, in particular to a trestle construction hazard source assessment method and system, and the method comprises the steps: constructing a hazard source data set corresponding to a trestle construction process, carrying out the entry recording of various potential hazard sources, and setting impact factors for representing risks; collecting field data of the construction process, and associating the field data with the influence factors to obtain risk characterization forming a hazard source; fusing the risk characterization with the environmental parameters, and constructing a space-time risk mapping layer; based on the space-time risk mapping layer, performing quantitative analysis on the hazard source through a multi-factor comprehensive model to obtain risk distribution characteristics in different level intervals; and according to the risk distribution characteristics, generating a space-marked trestle risk map. According to the invention, the problems of strong subjectivity and lack of systematic analysis of existing trestle construction hazard source assessment are effectively solved, the objectivity of risk assessment is enhanced, and the capability of accurately judging the risk state in a complex construction environment is improved.

The invention relates to the technical field of fault prediction and health management, and discloses a power inspection unmanned aerial vehicle cluster state monitoring method and system. The method comprises the following steps: acquiring a cluster equipment state, environmental interference and historical inspection data; extracting node feature vectors to generate an adjacent matrix, constructing a dynamic collaboration diagram and calculating node connection strength; fusing the interference feature vectors to obtain an edge weight change value, and generating an abnormal cooperation sequence if the edge weight change value exceeds a threshold value; analyzing a sequence trend and combining a historical track prediction state to generate a prediction state vector; calculating a deviation degree with a normal vector, and dividing potential risk levels; generating evaluation parameters by referring to historical data, and substituting the evaluation parameters into the model to obtain risk evaluation values; and if the warning threshold value is exceeded, correcting the collaboration diagram, generating a new collaboration path and supplementing the inspection blind area to obtain a path optimization result. According to the method, dynamic and accurate state monitoring and collaborative optimization of the electric power inspection unmanned aerial vehicle cluster can be realized, and the dual requirements of safety and high efficiency of power grid inspection in a complex environment are met.

The invention relates to the technical field of public health safety, and discloses a quantitative evaluation method and system for pathogen transmission risk, and the evaluation method comprises the following steps: step 1, obtaining virulence genes, reference virulence genes, environmental parameters, transmission factors and host risk factor parameters of pathogen samples; 2, performing variation detection on the virulence gene by adopting various variation detection algorithms to obtain all variation sites, and processing all variation sites to obtain effective variation sites; 3, constructing a virulence index model, inputting the variation distance into the virulence index model, and calculating the virulence index of the pathogenic bacteria sample; and 4, constructing a propagation risk assessment model, inputting the virulence index, the environmental factor, the propagation coefficient and the host susceptibility risk index into the propagation risk assessment model, and outputting a risk value by the propagation risk assessment model. According to the method, accurate prediction of the pathogen transmission risk can be realized.

The invention discloses a construction engineering cost analysis system and method, and particularly relates to the technical field of construction cost behavior monitoring, and the method comprises the following steps: collecting the business information and field process information of an execution stage project, carrying out the field-level matching with a budget detail table, and constructing a data set; performing risk judgment according to the contract plan information and the actual execution information, and judging whether to start a risk identification process; executing multi-dimensional information cross-analysis to identify cost risk symptoms when the conditions are met; calculating dynamically updated first and second risk assessment factors based on the identification result, and inputting the first and second risk assessment factors into a risk assessment model to generate a comprehensive cost risk score; according to the method, accurate matching of the budget information and the execution data is realized, and the real-time sensing capability of the cost data is improved; through a dynamic risk identification and cross analysis mechanism, the early discovery and judgment capability of the cost abnormity is enhanced; and a two-factor evaluation and scoring model is constructed, so that quantitative expression and intelligent early warning of the cost risk are realized.

The invention discloses an intelligent driving control method and system for an unmanned ship, and belongs to the field of unmanned driving control. Firstly, the position and speed of the unmanned ship and the positions and speeds of surrounding obstacles are obtained through a sensor and a positioning device, then dynamic path tracking is carried out in combination with global path planning and local path planning, and finally the unmanned ship is driven to sail along an optimized path by controlling the course and an accelerator. Local path planning adopts an improved dynamic window method, and an unmanned ship trajectory prediction evaluation function is modified by introducing a dynamic risk evaluation layer and a rule compliance layer; the dynamic risk evaluation layer is used for designing a collision risk evaluation function to dynamically screen a speed sampling space and rejecting a speed vector conflicting with a predicted trajectory of a moving obstacle; and the rule compliance layer classifies the meeting situation, introduces a rule evaluation function to perform weight correction on the candidate velocity vector, and drives the unmanned ship to generate a course adjustment strategy conforming to the collision avoidance rule. The unmanned ship realizes full-autonomous cruise, high-precision obstacle avoidance and path tracking in a complex marine environment.

The invention relates to air route quantitative evaluation, in particular to an unmanned aerial vehicle air route dynamic quantitative evaluation method based on comprehensive risk avoidance, and the method comprises the steps: constructing a multi-dimensional air route risk quantitative evaluation system, and enabling each dimension to comprise a plurality of specific and quantifiable risk evaluation factors; mapping the observation value of each risk assessment factor into a standardized risk score; determining the weight of each risk assessment factor according to a dynamic weight adjustment strategy, and calculating a route comprehensive risk score by combining the standardized risk score of each risk assessment factor; executing the evaluation process on all the candidate paths, calculating an air route comprehensive risk score for each candidate path, preferentially recommending the candidate path with the highest air route comprehensive risk score, and generating an air route risk evaluation report at the same time; according to the technical scheme provided by the invention, the defect that accurate dynamic quantitative evaluation of the route of the unmanned aerial vehicle is difficult in the prior art can be effectively overcome.

The invention belongs to the technical field of ecological restoration evaluation, and provides a geological ecological restoration evaluation method and system for geological disaster prevention and control, electronic equipment and a storage medium. The method comprises the steps of research data acquisition, geological disaster type setting, initial evaluation, data preprocessing, model construction and training, risk determination before restoration, information acquisition after restoration, geological disaster risk prediction and geological ecological restoration evaluation. According to the method, the model is trained by using the remote sensing image, the sound wave data and the initial evaluation data, the evaluation result is predicted through the remote sensing image and the sound wave data, the risk evaluation difficulty and time are reduced, in addition, remote sensing and sound waves are combined, meanwhile, surface features and structural features are extracted, and the prediction effect of the model is optimized; through a multi-mode proximity perception model, the perception capability of the surrounding environment is improved, and the prediction precision of a single block is improved; and through the repair effect evaluation rule, a repair effect evaluation mechanism is optimized.

The application discloses a kind of urban road physical structure hidden danger investigation method, device and electronic equipment, method includes: obtaining the road physical structure data in target road network;According to the road physical structure data, determine the risk evaluation index data corresponding to target type road, the risk evaluation index data is determined according to the road attribute of corresponding type road;According to the risk evaluation rule and the risk evaluation index data of the preset, the risk of target type road is evaluated;According to the risk evaluation result of target type road, determine the hidden danger investigation scheme of target type road.The method of the application can make the road hidden danger investigation more efficient, effectively solve the problem that the dependence degree of existing road hidden danger investigation scheme on the working experience of investigators is high and the investigation is not thorough, greatly save the labor cost.

The invention provides a movement control method, system and device for adaptively following a target object and a medium, and belongs to the field of robot automatic control, and the method comprises the steps: obtaining the motion state information of a robot, the obstacle information of the surrounding environment and the position information of the target object; generating a prediction track of the dynamic obstacle based on the dynamic obstacle information, and constructing a risk area according to the static obstacle information and the prediction track; sampling by adopting a dynamic window algorithm to obtain a plurality of groups of candidate speed pairs and generating a plurality of candidate tracks; on the basis of candidate trajectories, the risk area and the predicted trajectory of the dynamic obstacle, a trajectory risk evaluation value is obtained through calculation; and based on a preset evaluation index, scoring the candidate trajectory, selecting an optimal speed pair according to a scoring result, and based on the optimal speed pair and the current actual speed of the robot, generating a control instruction to drive the robot to move. The autonomous following performance and the user experience of the robot in the dynamic environment are improved.

The invention relates to the technical field of new energy power distribution planning, in particular to a new energy power distribution planning intelligent simulation system oriented to a novel power system. Comprising a multi-source data dynamic modeling unit; the multi-objective optimization decision-making unit is used for realizing collaborative optimization of economy and reliability of the power distribution network and generating a planning scheme of equipment configuration, energy storage layout and network reconstruction by improving a heuristic algorithm; and the uncertainty analysis unit is used for quantifying the influence of the wind and light output fluctuation on the planning scheme and outputting a risk assessment index through a scene generation technology and a probability sampling method. Through multi-modal data fusion, time sequence model construction and a self-adaptive updating mechanism of the multi-source data dynamic modeling unit, the dynamic adaptation capability of the model to new energy power fluctuation and load characteristic change can be improved, and meanwhile, the space-time correlation characteristics between the source and the load are mined, so that the power grid model can be synchronously evolved along with the time sequence data change; and the deviation between the model and the actual operation condition is reduced.

The invention discloses a real-time system task scheduling resource allocation risk defense method and device, and relates to the technical field of real-time system resource scheduling. The method comprises the steps of obtaining task data submitted to a system, and constructing a task dependency topological graph with dynamic characteristics based on task description, code logic and interface calling information of the task data; in a task initialization stage, resource pre-allocation is performed according to a task dependency topological graph and resource demand information of each task; in the task execution process, task state changes are captured through a system monitoring service, task resource allocation risks are evaluated, the task dependency topological graph is updated according to state capture and risk evaluation results, and task resource allocation is adjusted based on the updated task dependency topological graph. According to the invention, through dynamic management of the whole life cycle of the task, a full-link anti-overload and anti-hunger solution is provided for real-time system scheduling, and the method is especially suitable for high-complexity industrial scenes.

The invention discloses a geological risk identification method based on multi-source data fusion, belongs to the technical field of geological risk monitoring and analysis, and can solve the problems that the existing address risk identification mode is single, the identification precision is poor, and the identification efficiency is low due to the lack of dynamic monitoring means. The method comprises the following steps: S1, collecting geological data of a target area, and constructing a three-dimensional geological risk dynamic model according to the geological data; s2, determining risk index calculation data of the target area according to debris flow source data and rainfall data of the target area and the three-dimensional geological risk dynamic model; s3, determining a risk evaluation coefficient of the target area according to the risk index calculation data and risk index reference data of the target area, and generating a dynamic risk partition map according to the risk evaluation coefficient; and S4, generating early warning information of the target area according to the dynamic risk partition map, and performing geological risk identification according to the early warning information. The method is used for geological risk identification.

The invention provides a subway tunnel water disaster risk evaluation method under extreme rainfall based on multi-source data, and belongs to the technical field of urban rail transit disaster prevention. The method comprises the following steps: establishing a grading evaluation system on the basis of field rainstorm monitoring data, hydrogeological parameters, field geological exploration, rock-soil physical and mechanical parameters, monitoring data of a tunnel lining structure and material strength parameters of a tunnel site area in combination with a plurality of developed tests, dividing corresponding grades and determining an influence index-; and S4, respectively carrying out weight calculation on the influence index-by adopting an analytic hierarchy process and an entropy weight method, then calculating a dynamic weight of the influence index-, correcting an abnormal dynamic weight, generating a comprehensive score, dividing risk grades, matching graded prevention and control measures, and optimizing and adjusting the prevention and control measures in real time through a parameter risk grade dynamic judgment and construction method matching mechanism. According to the method, minute-level accurate interpretation of a disaster evolution mechanism and dynamic optimization adaptation of a prevention and control scheme are realized.

The invention discloses a heterogeneous database migration strategy generation method and device and a medium, and the method comprises the steps: respectively obtaining structures and constraint conditions of a source database and a target database, carrying out the structured conversion of the structures and the constraint conditions, and generating a structured parameter set; performing risk analysis on the target database constraint condition to generate a migration verification data strategy; acquiring a migration verification data set according to the migration verification data strategy; in a preset isolation environment, executing a data migration operation on the migration verification data set, and recording behavior parameters generated in a data migration process; performing risk analysis on the behavior parameters to generate risk assessment parameters; and according to the risk assessment parameter, matching a corresponding migration strategy type from a preset migration processing rule set to generate a migration strategy parameter which can be directly applied to formal migration. The defects that in the prior art, manual experience is relied on, and risk discovery is lagged are overcome, and the certainty and overall efficiency of the migration process are remarkably 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 a coal mine coal dust explosion risk dynamic evaluation and explosion suppression system, which comprises a sensing module, a data processing module, a data processing module and a data processing module, wherein the sensing module is used for acquiring direct and indirect parameters of an underground environment; the central processing module is used for operating a risk assessment algorithm and generating a control instruction; the explosion suppression execution modules are distributed in pipelines and roadways of underground key nodes and are used for executing explosion suppression actions; the active chemical detection module is arranged in the main ventilation pipeline and is used for carrying out pre-chemical sampling and analysis on the inflowing air so as to judge the abnormal enrichment trend of the explosive gas; according to the system, a dynamic risk assessment system based on multi-source indirect perception and active chemical detection is constructed, and precise explosion suppression of time-space cooperation is realized. The method has the advantages of being capable of achieving early warning, dynamic evaluation and precise collaborative explosion suppression.

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

The invention provides a tunnel grouting fracture network generation and risk dynamic assessment method and system, and belongs to the technical field of tunnel engineering grouting reinforcement and numerical simulation. Comprising the following steps: performing fracture topological parameter extraction on acquired multi-modal data in a tunnel by adopting a three-dimensional semantic segmentation method to construct a fracture feature database; the fracture statistical features in the fracture feature database are input into the generative adversarial network to generate a tunnel grouting fracture network; based on the tunnel grouting fracture network, graph nodes are selected, and graph edges are constructed; a graph neural network is utilized to learn a connection relation between graph nodes to obtain a fracture connectivity coefficient; constructing a Bayesian network according to the fracture connectivity coefficient; and based on the Bayesian network, constructing a continuous three-dimensional risk field distribution map to realize risk dynamic assessment. According to the method, the three-dimensional random fracture model conforming to the special geological statistical characteristics can be constructed, and minute-level accurate evaluation of the grouting risk probability in the tunnel under the geology is realized.

The invention discloses a fixed source carbon emission data quality comprehensive evaluation method and system, and relates to the technical field of carbon emission monitoring, and the method comprises the steps: S1, collecting dual-source monitoring and production data, carrying out the invalid recognition, emission amount accounting aggregation, working condition classification and correlation coefficient construction, and building a score working condition basic data set through variable coefficient detection; s2, calculating a correlation coefficient of the to-be-inspected valid data, and obtaining a valid data risk comprehensive score through multi-dimensional inspection scoring and in combination with an industry weight; s3, calculating an invalid data proportion as an invalid data risk score; and S4, performing two-dimensional risk rating on the valid data and the invalid data, and outputting a four-dimensional result. The system realizes the method through four layers of modules of data input, processing, risk evaluation and result output, and solves the technical problems that the existing data quality evaluation only pays attention to valid data, the inspection method is single, and a complete dual-source collaborative quality control and full-dimensional quality control scheme is lacked.

The invention relates to a knowledge-data double-drive karst tunnel construction period water inrush risk assessment method, which comprises the steps of obtaining influence factors of a target karst tunnel water inrush disaster, and constructing a risk assessment index comprehensive layering system; obtaining a comprehensive weight according to the risk evaluation index comprehensive layering system; according to the comprehensive weight and a VIKOR model, constructing a knowledge-driven water inrush risk assessment model; constructing a training sample library according to the knowledge-driven water inrush risk assessment model; a DE-GWO-ELM model is constructed, the DE-GWO-ELM model is trained according to the training sample library, and a knowledge-data dual-drive DE-GWO-ELM model is obtained; and inputting to-be-evaluated monitoring data into the DE-GWO-ELM model, and outputting a water burst risk value. According to the invention, the method can effectively and accurately achieve the risk assessment of the water inrush, and is higher in real-time performance and interpretability.

The invention relates to the technical field of machine learning models, in particular to a geological disaster risk dynamic assessment method based on multi-source data fusion, which comprises the following steps: constructing a basic geographic information database; dividing geological disaster risk areas by adopting a machine learning algorithm; calculating the contribution degree of each environment factor to the geological disaster through an information amount model; fusing the dynamic rainfall data, carrying out weighted fusion on the dynamic rainfall data and the static geological disaster factors, and calculating a comprehensive risk value; dividing risk levels according to the risk values, and generating a geological disaster risk zoning map; compared with the prior art which mainly depends on single static geological data for analysis and has the problems of incomplete evaluation dimensions and poor timeliness, the scheme realizes multi-dimensional fusion analysis of geological conditions and real-time meteorological factors by constructing the geographic information database integrating the multi-source environmental factors and the dynamic rainfall data; and the comprehensiveness and the momentality of risk assessment are obviously improved.

The invention discloses a large-scale flood risk dynamic assessment method for a power grid equipment flood-related area, and aims to solve the problems of risk assessment result distortion under complex hydrology, insufficient pertinence of power grid equipment flood risk assessment, insufficient risk assessment dynamic updating capability and the like in a traditional flood risk assessment method. According to the method, basic data such as meteorology, hydrology, terrain, remote sensing images and power grid historical flood conditions are integrated, and flood-prone areas are analyzed and determined; the method comprises the following steps: aiming at a flood-related area of power grid equipment, constructing a district distributed flood forecasting model based on a three-water-source Xinanjiang model, taking a hydrological forecasting model result as input, constructing a refined one-two-dimensional coupling flood routing analysis model, and simulating a flood inundation risk evolution process of the power grid equipment in different scenes; and constructing a power grid equipment multi-index flood inundation risk grading system to clarify the risk grade, constructing a large-scale flood risk assessment model, and outputting a dynamic risk assessment result by associating real-time data.

The invention discloses a hazardous waste disposal traceability management system and method based on a block chain, and relates to the technical field of block chains. The system comprises a data integration module, a risk dimension evaluation module, a risk level analysis module and a real-time management and control module. The data integration module collects hazardous waste full life cycle data and uploads the data to the block chain, and a mapping relation table is established; the risk dimension assessment module analyzes the data chain to identify a full-link state and generate a multi-dimensional risk assessment parameter; the risk level analysis module calculates risk indexes in combination with the state matrix, and divides high, medium and low risk levels; and the real-time management and control module monitors the real-time data of the current link, judges the risk controllability, adjusts the processing strategy, and updates the block chain data. According to the invention, real-time traceability and dynamic management and control of hazardous waste disposal whole-process risks are realized, and the comprehensiveness and timeliness of risk assessment are improved.

The invention discloses a driver medication risk assessment method based on evidence-based big data, and particularly relates to the technical field of risk assessment. The method comprises the following steps: acquiring driving record data, medication record data and physiological index data of a driver; based on the medication record data and the physiological index data, analyzing the side effect risk of medication of the driver, and evaluating the occurrence probability of adverse drug reaction; based on the driving record data and the medicine use record data, analyzing the compatibility risk of the medicine use and the driving task of the driver, and evaluating the potential interference degree of the medicine to the driving operation; combining the side effect risk assessment result and the compatibility risk assessment result to generate a preliminary risk summary, processing a risk weight by applying a decision tree algorithm, and calculating a risk score of driver medication; and generating a driver medication risk assessment report according to the driver medication risk score, thereby realizing comprehensive risk quantitative assessment of the driver medication behavior based on the multi-source evidence-based data.