1192 results about "Risk evaluation" patented technology

The invention discloses an underground engineering geological safety dynamic risk assessment method based on multi-source data fusion, which relates to the technical field of risk assessment, and comprises the following steps: collecting multi-source heterogeneous data related to underground engineering, extracting implicit information, modeling underground engineering geological safety risk factors into a risk network, and establishing a risk network model; calculating the comprehensive importance of the nodes based on a Stacking integration algorithm, and identifying key risk factors; acquiring characteristic parameters of key risk factors by using spatio-temporal characteristics of implicit information, introducing a random walk mechanism to acquire a dynamic accident prediction chain, and performing learning representation by using a graph attention network to acquire probability distribution of an accident evolution path; and assessing the vulnerability of the connection edge in the risk network, establishing a dynamic risk assessment model based on the node importance and the edge vulnerability, and obtaining a dynamic risk value corresponding to the accident according to the accident occurrence probability and the risk mitigation factor. According to the invention, intelligent identification, dynamic evaluation and accurate early warning of risk factors are realized, and the accuracy and real-time performance of risk identification and evaluation are improved.

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 relates to the technical field of water quality monitoring, in particular to a mining area groundwater environment monitoring system and method, and the system comprises a mining area groundwater partition recognition module, a groundwater pollution risk assessment module, a groundwater monitoring point optimization module, a groundwater pollution path analysis module and a groundwater dynamic monitoring planning module. According to the invention, through data-driven zoning and risk assessment processes, fine and dynamic management is realized in underground water environment monitoring, functional zone zoning boundaries are accurately calibrated, understanding of an underground water flowing mode is enhanced, pollution source control is more targeted, diffusion risk intensity is assessed in combination with flow velocity and pollutant release intensity, and the risk assessment accuracy is improved. The risk management strategy is optimized, the pollution diffusion range is predicted and identified more efficiently, the emergency response accuracy is improved, monitoring point arrangement is optimized through risk data, reasonable monitoring resource allocation is ensured, the sampling task weight and the time interval are dynamically adjusted, and resource optimization and monitoring efficiency improvement are achieved.

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

The invention belongs to the technical field of intelligent supply chain management and optimization, discloses a product supply chain comprehensive optimization system and method based on big data, and aims to solve the problem that a traditional supply chain management system based on historical data and a static model is difficult to quickly adapt to changes. Forming a real-time evaluation vector; integrating the environment data, constructing an environment feature vector, and training to obtain a risk prediction model in combination with a real-time evaluation vector, thereby obtaining a risk probability of each node in the future, and obtaining a risk evaluation vector; based on the risk assessment vector, in combination with the inventory level and the transportation path, performing joint optimization on the inventory configuration and the distribution path to generate an optimization strategy; updating each node of the supply chain according to the optimization strategy, monitoring the state of the updated supply chain in real time, and generating a cost prediction vector when abnormity is monitored; and taking corrective measures on the supply chain according to the cost prediction vector to ensure stable operation of the supply chain.

The invention discloses a risk assessment method based on soil heavy metal pollution pathway analysis, and particularly relates to the technical field of land risk assessment. Heavy metal pollutant data and geographical location information of a target area are collected to generate a basic data set; constructing a heavy metal migration model based on the basic data set, and predicting a pollutant migration path and concentration change in combination with the comprehensive characteristics of the regional environment to identify a first high-risk region; a second high-risk area is identified by analyzing retention and release behaviors of pollutants in the microfractures in combination with quantitative soil microfracture connectivity characteristics and pollution migration behaviors of a dynamic entropy model; union processing is carried out on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area; heavy metal concentration and land utilization changes in different time periods of the high-risk area are analyzed and synthesized through a multi-dimensional data fusion algorithm, and cumulative risk distribution is generated; and comparing the accumulated risk distribution with enterprise land use planning, evaluating potential environmental responsibility and generating a risk evaluation report.

The invention relates to the technical field of data security, in particular to a financial data security management system and method, and the system comprises a data monitoring module, a vulnerability prediction module, a dynamic isolation module, a behavior analysis module and a security response module. According to the invention, through deep monitoring and analysis of financial data, the capability of identifying and processing abnormal operation is improved, so that the data security is enhanced, and through real-time collection of financial transaction and account behavior data and detailed analysis of fund flow frequency and account access frequency, risks are effectively identified and potential security vulnerabilities are predicted. By dynamically analyzing the association between the transaction node and the risk event, evaluating the risk level and automatically adjusting the data storage position and the access authority according to the risk evaluation, the security of the data in the transmission and storage process is remarkably improved, in addition, by monitoring the behavior mode change of the isolated account, the abnormal activity is further identified and corrected, and the safety of the data transmission and storage process is improved. And the overall safety and stability of the financial data environment are ensured.

The invention discloses a method and system for searching a risk enterprise group based on a knowledge graph, and the method comprises the steps: obtaining the related information of an enterprise industrial chain graph, carrying out the layering of hierarchical labels, selecting the related elements of the labels of the enterprise industrial chain graph according to the basic information of the enterprise industrial chain, forming an enterprise industrial chain key element knowledge base, and carrying out the searching of the risk enterprise group. Performing label keyword extraction and matching, generating an enterprise portrait and enterprise data report information, constructing an enterprise industry chain knowledge graph, performing enterprise risk evaluation according to the enterprise industry chain knowledge graph, and then performing enterprise investment attraction management strategy processing; according to the method, the business feature tag of the enterprise can be formed, and after the business feature tag is accurately matched with the industrial chain graph, the enterprise industrial chain knowledge graph is constructed, and the risk of the enterprise ethnic group is evaluated and analyzed in multiple dimensions, so that the risk evaluation of the enterprise ethnic group is more accurate, efficient and comprehensive compared with a traditional method, and the risk evaluation efficiency of the enterprise ethnic group is improved. And an enterprise investment attraction management strategy is given.

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a well-seismic integrated horizontal well geosteering risk evaluation method, which comprises the following steps: S1, acquiring three-dimensional seismic data, measurement while drilling data and geological logging data through a well-seismic integrated data acquisition platform; s2, constructing a horizontal well geosteering dynamic model based on multi-source heterogeneous data, and integrating a seismic inversion result and real-time drilling parameters; by establishing a well-seismic data dynamic fusion mechanism and a multi-source risk quantification model, the problem of space-time dislocation of seismic inversion and measurement-while-drilling data is solved, the recognition precision of a fault boundary, a lithologic interface and a pressure abnormal zone is ensured to be matched with drilling position changes in real time, risk misjudgment caused by model updating lag in a traditional method is eliminated, and the accuracy of the method is improved. The accuracy and timeliness of geosteering decision making of the complex structure area are improved; by monitoring well track deviation and geological model prediction deviation in real time, the risks of well wall instability and target deviation are avoided, and the operation safety of the horizontal well in the full life cycle is guaranteed.

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 relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

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

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The invention discloses a seismic source inversion data driven rock burst prediction method and system, and the method comprises the steps: carrying out the quantitative inversion calculation of the core parameters of a coal rock fracture seismic source mechanism through original sound wave data collected on site; a rock burst seismic source data prediction model is constructed based on an Informer algorithm, and the occurrence time and position of rock burst are accurately predicted. A rock burst risk evaluation model is constructed by combining the predicted occurrence time and position of a future fracture source and seismic source mechanism core parameters, the magnitude and property of rock burst are predicted, and a dual-drive rock burst intelligent model for seismic source data prediction and rock burst risk evaluation is established; and multi-dimensional intelligent prediction of sequential response, spatial distribution and evolution attributes in the rock burst catastrophe process is realized. According to the method, seismic source data prediction and risk evaluation are combined, the rock burst catastrophe risk can be predicted, and the rock burst evolution process and the disaster-causing mechanism can be visually represented.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention relates to the technical field of monitoring and identification, and discloses a rainfall type group landslide risk area and hidden danger point comprehensive identification and evaluation method. The method comprises the following steps: determining a landslide danger index and a disaster-bearing body vulnerability index of each calculation unit in a target area according to an updated comprehensive database; determining a risk evaluation result according to a landslide risk index of each sub-region determined by the landslide risk index and the disaster-bearing body vulnerability index, wherein the risk evaluation result comprises a landslide risk grade corresponding to each sub-region; determining a deformation area in the target area according to synthetic aperture radar data; determining a hidden danger point identification result corresponding to the deformation area, wherein the hidden danger point identification result comprises disaster hidden danger points in the deformation area and hidden danger risk levels corresponding to the disaster hidden danger points; adjusting a hidden danger point identification result according to the risk evaluation result; and adjusting a risk evaluation result according to a hidden danger point identification result and a disaster hidden danger point treatment result. According to the method, dynamic feedback and optimization of risk and hidden danger assessment can be realized, and misjudgment is reduced.

Techniques for beacon and threat intelligence based Advanced Persistent Threat (APT) detection are disclosed. In some embodiments, a system / process / computer program product for beacon and threat intelligence based APT detection includes collecting firewall log data from monitored network traffic; analyzing the firewall log data at a cloud security service to identify beacon traffic based on a plurality of heuristics; performing a risk evaluation of the beacon traffic to detect malicious beacon traffic; and performing an action in response to detecting the malicious beacon traffic.

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

The invention 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 discloses an underground space flood emergency rescue and dispatching command model construction method and device under a rainstorm situation, belongs to the technical field of construction safety management, and solves the problem that effective landing of an emergency rescue scheme cannot be ensured in the prior art. According to the method, flood simulation under multiple scenes of different rainfall capacities is carried out based on rainstorm scene data and a cellular automaton model, flood simulation results are obtained, key risk influence factors are identified, severity evaluation is carried out, and risk grade evaluation is carried out based on an underground space disaster risk evaluation index system under the rainstorm scene. The method comprises the following steps: constructing a knowledge base, matching a target underground space emergency rescue resource list with the constructed knowledge base to obtain a first emergency task list, applying the first emergency task list to a corresponding rainfall scene, performing pre-deduction by adopting a cellular automaton model, and generating a second emergency resource list according to a pre-deduction result to obtain an emergency rescue and scheduling command decision. The method is used for underground space flood emergency rescue and dispatching command under the rainstorm situation.

The invention discloses a contract risk dynamic scoring method and a multi-level approval device, and belongs to the technical field of contract management, the method comprises the steps that risk fields in a contract form are extracted to form a to-be-verified list, and the risk fields are fields needing risk management and control in the contract form; extracting risk fields meeting trigger conditions in the to-be-verified list to form a target list, wherein the trigger conditions have corresponding weights; calculating the sum value of the weights corresponding to the risk fields in the target list to obtain a risk score; and matching a corresponding risk level according to the risk score. According to the invention, the risk assessment of the contract is realized through the preset triggering condition, and the efficiency and accuracy of contract management are improved.

The embodiment of the invention relates to the field of natural disaster risk prediction, and discloses a green land optimization method based on an urban inland inundation risk assessment model, parameters in a maximum entropy model are optimized, and the maximum entropy model after parameter optimization is used as the urban inland inundation risk assessment model, so that model parameters suitable for a city can be optimized; compared with a default parameter method adopted in the prior art, the urban inland inundation risk assessment method has the advantage that the assessment precision can be effectively improved. Meanwhile, according to the method, the obtained urban environment variable test data is pre-evaluated to obtain the urban environment variable evaluation data, so that the operation precision and evaluation accuracy of the urban waterlogging risk model can be improved. In addition, according to the urban inland inundation risk probability distribution diagram and the urban supplementary greenbelt rate output by the urban inland inundation risk assessment model, the greenbelt optimization scheme is determined, and the urban inland inundation risk probability can be effectively reduced.

The invention provides a burnt rock aquifer water disaster prevention and control design method which comprises the following steps: exploring mine geological and hydrogeological conditions, determining burnt rock distribution characteristics, an aquifer range and a water-rich abnormal region, and constructing a burnt rock aquifer water diversion characteristic judgment system; on the basis of the residual bursting coefficient of the burnt rock, a calculation formula of the development height of the burnt rock stratum water-flowing fractured zone is corrected, and the development height of the working face mining overlying strata water-flowing fractured zone is accurately judged through the formula; constructing a burnt rock water-bearing layer risk evaluation system according to the relationship between the burnt rock water-bearing layer position and the development height of the water-flowing fractured zone; according to the danger evaluation system, the'intercepting-dredging-building 'burnt rock aquifer layering treatment method is provided. Compared with traditional aquifer water disaster treatment, the method has the advantages that the aquifer damage of the burnt rock can be judged more accurately, layered treatment is more economical and efficient, and the method is of great significance to prevention of underground water inrush accidents and underground water loss and realization of safe, efficient and green mining of coal.

The invention relates to the technical field of power system operation or management, in particular to a partial discharge monitoring strategy optimization method and system based on dynamic resource allocation, and the method comprises the steps: constructing a three-stage monitoring system comprising a sensor node, a sink node and a cloud processing center, firstly initializing monitoring parameters, and then obtaining system state information periodically or in a triggering manner, calculating the risk level of each monitoring point in combination with a dynamic risk evaluation model; constructing an efficiency-maximized resource allocation optimization model based on risk levels and resource constraints, solving an optimal scheme by adopting an improved multi-target particle swarm algorithm, and issuing the optimal scheme to each node to adjust monitoring behaviors to form closed-loop optimization; and meanwhile, model parameters are dynamically updated through an online learning mechanism. According to the method, dynamic matching of risks and resources is realized, the monitoring accuracy and the resource utilization rate are improved, the adaptability of the system to the equipment state and the environment change is enhanced, and the method is suitable for partial discharge monitoring scenes of various power equipment.

The invention provides a method and a system for dynamically evaluating the blockage risk of a concrete conveying pipeline. According to the method, firstly, geometric feature information of the concrete conveying pipeline is obtained, meanwhile, in the concrete conveying process, pressure data and flow velocity data in the concrete conveying pipeline are collected in real time, then, based on the geometric feature information, dynamic compensation processing is conducted on the pressure data, and compensated pressure data are generated; and then combining the compensated pressure data and flow velocity data to calculate a blockage risk evaluation value in the current concrete conveying state, and finally outputting a blockage risk evaluation report according to the blockage risk evaluation value. According to the technical scheme provided by the invention, the structure interference false alarm is effectively eliminated, the accurate positioning and graded response of the blockage risk position are realized, and the blockage treatment efficiency and reliability are improved.

The invention discloses a deep foundation pit intelligent safety monitoring method for an intelligent construction site, and relates to the field of safety monitoring, and the method comprises the steps: collecting a plurality of pieces of foundation pit point cloud data of a target foundation pit; marking a point cloud vacant area; collecting regional point cloud data of the point cloud vacant region, and combining the regional point cloud data and all the foundation pit point cloud data to construct a foundation pit three-dimensional model; constructing a foundation pit map network, and determining a foundation pit key monitoring area according to the foundation pit map network; generating a foundation pit monitoring strategy of the target foundation pit; obtaining foundation pit monitoring data of the target foundation pit; updating the three-dimensional model of the foundation pit in real time; and risk evaluation is carried out on the target foundation pit, and a risk evaluation result is visually displayed through the foundation pit three-dimensional model. According to the invention, continuous risk monitoring and risk visual display of the foundation pit can be effectively realized.

The invention discloses a longitudinal analysis-based lung cancer chemotherapy combined immunotherapy patient symptom prediction method, which comprises the following steps: S1, collecting a multi-modal medical data set of a patient, and carrying out data preprocessing; S2, carrying out time sequence integration on the preprocessed multi-modal medical data set to construct a longitudinal analysis data set; s3, constructing a symptom weight adaptive modeling method based on an attention mechanism; s4, forming a symptom-treatment causal relationship evaluation model; s5, inputting real-time multi-modal medical data of the patient into the symptom-treatment causal relationship evaluation model, and classifying prediction results; s6, generating an individual symptom risk assessment report of the patient according to a prediction result; s7, the clinician dynamically optimizes the chemotherapy dose, the immunotherapy dose and the drug use time of the patient based on the individualized symptom risk assessment report. The invention provides an effective technical means for accurately optimizing a lung cancer chemotherapy and immunotherapy combined scheme.

The invention relates to an enterprise park meteorological fire risk assessment method based on an adaptive random forest model, and the method comprises the steps: building a risk assessment model and a visual early warning platform, enabling the risk assessment model to collect alarm data, meteorological data, enterprise data and park division data, and forming a risk assessment data set, and integrating internal characteristics and external data of the park into a data set, inputting new data into the self-adaptive random forest model by means of a real-time data flow technology, updating and evaluating a fire risk index value of the park in real time, and optimizing a model decision tree to automatically adjust a risk evaluation model and adapt to changes of environment and operation. Accurate early warning information is provided for a visual early warning platform, the risk level of the park is displayed in real time, and a risk alarm is given out in time.

The invention discloses a meteorological disaster risk assessment method and system based on disaster risk grading, and belongs to the technical field of meteorological disaster assessment, and the method specifically comprises the steps: collecting the meteorological and disaster-bearing body data and geographic information of a disaster area, and carrying out the preprocessing through a spatial interpolation method; setting meteorological disaster-inducing factors, disaster-pregnant environments and disaster-bearing body vulnerability indexes, and constructing a judgment matrix and distributing index weights by using an analytic hierarchy process; constructing a disaster risk grading model according to the risk indexes and the corresponding weights, inputting data of the to-be-evaluated region, calculating a risk value, judging a risk grade, and generating a report; establishing a real-time monitoring system, updating a risk assessment result, and issuing early warning information when a risk value reaches an early warning threshold value; according to the invention, through comprehensive analysis and real-time monitoring, the accuracy and timeliness of meteorological disaster risk assessment are improved.

The invention discloses a coal mining rock burst prevention and early warning system which comprises a data acquisition system, and the data acquisition system comprises a sensor network, a self-adaptive sensor layout module, a signal sending module and a power supply module. The sensor network comprises a stress sensor, a displacement sensor and a vibration sensor which are arranged in a coal mine tunnel; the signal sending module is used for sending data acquired by the sensor; the power module is used for supplying power to the sensor network and the signal sending module. The coal mining rock burst prevention and early warning system is compatible with three kinds of sensing data of a stress sensor, a displacement sensor and a vibration sensor, and the positions and density of the sensors in a high-risk area are dynamically adjusted. And establishing a coal seam rock burst risk evaluation expert system by using the monitored result, and evaluating the early warning result. The sensor layout strategy of the coal mining rock burst prevention and early warning system is optimized, the monitoring capability of a high-risk area is improved, and the rock burst danger early warning level can be improved.