962 results about "Risk evaluation" 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 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 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 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 container type battery environment monitoring system based on edge calculation, which relates to the technical field of battery environment monitoring, and is characterized in that sensors and edge nodes are arranged in cabins of a battery energy storage device to acquire internal operation environment data of the container type battery energy storage device; a pneumatic environment data set and a water vapor critical data set are formed after data processing, cross-cabin air exchange efficiency Eex is constructed for air exchange qualification evaluation, a condensation nucleation potential index Cnp is constructed when air exchange is abnormal, and a comprehensive condensation risk index Rall is generated in combination with the cross-cabin air exchange efficiency Eex for condensation risk evaluation. Instructions such as fan power adjustment, dehumidification device start and stop and power limitation are issued according to the risk level, and information labels are added to the evaluation data set and uploaded to the cloud in real time. According to the invention, the real-time monitoring of the container type battery environment, the dynamic prediction of the condensation risk and the automatic control are realized, and the safety and reliability of the operation of the energy storage system are improved.

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.

Various aspects of the present disclosure relate to adaptive monitoring of radio intelligent controller (RIC) key performance indicators (KPIs). An apparatus, such as a RIC, receives performance data of one or more KPIs. The RIC updates a sampling rate of at least one KPI based at least in part on a determination to mitigate use of one or more operational resources of the RIC, where the determination includes a quantized risk evaluation for adjusting the sampling rate of the at least one KPI. The RIC can transmit an indication of an updated sampling rate for the at least one KPI to a network equipment (NE) and / or to a wireless access point for communication to a user equipment (UE).

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 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 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 discloses an oil and gas pipeline leakage monitoring method based on SCADA, and relates to the technical field of safety monitoring, and the method comprises the following steps: S1, building an SCADA data preprocessing model; s2, establishing a leakage feature extraction model; s3, performing leakage state identification through the leakage detection classification model; s4, positioning a leakage point; s5, calculating the leakage rate; s6, evaluating a risk level based on the leakage risk evaluation model; s7, performing real-time early warning, and dynamically adjusting the early warning level; and S8, making an emergency scheme. According to the oil and gas pipeline leakage monitoring method, the influence of environmental noise and operation disturbance can be effectively removed by establishing a plurality of models, the accuracy of leakage feature extraction is ensured, and high precision and dynamic adaptability of leakage positioning and rate estimation are realized. Compared with a traditional method, the method has the advantages that the sensitivity and accuracy of leakage detection are remarkably improved, and monitoring parameters can be dynamically adjusted in a complex environment.

The invention provides a cascade reservoir operation risk and benefit evaluation method considering prediction uncertainty, and the evaluation method comprises the steps: building a day-ahead wind-light-water optimal complementary model, and employing a two-stage robust method to process the power generation amount deviation generated by the wind-light uncertainty; the method comprises the following steps: identifying the worst condition of photovoltaic power output which causes maximum water consumption and power imbalance to obtain a day-ahead total power generation plan of the wind-solar hybrid system capable of realizing maximum resource utilization rate; according to the evaluation method, a benefit and risk evaluation model based on power generation complementary benefit, power generation stability, power supply reliability and water level fluctuation is further provided, the risk of complementary operation of the reservoir system is quantified through indexes, the risk and benefit of cascade reservoir operation can be reasonably evaluated and balanced on the basis of considering the uncertainty of wind and light power generation, and the method is suitable for popularization and application. And the overall efficiency and reliability of the power system are improved.

An intelligent driving risk early-warning method and apparatus, an electronic device, and a storage medium. The intelligent driving risk early-warning method is applied to a first vehicle, and comprises the following steps: when it is determined that the first vehicle is in an intelligent driving state, acquiring vehicle traveling information (S10); when it is determined that the vehicle traveling information satisfies a preset potential risk condition, performing risk data detection (S20); and when risk evaluation is performed by means of a preset risk evaluation model on the basis of the risk data to determine that the first vehicle has an intelligent driving risk, outputting intelligent driving risk early-warning information (S30).

The invention relates to the technical field of power network space optimization management, and particularly discloses a power network space optimization analysis method and system based on a power grid GIS, and the method comprises the steps: firstly enabling a power grid GIS port to firstly match the space coordinates of a high-voltage side bus of a power transmission substation and a power distribution feeder starting node, building an electrical connection topology after verification, and evaluating a capacity coupling factor; dividing feeder segments according to topology, calculating a loss rate and a risk index, and correcting the risk index in combination with a capacity coupling factor, thereby realizing accurate quantification of the feeder state and improving risk assessment scientificity; secondly, feature vectors are constructed according to the GIS center points, the loss rates and the risk indexes of the feeder line sections for clustering, GIS layers with labels are output, feeder line clusters in the same state can be recognized conveniently, and support is provided for regional analysis; and finally, detecting a loop gap based on a layer, analyzing connectivity, identifying a weak link and optimizing feedback, accurately positioning a network short board, and providing a targeted reference for an optimization scheme.

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 relates to the technical field of human resources, and discloses a multi-dimensional automatic background investigation method and system based on Ai assistance. The system comprises a multi-source data acquisition module, an intelligent analysis engine module, a real-time interview processing module, a quality verification control module, a dynamic decision support module, a privacy security protection module and a visual report generation module. According to the method, public data of candidates are acquired through a multi-thread crawler technology, and the authenticity of educational background is verified; analyzing the paper certificate by adopting an optical character recognition technology; constructing a space-time relation knowledge graph to detect work experience abnormality; interview content is transcribed in real time, and emotional tendency is analyzed; dynamically adjusting the evaluation dimension weight; and generating a visual risk assessment report. According to the method, the enterprise employment risk is reduced, the comprehensiveness and depth of background investigation are improved, the accuracy and applicability of a background investigation conclusion are improved, a safe and reliable background investigation environment is constructed, and the enterprise human resource allocation effect is optimized.

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 the technical field of intelligent medical information, in particular to a urinary surgery risk AI intelligent assessment method and system. The method comprises the steps that intraoperative data in the urinary surgery operation process are collected in real time; inputting the intraoperative data into a risk feature fusion AI model, performing context association analysis of different stages of the operation, and fusing each intraoperative multi-source modal feature to generate a risk assessment comprehensive feature set; analyzing the risk assessment comprehensive feature set by using a risk assessment prediction AI model, dynamically adjusting the priority and prediction weight of each risk index in combination with a doctor judgment priority weight adjustment mechanism, and predicting to obtain an intra-operative real-time comprehensive risk score; and visually displaying the intraoperative real-time comprehensive risk score and the risk prompt information in a terminal in real time. According to the AI intelligent assessment method, the causal relationship between the risks is constructed by associating the data of each stage of the operation with doctor judgment, and the AI intelligent assessment method capable of dynamically predicting the key risks in the operation process is realized.

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 discloses an export enterprise risk prediction method and system based on Transform, relates to the field of data processing, is applied to an enterprise risk control system, and comprises the following steps: constructing a multi-dimensional index system, and dynamically distributing index weights by adopting a subjective and objective fused multi-target combination weighting model; designing a Transform risk prediction model, capturing dynamic association of risk indexes through a space-time attention mechanism, and training the risk prediction model in combination with historical risk evaluation index data; establishing a weight comparison correction mechanism, comparing a combined weighting result with a model prediction value, and optimizing weight distribution through error feedback; and establishing a risk early warning linkage mechanism of a regional level by combining a corrected weighting result, and triggering graded early warning when an enterprise risk value exceeds a threshold value, thereby realizing accurate prediction and collaborative prevention and control of export enterprise risk indexes.

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 invention discloses a dam landslide dangerous case area identification system and method based on an unmanned aerial vehicle and a remote sensing image, and belongs to the technical field of dam slope detection. The system comprises a task planning module, a data acquisition module, a feature extraction module, a region division module and a dangerous case issuing module, through highlighting and focusing on important features of key parts of a dam, shallow layer features are constructed into a graph structure, and through feature propagation and aggregation between nodes, a dangerous case is issued. According to the method, local feature information is expanded into deep semantic features including global semantic information, and spatial multi-scale information and time variation trends of the deep semantic features are captured, so that the dynamic change process of a dam can be described more comprehensively, feature modes related to landslide can be identified, probability values belonging to landslide areas are allocated to each pixel point, and the dynamic change process of the dam can be described more comprehensively. A multi-index evaluation system is established to carry out quantitative evaluation on the landslide degree and the risk level, a plurality of physical parameters and characteristic parameters are selected as evaluation indexes, and more comprehensive risk evaluation is carried out on a landslide area.