189results about How to "Improve evaluation accuracy" patented technology

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention discloses a crop disease remote sensing monitoring and early warning system based on time sequence spectral information analysis, and relates to the technical field of agricultural disease and insect pest monitoring. Computer drawing software is utilized to divide a corn planting field in equal proportion, and sensors are deployed to monitor the growth environment change in each grid in real time and generate microenvironment data information; the method comprises the following steps: calculating a microenvironment difference index Xbd according to microenvironment data, identifying a microenvironment difference aggregation area and sending a risk analysis instruction, after receiving the risk analysis instruction, capturing related spectral data information of the microenvironment difference aggregation area by using an unmanned aerial vehicle and a multispectral sensor, and constructing a multispectral fluctuation coefficient Xgp by analyzing related spectral data, and comparing the calculated rust disease risk assessment index Zpg with a preset threshold W in combination with the microenvironment difference index Xbd and the trained risk assessment model, and obtaining corresponding grade early warning according to the rust disease risk degree.

The invention provides a marine fishery habitat assessment method and device and a storage medium. The method comprises the steps that acoustic data collected by a light-induced fishing boat shipborne fish finder in voyage times, fishery yield data of fishing boats homologous with the acoustic data and marine environment element data in a research sea area are obtained; processing the data to obtain a single target number, a fish school NASC and a full voyage NASC; establishing three optimal generalized additive models by taking the single target number, the fish school NASC and the full-voyage NASC as response variables and the environmental elements as explanatory variables, selecting an optimal response variable and calculating the weight of each environmental element in the model; combining the suitability index of each environment element based on the calculated weight, constructing two weighted habitat suitability index models, and selecting an optimal model; and calculating a habitat suitability index HSI value by adopting the optimal model, and verifying by means of yield data. According to the invention, the accuracy of northwest Pacific Ocean fishery habitat evaluation can be improved.

The invention relates to the technical field of smart power grids, in particular to a virtual power plant real-time regulation capability assessment method and system, and the method comprises the steps: obtaining real-time power consumption data and historical power consumption data of each power device in a virtual power plant, determining a power minimum interval unit, correcting data abnormity, and the like. Accurate evaluation of the real-time adjustment capability of the virtual power plant is realized; the method specifically comprises the following steps: acquiring power consumption data and historical data of power equipment, generating a load curve, determining a power minimum interval unit based on the historical data, and further performing anomaly correction on the data; on this basis, the maximum and minimum real-time adjustment capability of each power device is evaluated, and the overall adjustment capability of the virtual power plant is comprehensively evaluated; through the implementation of the method, the precision and efficiency of virtual power plant scheduling can be improved, and the stability and flexibility of a power grid under different load conditions are ensured; the scheme has high real-time performance and accuracy, and is suitable for scheduling optimization of a modern intelligent power grid.

The invention discloses a child massage teaching auxiliary system based on artificial intelligence, and particularly relates to the technical field of massage auxiliary teaching, a Kalman filtering technology is adopted to effectively filter out large-amplitude interference in hand motion data, time-frequency analysis is carried out in combination with fast Fourier transform or wavelet transform, and frequency domain characteristics of microfibrillation signals are accurately captured; through fine micro-tremor signal analysis, noise interference is removed, the fine change of micro-tremor of the hands of a student is accurately identified, and the evaluation precision of the massage action stability is remarkably improved; and the integrated learning progress management module automatically adjusts teaching contents according to the learning progress and skill mastering conditions of the students, provides real-time feedback and forward excitation, provides excitation when the students have good performance, prompts and optimizes follow-up actions in real time when deviation occurs, helps the students to continuously improve skills, avoids error accumulation, and effectively improves the teaching effect.

The invention relates to the technical field of cement-based composite cementing material preparation, in particular to a low-carbon environment-friendly cement-based composite cementing material and a preparation method thereof.The preparation method comprises the steps that an initial forming object is subjected to regional division according to position characteristics so as to obtain real-time monitoring results of all target monitoring regions, determining the actual state of each target monitoring area according to the real-time monitoring result; analyzing the actual state to determine a single mixing amount set of each target monitoring area; integrating the single mixing amount sets to obtain a target mixing amount set, and sequentially performing stirring and pouring molding according to the target mixing amount set to obtain a target molded object; in the stirring process, the initial stirring parameters are adjusted according to the actual stirring degree in combination with the overall analysis result, so that actual stirring parameters are determined; and in the pouring forming process, the initial pouring parameters are adjusted according to the bubble generation rate in combination with the overall analysis result, so that the actual pouring parameters are determined.

The invention provides a method for characterizing the heterogeneity of a millimeter-level shale oil and gas reservoir. The method mainly solves the problem that the reliability and accuracy of a reservoir evaluation result of an existing volume sampling method are low. The method is characterized by comprising the following steps that S1, shale millimeter-level mineral components are recognized and drilled; s2, based on the obtained powdery sample, oil-containing and non-oil-containing tests are carried out; determining occurrence pore size distribution to carry out reservoir characterization through comparison before and after pumping; s3, on the basis of the obtained powdery sample, carrying out oiliness characterization on different lamina / layered components; and S4, carrying out millimeter-level shale oil reservoir heterogeneity characterization on the basis of the reservoir property and oiliness results of the different strata / layered components. According to the method provided by the invention, the identification precision of the shale formation / stratified structure can be enhanced, millimeter-level fine representation of lithology of different formation / strata is realized, and favorable support is provided for shale oil enrichment mechanism, sweet spot prediction and the like.

The invention relates to the field of ocean energy development and utilization, in particular to a tidal current energy array field site selection method based on a three-dimensional high-precision numerical model, which comprises the following steps: (1) a macroscopic site selection method; (2) a microscopic site selection method; (3) establishing a high-precision layered three-dimensional numerical model; (4) a tidal current energy resource refined evaluation method; analyzing a model calculation result, analyzing tidal current properties such as a large tide rising and falling moment, a small tide rising and falling moment, tidal current stability, energy flow density, theoretical reserve of tidal current energy resources, single-width energy flow flux and the like of a target sea area, and presenting an evaluation result through a visualization technology so as to guide a laying mode of a tidal current energy unit; compared with a traditional two-dimensional or simplified three-dimensional model, the evaluation precision is greatly improved. The tidal current characteristics at different water depths can be meticulously simulated, so that tidal current energy resource assessment is more comprehensive and accurate.

The invention discloses a high-capacity solid state disk single event effect testing method and device based on high-energy particles. The testing method comprises the steps that an SSD user data preset experiment is carried out; based on the data preset experiment, utilizing a device to carry out an irradiation test, judging whether the SSD module has errors such as single event locking and function interruption under an irradiation condition, determining a judgment result, carrying out quantitative calculation on the errors, and outputting a function test result; based on the function test result, performing a silent data destruction test in a post-irradiation test stage; recording silent data destruction and fixed bit error data according to the silent data destruction test; and based on the silent data destruction and the fixed bit error data, carrying out error characteristic quantification calculation, and outputting a calculation result. According to the method, parameterized evaluation of the radiation resistance of the SSD can be realized.

The invention discloses a mine geological environment evaluation system based on multi-scale characteristics and an evaluation method thereof. The mine geological environment evaluation system comprises a remote sensing image acquisition unit, a mine geological environment problem analysis unit, a mine geological environment element extraction unit, a mine geological environment problem identification unit and a mine geological environment evaluation unit. The method comprises the following steps: collecting a remote sensing image of a target mine area, extracting mine geological environment elements, analyzing temporal and spatial change characteristics of the elements, performing similarity comparison calculation with change characterization of different mine geological environment problems, identifying the geological environment problems, and training an evaluation model to obtain a geological environment evaluation prediction result. According to the method, index factor characterization of different mine geological environment problems is analyzed from remote sensing image modal data, through automatic comparison and matching with target remote sensing image multi-scale features, the problem that the efficiency and quality of a mine geological environment investigation mode and a sample selection method are low is solved, and scientific evaluation of the mine geological environment is achieved.

The invention relates to the field of hydroelectric equipment, and provides an intelligent object construction method of a hydroelectric equipment body, which comprises the following steps: establishing a hydroelectric equipment state intelligent sensing system, and obtaining state data of hydroelectric equipment; establishing a multi-source information fusion, feature extraction and self-learning analysis system based on the hydroelectric equipment; the state of the hydroelectric equipment is intelligently evaluated; carrying out early warning on faults of the hydroelectric equipment; through the above steps, the construction and evaluation of the intelligent object of the hydroelectric equipment are realized. By constructing a full-service process of multi-dimensional situation intelligent perception, multi-source information fusion, feature self-learning, fault self-diagnosis and maintenance self-decision making of the hydroelectric equipment, comprehensive monitoring evaluation and intelligent management of a hydroelectric equipment body are realized, the evaluation precision of the operating state of the hydroelectric equipment is greatly improved, early warning can be realized, and the service life of the hydroelectric equipment is prolonged. And the operation efficiency and the safety of the equipment are improved.

The invention discloses a safety construction management method based on digitization, and belongs to the field of construction management. The safety construction management method comprises the steps that disk saw operation data of a construction site are collected; preprocessing the collected disk saw operation data; according to the preprocessed disk saw operation data, the safe operation state of the disk saw is judged through a support vector machine (SVM) algorithm; according to the safe operation state, performing risk assessment by adopting a risk assessment method based on combination of an evidence theory and a fuzzy set theory to obtain a safety risk level; and according to the safe operation state and the safety risk level, carrying out safety management on the disk saw through a multi-level early warning control strategy based on reinforcement learning. In view of low risk assessment precision in disk saw construction in the prior art, the risk assessment precision is improved through risk assessment combining an evidence theory and a fuzzy set theory and by using a reinforcement learning early warning control strategy and the like.

The invention discloses a concrete time-varying Poisson's ratio testing method. The method comprises the following steps: placing concrete in a preset concrete creep measuring device; after multi-axial pressure is applied to the concrete, creep data of the concrete in the pressed direction, stress data and strain data of the concrete in other directions and concrete test parameters are obtained; calculating a creep coefficient corresponding to the concrete based on the concrete test parameters; calculating the time-varying elastic modulus of the concrete in the multi-axis state based on the creep coefficient and the creep data; and calculating the time-varying Poisson's ratio corresponding to the concrete based on the time-varying elastic modulus, the creep data, the stress data and the strain data. According to the improved concrete creep measurement device and method, the Poisson's ratio of the concrete in the multi-axis stress state can be more accurately tested, the precision of the test result is improved, a research method about the change of the Poisson's ratio of the concrete along with time under the multi-axis stress is provided, and the application prospect is wide. Therefore, the safety and durability of the concrete structure are effectively guaranteed.

The invention belongs to the technical field of remote rehabilitation guidance, and particularly relates to an intelligent remote rehabilitation guidance system. The system integrates the functions of man-machine interaction, information encryption, physiological and psychological assessment, rehabilitation grade division, physician professional assessment, rehabilitation knowledge base construction, rehabilitation matching planning, rehabilitation training monitoring and feedback, rehabilitation effect prediction and the like, and a fuzzy assessment algorithm and an emotional depression detection algorithm are integrated. The system comprehensively evaluates the physiological and psychological health conditions of a rehabilitative person, recommends appropriate rehabilitation doctors or health planning guidance suggestions for the rehabilitative person, monitors the standard degree and micro-expression changes of training actions in real time and gives timely feedback and adjustment suggestions in the rehabilitation process, and meanwhile, predicts a rehabilitation track by utilizing a machine learning algorithm, so that the rehabilitation effect is improved. Subsequent rehabilitation planning is optimized, and personalized precise rehabilitation is ensured; according to the invention, the participation degree and training scientificity of the convalescent are improved, and an effective solution is provided for remote rehabilitation guidance.

The invention provides a fertility auxiliary judgment method and system for a hysteromyoma patient, and the method comprises the steps: obtaining a sample detection result and a scanning graph of the hysteromyoma of the patient, and recognizing the sample detection data of each detection type of the hysteromyoma based on the sample detection result; identifying three-dimensional structure data and position distribution information of the hysteromyoma on the basis of the scanning graph of the hysteromyoma, and identifying a detection evaluation value of each detection type through a sample evaluation strategy on the basis of the sample detection data of each detection type; based on the three-dimensional structure data and the position distribution information of the hysteromyoma, identifying the fertility influence degree of the hysteromyoma on the fertility influence type of the patient, and based on the detection evaluation value of each detection type and the fertility influence degree of the fertility influence type, identifying the fertility probability value of the patient through a fertility influence evaluation model; and taking the fertility probability value of the patient as a fertility auxiliary judgment result of the patient. By adopting the scheme, the evaluation accuracy of the fertility can be improved.

The invention discloses a rapid assessment method for seawater intrusion and sustainable freshwater exploitation in an phreatic aquifer considering hydraulic boundary changes. The method comprises the following steps: constructing a conceptual model based on specific working conditions; potential energy distribution in the phreatic aquifer is calculated; the seawater intrusion range is determined by searching an equipotential line equal to potential energy at a salt toe position in a potential energy field through interpolation and the height of a salt-fresh water interface; and calculating the sustainable fresh water exploitation rate: calculating the stagnation point position, wherein the water pumping rate when the stagnation point coincides with the salt toe position is the sustainable fresh water exploitation rate. According to the method, the evaluation precision and calculation efficiency of the seawater invasion range and the sustainable fresh water exploitation rate can be remarkably improved while the result accuracy is ensured; by adopting the conceptual model based on the working condition, the research area range and parameter setting can be flexibly adjusted according to actual requirements, and by optimizing the calculation model, the requirements of calculation resources and manpower and material resources are remarkably reduced under the condition that additional equipment or resource investment is not increased.

The invention discloses a method for rapidly evaluating seawater intrusion and sustainable freshwater exploitation in a confined aquifer by considering hydraulic boundary change. The method comprises the following steps: constructing a conceptual model based on specific working conditions; potential energy distribution in the confined aquifer is calculated; the seawater intrusion range is determined by searching an equipotential line equal to potential energy at a salt toe position in a potential energy field through interpolation and the height of a salt-fresh water interface; and calculating the sustainable fresh water exploitation rate: calculating the stagnation point position, wherein the water pumping rate when the stagnation point coincides with the salt toe position is the sustainable fresh water exploitation rate. According to the method, the evaluation precision and calculation efficiency of the seawater invasion range and the sustainable fresh water exploitation rate can be remarkably improved while the result accuracy is ensured; by adopting the conceptual model based on the working condition, the research area range and parameter setting can be flexibly adjusted according to actual requirements, and by optimizing the calculation model, the requirements of calculation resources and manpower and material resources are remarkably reduced under the condition that additional equipment or resource investment is not increased.

The invention discloses a landslide deformation early warning system and method, and belongs to the technical field of landslide deformation early warning. Nx clinometers are transversely arranged in a to-be-detected area, Ny clinometers are longitudinally arranged in the to-be-detected area, and Nz clinometers are vertically arranged in the to-be-detected area, so that a three-dimensional monitoring network is formed. According to the method, the space angle value of each inclinometer is extracted, a three-dimensional space angle value matrix is constructed, the transaction area is screened out according to the angle values, and the transaction coefficient is calculated. Thirdly, separating the three-dimensional space angle value matrix at each moment into a transverse-longitudinal angle matrix, a longitudinal-vertical angle matrix and a transverse-vertical angle matrix, and constructing a corresponding gradient matrix based on the angle matrixes at adjacent moments; and finally, the landslide deformation early warning subsystem processes the matrixes and adds a transaction coefficient for reinforcement to obtain a landslide deformation degree value at each moment. According to the invention, through multi-dimensional monitoring and accurate evaluation, the identification and early warning capability of the landslide risk is significantly improved.

The invention belongs to the cross technical field of remote sensing data processing and soil environment monitoring, and discloses a multi-source remote sensing collaborative soil quality index inversion and comprehensive evaluation system and method, and the method comprises the following steps: obtaining different types of remote sensing data in a target area, and constructing a multi-source data set; the acquired multi-source remote sensing data are preprocessed, data noise and system errors are eliminated, and data consistency is ensured; performing multi-source remote sensing data fusion by adopting a spectrum-polarization-texture collaborative fusion strategy in combination with a multi-scale decomposition and weighted fusion model; based on the fusion data, constructing a machine learning inversion model, and inverting soil quality key indexes; and carrying out comprehensive evaluation on the soil quality by adopting an analytic hierarchy process-matter element extension model to realize grading of the soil quality. By adopting the multi-source remote sensing collaborative soil quality index inversion and comprehensive evaluation system and method disclosed by the invention, rapid, accurate, large-area and dynamic monitoring and comprehensive evaluation of soil quality are realized.

The invention discloses a gas turbine fault diagnosis system based on multiple sensors and a control method, the gas turbine fault diagnosis system comprises a data acquisition device, a data core service system and a monitoring center, and sensor nodes of the data acquisition device acquire dynamic signals of multiple parts of a gas turbine; a data acquisition module of the data acquisition equipment is connected with a sensor node, receives a dynamic signal and transmits the dynamic signal to a data receiving module; the data receiving module transmits the dynamic signal to the data processing module, the data processing module extracts each characteristic parameter through a characteristic parameter extraction algorithm, performs standardized score calculation on each dynamic signal characteristic data point acquired in real time, and sets a single score threshold value corresponding to each characteristic parameter; the standardized scores of all the features are fused, a comprehensive abnormal scoring formula is obtained, and a comprehensive scoring threshold value is set; when the characteristic data point of the current dynamic signal exceeds the comprehensive score threshold value or the single score threshold value corresponding to each characteristic parameter, the data processing module sends an abnormal signal to the alarm module.

The invention relates to the technical field of disaster assessment, and discloses a tree lodging risk assessment method and device based on a disaster risk theory. The assessment method comprises the following steps: acquiring a basic data set for disaster risk assessment; and inputting the basic data into a pre-constructed tree lodging risk index calculation model, and calculating and outputting a predicted tree lodging risk index. According to the embodiment of the invention, the assessment precision is improved: real-time and future predicted risk reminding is carried out by establishing a quantitative tree lodging risk index, and the precision and accuracy of disaster risk assessment are improved; overall-process risk prompting is realized; overall-process dynamic risk prompting is carried out around historical, real-time and future key time points, so that the disaster response efficiency is improved; and through more accurate disaster assessment and early warning, the landscaping maintenance cost and loss are reduced, and the social benefit of disaster response is improved.

The invention provides an alopecia type identification and detection method and system based on comparative learning, and solves the problems of alopecia type identification and the like, and the method comprises the following steps: S1, a doctor participates in data set production and processing; s2, fusing the data into a doctor knowledge base; s3, model training is carried out, and a model result is evaluated and fed back; s4, carrying out centralized learning on the model by adopting a stacking method; and S5, outputting a prediction result. The method has the advantages of good identification effect, high adaptability and the like.

The invention relates to the technical field of magnetic resonance imaging, and discloses a magnetic resonance four-dimensional dynamic imaging method and device driven by a physical model, and the method comprises the steps: obtaining the magnetic resonance K space data of a to-be-imaged part in a complete motion process; extracting a preset number of three-dimensional static images in the magnetic resonance K space data; analyzing the three-dimensional static image by using the target training model to obtain a dynamic change mode of the three-dimensional static image, and predicting a static image of a future time frame based on the dynamic change mode to obtain a four-dimensional image sequence; and applying physical constraint to the four-dimensional image sequence and correcting data in the K space domain by using a preset physical driving module, and carrying out multiple iterations to obtain a target four-dimensional dynamic imaging sequence. According to the method, high-resolution imaging of the prediction model is utilized, a small number of static images can generate the magnetic resonance four-dimensional dynamic sequence, and the problems that magnetic resonance imaging is limited in providing organ tissue dynamic motion information and complex dynamic motion joint functions and lesion conditions are difficult to comprehensively evaluate are solved.

The invention provides a road congestion evaluation method based on new energy automobile network connection big data, and the method comprises the steps: obtaining the real-time operation data of a new energy automobile, carrying out the preprocessing of the real-time operation data of the new energy automobile, and obtaining a vehicle basic information table; acquiring urban road network data, and analyzing the urban road network data to obtain a road network information table; obtaining a vehicle-road matching relation information table according to the vehicle operation table and the road network information table; obtaining road congestion evaluation information according to the vehicle-road matching relation information table; and obtaining a congestion analysis result according to the road congestion evaluation information. According to the invention, the Internet of Vehicles data and the urban road network data can be obtained, the diversity and real-time performance of the data are ensured, the road congestion condition can be evaluated and predicted more accurately, and more accurate traffic flow prediction and congestion early warning are provided. The evaluation and prediction precision is improved, and the intelligent and informatization level of urban traffic management can be improved.

According to the postpartum fumigation conditioning device and method for the obstetrics and gynecology department, the distribution gradient of the temperature in the horizontal direction in a fumigation box is determined, and then the monitoring temperature is divided into a normal temperature section and an interference temperature section based on the distribution gradient; determining a temperature confidence interval of each monitoring area in the fumigation box, and performing fusion estimation on the state vector of the temperature of each monitoring area through each temperature confidence interval and the distribution gradient to obtain a temperature response state of each monitoring area; further determining the disturbance margin of the temperature of each monitoring area in the circulating fumigation process of the fumigation gas; and according to all the disturbance margins and the temperature response state of each monitoring area, determining the thermal compensation amount of the corresponding monitoring area, obtaining the thermal compensation amount of each monitoring area, and adjusting the power of the heater of the corresponding monitoring area according to each thermal compensation amount. By the adoption of the scheme, dynamic balanced temperature control of the postpartum fumigation conditioning device for the obstetrics and gynecology department based on the regional temperature gradient can be achieved.

The invention discloses an intelligent fault early warning system of ship environment monitoring equipment, which relates to the technical field of data prediction analysis and comprises a data transmission monitoring module, a computing resource monitoring module, a real-time evaluation module, a classification module and a decision optimization module. The CPU occupancy rate and the fluctuation amplitude are collected in real time by combining the computing resource monitoring module, the real-time performance evaluation module comprehensively analyzes delay and the abnormal degree of computing resources and accurately evaluates the real-time performance ability of the system, and the classification module divides early warning into real-time early warning and non-real-time early warning. And the decision optimization module carries out deep analysis and decision optimization on non-real-time early warning, so that the accuracy and response efficiency of fault early warning are effectively improved, efficient and accurate early warning of ship faults in a complex environment is realized, and the stability of the system and the reliability of decision making of operators are remarkably improved.

The invention relates to an intelligent construction method for a water-rich fault tunnel excavation support, and aims to improve the safety and efficiency of tunnel excavation in a water-rich fault region through advanced numerical simulation, model test and intelligent control technologies. The method comprises the steps that geological conditions of the water-rich fault are analyzed in details, optimal excavation parameters and an optimal supporting scheme are determined through numerical simulation, and the tunnel excavation process is dynamically adjusted in combination with a real-time monitoring technology. And the support effect is evaluated and optimized by adopting fuzzy logic and a deep learning algorithm, so that intelligent management of tunnel excavation support under complex geological conditions is realized. The construction risk can be effectively reduced, the construction period is shortened, and the construction quality is improved.

The invention discloses a patient nursing grade intelligent evaluation system based on high-dimensional tumor data, and relates to the technical field of nursing grade evaluation. The nursing grade of the patient is intelligently evaluated through a comprehensive evaluation algorithm. The AHP can help the system to carry out hierarchical processing on factors influencing the nursing grade, and the entropy weight rule carries out objective optimization on the weight of each evaluation factor, so that a comprehensive nursing grade evaluation result is generated. The process is automatically completed, manual intervention is not needed, and the evaluation efficiency and precision are greatly improved.

The invention provides a geological disaster risk evaluation method and device fusing ascending and descending orbit time sequence InSAR deformation, and the method comprises the steps: carrying out the deformation inversion based on ascending and descending orbit time sequence InSAR data, obtaining an ascending orbit surface deformation quantity and a descending orbit surface deformation quantity, and obtaining a dynamic deformation factor corresponding to each grid in a research region after fusion; determining a multi-dimensional static environment factor corresponding to each grid in the research area based on the optical remote sensing data and the environment data; traversing each grid in the research area, and screening the dynamic deformation factors and the multi-dimensional static environment factors corresponding to the grids to determine a plurality of target evaluation factors corresponding to the grids; and based on the contribution degree of the target evaluation factor corresponding to each grid to the geological disaster event occurring in the research area, generating a geological disaster risk evaluation result corresponding to the research area. According to the method, geological disaster deformation can be represented more comprehensively and accurately, so that the geological disaster risk evaluation precision is improved.

The invention discloses a surface water pollution grade evaluation method based on multi-source data. The method comprises the following steps: acquiring underground water data, standard data and multi-source monitoring data; obtaining a comprehensive quality grade according to the standard data and the multi-source monitoring data, obtaining a dynamic environmental pollution limit value according to the standard data and the underground water data, and obtaining a single pollution index according to the multi-source monitoring data and the environmental pollution limit value; obtaining the comprehensive pollution index according to the single pollution index, clustering the comprehensive pollution index and the comprehensive quality grade to obtain a comprehensive pollution evaluation grade, and establishing a pollution grade evaluation model by adopting the comprehensive pollution evaluation grade; and optimizing the pollution level evaluation model by adopting a bat algorithm, inputting to-be-evaluated data into the pollution level evaluation model, and outputting an evaluation result. The method not only can improve the precision of surface water pollution grade evaluation, but also has good interpretability, and can be directly applied to a surface water pollution grade evaluation system.