67 results about "Natural hazard" patented technology

The invention discloses an urban inland inundation risk multi-level prediction method and device based on time-space diagram learning, a storage medium and a computer program product, and relates to the technical field of natural disaster risk prediction, and the method comprises the steps: collecting multi-modal urban hydrological data; performing hierarchical time modeling on the multi-modal urban hydrological data, and extracting a time embedding vector; constructing a heterogeneous graph based on the time embedding vector, and performing spatial feature aggregation calculation on the heterogeneous graph to obtain spatial embedding representation; and performing multi-level prediction according to the spatial embedding representation to obtain a multi-granularity waterlogging risk index. Through multi-modal data acquisition and preprocessing, layered time modeling, heterogeneous graph construction, spatial feature aggregation calculation and multi-level prediction, multi-modal urban hydrological data are effectively fused, and comprehensive and accurate urban inland inundation risk prediction is realized.

The invention relates to a BIM-based building structure intelligent management method. The method comprises the following steps: step 1, dynamic monitoring factor mining and multi-modal sensing: collecting meteorological hydrology, geological conditions, microenvironment and natural disaster historical data; 2, neural operator-driven virtual-real fusion dynamic analysis: converting the format of the collected data, mapping the data into a BIM model, integrating a virtual-real synchronization engine, and pre-training a Fourier neural operator through real-time displacement and strain data reverse calibration model boundary conditions in a construction stage, and establishing a direct mapping relationship between an external load and a structural response; 3, multi-modal data collaboration and hierarchical permission interaction: forming a multi-dimensional feature matrix through association of timestamps and space coordinates; 4, multi-modal graph convolutional network damage identification: learning dynamic relevance between nodes through an adaptive graph convolutional layer, and extracting damage sensitive features; and step 5, dynamic life early warning of digital twinning drive: predicting the remaining service period of the structure.

The embodiment of the invention provides a power grid natural disaster early warning method and system based on artificial intelligence, and belongs to the technical field of artificial intelligence. The method comprises the following steps of: firstly, constructing a multi-source power grid and disaster information evolution association set which comprises power grid equipment operation dynamic information and natural disaster occurrence dynamic information of a corresponding region and is associated in real time through a region identifier and annotated with an information evolution trend, and then carrying out sequential risk conduction analysis on the multi-source power grid and disaster information evolution association set; then calling a pre-trained power grid disaster intelligent hierarchical prediction model to carry out multi-stage feedback prediction processing on the result, generating a power grid natural disaster hierarchical prediction result, executing early warning strategy dynamic iteration optimization according to the result, generating early warning strategy dynamic iteration adjustment parameters, and carrying out early warning strategy dynamic iteration adjustment. And finally, generating a power grid natural disaster accurate early-warning instruction based on the early-warning strategy dynamic iteration adjustment parameters, and sending the power grid natural disaster accurate early-warning instruction to a power grid monitoring center terminal. The power grid natural disaster early-warning method can comprehensively, accurately and dynamically perform early warning on the power grid natural disaster.

The invention discloses a risk management and control method, system and equipment based on a water conservancy project, and relates to the technical field of risk management and control. A sensor data drift abnormal index is calculated by obtaining multi-source monitoring data of a target water conservancy project after an earthquake, and an engineering structure change abnormal index is calculated based on a preset structure health model and epicenter parameters; and calculating an earthquake propagation influence index by combining the seismic source distance, calculating an earthquake influence coefficient by combining the three factors, and judging whether an original risk early warning evaluation system needs to be updated according to the earthquake influence coefficient. According to the method, after a natural disaster event (such as a slight earthquake) is encountered, whether the risk early warning evaluation system needs to be updated or not can be judged according to the actual disaster degree; it is ensured that early warning is not delayed or invalid, and the possibility of causing larger potential safety hazards is reduced; meanwhile, whether the risk early warning evaluation system is updated or not can be reasonably judged according to the influence of the actual earthquake on the construction of the hydraulic engineering project, and unnecessary waste of computing resources is reduced.

The invention discloses a satellite precipitation product improvement method, device and system based on a dual-time scale minimum error, and the method comprises the steps: carrying out the improvement of a first satellite precipitation product, a second satellite precipitation product and an interpolation precipitation product according to the obtained first satellite precipitation product, second satellite precipitation product and interpolation precipitation product; sequentially adopting a dual tool variable algorithm, a least square fusion algorithm and an inverse error weighting algorithm to respectively calculate first fusion rainfall products under a daily scale and a monthly scale; and fusing the first fused precipitation products under the daily scale and the monthly scale to generate a second fused precipitation product. Ground observation data are not needed, daily scale and monthly scale rainfall errors can be minimized at the same time, the thought of data fusion is incorporated, the performance of satellite rainfall products is enhanced, and detailed data support can be provided for natural disaster monitoring and early warning and regional water resource management.

The invention provides an overhead transmission line fault loss assessment method based on spherical fuzzy and entropy weight methods, and relates to the technical field of transmission line fault loss assessment. Comprising the following steps: S1, acquiring natural disaster data of a to-be-evaluated line area; s2, dividing the fault loss into a plurality of evaluation dimensions, and constructing a hierarchical fault loss evaluation system by taking natural disasters as influence factors; s3, based on the disaster data, calculating fault loss cost of each evaluation dimension under different natural disasters; s4, using a fuzzy set theory to quantify the influence degree of the natural disaster on each evaluation dimension, and calculating a subjective weight; s5, calculating the objective weight of each evaluation dimension by adopting an entropy weight method; and S6, determining a comprehensive weight through the subjective and objective weight fusion model, and evaluating the fault loss of the overhead transmission line based on the comprehensive weight. In the whole life cycle cost calculation, fault loss evaluation is added, accurate quantification of multi-dimensional loss is realized, and the reliability of the line is improved in the later line design process.

The invention discloses an elasticity evaluation and improvement method for a power transmission system under the action of debris flow, is used for solving the technical problem of single fault rate consideration factor in the prior art, and belongs to the technical field of evaluation of anti-disaster capability and recovery capability of the power transmission system under extreme natural disasters (such as debris flow). The method comprises the following specific steps: step 1, constructing a debris flow model considering an impact force attenuation effect; s11, describing the influence area of the debris flow by using the three parameters; s12, carrying out stress analysis on the tower impacted by the debris flow; 2, constructing a fault model considering the accumulated impact process of debris flow on the power transmission element; s21, calculating a tower failure probability; s22, calculating the failure probability of the power transmission line; s23, calculating the overall failure probability of the power transmission line; 3, sampling at a constant time interval delta t based on the solved failure probability of the power transmission line within the debris flow duration delta T; 4, drawing an expected power supply power curve of the power transmission system; and 5, calculating an elastic index.

The invention discloses a mountain disaster susceptibility prediction method and device considering a composite terrain distance, and relates to the field of natural disaster risk assessment, and the method comprises the steps: obtaining a mountain disaster sample and a multi-source environment factor in a research area range; terrain factors are screened from the multi-source environment factors, and the weight of each terrain factor is calculated based on an entropy weight method; on the basis of the Euclidean space distance, a weighted terrain difference distance is introduced, and a composite terrain distance is constructed through fusion; sorting and screening the mountain disaster sample data according to the composite terrain distance, and determining a local reference sample set and a geographic weighted weight corresponding to each local reference sample; adopting a Bayesian optimization method to perform joint optimization on the bandwidth parameter of the geographical weighted weight, the weight parameter of the composite terrain distance and the structure parameter of the random forest model to obtain an optimal parameter combination; and under the constraint of the optimal parameter combination, carrying out point-by-point prediction on the research area. According to the invention, the precision of mountain disaster susceptibility prediction can be improved.

The utility model provides a windproof anti-seismic photovoltaic tracking support system, which relates to the field of photovoltaic supports, and comprises a plurality of groups of synchronously adjusted photovoltaic support mechanisms and two groups of symmetrically arranged connecting rod damping mechanisms, the plurality of groups of photovoltaic support mechanisms are arranged side by side, and each group of connecting rod damping mechanism is connected in series with two ends of the photovoltaic support mechanism. The gravity center design of the photovoltaic tracking support system is optimized, the multiple sets of photovoltaic support mechanisms arranged side by side form an integral structure through the connecting rod damping mechanism, it is ensured that under the action of strong wind, the multiple sets of photovoltaic support mechanisms can jointly resist the extreme environment and keep balance, and the overturning risk is reduced; according to the utility model, the influence of natural disasters such as strong wind and earthquake on the photovoltaic tracking support system is effectively reduced while the power generation efficiency is improved, and the popularization value is high.

The application discloses a kind of based on ST-GAN's multi-source data dynamic risk early warning method and system, the method includes the following steps: S1. constructing the spatiotemporal generation confrontation network suitable for spatiotemporal data characteristics, utilize the network to generate extreme precipitation data, realize spatiotemporal unbalanced data reduction, obtain the equalization data of final output;S2. the equalization data of final output with high-dimensional space variable is carried out multi-source data fusion, and nonlinear spatiotemporal information conversion equation, and by local linearization obtains linear approximation model, to predict future time series;S3. under the present situation that extreme rainfall data amount is relatively insufficient, neural network based on dual learning theory accurately learns the parameter of nonlinear spatiotemporal conversion, estimates extreme weather event.The application is through spatiotemporal generation confrontation network (ST-GAN) and multi-source data fusion engine, significantly improves the precision and efficiency of natural disaster warning.

A method for displaying an augmented reality (AR) representation of physical effects and property damage resulting from a parametric earthquake event is provided. The method includes scanning scenes with a common boundary and identifying the common boundary and objects in the first and second scenes. The method also includes creating an AR boundary for the common boundary and AR objects for the objects. The method further includes receiving a seismic characteristic. The method additionally includes, while scanning the first scene, displaying an aperture effect on the AR boundary and at least one seismic effect on the AR objects in the second scene based on the at least one received seismic characteristic, wherein the aperture effect allows viewing the seismic effect on AR objects in the second scene on the user device while the second scene is not visible to the one or more sensors of the user device.

The invention discloses an intelligent management system mainly used for reducing the damage risk of a photovoltaic power station caused by extreme weather, and the system comprises an equipment installation backboard, one side of the equipment installation backboard is fixedly provided with a photovoltaic inverter, and the other side of the photovoltaic inverter is provided with an equipment installation frame in a penetrating manner. And equipment mounting bolts are mounted in the equipment mounting racks in a threaded manner. According to the intelligent management system mainly used for reducing the damage risk of the photovoltaic power station caused by the extreme weather, in the daily use process, the photovoltaic inverter is fixedly installed on one side of the installation backboard through cooperation of the equipment installation frame and the equipment installation bolts; and then the photovoltaic intelligent management system is utilized to perform intelligent management on the photovoltaic power station, so that the damage to the photovoltaic power station caused by natural disasters can be detected in real time, and when the natural disasters encounter, the management can automatically judge a response mode according to the current state, and the whole set of system can be controlled to take corresponding response measures.

This invention relates to a method for constructing a disaster-bearing entity based on a structure-function-behavior model, comprising the following steps: S1 entity structural semantic modeling; S2 entity functional semantic modeling; S3 behavioral semantic modeling; S4 constructing a semantic fusion module and building urban natural disaster risk factor data and knowledge data, and using the risk factor data and knowledge data to fuse the semantics formed in the models built in steps S1-S3 to realize the construction of the disaster-bearing entity. The method also includes a data and mechanism-driven twin model update method, specifically including: updating the twin model using a change detection model, and update management, wherein the change detection model includes the detection of structural and semantic changes. The method also includes a disaster risk prediction method, which uses a hybrid approach consisting of decision trees and node data formed by urban natural disaster risk factor data and knowledge data. This achieves data analysis and prediction of urban entity disaster situations with visualization of the sum of structure-function-behavior factors.

This invention discloses a parallelized method for surface water dynamic simulation based on a pipeline network. First, it constructs a water flow path from the water source to the watershed outlet, simplifying the three-dimensional surface into a one-dimensional pipeline network. Then, it improves the classic Manning formula using topographic parameters to estimate the flow velocity of each water flow path. Combined with a preset travel time, it simulates the surface runoff. Finally, it uses parallel computing to parallelize the velocity estimation function and the surface runoff simulation function, resulting in a high-precision and high-efficiency parallelized method for surface water dynamic simulation. This invention not only considers the influence of topographic parameters on runoff velocity in surface water dynamic simulation but also applies the parallel technology CUDA to the field of surface water dynamic simulation. While ensuring the accuracy of the surface water dynamic simulation algorithm, it improves the real-time response efficiency of surface water dynamic simulation, laying the foundation for scientific land planning, water resource management, and natural disaster response strategies.

1. The name of the design product: lighting column assembly. 2. The use of the design product: used as a lighting column assembly for safety and hazard identification in various work areas (such as infrastructure, road construction and maintenance, aviation, port, railway, mining, traffic congestion area, and public access area); or used to guide pedestrian traffic during work area, public activity, during natural disaster, and help identify the location of various landmarks. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1. 5. The rear view and right view are omitted as the design points have been shown in other views. 6. Other changes in the state of the case need to be explained: the product with color is shown in the figure.

This invention discloses a method and system for comprehensive risk assessment of natural disasters in coal mines under complex geological conditions, belonging to the field of coal mine disaster risk assessment technology. The method involves collecting relevant coal mine data; determining comprehensive risk assessment indicators for natural disasters in coal mines under complex geological conditions, and calculating the comprehensive indicator weights using a combined weighting method; constructing an improved transfer learning-BP neural network model, optimizing scoring parameters by combining relevant coal mine data under complex geological conditions, and determining the scoring values ​​for the comprehensive risk assessment of natural disasters in coal mines under complex geological conditions; and conducting a comprehensive risk assessment of natural disasters in coal mines under complex geological conditions based on the comprehensive indicator weights and the scoring values ​​to obtain the assessment results. This invention achieves comprehensive, accurate, and real-time assessment of natural disasters in coal mines under complex geological conditions, effectively preventing and reducing the risks of underground natural disasters under complex geological conditions, and ensuring safe production and the safety of life and property of coal mine workers.

The present application discloses an intelligent railway operation and maintenance system, method, equipment, medium and product, which relate to the field of railway operation and maintenance. The system includes an air-ground-vehicle integrated transmission module for collecting and transmitting sensor data of key train components; a drone cluster module for collecting and transmitting environmental image data within a set range of the railway line; a digital twin big model training module for constructing a digital twin big model based on the sensor data and environmental image data, determining an integrated big model, and using the integrated big model to predict train fault types and natural disasters, and generate emergency guidance plans; the integrated big model is a neural network model. The present application improves the real-time detection and is more suitable for future ultra-high-speed scenarios.

A method and system for monitoring low-temperature rain, snow, and freezing in complex terrain areas comprises the following steps: Step 1: Collecting satellite observation data of the monitoring area for large-scale monitoring of cold air movement, snow cover, and surface temperature, and identifying lower atmospheric inversion layer data; Step 2: Obtaining atmospheric vertical structure data through a ground-based remote sensing vertical observation system; Step 3: Collecting image data of the monitoring area through UAV-based remote sensing; Step 4: Deploying ground data acquisition units to collect data including rainfall and snowfall, snow thickness, temperature, road icing, and power line icing; Step 5: Collecting historical disaster data for the monitoring area; Step 6: Cleaning, fusing, and extracting features from various data using machine learning algorithms; Step 7: Constructing an assessment model reflecting low-temperature rain, snow, and freezing disasters based on the extracted feature data, and generating prediction results for low-temperature rain, snow, and freezing natural disasters in the monitoring area.

The invention provides a non-coal mine natural disaster-causing risk assessment method and system, and the method comprises the steps: carrying out the division according to the factors of non-coal mine natural disaster-causing disasters and an assessment target, and forming a disaster-causing risk assessment system which comprises a target layer, a criterion layer and an index layer; calculating the index weight of each layer of index through an analytic hierarchy process; adopting a fuzzy analytic hierarchy process to obtain a fuzzy evaluation matrix of each layer of indexes; obtaining a comprehensive evaluation vector according to the index weight of each layer of index and the fuzzy evaluation matrix; according to the method, an occurrence possibility grade value and a consequence severity grade value are calculated according to evaluation vectors, a final risk grade is determined, influence weights of all indexes on disaster-causing risks are calculated by introducing fuzzy mathematics, contributions of different indexes to the disaster-causing risks are optimized, and the risk assessment accuracy is improved. According to the method, the defect of lack of difference in calculation of the weight of each disaster-causing index of the non-coal mine in a traditional method is overcome, the evaluation result can better conform to the actual situation, and the method has a feasible guiding effect.

The application discloses a mountain torrent disaster early warning method and system, and the method comprises the following steps: acquiring cloud image data; processing the cloud image data based on a pre-trained deep learning model to obtain a predicted rainfall sequence; constructing a hydrological and hydrodynamic model and performing scenario simulation to demarcate a disaster threshold rainfall; and combining the predicted rainfall sequence and the disaster threshold rainfall to perform early warning. The system comprises an image acquisition module, a rainfall prediction module, a threshold determination module and an early warning module. By using the application, mountain torrent disaster early warning can be performed in advance, important disaster relief and risk avoidance time can be obtained for mountain torrent disasters, and the prediction accuracy is well guaranteed due to the combination of the rainfall prediction module and the atmospheric pressure observation and the cloud rainfall estimation method based on deep learning. The application can be widely applied in the field of natural disaster early warning.

The application provides a general wind speed prediction method and device for real-time typhoon rapid warning, and relates to the technical field of natural disaster warning. The method comprises the following steps: based on the simulation circle method, typhoon data analysis is performed according to coastal city information and typhoon historical information to obtain typhoon basic information; based on a mathematical statistical inference method, a model is constructed according to the typhoon basic information to obtain a parameter probability distribution model; according to the parameter probability distribution model, random sample generation is performed using the Monte Carlo method to obtain a first training data set; based on a dynamic elite reservation strategy, the first training data set and typhoon real-time information are used to optimize and train a general wind speed prediction model to be trained to obtain an optimized general wind speed prediction model. The application is an efficient and accurate general wind speed prediction method based on a GA-BP neural network and a dynamic elite reservation strategy.

The utility model discloses a curtain wall truss with an anti-seismic structure, which relates to the technical field of curtain walls and comprises an inner vertical frame and an inner transverse frame which are used for mounting curtain wall glass. Wherein the inner side vertical frame is also fixedly connected with the connecting piece; the outer end of the connecting piece is connected with a C-shaped connecting piece through a corner connector; an outer side vertical frame is fixed in the C-shaped connecting piece, and outer side transverse frames are fixed on the two sides of the outer side vertical frame; according to the utility model, the rubber strip has better sealing and protecting effects on the curtain wall glass, and when vibration occurs, the rubber strip has a buffering effect on the curtain wall glass, so that the condition of stress cracking can be avoided; due to the structural design of the C-shaped connecting piece, the whole curtain wall truss system has a certain displacement space when subjected to external force, and therefore vibration energy is effectively absorbed and dispersed; when natural disasters such as earthquakes occur, the anti-seismic structure can guarantee the overall stability of the curtain wall, and the risks of glass breakage and structural damage caused by vibration are reduced.

The application provides a kind of glacier mud-rock flow dynamic risk early warning method and system based on seismic signal, belongs to natural disaster warning technical field, first constructs the response chain of seismic signal glacier, obtains the information such as the propagation path of seismic signal, the response order of each circle of glacier and the response characteristic transmission relationship, then analyzes the risk evolution node in response chain information, accurately determines the key nodes such as response trigger, energy accumulation and diffusion start, forms the glacier mud-rock flow risk evolution node set, then links the glacier mud-rock flow risk evolution node set with the topographic features of glacier area, generates the dynamic risk evolution path that can reflect the dynamic development process of glacier mud-rock flow, generates the dynamic risk warning content containing detailed warning information based on this dynamic risk evolution path, finally reversely associates the on-site response data to the response chain construction process, realizes the optimization update of the response chain information of seismic signal glacier, effectively improves the early warning ability to glacier mud-rock flow disaster.

The low-cost structural vibration monitoring device with intelligent electrical control system measures three-axis acceleration — two orthogonal horizontal directions and one vertical direction — at its installation point. It is designed to be easy for building or structure owners to install, offering a cost-effective alternative to imported products for measuring floor acceleration or vibration levels. The device can reliably detect vibration caused by natural hazards such as storms or earthquakes, and when acceleration exceeds a preset threshold it immediately alerts the responsible owner or authorities. This enables timely planning of user warning systems and provides data for future engineering assessments of structural performance. Measurement accuracy has been confirmed by comparison with high-end standard instruments. Beyond natural - hazard monitoring, the device can also be applied to other vibration scenarios, including roadway vibrations from vehicular traffic, pile-driving activities, and machinery -induced vibration in industrial facilities. The intelligent electrical control system not only serves as a backup power source for the low-cost monitoring device but also permits remote on / off control of its power supply via the Internet, allowing operation from anywhere without requiring physical access to the installation site.

This invention discloses a spatial simulation method and system for the relative humidity field during the rainy season, comprising: acquiring the monthly relative humidity baseline field of the dry season reference month, the annual relative humidity baseline field, and the reference monthly relative humidity and the relative humidity of the month to be simulated at meteorological observation stations; calculating the monthly anomaly baseline field of the reference monthly relative humidity; acquiring the monthly anomaly baseline value and the annual relative humidity baseline value of the reference monthly relative humidity at the meteorological observation station, and calculating the monthly relative humidity anomaly and anomaly difference of the meteorological observation station in the reference month; acquiring the monthly relative humidity anomaly difference field of the reference month and the non-monsoon influence field of the relative humidity anomaly of the month to be simulated; calculating the monsoon influence of the relative humidity anomaly of the month to be simulated at the meteorological station, and after acquiring the monsoon influence component field of the relative humidity anomaly of the month to be simulated, calculating the relative humidity field of the rainy season month to be simulated. This invention can improve its application level in the fields of ecology, forestry, agriculture, and natural disaster monitoring and early warning.

The invention discloses a power distribution network multi-disaster toughness improvement strategy, an evaluation method, a system, equipment and a medium, and belongs to the technical field of power distribution network toughness evaluation and toughness improvement, and the method comprises the steps: obtaining meteorological and geological disaster data and power grid structure data, and constructing a multi-disaster coupling fault probability model; based on the multi-disaster coupling fault probability model, a comprehensive toughness evaluation index is obtained by combining a power distribution network information entropy index, a graph structure toughness index and a load recovery rate; carrying out joint representation learning on the topological structure and node operation data of the power distribution network through a graph convolutional network, extracting high-order topological features through multilayer message transmission, and carrying out state integration to obtain a comprehensive power distribution network state vector; and constructing a soft actor-commentator reinforcement learning model, and dynamically outputting an optimal resource scheduling strategy by taking maximization of a comprehensive toughness index as a target. According to the invention, the restoring force and the operation efficiency of the power distribution network in the face of natural disasters are effectively improved.

The invention discloses a driving guidance method, device and equipment for a geological disaster road section and a storage medium, and relates to the technical field of geological disaster prevention and control, and the method further analyzes potential driving guidance of the geological disaster road section by means of the visual capability and analysis capability of a small baseline integrated interferometric synthetic aperture radar in cooperation with an extreme learning machine having good compatibility with vectors. According to the method and the system, the situation that the unhappened geological disasters occur before the geological disasters occur is prevented, the vehicle is reminded to change the route in time and the monitoring role is notified to perform prevention and control in time after the geological potential risk is predicted, so that the double insurance of early warning and prevention and control is realized, and the loss of life and property safety caused by natural disasters caused by unfavorable geological bodies is prevented; and personal safety and property safety are guaranteed.

The invention relates to the technical field of precipitation analysis, and discloses a basin precipitation space-time evolution characteristic and development trend analysis method, which comprises the following steps of: selecting precipitation data and foundation laser radar water vapor data of a plurality of ground meteorological stations in a target basin, and performing monthly correlation analysis on the precipitation data and precipitation data parameters respectively; and analyzing the variation trend of the four-season precipitable water in the recent N years along with the years, analyzing the evolution rule of the four-season precipitable water in the recent N years along with the years by utilizing a Mangkendall test method, and analyzing the evolution trend of the precipitable water in the recent N years by utilizing an empirical orthogonal function analysis method. According to the invention, through analyzing the annual, latitude and longitude and seasonal spatial-temporal distribution of the precipitable water amount of the target drainage basin in recent 20 years, the spatial-temporal distribution evolution characteristics of the precipitable water amount of the target drainage basin in recent 20 years and the long-time sequence development change trend of the target drainage basin can be obtained; therefore, important reference can be provided for the climate and natural disaster early warning of the target drainage basin.

The present application relates to the technical field of substation risk assessment, in particular to a substation resilience assessment method based on zero-sum game and vulnerability analysis. The method models the substation through network topology analysis, calculates the system function using the reachability matrix thereof; then, a vulnerability model of the element is established, Monte Carlo simulation is performed using the vulnerability function thereof, and the risk of the substation under earthquake disaster is calculated; then, the operation risk and loss of the substation under human attack are modeled, a zero-sum game model of the defender and the attacker is constructed, an effective protection strategy is proposed according to the specific budget limit and the known attack scenario, the influence of the two disasters is comprehensively considered, the substation resilience assessment method considering natural disaster and human attack is established, the safety risk of the substation under extreme events is effectively reduced, and the safe operation ability of the substation is improved.

The invention discloses a pole and tower inclination monitoring device based on an AI learning module and a working method thereof. A multi-sensor fusion monitoring mode is adopted, a single set of device can realize omnibearing state monitoring of a base pole and tower, important power transmission channel poles and towers need to be installed on each base, and a set of device can be configured for each 3-5 base pole and towers according to geological conditions in a general line. When a tower inclines due to factors such as foundation settlement, external force damage or natural disasters, the spatial attitude of the tower changes continuously, and systematic deviation of a measured value of a tilt angle sensor and Beidou positioning coordinates is caused. The change forms digital representation of the health state of the tower through time-space correlation analysis of multi-source data. The device performs fusion processing on acquired multi-dimensional data such as inclination angles, coordinates and environments, identifies an inclination mode and predicts a development trend by using an intelligent algorithm, and realizes early warning and accurate diagnosis of tower inclination.