123 results about "Weather effect" patented technology

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses a multimodal transport organization mode dynamic optimization method based on big data processing, which relates to the technical field of multimodal transport data optimization, and comprises the steps of collecting various data in a transport process in real time, and carrying out data optimization based on transport network nodes of a target area. A multimodal transport grid model is constructed, first site information and second site information in a multimodal transport planning path are collected, first weather prediction data and second weather prediction data are collected in real time, and the multimodal transport grid model based on weather changes is formed. An optimization algorithm is adopted to solve the multimodal transport path optimization model with constraint conditions and based on weather changes; according to the method, the influence of the weather factors on the transportation mode can be fully considered when the multimodal transportation organization mode is dynamically optimized, the situation that the established transportation mode cannot be safely and successfully executed on schedule due to the weather influence is avoided, the transportation mode can be flexibly adjusted by introducing the first weather prediction data and the second weather prediction data, and the transportation efficiency is improved. Therefore, the smooth operation of multimodal transport is ensured.

The invention provides an artificial influence weather-based image recognition method and system, and the method comprises the steps: firstly obtaining a meteorological image data set of a target region containing a plurality of original meteorological images in sequential arrangement, carrying out the feature extraction of the original meteorological images, and obtaining a meteorological element distribution feature and a dynamic evolution feature; and then, based on a preset weather influence analysis model, carrying out weather influence analysis on the meteorological element distribution characteristics and the dynamic evolution characteristics, generating weather intervention decision characteristics, finally, according to the weather intervention decision characteristics, generating an operation strategy of artificial influence weather, and sending the operation strategy to operation execution equipment to trigger weather intervention operation. Therefore, meteorological image features can be comprehensively and deeply analyzed, the operation strategy is scientifically and accurately generated, and the effect and efficiency of artificial influence weather operation are improved.

The invention discloses a multi-weather degraded image restoration method based on a state space model. The method comprises the following steps: acquiring multi-scene clear images, synthesizing images under rain, snow and fog weather conditions by using an image synthesis technology, and constructing a standard multi-weather data set; preprocessing the data set and then dividing the data set; constructing a multi-weather degradation image restoration model based on the state space model, and performing training optimization on the model; the method comprises the following steps: acquiring an image which is degraded due to weather influence, preprocessing the image, inputting the processed image into a pre-trained multi-weather degraded image restoration model for restoration, and outputting the image as a processed clear image. According to the method, a self-adaptive channel attention mechanism is introduced based on the state space model, the capturing capability of the network on image local features is enhanced, images of various scales can be effectively processed through overlapped block embedding and multi-scale processing strategies, and the image recovery performance is remarkably improved. The method provided by the invention can process image restoration tasks including but not limited to various weather degradation caused by rain, snow and fog, and has a wide application prospect.

The invention is suitable for the technical field of fault first-aid repair, and provides a power fault first-aid repair path optimization method and system under the influence of extreme weather, and the method comprises the steps: building a power fault first-aid repair path initial scheduling model based on the shortest total first-aid repair time and the minimum loss under the influence of extreme weather as parameters; setting a constraint condition to allocate at least one first-aid repair team for each fault point, wherein the first-aid repair capability of the first-aid repair team under the influence of extreme weather is an integer greater than zero and the loss caused by the influence of extreme weather is less than the loss of the regional fault point; determining a target parameter variable according to the constraint condition, and optimizing the target parameter variable by using an improved genetic algorithm to obtain an optimized power fault repair path optimization scheduling model; and inputting the obtained power fault first-aid repair response data into the power fault first-aid repair path optimization scheduling model to generate a target first-aid repair path. According to the method, the loss amount generated by extreme weather influence factors is considered, and the accuracy of path prediction is effectively improved.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

The invention relates to the technical field of artificial influence weather, and discloses an artificial influence weather knowledge graph construction method and system based on a large model. The method comprises the steps that multi-source meteorological data are collected and preprocessed to generate a standardized data set; extracting a meteorological feature set by using the pre-trained large model; detecting a data dynamic change frequency, and starting a dynamic strategy to switch and mark an abnormal data segment when the data dynamic change frequency exceeds a threshold value; in combination with a similarity matching result of the historical knowledge graph and the current features, decomposing the feature set of the non-abnormal data segments to generate knowledge feature subsets; correcting the knowledge feature subset based on the coupling relationship between the meteorological kinetic parameters and the environmental parameters; and reversely backtracking the key influence factors along the weather influence propagation map, and constructing the knowledge map. According to the method, multi-source data are integrated through standardized processing, abnormal data are dynamically identified, historical knowledge and physical mechanism are combined to correct features, key factors are finally backtracked to complete graph construction, and the method is suitable for knowledge integration and analysis in the field of artificial influence weather.

The invention relates to the technical field of intelligent logistics, in particular to a vehicle scheduling method and system and a readable storage medium, and the method comprises the steps: obtaining the platform dynamic information of all platforms in a park, the vehicle information of all to-be-scheduled vehicles, and the current weather state of the park; determining the weather influence degree based on the current weather state and the cargo type of each to-be-dispatched vehicle, and obtaining the dispatching emergency degree based on the weather influence degree of each to-be-dispatched vehicle and the vehicle information; based on the platform dynamic information and the platform static information of all platforms and the vehicle information of each to-be-dispatched vehicle, the matching degree of the to-be-dispatched vehicle and the platform is obtained to determine a distribution matrix between the vehicle and the platform, and based on constraint conditions, the distribution matrix is solved to obtain a dispatching strategy corresponding to each to-be-dispatched vehicle. According to the method, automatic scheduling is performed through the real-time data, so that the risk caused by weather and illegal scheduling is reduced while the scheduling efficiency is improved and the resource utilization rate is optimized.

The invention belongs to the technical field of port scheduling, and particularly relates to a container yard AGV scheduling method and system, and the method comprises the steps: S1, collecting and processing the data of a field environment; s2, setting a target function F according to the transportation distance D, the waiting time T, the weather influence W and the storage space storage factor C; s3, using an ant colony algorithm to optimize the transportation distance D and the storage space storage factor C; optimizing the waiting time T and the weather influence W by using a neural network; and S4, selecting a scheme with the minimum comprehensive objective function F according to the transportation distance D and the storage space storage factor C provided by the ant colony algorithm and the waiting time T and the weather influence W predicted by the neural network. The method has the advantages that different key independent variables are optimized through the ant colony algorithm and the neural network respectively, and optimization of the efficiency and stability of the whole dispatching system is facilitated. According to the method and the system, a brand new algorithm is constructed for a port environment, and real-time and dynamic optimization can be realized on path and resource scheduling.

The invention discloses a brick and stone cultural heritage double-control environment test box and a use method thereof.The brick and stone cultural heritage double-control environment test box comprises a box body, the interior of the box body is divided into two test cavities through an experiment sample, the experiment sample is fixed in the box body through a detachable module, and temperature and humidity control assemblies are arranged in the two test cavities; wherein one of the test cavities is also internally provided with a component for simulating an outdoor environment. Compared with the prior art, environments on the inner and outer sides of the grotto can be simulated at the same time, so that the weathering influence of an experimental sample on the sample under the combined action of different environments on the inner and outer sides can be conveniently monitored, and the simulated environment in the experiment better fits the actual condition of an engineering site; therefore, the effect of better revealing the disease mechanism of the grotto or masonry cultural heritage is achieved.

The invention discloses a flight fly-around path planning and evaluation method under a severe weather condition, and the method comprises the steps: obtaining a meteorological distribution diagram and all planned flight paths, judging whether each planned flight path is affected by weather according to the meteorological distribution diagram, dividing the planned flight paths affected by weather as abnormal flight paths, and carrying out the calculation of the abnormal flight paths. Dividing the planned track which is not influenced by weather as a normal track; for each abnormal track, planning a plurality of pre-selected tracks by avoiding weather influence according to an A * algorithm in combination with the meteorological distribution map, calculating a comprehensive score of each pre-selected track according to the meteorological distribution map, selecting the pre-selected track with the highest score to replace the abnormal track, and classifying the replaced abnormal track as a normal track; and S3, repeating the step S2 until each planned track is not influenced by weather, and completing the path planning. According to the method, the plurality of fly-around paths are solved through the A * algorithm, and the plurality of fly-around paths are comprehensively evaluated, so that the path planning is preferentially carried out, and the problems of single concentration point and low planning efficiency of the path planning method in the prior art are solved.

The invention discloses a mountainous area meteorological rainfall supplementation observation station site selection evaluation method based on a high-influence weather influence path in the technical field of meteorological observation, and the method comprises the steps: obtaining meteorological grid point data in an observation region, screening the meteorological grid point data to obtain a rainstorm process, generating a path sequence based on the rainstorm data in the rainstorm process, and carrying out the site selection evaluation of a mountainous area meteorological rainfall supplementation observation station. A dynamic time warping (DTW) method is adopted to generate an influence area with a widened rainstorm path, and grid point rainfall forecast error data is generated based on multi-source forecast data and station meteorological data. According to the invention, by extracting the evolution path of the rainstorm process in time and space and combining the multi-source mode forecast error, the regional rainfall distribution, the existing station network density, the topographic feature and the settlement distance element, the evaluation method can be dynamically quantified and the monitoring demand and cost constraint can be balanced; the problems of data missing in the initial stage of a rainstorm system, insufficient upstream observation of a weather system path, insufficient station network coverage rate and weak data representativeness are solved.

The greening curtain wall of the green building comprises a wall body, a plurality of containing covers are connected to the wall body, one end of each containing cover is provided with an opening, a moving frame and a moving part for pushing the moving frame to move are arranged in each containing cover, green plant boxes and a mounting assembly for limiting the green plant boxes are arranged on each moving frame, and a containing groove is formed in the wall body; a containing groove is formed in the wall body, a shielding plate and a pushing piece for pushing the shielding plate to move are arranged in the containing groove, a connecting rope is connected between the shielding plate and the moving frame, the two ends of the shielding plate are connected with connecting columns, limiting grooves are formed in the positions, corresponding to the connecting columns, of the wall body, and a limiting assembly for limiting movement of the shielding plate is arranged in the wall body. The limiting assembly limits the shielding plate, when the green plant box is located in the containing cover, the connecting column is located at the end of the limiting groove, and the shielding plate is in a vertical state and covers the opening of the containing cover. The method has the effect of preventing the green plants from being influenced by external weather.

The invention relates to the technical field of wind profile radar data processing, in particular to a wind profile radar data adaptive quality control method and system under the influence of extreme weather. The method comprises the following steps: acquiring wind profile radar spectrum data, and calculating radial speeds of four inclined beams in the east, the south, the west and the north according to the wind profile radar spectrum data; obtaining a horizontal wind component of an inclined beam according to the radial speed; synthesizing the horizontal wind component to obtain a new wind speed of the inclined wave beam; and constructing a wind speed profile by using the new wind speed, obtaining an optimal wind speed profile in combination with a balloon sounding profile and the wind speed profile, and taking wind speed data of the optimal wind speed profile as product data. According to the three-beam detection principle, the optimal wind speed profile is obtained through the wind speed data resynthesized by the wind profile radar data, and the problem of poor availability of the wind profile radar observation data in extreme weather is solved.

A processor may receive weather event data. The processor may determine, utilizing an artificial intelligence model mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in the latent space, a weather impact associated with a weather event. In some embodiments, the artificial intelligence model may be trained using historical weather event data and historical weather impact data associated with the historical weather event data. The processor may output the weather impact associated with the weather event to a user.

The embodiment of the invention provides a traffic safety early warning method and device and computer equipment, and the method comprises the steps: carrying out the superposition of a known weather monitoring network and a known rail traffic network, and displaying the weather state data of a first target area after the superposition; determining a second target area with a disaster weather risk in the first target area according to the weather state data; determining a rail transit network operation safety factor association relationship and strength thereof in the second target area; and constructing a disaster weather and second target area rail transit network operation safety factor influence association view, forming a disaster weather influence knowledge graph, and based on the weather influence knowledge graph, combining the rail transit network operation safety factor association relationship and the strength thereof to obtain a second target area rail transit network operation safety factor influence association view. The grade of the evaluation result of the operation safety toughness of the rail transit network in the disaster weather is comprehensively determined so as to trigger combined early warning, and the coping requirements of the sudden severe weather in the local area or part of stations of the network can be met.

The invention relates to an overhead line monitoring method in which an operating state of an overhead line is characterized metrologically in order to keep the operating state of the overhead line within a predefinable permissible range depending on weather influences by regulating an energy feed-in into the overhead line and at the same time to maximize an energy utilization of the overhead line within the permissible range of the operating state, comprising the steps: • Generation of evaluable measurements depending on wind speeds v i at a plurality n of measuring points i=1...n along an overhead line, in particular at locations where overhead line masts are erected, wherein ◯ Wind energy at a measuring point i is converted into a rotational movement of a rotor (2) of a sensor (1), ◯ the rotational movement is converted into a periodic, local deformation of at least one optical waveguide (3) running parallel to the overhead line at the measuring point i, • Calculation of wind speeds v i at the n measuring points by evaluating periodic changes in signal attenuation caused by deformation in the optical waveguide (3) • Assessment of the operating condition of the overhead line based on wind speeds v i . The invention further relates to a measuring transmitter for generating an evaluable measured quantity as a function of wind speed.

The invention relates to a physical information guidance artificial intelligence weather forecast method and device for a wind power plant. Comprising the steps that first historical data of multiple key meteorological elements in a target wind power plant are acquired, and the influence degrees of the multiple key meteorological elements on weather in the target wind power plant are all larger than a preset threshold value; performing weather prediction on the first historical data of the multiple key meteorological elements by using a weather prediction model of the target wind power plant to obtain predicted weather data of the target wind power plant at a future moment; the weather prediction model is pre-trained through the various meteorological parameters of the coarse resolution mode, and then fine adjustment of the fine resolution mode is realized by using the historical data of the various key meteorological elements having great influence on the weather, so that the micro-scale weather prediction model is obtained to adapt to weather prediction of the corresponding wind power plant; the method improves the resolution and efficiency of weather prediction, reduces the consumption of computing resources, and reduces the cost of weather prediction.

The invention relates to a model for automatic driving image segmentation under challenging weather conditions, said model comprising an encoder and a decoder corresponding to the encoder. The model is specially used for automatic driving scene graph segmentation under extremely severe weather conditions. Coordinate attention is used to carefully encode spatial information, complex cross-dimensional interaction between spatial and channel dimensions in input is captured in combination with triple attention, and exhibition attention is used to encode fine-grained features and context nuance as marks. In a scene influenced by weather, the model of the invention can skillfully capture fine features such as fog, snow and rain, thereby facilitating identification of objects in challenging environments. Practical tests prove that the method can clearly segment the input image and identify buildings, automobiles and sidewalks, and the method is very close to the actual ground condition in the aspect of prediction.

The present invention relates to a carrying apparatus designed for roofing applications, featuring a container with a planar base and multiple sidewalls defining a cavity for carrying materials, particularly roof nails. A friction-based material, such as polyurethane open-cell foam, is removably attached to the lower surface of the container to ensure stable, removable adhesion to sloped roof surfaces. The foam condenses under pressure, providing stability. The apparatus includes multiple attachment points, such as pins and pin holes, for secure and easy replacement of the friction material. The container can be fitted with a cover to protect contents from weather and prevent spillage. Additionally, the sidewalls are collapsible for convenient storage. The container is typically shaped as a rectangular prism, cuboid, or parallelepiped, and includes evacuation holes for water drainage. This innovative design enhances safety, durability, and efficiency of roofing operations by preventing sliding and allowing easy refilling of materials.

The application relates to the technical field of robot navigation, in particular to an autonomous decision optimization method for an aerial robot. Flight environment data are acquired, an obstacle density factor, a weather influence factor and a height risk factor are calculated, and environment influence parameters are generated to be used for dynamically adjusting a control strategy; a hybrid control framework is constructed, containing two parallel branches of global path tracking control and local obstacle avoidance control, and the control weight is dynamically adjusted according to the environment influence parameters; a three-layer hierarchical control architecture is established, including a macro task constraint layer, a medium maneuver decision layer and a micro flight control layer, the upper layer constraints are converted into bottom layer control instructions through a control constraint propagation mechanism, and consistency and safety verification are carried out; a state-weight mapping controller is adopted, the multi-objective optimization weight coefficient is dynamically adjusted according to a real-time state vector, and optimal control instructions are generated. The application improves the autonomous decision-making ability and flight safety of the aerial robot in a dynamic environment.

The invention relates to the technical field of intelligent regulation and control, in particular to an intelligent regulation method and system for a high-waterproof metal enclosure structure, and the method comprises the following steps: calculating weather influence and comparing a threshold value, transmitting a closing signal when the weather influence exceeds the threshold value, recording state generation control information, recognizing load abnormity and adjusting power, monitoring a deviation trend, and marking an abnormity generation early warning result. The weather influence strength index is established by calculating the wind speed change rate and the rainfall change value, rapid response to extreme weather can be achieved, water seepage caused by delayed closing of the skylight is avoided, a disturbance suppression mechanism is established by combining angular position and stress data, the structural stability in the opening and closing process is guaranteed, and the service life of the skylight is prolonged. By means of comparison of multi-frame displacement and current amplification, transmission load abnormity can be accurately recognized, a driving part is made to operate more stably, trend recognition of the long-period abnormal state of the hoist mechanism can be achieved through quantitative statistics and residual value mapping of the angle deviation trend, and risk early warning is carried out in advance when the deviation rate continuously exceeds the limit.

The application discloses a high-thick-brick city wall surface layer weathering prevention method and belongs to the field of historical building protection. The weathering prevention method controls the chemical reaction of a hydrophobic material, reduces the mass of hydrophobic substances generated by the chemical reaction in the penetration process, avoids the influence of the penetration process of weak hydrophobic substances on the penetration coefficient of the city wall surface layer bricks, prolongs the penetration time of the hydrophobic material, improves the penetration depth of the hydrophobic material in the city wall surface layer, weakens the dry shrinkage reciprocating change, realizes the deformation and stress coordination of the waterproof layer of the city wall surface layer and the internal wall body, avoids the stress concentration phenomenon of the waterproof layer shell, further achieves the purpose of preventing the weathering effect of external rain and snow immersion into the city wall surface layer and limiting the salt accumulation caused by the evaporation of the water in the city wall, realizes the purpose of deeper penetration of the hydrophobic material into the city wall, and solves the weathering diseases of the high-thick-brick city wall.

The present invention discloses a method, system and equipment for patrolling, monitoring and early warning of the entire route of highway maintenance and management, and belongs to the field of video monitoring technology. The equipment includes an intelligent terminal device capable of patrolling and moving and a monitoring sensing device at a fixed point. The intelligent terminal device is arranged on guardrails on both sides of the highway, and has functions such as patrolling, data collection, transmission, monitoring, light warning, broadcasting, and voice intercom. The monitoring sensing device is arranged in tunnels, bridges, high slopes, sharp bends, sections with poor visibility, and sections susceptible to rain and snow weather, and can realize the monitoring of images, environmental changes, topography, and real-time data of road conditions. The present invention can realize all-round monitoring and management of the entire highway through the movable intelligent terminal and the collection terminals at various fixed points, and timely collect, organize, and transmit management data such as real-time changes in road conditions, weather and environmental impacts, and driving safety status to a monitoring command and management platform.

The invention discloses an extreme rainstorm weather rendering method based on generative artificial intelligence, and belongs to the technical field of image render.The method comprises the steps that a data set corresponding to rainstorm weather is made and processed in the training stage, and a training set is established by using image blocks including wind power, precipitation and other characteristics; and then the generative artificial intelligence network is used to learn the data to obtain a high-simulation rendering model, for new predicted rainfall data, the method can automatically process and output a highly realistic rainstorm weather visual effect, and the method can efficiently generate a realistic rainstorm weather effect, and can improve the rainfall prediction efficiency. By means of the method, the upcoming rainstorm weather condition can be processed and displayed more efficiently, weather information which is more visual and easy to understand is provided for the public and decision makers, and emergency management and disaster prevention work are effectively supported.

The present invention provides a method for removing weather effects from a single image based on a generative adversarial network. The method includes obtaining a data set for training, wherein the data set includes blurred images affected by weather and clear images not affected by weather; constructing a deep learning network for learning the relationship between blurred images and clear images, wherein the deep learning network is based on a generative adversarial network, and the generative adversarial network includes a first generator for generating blurred images affected by weather and a second generator for generating clear images not affected by weather; training the constructed deep learning network using the obtained data set for training to obtain the optimal second generator as the image removal model; inputting the image to be processed into the image removal model; and processing the input image and outputting a clear image corresponding to the input image.

The invention relates to the technical field of security alarm control equipment, in particular to smart park intelligent security alarm control equipment which comprises a base, a smoke alarm and a rotating mechanism, the rotating mechanism is fixedly mounted on the base, and the smoke alarm is fixedly mounted in the rotating mechanism. The rotating mechanism drives a duplicate gear to coaxially rotate at the same frequency through a worm, a differential rotating shaft can do whole-circle motion under the cooperation of a limiting rod, then a swing support swings around a base in a variable speed mode in a fan-shaped area, and therefore more air enters a shutter grid through an air inlet. The detection of the content of particulate matters in the air in one direction is abandoned, and if the wind speed is high outdoors in a park, the equipment can be enlarged to carry out air detection in a wider angle and a larger range, so that the sensitivity of the alarm control equipment is improved, and especially in an outdoor scene or a scene with a large weather influence, the alarm control equipment can be used for detecting the air in a wider range. And the life and property safety of people is further ensured.

Freestanding, self-supporting device for protection against the elements, especially for use with a roof tent mounted on a vehicle, characterized by that the device has a load-bearing frame system, - which requires no ground anchoring or guy wires at all, - consisting of at least two telescopic support elements, - wherein the support elements are attached horizontally or diagonally to a support structure of the vehicle or directly to the base plate of the roof tent, and -where a textile covering is stretched over the frame system and connected to the roof tent via a zipper connection or an equivalent textile coupling.

The invention provides a video generation method and device, electronic equipment, a storage medium and a chip, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a depth map video, a line map video and a contour map video of a to-be-processed video, and obtaining the target weather information needed by the to-be-processed video; splicing the depth map video, the line draft map video and the contour map video according to a video dimension channel to obtain a spliced video; generating a target video according to the spliced video and the target weather information; the target video comprises video features of the target weather information. Processing is carried out based on the video, the demand of the automatic driving scene for the time sequence video is met, the depth map video provides the scene space structure, the line draft map video defines the object edge, the contour map video defines the main body shape, the physical rationality can be improved through joint constraint of the three, the target weather information is combined, and the weather effect intensity is adjusted. And the contradiction between the intensity of the weather effect of the video and the physical rationality of the object and the motion thereof in the video is reduced.

The invention discloses a wind power generation predicted power correction method and device influenced by extreme weather, equipment and a medium, and the method comprises the steps: obtaining the current environment data of a place where a target wind power generator is located, inputting the current environment data into an environment recognition training network, and obtaining an environment recognition result; if the environment identification result simultaneously comprises an extreme weather identification result and an extreme weather identification type, determining the operation deterioration probability of the target wind driven generator; according to the extreme weather identification type, the operation power of the target wind driven generator is corrected for the first time, and first correction power is obtained; and according to the operation deterioration probability and the first correction power, carrying out second correction on the operation power of the target wind driven generator, and taking the second correction power as the target prediction power of the target wind driven generator under the current environment data. The invention belongs to the field of wind power generation power prediction. Wind power generation prediction under the influence of extreme weather can be realized.