141 results about "Weather factor" patented technology

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention provides a path planning method and electronic equipment, relates to the technical field of navigation, solves the problem that a path planning scheme in the related technology is difficult to adjust a path planning strategy in combination with factors such as current road conditions and environment, and can perform path planning in combination with road conditions and weather factors in response to user demands, thereby improving the user experience. According to the method and the system, the traffic cost is acquired, the planned path is further analyzed based on the traffic cost, a flexible path planning scheme is realized, a path planning strategy can be dynamically adjusted, and the preference of the user is injected into path screening, so that a rapid and safe path can be better provided for the user, and the user experience is improved. And a path serving as an optimal solution is screened in combination with user preferences, so that the user experience is improved.

An expressway holiday travel traffic volume prediction method based on the influence of a weather factor, said method belonging to the technical field of road traffic flow prediction. In order to solve the problem of accurate holiday expressway travel traffic volume prediction based on the influence of a weather factor, the method of the present application comprises: establishing a relative influence coefficient between historical weather and travel traffic volume; constructing a travel traffic volume transfer model based on weather data; selecting basic travel traffic volume features, constructing derived travel traffic volume features by means of a cross operation between the basic travel traffic volume features, and then constructing a multiple linear regression model based on historical travel traffic volume features; correcting a predicted travel traffic volume obtained by the multiple linear regression model; correcting a predicted travel traffic volume of the remaining days of a holiday; constructing a layered flow road network travel traffic volume model; and constructing an objective function, and using a reverse gradient propagation method to optimize and solve the layered flow road network travel traffic volume model, so as to obtain an expressway holiday travel traffic volume prediction result based on the influence of a weather factor.

The invention is suitable for the technical field of power distribution network abnormity monitoring, and provides a power distribution network abnormity monitoring method and system oriented to the Internet of Things, and related equipment, and the method comprises the steps: collecting the abnormal data of electrical parameters, weather data and geographic position information data from each monitoring point of a power distribution network, and carrying out the data preprocessing of the collected data; inputting the preprocessed data into a trained CNN-RNN model to evaluate whether the electrical parameters are influenced by weather factors or geographic position factors; when the electrical parameters are not influenced by weather factors or geographic position factors, determining target characteristic variables of the operation state of each monitoring point device of the power distribution network; judging whether the target feature vector meets an abnormal condition or not; and if yes, abnormal analysis is carried out on the electrical parameters by monitoring abnormal data of the operation state of each monitoring point device of the power distribution network, so that the comprehensiveness of abnormal monitoring is effectively improved.

The invention discloses a power transmission line icing early warning method based on weather factors, and belongs to the technical field of power transmission line inspection. According to the invention, the problem of insufficient advanced time of an existing power transmission line icing early warning method is solved. According to the method, an icing susceptibility degree description acquisition module and an ultra-long advance periodic icing feature calculation module are constructed, typical weather cycle conditions can be described by using the two modules, then an ultra-long advance period identification model is obtained, and then ultra-long time advance early warning is carried out in power transmission line icing high-incidence areas. By means of the method, it can be found that the weather cycle period in which the weather is likely to enter the icing high-incidence weather, the indirect incidence relation between the weather and icing is established, and the weather state in which the weather state related to icing can be entered with the large probability under the continuous multi-day condition is found; and the icing condition can be pre-warned for many days in advance. The method can be applied to power transmission line icing early warning.

The invention discloses an intelligent irrigation dynamic decision-making method based on reference crop evapotranspiration estimation. The method comprises the following steps: firstly, acquiring historical day-by-day weather data of a farmland area, calculating the evapotranspiration of a reference crop, and preprocessing the evapotranspiration; secondly, weather factors are screened to serve as input features, reference crop evapotranspiration serves as a target variable, and a reference crop evapotranspiration estimation model is constructed and trained; and then, through a reference crop evapotranspiration estimation model, obtaining reference crop evapotranspiration estimation results of the current day and the future n days. And finally, according to an evapotranspiration estimation result in combination with a crop coefficient corresponding to the growth stage of the rice, evapotranspiration of the current day and n days in the future is calculated, an irrigation decision is made, and the water storage capacity of the reservoir is adjusted based on the irrigation water consumption of the current day and the irrigation water consumption of the n days in the future. The invention provides an efficient, economic and sustainable technical scheme for intelligent irrigation management of large-scale farmland, and has important value for promoting agricultural precision and ecological development.

The invention discloses a short-term power load prediction method, and provides a CNN-BiLSTM-Attention power load short-term prediction model optimized based on an improved crested porcupine optimization algorithm in order to excavate hidden information in power load data and improve the precision of short-term load prediction. The method comprises the following steps: firstly, taking historical load data as the input of a CNN-BiLSTM-Attention network in combination with the influence of weather factors; then, the VMD decomposes the original signal, and extracts information in the data to improve the data quality; meanwhile, CPO is improved through Circle chaotic mapping, spiral fluctuation search and an adaptive t variation strategy, then hyper-parameters of the CNN-BiLSTM-Attention network are optimized by using ICPO, and different sequence prediction values are superposed to obtain a final prediction result.

The invention provides a rail transit platform passenger flow prediction method and system, computer equipment and a storage medium. The method comprises the following steps: extracting directed OD pairs from entry and exit records of an automatic fare collection system based on a completely directed network; determining travel time of non-transfer and one-time transfer; determining the real-time position of the passenger according to the directed OD pair and the travel time; according to the real-time position of the passenger, determining the passenger flow volume of each station in the target time period; acquiring and processing weather factors; a CNN-BiLSTM-Attention passenger flow prediction model is constructed; and taking the passenger flow volume of each station in the target time period and the weather factors as input variables, and predicting the passenger flow volume of each station by using the CNN-BiLSTM-Attention passenger flow prediction model. According to the method, the influence of weather factors, time dependence and spatial features is considered, the attention on key features is enhanced, and the prediction precision of the platform passenger flow is improved.

The invention discloses an enhanced GAN extreme scene generation method and system considering weather factors, and relates to the technical field of scene generation in new energy power system planning and operation, and the method comprises the steps: extracting key factors from multi-time-delay meteorological data, forming an extreme label, converting the extreme label into a generation condition, and achieving the output through two-stage confrontation generation, wherein a scarce sample is stably amplified by WGAN-GP, a joint curve is generated according to conditions, and related structures, climbing and power boundary constraints are synchronously embedded to ensure statistical and physical consistency. According to the method, the authenticity of the distribution tail, the cross-variable correlation and the time structure can be considered when the sample is scarce. Extreme situation deduction of a novel power system can be supported while extreme situation coverage and credibility are improved, and an existing new energy scene analysis and planning process is integrated.

The invention provides a traffic time estimation method based on deep learning in a complex environment, and the method comprises the steps: firstly, obtaining a track data set comprising a plateau environment region and a mountain environment region; the trajectory data set is then divided into a training data set, a verification data set, and a test data set. Next, constructing a transit time estimation model, and training and optimizing the transit time estimation model according to the training data set and the verification data set to obtain an optimized transit time estimation model; and testing the optimized transit time estimation model according to the test data set, and determining a model evaluation index value. And finally, under the condition that the model evaluation index value meets the index threshold value, inputting to-be-estimated data into the optimized passing time estimation model, and outputting to obtain a passing time estimation result. According to the method, the adaptability of the traffic time estimation model to modeling of complex environmental terrains and weather factors is improved, so that the accuracy of estimating the traffic time in a complex environment can be effectively improved.

The invention discloses an electricity consumption prediction method, which comprises the following steps: S1, obtaining historical electricity consumption data of a to-be-predicted region, and has the beneficial effects that a TCN-GAT model, a gray model, an autoregressive moving average prediction model and an electricity consumption prediction model are built through the obtained historical electricity consumption data of the to-be-predicted region; the power consumption prediction work to be predicted can be analyzed and predicted through the above models, and finally, whether abnormity occurs or not is detected and predicted through the power prediction model under the conditions of various factors such as weather factors, seasonal factors, date difference factors, holiday factors and extreme weather factors. And carrying out final power consumption prediction analysis under various factors such as a weather factor, a season factor, a date difference factor, a holiday factor and an extreme weather factor of the to-be-predicted region under the condition of ensuring correct prediction.

An integrated energy system short-term load prediction method based on an improved multi-task model includes the steps that in the input stage, the MIC is adopted to analyze the correlation degree of multi-element loads and weather factors, and STL is used for decomposing a load sequence to construct a component data set; in the modeling stage, the CNN is used as a feature extraction network, the spatial coupling relation of an input sequence is directly learned, the general representation of the multi-element load is learned by applying a hierarchical progressive routing strategy, and specific parameter information of different tasks is separated; in the output stage, modeling is conducted on time sequence characteristics through combination of LSTM, multi-element load prediction is achieved, aiming at the complex nonlinear relation between loads, a CPLE joint prediction model is established through combination of the characteristics of CNN and LSTM networks and a PLE multi-task learning method, and while multiple networks are adopted to learn the space-time coupling characteristics between the multi-element loads, the multi-element load prediction efficiency is improved. And different load specific knowledge is highlighted by using a progressive routing mode.

The invention discloses a method for estimating subsidence of ground around a subway under construction by considering weather factors. Selecting a ground subsidence research area around the subway under construction, and obtaining ground subsidence data and weather data in the research area; a settlement estimation subarea module is built based on a time sequence clustering algorithm, monitoring points in a research area are divided into a plurality of settlement estimation subareas according to settlement rule similarity, and the influence of data heterogeneity of different areas is reduced; a graph neural network is adopted to construct a rule capturing module, and the spatial topological relation between monitoring points in the settlement estimation subarea is analyzed; a weather factor module is built through a gating circulation unit neural network, time sequence data corresponding to weather factors are processed, and the influence of the time sequence data on land subsidence is captured; and fusing the settlement spatial-temporal characteristics and the weather characteristics to obtain a land settlement estimated value of the research area. Compared with an existing estimation method, the land subsidence estimation method can obtain more accurate regional future land subsidence data, and reliable data support is provided for subway construction.

The invention discloses a power transmission line icing prediction method, system and device based on satellite data assimilation and a medium, and relates to the technical field of ice and snow monitoring, and the method comprises the steps: obtaining satellite microwave radiance observation data, fusing the observation data and a numerical mode background field through data assimilation, generating grid meteorological data, and carrying out the prediction of the icing of a power transmission line. The method comprises the following steps: processing gridding meteorological data by using a spatial-temporal feature joint modeling technology, reconstructing meteorological data of continuous time steps into a spatial-temporal coupled four-dimensional tensor, synchronously extracting spatial-temporal dimension dynamic evolution features through a three-dimensional convolution kernel, and fusing historical states and current features based on a gating mechanism to obtain a spatial-temporal feature fusion model; and outputting a spatial distribution prediction result of the icing thickness of the power transmission corridor. According to the method, the high-precision meteorological field is obtained, the time-space dynamic characteristics of icing evolution are synchronously extracted, the key meteorological factors are screened in combination with an improved gating mechanism and attention, and accurate closed-loop simulation of power transmission corridor icing is realized based on iterative prediction of physical constraints, so that the accuracy and reliability of icing early warning are remarkably improved.

The invention relates to the technical field of data processing, in particular to a data processing method based on a large model, which monitors whether a transmission cable and a corresponding transmission node are safe or not by setting a damage contrast library and inputting an acquired image into the large model, and verifies the corresponding safety according to the transmission result. According to the method, the safety of the transmission cable is effectively improved, the real-time performance of detection is enhanced, the problem that the influence of weather factors on the transmission cable cannot be accurately predicted through simple parameter translation is solved, a large model can be effectively trained, and then the safety of the transmission cable is effectively improved.

The invention discloses an urban atmospheric pollution real-time monitoring method and system. The method comprises the following steps: acquiring pollutant concentration, weather and geographic position data through multi-source monitoring equipment; performing data fusion by adopting space-time Kriging interpolation and wavelet threshold denoising to generate a high-quality urban pollution distribution initial field; a machine learning model of an encoder-decoder structure is used for prediction, an encoder is a convolutional long-short term memory network, a decoder is a dynamic graph convolutional network fused with meteorological factors, and a dynamic pollution situation map is generated; polluted area identification is carried out based on a wind field streamline, and accurate tracing is carried out through combination of reverse simulation of a Lagrange particle diffusion model and a control variable method. The whole process optimization of pollution monitoring from data fusion, accurate prediction to quantitative traceability is realized, and the accuracy and timeliness of urban atmospheric pollution supervision are remarkably improved.

The invention discloses an automatic driving vehicle driving behavior compliance simulation test method, which comprises the following steps of S1, decomposing a natural language traffic regulation into an initial premise and a constraint condition, digitalizing the initial premise and the constraint condition by utilizing measurement sequential logic, and generating a mathematical formula which can be analyzed by a machine; s2, constructing a simulation scene according to the initial premise; s3, accessing the tested automatic driving algorithm to the self-vehicle in the simulation scene, and collecting vehicle state and environment data in real time; and S4, dynamically adjusting a violation detection threshold based on the constraint condition and the current weather and road conditions, and outputting a detection result. The method is different from a traditional real vehicle testing method, and various possible scenes including extreme weather, road conditions and the like can be covered more comprehensively; different from a traditional violation detection method, the method considers the influence of road and weather factors on violation detection conditions for different test scenes, and is higher in universality.

The invention relates to a digital intelligent trajectory prediction method and system based on logistics transportation order tracking. The method comprises the steps of collecting multi-source transportation data including vehicle GPS data, AI telephone interaction data, ETC passing record data and freight station transfer operation node data; performing space-time alignment, data cleaning and map matching based on road network information on the multi-source transportation data; utilizing the processed multi-source data to generate a continuous digital intelligent track through an interpolation algorithm and a path deduction method; calculating the latest predicted position of the vehicle through a prediction algorithm model based on the digital intelligent track in combination with real-time traffic information and a historical driving mode; residual paths, real-time road conditions, weather factors and driver behavior modes are comprehensively considered, and the predicted arrival time is calculated through a dynamic ETA model. According to the invention, the GPS signal blind area problem is effectively solved, the whole-course blind-area-free tracking and high-precision arrival time prediction are realized, and the transparency and intelligent management level of logistics transportation are greatly improved.

The invention relates to the technical field of digital geological detection, in particular to a Danxia landform corrosion detection method and system.The Danxia landform corrosion detection method mainly comprises the steps that an initial collection interval is set, weather data of each group of basic data is obtained, and the basic data are grouped according to the weather data of the basic data; obtaining a plurality of groups of basic data sets of different weather data; and establishing a neural network prediction model, obtaining a plurality of neural network prediction model output values, and outputting the future corrosion degree of the Danxia landform through the comprehensive evaluation model according to the plurality of neural network prediction model output values. According to the method, the influence of weather is taken as a consideration factor, and different weather data are grouped, so that the data of different weather conditions are analyzed in the same group, detection errors caused by weather factors are reduced as much as possible, and meanwhile, the neural network model is adopted to analyze the basic data, so that the detection accuracy is improved. And the accuracy of the prediction result based on the basic data is further improved.

The short-term load forecasting method based on SARIMA-random forest combination model comprises the following steps: grouping the original load data by using a sliding window, decomposing the to-be-tested week-before-next day data set of each group to obtain a trend item, a seasonal item and a residual item; establishing a SARIMA model, predicting the trend item to obtain a preliminary prediction result and a residual; clustering weather factors to obtain similar days, grouping to construct a weather-residual data set and establishing a random forest regression model, learning the influence of the weather factors on the residual, and selecting model parameters by using a grid search method; combining the prediction results of the model, and comparing the influence of weather clustering and residual training on the load prediction accuracy. The method can accurately predict the next day load under the condition that the historical load and weather factors of the to-be-tested day are known, and improves the prediction accuracy.

This invention relates to a JP complete power distribution cabinet, belonging to the technical field of power distribution cabinets. It includes a housing with a pre-reserved cavity on its inner side. A temperature control platform is mounted on the inner side of the cavity. Several mounting brackets are arranged on one side of the inner side of the temperature control platform. Several temperature-conducting plates are arranged on the side of the temperature control platform closest to the inner side of the cavity. The temperature-conducting plates are wavy and composed of several semi-circular rings. This invention solves the problem that cooling efficiency is easily affected by weather factors, and that while vents allow air to pass through, liquid and contaminant particles can easily enter with the airflow. In this invention, by using a temperature control platform, the wavy shape of the temperature-conducting plates increases their surface area, thereby increasing the efficiency of absorbing heat energy inside the cavity and the heat release efficiency inside the solution cavity. The calcium chloride solution inside the friction wheel has the characteristics of high specific heat capacity and low freezing point.

The invention relates to the technical field of solar photovoltaic panel cleaning, and discloses an online solar panel obstacle avoidance cleaning machine which comprises two solar photovoltaic panels, an obstacle is arranged between the two solar photovoltaic panels, and two symmetrically-distributed cleaning brushes are arranged on the solar photovoltaic panels in a close contact mode. The robot further comprises a rotating mechanism fixed to the ends, away from each other, of the two cleaning brushes, and the rotating mechanism is used for adjusting the positions of the cleaning brushes when the cleaning brushes approach an obstacle. Under the action of the designed rotating mechanism, the reciprocating driving mechanism and the triggering mechanism, obstacle avoidance of the cleaning brushes is achieved in a pure mechanical rotating mode; compared with the prior art, the obstacle avoidance device can automatically trigger the obstacle avoidance action at a specified position, does not need extra power supply when executing the obstacle avoidance work, reduces extra power supply cost, is lower in maintenance cost, and is less affected by external weather factors.

The application discloses a complex environment water level monitoring system and method based on heterogeneous light field cooperation, which is characterized in that: a water gauge image is segmented from a water gauge point cloud and a joint external parameter; the water gauge image is further segmented after image enhancement, and the tilted water gauge is corrected; the intensity of the water gauge point cloud is accumulated, and the water level line is obtained through the maximum gradient method; the corrected water gauge image is detected by using a target detection algorithm to detect digital characters, the character recognition algorithm is used to perform OCR recognition on the detected digital characters, and the water level value is calculated according to the digital characters, the water level line and the water gauge image. Through the combination of laser radar and visible light field, the reflection characteristics of different materials are utilized, so that the water level in the complex environment of a river channel, a reservoir and a coast can be monitored in real time. The interference of floating objects and weather factors on water level detection is effectively avoided, and the accuracy and robustness of water level monitoring are significantly improved.

The application discloses a rain weather multi-view feedforward Gaussian reconstruction method and system based on weather factor gating, and the method comprises the following steps: acquiring rain weather context images under different views and camera parameters thereof; inputting the rain weather context images into a weather factor decomposition network to generate predicted weather factors; constructing a matching reliability graph for representing the corresponding reliability degree across views according to the predicted weather factors; extracting multi-scale features of the rain weather context images; generating gated features according to the multi-scale features, the matching reliability graph and reliability gating; performing multi-view aggregation based on the gated features to generate fusion representation; inputting the fusion representation into a three-dimensional reconstruction network for feedforward prediction to obtain a three-dimensional Gaussian set; inputting the three-dimensional Gaussian set into a differentiable renderer to render a clean predicted image of a target view in combination with target view camera parameters; acquiring clean context images of multiple views to construct rain weather training samples; and constructing a loss function to train the weather factor decomposition network and the three-dimensional reconstruction network.

The invention provides a power grid bus load prediction method based on subtropical monsoon climate characteristics. The method comprises the following steps: S1, collecting and preprocessing historical data; s2, extracting features related to time and weather, performing coding, and performing corresponding association with the load data; s3, extracting time and weather features to perform interaction enhancement, and creating time-weather interaction features; s4, meteorological expansion features are generated after meteorological features are extracted; s5, constructing a machine learning model, and randomly dividing the features into a training set, a verification set and a test set for training, verifying and testing the machine learning model to obtain a load prediction model for the power grid bus; and S6, predicting the load of the power grid bus by using the load prediction model. The time-weather interaction characteristics can capture the complex influence of the weather factors on the power grid bus load in different time periods, so that the bus load is predicted more accurately.

A refueling prediction mode in a microgrid power system may include inputting historical load and weather date of the microgrid power system to a forecasting block and determining forecasted site load and weather factors for the microgrid power system. The forecasted site load and weather factors may be used along with fuel efficiency curves and asset fuel levels for power assets of the microgrid power system, along with other information, to determine an asset dispatch schedule and a refueling timeline for the power assets. Asset dispatch commands based on the asset dispatch schedule and an actual site load may be output to the power assets to meet the load demand on the microgrid power system. A sustained reliability mode may use the information plus real-time fuel costs for the power assets to determine an asset dispatch schedule for longest microgrid sustenance for the power assets.

The invention discloses an image correction method and device, a storage medium and electronic equipment. The method relates to the field of artificial intelligence and big data, and comprises the following steps: obtaining a target image acquired by a target camera at a target moment and a reference image acquired at a reference moment, the target moment being later than the reference moment; according to an image offset scene between the target image and the reference image, a target offset correction mode for the target camera is determined, and the image offset scene is one of a scene generating image offset along with time accumulation and a scene generating image offset due to weather factors or human factors; determining offset correction information based on the target offset correction mode, the target image and the reference image; and performing image correction on the target image based on the offset correction information. Through the method and the device, the problem of low correction accuracy when the image shot by the camera is corrected under the condition that the pose of the camera is changed in the prior art is solved.

This invention provides a method, system, computer equipment, and storage medium for predicting passenger flow at rail transit stations. The method extracts directed origin-destination (OD) pairs from the entry and exit records of an automated fare collection system using a fully directed network; determines the travel time for non-transfer and single-transfer journeys; determines the real-time location of passengers based on the OD pairs and travel time; determines the passenger flow at each station within a target time period based on the passengers' real-time locations; acquires and processes weather factors; constructs a CNN-BiLSTM-Attention passenger flow prediction model; and uses the passenger flow at each station within the target time period and weather factors as input variables to predict the passenger flow at each station using the CNN-BiLSTM-Attention passenger flow prediction model. This invention considers the influence of weather factors, time dependence, and spatial characteristics, enhancing the focus on key features and improving the accuracy of station passenger flow prediction.

The invention belongs to the technical field of power systems, and relates to a reliability evaluation method and system for an extreme meteorological new energy high-permeability power system. The method comprises the following steps: judging extreme meteorology, and constructing a new energy output and meteorological factor coupling model under the extreme meteorology; establishing a system operation model considering extreme meteorology, and obtaining system operation state parameters and power distribution parameters; calculating a short-time index reliability score and an ultra-short-time index reliability score of the system under each extreme weather and different time length scales; and fusing the reliability scores to obtain a comprehensive reliability score. The method can accurately describe the instantaneous impact and short-term influence of the extreme weather on the operation of the new energy high-permeability power system, and realizes the quantitative analysis of the reliability of the system; the reliability indexes of the system are divided into a short-time type and an ultra-short-time type, the power shortage risks under different time scales can be captured respectively, and more refined and dynamic reliability evaluation is provided for system operation under extreme weather.

The invention discloses a wind measurement data correction method based on flow field simulation, and the method comprises the following steps: obtaining the current topographic data and historical target data of a target mountainous area; identifying key weather factors according to historical target data; constructing a quantitative relation model of the key weather factors and the surface form change; obtaining a topographic data prediction value of the target mountainous area; constructing a prediction digital elevation model according to the topographic data prediction value; obtaining corrected wind speed prediction data; comparing the corrected wind speed prediction data with the actually measured wind speed data of the field anemometer tower, and calculating the deviation; and constructing a space correction function based on the deviation, and applying the correction function to subsequent wind resource evaluation or fan layout optimization to realize dynamic correction of the wind measurement data. The fan flow field prediction method solves the problems that fan flow field prediction precision has a large error and the prediction result is not high in accuracy.