Meteorological-based deduction method and system

Abstract

The invention relates to a weather-based deduction method, which comprises the steps of obtaining historical weather information and weather influence information corresponding to a target region, extracting weather features and influence features, and packaging the weather features and the influence features into a first training feature set; training a meteorological deduction model through the first training feature set; setting initial conditions and boundary conditions of the deduction model, obtaining current meteorological information and meteorological influence information, and inputting the information into the meteorological deduction model for preliminary deduction; when the deduction information is received, inputting the deduction information into a deviation correction model for deviation correction; and when the corrected deduction information is received, presenting the corrected deduction information in a visual mode. According to the method, the historical meteorological information and the meteorological influence information are comprehensively utilized, the key features are extracted for training, the more accurate meteorological deduction model is constructed, meanwhile, the deviation correction model is introduced to carry out refined correction on the deduction result, and the method has the effect of remarkably improving the accuracy and practicability of meteorological deduction.

Description

Technical Field

[0001] The present application relates to the technical field of meteorological deduction, and in particular to a meteorological-based deduction method and system. Background Art

[0002] Meteorological simulation is a technology for predicting and simulating future weather conditions. It is widely used in weather forecasting, climate research, agricultural production, aviation and navigation, urban planning and other fields. Through meteorological simulation, people can better understand the laws of meteorological changes, provide scientific basis for decision-making, reduce losses caused by natural disasters, and improve social and economic benefits.

[0003] At present, there are still some problems that need to be solved in traditional meteorological simulation technology:

[0004] First, traditional meteorological deduction methods often only focus on past historical meteorological data themselves, and tend to ignore factors that have an indirect but important impact on the weather, such as topography, vegetation cover, water distribution, human activities, etc., which can easily lead to inaccurate deduction results or even misleading conclusions; at the same time, in the meteorological deduction process, due to the limitations of the model itself, the incompleteness of the data and the complexity of meteorological phenomena, the deduction results are prone to certain deviations, and most of the existing meteorological deduction methods are based on simple statistical models and lack the ability to accurately correct the deviations of the deduction results. The existence of deduction deviations not only affects the accuracy of the deduction results, but also limits the reliability and practicality of meteorological deduction technology in practical applications. Summary of the invention

[0005] In order to improve the accuracy and practicality of meteorological deduction, the present application provides a meteorological-based deduction method and system.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0007] A meteorological deduction method comprises the following steps:

[0008] Obtain historical meteorological information and meteorological impact information corresponding to the target area;

[0009] Extracting meteorological features and impact features from historical meteorological information and meteorological impact information respectively, and packaging the extracted meteorological features and impact features into a first training feature set;

[0010] Training the pre-built meteorological deduction model through the first training feature set;

[0011] Set the initial conditions and boundary conditions of the deduction model, obtain the current meteorological information and meteorological impact information and input them into the meteorological deduction model for preliminary deduction;

[0012] When receiving the deduction information output by the deduction model, the deduction information is input into the pre-trained bias correction model for bias correction;

[0013] When the correction deduction information output by the deviation correction model is received, it is presented in a visual manner.

[0014] By adopting the above technical solution, historical meteorological data information and information on factors affecting meteorological changes in the target area are collected, and key meteorological features and impact features are extracted from the collected historical meteorological information and meteorological impact information and packaged as a first training feature set for training a pre-constructed meteorological deduction model; according to actual needs, initial conditions and boundary conditions are set for the meteorological deduction model, and the latest current meteorological information and meteorological impact information are obtained, and the current meteorological information and meteorological impact information are input into the meteorological deduction model for preliminary meteorological deduction. Since various uncertain factors may exist in the meteorological deduction process, there is a certain deviation between the deduction result and the actual meteorological conditions, so a pre-trained deviation correction model is introduced to perform refined deviation correction on the deduction information output by the deduction model to improve the accuracy of the prediction; finally, the deduction information after deviation correction is visualized in intuitive forms such as charts, maps, animations, etc.; This application constructs a more accurate meteorological deduction model by comprehensively utilizing historical meteorological information and meteorological impact information, extracting key features for training, and at the same time, by introducing a deviation correction model to perform refined correction on the deduction results, it has the effect of significantly improving the accuracy and practicality of meteorological deduction.

[0015] In a preferred example, the present application may be further configured as follows: the step of extracting meteorological features and impact features from historical meteorological information and meteorological impact information respectively, and packaging the extracted meteorological features and impact features into a first training feature set, comprises the steps of:

[0016] Extracting element features from historical meteorological information, and performing trend analysis and interpolation analysis on the element features to obtain meteorological features, wherein the meteorological features include time trend features and spatial distribution features;

[0017] Identifying direct impact data, indirect impact data, and delayed impact data from the meteorological impact information, and extracting impact features, wherein the impact features include direct impact features, indirect impact features, and delayed impact features;

[0018] Combining the extracted meteorological features and the corresponding associated influencing features into a plurality of training feature vectors, wherein the training feature vectors include the meteorological features at the corresponding time points and spatial positions and the corresponding influencing features;

[0019] Several training feature vectors are packaged into a first training feature set.

[0020] By adopting the above technical scheme, element features are extracted from historical meteorological information. After extracting these element features, trend analysis is performed on them to reveal the changing rules of meteorological elements over time; at the same time, interpolation analysis is performed to supplement and improve the spatial distribution information of meteorological elements; direct impact data, indirect impact data and hysteresis effect data are identified from meteorological impact information, and direct impact data include factors such as topography and vegetation coverage that directly affect the meteorology; indirect impact data involve factors such as human activities and economic development that indirectly affect the meteorology through certain channels; hysteresis effect data considers the delayed effects that may exist in meteorological changes, such as the impact of previous precipitation on later soil moisture; after identifying direct impact data, indirect impact data and hysteresis effect data, their impact features on the meteorology are extracted, including direct impact features, indirect impact features and hysteresis impact features; the extracted meteorological features and the corresponding associated impact features are combined into a number of training feature vectors, each of which contains the meteorological features and their corresponding impact features at the corresponding time point and spatial position. This combination method ensures that the training feature vector can fully reflect the complex relationship between meteorological changes and their influencing factors, and the combined several training feature vectors are packaged as the first training feature set.

[0021] In a preferred example, the present application can be further configured as follows: the steps of setting the initial conditions and boundary conditions of the deduction model, obtaining the current meteorological information and meteorological impact information and inputting them into the meteorological deduction model for preliminary deduction include the following steps:

[0022] Obtain the simulation demand information and determine the initial conditions of the simulation model based on the historical meteorological information and the simulation demand information;

[0023] The boundary conditions of the deduction model are determined based on the deduction requirement information, and the boundary conditions include space boundary conditions, time boundary conditions and external boundary conditions.

[0024] By adopting the above technical solution, the deduction demand information is obtained, including the specific requirements of users or application scenarios for meteorological deduction, such as the geographical area of ​​concern, time range, meteorological elements, etc., based on the combination of deduction demand information and historical meteorological information, the initial conditions of the deduction model are determined, and the boundary conditions of the deduction model are further refined according to the deduction demand information. Boundary conditions are crucial in meteorological deduction. They limit the spatial scope, time span and external factors that may be affected by the model deduction. Among them, the spatial boundary conditions define the geographical area of ​​the deduction to ensure that the model pays attention to the area of ​​concern to the user. The time boundary conditions set the start and end time of the deduction so that the deduction results can cover the time period required by the user. The external boundary conditions take into account The application considers the external factors that may affect the internal meteorological conditions in the simulation area, such as the meteorological conditions and terrain features of the surrounding areas; by comprehensively considering the simulation needs, historical meteorological information and detailed boundary condition settings, it has the effect of significantly improving the prediction accuracy and applicability of the meteorological simulation model. First, by accurately setting the initial conditions, the model can start the simulation from a meteorological state that is closer to the actual state, reducing the prediction error caused by inaccurate initial conditions. Secondly, the carefully divided boundary conditions ensure the rationality of the simulation process in space, time and external influences, making the simulation results closer to the actual situation, improving the accuracy of weather forecasts, and providing a more reliable scientific basis for disaster prevention and mitigation, agricultural production, urban planning and other fields.

[0025] In a preferred example, the present application may be further configured as follows: when receiving the deduction information output by the deduction model, before inputting the deduction information into the pre-trained deviation correction model for deviation correction, the following steps are performed:

[0026] Obtain the historical weather forecast information corresponding to the target area, and extract deviation characteristics based on the historical weather information and historical weather forecast information;

[0027] Packing meteorological features and deviation features into a second training feature set in time sequence;

[0028] The deviation correction models are trained by the second training feature set to determine the weights of the deviation correction models, wherein the deviation correction models include a linear regression model, a random forest model, and a neural network model.

[0029] By adopting the above technical solution, the historical meteorological forecast information corresponding to the target area is obtained, and the deviation features are extracted based on the historical meteorological information (i.e., the actually observed meteorological data) and the historical meteorological forecast information (i.e., the previously predicted meteorological data), and the meteorological features and the extracted deviation features are packaged into a second training feature set in time series. The deviation correction model is trained through the second training feature set to determine the weight of each deviation correction model. The deviation correction model includes a linear regression model, a random forest model, and a neural network model, each of which has different advantages and characteristics and can capture different types of deviation patterns. Through training, the contribution (i.e., weight) of each model in the deviation correction can be determined, thereby constructing a more accurate and robust deviation correction system, which has the effect of improving the accuracy of meteorological deduction.

[0030] In a preferred example, the present application can be further configured as follows: the deviation correction model is trained by the second training feature set to determine the weight of each deviation correction model, and the deviation correction model includes a linear regression model, a random forest model and a neural network model, including the steps of:

[0031] Identify the meteorological type of the historical meteorological forecast information corresponding to each deviation feature in the second training feature set;

[0032] Determine the dynamic weights of each bias correction model based on all identified weather types.

[0033] By adopting the above technical scheme, the meteorological type of the historical meteorological forecast information corresponding to each deviation feature in the second training feature set is identified to understand the characteristics and laws of the prediction deviation under different meteorological conditions. For example, some meteorological types (such as heavy rain, typhoon, etc.) may have more complex and difficult to predict characteristics, so the corresponding deviation patterns and correction requirements may also be more special; based on the identified meteorological type, the dynamic weight of each deviation correction model (including linear regression model, random forest model and neural network model) is determined. Because different deviation correction models may have different advantages and effects when processing different types of meteorological data, by dynamically adjusting the weights of each model, the prediction deviation can be corrected more effectively and adaptively under different meteorological types, which has the effect of making the deviation correction more refined and enhancing the adaptability of the deviation correction model.

[0034] In a preferred example, the present application can be further configured as follows: the step of determining the dynamic weights of each deviation correction model based on all identified weather types includes the steps of:

[0035] Based on the preset preliminary weight allocation strategy, the physical mechanism of the identified meteorological type is analyzed and a preliminary weight range is assigned to each bias correction model;

[0036] Each deviation correction model is trained and cross-validated using the second training feature set, and the preliminary weight range of each deviation correction model is adjusted based on the cross-validation result to determine the dynamic weight of each deviation correction model based on each meteorological type.

[0037] By adopting the above technical scheme, the physical mechanism of the identified meteorological type is analyzed based on the preset preliminary weight allocation strategy, so as to allocate a preliminary weight range matching the meteorological type to each model, so as to ensure that the weight allocation is consistent with the physical mechanism of meteorology, thereby improving the physical rationality and accuracy of the model, and using the second training feature set to train and cross-validate each deviation correction model. The second training feature set contains meteorological features and deviation features packaged in time series, which can fully reflect the changing laws and deviation characteristics of meteorological data. Through training and cross-validation, the performance of each model under different weights is evaluated, and the preliminary weight range of each model is adjusted based on the verification results, so as to optimize the weight allocation through actual data, ensure the accuracy and stability of the model in practical applications, and after multiple iterations and adjustments, determine the dynamic weight of each deviation correction model based on each meteorological type, which has the effect of improving the adaptability and physical rationality of the deviation correction model.

[0038] The second object of the invention is achieved by the following technical solutions:

[0039] A weather-based deduction system, comprising:

[0040] An information acquisition module is used to obtain historical meteorological information and meteorological impact information corresponding to the target area;

[0041] A feature extraction module, used to extract meteorological features and impact features from historical meteorological information and meteorological impact information, respectively, and package the extracted meteorological features and impact features into a first training feature set;

[0042] A first training module, used for training a pre-built meteorological deduction model through a first training feature set;

[0043] The deduction model input module is used to set the initial conditions and boundary conditions of the deduction model, obtain the current meteorological information and meteorological impact information, and input them into the meteorological deduction model for preliminary deduction;

[0044] The deviation model input module is used to input the deduction information into the pre-trained deviation correction model for deviation correction when receiving the deduction information output by the deduction model;

[0045] The visualization module is used to present the correction deduction information output by the deviation correction model in a visual manner when it is received.

[0046] By adopting the above technical solution, the information acquisition module is used to obtain the historical meteorological information and meteorological impact information corresponding to the target area; the feature extraction module is used to extract meteorological features and impact features from the historical meteorological information and meteorological impact information respectively, and pack the extracted meteorological features and impact features into a first training feature set; the first training module is used to train a pre-constructed meteorological deduction model through the first training feature set; the deduction model input module is used to set the initial conditions and boundary conditions of the deduction model, obtain the current meteorological information and meteorological impact information and input them into the meteorological deduction model for preliminary deduction; the deviation model input module is used to input the deduction information into the pre-trained deviation correction model for deviation correction when receiving the deduction information output by the deduction model; the visualization module is used to present the corrected deduction information output by the deviation correction model in a visual manner when receiving it.

[0047] In a preferred example, the present application can be further configured as follows: the feature extraction module includes:

[0048] The meteorological feature acquisition submodule is used to extract element features from historical meteorological information and perform trend analysis and interpolation analysis on the element features to obtain meteorological features, wherein the meteorological features include time trend features and spatial distribution features;

[0049] An image feature extraction submodule is used to identify direct impact data, indirect impact data and delayed effect data from meteorological impact information, and extract impact features, wherein the impact features include direct impact features, indirect impact features and delayed impact features;

[0050] A feature vector combination submodule, used to combine the extracted meteorological features and the corresponding associated influencing features into a plurality of training feature vectors, wherein the training feature vectors include the meteorological features corresponding to the time point and spatial position and the corresponding influencing features;

[0051] The feature packaging submodule is used to package a plurality of training feature vectors into a first training feature set.

[0052] By adopting the above technical solution, a meteorological feature acquisition submodule is used to extract element features from historical meteorological information, and perform trend analysis and interpolation analysis on the element features to obtain meteorological features, wherein the meteorological features include time trend features and spatial distribution features; an image feature extraction submodule is used to identify direct impact data, indirect impact data and lag effect data from meteorological impact information, and extract impact features, wherein the impact features include direct impact features, indirect impact features and lag impact features; a feature vector combination submodule is used to combine the extracted meteorological features and the corresponding associated impact features into a plurality of training feature vectors, wherein the training feature vectors include the meteorological features of the corresponding time points and spatial positions and their corresponding impact features; a feature packaging submodule is used to package a plurality of training feature vectors into a first training feature set.

[0053] In summary, the present application includes at least one of the following beneficial technical effects:

[0054] 1. This application constructs a more accurate meteorological deduction model by comprehensively utilizing historical meteorological information and meteorological impact information, extracting key features for training, and at the same time, by introducing a deviation correction model to fine-tune the deduction results, it has the effect of significantly improving the accuracy and practicality of meteorological deduction;

[0055] 2. This application has the effect of significantly improving the prediction accuracy and applicability of the meteorological deduction model by comprehensively considering the deduction needs, historical meteorological information and detailed boundary condition settings. First, by accurately setting the initial conditions, the model can start the deduction from a meteorological state that is closer to the actual state, reducing the prediction error caused by inaccurate initial conditions. Secondly, the carefully divided boundary conditions ensure the rationality of the deduction process in space, time and external influences, making the deduction results closer to the actual situation, improving the accuracy of weather forecasts, and providing a more reliable scientific basis for disaster prevention and mitigation, agricultural production, urban planning and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of an embodiment of a weather-based deduction method of the present application;

[0057] Figure 2 is a flowchart of an implementation of step S20 in an embodiment of a deduction method based on meteorology of the present application;

[0058] Figure 3 It is an implementation flow chart before step S50 in an embodiment of a weather-based deduction method of the present application. DETAILED DESCRIPTION

[0059] The following is combined with Figure 1-3 This application is described in further detail.

[0060] In one embodiment, if Figure 1 As shown, the present application discloses a meteorological deduction method, which specifically includes the following steps:

[0061] S10: Obtain historical meteorological information and meteorological impact information corresponding to the target area;

[0062] In this embodiment, the target area is the area where the weather simulation is about to be carried out; the historical weather information is the weather data recorded in the past, including but not limited to temperature, humidity, precipitation, wind speed, wind direction, air pressure, etc.; the weather impact information is the relevant data that may affect the weather, including but not limited to geographical and environmental data, ocean and atmospheric data, solar radiation and astronomical data, human activity data and other weather observation data;

[0063] Furthermore, geographic and environmental data include:

[0064] Topographic data: including altitude, mountain ranges, and the location of rivers and lakes, which can affect local climate and weather patterns;

[0065] Land use and vegetation cover data: Different types of land cover (such as forests, grasslands, cities, etc.) have different effects on climate. For example, forests can affect rainfall and temperature.

[0066] Soil type and moisture data: Soil moisture and type can affect surface albedo and water evaporation, thus affecting local climate;

[0067] Ocean and atmosphere data include:

[0068] Sea surface temperature (SST): The ocean is an important heat source for the atmosphere, and changes in SST can affect atmospheric circulation and weather patterns;

[0069] Ocean currents and current data: Ocean currents carry heat and moisture and have a significant impact on the climate of coastal areas;

[0070] Atmospheric circulation indices: such as El Nino-Southern Oscillation (ENSO) and Arctic Oscillation (AO). These indices can reflect changes in atmospheric circulation and have an important impact on climate.

[0071] Solar radiation and astronomical data including:

[0072] Solar radiation intensity: Solar radiation is the main source of energy for the Earth, and its changes can affect climate and weather;

[0073] The distance between the Earth and the Sun and the speed of the Earth's rotation: Although this factor has little impact on long-term climate change, it may have some impact on weather patterns under certain conditions (such as changes in the Earth's rotation speed);

[0074] Human activity data include:

[0075] Greenhouse gas emission data: The emission of greenhouse gases such as carbon dioxide and methane is one of the main causes of global warming;

[0076] Land use change data: such as urbanization and agricultural expansion, which can change the surface albedo, water cycle, etc., and thus affect the climate;

[0077] Energy use and industrial emissions data: Pollutants and particulate matter emitted by industry can affect local climate and weather;

[0078] Other meteorological observations include:

[0079] Meteorological observation data of neighboring areas: Meteorological conditions in neighboring areas can affect the meteorological changes in the target area, so they can be used as a reference for meteorological deduction and correction;

[0080] Satellite remote sensing data: Satellites can provide large-scale, high-resolution meteorological observation data, such as cloud maps, temperature maps, water vapor maps, etc., which are helpful for meteorological deduction and correction;

[0081] Specifically, collect historical meteorological data information of the target area and information on factors affecting meteorological changes.

[0082] S20: extracting meteorological features and impact features from the historical meteorological information and the meteorological impact information respectively, and packaging the extracted meteorological features and impact features into a first training feature set;

[0083] In this embodiment, meteorological characteristics are representative or key meteorological characteristics extracted after in-depth analysis of historical meteorological information, including daily changes in temperature, intensity and distribution of precipitation, direction and speed of wind, etc., which can reflect the essential attributes and changing laws of meteorological phenomena and are important inputs to meteorological deduction models; impact characteristics are key characteristics that have an important impact on meteorological deduction extracted from meteorological impact information, including topographic and geomorphic characteristics, land use and vegetation cover characteristics, soil type and humidity characteristics, etc., which can reflect the interactive relationship between meteorological conditions and geographical environment, and provide necessary background information for meteorological deduction;

[0084] Specifically, key meteorological features and impact features are extracted from the collected historical meteorological information and meteorological impact information and packaged into a first training feature set for training a pre-built meteorological deduction model.

[0085] S30: training the pre-built meteorological deduction model by using the first training feature set;

[0086] In this embodiment, the meteorological deduction model is a model constructed based on historical meteorological information and meteorological principles, and is used to predict future meteorological conditions and simulate the development process of meteorological events;

[0087] Specifically, a meteorological deduction model pre-constructed based on historical meteorological information and meteorological principles is trained through the first training feature set.

[0088] S40: setting initial conditions and boundary conditions of the deduction model, obtaining current meteorological information and meteorological impact information and inputting them into the meteorological deduction model for preliminary deduction;

[0089] In this embodiment, the initial conditions are the starting state set when the meteorological deduction model starts running, including the spatial distribution and time evolution of meteorological elements; the boundary conditions are the constraints followed by the meteorological deduction model when running, including spatial boundary conditions, time boundary conditions and external boundary conditions; the current meteorological information is the current meteorological data, including but not limited to temperature, humidity, precipitation, wind speed, wind direction, air pressure, etc.; the preliminary deduction is the deduction of the meteorological deduction model based on the current meteorological information and meteorological impact information;

[0090] Specifically, according to actual needs, initial conditions and boundary conditions are set for the meteorological simulation model, and the latest current meteorological information and meteorological impact information are obtained, and the current meteorological information and meteorological impact information are input into the meteorological simulation model for preliminary meteorological simulation.

[0091] S50: When receiving the deduction information output by the deduction model, inputting the deduction information into the pre-trained deviation correction model for deviation correction;

[0092] In this embodiment, the deduction information is the result information of the predicted future weather conditions and simulated weather events output by the meteorological deduction model after preliminary deduction; the deviation correction model is a model used to correct the output results of the meteorological deduction model;

[0093] Specifically, since there may be various uncertain factors in the meteorological deduction process, which may lead to a certain deviation between the deduction results and the actual meteorological conditions, a pre-trained deviation correction model is introduced to perform refined deviation correction on the deduction information output by the deduction model to improve the accuracy of the prediction.

[0094] S60: When receiving the correction deduction information output by the deviation correction model, presenting it in a visual manner;

[0095] In this embodiment, the corrected deduction information is the result information output after the deviation correction model corrects the deduction information; the visual presentation is to display the data or results output by the meteorological deduction model in the form of graphics, images or animations, so as to facilitate understanding and analysis;

[0096] Specifically, the bias-corrected deduction information is visualized in intuitive forms such as charts, maps, and animations.

[0097] In one embodiment, if Figure 2 As shown, step S20 includes the steps of:

[0098] S21: extracting element features from historical meteorological information, and performing trend analysis and interpolation analysis on the element features to obtain meteorological features, wherein the meteorological features include time trend features and spatial distribution features;

[0099] S22: identifying direct impact data, indirect impact data and delayed impact data from the meteorological impact information, and extracting impact features, wherein the impact features include direct impact features, indirect impact features and delayed impact features;

[0100] S23: combining the extracted meteorological features and the corresponding associated influencing features into a plurality of training feature vectors, wherein the training feature vectors include the meteorological features corresponding to the time point and spatial position and the corresponding influencing features;

[0101] S24: Packing a plurality of training feature vectors into a first training feature set;

[0102] In this embodiment, element features are features of basic meteorological elements extracted from historical meteorological information, including but not limited to temperature, humidity, wind speed, wind direction, precipitation, etc., which are basic components of meteorological data and are used to reflect the basic features of meteorological conditions; trend analysis is a statistical method for identifying long-term trends or patterns in data, specifically identifying the evolution of meteorological element features over time, such as whether the temperature rises year by year, seasonal changes in precipitation, etc.; interpolation analysis is a mathematical method for estimating values ​​at unknown points, which is usually based on data at known points, specifically used to fill gaps in meteorological observation data, or to generate higher-resolution meteorological data; time trend features are features that describe how meteorological elements change over time, such as seasonal changes, interannual changes, etc.; spatial distribution features are features that describe the spatial distribution of meteorological elements, such as the geographical distribution of precipitation, the vertical gradient of temperature, etc.; directly affecting data are data that have a direct and immediate impact on meteorological conditions, such as topography, land use type, etc.; indirect affecting data are data that have a direct and immediate impact on meteorological conditions, such as topography, land use type, etc. The data refers to data that indirectly affects meteorological conditions by affecting other factors, such as sea surface temperature, atmospheric circulation index, etc.; the data with delayed effect refers to data that has a delayed effect on meteorological conditions, that is, its impact will not appear until a period of time has passed, such as the long-term impact of greenhouse gas emissions on the global climate; direct impact features are features extracted from direct impact data, which directly reflect the immediate impact on meteorological conditions; indirect impact features are features extracted from indirect impact data, which reflect the characteristics of indirect impact on meteorological conditions through other factors; delayed impact features are features extracted from delayed effect data, which reflect its long-term or delayed impact on meteorological conditions; the training feature vector is a data structure containing multiple features, which is used to train machine learning models. Specifically, each training feature vector contains the meteorological features and their corresponding impact features at the corresponding time point and spatial position, where the corresponding time point refers to the specific time corresponding to the meteorological features and the impact features, and the corresponding spatial position refers to the specific geographical location corresponding to the meteorological features and the impact features;

[0103] Specifically, extract element features from historical meteorological information, and after extracting these element features, perform trend analysis on them to reveal the changing rules of meteorological elements over time; at the same time, perform interpolation analysis to supplement and improve the spatial distribution information of meteorological elements; identify direct impact data, indirect impact data and lag effect data from meteorological impact information, direct impact data include factors such as topography, vegetation coverage, etc. that directly affect the meteorology; indirect impact data involve factors such as human activities and economic development that indirectly affect the meteorology through certain channels; lag effect data considers possible delayed effects in meteorological changes, such as the impact of previous precipitation on later soil moisture; after identifying direct impact data, indirect impact data and lag effect data, extract their impact features on the meteorology, including direct impact features, indirect impact features and lag impact features; combine the extracted meteorological features and the corresponding associated impact features into a number of training feature vectors, each of which contains the meteorological features and their corresponding impact features at the corresponding time point and spatial position, and this combination method ensures that the training feature vector can fully reflect the complex relationship between meteorological changes and their influencing factors, and package the combined several training feature vectors into the first training feature set.

[0104] In one embodiment, step S40 includes the steps of:

[0105] S41: Obtaining deduction demand information, and determining initial conditions of the deduction model based on historical meteorological information and the deduction demand information;

[0106] S42: Determine boundary conditions of the deduction model based on the deduction requirement information, where the boundary conditions include space boundary conditions, time boundary conditions, and external boundary conditions;

[0107] In this embodiment, the deduction requirement information is the specific requirements for weather deduction proposed by the user or the system, including the time range, spatial range, meteorological elements, etc. of the deduction, and further includes specific meteorological events or phenomena, such as typhoon path prediction, rainstorm warning, etc.; the spatial boundary condition defines the geographical area of ​​the deduction to ensure that the model focuses on the area of ​​concern to the user, the time boundary condition sets the start and end time of the deduction so that the deduction result can cover the time period required by the user, and the external boundary condition takes into account the factors outside the deduction area that may affect the internal meteorological conditions, such as the meteorological conditions and terrain characteristics of the surrounding areas;

[0108] Specifically, the deduction demand information is obtained, including the specific requirements of the user or application scenario for meteorological deduction, such as the geographical area of ​​concern, time range, meteorological elements, etc., and the initial conditions of the deduction model are determined in combination with the deduction demand information and historical meteorological information: these data are statistically analyzed to understand the average value, change trend, extreme value, etc. of the characteristics of the meteorological elements, and the initial conditions of the deduction model are determined by the time range, spatial range, meteorological elements, etc. in the deduction demand information. Among them, if the deduction demand information is to predict the temperature change in the next few days, then the initial conditions may include the temperature distribution at the beginning of the deduction, the recent temperature change trend, etc., and the boundary conditions of the deduction model are further refined according to the deduction demand information. The boundary conditions are crucial in meteorological deduction. They limit the spatial scope, time span and external factors that may be affected by the model deduction. Among them, the spatial boundary conditions define the geographical area of ​​the deduction to ensure that the model pays attention to the area of ​​concern to the user. The time boundary conditions set the start and end time of the deduction so that the deduction results can cover the time period required by the user. The external boundary conditions take into account the factors outside the deduction area that may affect the internal meteorological conditions, such as the meteorological conditions and terrain characteristics of the surrounding areas.

[0109] In one embodiment, if Figure 3 As shown, before step S50, the following steps are performed:

[0110] S47: Obtain historical meteorological forecast information corresponding to the target area, and extract deviation characteristics based on the historical meteorological information and the historical meteorological forecast information;

[0111] S48: Packing the meteorological features and the deviation features into a second training feature set in time sequence;

[0112] S49: training the deviation correction model by using the second training feature set to determine the weight of each deviation correction model, wherein the deviation correction model includes a linear regression model, a random forest model, and a neural network model;

[0113] In this embodiment, the historical weather forecast information is the weather forecast data released for the target area in the past; the deviation feature is the feature extracted from the difference or deviation between the historical weather forecast information and the actual weather information; the time-series packaging is to organize the weather features and the deviation features in time sequence to form a series of feature sets with time-series relationships; the second training feature set is a data set composed of the weather features and the deviation features in time sequence, which is used to train the deviation correction model; the linear regression model is a machine learning model used to establish a linear relationship between input features (such as weather features and deviation features) and output targets (such as deviation correction values); the random forest model is an integrated learning method that improves the accuracy of prediction by constructing multiple decision trees and integrating their prediction results; the neural network model is a machine learning model that simulates the working mode of the human brain neural network. The neural network model can learn the complex relationship between the input features and output the prediction results through nonlinear transformation of multiple layers of neurons;

[0114] Specifically, historical meteorological forecast information corresponding to the target area is obtained, and deviation features are extracted based on the historical meteorological information (i.e., actually observed meteorological data) and the historical meteorological forecast information (i.e., previously predicted meteorological data). The meteorological features and the extracted deviation features are packaged in time series as a second training feature set, and the deviation correction model is trained through the second training feature set to determine the weight of each deviation correction model. The deviation correction model includes a linear regression model, a random forest model, and a neural network model, each of which has different advantages and characteristics and can capture different types of deviation patterns. Through training, the contribution (i.e., weight) of each model in the deviation correction can be determined, thereby constructing a more accurate and robust deviation correction system, which has the effect of improving the accuracy of meteorological deduction.

[0115] In one embodiment, step S43 includes the steps of:

[0116] S431: Identify the meteorological type of the historical meteorological forecast information corresponding to each deviation feature in the second training feature set;

[0117] S432: Determine the dynamic weight of each deviation correction model based on all identified meteorological types;

[0118] In this embodiment, the meteorological type is a classification of the meteorological conditions according to meteorological elements and weather phenomena, including sunny days, rainy days, snowy days, windy days, etc.; the dynamic weight is a weight dynamically adjusted according to the meteorological type and other conditions to correct the deviation more accurately;

[0119] Specifically, the meteorological type of the historical meteorological forecast information corresponding to each deviation feature in the second training feature set is identified to understand the characteristics and laws of the prediction deviation under different meteorological conditions. For example, some meteorological types (such as heavy rain, typhoon, etc.) may have more complex and difficult to predict characteristics, so the corresponding deviation patterns and correction requirements may also be more special; based on the identified meteorological type, the dynamic weight of each deviation correction model (including linear regression model, random forest model and neural network model) is determined. Because different deviation correction models may have different advantages and effects when processing different types of meteorological data, by dynamically adjusting the weights of each model, the prediction deviation can be corrected more effectively and adaptively under different meteorological types, which has the effect of making the deviation correction more refined and enhancing the adaptability of the deviation correction model.

[0120] In one embodiment, step S432 includes the steps of:

[0121] S4321: Based on a preset preliminary weight allocation strategy, analyzing the physical mechanism of the identified meteorological type and assigning a preliminary weight range to each deviation correction model;

[0122] S4322: training and cross-validating each deviation correction model using the second training feature set, and adjusting a preliminary weight range of each deviation correction model based on the cross-validation result to determine a dynamic weight of each deviation correction model based on each meteorological type;

[0123] In this embodiment, the preliminary weight allocation strategy is to analyze the physical mechanism of the meteorological type based on experience and preset rules, and to allocate preliminary weight ranges to each deviation correction model based on the analysis results of the physical mechanism; the physical mechanism is the physical principle or law behind the meteorological phenomenon, which is used to guide the preliminary allocation of weights; cross-validation is a model evaluation method, which divides the data set into a training set and a validation set, trains the model with the training set, and evaluates the performance of the deviation correction model with the validation set to adjust the parameters or weights of the deviation correction model; adjusting the preliminary weight range is to optimize the preliminary weight range based on the cross-validation results to determine a more accurate dynamic weight;

[0124] Specifically, the physical mechanism of the identified meteorological type is analyzed based on the preset preliminary weight allocation strategy, so as to allocate a preliminary weight range matching the meteorological type to each model, so as to ensure that the weight allocation is consistent with the physical mechanism of meteorology, thereby improving the physical rationality and accuracy of the model, and using the second training feature set to train and cross-validate each deviation correction model. The second training feature set contains meteorological features and deviation features packaged in time series, which can fully reflect the changing laws and deviation characteristics of meteorological data. Through training and cross-validation, the performance of each model under different weights is evaluated, and the preliminary weight range of each model is adjusted based on the verification results, so as to optimize the weight allocation through actual data, ensure the accuracy and stability of the model in practical applications, and after multiple iterations and adjustments, determine the dynamic weight of each deviation correction model based on each meteorological type, which has the effect of improving the adaptability and physical rationality of the deviation correction model.

[0125] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0126] In one embodiment, a weather-based deduction system is provided, and the weather-based deduction system corresponds one-to-one to a weather-based deduction method in the above embodiment.

[0127] A weather-based deduction system, comprising:

[0128] An information acquisition module is used to obtain historical meteorological information and meteorological impact information corresponding to the target area;

[0129] A feature extraction module, used to extract meteorological features and impact features from historical meteorological information and meteorological impact information, respectively, and package the extracted meteorological features and impact features into a first training feature set;

[0130] A first training module, used for training a pre-built meteorological deduction model through a first training feature set;

[0131] The deduction model input module is used to set the initial conditions and boundary conditions of the deduction model, obtain the current meteorological information and meteorological impact information, and input them into the meteorological deduction model for preliminary deduction;

[0132] The deviation model input module is used to input the deduction information into the pre-trained deviation correction model for deviation correction when receiving the deduction information output by the deduction model;

[0133] A visualization module, for presenting the correction deduction information output by the deviation correction model in a visual manner when receiving the correction deduction information output by the deviation correction model;

[0134] Optionally, the feature extraction module includes:

[0135] The meteorological feature acquisition submodule is used to extract element features from historical meteorological information and perform trend analysis and interpolation analysis on the element features to obtain meteorological features, wherein the meteorological features include time trend features and spatial distribution features;

[0136] An image feature extraction submodule is used to identify direct impact data, indirect impact data and delayed effect data from meteorological impact information, and extract impact features, wherein the impact features include direct impact features, indirect impact features and delayed impact features;

[0137] A feature vector combination submodule, used to combine the extracted meteorological features and the corresponding associated influencing features into a plurality of training feature vectors, wherein the training feature vectors include the meteorological features corresponding to the time point and spatial position and the corresponding influencing features;

[0138] A feature packing submodule, used for packing a plurality of training feature vectors into a first training feature set;

[0139] Optionally, the deduction model input module includes:

[0140] The initial condition determination submodule is used to obtain the deduction requirement information and determine the initial conditions of the deduction model based on the historical meteorological information and the deduction requirement information;

[0141] A boundary condition determination submodule, used to determine the boundary conditions of the deduction model based on the deduction requirement information, wherein the boundary conditions include space boundary conditions, time boundary conditions and external boundary conditions;

[0142] Optionally, also include:

[0143] Deviation feature extraction module, used to obtain historical meteorological forecast information corresponding to the target area, and extract deviation features based on historical meteorological information and historical meteorological forecast information;

[0144] A feature packaging module, used for packaging meteorological features and deviation features into a second training feature set in time sequence;

[0145] A second training module, used for training the deviation correction model through a second training feature set to determine the weight of each deviation correction model, wherein the deviation correction model includes a linear regression model, a random forest model, and a neural network model;

[0146] Optionally, the second training module includes:

[0147] A meteorological type identification submodule, used to identify the meteorological type of the historical meteorological forecast information corresponding to each deviation feature in the second training feature set;

[0148] A weight determination submodule, for determining the dynamic weights of each deviation correction model based on all identified meteorological types;

[0149] Optionally, the weight determination submodule includes:

[0150] A weight range allocation submodule, for analyzing the physical mechanism of the identified meteorological type and allocating a preliminary weight range to each deviation correction model based on a preset preliminary weight allocation strategy;

[0151] The weight adjustment submodule is used to train and cross-validate each deviation correction model through the second training feature set, and adjust the preliminary weight range of each deviation correction model based on the cross-validation result to determine the dynamic weight of each deviation correction model based on each meteorological type.

[0152] For the specific definition of a meteorological-based deduction system, please refer to the definition of a meteorological-based deduction method above, which will not be repeated here. Each module in the above-mentioned meteorological-based deduction system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0153] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A weather-based deduction method, characterized in that: Includes steps: Obtain historical meteorological information and meteorological impact information corresponding to the target area; Extracting meteorological features and impact features from historical meteorological information and meteorological impact information respectively, and packaging the extracted meteorological features and impact features into a first training feature set; Training the pre-built meteorological deduction model through the first training feature set; Set the initial conditions and boundary conditions of the deduction model, obtain the current meteorological information and meteorological impact information and input them into the meteorological deduction model for preliminary deduction; When receiving the deduction information output by the deduction model, the deduction information is input into the pre-trained bias correction model for bias correction; When the correction deduction information output by the deviation correction model is received, it is presented in a visual manner.

2. A meteorological deduction method according to claim 1, characterized in that: The step of extracting meteorological features and impact features from historical meteorological information and meteorological impact information respectively, and packaging the extracted meteorological features and impact features into a first training feature set comprises the steps of: Extracting element features from historical meteorological information, and performing trend analysis and interpolation analysis on the element features to obtain meteorological features, wherein the meteorological features include time trend features and spatial distribution features; Identifying direct impact data, indirect impact data, and delayed effect data from the meteorological impact information, and extracting impact features, wherein the impact features include direct impact features, indirect impact features, and delayed impact features; Combining the extracted meteorological features and the corresponding associated influencing features into a plurality of training feature vectors, wherein the training feature vectors include the meteorological features at the corresponding time points and spatial positions and the corresponding influencing features; Several training feature vectors are packaged into a first training feature set.

3. The weather-based deduction method according to claim 1, characterized in that: The step of setting the initial conditions and boundary conditions of the deduction model, obtaining the current meteorological information and meteorological impact information and inputting them into the meteorological deduction model for preliminary deduction includes the following steps: Obtain the simulation demand information and determine the initial conditions of the simulation model based on the historical meteorological information and the simulation demand information; The boundary conditions of the deduction model are determined based on the deduction requirement information, and the boundary conditions include space boundary conditions, time boundary conditions and external boundary conditions.

4. The weather-based deduction method according to claim 1, characterized in that: When receiving the deduction information output by the deduction model, before inputting the deduction information into the pre-trained deviation correction model for deviation correction, the following steps are performed: Obtain the historical weather forecast information corresponding to the target area, and extract deviation characteristics based on the historical weather information and historical weather forecast information; Packing meteorological features and deviation features into a second training feature set in time sequence; The deviation correction models are trained by the second training feature set to determine the weights of the deviation correction models, wherein the deviation correction models include a linear regression model, a random forest model, and a neural network model.

5. The weather-based deduction method according to claim 4, characterized in that: The deviation correction model is trained by the second training feature set to determine the weight of each deviation correction model, wherein the deviation correction model includes the steps of a linear regression model, a random forest model and a neural network model, including the steps of: Identify the meteorological type of the historical meteorological forecast information corresponding to each deviation feature in the second training feature set; Determine the dynamic weights of each bias correction model based on all identified weather types.

6. A meteorological deduction method according to claim 5, characterized in that: The step of determining the dynamic weights of each deviation correction model based on all identified meteorological types comprises the steps of: Based on the preset preliminary weight allocation strategy, the physical mechanism of the identified meteorological type is analyzed and a preliminary weight range is assigned to each bias correction model; Each deviation correction model is trained and cross-validated using the second training feature set, and the preliminary weight range of each deviation correction model is adjusted based on the cross-validation result to determine the dynamic weight of each deviation correction model based on each meteorological type.

7. A weather-based deduction system, characterized by: include: An information acquisition module is used to obtain historical meteorological information and meteorological impact information corresponding to the target area; A feature extraction module, used to extract meteorological features and impact features from historical meteorological information and meteorological impact information, respectively, and package the extracted meteorological features and impact features into a first training feature set; A first training module, used for training a pre-built meteorological deduction model through a first training feature set; The deduction model input module is used to set the initial conditions and boundary conditions of the deduction model, obtain the current meteorological information and meteorological impact information, and input them into the meteorological deduction model for preliminary deduction; The deviation model input module is used to input the deduction information into the pre-trained deviation correction model for deviation correction when receiving the deduction information output by the deduction model; The visualization module is used to present the correction deduction information output by the deviation correction model in a visual manner when it is received.

8. The weather-based deduction system according to claim 7, characterized in that: The feature extraction module comprises: The meteorological feature acquisition submodule is used to extract element features from historical meteorological information and perform trend analysis and interpolation analysis on the element features to obtain meteorological features, wherein the meteorological features include time trend features and spatial distribution features; An image feature extraction submodule is used to identify direct impact data, indirect impact data and delayed effect data from meteorological impact information, and extract impact features, wherein the impact features include direct impact features, indirect impact features and delayed impact features; A feature vector combination submodule, used to combine the extracted meteorological features and the corresponding associated influencing features into a plurality of training feature vectors, wherein the training feature vectors include the meteorological features corresponding to the time point and spatial position and the corresponding influencing features; The feature packaging submodule is used to package a plurality of training feature vectors into a first training feature set.

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