Data processing and analysis method for multi-source numerical prediction data based on cloud edge fusion

Abstract

The application discloses a cloud edge fusion-based data processing and analysis method for multi-source numerical prediction data, and relates to the technical field of data analysis, comprising the following steps: establishing a meteorological multi-source numerical prediction database; creating decoding objects of various meteorological general data to form a meteorological general data decoding pool; fitting and matching the meteorological general data decoding pool and the meteorological multi-source numerical prediction database to obtain a meteorological multi-source numerical prediction unified data set; based on the meteorological multi-source numerical prediction unified data set, performing data labeling according to unit time to construct a meteorological multi-source numerical prediction unified time series data set; fusing and integrating the meteorological multi-source numerical prediction unified time series data set according to a meteorological multi-source numerical prediction mode to predict the meteorological precipitation probability of a target area; and generating a meteorological precipitation probability prediction map of the target area based on the meteorological precipitation probability of the target area. The application has the advantage of improving the prediction accuracy and precision of meteorological data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a data processing and analysis method for multi-source numerical prediction data based on cloud-edge fusion. BACKGROUND

[0002] The existing weather forecast mainly relies on regression analysis of observed influencing factors within a short time scale and macroscopic estimation of meteorological turbulent fluid using Navier-Stokes equation to predict future weather data. However, due to the unobservable nature of turbulent microstate, the accuracy of actual prediction of future weather data is poor. SUMMARY

[0003] To solve the above technical problems, the data processing and analysis method for multi-source numerical prediction data based on cloud-edge fusion is provided. The technical solution solves the problem that the existing weather forecast mainly relies on regression analysis of observed influencing factors within a short time scale and macroscopic estimation of meteorological turbulent fluid using Navier-Stokes equation to predict future weather data. However, due to the unobservable nature of turbulent microstate, the accuracy of actual prediction of future weather data is poor.

[0004] To achieve the above purpose, the technical solution adopted by the present application is:

[0005] The data processing and analysis method for multi-source numerical prediction data based on cloud-edge fusion comprises:

[0006] Obtaining meteorological general data of several weather forecast systems, establishing a meteorological multi-source numerical prediction database;

[0007] Creating decoding objects of each meteorological general data to form a meteorological general data decoding pool;

[0008] Based on the meteorological general data decoding pool and the meteorological multi-source numerical prediction database, fitting and matching is performed to obtain a meteorological multi-source numerical prediction unified data set;

[0009] Based on the meteorological multi-source numerical prediction unified data set, data labeling is performed according to unit time to construct a meteorological multi-source numerical prediction unified time series data set;

[0010] The meteorological multi-source numerical prediction unified time series data set is integrated according to the meteorological multi-source numerical prediction mode to predict the meteorological precipitation probability of the target area;

[0011] Based on the meteorological precipitation probability of the target area, a meteorological precipitation probability prediction map of the target area is generated.

[0012] Preferably, fitting and matching the meteorological general data decoding pool with the meteorological multi-source numerical forecast database to obtain a unified meteorological multi-source numerical forecast data set specifically includes:

[0013] Based on the meteorological general data decoding pool, a meteorological general data decoding public class is established with each meteorological general data decoding method;

[0014] The meteorological general data decoding public class is used as the outer loop, and the meteorological multi-source numerical forecast data is nested as the inner loop. According to the matching formula, the matching index between each meteorological multi-source numerical forecast data and each meteorological general data decoding method is calculated as the decoding loop stop condition, and the data decoding demand function is established;

[0015] According to the data decoding requirement function, the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method is obtained, a data decoding model is constructed, and a unified meteorological multi-source numerical forecast data set is generated;

[0016] The matching formula is specifically:

[0017]

[0018] Where, is the i-th meteorological multi-source numerical forecast data, is the jth general meteorological data decoding method, is the kth dimension parameter value in the i-th meteorological multi-source numerical forecast data, is the kth dimension parameter value in the jth meteorological general data decoding method, is the weight of the k-th dimension parameter value.

[0019] Preferably, according to the data decoding requirement function, obtaining the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method, constructing the data decoding model, and generating the meteorological multi-source numerical forecast unified data set specifically include:

[0020] Based on the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method as the root node, and each meteorological general data decoding method as the decoding branch node, a meteorological multi-source numerical forecast data decoding demand decision tree is established, and the data decoding model is connected in series.

[0021] Based on the data decoding model, the meteorological multi-source numerical forecast database is used as input, and the meteorological multi-source numerical forecast data is divided according to the maximum gain ratio of the decoding branch nodes to generate a unified meteorological multi-source numerical forecast data set;

[0022] The maximum gain ratio of the multi-source meteorological numerical forecast data to the decoding branch nodes is divided into:

[0023]

[0024] Where, is the maximum gain ratio of meteorological multi-source numerical forecast data D to decoding branch node A, is the information gain of the meteorological multi-source numerical forecast data D for the decoding branch node A, is the attribute entropy of the meteorological multi-source numerical forecast data D for the decoding branch node A, Divide the branch nodes into subsets for matching the jth meteorological general data decoding method for meteorological multi-source numerical forecast data, is the total set of meteorological multi-source numerical forecast data, is a logarithmic function, is the total number of decoding branch nodes A.

[0025] Preferably, the unified time series data set of meteorological multi-source numerical prediction is integrated according to the meteorological multi-source numerical prediction model to predict the meteorological precipitation probability in the target area;

[0026] Based on the unified time series data of meteorological multi-source numerical prediction, the correlation factors are screened according to the meteorological precipitation type of the target area to be predicted, and the time series data of meteorological multi-source numerical prediction influence is obtained;

[0027] Based on the meteorological multi-source numerical prediction impact time series data, data alignment is performed according to the spatial resolution and temporal resolution, the temporal resolution is used as the observation window, the meteorological multi-source numerical prediction impact time series data within the spatial resolution is used as the observation attribute, and the meteorological characteristic time series parameters of the target area to be predicted are obtained through a sliding window; the spatial resolution is the target distance, and the temporal resolution is hours;

[0028] According to the meteorological characteristic time series parameters of the target area to be predicted, the optimal meteorological precipitation type characteristic time series parameters are screened and integrated for the posterior probability of the unified time series data of the meteorological multi-source numerical prediction of the target area, and a multi-mode meteorological characteristic time series parameter set of the target area to be predicted is obtained;

[0029] Based on the multi-mode meteorological characteristic time series parameter set of the target area to be predicted, a meteorological prediction model for the target area is constructed to predict the meteorological precipitation probability of the target area;

[0030] The posterior probability of the meteorological characteristic time series parameters of the target area to be predicted for the unified time series data of the multi-source numerical prediction of the meteorological target area is specifically:

[0031]

[0032] Where, is the posterior probability of the characteristic parameter of the vth meteorological precipitation type under the tth unit time of the uth target area to be predicted, is the conditional probability of the uth target area at the tth unit time given the vth meteorological precipitation type characteristic parameter, is the prior probability of the u-th target area to be predicted under the t-th unit time, is the overall probability distribution of the characteristic parameters of the vth meteorological precipitation type.

[0033] The target area's weather forecast model is specifically:

[0034]

[0035] Where, is the meteorological precipitation probability of the uth target area to be predicted under the tth unit time, is an indicator function, if ,but =1, indicating that precipitation occurred in the area at that time point. ,but =0, indicating that there is no precipitation in the area at that time point, T is the precipitation threshold, is the number of parameters in the multi-mode meteorological characteristic time series parameter set of the target area to be predicted.

[0036] The unified time series data set of meteorological multi-source numerical prediction is integrated according to the meteorological multi-source numerical prediction model to predict the meteorological precipitation probability in the target area;

[0037] Based on the unified time series data of meteorological multi-source numerical prediction, the correlation factors are screened according to the meteorological precipitation type of the target area to be predicted, and the time series data of meteorological multi-source numerical prediction influence is obtained;

[0038] Based on the meteorological multi-source numerical prediction impact time series data, data alignment is performed according to the spatial resolution and temporal resolution, the temporal resolution is used as the observation window, the meteorological multi-source numerical prediction impact time series data within the spatial resolution is used as the observation attribute, and the meteorological characteristic time series parameters of the target area to be predicted are obtained through a sliding window; the spatial resolution is the target distance, and the temporal resolution is hours;

[0039] According to the meteorological characteristic time series parameters of the target area to be predicted, the optimal meteorological precipitation type characteristic time series parameters are screened and integrated for the posterior probability of the unified time series data of the meteorological multi-source numerical prediction of the target area, and a multi-mode meteorological characteristic time series parameter set of the target area to be predicted is obtained;

[0040] Based on the multi-mode meteorological characteristic time series parameter set of the target area to be predicted, a meteorological prediction model for the target area is constructed to predict the meteorological precipitation probability of the target area;

[0041] The posterior probability of the meteorological characteristic time series parameters of the target area to be predicted for the unified time series data of the multi-source numerical prediction of the meteorological target area is specifically:

[0042]

[0043] Where, is the posterior probability of the characteristic parameter of the vth meteorological precipitation type under the tth unit time of the uth target area to be predicted, is the conditional probability of the uth target area at the tth unit time given the vth meteorological precipitation type characteristic parameter, is the prior probability of the u-th target area to be predicted under the t-th unit time, is the overall probability distribution of characteristic parameters of the vth meteorological precipitation type;

[0044] The weather forecast model for the target area is specifically:

[0045]

[0046] Where, is the meteorological precipitation probability of the uth target area to be predicted under the tth unit time, is an indicator function, if ,but =1, indicating that precipitation occurred in the area at that time point. ,but =0, indicating that there is no precipitation in the area at that time point, T is the precipitation threshold, It is a set of multi-mode meteorological characteristic time series parameters of the target area to be predicted.

[0047] Preferably, based on the unified time series data of meteorological multi-source numerical prediction, the associated factors are screened according to the meteorological precipitation type of the target area to be predicted, and the meteorological multi-source numerical prediction influencing time series data is obtained, which specifically includes:

[0048] Determine the meteorological precipitation type attention preference influencing factors of the target area to be detected, and establish the meteorological precipitation type preference array of the target area to be detected;

[0049] Using linear algebra, linear mapping is performed on the meteorological precipitation type preference array of the target area to be detected to obtain the meteorological precipitation type preference vector matrix Ns of the target area to be detected;

[0050]

[0051] in, Match the feature vector of the vth meteorological precipitation type of the uth target area to be predicted, is the total number of target areas to be predicted, is the total number of meteorological precipitation types;

[0052] For the unified time series data of meteorological multi-source numerical prediction, data dimension reduction is performed according to the principal component analysis method to obtain the unified time series dimension reduction data of meteorological multi-source numerical prediction;

[0053] Based on the similarity between the unified time series dimensionality reduction data of meteorological multi-source numerical prediction and each element in the meteorological precipitation type preference vector matrix of the target area to be detected, positive correlation factors are screened to obtain the time series data of meteorological multi-source numerical prediction;

[0054] The similarity between the unified time series dimensionality reduction data of the meteorological multi-source numerical prediction and each element in the meteorological precipitation type preference vector matrix of the target area to be detected is specifically:

[0055]

[0056] Where, It is the similarity between the unified time series dimensionality reduction data of the meteorological multi-source numerical prediction of the u-th target area to be predicted and the v-th meteorological precipitation type preference vector of the u-th target area to be detected.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] This paper proposes a data processing and analysis solution for multi-source numerical forecast data based on cloud-edge fusion. By integrating common meteorological data from multiple weather forecast systems, a database and decoding pool are established to unify the decoding format. This is then fitted and matched to form a unified data set. A time series data set is then constructed based on time series annotations. By integrating and fusing multi-source numerical forecast models, meteorological characteristics of the target area are predicted and intuitive precipitation probability forecast maps are generated. This has the beneficial effect of effectively improving the forecast precision and accuracy of meteorological data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Flowchart of the method for obtaining unified data set for multi-source numerical forecast of meteorology;

[0060] Figure 2 Flowchart of the unified data collection method for multi-source numerical weather forecasting;

[0061] Figure 3 A flow chart of the method for constructing a data decoding model;

[0062] Figure 4 Flowchart of the method for predicting meteorological precipitation probability in the target area;

[0063] Figure 5 Flowchart of the method for obtaining time series data of multi-source numerical prediction of meteorology;

[0064] Figure 6 This is a schematic diagram of the structure of the electronic device of the present invention;

[0065] Figure 7 It is a schematic diagram of the computer-readable storage medium structure of the present invention. DETAILED DESCRIPTION

[0066] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0067] Reference Figure 1 As shown in the figure, the data processing and analysis method of multi-source numerical forecast data based on cloud-edge fusion includes:

[0068] Obtain general meteorological data from several weather forecast systems and establish a meteorological multi-source numerical forecast database;

[0069] Create decoding objects for various general meteorological data and form a general meteorological data decoding pool;

[0070] Based on the meteorological general data decoding pool and the meteorological multi-source numerical forecast database, a unified meteorological multi-source numerical forecast data set is obtained;

[0071] Based on the unified data set of meteorological multi-source numerical forecast, data is labeled according to unit time to construct a unified time series data set of meteorological multi-source numerical forecast;

[0072] The unified time series data set of meteorological multi-source numerical prediction is integrated according to the meteorological multi-source numerical prediction model to predict the meteorological precipitation probability in the target area;

[0073] Based on the predicted meteorological precipitation probability of the target area, a meteorological precipitation probability forecast map of the target area is generated.

[0074] This solution integrates common meteorological data from multiple weather forecast systems, establishes a database, and creates a decoding pool to unify the decoding format. This approach then forms a unified data set through fitting and matching, and constructs a time series data set based on time series annotation. By integrating multi-source numerical forecast models, the solution predicts the meteorological characteristics of the target area and generates intuitive precipitation probability forecast maps. This has the beneficial effect of effectively improving the forecast precision and accuracy of meteorological data.

[0075] Reference Figure 2 As shown in the figure, based on the fitting and matching of the meteorological general data decoding pool and the meteorological multi-source numerical forecast database, the unified meteorological multi-source numerical forecast data set is obtained, which specifically includes:

[0076] Based on the meteorological general data decoding pool, a meteorological general data decoding public class is established with each meteorological general data decoding method;

[0077] The meteorological general data decoding public class is used as the outer loop, and the meteorological multi-source numerical forecast data is nested as the inner loop. According to the matching formula, the matching index between each meteorological multi-source numerical forecast data and each meteorological general data decoding method is calculated as the decoding loop stop condition, and the data decoding demand function is established;

[0078] According to the data decoding requirement function, the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method is obtained, a data decoding model is constructed, and a unified meteorological multi-source numerical forecast data set is generated;

[0079] The matching formula is specifically:

[0080]

[0081] Where, is the i-th meteorological multi-source numerical forecast data, is the jth general meteorological data decoding method, is the kth dimension parameter value in the i-th meteorological multi-source numerical forecast data, is the kth dimension parameter value in the jth meteorological general data decoding method, is the weight of the k-th dimension parameter value.

[0082] Reference Figure 3 As shown in the figure, according to the data decoding requirement function, the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method is obtained, a data decoding model is constructed, and a unified meteorological multi-source numerical forecast data set is generated. Specifically, the following steps are involved:

[0083] Based on the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method as the root node, and each meteorological general data decoding method as the decoding branch node, a meteorological multi-source numerical forecast data decoding demand decision tree is established, and the data decoding model is connected in series.

[0084] Based on the data decoding model, the meteorological multi-source numerical forecast database is used as input, and the meteorological multi-source numerical forecast data is divided according to the maximum gain ratio of the decoding branch nodes to generate a unified meteorological multi-source numerical forecast data set;

[0085] The maximum gain ratio of the multi-source meteorological numerical forecast data to the decoding branch nodes is divided into:

[0086]

[0087] Where, is the maximum gain ratio of meteorological multi-source numerical forecast data D to decoding branch node A, is the information gain of the meteorological multi-source numerical forecast data D for the decoding branch node A, is the attribute entropy of the meteorological multi-source numerical forecast data D for the decoding branch node A, Divide the branch nodes into subsets for matching the jth meteorological general data decoding method for meteorological multi-source numerical forecast data, is the total set of meteorological multi-source numerical forecast data, is a logarithmic function, is the total number of decoding branch nodes A.

[0088] It is understandable that since there are multiple categories of encoding formats in meteorological multi-source numerical forecast data, the conventional matching method for decoding selection will have too many categories and cannot make decisions based on the data itself. Therefore, the matching formula is embedded in the self-iterative function to determine the optimal decoding method, and a decision tree for decoding requirements of meteorological multi-source numerical forecast data is constructed, which is connected in series as a data decoding model to achieve fast and unified decoding of data.

[0089] Reference Figure 4 As shown in the figure, the unified time series data set of meteorological multi-source numerical prediction is integrated according to the meteorological multi-source numerical prediction model to predict the meteorological precipitation probability in the target area;

[0090] Based on the unified time series data of meteorological multi-source numerical prediction, the correlation factors are screened according to the meteorological precipitation type of the target area to be predicted, and the time series data of meteorological multi-source numerical prediction influence is obtained;

[0091] Based on the meteorological multi-source numerical prediction impact time series data, data alignment is performed according to the spatial resolution and temporal resolution, the temporal resolution is used as the observation window, the meteorological multi-source numerical prediction impact time series data within the spatial resolution is used as the observation attribute, and the meteorological characteristic time series parameters of the target area to be predicted are obtained through a sliding window; the spatial resolution is the target distance, and the temporal resolution is hours;

[0092] According to the meteorological characteristic time series parameters of the target area to be predicted, the optimal meteorological precipitation type characteristic time series parameters are screened and integrated for the posterior probability of the unified time series data of the meteorological multi-source numerical prediction of the target area, and a multi-mode meteorological characteristic time series parameter set of the target area to be predicted is obtained;

[0093] Based on the multi-mode meteorological characteristic time series parameter set of the target area to be predicted, a meteorological prediction model for the target area is constructed to predict the meteorological precipitation probability of the target area;

[0094] The posterior probability of the meteorological characteristic time series parameters of the target area to be predicted for the unified time series data of the multi-source numerical prediction of the meteorological target area is specifically:

[0095]

[0096] Where, is the posterior probability of the characteristic parameter of the vth meteorological precipitation type under the tth unit time of the uth target area to be predicted, is the conditional probability of the uth target area at the tth unit time given the vth meteorological precipitation type characteristic parameter, is the prior probability of the u-th target area to be predicted under the t-th unit time, is the overall probability distribution of the characteristic parameters of the vth meteorological precipitation type.

[0097] The weather forecast model for the target area is specifically:

[0098]

[0099] Where, is the meteorological precipitation probability of the uth target area to be predicted under the tth unit time, is an indicator function, if ,but =1, indicating that precipitation occurred in the area at that time point. ,but =0, indicating that there is no precipitation in the area at that time point, T is the precipitation threshold, is the number of parameters in the multi-mode meteorological characteristic time series parameter set of the target area to be predicted.

[0100] This solution predicts the probability of precipitation in a target area by fusing multi-source numerical forecast data. Utilizing unified time series data from multi-source numerical forecasts, this approach screens for correlated influencing factors and performs data alignment. Based on spatial and temporal resolution, meteorological characteristic time series parameters are extracted. By selecting and fusing optimal meteorological characteristic time series parameters, a multi-model meteorological characteristic time series parameter set is constructed, which is then used to train a meteorological forecast model to predict the probability of precipitation in the target area. The beneficial effect lies in the ability to accurately integrate multi-source information and, through data processing and feature screening, improve the accuracy of meteorological precipitation probability forecasts.

[0101] Reference Figure 5 As described above, based on the unified time series data of meteorological multi-source numerical prediction, the associated factors are screened according to the meteorological precipitation type of the target area to be predicted, and the time series data of meteorological multi-source numerical prediction influence is obtained, which specifically includes:

[0102] Determine the meteorological precipitation type attention preference influencing factors of the target area to be detected, and establish the meteorological precipitation type preference array of the target area to be detected;

[0103] Using linear algebra, linear mapping is performed on the meteorological precipitation type preference array of the target area to be detected to obtain the meteorological precipitation type preference vector matrix Ns of the target area to be detected;

[0104]

[0105] in, Match the feature vector of the vth meteorological precipitation type of the uth target area to be predicted, is the total number of target areas to be predicted, is the total number of meteorological precipitation types;

[0106] For the unified time series data of meteorological multi-source numerical prediction, data dimension reduction is performed according to the principal component analysis method to obtain the unified time series dimension reduction data of meteorological multi-source numerical prediction;

[0107] Based on the similarity between the unified time series dimensionality reduction data of meteorological multi-source numerical prediction and each element in the meteorological precipitation type preference vector matrix of the target area to be detected, positive correlation factors are screened to obtain the time series data of meteorological multi-source numerical prediction;

[0108] The similarity between the unified time series dimensionality reduction data of the meteorological multi-source numerical prediction and each element in the meteorological precipitation type preference vector matrix of the target area to be detected is specifically:

[0109]

[0110] Where, It is the similarity between the unified time series dimensionality reduction data of the meteorological multi-source numerical prediction of the u-th target area to be predicted and the v-th meteorological precipitation type preference vector of the u-th target area to be detected.

[0111] Furthermore, the method or system according to the embodiment of the present application may also be Figure 6 The electronic device architecture shown in FIG. Figure 6As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, and the like. The storage device in the electronic device 500, such as ROM503 or hard disk 507, can store the data processing and analysis method of multi-source numerical forecast data based on cloud-edge fusion provided by the present application, including: obtaining meteorological general data of several weather forecast systems and establishing a meteorological multi-source numerical forecast database; creating decoding objects for each meteorological general data and forming a meteorological general data decoding pool; performing fitting and matching based on the meteorological general data decoding pool and the meteorological multi-source numerical forecast database to obtain a meteorological multi-source numerical forecast unified data set; based on the meteorological multi-source numerical forecast unified data set, data is labeled according to unit time to construct a meteorological multi-source numerical prediction unified time series data set; the meteorological multi-source numerical prediction unified time series data set is integrated and integrated according to the meteorological multi-source numerical forecast mode to predict the meteorological precipitation probability of the target area; based on the predicted meteorological precipitation probability of the target area, a meteorological precipitation probability forecast map of the target area is generated. The electronic device 500 may also include a user interface 508. Of course, Figure 6 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 6 One or more components of an electronic device are shown.

[0112] Figure 7 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 7 As shown, a computer-readable storage medium 600 according to one embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, the data processing and analysis method of multi-source numerical forecast data based on cloud-edge fusion according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0113] When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are performed.

[0114] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A data processing and analysis method for multi-source numerical forecast data based on cloud-edge fusion, characterized by: include: Obtain general meteorological data from several weather forecast systems and establish a meteorological multi-source numerical forecast database; Create decoding objects for various general meteorological data and form a general meteorological data decoding pool; Based on the meteorological general data decoding pool and the meteorological multi-source numerical forecast database, a unified meteorological multi-source numerical forecast data set is obtained; Based on the unified data set of meteorological multi-source numerical forecast, data is labeled according to unit time to construct a unified time series data set of meteorological multi-source numerical forecast; The unified time series data set of meteorological multi-source numerical prediction is integrated according to the meteorological multi-source numerical prediction model to predict the meteorological precipitation probability in the target area; Based on the predicted meteorological precipitation probability of the target area, a meteorological precipitation probability forecast map of the target area is generated; Among them, based on the fitting and matching of the meteorological general data decoding pool and the meteorological multi-source numerical forecast database, the unified meteorological multi-source numerical forecast data set is obtained, which specifically includes: Based on the meteorological general data decoding pool, a meteorological general data decoding public class is established with each meteorological general data decoding method; The meteorological general data decoding public class is used as the outer loop, and the meteorological multi-source numerical forecast data is nested as the inner loop. According to the matching formula, the matching index between each meteorological multi-source numerical forecast data and each meteorological general data decoding method is calculated as the decoding loop stop condition, and the data decoding demand function is established; According to the data decoding requirement function, the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method is obtained, a data decoding model is constructed, and a unified meteorological multi-source numerical forecast data set is generated; The matching formula is specifically: ； Where, is the jth general meteorological data decoding method, is the kth dimension parameter value in the i-th meteorological multi-source numerical forecast data, is the kth dimension parameter value in the jth meteorological general data decoding method, is the weight of the k-th dimension parameter value; , for meteorological multi-source numerical prediction, the unified time series data set is integrated according to the meteorological multi-source numerical forecast model to predict the meteorological precipitation probability in the target area; Based on the unified time series data of meteorological multi-source numerical prediction, the correlation factors are screened according to the meteorological precipitation type of the target area to be predicted, and the time series data of meteorological multi-source numerical prediction influence is obtained; Based on the meteorological multi-source numerical prediction impact time series data, data alignment is performed according to the spatial resolution and temporal resolution, the temporal resolution is used as the observation window, the meteorological multi-source numerical prediction impact time series data within the spatial resolution is used as the observation attribute, and the meteorological characteristic time series parameters of the target area to be predicted are obtained through a sliding window; the spatial resolution is the target distance, and the temporal resolution is hours; According to the meteorological characteristic time series parameters of the target area to be predicted, the optimal meteorological precipitation type characteristic time series parameters are screened and integrated for the posterior probability of the unified time series data of the meteorological multi-source numerical prediction of the target area, and a multi-mode meteorological characteristic time series parameter set of the target area to be predicted is obtained; Based on the multi-mode meteorological characteristic time series parameter set of the target area to be predicted, a meteorological prediction model for the target area is constructed to predict the meteorological precipitation probability of the target area; The posterior probability of the meteorological characteristic time series parameters of the target area to be predicted for the unified time series data of the multi-source numerical prediction of the meteorological target area is specifically: ； Where, is the posterior probability of the characteristic parameter of the vth meteorological precipitation type under the tth unit time of the uth target area to be predicted, is the conditional probability of the uth target area at the tth unit time given the vth meteorological precipitation type characteristic parameter, is the prior probability of the u-th target area to be predicted under the t-th unit time, is the overall probability distribution of characteristic parameters of the vth meteorological precipitation type; The target area's weather forecast model is specifically: ； Where, is the meteorological precipitation probability of the uth target area to be predicted under the tth unit time, is an indicator function, if ,but =1, indicating that precipitation occurred in the area at that time point. ,but =0, indicating that there is no precipitation in the area at that time point, T is the precipitation threshold, is the number of parameters in the multi-mode meteorological characteristic time series parameter set of the target area to be predicted.

2. The data processing and analysis method for multi-source numerical forecast data based on cloud-edge fusion according to claim 1 is characterized in that: According to the data decoding requirement function, the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method is obtained, a data decoding model is constructed, and a unified meteorological multi-source numerical forecast data set is generated. Specifically, the following steps are involved: Based on the meteorological multi-source numerical forecast data corresponding to the meteorological general data decoding method as the root node, and each meteorological general data decoding method as the decoding branch node, a meteorological multi-source numerical forecast data decoding demand decision tree is established, and the data decoding model is connected in series. Based on the data decoding model, the meteorological multi-source numerical forecast database is used as input, and the meteorological multi-source numerical forecast data is divided according to the maximum gain ratio of the decoding branch nodes to generate a unified meteorological multi-source numerical forecast data set.

3. The data processing and analysis method for multi-source numerical forecast data based on cloud-edge fusion according to claim 2 is characterized in that: The maximum gain ratio of the decoding branch nodes based on the meteorological multi-source numerical forecast data is divided into: ； Where, is the maximum gain ratio of meteorological multi-source numerical forecast data D to decoding branch node A, is the information gain of the meteorological multi-source numerical forecast data D for the decoding branch node A, is the attribute entropy of the meteorological multi-source numerical forecast data D for the decoding branch node A, Divide the branch nodes into subsets for matching the jth meteorological general data decoding method for meteorological multi-source numerical forecast data, is the total set of meteorological multi-source numerical forecast data, is a logarithmic function, is the total number of decoding branch nodes A.

4. The data processing and analysis method for multi-source numerical forecast data based on cloud-edge fusion according to claim 3 is characterized in that: Based on the unified time series data of meteorological multi-source numerical prediction, the correlation factors are screened according to the meteorological precipitation type of the target area to be predicted, and the time series data of meteorological multi-source numerical prediction influencing factors are obtained, including: Determine the meteorological precipitation type attention preference influencing factors of the target area to be detected, and establish the meteorological precipitation type preference array of the target area to be detected; Using linear algebra, linear mapping is performed on the meteorological precipitation type preference array of the target area to be detected to obtain the meteorological precipitation type preference vector matrix Ns of the target area to be detected; ； in, Match the feature vector of the vth meteorological precipitation type of the uth target area to be predicted, is the total number of target areas to be predicted, is the total number of meteorological precipitation types; For the unified time series data of meteorological multi-source numerical prediction, data dimension reduction is performed according to the principal component analysis method to obtain the unified time series dimension reduction data of meteorological multi-source numerical prediction; Based on the similarity between the unified time series dimensionality reduction data of meteorological multi-source numerical prediction and each element in the meteorological precipitation type preference vector matrix of the target area to be detected, positive correlation factors are screened to obtain the time series data of meteorological multi-source numerical prediction; The similarity between the unified time series dimensionality reduction data of the meteorological multi-source numerical prediction and each element in the meteorological precipitation type preference vector matrix of the target area to be detected is specifically: ； Where, It is the similarity between the unified time series dimensionality reduction data of the meteorological multi-source numerical prediction of the u-th target area to be predicted and the v-th meteorological precipitation type preference vector of the u-th target area to be detected.

5. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data processing and analysis method for multi-source numerical forecast data of cloud-edge fusion as described in any one of claims 1-4.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data processing and analysis method of multi-source numerical forecast data of cloud-edge fusion described in any one of claims 1-4 is implemented.

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