Weather prediction method and device, computer equipment, storage medium and program product

Abstract

The invention relates to a weather prediction method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring first weather associated data acquired by vehicles in a target area and second weather associated data acquired by roadside equipment, and acquiring third weather associated data of the target area from a trusted data end; based on the third weather associated data, performing data fusion on the first weather associated data and the second weather associated data to obtain target fusion data; and on the basis of a weather prediction model, according to the target fusion data and the third weather associated data, determining weather prediction data of the target area in a future time period. By adopting the method, accurate prediction of weather can be realized.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to a weather forecasting method, apparatus, computer equipment, storage medium and program product. Background Art

[0002] With the rapid development of intelligent driving, Internet of Vehicles, cloud computing, and big data analysis technology, real-time weather monitoring and forecasting systems based on sensor networks have become a research hotspot.

[0003] However, the current real-time weather monitoring and forecasting systems have problems such as single data source and weak data source, which makes it impossible to achieve real-time monitoring and accurate forecasting of weather conditions. Summary of the invention

[0004] Based on this, it is necessary to provide a weather forecasting method, device, computer equipment, storage medium and program product to address the above technical problems, which can achieve accurate weather forecasting.

[0005] In a first aspect, the present application provides a weather prediction method, the method comprising:

[0006] Acquire first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal;

[0007] Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data;

[0008] Based on the weather forecast model, the weather forecast data of the target area in the future period is determined according to the target fusion data and the third weather correlation data.

[0009] In one embodiment, based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data, including:

[0010] Based on the third weather-related data, the first weather-related data and the second weather-related data are unified; the unified first weather-related data and the second weather-related data are fused to obtain first fused data; the first fused data is input into a hierarchical data warehouse model to obtain target fused data.

[0011] In one embodiment, the unified first weather-related data and the second weather-related data are fused to obtain first fused data, including:

[0012] The first weather-related data of different vehicles after unified processing are input into the multi-source heterogeneous fusion model to obtain second fused data; the second weather-related data of different roadside equipment after unified processing are input into the multi-source heterogeneous fusion model to obtain third fused data; the second fused data and the third fused data are fused to obtain first fused data.

[0013] In one embodiment, based on the weather forecast model, according to the target fusion data and the third weather correlation data, determining the weather forecast data of the target area in the future period includes:

[0014] Based on the third weather-related data, a meteorological data threshold in the weather prediction model is determined; the weather prediction model is controlled, and the target fusion data is processed based on the meteorological data threshold to obtain weather prediction data for the target area in the future period.

[0015] In one embodiment, based on the weather forecast model, according to the target fusion data and the third weather correlation data, determining the weather forecast data of the target area in the future period includes:

[0016] For any sub-region, the target weather data corresponding to the sub-region is obtained from the target fusion data; the target weather data and the third weather-related data corresponding to the sub-region are input into the weather prediction model to obtain the weather prediction data of the sub-region in the future period.

[0017] In one embodiment, obtaining first weather-related data collected by vehicles in a target area and second weather-related data collected by roadside equipment, and obtaining third weather-related data of the target area from a trusted data terminal include:

[0018] The first initial data collected from vehicles in the target area, the second initial data collected from the roadside equipment, and the third initial data of the target area obtained from the trusted data terminal are cleaned to obtain the first weather-related data, the second weather-related data, and the third weather-related data.

[0019] In a second aspect, the present application also provides a weather prediction device, the device comprising:

[0020] An acquisition module, used to acquire first weather-related data collected by vehicles in a target area and second weather-related data collected by roadside equipment, and to acquire third weather-related data of the target area from a trusted data terminal;

[0021] A fusion module, configured to fuse the first weather-related data and the second weather-related data based on the third weather-related data to obtain target fused data;

[0022] The prediction module is used to determine the weather prediction data of the target area in the future period based on the weather prediction model, according to the target fusion data and the third weather correlation data.

[0023] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0024] Acquire first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal;

[0025] Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data;

[0026] Based on the weather forecast model, the weather forecast data of the target area in the future period is determined according to the target fusion data and the third weather correlation data.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0028] Acquire first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal;

[0029] Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data;

[0030] Based on the weather forecast model, the weather forecast data of the target area in the future period is determined according to the target fusion data and the third weather correlation data.

[0031] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0032] Acquire first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal;

[0033] Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data;

[0034] Based on the weather forecast model, the weather forecast data of the target area in the future period is determined according to the target fusion data and the third weather correlation data.

[0035] The above-mentioned weather prediction method, device, computer equipment, storage medium computer program product, by acquiring the first weather-related data collected by vehicles in the target area and the second weather-related data collected by roadside equipment, and acquiring the third weather-related data of the target area from the trusted data terminal, realizes the multi-dimensional acquisition of weather-related data; further, based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain the target fused data; then, based on the weather prediction model, the weather forecast data of the target area in the future period is determined according to the target fused data and the third weather-related data. This scheme realizes the analysis of weather-related data from multiple monitoring dimensions by introducing the first weather-related data collected by vehicles, the second weather-related data collected by roadside equipment, and the third weather-related data obtained from the trusted data terminal, thereby ensuring the accuracy of the data; further, by fusing the weather-related data of multiple dimensions and performing weather forecasting based on the fused data, the accurate processing of the source data is realized, thereby ensuring the accuracy of the weather forecast. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] FIG1 is an application environment diagram of a weather forecasting method provided in an embodiment of the present application;

[0038] FIG2 is a schematic diagram of a weather forecast process provided in an embodiment of the present application;

[0039] FIG3 is a schematic diagram of a process for obtaining target fusion data provided in an embodiment of the present application;

[0040] FIG4 is a schematic diagram of a process for determining weather forecast data provided in an embodiment of the present application;

[0041] FIG5 is a schematic diagram of a flow chart of another weather forecasting method provided in an embodiment of the present application;

[0042] FIG6 is a structural block diagram of a weather forecasting device provided in an embodiment of the present application;

[0043] FIG. 7 is an internal structure diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] The weather prediction method provided in the embodiment of the present application can be applied in the application environment shown in FIG. 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 can be installed on the vehicle and the roadside to realize the real-time collection of weather-related data; then, the server 104 can obtain the first weather-related data collected by the vehicle in the target area and the second weather-related data collected by the roadside equipment through the terminal 102, and can directly obtain the third weather-related data of the target area through the trusted data terminal; then, the acquired data can be analyzed, and the weather forecast data of the target area in the future period can be determined according to the results of the analysis. Among them, the terminal 102 can be, but not limited to, various sensor devices and monitoring devices, etc., and the sensor devices can include, but are not limited to, temperature and humidity sensors, rainfall sensors, millimeter wave radars, visual sensors, and wind speed transmitters. Monitoring equipment can include, but are not limited to cameras, millimeter wave radars, environmental perception devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0046] In an exemplary embodiment, as shown in FIG. 2 , a weather prediction method is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and may include the following steps:

[0047] S201, obtaining first weather-related data collected by vehicles in a target area and second weather-related data collected by roadside equipment, and obtaining third weather-related data of the target area from a trusted data terminal.

[0048] Among them, the first weather-related data can represent the data related to the weather monitored by various sensor devices installed on the vehicle (such as temperature and humidity sensors, rainfall sensors, millimeter-wave radars, visual sensors and wind speed sensors, etc.); the second weather-related data can represent the data related to the weather monitored by various roadside devices installed on both sides of the road (such as cameras, millimeter-wave radars, environmental perception devices, etc.); the third weather-related data can represent the data related to the weather in the target area directly obtained from the trusted data terminal (such as the Meteorological Bureau and meteorological satellites, etc.).

[0049] Optionally, data of various meteorological parameters may be determined based on the first weather-related data, the second weather-related data, and the third weather-related data, wherein the meteorological parameters may include but are not limited to parameters such as temperature, air pressure, wind, humidity, cloud, and rainfall.

[0050] Exemplarily, terminal 102 may send weather-related data to server 104 at preset intervals and through a communication module; server 104 may then obtain first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment; and server 104 may send a request, such as a GET or POST request, to a trusted data terminal, and the trusted data terminal may perform corresponding processing based on the received request and return the processing result to server 104, so that server 104 may obtain third weather-related data of the target area.

[0051] S202: Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data.

[0052] Among them, the target fusion data can represent the data after the characteristics of the first weather-related data and the second weather-related data are integrated, which can more accurately represent the meteorological characteristics.

[0053] Exemplarily, the first weather-related data and the second weather-related data may be fused based on information such as consistent timestamps and geographic locations. Optionally, key features such as temperature, humidity, wind speed and wind direction may be extracted from the first weather-related data and the second weather-related data; further, the extracted key features may be matched with the third weather-related data based on information such as timestamps and geographic locations to ensure the accuracy of the key features and the temporal and spatial consistency of the data; further, the matched key features may be fused and calculated based on a fusion method (such as a statistical method, a machine learning method and / or a deep learning method) to obtain target fused data.

[0054] S203, based on the weather prediction model, determining the weather prediction data of the target area in the future period according to the target fusion data and the third weather correlation data.

[0055] Among them, the weather forecast model can be built through NWP (Numerical Weather Predictio), statistical learning methods and machine learning; weather forecast data can include but is not limited to time, temperature, humidity, rainfall, wind speed, wind direction and other factors.

[0056] Exemplarily, based on the weather forecast model, the target fusion data can be analyzed, and the weather in the target area in the future period can be predicted based on the analysis results; further, the predicted weather can be calibrated based on the third weather-related data, and the weather forecast data for the target area in the future period can be obtained to ensure accurate weather prediction.

[0057] The above-mentioned weather prediction method realizes multi-dimensional acquisition of weather-related data by acquiring the first weather-related data collected by vehicles in the target area and the second weather-related data collected by roadside equipment, and acquiring the third weather-related data of the target area from the trusted data terminal; further, based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data; then, based on the weather prediction model, the weather forecast data of the target area in the future period is determined according to the target fused data and the third weather-related data. This solution realizes analysis of weather-related data from multiple monitoring dimensions and ensures data accuracy by introducing the first weather-related data collected by vehicles, the second weather-related data collected by roadside equipment, and the third weather-related data obtained from the trusted data terminal; further, by fusing weather-related data of multiple dimensions and performing weather forecasting based on the fused data, accurate processing of source data is achieved, thereby ensuring the accuracy of weather forecasting.

[0058] Based on the above embodiment, the present application embodiment explains the above embodiment S202 in detail. Specifically, the process of obtaining target fusion data in the present application embodiment, as shown in FIG3, specifically includes the following steps:

[0059] S301, based on the third weather-related data, the first weather-related data and the second weather-related data are uniformly processed.

[0060] It should be noted that the first weather-related data is obtained through sensor devices installed on vehicles, but since the configurations of each vehicle may be different, the data forms monitored by each sensor device may be different, that is, the first weather-related data may include video data and electromagnetic wave data, etc. Therefore, it is necessary to unify the first weather-related data so that it can represent weather data in the same data form; correspondingly, the second weather-related data is obtained through various roadside devices installed on the road side, but the roadside devices are different, resulting in the data forms monitored by each roadside device may be different, that is, the second weather-related data may include video data and electromagnetic wave data, etc. Therefore, it is necessary to unify the second weather-related data.

[0061] Exemplarily, the first weather-related data and the second weather-related data can be uniformly processed based on a preset data format; further, the threshold of each meteorological parameter can be determined based on the third weather-related data, the first value of each meteorological parameter can be determined based on the first weather-related data after the uniform processing, and the second value of each meteorological parameter can be determined based on the second weather-related data after the uniform processing; and then, for each meteorological parameter, the first value and the second value of the meteorological parameter can be checked based on the threshold of the meteorological parameter, the first value and / or the second value that exceeds the threshold can be determined as abnormal data, and the abnormal data and the first weather-related data and the second weather-related data after the uniform processing related to the meteorological data can be eliminated to ensure the accuracy of the data.

[0062] S302, fusing the unified first weather-related data and the second weather-related data to obtain first fused data.

[0063] Among them, the first fusion data can represent the data after the first weather-related data and the second weather-related data are fused after unified processing based on the timestamp and geographic location information; optionally, the fusion processing process can be the process of performing mean processing on the first weather-related data and the second weather-related data based on the timestamp and geographic location information.

[0064] One feasible method is to input the first weather-related data of different vehicles after unified processing into a multi-source heterogeneous fusion model to obtain second fused data; input the second weather-related data of different roadside equipment after unified processing into a multi-source heterogeneous fusion model to obtain third fused data; and fuse the second fused data and the third fused data to obtain first fused data.

[0065] Among them, the second fused data can represent the data after the weather-related data collected from all vehicles (i.e., the first weather-related data) are fused and processed; the third fused data can represent the data after the weather-related data collected from all roadside equipment (i.e., the second weather-related data) are fused and processed.

[0066] Exemplarily, the first weather-related data of different vehicles after unified processing can be input into the multi-source heterogeneous fusion model, and the multi-source heterogeneous fusion model performs mean processing on the first weather-related data of different vehicles after unified processing according to the unified timestamp and unified geographic location information, and obtains second fused data; further, the second weather-related data of different roadside devices after unified processing can be input into the multi-source heterogeneous fusion model, and the multi-source heterogeneous fusion model performs fusion processing on them, and obtains third fused data; if roadside devices 1, roadside devices 2, , , , and roadside devices n can all monitor sub-area A in the target area, then the multi-source heterogeneous fusion model can perform mean processing on the second weather-related data of different roadside devices after unified processing corresponding to sub-area A.

[0067] Another possible implementation method is to directly input the unified first weather-related data and the second weather-related data into the fusion model, and the fusion model fuses the unified first weather-related data and the second weather-related data to finally obtain the first fusion data. Optionally, the fusion model can be built through a Bayesian network, and the fusion model can obtain a rainfall data set based on the unified first weather-related data and the second weather-related data: , each observation represents the rainfall per unit time, and n represents the number of rainfall data. The likelihood function of rainfall data can be expressed as:

[0068]

[0069] Among them, Exponential() can represent an exponential distribution; λ can represent the average intensity of rainfall per unit time.

[0070] Furthermore, the posterior probability distribution of parameter λ can be calculated by Bayes' theorem: , thereby obtaining an estimate of the probability distribution of rainfall and ultimately determining the first fused data.

[0071] S303, input the first fused data into the hierarchical data warehouse model to obtain target fused data.

[0072] Among them, the hierarchical data warehouse model can be constructed based on the historical fusion data and the historical third weather correlation data. The model can extract, convert and load the first fusion data, thereby realizing the hierarchical classification of the data, which is more convenient for data maintenance and management.

[0073] Exemplarily, the first fused data may be input into a hierarchical data warehouse model, and the hierarchical data warehouse model may analyze the first fused data in terms of time dimension and space dimension to obtain target fused data.

[0074] In the embodiment of the present application, by fusing the unified first weather-related data and the second weather-related data, a comprehensive analysis of the weather-related monitoring data from multiple dimensions is achieved, thereby improving the accuracy of weather forecasts.

[0075] Based on the above embodiment, the present application embodiment explains the above embodiment S203 in detail. Specifically, the present application embodiment involves a process of determining weather forecast data for a target area in a future period, as shown in FIG4 , and specifically includes the following steps:

[0076] S401, determining a meteorological data threshold in a weather prediction model based on third weather-related data.

[0077] The meteorological data threshold may represent the limit value of the meteorological parameter.

[0078] Exemplarily, based on the third weather-related data, mean calculation or extreme value selection and other processing can be performed on the meteorological data of each meteorological parameter, and the meteorological data threshold in the weather prediction model can be determined according to the processing result.

[0079] S402, controlling the weather prediction model, processing the target fusion data based on the meteorological data threshold, and obtaining the weather prediction data of the target area in the future period.

[0080] Among them, the weather prediction model can be built by multiple algorithms and networks, such as regression algorithms and convolutional neural networks. Optionally, the typhoon path can be predicted through the spatiotemporal convolutional neural network; the rainfall can be interpolated and predicted through the spatiotemporal kriging method in the regression algorithm to determine a detailed rainfall distribution map.

[0081] Exemplarily, the meteorological data threshold and the target fusion data can be input into the weather prediction model, which analyzes the target fusion data and predicts the weather in the target area in the future period based on the analysis results; further, the weather prediction model verifies the predicted weather based on the meteorological data threshold. If the verification fails, the model continues to return to the step of analyzing the target fusion data until the verification passes and the predicted weather data is determined.

[0082] In the embodiment of the present application, by determining the meteorological data threshold in the weather prediction model according to the third weather-related data, and determining the weather prediction data based on the meteorological data threshold, the accuracy of the weather prediction data is ensured.

[0083] Based on the above embodiment, the present application embodiment explains the above embodiment S203 in detail. Specifically, in the present application embodiment, the target area may include multiple sub-areas, and accordingly, the process of determining the weather forecast data of the target area in the future period includes the following steps:

[0084] For any sub-region, the target weather data corresponding to the sub-region is obtained from the target fusion data; the target weather data and the third weather-related data corresponding to the sub-region are input into the weather prediction model to obtain the weather prediction data of the sub-region in the future period.

[0085] It should be noted that by obtaining weather-related data in the target area, it is possible to obtain weather-related data in a small area (such as a sub-area within the target area) from a larger range (for example, some road test equipment in the target area is not installed in the sub-area, but the weather-related data in the sub-area can be monitored).

[0086] Exemplarily, for a sub-area T in the target area, the target weather data q corresponding to the sub-area T can be obtained from the target fusion data based on the geographic location information of the sub-area T; further, the target weather data q and the third weather-related data corresponding to the sub-area T can be input into the weather prediction model, which is analyzed by the weather prediction model to obtain the weather forecast data of the sub-area T in the future period.

[0087] In the embodiment of the present application, by introducing sub-regions, the weather in smaller areas can be predicted, thereby achieving accurate prediction of the weather in various regions.

[0088] Based on the above-mentioned embodiments, the embodiments of the present application explain the above-mentioned embodiment S201 in detail. Specifically, the embodiments of the present application involve the process of obtaining the first weather-related data collected by vehicles in the target area and the second weather-related data collected by roadside equipment, and obtaining the third weather-related data of the target area from a trusted data terminal, including the following steps: performing data cleaning on the first initial data collected by vehicles in the target area and the second initial data collected by roadside equipment, and obtaining the third initial data of the target area from the trusted data terminal, to obtain the first weather-related data, the second weather-related data, and the third weather-related data.

[0089] Exemplarily, the real-time collected data of each sensor on the vehicle (i.e., the first initial data) and the real-time monitoring data of each roadside equipment (i.e., the second initial data) can be denoised and filtered to complete data cleaning; and the third initial data sent by the trusted data terminal can be denoised and the measurement unit can be unified to complete the cleaning operation of the third initial data.

[0090] In the embodiment of the present application, by performing data cleaning on the initial data, the accuracy of the data is ensured, laying the foundation for the accuracy of the weather forecast results.

[0091] Based on the above embodiment, this embodiment provides an optional example of a weather forecasting method. As shown in FIG5 , the specific implementation process is as follows:

[0092] S501, performing data cleaning on the first initial data collected from vehicles in the target area, the second initial data collected from the roadside equipment, and the third initial data of the target area obtained from the trusted data terminal to obtain the first weather-related data, the second weather-related data, and the third weather-related data.

[0093] S502: Based on the third weather-related data, the first weather-related data and the second weather-related data are uniformly processed.

[0094] S503, inputting the unified first weather-related data of different vehicles into a multi-source heterogeneous fusion model to obtain second fusion data.

[0095] S504, inputting the unified second weather-related data of different roadside equipment into a multi-source heterogeneous fusion model to obtain third fusion data.

[0096] S505: Fuse the second fused data and the third fused data to obtain first fused data.

[0097] S506, input the first fused data into the hierarchical data warehouse model to obtain target fused data.

[0098] S507: Determine a meteorological data threshold in a weather prediction model based on the third weather-related data.

[0099] S508, controlling the weather prediction model to process the target fusion data based on the meteorological data threshold to obtain weather prediction data for the target area in the future period.

[0100] In one possible implementation method, the target area may include multiple sub-areas; accordingly, for any sub-area, the target weather data corresponding to the sub-area is obtained from the target fusion data; the target weather data and the third weather-related data corresponding to the sub-area are input into the weather prediction model to obtain the weather prediction data of the sub-area in the future period.

[0101] The specific process of the above S501-S508 can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0102] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0103] Based on the same inventive concept, the embodiment of the present application also provides a weather forecasting device for implementing the weather forecasting method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more weather forecasting device embodiments provided below can refer to the limitations on the weather forecasting method above, and will not be repeated here.

[0104] In an exemplary embodiment, as shown in FIG6 , a weather prediction device 1 is provided, comprising: an acquisition module 10, a fusion module 20 and a prediction module 30, wherein:

[0105] An acquisition module 10 is used to acquire first weather-related data collected by vehicles in a target area and second weather-related data collected by roadside equipment, and to acquire third weather-related data of the target area from a trusted data terminal;

[0106] A fusion module 20, configured to fuse the first weather-related data and the second weather-related data based on the third weather-related data to obtain target fused data;

[0107] The prediction module 30 is used to determine the weather prediction data of the target area in the future period based on the weather prediction model according to the target fusion data and the third weather correlation data.

[0108] In one embodiment, the fusion module 20 is specifically used for:

[0109] Based on the third weather-related data, the first weather-related data and the second weather-related data are unified; the unified first weather-related data and the second weather-related data are fused to obtain first fused data; the first fused data is input into a hierarchical data warehouse model to obtain target fused data.

[0110] In one embodiment, the fusion module 20 is further configured to:

[0111] The first weather-related data of different vehicles after unified processing are input into the multi-source heterogeneous fusion model to obtain second fused data; the second weather-related data of different roadside equipment after unified processing are input into the multi-source heterogeneous fusion model to obtain third fused data; the second fused data and the third fused data are fused to obtain first fused data.

[0112] In one embodiment, the prediction module 30 is specifically used for:

[0113] Based on the third weather-related data, a meteorological data threshold in the weather prediction model is determined; the weather prediction model is controlled, and the target fusion data is processed based on the meteorological data threshold to obtain weather prediction data for the target area in the future period.

[0114] In one embodiment, the prediction module 30 is further configured to:

[0115] For any sub-region, the target weather data corresponding to the sub-region is obtained from the target fusion data; the target weather data and the third weather-related data corresponding to the sub-region are input into the weather prediction model to obtain the weather prediction data of the sub-region in the future period.

[0116] In one embodiment, the acquisition module 10 is specifically used for:

[0117] The first initial data collected from vehicles in the target area, the second initial data collected from the roadside equipment, and the third initial data of the target area obtained from the trusted data terminal are cleaned to obtain the first weather-related data, the second weather-related data, and the third weather-related data.

[0118] Each module in the weather forecast device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0119] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG7. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store weather-related data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a weather prediction method is implemented.

[0120] Those skilled in the art will understand that the structure shown in FIG. 7 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0121] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0122] Acquire first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal;

[0123] Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data;

[0124] Based on the weather forecast model, the weather forecast data of the target area in the future period is determined according to the target fusion data and the third weather correlation data.

[0125] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0126] Based on the third weather-related data, the first weather-related data and the second weather-related data are unified; the unified first weather-related data and the second weather-related data are fused to obtain first fused data; the first fused data is input into a hierarchical data warehouse model to obtain target fused data.

[0127] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0128] The first weather-related data of different vehicles after unified processing are input into the multi-source heterogeneous fusion model to obtain second fused data; the second weather-related data of different roadside equipment after unified processing are input into the multi-source heterogeneous fusion model to obtain third fused data; the second fused data and the third fused data are fused to obtain first fused data.

[0129] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0130] Based on the third weather-related data, a meteorological data threshold in the weather prediction model is determined; the weather prediction model is controlled, and the target fusion data is processed based on the meteorological data threshold to obtain weather prediction data for the target area in the future period.

[0131] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0132] For any sub-region, the target weather data corresponding to the sub-region is obtained from the target fusion data; the target weather data and the third weather-related data corresponding to the sub-region are input into the weather prediction model to obtain the weather prediction data of the sub-region in the future period.

[0133] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0134] The first initial data collected from vehicles in the target area, the second initial data collected from the roadside equipment, and the third initial data of the target area obtained from the trusted data terminal are cleaned to obtain the first weather-related data, the second weather-related data, and the third weather-related data.

[0135] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0136] Acquire first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal;

[0137] Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data;

[0138] Based on the weather forecast model, the weather forecast data of the target area in the future period is determined according to the target fusion data and the third weather correlation data.

[0139] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0140] Based on the third weather-related data, the first weather-related data and the second weather-related data are unified; the unified first weather-related data and the second weather-related data are fused to obtain first fused data; the first fused data is input into a hierarchical data warehouse model to obtain target fused data.

[0141] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0142] The first weather-related data of different vehicles after unified processing are input into the multi-source heterogeneous fusion model to obtain second fused data; the second weather-related data of different roadside equipment after unified processing are input into the multi-source heterogeneous fusion model to obtain third fused data; the second fused data and the third fused data are fused to obtain first fused data.

[0143] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0144] Based on the third weather-related data, a meteorological data threshold in the weather prediction model is determined; the weather prediction model is controlled, and the target fusion data is processed based on the meteorological data threshold to obtain weather prediction data for the target area in the future period.

[0145] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0146] For any sub-region, the target weather data corresponding to the sub-region is obtained from the target fusion data; the target weather data and the third weather-related data corresponding to the sub-region are input into the weather prediction model to obtain the weather prediction data of the sub-region in the future period.

[0147] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0148] The first initial data collected from vehicles in the target area, the second initial data collected from the roadside equipment, and the third initial data of the target area obtained from the trusted data terminal are cleaned to obtain the first weather-related data, the second weather-related data, and the third weather-related data.

[0149] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0150] Acquire first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal;

[0151] Based on the third weather-related data, the first weather-related data and the second weather-related data are fused to obtain target fused data;

[0152] Based on the weather forecast model, the weather forecast data of the target area in the future period is determined according to the target fusion data and the third weather correlation data.

[0153] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0154] Based on the third weather-related data, the first weather-related data and the second weather-related data are unified; the unified first weather-related data and the second weather-related data are fused to obtain first fused data; the first fused data is input into a hierarchical data warehouse model to obtain target fused data.

[0155] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0156] The first weather-related data of different vehicles after unified processing are input into the multi-source heterogeneous fusion model to obtain second fused data; the second weather-related data of different roadside equipment after unified processing are input into the multi-source heterogeneous fusion model to obtain third fused data; the second fused data and the third fused data are fused to obtain first fused data.

[0157] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0158] Based on the third weather-related data, a meteorological data threshold in the weather prediction model is determined; the weather prediction model is controlled, and the target fusion data is processed based on the meteorological data threshold to obtain weather prediction data for the target area in the future period.

[0159] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0160] For any sub-region, the target weather data corresponding to the sub-region is obtained from the target fusion data; the target weather data and the third weather-related data corresponding to the sub-region are input into the weather prediction model to obtain the weather prediction data of the sub-region in the future period.

[0161] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0162] The first initial data collected from vehicles in the target area, the second initial data collected from the roadside equipment, and the third initial data of the target area obtained from the trusted data terminal are cleaned to obtain the first weather-related data, the second weather-related data, and the third weather-related data.

[0163] It should be noted that the information (including but not limited to vehicle equipment information, roadside equipment information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0165] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A weather forecasting method, characterized in that: The method comprises: Acquire first weather-related data collected by vehicles in a target area and second weather-related data collected by roadside equipment, and acquire third weather-related data of the target area from a trusted data terminal; Based on the third weather-related data, performing data fusion on the first weather-related data and the second weather-related data to obtain target fused data; Based on the weather prediction model, the weather prediction data of the target area in the future period is determined according to the target fusion data and the third weather-related data.

2. The method according to claim 1, characterized in that The step of fusing the first weather-related data and the second weather-related data based on the third weather-related data to obtain target fused data includes: Based on the third weather-related data, performing unified processing on the first weather-related data and the second weather-related data; Fusing the unified first weather-related data and the second weather-related data to obtain first fused data; The first fused data is input into a hierarchical data warehouse model to obtain target fused data.

3. The method according to claim 2, characterized in that The first weather-related data and the second weather-related data after the unified processing are fused to obtain the first fused data, including: Inputting the first weather-related data of different vehicles after unified processing into the multi-source heterogeneous fusion model to obtain second fusion data; Inputting the unified processed second weather-related data of different roadside equipment into the multi-source heterogeneous fusion model to obtain third fusion data; The second fused data and the third fused data are fused to obtain first fused data.

4. The method according to claim 1, characterized in that: The step of determining weather forecast data of the target area in a future period based on the weather forecast model and according to the target fusion data and the third weather-related data includes: Determining a meteorological data threshold in a weather prediction model based on the third weather-related data; The weather forecast model is controlled to process the target fusion data based on the meteorological data threshold to obtain weather forecast data for the target area in a future period.

5. The method according to claim 1, characterized in that The target area includes a plurality of sub-areas; The step of determining weather forecast data of the target area in a future period based on the weather forecast model and according to the target fusion data and the third weather-related data includes: For any sub-region, obtaining target weather data corresponding to the sub-region from the target fusion data; The target weather data and the third weather-related data corresponding to the sub-area are input into a weather prediction model to obtain weather prediction data for the sub-area in a future period.

6. The method according to claim 1, characterized in that The step of acquiring first weather-related data collected by vehicles in the target area and second weather-related data collected by roadside equipment, and acquiring third weather-related data of the target area from a trusted data terminal, includes: The first initial data collected from vehicles in the target area and the second initial data collected from the roadside equipment, as well as the third initial data of the target area obtained from the trusted data terminal are cleaned to obtain the first weather-related data, the second weather-related data and the third weather-related data.

7. A weather forecasting device, characterized in that: The device comprises: An acquisition module, used to acquire first weather-related data collected by vehicles in a target area and second weather-related data collected by roadside equipment, and to acquire third weather-related data of the target area from a trusted data terminal; a fusion module, configured to perform data fusion on the first weather-related data and the second weather-related data based on the third weather-related data to obtain target fused data; The prediction module is used to determine the weather prediction data of the target area in the future period based on the weather prediction model according to the target fusion data and the third weather-related data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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