阅读说明：本技术 一种基于雷达图的降水预测方法以及装置 (Rainfall prediction method and device based on radar map ) 是由 张志远 黄耀海 李进 于 2020-12-30 设计创作，主要内容包括：本申请公开了一种基于雷达的降水预测方法以及装置,方法包括：获取雷达图,对雷达图中的微团进行检测,微团包括雷达图中的边缘点；对微团的运动方向进行检测,并将检测结果作为云团运动方向；根据云团运动方向确定云团的移动区域,并对移动区域和云团所在区域进行降水预测,得到预测结果。通过对雷达图中边缘点构建的微团的运动范围进行检测,进而检测出云团的运动方向,将云团的运动方向结合外推后的雷达图,预测云团的移动区域内的降水情况,有效降低降水预测的误报率或者漏报率,提高降水预测的准确率和降水预测性能。(The application discloses rainfall prediction method and device based on radar, and the method comprises the following steps: acquiring a radar map, and detecting a micelle in the radar map, wherein the micelle comprises an edge point in the radar map; detecting the moving direction of the micro-cluster, and taking the detection result as the moving direction of the cloud cluster; and determining a moving area of the cloud cluster according to the movement direction of the cloud cluster, and predicting precipitation in the moving area and the area where the cloud cluster is located to obtain a prediction result. The movement range of the micro-cluster constructed by the edge points in the radar map is detected, so that the movement direction of the cloud cluster is detected, the movement direction of the cloud cluster is combined with the radar map after extrapolation, the precipitation condition in the moving area of the cloud cluster is predicted, the false alarm rate or the missing report rate of precipitation prediction is effectively reduced, and the accuracy rate of precipitation prediction and the precipitation prediction performance are improved.)

1. A radar map-based precipitation prediction method, comprising:

acquiring a radar map, and detecting a micro-cluster in the radar map, wherein the micro-cluster comprises edge points in the radar map;

detecting the motion direction of the micro-cluster, and determining the motion direction of the cloud cluster according to the detection result;

and determining a moving area of the cloud cluster according to the motion direction of the cloud cluster, and predicting precipitation of the moving area and the area where the cloud cluster is located to obtain a prediction result.

2. The method of claim 1, wherein detecting the moving direction of the micro-cluster and determining the moving direction of the cloud cluster according to the detection result comprises:

detecting the area where the micro-cluster is located at the time tAverage gray value V of_tAverage gray value V at time t-1_t-1T is greater than or equal to 1;

at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the advancing direction of the cloud cluster under the condition that the difference value of the micro-cluster is larger than the threshold value.

3. The method of claim 2, further comprising:

at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the cloud cluster leaving direction when the difference value of (a) is less than or equal to the threshold value.

4. The method of claim 1, wherein detecting the moving direction of the micro-cluster and determining the moving direction of the cloud cluster according to the detection result comprises:

and detecting the high-altitude wind speed and the wind direction of the area where the micro-cluster is located at the time t, and taking the high-altitude wind speed and the wind direction as the moving direction and the moving speed of the cloud cluster.

5. The method of claim 1, wherein detecting the moving direction of the micro-cluster and determining the moving direction of the cloud cluster according to the detection result comprises:

and detecting user feedback information of the area where the micro-cluster is located within the time range of 0-t, wherein the user feedback information comprises a direction from no rain to rain and a direction from rain to no rain, the direction from no rain to rain is taken as a cloud cluster leaving direction, and the direction from rain to no rain is taken as a cloud cluster advancing direction.

6. The method of claim 1, wherein predicting precipitation for the moving area to obtain a prediction comprises:

processing the radar graph by using an extrapolation algorithm to obtain an extrapolated radar graph;

and in the extrapolated radar map, determining a precipitation prediction result of the moving area of the cloud cluster by reducing a precipitation threshold value in the moving area of the cloud cluster.

7. A radar map-based precipitation prediction device, comprising:

the micro-cluster detection module is used for acquiring a radar map and detecting micro-clusters in the radar map, wherein the micro-clusters comprise edge points in the radar map;

the cloud cluster movement direction detection module is used for detecting the movement direction of the micro cluster and determining the cloud cluster movement direction according to the detection result;

and the precipitation prediction module is used for determining a moving area of the cloud cluster according to the movement direction of the cloud cluster, and performing precipitation prediction on the moving area and the area where the cloud cluster is located to obtain a prediction result.

8. The apparatus of claim 7, wherein the cloud motion direction detection module comprises:

a first detection submodule for detecting an average gray value V of the region where the micelle is located at time t_tAverage gray value V at time t-1_t-1T is greater than or equal to 1; at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the advancing direction of the cloud cluster under the condition that the difference value of the micro-cluster is larger than the threshold value.

9. The apparatus of claim 8, wherein the cloud motion direction detection module comprises:

a second detection submodule for detecting at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the cloud cluster leaving direction when the difference value of (a) is less than or equal to the threshold value.

10. The apparatus of claim 7, wherein the cloud motion direction detection module comprises:

and the third detection submodule is used for detecting the high-altitude wind speed and the wind direction of the area where the micro-cluster is located at the time t, and taking the high-altitude wind speed and the wind direction as the moving direction and the moving speed of the cloud cluster.

11. The apparatus of claim 7, wherein the cloud motion direction detection module comprises:

and the fourth detection submodule is used for detecting user feedback information of the area where the micro-cluster is located within the time range of 0-t, wherein the user feedback information comprises a direction from no rain to rain and a direction from rain to no rain, the direction from no rain to rain is taken as a cloud cluster leaving direction, and the direction from rain to no rain is taken as a cloud cluster advancing direction.

12. The apparatus of claim 7, wherein the precipitation prediction module comprises:

the extrapolation algorithm submodule is used for processing the radar graph by using an extrapolation algorithm to obtain an extrapolated radar graph;

and the precipitation prediction submodule is used for determining a precipitation prediction result of the moving area of the cloud cluster by reducing a precipitation threshold value in the moving area of the cloud cluster in the extrapolated radar map.

13. An electronic device, comprising:

at least one processor; and a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

Technical Field

The application relates to the field of deep learning, in particular to the field of rainfall prediction.

Background

The current method for predicting precipitation through radar echo usually adopts a space-time extrapolation method to extrapolate a radar, and then judges whether each point is precipitated according to the extrapolated radar echo value. However, the extrapolated radar image has a certain error, especially in the marginal area, because in practical situations, the marginal area of the radar echo is usually the place where precipitation just starts or just ends, and such a local radar echo value is different from the central area of the radar echo value, and the situation in such an area is usually more complicated. However, the existing precipitation estimation method is essentially based on the application of a space-time radar extrapolation method, does not solve the problems of error accumulation and loss in the extrapolation process, and cannot well estimate the actual precipitation condition of the radar edge part.

Disclosure of Invention

The embodiment of the application provides a rainfall prediction method and device based on a radar map, which are used for solving the problems in the related art, and the technical scheme is as follows:

in a first aspect, an embodiment of the present application provides a precipitation prediction method based on a radar map, including:

acquiring a radar map, and detecting a micelle in the radar map, wherein the micelle comprises an edge point in the radar map;

detecting the moving direction of the micro-cluster, and determining the moving direction of the cloud cluster according to the detection result;

and determining a moving area of the cloud cluster according to the movement direction of the cloud cluster, and predicting precipitation in the moving area and the area where the cloud cluster is located to obtain a prediction result.

In one embodiment, detecting the moving direction of the micro-cluster and determining the moving direction of the cloud cluster according to the detection result comprises:

detecting the average gray value V of the area where the micelle is located at the time t_tAverage gray value V at time t-1_t-1T is greater than or equal to 1;

at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the advancing direction of the cloud cluster under the condition that the difference value of the micro-cluster is larger than the threshold value.

In one embodiment, the method further comprises:

at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the cloud cluster leaving direction under the condition that the difference value of the micro-cluster is smaller than or equal to the threshold value.

In one embodiment, detecting the moving direction of the micro-cluster and determining the moving direction of the cloud cluster according to the detection result comprises:

and detecting the high-altitude wind speed and the wind direction of the area where the micelle is located at the time t, and taking the high-altitude wind speed and the wind direction as the moving direction and the moving speed of the cloud cluster.

In one embodiment, detecting the moving direction of the micro-cluster and determining the moving direction of the cloud cluster according to the detection result comprises:

and detecting user feedback information of the area where the micro-cluster is located within the range of 0-t time, wherein the user feedback information comprises a direction from no rain to rain and a direction from rain to no rain, the direction from no rain to rain is taken as a cloud cluster leaving direction, and the direction from rain to no rain is taken as a cloud cluster advancing direction.

In one embodiment, the predicting precipitation for a moving area to obtain a prediction comprises:

processing the radar map by using an extrapolation algorithm to obtain an extrapolated radar map;

in the extrapolated radar map, a precipitation prediction result of the moving region of the cloud cluster is determined by reducing a precipitation threshold in the moving region of the cloud cluster.

In a second aspect, the present embodiment provides a radar map-based precipitation prediction apparatus, including:

the micro-cluster detection module is used for acquiring a radar map and detecting micro-clusters in the radar map, wherein the micro-clusters comprise edge points in the radar map;

the cloud cluster motion direction detection module is used for detecting the motion direction of the micro cluster and determining the motion direction of the cloud cluster according to the detection result;

and the precipitation prediction module is used for determining the moving area of the cloud cluster according to the movement direction of the cloud cluster and predicting precipitation in the moving area and the area where the cloud cluster is located to obtain a prediction result.

In one embodiment, the cloud cluster movement direction detection module comprises:

a first detection submodule for detecting the average gray value V of the region where the micelle is located at the time t_tAverage gray value V at time t-1_t-1T is greater than or equal to 1; at V_tAnd V_t-1In the case that the difference value of (A) is greater than the threshold value, the moving direction of the micro-cluster is determined asThe advancing direction of the cloud cluster.

In one embodiment, the cloud cluster movement direction detection module comprises:

a second detection submodule for detecting at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the cloud cluster leaving direction under the condition that the difference value of the micro-cluster is smaller than or equal to the threshold value.

In one embodiment, the cloud cluster movement direction detection module comprises:

and the third detection submodule is used for detecting the high-altitude wind speed and wind direction of the area where the micro-cluster is located at the time t, and taking the high-altitude wind speed and the wind direction as the movement direction and the movement speed of the cloud cluster.

In one embodiment, the cloud cluster movement direction detection module comprises:

and the fourth detection submodule is used for detecting user feedback information of the area where the micro-cluster is located within the time range of 0-t, wherein the user feedback information comprises a direction from no rain to rain and a direction from rain to no rain, the direction from no rain to rain is taken as a cloud cluster leaving direction, and the direction from rain to no rain is taken as a cloud cluster advancing direction.

In one embodiment, the precipitation prediction module comprises:

the extrapolation algorithm submodule is used for processing the radar map by using an extrapolation algorithm to obtain an extrapolated radar map;

and the precipitation prediction submodule is used for determining a precipitation prediction result of the moving area of the cloud cluster by reducing a precipitation threshold value in the moving area of the cloud cluster in the extrapolated radar map.

In a third aspect, an electronic device is provided, including:

at least one processor; and a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the methods described above.

In a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any of the above.

One embodiment in the above application has the following advantages or benefits: the movement range of the micro-cluster constructed by the edge points in the radar map is detected, so that the movement direction of the cloud cluster is detected, the movement direction of the cloud cluster is combined with the radar map after extrapolation, the precipitation condition in the moving area of the cloud cluster is predicted, the false alarm rate or the missing report rate of precipitation prediction is effectively reduced, and the accuracy rate of precipitation prediction and the precipitation prediction performance are improved.

Other effects of the above-described alternative will be described below with reference to specific embodiments.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

FIG. 1 is a flow chart of a prior art precipitation prediction method provided in accordance with the present application;

FIG. 2 is a schematic illustration of a radar map based precipitation prediction method according to an embodiment of the present application;

FIG. 3 is a flow chart of edge point detection according to another embodiment of the present application;

FIG. 4 is a diagram illustrating a background difference-based micro-cluster motion detection scenario according to an embodiment of the present application;

FIG. 5 is a schematic illustration of a radar map based precipitation prediction method according to another embodiment of the present application;

FIG. 6 is a block diagram of a radar map based precipitation prediction apparatus according to an embodiment of the present application;

fig. 7 is a block diagram of an electronic device for implementing a radar map based precipitation prediction method according to an embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

As shown in fig. 1, in the flow chart of primary precipitation prediction, a radar map is obtained, precipitation is judged according to the radar map, and finally, a precipitation map is output. However, in an actual process, most of the current radar precipitation judgment methods are uniformly applied to the whole radar chart, and based on the same precipitation radar echo precipitation judgment algorithm, the judgment of precipitation starting time is missed, and meanwhile, false alarm is generated in an area when precipitation is finished. The cloud motion is not rigid, changes such as living and disappearing exist in the motion process, phenomena such as cloud dissipation and the like are more obvious, the motion is more irregular, the accuracy of forecast rainfall at the cloud edge part is lower, and errors in radar extrapolation prediction can be caused. Therefore, if the precipitation prediction performance is to be improved, the whole cloud part and the edge part of the cloud need to be separately processed, and how to identify the two parts of the cloud part and the edge part becomes a key.

The embodiment provides a precipitation prediction method based on micelle direction detection, which is used for detecting whole and edge precipitation areas of a cloud. The cloud motion is represented by the motion of the micro-cluster, so that an upcoming place and an upcoming leaving place of the cloud are identified, and the accuracy of prediction when precipitation starts and precipitation ends is improved by finely processing the places.

In a specific embodiment, as shown in fig. 2, there is provided a radar-based precipitation prediction method, comprising the steps of:

step S110: acquiring a radar map, and detecting a micelle in the radar map, wherein the micelle comprises an edge point in the radar map;

step S120: detecting the moving direction of the micro-cluster, and taking the detection result as the moving direction of the cloud cluster;

step S130: and determining a moving area of the cloud cluster according to the movement direction of the cloud cluster, and predicting precipitation in the moving area and the area where the cloud cluster is located to obtain a prediction result.

In one example, the optical flow method needs to extract feature points of a representative object, but because the change between different frames of a cloud layer is large, similar feature points are difficult to find and are based on the overall rigid transformation of the object, but the movement of the cloud is always accompanied with growth and dissipation, and meanwhile, the movement of the cloud at edge points is in various directions, and the movement in a uniform direction by the optical flow method cannot meet the change requirement of the edge. Therefore, in the embodiment, the cloud movement direction is represented by the movement direction of the "micro-cluster" mainly concentrated on the edge part of the cloud, and the cloud movement direction can be used as a feature to be added into precipitation prediction or training.

First, a radar map, which may be a radar return reflectivity picture, is acquired. The radar echo reflectivity picture at the previous t moment can be predicted to obtain a radar echo reflectivity picture at the t + N moment through a pre-trained space-time extrapolation model, such as a (convlstm) multilayer recurrent neural network model and the like. The radar echo reflectivity picture at the previous t moment needs to go through a micro-cluster detection program, wherein the micro-cluster represents a single point or a plurality of points which are gathered together to represent the motion trend of the cloud cluster. In this embodiment, edge points of the radar map are detected, and a cloud cluster including the edge points is marked as a "micro cluster". It is particularly noted that all edge points may be taken as edge pictures of the radar. The edge point detection algorithm may be based on the commonly used sober or cannon edge detection algorithms. The edge point detection flowchart is shown in fig. 3.

Then, the direction of movement of the "micelle" is detected. The background difference-based method for detecting the motion of the micelles as shown in fig. 4 can be used, that is, the radar reflectivity value on the path of the motion direction of the micelles is always at a very low value before the micelles arrive, so that the direction in which the gradient of the micelles is the largest is the direction of the motion of the cloud clusters. Of course, the method is not limited to the radar echo itself, and can also be described by combining other element characteristics (such as high wind speed and wind direction of the mode data) with the radar echo edge. And taking the detection result as the moving direction of the cloud cluster, such as the advancing direction of the cloud cluster or the leaving direction of the cloud cluster. The direction of motion of different micelles may be different.

And finally, determining the moving area of the cloud cluster according to the moving direction of the cloud cluster. And (4) carrying out precipitation prediction on the moving area and the area where the cloud cluster is located to obtain a prediction result. The rainfall prediction method not only predicts the rainfall of the current position of the cloud cluster, but also predicts the rainfall of a moving area to which the cloud cluster is about to move. The movement direction of the cloud cluster is reflected and detected through the change of the radar echo value, so that the area to which the cloud moves can be detected through the change of the radar echo value. And processing the radar map by using an extrapolation algorithm to obtain an extrapolated radar map. In the extrapolated radar map, the precipitation threshold determination for the area in which the cloud cluster is about to move is adjusted, and in order to increase the recall rate of precipitation forecast, the precipitation threshold for that area is usually decreased. Determining a precipitation prediction result for the moving region of the cloud by lowering a precipitation threshold in the moving region of the cloud. The area estimated by the micelle detection motion is processed in a targeted mode to improve the accuracy of precipitation starting, the data of the national sampling point beginning to rain and the data of the national sampling point stopping rain in 1 month to 10 months in 2020 are collected as test samples, and the final test result is as follows:

	detection of motion by addition of micelles	Detection of motion without micelles
			Rate of accuracy	0.643	0.602

In the embodiment, the moving range of the micro-cluster constructed by the edge points in the radar map is detected, so that the moving direction of the cloud cluster is detected, the moving direction of the cloud cluster is combined with the radar map after extrapolation, the precipitation condition in the moving area of the cloud cluster is predicted, the false alarm rate or the missing report rate of precipitation prediction is effectively reduced, and the accuracy rate and the precipitation prediction performance of precipitation prediction are improved.

In one embodiment, step S120: the method comprises the following steps:

step S121: detecting the average gray value V of the area where the micelle is located at the time t_tAverage gray value V at time t-1_t-1T is greater than or equal to 1;

step S122: at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the advancing direction of the cloud cluster under the condition that the difference value of the micro-cluster is larger than the threshold value.

In one embodiment, the method further comprises:

step S123: at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the cloud cluster leaving direction under the condition that the difference value of the micro-cluster is smaller than or equal to the threshold value.

In one example, as shown in fig. 5, it is assumed that the average grayscale value of the detected region in which the micelle B is located at time t is V_tThe average gray value at time t-1 is V_t-1By comparison of V_tAnd V_t-1And judging whether the micro-cluster is in the movement direction of the radar echo or the radar leaving direction. Since the cloud is represented by radar signals, the direction of motion of the cloud is the direction of motion of the radar echo. At V_tAnd V_t-1And determining the moving direction of the micro-cluster as the advancing direction of the cloud cluster under the condition that the difference value of the micro-cluster is larger than the threshold value. At V_tAnd V_t-1And determining the moving direction of the micro-cluster as the cloud cluster leaving direction under the condition that the difference value of the micro-cluster is smaller than or equal to the threshold value. The threshold is a preset threshold.

In one embodiment, step S120: the method comprises the following steps:

step S124: and detecting the high-altitude wind speed and the wind direction of the area where the micelle is located at the time t, and taking the high-altitude wind speed and the wind direction as the moving direction and the moving speed of the cloud cluster.

In one example, as shown in FIG. 5, the movement of the micelles may be described using other weather elements, such as high altitude wind speed and direction. In the embodiment, the high-altitude wind speed and direction at the time T are added on the basis of the previous embodiment, and the high-altitude wind speed and direction at the corresponding position of each micro-cluster are found as the moving direction and speed of the cloud cluster. Finally, the cloud movement range at each time is obtained.

In one embodiment, step S120: the method comprises the following steps:

step S125: and detecting user feedback information of the area where the micro-cluster is located within the range of 0-t time, wherein the user feedback information comprises a direction from no rain to rain and a direction from rain to no rain, the direction from no rain to rain is taken as a cloud cluster leaving direction, and the direction from rain to no rain is taken as a cloud cluster advancing direction.

In one embodiment, step S130 includes:

step S131: processing the radar map by using an extrapolation algorithm to obtain an extrapolated radar map;

step S132: in the extrapolated radar map, a precipitation prediction result of the moving region of the cloud cluster is determined by reducing a precipitation threshold in the moving region of the cloud cluster.

In one example, if the area where the radar motion advances is finally obtained is set as area a, when a radar extrapolated picture is obtained, the precipitation probability for all points in area a is weighted by a weight to increase the probability that it forecasts precipitation. a is a changeable hyper-parameter setting. Similarly, for the area where the radar leaves, the weight of the precipitation probability of the radar is reduced, and the probability of forecasting is reduced. The precipitation probability is the probability of judging that the area is enough to precipitate, for example, the calculated precipitation probability value is 0.6, the previously set precipitation threshold value is 0.5, and then the system judges that the precipitation threshold value is smaller and the precipitation probability is larger because 0.6> 0.5.

The embodiment provides a method for judging cloud motion edge trend based on the micro-cluster, and the finally obtained edge motion range information and radar reflectivity information obtained through extrapolation can be integrated together for judging precipitation through judgment of motion reversal of the edge micro-cluster, so that the accuracy of forecasting of precipitation starting or precipitation ending is improved.

In another embodiment, as shown in fig. 6, there is provided a radar map-based precipitation prediction apparatus, including:

the micelle detection module 110 is configured to obtain a radar map and detect a micelle in the radar map, where the micelle includes an edge point in the radar map;

the cloud cluster movement direction detection module 120 is used for detecting the movement direction of the micro cluster and determining the cloud cluster movement direction according to the detection result;

and the precipitation prediction module 130 is configured to determine a moving area of the cloud cluster according to the movement direction of the cloud cluster, and predict precipitation in the moving area and the area where the cloud cluster is located to obtain a prediction result.

In one embodiment, the cloud cluster movement direction detection module comprises:

a first detection submodule for detecting the average gray value V of the region where the micelle is located at the time t_tAverage gray value V at time t-1_t-1T is greater than or equal to 1; at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the advancing direction of the cloud cluster under the condition that the difference value of the micro-cluster is larger than the threshold value.

In one embodiment, the cloud cluster movement direction detection module comprises:

a second detection submodule for detecting at V_tAnd V_t-1And determining the moving direction of the micro-cluster as the cloud cluster leaving direction under the condition that the difference value of the micro-cluster is smaller than or equal to the threshold value.

In one embodiment, the cloud cluster movement direction detection module comprises:

and the third detection submodule is used for detecting the high-altitude wind speed and wind direction of the area where the micro-cluster is located at the time t, and taking the high-altitude wind speed and the wind direction as the movement direction and the movement speed of the cloud cluster.

In one embodiment, the cloud cluster movement direction detection module comprises:

and the fourth detection submodule is used for detecting user feedback information of the area where the micro-cluster is located within the time range of 0-t, wherein the user feedback information comprises a direction from no rain to rain and a direction from rain to no rain, the direction from no rain to rain is taken as a cloud cluster leaving direction, and the direction from rain to no rain is taken as a cloud cluster advancing direction.

In one embodiment, the precipitation prediction module comprises:

the extrapolation algorithm submodule is used for processing the radar map by using an extrapolation algorithm to obtain an extrapolated radar map;

and the precipitation prediction submodule is used for determining a precipitation prediction result of the moving area of the cloud cluster by reducing a precipitation threshold value in the moving area of the cloud cluster in the extrapolated radar map.

The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.

According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.

Fig. 7 is a block diagram of an electronic device for a radar map-based precipitation prediction method according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.

As shown in fig. 7, the electronic apparatus includes: one or more processors 701, a memory 702, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display Graphical information for a Graphical User Interface (GUI) on an external input/output device, such as a display device coupled to the Interface. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 7, one processor 701 is taken as an example.

The memory 702 is a non-transitory computer readable storage medium as provided herein. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform a radar map based precipitation prediction method as provided herein. A non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform a radar map-based precipitation prediction method provided herein.

The memory 702, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to a radar map based precipitation prediction method in embodiments of the present application. The processor 701 executes various functional applications of the server and data processing by executing non-transitory software programs, instructions and modules stored in the memory 702, so as to implement a radar map-based precipitation prediction method in the above method embodiment.

The memory 702 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an electronic device of a radar map-based precipitation prediction method, and the like. Further, the memory 702 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 702 may optionally include memory located remotely from the processor 701, which may be connected to the electronic devices via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The electronic device may further include: an input device 703 and an output device 704. The processor 701, the memory 702, the input device 703 and the output device 704 may be connected by a bus or other means, and fig. 7 illustrates an example of a connection by a bus.

The input device 703 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 704 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD) such as a Cr7 star display 7, a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, Integrated circuitry, Application Specific Integrated Circuits (ASICs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (Cathode Ray Tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.