A weather large model driven high-resolution frequency prediction system and method

Abstract

The application provides a weather large model driven high-resolution frequency prediction system and method, and belongs to the technical field of data processing, so as to realize more refined and higher-precision weather prediction. The method comprises the following steps: in the case that a weather prediction platform predicts the change of weather conditions of a first region in a low-frequency low-resolution prediction mode, if it is detected that the change of weather observation data of the first region exceeds a threshold value, the weather prediction platform determines to switch from the low-frequency low-resolution prediction mode to a high-frequency high-resolution prediction mode, and the first region is a service region of the weather prediction platform; the weather prediction platform predicts the change of weather conditions of a second region in the high-frequency high-resolution prediction mode to obtain a prediction result, and the second region is a subregion of the first region; and the weather prediction platform sends the prediction result to users in the second region.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a high-resolution frequency prediction system and method driven by a large meteorological model. Background Technology

[0002] In modern society, weather forecasting has become an indispensable part of our daily lives. From weather forecasts on television to weather alerts on mobile apps, we receive information about the future weather every day. However, traditional weather forecasts are often based on broad areas, such as entire cities or regions, which limits their accuracy to some extent. This article will explore current weather forecasting technologies and look ahead to future development directions. The core of weather forecasting lies in the monitoring and analysis of atmospheric conditions. Currently, meteorological departments mainly rely on equipment such as satellites, radars, and weather stations to collect data, and then perform calculations using numerical weather prediction models. These models are based on physical laws and atmospheric dynamics principles, and predict future weather changes through the processing and simulation of large amounts of meteorological data. Current forecasting methods are mostly based on broad areas, such as a specific city, county, or district, predicting weather changes in that area.

[0003] However, as technology continues to develop and user needs continue to change, this kind of broad-area weather forecast may not be able to meet future user needs. Summary of the Invention

[0004] This invention provides a high-resolution frequency prediction system and method driven by a large meteorological model to achieve more refined and accurate meteorological forecasts.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, a high-resolution frequency forecasting method driven by a large meteorological model is provided. The method is applied to a meteorological forecasting platform deployed at the edge. The method includes: when the meteorological forecasting platform forecasts changes in meteorological conditions in a first region using a low-frequency, low-resolution forecasting method, if it detects that changes in meteorological observation data in the first region exceed a threshold, the meteorological forecasting platform determines to switch from a low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method. The first region is the service area of ​​the meteorological forecasting platform. The meteorological forecasting platform forecasts changes in meteorological conditions in a second region using a high-frequency, high-resolution forecasting method, and obtains the forecast results. The second region is a sub-region of the first region. The meteorological forecasting platform sends the forecast results to users in the second region.

[0007] Optionally, the meteorological forecasting platform predicts the changes in meteorological conditions in the second region using a high-frequency, high-resolution forecasting method to obtain the forecast results, including: the meteorological forecasting platform acquiring the intentions of users in the first region; the meteorological forecasting platform dividing the first region into multiple sub-regions based on the intentions of users in the first region, where the intentions of users in each sub-region match, and the intentions of users in different sub-regions do not match, and the second region being any one of the multiple sub-regions; the meteorological forecasting platform predicting the changes in meteorological conditions in the second region at a high frequency to obtain the forecast results.

[0008] Optionally, the weather forecasting platform is a third-party application. The weather forecasting platform obtains the intent of users in the first area by requesting the operator's network to open the traffic data in the first area, thereby obtaining the traffic data of users in the first area. The traffic data of users in the first area is provided by the access network equipment of the serving cell in the operator's network covering the first area. The weather forecasting platform determines the intent of users in the first area by analyzing the traffic data of users in the first area.

[0009] Optionally, the traffic data of users in the first area includes: the packet header of each user's data packet in the first area and the information of the access network device accessed by each user in the first area, and the traffic data of users in the first area does not include: the payload of each user's data packet in the first area; the weather forecasting platform determines the intention of users in the first area by analyzing the traffic data of users in the first area, including: the intelligent agent built into the weather forecasting platform determines the intention of each user in the first area by analyzing the address information in the packet header of each user's data packet, and the intention of each user in the first area can indicate the operation currently performed by the corresponding user.

[0010] Optionally, the traffic data of users in the first area includes: traffic characteristics of the traffic data and information on the access network devices accessed by each user in the first area. The traffic characteristics indicate at least one of the following for each user's data packets in the first area: packet size distribution, packet sending interval, or traffic rate. The traffic data of users in the first area does not include: data packets of each user in the first area. The weather forecasting platform determines the intention of users in the first area by analyzing the traffic data of users in the first area, including: the intelligent agent built into the weather forecasting platform determines the intention of each user in the first area by analyzing the traffic characteristics. The intention of each user in the first area can indicate the operation currently performed by the corresponding user.

[0011] Optionally, the weather forecasting platform divides the first area into multiple sub-areas based on the intentions of users within the first area. This includes: the weather forecasting platform determining multiple types of users within the first area based on the intentions of each user within the first area, where each user type contains multiple users with matching intentions, indicating that multiple users are currently performing the same operation; and the weather forecasting platform dividing the first area into multiple sub-areas based on the location of each user type, where each sub-area covers one corresponding user type, and the location of each user in each user type is characterized by the location of the access network device to which the corresponding user is connected.

[0012] Optionally, the meteorological forecasting platform frequently forecasts changes in meteorological conditions in the second region to obtain forecast results, including: the meteorological forecasting platform determines the type of meteorological conditions corresponding to the user's intent in the second region, wherein for two mismatched intents, the two types of meteorological conditions corresponding to the two intents are different; the meteorological forecasting platform predicts changes in meteorological conditions in the second region at a high frequency according to the type of meteorological conditions corresponding to the user's intent in the second region, and obtains multiple preset results in sequence.

[0013] Optionally, the forecast results are multiple forecast results. The meteorological forecasting platform sends the forecast results to users in the second area, including: the meteorological forecasting platform sequentially sends multiple forecast results to the access network equipment corresponding to the second area. The access network equipment corresponding to the second area refers to the access network equipment whose service cell covers the second area.

[0014] Optionally, the meteorological forecasting platform sequentially sends multiple forecast results to the access network equipment corresponding to the second area, including: for any one of the multiple forecast results, the meteorological forecasting platform sends the forecast result and indication information to the access network equipment corresponding to the second area, and the indication information indicates that the forecast result needs to be forcibly broadcast to each user accessing the access network equipment corresponding to the second area.

[0015] Secondly, a high-resolution frequency forecasting system driven by a large meteorological model is provided. This system includes an edge-deployed meteorological forecasting platform, configured as follows: when the meteorological forecasting platform forecasts changes in meteorological conditions in a first region using a low-frequency, low-resolution forecasting method, if it detects that changes in meteorological observation data in the first region exceed a threshold, the meteorological forecasting platform determines to switch from a low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method. The first region is the service area of ​​the meteorological forecasting platform. The meteorological forecasting platform then forecasts changes in meteorological conditions in a second region using a high-frequency, high-resolution forecasting method, obtaining the forecast results. The second region is a sub-region of the first region. The meteorological forecasting platform then sends the forecast results to users within the second region.

[0016] Optionally, the meteorological forecasting platform predicts the changes in meteorological conditions in the second region using a high-frequency, high-resolution forecasting method to obtain the forecast results, including: the meteorological forecasting platform acquiring the intentions of users in the first region; the meteorological forecasting platform dividing the first region into multiple sub-regions based on the intentions of users in the first region, where the intentions of users in each sub-region match, and the intentions of users in different sub-regions do not match, and the second region being any one of the multiple sub-regions; the meteorological forecasting platform predicting the changes in meteorological conditions in the second region at a high frequency to obtain the forecast results.

[0017] Optionally, the weather forecasting platform is a third-party application. The weather forecasting platform obtains the intent of users in the first area by requesting the operator's network to open the traffic data in the first area, thereby obtaining the traffic data of users in the first area. The traffic data of users in the first area is provided by the access network equipment of the serving cell in the operator's network covering the first area. The weather forecasting platform determines the intent of users in the first area by analyzing the traffic data of users in the first area.

[0018] Optionally, the traffic data of users in the first area includes: the packet header of each user's data packet in the first area and the information of the access network device accessed by each user in the first area, and the traffic data of users in the first area does not include: the payload of each user's data packet in the first area; the weather forecasting platform determines the intention of users in the first area by analyzing the traffic data of users in the first area, including: the intelligent agent built into the weather forecasting platform determines the intention of each user in the first area by analyzing the address information in the packet header of each user's data packet, and the intention of each user in the first area can indicate the operation currently performed by the corresponding user.

[0019] Optionally, the traffic data of users in the first area includes: traffic characteristics of the traffic data and information on the access network devices accessed by each user in the first area. The traffic characteristics indicate at least one of the following for each user's data packets in the first area: packet size distribution, packet sending interval, or traffic rate. The traffic data of users in the first area does not include: data packets of each user in the first area. The weather forecasting platform determines the intention of users in the first area by analyzing the traffic data of users in the first area, including: the intelligent agent built into the weather forecasting platform determines the intention of each user in the first area by analyzing the traffic characteristics. The intention of each user in the first area can indicate the operation currently performed by the corresponding user.

[0020] Optionally, the weather forecasting platform divides the first area into multiple sub-areas based on the intentions of users within the first area. This includes: the weather forecasting platform determining multiple types of users within the first area based on the intentions of each user within the first area, where each user type contains multiple users with matching intentions, indicating that multiple users are currently performing the same operation; and the weather forecasting platform dividing the first area into multiple sub-areas based on the location of each user type, where each sub-area covers one corresponding user type, and the location of each user in each user type is characterized by the location of the access network device to which the corresponding user is connected.

[0021] Optionally, the meteorological forecasting platform frequently forecasts changes in meteorological conditions in the second region to obtain forecast results, including: the meteorological forecasting platform determines the type of meteorological conditions corresponding to the user's intent in the second region, wherein for two mismatched intents, the two types of meteorological conditions corresponding to the two intents are different; the meteorological forecasting platform predicts changes in meteorological conditions in the second region at a high frequency according to the type of meteorological conditions corresponding to the user's intent in the second region, and obtains multiple preset results in sequence.

[0022] Optionally, the forecast results are multiple forecast results. The meteorological forecasting platform sends the forecast results to users in the second area, including: the meteorological forecasting platform sequentially sends multiple forecast results to the access network equipment corresponding to the second area. The access network equipment corresponding to the second area refers to the access network equipment whose service cell covers the second area.

[0023] Optionally, the meteorological forecasting platform sequentially sends multiple forecast results to the access network equipment corresponding to the second area, including: for any one of the multiple forecast results, the meteorological forecasting platform sends the forecast result and indication information to the access network equipment corresponding to the second area, and the indication information indicates that the forecast result needs to be forcibly broadcast to each user accessing the access network equipment corresponding to the second area.

[0024] Thirdly, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the method described in the first aspect.

[0025] In one possible design, the electronic device described in the third aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the third aspect and other electronic devices.

[0026] In the embodiments of the present invention, the electronic device described in the third aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0027] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to perform the method described in the first aspect.

[0028] In summary, the above methods and systems have the following technical effects:

[0029] When a weather forecasting platform predicts changes in weather conditions in a first region using low-frequency, low-resolution forecasting, if it detects that changes in weather observation data in the first region exceed a threshold, indicating a potential for significant weather changes, the platform switches from low-frequency, low-resolution forecasting to high-frequency, high-resolution forecasting. This high-frequency, high-resolution forecast then predicts weather condition changes in sub-regions within the first region, yielding more frequent, higher-resolution forecasts for finer-grained areas. This results in more refined and accurate weather forecasts. Consequently, when the weather forecasting platform sends forecast results to users within the sub-regions of the first region, it can more precisely meet the needs of specific users. Attached Figure Description

[0030] Figure 1 A schematic diagram of the architecture of a high-resolution frequency prediction system driven by a large meteorological model provided in an embodiment of the present invention;

[0031] Figure 2 A flowchart illustrating a high-resolution frequency prediction method driven by a large meteorological model, provided in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0034] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0035] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0036] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0037] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0038] In the embodiments of this invention, "protocol" may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol to be applied in future systems. The embodiments of this invention do not specifically limit this.

[0039] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0040] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0041] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0042] To facilitate understanding of the embodiments of the present invention, firstly, using Figure 1 Taking the high-resolution frequency prediction system driven by a large meteorological model shown in the figure as an example, such as Figure 1 As shown, the high-resolution frequency forecasting system driven by the large meteorological model can include multiple meteorological forecasting platforms.

[0043] These multiple weather forecasting platforms can be deployed at the edge, meaning they are not deployed in the cloud (or control center), but rather near the business operations, i.e., at the edge. Each of these weather forecasting platforms can also be understood in terms of its form as a multi-access edge computing (MEC) platform, meaning each platform has computing power and can use that power to perform weather forecasting.

[0044] The physical form of each weather forecasting platform can be a terminal. A terminal can be a passive terminal, or it can be user equipment (UE), access terminal, subscriber unit, user station, mobile station (MS), mobile station, remote station, remote terminal, mobile device, user terminal, wireless communication equipment, user agent, or user device. The terminals in the embodiments of this application may be mobile phones, cellular phones, smartphones, tablets, wireless data cards, personal digital assistants (PDAs), wireless modems, handsets, laptop computers, machine type communication (MTC) terminals, computers with wireless transceiver capabilities, virtual reality (VR) terminals, augmented reality (AR) terminals, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical care, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, vehicle-mounted terminals, roadside units (RSUs) with terminal functions, etc. The terminal in this application may also be an on-board module, on-board unit, on-board component, on-board chip, or on-board unit that is built into the vehicle as one or more components or units. Alternatively, the terminal may also be customer-premises equipment (CPE).

[0045] In this embodiment, each weather forecasting platform has its corresponding service area. This means that each platform provides weather forecasting services within its designated service area. Since each platform is deployed within its own service area, forecast latency can be lower, data exchange volume can be larger, and real-time performance can be stronger. Different weather forecasting platforms correspond to different service areas. For ease of understanding, the following method embodiments will use any one of multiple weather forecasting platforms as an example.

[0046] Figure 2 This is a flowchart illustrating a high-resolution frequency prediction method driven by a large meteorological model, provided in an embodiment of the present invention. The method is applicable to the aforementioned high-resolution frequency prediction system driven by a large meteorological model and involves execution on any one of multiple meteorological prediction platforms. The specific process is as follows:

[0047] S201, if the meteorological forecasting platform detects that the change in meteorological observation data of the first region exceeds a threshold when it is forecasting changes in meteorological conditions of the first region using a low-frequency, low-resolution forecasting method, the meteorological forecasting platform determines to switch from a low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method.

[0048] The first area can be the service area of ​​the weather forecasting platform. The service area of ​​the weather forecasting platform can be a region at the service cell level, such as a collection of multiple service cells. Multiple service cells can be provided by different access network devices, which is the area that the operator network can understand, so as to facilitate the subsequent acquisition of traffic data from the operator network.

[0049] It should be understood that the "resolution" mentioned in the embodiments of this application does not refer to the resolution of weather forecasts on a map, that is, the "resolution" mentioned in the embodiments of this application does not refer to the traditional concept of "resolution" in the art. The "resolution" mentioned in the embodiments of this application refers to the granularity of the region when the weather forecasting platform performs weather forecasts, such as whether the weather forecast is performed on a first region or on a sub-region within the first region. The "resolution" mentioned in the embodiments of this application also refers to the fineness of the weather forecasting strategy, such as whether the same weather forecasting method is used for the first region or different weather forecasting methods are used based on the different intentions of users in different sub-regions. For details, please refer to the relevant descriptions below.

[0050] Therefore, the low-frequency, low-resolution forecasting method is to use conventional meteorological forecasting methods (i.e., relatively comprehensive meteorological condition forecasting) and then use a relatively long period (such as 4 hours or 6 hours) to make meteorological forecasts for the entire first region.

[0051] Meteorological observation data can be key changes in atmospheric conditions observed by satellites, radars in the first region, or ground stations in the first region, such as the initial formation of convection, the passage of fronts, and sudden changes in wind speed. When these key changes exceed the corresponding thresholds, such as sudden changes in wind speed exceeding the threshold, the meteorological forecasting platform will be triggered to switch from a low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method.

[0052] High-frequency, high-resolution forecasting can be achieved by using personalized weather forecasting (i.e., forecasting weather conditions to correspond to the user's intent, rather than comprehensive forecasting), and then using relatively short intervals (such as 5 minutes or 10 minutes) to forecast the weather for the entire sub-region within the first region. For details, please refer to the relevant introduction in S202.

[0053] S202, the meteorological forecasting platform predicts changes in meteorological conditions in the second region using high-frequency, high-resolution forecasting, and obtains the forecast results.

[0054] The second region is a sub-region of the first region. It can be any one of the multiple sub-regions of the first region. A sub-region can be a serving cell.

[0055] Weather forecasting platforms can obtain the intentions of users in the first region.

[0056] For example, a weather forecasting platform, being a third-party application (from the operator's network perspective), can request the operator's network to open up traffic data within a first area to obtain user traffic data within that area. This user traffic data is provided by access network equipment serving cells within the operator's network that cover the first area. For instance, the weather forecasting platform can send a traffic data access request to the operator's network. This request can include information about the first area, such as the identifiers of the various cells within that area. The operator's network can then use these cell identifiers to locate the corresponding access network equipment, which in turn provides the traffic data of the users it serves to the weather forecasting platform, thus enabling the access of traffic data within the first area.

[0057] The weather forecasting platform determines the intentions of users within the first region by analyzing their traffic data.

[0058] Specifically, in one possible approach, the traffic data of users within the first area may include: the packet header of each user's data packet within the first area and information about the access network device to which each user is connected. However, the traffic data of users within the first area does not include: the payload of each user's data packet, meaning that the user's private information in the payload is not disclosed to third parties to prevent privacy leaks. The packet header of each user's data packet within the first area may specifically include the corresponding destination address and / or destination port number, but the source address and source port number are deleted by the access network device, i.e., the user's information is not disclosed to third parties. Therefore, the agent built into the weather forecasting platform determines the intent of each user within the first area by analyzing the address information in the packet header of each user's data packet. The user's intent can be understood as a description of the user's behavior, or expressed as the user's behavioral intention. The intent of each user within the first area indicates the corresponding user's current operation, such as whether the user is driving, listening to navigation, watching a live game, attending a live concert, playing a mobile game, or watching videos on their phone, without specific restrictions.

[0059] Alternatively, in another possible approach, the traffic data for users within the first area may include: traffic characteristics of the traffic data and information about the access network devices accessed by each user within the first area. The traffic characteristics indicate at least one of the following for each user's data packets within the first area: packet size distribution, packet sending interval, or traffic rate. The traffic data for users within the first area does not include: data packets for each user within the first area, thus avoiding the leakage of user information. Therefore, the intelligent agent built into the weather forecasting platform determines the intent of each user within the first area by analyzing the traffic characteristics, and the intent of each user within the first area can indicate the corresponding user's current action.

[0060] It should be understood that an intelligent agent can be a functional entity with a large model (LLM) deployed, which can be used to analyze and process intent information, such as converting intent information into tokens and then processing them.

[0061] Therefore, the weather forecasting platform can divide the first region into multiple sub-regions based on the intentions of users within the first region. Within each sub-region, user intentions match (or users within each sub-region perform the same action, such as driving or watching a game). However, user intentions do not match across different sub-regions (or users in different sub-regions perform different actions, such as users in sub-region A driving while users in sub-region B are watching a game). For example, the weather forecasting platform can determine multiple user categories within the first region based on the intentions of each user. Each user category contains multiple users with matching intentions, indicating that multiple users are currently performing the same action. Then, the weather forecasting platform can further divide the first region into multiple sub-regions based on the location of each user category. Each sub-region covers one user category, and the location of each user within each category is represented by the location of the access network device to which that user is connected.

[0062] It should be understood that the second region is any one of multiple sub-regions. In other words, for any one of these sub-regions, the weather forecasting platform frequently predicts changes in weather conditions in the second region to obtain the forecast results. For example, the weather forecasting platform determines the type of weather conditions corresponding to a user's intent within the second region. For two mismatched intents, the two types of weather conditions corresponding to the intents are different. For instance, in sub-region A, one type of user is someone driving; their corresponding weather condition types could include wind speed, precipitation, and visibility. The weather forecasting platform could predict changes in wind speed, precipitation, and visibility in sub-region A within the next 5 minutes, based on meteorological observation data collected in the current period, using a 5-minute cycle. Similarly, in sub-region B, one type of user is someone watching a game live; their corresponding weather condition types could include minute-level predictions of the start and end times of rainfall. The weather forecasting platform could predict minute-level predictions of the start and end times of rainfall in sub-region B within the next 5 minutes, based on meteorological observation data collected in the current period, using a 5-minute cycle. For example, one category of users in sub-region C are those engaged in agricultural work. Their corresponding meteorological conditions could include hourly precipitation probability, sunshine intensity, and frost risk predictions. The meteorological forecasting platform could predict the hourly precipitation probability, sunshine intensity, and frost risk for sub-region C within the next 5 minutes, based on meteorological observation data collected in the current cycle, using a 5-minute cycle. In short, the meteorological forecasting platform predicts changes in meteorological conditions in the second region at a high frequency, according to the meteorological condition types corresponding to the users' intentions, thus obtaining multiple preset results.

[0063] This allows for weather forecasting based on user intent and different scenarios, thus meeting user needs in the event of special weather changes.

[0064] S203, the weather forecasting platform sends forecast results to users in the second region.

[0065] It is understandable that, since the above forecast results are multiple, the meteorological forecasting platform sequentially sends these multiple forecast results to the access network equipment corresponding to the second area. The access network equipment corresponding to the second area refers to the access network equipment whose service cell covers the second area. Specifically, for any one of the multiple forecast results, the meteorological forecasting platform sends the forecast result and an indication message to the access network equipment corresponding to the second area. The indication message indicates that the forecast result needs to be forcibly broadcast to every user accessing the access network equipment corresponding to the second area.

[0066] In summary, when the weather forecasting platform predicts changes in meteorological conditions in the first region using a low-frequency, low-resolution forecasting method, if it detects that changes in meteorological observation data in the first region exceed a threshold, indicating a potential for significant weather changes, the platform switches from a low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method. This high-frequency, high-resolution forecasting method then predicts the meteorological conditions in sub-regions of the first region, yielding more accurate forecasts. This means that more frequent, high-resolution weather forecasts are performed on finer-grained areas, resulting in more refined and accurate weather predictions. Consequently, when the weather forecasting platform sends forecast results to users within the sub-regions of the first region, it can more precisely meet the needs of specific users.

[0067] The above combination Figure 2 The method provided by the embodiments of the present invention has been described in detail. The following description, in conjunction with the system described above for performing the method provided by the embodiments of the present invention, is configured as follows:

[0068] If the meteorological forecasting platform predicts changes in meteorological conditions in the first region using a low-frequency, low-resolution forecasting method, and detects that the changes in meteorological observation data in the first region exceed a threshold, the meteorological forecasting platform will switch from a low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method. The first region is the service area of ​​the meteorological forecasting platform. The meteorological forecasting platform then predicts changes in meteorological conditions in the second region using a high-frequency, high-resolution forecasting method, obtaining the forecast results. The second region is a sub-region of the first region. The meteorological forecasting platform then sends the forecast results to users within the second region.

[0069] Optionally, the meteorological forecasting platform predicts the changes in meteorological conditions in the second region using a high-frequency, high-resolution forecasting method to obtain the forecast results, including: the meteorological forecasting platform acquiring the intentions of users in the first region; the meteorological forecasting platform dividing the first region into multiple sub-regions based on the intentions of users in the first region, where the intentions of users in each sub-region match, and the intentions of users in different sub-regions do not match, and the second region being any one of the multiple sub-regions; the meteorological forecasting platform predicting the changes in meteorological conditions in the second region at a high frequency to obtain the forecast results.

[0070] Optionally, the weather forecasting platform is a third-party application. The weather forecasting platform obtains the intent of users in the first area by requesting the operator's network to open the traffic data in the first area, thereby obtaining the traffic data of users in the first area. The traffic data of users in the first area is provided by the access network equipment of the serving cell in the operator's network covering the first area. The weather forecasting platform determines the intent of users in the first area by analyzing the traffic data of users in the first area.

[0071] Optionally, the traffic data of users in the first area includes: the packet header of each user's data packet in the first area and the information of the access network device accessed by each user in the first area, and the traffic data of users in the first area does not include: the payload of each user's data packet in the first area; the weather forecasting platform determines the intention of users in the first area by analyzing the traffic data of users in the first area, including: the intelligent agent built into the weather forecasting platform determines the intention of each user in the first area by analyzing the address information in the packet header of each user's data packet, and the intention of each user in the first area can indicate the operation currently performed by the corresponding user.

[0072] Optionally, the traffic data of users in the first area includes: traffic characteristics of the traffic data and information on the access network devices accessed by each user in the first area. The traffic characteristics indicate at least one of the following for each user's data packets in the first area: packet size distribution, packet sending interval, or traffic rate. The traffic data of users in the first area does not include: data packets of each user in the first area. The weather forecasting platform determines the intention of users in the first area by analyzing the traffic data of users in the first area, including: the intelligent agent built into the weather forecasting platform determines the intention of each user in the first area by analyzing the traffic characteristics. The intention of each user in the first area can indicate the operation currently performed by the corresponding user.

[0073] Optionally, the weather forecasting platform divides the first area into multiple sub-areas based on the intentions of users within the first area. This includes: the weather forecasting platform determining multiple types of users within the first area based on the intentions of each user within the first area, where each user type contains multiple users with matching intentions, indicating that multiple users are currently performing the same operation; and the weather forecasting platform dividing the first area into multiple sub-areas based on the location of each user type, where each sub-area covers one corresponding user type, and the location of each user in each user type is characterized by the location of the access network device to which the corresponding user is connected.

[0074] Optionally, the meteorological forecasting platform frequently forecasts changes in meteorological conditions in the second region to obtain forecast results, including: the meteorological forecasting platform determines the type of meteorological conditions corresponding to the user's intent in the second region, wherein for two mismatched intents, the two types of meteorological conditions corresponding to the two intents are different; the meteorological forecasting platform predicts changes in meteorological conditions in the second region at a high frequency according to the type of meteorological conditions corresponding to the user's intent in the second region, and obtains multiple preset results in sequence.

[0075] Optionally, the forecast results are multiple forecast results. The meteorological forecasting platform sends the forecast results to users in the second area, including: the meteorological forecasting platform sequentially sends multiple forecast results to the access network equipment corresponding to the second area. The access network equipment corresponding to the second area refers to the access network equipment whose service cell covers the second area.

[0076] Optionally, the meteorological forecasting platform sequentially sends multiple forecast results to the access network equipment corresponding to the second area, including: for any one of the multiple forecast results, the meteorological forecasting platform sends the forecast result and indication information to the access network equipment corresponding to the second area, and the indication information indicates that the forecast result needs to be forcibly broadcast to each user accessing the access network equipment corresponding to the second area.

[0077] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Exemplarily, the electronic device may be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. Figure 3 As shown, the electronic device 400 may include a processor 401. Optionally, the electronic device 400 may also include a memory 402 and / or a transceiver 403. The processor 401 is coupled to the memory 402 and the transceiver 403, for example, via a communication bus.

[0078] The following is combined Figure 3 A detailed description of each component of the electronic device 400 is provided below:

[0079] The processor 401 is the control center of the electronic device 400. It can be a single processor or a collective term for multiple processing elements. For example, the processor 401 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0080] Optionally, the processor 401 can perform various functions of the electronic device 400 by running or executing software programs stored in the memory 402 and calling data stored in the memory 402, such as performing the aforementioned functions. Figure 2 This paper presents a high-resolution frequency prediction method driven by a large meteorological model.

[0081] In a specific implementation, as one example, processor 401 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0082] In a specific implementation, as one example, the electronic device 400 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0083] The memory 402 is used to store the software program that executes the solution of the present invention, and is controlled by the processor 401 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0084] Optionally, the memory 402 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 402 may be integrated with the processor 401 or may exist independently and be accessible through the interface circuit of the electronic device 400. Figure 3 (Not shown in the image) is coupled to processor 401, and this embodiment of the invention does not specifically limit this.

[0085] Transceiver 403 is used for communication with other electronic devices. For example, if electronic device 400 is a terminal, transceiver 403 can be used to communicate with a network device or with another terminal device. As another example, if electronic device 400 is a network device, transceiver 403 can be used to communicate with a terminal or with another network device.

[0086] Alternatively, transceiver 403 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0087] Alternatively, the transceiver 403 can be integrated with the processor 401, or it can exist independently and be connected via the interface circuit of the electronic device 400. Figure 3 (Not shown in the image) is coupled to processor 401, and this embodiment of the invention does not specifically limit this.

[0088] Understandable, Figure 3 The structure of the electronic device 400 shown does not constitute a limitation on the electronic device. Actual electronic devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0089] Furthermore, the technical effects of the electronic device 400 can be referred to the technical effects of the methods described in the above method embodiments, and will not be repeated here.

[0090] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0091] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0093] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0094] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0095] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0096] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0097] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0098] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0099] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0101] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A high-resolution frequency prediction method driven by a large meteorological model, characterized in that, The method is applied to an edge-deployed weather forecasting platform, and the method includes: If the meteorological forecasting platform detects that the change in meteorological observation data in the first region exceeds a threshold when it predicts changes in meteorological conditions in the first region using a low-frequency, low-resolution forecasting method, then the meteorological forecasting platform determines to switch from the low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method. The first region is the service area of ​​the meteorological forecasting platform. The meteorological forecasting platform predicts the changes in meteorological conditions in the second region using the high-frequency, high-resolution forecasting method, and obtains the forecast results. The second region is a sub-region of the first region. The weather forecasting platform sends the forecast results to users in the second area; The meteorological forecasting platform predicts changes in meteorological conditions in the second region using the high-frequency, high-resolution forecasting method, and obtains the forecast results, including: The weather forecasting platform obtains the intent of users within the first area; The weather forecasting platform divides the first region into multiple sub-regions based on the intentions of users within the first region. The intentions of users in each of the multiple sub-regions match, while the intentions of users in different sub-regions do not match. The second region is any one of the multiple sub-regions. The meteorological forecasting platform frequently forecasts changes in meteorological conditions in the second region to obtain the forecast results; The weather forecasting platform is a third-party application. The platform obtains the intent of users within the first area, including: The weather forecasting platform obtains user traffic data within the first area by requesting the operator network to open the traffic data within the first area. The user traffic data within the first area is provided by the access network equipment in the operator network whose serving cell covers the first area. The weather forecasting platform determines the intentions of users within the first area by analyzing their traffic data.

2. The method according to claim 1, characterized in that, The user traffic data in the first area includes: the packet header of each user's data packet in the first area and information about the access network device accessed by each user in the first area, but does not include: the payload of each user's data packet in the first area; the weather forecasting platform determines the user's intent in the first area by analyzing the user traffic data in the first area, including: The built-in intelligent agent of the weather forecasting platform determines the intent of each user in the first area by analyzing the address information in the header of each user's data packet. The intent of each user in the first area can indicate the operation currently being performed by the corresponding user.

3. The method according to claim 1, characterized in that, The user traffic data in the first area includes: the traffic characteristics of the traffic data and information on the access network devices accessed by each user in the first area. The traffic characteristics indicate at least one of the following for each user's data packets in the first area: packet size distribution, packet interval, or traffic rate. The user traffic data in the first area does not include: the data packets of each user in the first area. The weather forecasting platform determines the user's intent in the first area by analyzing the user traffic data in the first area, including: The built-in intelligent agent of the weather forecasting platform analyzes the traffic flow characteristics to determine the intention of each user in the first area. The intention of each user in the first area can indicate the operation currently being performed by the corresponding user.

4. The method according to claim 1, characterized in that, The weather forecasting platform divides the first region into multiple sub-regions based on the intentions of users within the first region, including: The weather forecasting platform determines multiple types of users within the first area based on the intent of each user within the first area. Each type of user includes multiple users with matching intents, and the multiple users with matching intents indicate that the multiple users are currently performing the same operation. The weather forecasting platform divides the first region into multiple sub-regions based on the location of each type of user. Each sub-region covers one type of user among the multiple user types, and the location of each user in each user type is represented by the location of the access network device to which the corresponding user is connected.

5. The method according to any one of claims 1-4, characterized in that, The meteorological forecasting platform frequently forecasts changes in meteorological conditions in the second region to obtain the forecast results, including: The weather forecasting platform determines the weather condition type corresponding to the user's intent in the second area, wherein for two mismatched intents, the two weather condition types corresponding to the two intents are different; The weather forecasting platform predicts changes in weather conditions in the second region at a high frequency according to the weather conditions type corresponding to the user's intention in the second region, and obtains multiple preset results in sequence.

6. The method according to any one of claims 1-4, characterized in that, The forecast results are multiple forecast results, and the meteorological forecasting platform sends the forecast results to users in the second area, including: The meteorological forecasting platform sequentially sends the multiple forecast results to the access network devices corresponding to the second region. The access network devices corresponding to the second region refer to the access network devices whose service cells cover the second region.

7. The method according to claim 6, characterized in that, The meteorological forecasting platform sequentially sends the multiple forecast results to the access network equipment corresponding to the second area, including: For any one of the multiple forecast results, the meteorological forecasting platform sends the forecast result and indication information to the access network device corresponding to the second area. The indication information indicates that the forecast result needs to be forcibly broadcast to each user accessing the access network device corresponding to the second area.

8. A high-resolution frequency forecasting system driven by a large meteorological model, characterized in that, The system includes an edge-deployed weather forecasting platform, which is configured to: If the meteorological forecasting platform detects that the change in meteorological observation data in the first region exceeds a threshold when it predicts changes in meteorological conditions in the first region using a low-frequency, low-resolution forecasting method, then the meteorological forecasting platform determines to switch from the low-frequency, low-resolution forecasting method to a high-frequency, high-resolution forecasting method. The first region is the service area of ​​the meteorological forecasting platform. The meteorological forecasting platform predicts the changes in meteorological conditions in the second region using the high-frequency, high-resolution forecasting method, and obtains the forecast results. The second region is a sub-region of the first region. The weather forecasting platform sends the forecast results to users in the second area; The meteorological forecasting platform predicts changes in meteorological conditions in the second region using the high-frequency, high-resolution forecasting method, and obtains the forecast results, including: The weather forecasting platform obtains the intent of users within the first area; The weather forecasting platform divides the first region into multiple sub-regions based on the intentions of users within the first region. The intentions of users in each of the multiple sub-regions match, while the intentions of users in different sub-regions do not match. The second region is any one of the multiple sub-regions. The meteorological forecasting platform frequently forecasts changes in meteorological conditions in the second region to obtain the forecast results; The weather forecasting platform is a third-party application. The platform obtains the intent of users within the first area, including: The weather forecasting platform obtains user traffic data within the first area by requesting the operator network to open the traffic data within the first area. The user traffic data within the first area is provided by the access network equipment in the operator network whose serving cell covers the first area. The weather forecasting platform determines the intentions of users within the first area by analyzing their traffic data.

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