Artificial intelligence short-term and temporary weather forecast system based on large model

Abstract

The invention discloses an artificial intelligence short-term weather forecast system based on a large model, and relates to the technical field of short-term weather forecast. The data processing center is remotely associated with the real-time monitoring unit, and an earth surface monitoring unit and a satellite monitoring unit are arranged in the real-time monitoring unit; the data processing center is associated with a data receiving unit, an auditing unit is arranged in the data receiving unit, and the data receiving unit is remotely associated with the real-time monitoring unit. Through the arrangement of the model establishment unit, the model establishment system can perform preliminary model establishment on existing region and environment data, so that when the data processing center processes data transmitted by the real-time monitoring unit, simulation can be performed through region data in a preliminary model; the data transmitted by the real-time monitoring unit is used as rehearsal data to be rehearsed in the preliminary model, so that the data processing center can be more accurate when calculating the short-term weather data, and the accuracy of the system in calculating the short-term weather forecast is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of short-term weather forecasting, and in particular to an artificial intelligence short-term weather forecasting system based on a large model. Background Art

[0002] Short-term forecast is a short-term near-term weather forecast, which mainly forecasts the weather within a few hours. It is about forecasting small and medium-scale weather systems, especially severe convective weather systems. However, the existing short-term meteorological forecast system has some shortcomings, such as:

[0003] Application number: CN202211010895.8 An emergency short-term forecast method and system based on multi-source data fusion, the system can input the multi-time multi-channel satellite cloud image sequence, multi-time radar echo image sequence and basic meteorological forecast factors under the same time sequence that meet the short-term forecast validity period before the forecast area into the trained multi-source data fusion network, and make weather forecasts for several time periods in the future in the region, but the system is difficult to detect complex terrain, which may cause the monitoring agencies such as temperature, humidity and air pressure in the system to have excessive deviations when detecting a certain area, thereby reducing the accuracy of the short-term forecast system in prediction;

[0004] Therefore, we proposed an artificial intelligence short-term weather forecast system based on a large model to solve the problems raised above. Summary of the invention

[0005] The purpose of the present invention is to provide an artificial intelligence short-term weather forecast system based on a large model to solve the problem raised by the above background technology that most short-term weather forecast systems on the market are difficult to detect complex terrains, which may cause the temperature, humidity, air pressure and other monitoring mechanisms in the system to have excessive deviations when detecting a certain area, thereby reducing the accuracy of the short-term forecast system in prediction.

[0006] To achieve the above object, the present invention provides the following technical solutions: an artificial intelligence short-term weather forecast system based on a large model, comprising a data processing center, a real-time monitoring unit associated with the data processing center, and a model building unit; the data processing center is remotely associated with the real-time monitoring unit, and a surface monitoring unit and a satellite monitoring unit are provided inside the real-time monitoring unit;

[0007] The data processing center is associated with a data receiving unit, and an audit unit is arranged inside the data receiving unit, and the data receiving unit is remotely associated with the real-time monitoring unit.

[0008] Through the setting of the model building unit, the model building system can build a preliminary model of the existing regional and environmental data, so that when the data processing center processes the data transmitted by the real-time monitoring unit, it can simulate through the regional data within the preliminary model, so that the data transmitted by the real-time monitoring unit is previewed in the preliminary model as preview data, so that the data processing center can calculate the short-term weather data more accurately, thereby increasing the accuracy of the system in calculating short-term meteorological forecasts.

[0009] As a preferred technical solution of the present invention, the data processing center is internally provided with an algorithm loading module, a data analysis module, a data filling module, a model updating module and a data output module, and the algorithm loading module can load a preset basic algorithm, and the data filling module and the model updating module can integrate the preliminary model and data established by the real-time monitoring unit and the model establishment unit;

[0010] The basic algorithm formula of the algorithm loading module is:

[0011] The horizontal atmospheric motion equation is:

[0012] Atmospheric vertical motion equation:

[0013] Where: v h is the horizontal wind speed; t is the time; is a two-dimensional differential operator in the coordinate system; P * The upper limit pressure P8 is the surface pressure P T ; ∫ is the Coriolis parameter.

[0014] The adoption of the above technical solution can make it more convenient for the data processing center to process the measurement data of the real-time monitoring unit and the preliminary model established by the model establishment unit.

[0015] As a preferred technical solution of the present invention, the surface monitoring unit includes a temperature monitoring unit, a humidity monitoring unit, an air pressure monitoring unit, a wind monitoring unit, and a rainfall monitoring unit, and the temperature monitoring unit, the humidity monitoring unit, and the air pressure monitoring unit are environmental sensing structures, and the wind monitoring unit is a fan blade monitoring mechanism, which determines the wind conditions in the area by the fan blade rotation speed, and the rainfall monitoring unit is a rainwater cavity mechanism, which uses rainwater falling into the cavity mechanism to make the gravity sensing mechanism measure the rainfall;

[0016] The satellite monitoring unit is equipped with a cloud monitoring unit and a light monitoring unit.

[0017] The adoption of the above technical solution enables the surface monitoring unit to be more accurate when detecting data such as temperature, humidity and wind pressure in a specified area, thereby increasing the accuracy of the device in collecting meteorological environment data.

[0018] As a preferred technical solution of the present invention, the data processing center is remotely connected to the data receiving unit via Ethernet, and the audit unit is internally provided with an intelligent audit module and a manual audit module, and the data receiving unit is internally provided with a position calibration module and an information generation module;

[0019] The data processing center is associated with an artificial intelligence analysis unit, and the artificial intelligence analysis unit is associated with a large database retrieval unit.

[0020] The adoption of the above technical solution can make it more convenient for the data receiving unit to generate short-term weather forecast information.

[0021] As a preferred technical solution of the present invention, the data receiving unit is associated with a real-time weather reference unit, and the real-time weather reference unit is remotely associated with a surface monitoring unit and a satellite monitoring unit provided inside the real-time monitoring unit.

[0022] The adoption of the above technical solution can make the real-time weather reference unit more stable when receiving meteorological data of a specified area.

[0023] As a preferred technical solution of the present invention, when the data receiving unit determines the location of a designated area, it can be operated through a location calibration module, and the location information is transmitted to the location calibration module through a signal tower, so that the data receiving unit determines the location.

[0024] The adoption of the above technical solution enables the data receiving unit to more conveniently determine the location of the designated unit, thereby increasing the accuracy of the system in calculating short-term weather forecasts.

[0025] As a preferred technical solution of the present invention, when the audit unit inside the data receiving unit finds that an error occurs in the established data model, the data model can be transmitted back to the data processing center via Ethernet for model reconstruction.

[0026] The adoption of the above technical solution can make the data receiving unit more convenient when performing error correction, thereby increasing the accuracy of the system in calculating short-term weather forecasts.

[0027] As a preferred technical solution of the present invention, the data processing center is associated with an algorithm self-built module and an algorithm supplement module, and the data processing center is associated with an algorithm self-built module and an algorithm supplement module which can be associated with an algorithm loading module.

[0028] The adoption of the above technical solution enables the administrator to input algorithm formulas or other algorithm programs through the algorithm self-built module and the algorithm supplement module, thereby increasing the scalability of the system.

[0029] As a preferred technical solution of the present invention, the algorithm supplement module can be associated with a large database retrieval unit to supplement the algorithm information of different regions through the large database retrieval unit.

[0030] The adoption of the above technical solution enables the algorithm supplement module to search through a large database retrieval unit when supplementing algorithm programs in different regions, thereby increasing the scalability of the device in supplementing algorithms.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: through the setting of the model building unit, the model building system can build a preliminary model with the existing regional and environmental data, so that when the data processing center processes the data transmitted by the real-time monitoring unit, it can simulate through the regional data inside the preliminary model, so that the data transmitted by the real-time monitoring unit is previewed in the preliminary model as preview data, so that the data processing center can be more accurate when calculating the short-term weather data, thereby increasing the accuracy of the system in calculating the short-term weather forecast;

[0032] Furthermore, by setting two groups of audit units, namely, an intelligent audit module and a manual audit module, inside the audit unit, the data receiving unit can be more convenient when performing error correction, thereby increasing the accuracy of the system in calculating short-term weather forecasts;

[0033] Furthermore, through the setting of the algorithm self-built module and the algorithm supplement module, the administrator can input the algorithm formula or other algorithm program through the algorithm self-built module and the algorithm supplement module, thereby increasing the scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the structure of the weather forecast system of the present invention;

[0035] Figure 2 It is a structural schematic diagram of the real-time monitoring unit of the present invention;

[0036] Figure 3 It is a schematic diagram of the process of the present invention;

[0037] Figure 4 It is a detailed process schematic diagram of the present invention;

[0038] Figure 5 This is a schematic diagram of the structure of a weather forecast system according to Embodiment 2 of the present invention;

[0039] Figure 6 This is a schematic diagram of the process structure of Example 2 of the present invention.

[0040] In the figure: 100, data processing center; 101, algorithm loading module; 102, data analysis module; 103, data filling module; 104, model updating module; 105, data output module; 106, artificial intelligence analysis unit; 107, large database retrieval unit; 108, algorithm self-built module; 109, algorithm supplement module; 200, real-time monitoring unit; 210, surface monitoring unit; 211, temperature monitoring unit; 212, humidity monitoring unit; 213, air pressure monitoring unit Measurement unit; 214, wind monitoring unit; 215, rainfall monitoring unit; 220, satellite monitoring unit; 221, cloud monitoring unit; 222, light monitoring unit; 300, model building unit; 301, regional simulation unit; 302, environmental simulation unit; 400, data receiving unit; 401, position calibration module; 402, information generation module; 403, real-time weather reference unit; 410, audit unit; 411, intelligent audit module; 412, manual audit module. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0042] Embodiment 1:

[0043] See also Figure 1-4 The present invention provides a technical solution: an artificial intelligence short-term weather forecast system based on a large model, comprising a data processing center 100, a real-time monitoring unit 200 associated with the data processing center 100, and a model building unit 300; the data processing center 100 is remotely associated with the real-time monitoring unit 200, and the real-time monitoring unit 200 is internally provided with a surface monitoring unit 210 and a satellite monitoring unit 220;

[0044] The data processing center 100 is associated with a data receiving unit 400, and the data receiving unit 400 is provided with an audit unit 410, and the data receiving unit 400 is remotely associated with the real-time monitoring unit 200;

[0045] The data processing center 100 is associated with an algorithm self-building module 108 and an algorithm supplementing module 109, and the data processing center 100 is associated with an algorithm self-building module 108 and an algorithm supplementing module 109 which can be associated with the algorithm loading module 101; the algorithm supplementing module 109 can be associated with a large database retrieval unit 107, and the algorithm information of different regions is supplemented by the large database retrieval unit 107, and the large database retrieval unit 107 is associated with the artificial intelligence analysis unit 106;

[0046] The data processing center 100 is internally provided with an algorithm loading module 101, a data analysis module 102, a data filling module 103, a model updating module 104 and a data output module 105, and the algorithm loading module 101 can load a preset basic algorithm, and the data filling module 103 and the model updating module 104 can monitor the preliminary model and data fusion established by the unit 200 and the model establishment unit 300 in real time;

[0047] When the model building unit 300 is building a preliminary regional model, the regional simulation unit 301 builds the terrain by specifying the coordinate position of the region, and then the environmental simulation unit 302 uses the real shooting data as auxiliary data to build a preliminary model of the terrain coordinates through the real shooting device;

[0048] When the audit unit 410 provided inside the data receiving unit 400 audits the preview data, the intelligent audit module 411 and the manual audit module 412 inside the audit unit 410 audit the preview data, and the administrator can compare the meteorological calculation tolerance of the data model through the manual audit module 412, thereby comparing the result of the intelligent audit module 411;

[0049] The basic algorithm formula of the algorithm loading module 101 is:

[0050] The horizontal atmospheric motion equation is:

[0051] Atmospheric vertical motion equation:

[0052] Where: v h is the horizontal wind speed; t is the time; is a two-dimensional differential operator in the coordinate system; P * The upper limit pressure P8 is the surface pressure P T The difference between ∫ and ∫ is the Coriolis parameter;

[0053] The surface monitoring unit 210 includes a temperature monitoring unit 211, a humidity monitoring unit 212, an air pressure monitoring unit 213, a wind monitoring unit 214, and a rainfall monitoring unit 215. The temperature monitoring unit 211, the humidity monitoring unit 212, and the air pressure monitoring unit 213 are environment sensing structures, and the wind monitoring unit 214 is a fan blade monitoring mechanism, which determines the wind conditions in the area by the fan blade rotation speed. The rainfall monitoring unit 215 is a rainwater storage cavity mechanism, and the gravity sensing mechanism measures the rainfall by rainwater falling into the storage cavity mechanism; the satellite monitoring unit 220 is internally provided with a cloud layer monitoring unit 221 and a light monitoring unit 222;

[0054] When the surface monitoring unit 210 monitors the meteorological environment of the surface, the temperature monitoring unit 211, the humidity monitoring unit 212, the air pressure monitoring unit 213 and the wind force monitoring unit 214 will monitor the temperature, humidity, air pressure and wind force and other data of the designated area through the sensing mechanism structure. The sensing mechanism can be fixed on the signal tower, and multiple sets of sensing mechanisms can be arranged at different heights for detection, and the rainfall monitoring unit 215 monitors the rainfall in the designated area through the weight of rainwater inside the cavity; and multiple sets of surface monitoring units 210 can be arranged in the designated area to detect different directions, so that the data processing center 100 can be more accurate when performing calculations;

[0055] When the satellite monitoring unit 220 monitors the atmosphere of the designated area, the cloud monitoring unit 221 can detect the atmosphere, and the light intensity of the designated area can be monitored by the light monitoring unit 222;

[0056] The data processing center 100 is remotely connected to the data receiving unit 400 via Ethernet, and the audit unit 410 is internally provided with an intelligent audit module 411 and a manual audit module 412, and the data receiving unit 400 is internally provided with a position calibration module 401 and an information generation module 402, the data processing center 100 is associated with an artificial intelligence analysis unit 106, and the artificial intelligence analysis unit 106 is associated with a large database retrieval unit 107; the data receiving unit 400 is associated with a real-time weather reference unit 403, and the real-time weather reference unit 403 is remotely associated with a surface monitoring unit 210 and a satellite monitoring unit 220 provided inside the real-time monitoring unit 200;

[0057] When the data receiving unit 400 determines the location of a designated area, it can operate through the location calibration module 401, and transmit the location information to the location calibration module 401 through the signal tower, so that the data receiving unit 400 determines the location; when the review unit 410 inside the data receiving unit 400 finds that an error occurs in the established data model, the data model can be transmitted back to the data processing center 100 through Ethernet for model reconstruction;

[0058] Embodiment 2:

[0059] like Figure 5-6 As shown, the difference between this embodiment and embodiment 1 is that: the data processing center 100 is associated with an algorithm self-building module 108 and an algorithm supplement module 109, and the data processing center 100 is associated with an algorithm self-building module 108 and an algorithm supplement module 109 that can be associated with the algorithm loading module 101; the algorithm supplement module 109 can be associated with a large database retrieval unit 107, and the algorithm information of different regions is supplemented by the large database retrieval unit 107, and the large database retrieval unit 107 is associated with the artificial intelligence analysis unit 106;

[0060] The algorithm program built by the administrator is input into the data processing center 100 through the algorithm self-building module 108 and extracted through the algorithm loading module 101. When the administrator builds the algorithm himself, the previous algorithms of the specified area can be retrieved through the large database retrieval unit 107 through the algorithm supplement module 109, so that the algorithm supplement module 109 can extract the algorithm retrieved by the large database retrieval unit 107 for the administrator to refer to.

[0061] Working principle: When using the artificial intelligence short-term weather forecast system based on a large model, firstly, the meteorological environment and cloud movement environment of the designated area are monitored by the surface monitoring unit 210 and the satellite monitoring unit 220 set inside the real-time monitoring unit 200, and then the regional meteorological data is transmitted to the data processing center 100. At the same time, a preliminary model of the regional environment of the designated area is established by the model building unit 300 and transmitted to the data processing center 100;

[0062] After the regional meteorological data and the preliminary model are transmitted to the data processing center 100, the algorithm is loaded through the algorithm loading module 101, so that the regional meteorological data is integrated with the preliminary model, so that the short-term meteorological data is previewed in the preliminary model, and then the preview data is transmitted to the data receiving unit 400 through the data output module 105;

[0063] The preview data is reviewed by the review unit 410 provided inside the data receiving unit 400, so that the intelligent review module 411 and the manual review module 412 inside the review unit 410 review the preview data. When the review unit 410 finds that the difference of the preview data is too large or the position is inaccurate, the preview data is transmitted back to the data processing center 100 via Ethernet for processing;

[0064] When the surface monitoring unit 210 monitors the meteorological environment of the surface, the temperature monitoring unit 211, the humidity monitoring unit 212, the air pressure monitoring unit 213 and the wind force monitoring unit 214 will monitor the temperature, humidity, air pressure and wind force of the designated area through the sensing mechanism structure, and the rainfall monitoring unit 215 will monitor the rainfall of the designated area through the weight of the rainwater inside the cavity; and multiple groups of surface monitoring units 210 can be set in the designated area to detect different directions, so that the data processing center 100 can be more accurate when performing calculations;

[0065] When the satellite monitoring unit 220 monitors the atmosphere of the designated area, the cloud monitoring unit 221 can detect the atmosphere, and the light intensity of the designated area can be monitored by the light monitoring unit 222;

[0066] When the model building unit 300 is building a preliminary regional model, the regional simulation unit 301 builds the terrain by specifying the coordinate position of the region, and then the real-shot device can be used to enable the environment simulation unit 302 to build a preliminary model of the terrain coordinates using the real-shot data as auxiliary data.

[0067] Thereby completing a series of tasks, the contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0068] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An artificial intelligence short-term weather forecast system based on a large model, comprising a data processing center (100), a real-time monitoring unit (200) associated with the data processing center (100), and a model building unit (300); characterized in that: The data processing center (100) is remotely associated with the real-time monitoring unit (200), and the real-time monitoring unit (200) is internally provided with a surface monitoring unit (210) and a satellite monitoring unit (220); The data processing center (100) is associated with a data receiving unit (400), and an audit unit (410) is provided inside the data receiving unit (400), and the data receiving unit (400) is remotely associated with the real-time monitoring unit (200).

2. The artificial intelligence short-term weather forecast system based on a large model according to claim 1 is characterized in that: The data processing center (100) is internally provided with an algorithm loading module (101), a data analysis module (102), a data filling module (103), a model updating module (104) and a data output module (105), wherein the algorithm loading module (101) is capable of loading a preset basic algorithm, and the data filling module (103) and the model updating module (104) are capable of real-time monitoring the preliminary model established by the unit (200) and the model establishing unit (300) and fusing the data; The basic algorithm formula of the algorithm loading module (101) is: The horizontal atmospheric motion equation is: Atmospheric vertical motion equation: Where: v h is the horizontal wind speed; t is the time; is a two-dimensional differential operator in the coordinate system; P * The upper limit pressure P8 is the surface pressure P T ; ∫ is the Coriolis parameter.

3. The artificial intelligence short-term weather forecast system based on a large model according to claim 2 is characterized in that: The surface monitoring unit (210) comprises a temperature monitoring unit (211), a humidity monitoring unit (212), an air pressure monitoring unit (213), a wind monitoring unit (214), and a rainfall monitoring unit (215), wherein the temperature monitoring unit (211), the humidity monitoring unit (213), and the air pressure monitoring unit (213) are environment sensing structures, and the wind monitoring unit (214) is a fan blade monitoring mechanism, which determines the wind conditions in the area by the fan blade rotation speed, and the rainfall monitoring unit (215) is a rainwater storage cavity mechanism, which uses the gravity sensing mechanism to measure the rainfall by rainwater falling into the storage cavity mechanism; The satellite monitoring unit (220) is internally provided with a cloud layer monitoring unit (221) and a light monitoring unit (222).

4. The artificial intelligence short-term weather forecast system based on a large model according to claim 2 is characterized in that: The data processing center (100) is remotely connected to the data receiving unit (400) via Ethernet, and the audit unit (410) is internally provided with an intelligent audit module (411) and a manual audit module (412), and the data receiving unit (400) is internally provided with a position calibration module (401) and an information generation module (402); The data processing center (100) is associated with an artificial intelligence analysis unit (106), and the artificial intelligence analysis unit (106) is associated with a large database retrieval unit (107).

5. The large model-based artificial intelligence short-term weather forecast system according to claim 4 is characterized in that: The data receiving unit (400) is associated with a real-time weather reference unit (403), and the real-time weather reference unit (403) is remotely associated with a surface monitoring unit (210) and a satellite monitoring unit (220) provided inside the real-time monitoring unit (200).

6. The artificial intelligence short-term weather forecast system based on a large model according to claim 5 is characterized in that: When determining the location of a designated area, the data receiving unit (400) can be operated through a location calibration module (401), and the location information is transmitted to the location calibration module (401) through a signal tower, so that the data receiving unit (400) can determine the location.

7. The large model-based artificial intelligence short-term weather forecast system according to claim 6 is characterized in that: When the audit unit (410) inside the data receiving unit (400) finds that an error occurs in the established data model, the data model can be transmitted back to the data processing center (100) via Ethernet for model reconstruction.

8. The artificial intelligence short-term weather forecast system based on a large model according to claim 1 is characterized in that: The data processing center (100) is associated with an algorithm self-building module (108) and an algorithm supplementing module (109), and the data processing center (100) is associated with an algorithm self-building module (108) and an algorithm supplementing module (109) which can be associated with an algorithm loading module (101).

9. The large model-based artificial intelligence short-term weather forecast system according to claim 8 is characterized in that: The algorithm supplement module (109) can be associated with the large database search unit (107) to supplement the algorithm information of different regions through the large database search unit (107).

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