Artificial precipitation enhancement operation control method based on multi-source data fusion

Abstract

The invention relates to the technical field of artificial influence weather, and discloses a multi-source data fused artificial precipitation enhancement operation control method, which comprises the following steps: acquiring cloud body data of a target cloud body, including cloud body height, radar reflectivity and temperature intervals and boundaries of a plurality of temperature layers, and executing cloud body type judgment. Firstly, whether a preset temperature layer exists or not is judged, and if the preset temperature layer does not exist or the height of the cloud body is lower than a preset threshold value, the cloud body is determined as a non-operation cloud body; if the cloud body exists and the height meets the condition, calculating the fluctuation amount of the height, the temperature and the reflectivity, and dividing the cloud body into a first target cloud body or a second target cloud body according to the fluctuation amount. If the target cloud body is the first target cloud body, directly issuing a rainfall enhancement instruction; and if the target cloud body is a second target cloud body, carrying out secondary data acquisition and judgment, and if the target cloud body is still the second target cloud body, giving up the operation. Therefore, the scientificity and the accuracy of the rainfall enhancement operation are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial weather influencing, and in particular to a multi-source data fusion artificial rainfall control method. Background Art

[0002] Artificial rainfall enhancement, as a major means of artificially influencing the weather, has been widely used in the fields of increasing agricultural production, regulating water resources, and ecological restoration. Traditional artificial rainfall enhancement methods mainly rely on operators to select the catalytic time and target cloud body based on weather forecasts, radar echo patterns, and empirical judgment, and release catalysts such as silver iodide and liquid nitrogen into the clouds through rockets, artillery shells, or aircraft to promote the condensation of cloud droplets and form precipitation. With the development of remote sensing technology, meteorological data collection technology, and UAV platforms, artificial rainfall enhancement operations have gradually evolved towards intelligence and refinement, especially in operation path planning, catalytic window identification, and other aspects. However, the following technical problems often exist in the existing artificial rainfall enhancement process: First, existing cloud seeding methods cannot effectively and reliably determine whether a target cloud is operationally valuable under complex meteorological conditions, resulting in inaccurate identification, a low success rate for seeding operations, and a waste of resources. Second, existing cloud-seeding operations fail to consider the impact of pollutants on cloud-seeding operations, making it difficult to ensure environmental safety and operational reliability. Furthermore, cloud-seeding operations are often crude and lack specificity, reducing their accuracy. Third, existing technologies usually use fixed thresholds to judge pollutants and are unable to combine historical rainfall data to provide personalized early warnings on pollutant response trends, resulting in delayed risk identification. In addition, there is a lack of scientific evaluation methods for the effects of rain-making operations, and it is impossible to dynamically feedback the operation results or correct decision parameters based on the changing trends of pollutants, thereby limiting the refined management and continuous optimization capabilities of artificial rain-making operations. Summary of the Invention

[0003] This summary is intended to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] The present invention proposes a multi-source data fusion artificial rainmaking operation control method to solve one or more of the technical problems mentioned in the above background technology part.

[0005] The present invention provides a multi-source data fusion artificial rain enhancement operation control method, comprising: obtaining cloud data of a target cloud body, the cloud data including cloud body height, radar reflectivity, multiple temperature layers, and temperature intervals and boundaries corresponding to each temperature layer; Based on the cloud data, a cloud type determination step is performed: Based on multiple temperature layers and the temperature ranges corresponding to each temperature layer, it is determined whether the target cloud exists in a preset temperature layer. If not, the corresponding target cloud is determined as a non-operational cloud. If so, the cloud height is compared with a preset cloud height threshold. If the cloud height is less than the preset cloud height threshold, the corresponding target cloud is determined as a non-operational cloud. If the cloud height is greater than or equal to a preset cloud height threshold, multiple fluctuation quantities corresponding to the target cloud are determined based on the cloud height, radar reflectivity, multiple temperature layers, and the temperature range corresponding to each temperature layer. The cloud type of the target cloud is determined based on the multiple fluctuation quantities, and the cloud type is determined as the first target cloud or the second target cloud. For the first target cloud body, the corresponding rain enhancement instruction is sent to the execution device; for the second target cloud body, the cloud body data corresponding to the second target cloud body is collected, and the cloud body type judgment step is performed to determine the final cloud body type of the second target cloud body; if the final cloud body type is the first target cloud body, the corresponding rain enhancement instruction is sent to the execution device; if the final cloud body type is the second target cloud body, the corresponding target cloud body is determined as a non-operational cloud body.

[0006] Optionally, the plurality of fluctuations include altitude fluctuation, temperature fluctuation and radar reflectivity fluctuation; and Determining the cloud type of the target cloud based on the multiple fluctuation quantities, where the cloud type is either the first target cloud or the second target cloud, includes: If the multiple fluctuation amounts are all greater than or equal to the corresponding preset thresholds, the corresponding target cloud body is determined as the first target cloud body; if any of the multiple fluctuation amounts is less than the corresponding preset threshold, the target cloud body is determined as the second target cloud body.

[0007] Optionally, for the first target cloud body, sending a corresponding rain enhancement instruction to an execution device includes: According to the boundary of the preset temperature layer, the corresponding thickness is determined; if the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is greater than or equal to the corresponding threshold, the first flight plan of the execution device is determined; if the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is less than the corresponding threshold, the second flight plan of the execution device is determined.

[0008] Optionally, for the first target cloud body, sending a corresponding rain enhancement instruction to the execution device further includes: During the execution of the first flight plan or the second flight plan by the execution device, multiple meteorological data of a preset temperature layer are received, and each of the multiple meteorological data includes meteorological data and a corresponding execution device number; for each meteorological data, it is determined whether the meteorological data meets the delivery standards; if so, a rain enhancement instruction is generated and sent to the corresponding execution device.

[0009] Optionally, before obtaining the cloud data of the target cloud, include: Obtain historical meteorological data of source points in the target monitoring area, pollutant information at the source points, and pollutant information at the farmland points. The pollutant information at the farmland points includes the coordinates of the farmland points, multiple farmland pollutants, the farmland pollutant concentration corresponding to each farmland pollutant, the farmland pollutant type, and the safety threshold. The pollutant information at the source points includes the coordinates of the source points, multiple source pollutants, the source pollutant type corresponding to each source pollutant, the source pollutant concentration, the emission rate, the emission height, the lateral diffusion coefficient, the vertical diffusion coefficient, and the safety threshold. Based on the pollutant information of the farmland point, the farmland pollutant impact type corresponding to the farmland point is determined; based on the historical meteorological data, the historical wind rose diagram of the source point is generated; based on the historical wind rose diagram, the diffusion direction of the factory pollutants is determined; based on the coordinates of the farmland point and the coordinates of the source point, the relative position of the farmland point is determined; based on the relative position and diffusion direction of the farmland point, it is judged whether the source pollutants of the source point diffuse to the farmland point; if it does not diffuse to the farmland point, the farmland pollutant impact type is used as the pollutant impact type corresponding to the target monitoring area.

[0010] Optionally, before obtaining the cloud data of the target cloud, the following steps are also included: If the wind spreads to farmland points, determine the diffusion path and select multiple path points; for each path point, obtain the average wind speed, and determine the coordinates of each path point with the source point as the origin; Determine source monitoring pollutants based on multiple source pollutants and the corresponding source pollutant concentrations of each source pollutant; Based on the emission rate, emission height, diffusion coefficient, coordinates of each path point and average wind speed corresponding to the source monitoring pollutants, the pollutant concentration of the source monitoring pollutants at each path point is determined by the following formula:

[0011] in, The emission rate corresponding to the pollutant monitored at the source, is the average wind speed at each path point, The lateral diffusion coefficient and vertical diffusion coefficient corresponding to the source monitoring pollutants are is the coordinate of each path point determined with the source point as the origin, Monitor the pollutant concentration of each route point at the source of the pollutant. Emission height corresponding to pollutants monitored at the source; According to the path pollutant concentration of the source monitoring pollutants at each path point, the path pollutant concentration sequence of the diffusion path is obtained; according to the path pollutant concentration sequence of the diffusion path, the path pollutant impact type corresponding to the diffusion path is determined, and the path pollutant impact type is path rainfall pollution, path rainfall inhibition or path no impact; according to the farmland pollutant impact type and the path pollutant impact type, the pollutant impact type corresponding to the target monitoring area is determined, and the corresponding processing instructions are generated and sent to the job scheduling end, where the pollutant impact type is no impact, rainfall pollution or rainfall inhibition.

[0012] Optionally, based on the pollutant information at the farmland point, determine the farmland pollutant impact type corresponding to the farmland point, including: The farmland pollutant concentrations corresponding to each farmland pollutant are sorted in descending order to obtain a farmland pollutant concentration sequence; the farmland pollutant corresponding to the highest farmland pollutant concentration is selected as the farmland monitoring pollutant, and according to the type of farmland pollutant corresponding to the farmland monitoring pollutant, the farmland pollutant impact type corresponding to the farmland point is determined. The farmland pollutant impact type is farmland rainfall enhancement pollution, farmland rainfall inhibition or farmland no impact.

[0013] Optionally, the farmland pollutant impact type corresponding to the farmland point can be determined based on the farmland pollutant type corresponding to the farmland monitoring pollutant, including: According to the type of farmland pollutants corresponding to the farmland monitoring pollutants, the corresponding pollutant type is determined, where the pollutant type is a pollution-type pollutant or an inhibitory pollutant; if the pollutant type of the farmland monitoring pollutants is a pollution-type pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant impact type is determined to be farmland rainmaking pollution; if the pollutant type of the farmland monitoring pollutants is a pollution-type pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant impact type is determined to be no impact on farmland; If the pollutant type of the farmland monitoring pollutants is an inhibitory pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant impact type is determined to be farmland inhibition of rain increase; if the pollutant type of the farmland monitoring pollutants is an inhibitory pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant impact type is determined to be no impact on farmland.

[0014] Optionally, based on the farmland pollutant impact type and the path pollutant impact type, the pollutant impact type corresponding to the target monitoring area is determined, and corresponding processing instructions are generated and sent to the job scheduling end, including: If the pollutant impact type is no impact, a rain enhancement preparation instruction is generated and sent to the job scheduling end; if the pollutant impact type is rain enhancement pollution, a first processing instruction is generated and sent to the job scheduling end; if the pollutant impact type is rain enhancement suppression, a second processing instruction is generated and sent to the job scheduling end.

[0015] The present invention has the following beneficial effects: 1. Improve the success rate and resource utilization of catalytic operations. Specifically, survey aircraft equipped with multi-source sensors acquire key structural information, such as cloud height, radar reflectivity, and multiple temperature layers. A fluctuation index based on the ratio of standard deviation to mean is introduced to quantify cloud structural volatility, enabling accurate identification of target clouds with catalytic potential. By establishing a classification mechanism for primary and secondary target clouds and performing a secondary sampling and re-evaluation process on the secondary target cloud, a closed-loop judgment logic of "initial judgment-re-evaluation-execution" is established, effectively improving identification accuracy under boundary conditions. By matching the temperature layer thickness with the detection capabilities of the execution equipment, a dynamic flight plan is formulated to ensure that the catalyst deployment area covers the critical supercooled water area, enhancing operational efficiency and catalytic effectiveness. By determining whether deployment criteria are met based on real-time meteorological data, the timing and location of catalyst release are precisely controlled to avoid resource waste and ineffective operations. This enables accurate identification and reliable decision-making before cloud seeding operations, improving the success rate and resource utilization efficiency of catalytic operations.

[0016] 2. Improved operational reliability and accuracy. Specifically, by collecting pollutant information from farmland and source points, and combining it with historical meteorological data for diffusion simulation, an accurate assessment of the impact of pollution before artificial rainmaking operations is achieved. By distinguishing between polluting and inhibitory pollutants, it is determined whether they diffuse into farmland areas, and the type of pollutant impact in the target area is determined by combining the measured concentration with the simulation results. The system generates corresponding processing instructions based on the judgment results, realizing intelligent decision-making and dynamic regulation before operations. This solution improves the environmental safety and targeted operation of rainmaking operations, avoids acid rain or catalytic failure caused by excessive pollutants, and enhances the accuracy and reliability of artificial rainmaking operations.

[0017] 3. Improved the accuracy of risk identification and the scientific and safe nature of cloud seeding operations. Specifically, by analyzing the differences in pollutant responses before and after historical cloud seeding, personalized "warning thresholds" and "warning intervals" are generated to enhance the ability to proactively identify potential risks. Farmland pollutants and source pollutants are clearly modeled and judged separately. Through two-way determination and fusion, the "updated pollutant impact type" of the target monitoring area is refined. Using real-time pollutant concentration sequences, their changing trends are analyzed after the operation to judge the effectiveness of the cloud seeding operation and determine whether to adjust parameters or correct the path. If the concentration rises abnormally and meets the characteristics of pollution or suppression, the warning threshold or safety threshold is dynamically lowered. If the concentration rises but does not show pollution characteristics, the model deviation is inferred and the diffusion path point setting is further adjusted. By establishing a pollutant warning interval, updating the pollutant impact type, and providing a feedback mechanism for operation results, the accuracy of risk identification and the scientific and safe nature of cloud seeding operations are effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the elements are not necessarily drawn to scale.

[0019] Figure 1 This is a flow chart of a multi-source data fusion artificial rainmaking operation control method of the present invention; Figure 2 This is a schematic diagram of a survey machine surveying method for artificial rainfall enhancement operation control using multi-source data fusion according to the present invention; Figure 3 This is a schematic diagram of temperature and humidity changes in a multi-source data fusion artificial rainfall control method of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0021] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other.

[0022] It should be noted that the concepts of "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0023] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0024] The names of the messages or information exchanged between multiple devices of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0025] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0026] like Figure 1 FIG. 1 is a flowchart of a multi-source data fusion artificial rainmaking operation control method according to the present invention, which specifically includes the following steps: Step 101: Acquire cloud data of a target cloud, where the cloud data includes cloud height, radar reflectivity, multiple temperature layers, and temperature intervals and boundaries corresponding to each temperature layer. In some embodiments, the execution subject of the multi-source data fusion artificial rain enhancement operation control method of the present invention is a control terminal deployed on a survey aircraft, and the control terminal can be a background server. The survey aircraft is a high-altitude aircraft deployed with special detection sensors, data processing modules and communication modules, which can provide real-time cloud structure information and environmental parameters for rain enhancement operations, such as airplanes. Special detection sensors include sounding instruments, temperature and humidity probes, LWC detectors, millimeter wave radars, laser radars and other equipment. Among them, after the survey aircraft takes off from the airport, it first climbs to the cloud top of the target cloud body, and then spirally descends from the cloud top height (such as Figure 2(Figure 2 shows a schematic diagram of a survey aircraft survey). Data collected by dedicated detection sensors is sent to the control terminal as target cloud data. The control terminal then establishes a communication connection with the dedicated detection sensors to obtain the target cloud data collected by the sensors. The target cloud is the cloud that the survey aircraft is observing, analyzing, and evaluating for suitability as a target for rain enhancement operations. Cloud data refers to comprehensive parameters used to analyze cloud structure and characteristics, including cloud height, radar reflectivity, multiple temperature layers, and the temperature ranges and boundaries corresponding to each temperature layer. Cloud height is the vertical distance from cloud base to cloud top. Radar reflectivity refers to the intensity of the reflected signal from water vapor particles detected by the radar, reflecting changes in cloud droplet concentration or size. Multiple temperature layers refer to the different vertical temperature ranges within the cloud (e.g., -5°C to -15°C). The temperature range refers to the temperature range within each temperature layer, such as -5°C to -15°C. Boundaries refer to the upper and lower boundaries of a temperature layer. For example, the -5°C to -15°C layer is located between 6500m and 7000m. The value of the upper boundary refers to the vertical height from the ground to the upper boundary, and the value of the lower boundary refers to the vertical height from the ground to the lower boundary.

[0027] Step 102: Based on the cloud data, a cloud type determination step is performed. The following steps are performed: Based on the multiple temperature layers and the temperature ranges corresponding to each temperature layer, a determination is made as to whether the target cloud exists within a preset temperature layer. If not, the target cloud is determined to be a non-operational cloud. If so, the cloud height is compared with a preset cloud height threshold. If the cloud height is less than the preset cloud height threshold, the target cloud is determined to be a non-operational cloud. In step 103, if the cloud height is greater than or equal to the preset cloud height threshold, multiple fluctuation quantities corresponding to the target cloud are determined based on the cloud height, radar reflectivity, multiple temperature layers, and the temperature range corresponding to each temperature layer. Based on the multiple fluctuation quantities, the cloud type of the target cloud is determined, and the cloud type is determined as either the first target cloud or the second target cloud.

[0028] In some embodiments, after acquiring cloud data, the control terminal performs a cloud type determination step: matching the temperature range of the preset temperature layer with the temperature range of each temperature layer in the multiple temperature layers. If the match fails, it indicates that the preset temperature layer does not exist in the target cloud, and the corresponding target cloud is marked as a non-operational cloud. The preset temperature layer refers to the critical temperature range required for artificial rain enhancement operations. Non-operational clouds refer to clouds that do not meet the catalytic conditions and are unsuitable for rain enhancement operations (e.g., clouds that are too thin or lack a supercooled water layer). If the match succeeds, it indicates that the target cloud has the preset temperature layer. For example, the target cloud has temperature layers 1, 2, and 3. The temperature range of temperature layer 1 is 0°C to -5°C, the temperature range of temperature layer 2 is -5°C to -10°C, and the temperature range of temperature layer 3 is -10°C to -15°C. The preset temperature layer can be -5°C to -15°C. After traversing these three temperature layers, if the temperature ranges of temperature layers 2 and 3 are found to be within the temperature range corresponding to the preset temperature layer, the preset temperature layer is determined to exist. On this basis, determine whether the cloud height of the target cloud body is greater than the preset cloud height threshold. Among them, the preset cloud height threshold refers to the minimum allowable cloud vertical height value set by the system. If the height of the target cloud body is lower than this value, it is considered that the vertical development of the cloud body is insufficient and the target cloud body is marked as a non-operational cloud body, which is not suitable for artificial rain enhancement operations. If the height of the target cloud body is greater than or equal to this value, it is considered that the cloud body is suitable for artificial rain enhancement operations. The next step of judgment is to calculate the height fluctuation, temperature fluctuation and radar reflectivity fluctuation of the target cloud body according to the cloud height, radar reflectivity, multiple temperature layers and the temperature range corresponding to each temperature layer, and respectively calculate the height fluctuation, temperature fluctuation and radar reflectivity fluctuation of the target cloud body using the following formulas:

[0029] in, is a random variable The standard deviation of the random variable The intensity of fluctuations. is a random variable The mathematical expectation value of the random variable average level. is a random variable The fluctuation of the random variable The relative degree of fluctuation of the variable is larger, and the larger the value is, the more significant the heterogeneity or spatial or temporal fluctuation of the variable is.

[0030] In some embodiments, if multiple fluctuation measures are all greater than or equal to corresponding preset thresholds, the corresponding target cloud is identified as the first target cloud. If any of the multiple fluctuation measures is less than the corresponding preset threshold, the target cloud is identified as the second target cloud. The first target cloud is an ideal cloud whose all indicators (fluctuations) significantly meet the catalytic conditions and is prioritized for rain enhancement operations. The second target cloud is a less ideal cloud and requires further sampling and confirmation.

[0031] In step 104, for the first target cloud, a corresponding precipitation enhancement instruction is sent to the execution device. For the second target cloud, cloud data corresponding to the second target cloud is collected, and a cloud type determination step is performed to determine the final cloud type of the second target cloud. If the final cloud type is the first target cloud, a corresponding precipitation enhancement instruction is sent to the execution device. If the final cloud type is the second target cloud, the corresponding target cloud is determined as a non-operational cloud.

[0032] In some embodiments, if the target cloud body is identified as the first target cloud body in the initial judgment, no further verification is required, and the cloud body is directly considered to have significant catalytic potential. The execution device is a physical implementation unit that can release the catalyst at a specified position, method, and dosage after receiving the "rain enhancement instruction" from the control end. The execution device can be a group of drones. On this basis, the corresponding thickness is determined according to the boundary of the preset temperature layer. If the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is greater than or equal to the corresponding threshold, the first flight plan of the execution device is determined. If the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is less than the corresponding threshold, the second flight plan of the execution device is determined. During the execution of the first flight plan or the second flight plan by the execution device, multiple meteorological data of the preset temperature layer are received. Each of the multiple meteorological data includes meteorological data and the corresponding execution device number. For each meteorological data, it is determined whether the meteorological data meets the delivery standard. If it meets the standard, a rain enhancement instruction is generated and sent to the corresponding execution device. For the second target cloud body, the survey machine performs another spiral upsampling (such as Figure 2 (See the diagram of the survey aircraft survey). During the next upsampling process, the cloud type determination steps described above are executed, and the cloud type is verified again to determine the final cloud type of the second target cloud. If the final cloud type is the first target cloud, a rain enhancement command is sent to the execution device (such as a drone swarm). If the final cloud type is still the second target cloud, it means that even after the second verification, all fluctuation conditions are still not met. The cloud is then considered to be insufficiently stable or structurally unsatisfactory. To avoid catalysis failure or resource waste, it is marked as a non-operational cloud. The final cloud type refers to the final classification result of a target cloud by the control end after completing the initial judgment and secondary data collection and re-judgment. It is used to determine whether to execute rain enhancement operations.

[0033] The plurality of fluctuations include altitude fluctuation, temperature fluctuation and radar reflectivity fluctuation; and Determining the cloud type of the target cloud based on the multiple fluctuation quantities, where the cloud type is either the first target cloud or the second target cloud, includes: If the multiple fluctuation amounts are all greater than or equal to the corresponding preset thresholds, the corresponding target cloud body is determined as the first target cloud body; if any of the multiple fluctuation amounts is less than the corresponding preset threshold, the target cloud body is determined as the second target cloud body.

[0034] In some embodiments, height fluctuation measures the degree of vertical structural fluctuation in a cloud (i.e., the height difference between the upper and lower layers of the cloud), indicating the vertical activity of the cloud. Temperature fluctuation measures the significant temperature variations in each temperature layer, reflecting whether the thermal structure is conducive to condensation nucleus formation and cloud droplet growth. Radar reflectivity fluctuation measures the spatial variation in radar reflection signal intensity, representing the degree of variation in cloud droplet density / size and indirectly indicating the aggregation of supercooled water or ice crystals. For example, the preset thresholds for height fluctuation, temperature fluctuation, and radar reflectivity fluctuation are 0.25, 0.20, and 0.30, respectively. If the calculated height fluctuation is 0.32, the temperature fluctuation is 0.28, and the radar reflectivity fluctuation is 0.40, all of which are greater than the corresponding preset thresholds, the target cloud is identified as the first target cloud. If the calculated height fluctuation is 0.21, the temperature fluctuation is 0.28, and the radar reflectivity fluctuation is 0.40, and the height fluctuation is less than the corresponding preset thresholds, the target cloud is identified as the second target cloud.

[0035] For the first target cloud, sending the corresponding rain enhancement instruction to the execution device includes: Step 1: Determine the corresponding thickness based on the boundary of the preset temperature layer; if the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is greater than or equal to the corresponding threshold, determine the first flight plan of the execution device; if the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is less than the corresponding threshold, determine the second flight plan of the execution device; Step 2: During the execution of the first flight plan or the second flight plan by the execution device, multiple meteorological data of a preset temperature layer are received, where each meteorological data includes meteorological data and a corresponding execution device number; for each meteorological data, whether the meteorological data meets the delivery criteria is determined; if so, a rain enhancement instruction is generated and sent to the corresponding execution device.

[0036] In some embodiments, the thickness corresponding to the preset temperature layer is calculated based on the upper and lower boundaries of the preset temperature layer. The detection range of the execution device refers to the detection height of the drone in the vertical direction. The ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is calculated, and the ratio result is compared with the corresponding threshold. If the ratio is greater than or equal to the corresponding threshold, the first flight plan of the execution device is determined. Among them, the first flight plan is to perform layered detection of the target cloud body and spread rain-enhancing catalysts. As an example, the target cloud body is divided into two layers for detection and spreading of rain-enhancing catalysts. The first layer is the uppermost layer of the target cloud body. Multiple drones are separated by the detection range and jointly detect and spread. The drone's micrometeorological sensors detect supercooled water characteristics in a local area and transmit the collected meteorological data to the control terminal, which determines whether it meets the deployment criteria. If a supercooled water area meeting the deployment criteria is detected, the coordinates of the supercooled water area are recorded and a rain enhancement command is issued, instructing the executing device to disperse rain enhancement catalyst. When the second layer is executed, the area below the corresponding supercooled water area in the first layer (i.e., the second layer) is prioritized for determining whether there is a supercooled water area. Detection and dispersion are then performed. To account for diffusion around the area, the catalyst distribution can be reduced, for example, by 10%. (For example, assuming a supercooled water area exists at point A in the first layer, point B below point A in the second layer is prioritized for determining whether there is still a supercooled water area. The area around point B is then prioritized for determining whether there is still a supercooled water area. Assuming a nine-square grid centered on point B, if supercooled water exists at point B or in the surrounding nine-square grid, the catalyst distribution is appropriately reduced to account for gravity settling and wind field effects at point A.) If the ratio is less than the corresponding threshold, the second flight plan for the executing device is determined. Among them, the second flight plan is that the drones do not need to fly in layers, that is, fly in the same layer. During the flight, the drones will send the detected meteorological data to the control end. The control end will determine whether it meets the supercooled water area of ​​the release standard, record the coordinates and send a rain enhancement command to the drone. After receiving the rain enhancement command, the drone will spread the rain enhancement catalyst. Among them, each meteorological data includes temperature, humidity, wind speed, liquid water content and execution equipment number. The control end receives meteorological data in real time, such as Figure 3 As shown, a schematic diagram of the temperature and humidity changes collected by the drone is shown, including a schematic diagram of the temperature change over time and a schematic diagram of the humidity change with altitude. On this basis, it is judged whether the local area meets the deployment standards based on the temperature, humidity, wind speed, and liquid water content. If it meets the requirements, a rain enhancement instruction is issued to the corresponding execution device according to the execution device number. Among them, the deployment standards can be that the temperature is between -5℃ and -15℃, the relative humidity is not less than 90%, and the liquid water content is not less than 0.2g / m 3The rain enhancement instruction is a control command generated by the control end based on the target cloud type, meteorological conditions and pollutant assessment results, which is used to trigger the execution equipment to carry out the catalyst release operation. It usually contains specific flight parameters, trigger instructions and operation area information. The execution equipment number refers to the unique identification code assigned to each device that performs artificial rain enhancement operations (such as drones). It is a system number used for device-level operation instruction distribution, data matching and process tracking between the control end and multiple operation units.

[0037] These embodiments improve the success rate and resource utilization of catalytic operations. Specifically, a multi-source sensor onboard a survey aircraft acquires key structural information, such as cloud height, radar reflectivity, and multiple temperature layers. A fluctuation index based on the ratio of standard deviation to mean is introduced to quantify cloud structural volatility, enabling accurate identification of target clouds with catalytic potential. By establishing a classification mechanism for primary and secondary target clouds and performing a secondary sampling and re-evaluation process on the secondary target cloud, a closed-loop "initial evaluation-re-evaluation-execution" judgment logic is established, effectively improving identification accuracy under boundary conditions. By matching the temperature layer thickness with the detection capabilities of the execution equipment, a dynamic flight plan is formulated to ensure that the catalyst deployment area covers the critical supercooled water region, enhancing operational efficiency and catalytic effectiveness. By determining whether deployment criteria are met based on real-time meteorological data, the timing and location of catalyst release are precisely controlled, avoiding resource waste and ineffective operations. This enables accurate identification and reliable decision-making before cloud seeding operations, improving the success rate and resource utilization efficiency of catalytic operations.

[0038] In some embodiments, to further address the second technical problem described in the background technology section, namely, that "existing artificial rain enhancement operations fail to consider the impact of pollutants on rain enhancement operations, making it difficult to ensure environmental safety and operational reliability during the rain enhancement process, and that rain enhancement operations are single and extensive, lacking specificity, and reducing the accuracy of artificial rain enhancement operations," some embodiments of the present invention include, before obtaining cloud data of the target cloud, the following steps are performed: Step 1: Obtain historical meteorological data of the source points in the target monitoring area, pollutant information of the source points, and pollutant information of the farmland points. The pollutant information of the farmland points includes the coordinates of the farmland points, multiple farmland pollutants, the farmland pollutant concentration corresponding to each farmland pollutant, the type of farmland pollutant, and the safety threshold. The pollutant information of the source points includes the coordinates of the source points, multiple source pollutants, the type of source pollutant corresponding to each source pollutant, the source pollutant concentration, emission rate, emission height, lateral diffusion coefficient, vertical diffusion coefficient, and safety threshold. In some embodiments, the target monitoring area is one of the sampling points in the target area of ​​the rain enhancement operation, and is a reference point for assessing whether the impact of pollutants is acceptable. The target monitoring area includes farmland points and source points. On this basis, the historical meteorological data of the source points are extracted from the pre-stored historical meteorological database. And synchronized with the pollution source emission permit system of the ecological environment department to obtain pollutant information at the source points. Meteorological environment monitoring equipment deployed at farmland points regularly collects and transmits it back to the control end, so as to obtain pollutant information at the farmland points for pollutant impact determination and pre-operation assessment. Among them, historical meteorological data are meteorological records of the source point and its surroundings for the past 3 to 5 years, including wind speed, wind direction, etc., which are used for pollutant diffusion simulation and cloud formation prediction. The source point is the location of a single fixed pollution source (such as a factory exhaust chimney) and is the starting point for pollutant diffusion. Pollutant information at a source point refers to the pollutant emission parameters associated with specific pollution sources (such as factories) at their emission locations related to farmland pollution. This information is used to simulate pollutant diffusion under meteorological conditions. It includes the coordinates of the source point, various source pollutants, the type of source pollutant corresponding to each source pollutant, source pollutant concentration, emission rate, emission altitude, lateral diffusion coefficient, vertical diffusion coefficient, and safety threshold. Source point coordinates refer to the latitude and longitude coordinates of the source point. Source pollutants refer to the pollutants monitored at the source point. Source pollutant types refer to the specific chemical or physical components released into the atmosphere by the pollutant emission source, such as sulfur dioxide and nitrogen oxides. Source pollutant concentration refers to the mass or amount of pollutants per unit volume of air, reflecting the emission intensity of the pollution source. The emission rate refers to the total amount of pollutants released from the source into the atmosphere per unit time, reflecting the scale of the pollution source. The emission altitude refers to the initial vertical position of the source pollutant after it enters the atmosphere, affecting the range and pattern of diffusion. The lateral diffusion coefficient reflects the horizontal diffusion range. The vertical diffusion coefficient reflects the vertical diffusion range. Pollutant information at farmland sites refers to data such as the types and concentrations of pollutants actually monitored or acquired at the farmland site, reflecting the potential atmospheric pollution or background pollution status in the current farmland area. This information includes the site's coordinates, various farmland pollutants, the corresponding concentration of each farmland pollutant, the type of farmland pollutant, and safety thresholds. A farmland site refers to a farmland area with specific geographic coordinates or spatial extent within the target monitoring area. The coordinates of a farmland site can be latitude and longitude. Farmland pollutants refer to chemical substances, organisms, or physical particles present in farmland ecosystems that may negatively impact soil, water, air, or crops. Farmland pollutant concentration refers to the amount of pollutants present per unit volume or mass in farmland environmental media (soil, water, air) and is used to quantify the degree of pollution. Farmland pollutant types refer to the specific chemical or physical components in the atmosphere at the farmland site, such as sulfur dioxide and nitrogen oxides.

[0039] Step 2: Based on the pollutant information of the farmland points, determine the farmland pollutant impact type corresponding to the farmland points; based on the historical meteorological data, generate a historical wind rose diagram of the source point; based on the historical wind rose diagram, determine the diffusion direction of the factory pollutants; based on the coordinates of the farmland points and the coordinates of the source points, determine the relative position of the farmland points; based on the relative position and diffusion direction of the farmland points, determine whether the source pollutants of the source points diffuse to the farmland points; if they do not diffuse to the farmland points, the farmland pollutant impact type is used as the pollutant impact type corresponding to the target monitoring area; In some embodiments, the farmland pollutant concentrations corresponding to each farmland pollutant are sorted in order from large to small to obtain a farmland pollutant concentration sequence. The farmland pollutant corresponding to the highest farmland pollutant concentration is selected as the farmland monitoring pollutant, and the farmland pollutant impact type corresponding to the farmland point is determined according to the type of farmland pollutant corresponding to the farmland monitoring pollutant. The farmland pollutant impact type is farmland rain-increasing pollution, farmland rain-increasing inhibition or no impact on farmland. The farmland pollutant concentrations of all farmland pollutants are sorted in order from large to small to obtain a farmland pollutant concentration sequence. Among them, the farmland pollutant concentration sequence refers to an ordered list formed by sorting the concentration values ​​of multiple pollutants monitored at the farmland points from large to small. The farmland pollutant concentration sequence includes the types of farmland pollutants and the corresponding concentration values. As an example, the farmland pollutant concentration sequence includes SO2,80%, NO x , 60%. The pollutant with the highest concentration value in the farmland pollutant concentration sequence is selected as the farmland monitoring pollutant, for example, SO2 is selected as the farmland monitoring pollutant. The farmland monitoring pollutant refers to the pollutant with the highest concentration value in the above concentration sequence and is considered the pollutant with the "dominant influence" on the current farmland pollution situation. Based on this, the corresponding pollutant type is determined based on the farmland pollutant type corresponding to the farmland monitoring pollutant. The pollutant type table is queried based on the farmland pollutant type to determine the pollutant type corresponding to the farmland monitoring pollutant. The pollutant type table includes the farmland pollutant type, the pollutant type corresponding to each farmland pollutant, and the safety threshold. For example, if the farmland pollutant type is SO2, the corresponding pollutant type is a polluting pollutant; if the farmland pollutant type is PM2.5, the corresponding pollutant type is a polluting pollutant and an inhibiting pollutant. Polluting pollutants are pollutants that, in the context of artificial rain enhancement operations, may form acid rain or other environmental pollution byproducts after interacting with cloud water. They are inherently environmentally hazardous and pose pollution risks to farmland, soil, or ecosystems, such as acid rain. Inhibitory pollutants refer to substances that do not directly cause acidic pollution but interfere with cloud droplet formation, ice crystal growth, and catalyst action mechanisms. Their presence will significantly reduce the catalytic efficiency of artificial rainmaking operations or cause them to fail completely.

[0040] In some embodiments, if the pollutant type of the pollutant monitored by farmland is a pollution-type pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant impact type is determined to be farmland rain-increasing pollution; if the pollutant type of the pollutant monitored by farmland is a pollution-type pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant impact type is determined to be farmland no impact. If the pollutant type of the pollutant monitored by farmland is an inhibition-type pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant impact type is determined to be farmland inhibition of rain-increasing; if the pollutant type of the pollutant monitored by farmland is an inhibition-type pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant impact type is determined to be farmland no impact. Among them, the safety threshold refers to the maximum acceptable concentration limit allowed for a specific pollutant at a farmland point or a pollution diffusion path point. When the actual or simulated concentration of the pollutant exceeds the threshold, it is considered that there is an environmental risk or an operational inhibition risk, and the corresponding intervention strategy needs to be triggered. The threshold can be set with reference to national environmental standards or adjusted as needed. On this basis, if the pollutant type of the pollutant monitored in farmland is a pollution-type pollutant, the farmland pollutant concentration is compared with the corresponding safety threshold. If the farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant impact type corresponding to the farmland point is determined to be farmland rain enhancement pollution. If the farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant impact type corresponding to the farmland point is determined to be farmland no impact. Among them, farmland rain enhancement pollution means that the pollutant monitored at the farmland point is a pollution-type pollutant, and its actual concentration is greater than or equal to the corresponding safety threshold, which means that if artificial rain enhancement operations are carried out in this area, acid rain or other secondary pollution problems may be caused. If the pollutant type of the pollutant monitored in farmland is an inhibition-type pollutant, the farmland pollutant concentration is compared with the corresponding safety threshold. If the farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant impact type corresponding to the farmland point is determined to be farmland inhibition rain enhancement. If the farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant impact type corresponding to the farmland point is determined to be farmland no impact. Among them, farmland inhibition of rain enhancement means that the pollutants monitored at the farmland point are inhibitory pollutants, and their actual concentrations are greater than or equal to the corresponding safety thresholds. Although these pollutants do not directly pollute the environment, they can significantly reduce the nucleation efficiency of the catalyst or destroy the cloud structure, resulting in the failure of the rain enhancement operation or insignificant results. Farmland no impact means that the concentrations of all pollutants monitored at the farmland point are lower than their corresponding safety thresholds, or the type of pollutants monitored are not of the pollution or inhibitory type, and will not pose an environmental pollution risk or operational inhibition risk to the artificial rain enhancement operation.

[0041] In some embodiments, wind speed and wind direction are extracted from historical meteorological data, and a preset tool (such as Python) is used to draw a historical wind rose diagram of the source point: the wind direction is divided into 16 sectors (each 22.5 degrees). The wind direction frequency (percentage) and average wind speed of each sector are counted. Draw a graph: the frequency is represented by the length of the sector, and the wind speed is represented by a color bar. The historical wind rose diagram is a meteorological data visualization tool used to represent the statistical distribution of wind direction and wind speed at a specific location within a historical time period (such as the past 3-5 years). The dominant wind direction and frequency distribution in a specific season (such as previous years, similar time periods) or a specific weather form (such as similar wind direction, similar cloud distribution) are analyzed through the historical wind rose diagram. The dominant wind direction and its frequency distribution are obtained from the historical wind rose diagram. The dominant wind direction refers to the wind direction that appears most frequently in a certain area within a specific time period (such as a year, a season or a month). The dominant wind direction in the historical wind rose diagram is converted into a diffusion direction, and the diffusion direction can be a direction angle. Among them, the wind direction is the direction from which the wind comes, for example, east wind (90 。 ): The wind blows from the east, which means the wind is moving westward. West wind (270 。 ): The wind blows from the west, which means that the wind is moving eastward. The diffusion direction is consistent with the direction of wind movement. For example, if the dominant direction is east wind, the diffusion direction is westward. On this basis, the longitude and latitude coordinates of the farmland point and the longitude and latitude coordinates of the source point are converted into spherical coordinates respectively, and the initial azimuth is calculated, and the angle is mapped to 16 standard directions to obtain the relative position of the farmland point. Among them, the relative position of the farmland point refers to the relative position of the farmland point relative to the source point and the straight-line distance between the farmland point and the source point. For example, the farmland point is located due north of the source point (0 。 ), the relative position can be a direction angle. If the angle difference between the diffusion direction angle and the relative position angle of the farmland point is greater than the preset angle difference, the source pollutant of the source point will not diffuse to the farmland point, and the farmland pollutant impact type is used as the pollutant impact type. For example, the preset angle difference is 30 。 , the diffusion direction angle is 270 。 , the direction angle of the farmland point is (0 。 ), the angle difference between the two is 270 。 , greater than 30 。 , it means that the source pollutants at the source point will not spread to the farmland point. At this time, the corresponding farmland pollutant impact type is used as the pollutant impact type of the target monitoring area. If the diffusion direction angle is 75 。 The direction angle of the farmland point is 60 。 , it means that the source pollutants at the source point will spread to the farmland point.

[0042] Step 3: If the wind spreads to the farmland, determine the diffusion path and select multiple path points; for each path point, obtain the average wind speed and determine the coordinates of each path point with the source point as the origin; In some embodiments, if the pollutant is spreading toward a farmland site, a straight line diffusion path is established from the source site along the diffusion direction. This path simulates the route that pollutants would take to the farmland site under the influence of wind. Multiple path points are assigned to this path. The number of path points can be 5 or 10, depending on the required simulation accuracy. Each path point represents a location in the pollutant's propagation process. For example, path points are selected based on the following factors: turning points where pollutant concentrations change significantly (e.g., sharp drops or peaks), areas of high concentration or potential accumulation (e.g., densely built-up areas, valleys, and basins), the leading edges of potentially sensitive areas (e.g., near agricultural irrigation areas, schools, and drinking water sources), areas of wind direction change (e.g., intersections and gyre zones), and areas in front of terrain-enforced wind paths or obstacles (e.g., gorges and ventilation corridors). For each path point, a communication connection is established with an anemometer that collects real-time wind speeds at the path point to obtain real-time wind speeds for the past time period (e.g., the 30 minutes preceding the current time), and the average wind speed is calculated. On this basis, the coordinates of each path point are determined with the source point as the origin. The coordinates here are three-dimensional coordinates.

[0043] Step 4: Determine the source monitoring pollutants based on multiple source pollutants and the source pollutant concentration corresponding to each source pollutant; Step 5: Based on the emission rate, emission height, diffusion coefficient, coordinates of each path point, and average wind speed corresponding to the source monitoring pollutants, the pollutant concentration of the source monitoring pollutants at each path point is determined using the following formula:

[0044] in, The emission rate corresponding to the pollutant monitored at the source, is the average wind speed at each path point, The lateral diffusion coefficient and vertical diffusion coefficient corresponding to the source monitoring pollutants are is the coordinate of each path point determined with the source point as the origin, Monitor the pollutant concentration of each route point at the source of the pollutant. Emission height corresponding to pollutants monitored at the source; In some embodiments, the source pollutants with the most significant impact on farmland or the highest concentration are selected as source monitoring pollutants from multiple sources. Based on this, the emission rate, emission height, diffusion coefficient, coordinates of each path point, and average wind speed corresponding to the source monitoring pollutant are input into the following formula to obtain the path pollutant concentration of the source monitoring pollutant at each path point:

[0045] in, Monitor the emission rates of pollutants corresponding to the source. The average wind speed at each path point. The lateral diffusion coefficient and vertical diffusion coefficient corresponding to source monitoring of pollutants. The coordinates of each path point determined with the source point as the origin. The path pollutant concentration is monitored for the source of pollutants at each path point. The emission height corresponding to the source monitoring pollutant. Path pollutant concentration refers to the pollutant concentration value estimated or simulated at multiple path points along the path as the pollutant propagates from the source point to the farmland point along the diffusion path.

[0046] Step six: Based on the path pollutant concentration of the source monitoring pollutants at each path point, the path pollutant concentration sequence of the diffusion path is obtained; based on the path pollutant concentration sequence of the diffusion path, the path pollutant impact type corresponding to the diffusion path is determined, and the path pollutant impact type is path rainfall pollution, path rainfall inhibition or path no impact; based on the farmland pollutant impact type and the path pollutant impact type, the pollutant impact type corresponding to the target monitoring area is determined, and the corresponding processing instructions are generated and sent to the job scheduling end, where the pollutant impact type is no impact, rainfall pollution or rainfall inhibition.

[0047] In some embodiments, the simulated concentrations of source-monitored pollutants at each point along the diffusion path are recorded sequentially to form an ordered list, namely, a path pollutant concentration sequence. The pollutant concentration value at each path point in the path pollutant concentration sequence is compared with the safety threshold for the source-monitored pollutant. If the concentration value at any point in the sequence is greater than or equal to the safety threshold, and the source-monitored pollutant is of a contaminating type, the path pollutant impact type is path rain enhancement pollution; if the source-monitored pollutant is of an inhibitory type, the path pollutant impact type is path inhibition rain enhancement. If the concentrations at all points in the sequence are below the threshold, the path pollutant impact type is path no impact. The path pollutant impact type refers to a comprehensive assessment of whether a pollutant along the propagation path may affect rain enhancement operations at a farmland location, based on the concentration levels and pollutant properties of each point in the path pollutant concentration sequence. Path rain enhancement pollution refers to the simulation results along the pollutant diffusion path showing that the concentration of a contaminating pollutant at at least one path point is greater than or equal to its safety threshold. Such pollutants may co-precipitate with rainwater during rain enhancement, causing acid rain or farmland pollution. Pathway inhibition of rainfall enhancement means that the concentration of an inhibitory pollutant at at least one point along the pollutant diffusion path exceeds the safety threshold. Such pollutants can inhibit cloud droplet formation or interfere with catalyst activity, thereby reducing rainfall enhancement efficiency. Pathway no impact means that the simulated pollutant concentrations at all points along the pollutant diffusion path are below their corresponding safety thresholds, indicating that the current pollution source diffusion direction poses no significant risk or interference to farmland points.

[0048] In some embodiments, based on the farmland pollutant impact type and the path pollutant impact type, combined with preset rules, the pollutant impact type corresponding to the target monitoring area is determined, along with the dominant pollutant. Pollutant impact types include no impact, rain enhancement pollution, or rain enhancement inhibition. The pollutant impact type is the final classification result derived from a comprehensive assessment of the impact of pollutants measured at the farmland point (farmland pollutant impact type) and the impact of pollutants along the diffusion path from the pollution source to the farmland (path pollutant impact type). It reflects whether the target area is suitable for artificial rain enhancement operations under current environmental conditions. No impact means that the measured pollutant concentrations at the current farmland point are all below the corresponding safety threshold, and no areas with excessive concentrations are simulated along the pollution source diffusion path. This means that there is no environmental pollution risk or catalytic inhibition risk in the area, and rain enhancement operations can be carried out directly. Rain enhancement pollution refers to the detection of excessive levels of pollutants in the target area (possibly derived from actual measurements at the farmland point or path diffusion simulations). Performing artificial rain enhancement operations at this time could cause pollutants to fall into farmland with precipitation, resulting in acid rain or secondary environmental pollution risks. Rainfall enhancement suppression refers to the detection of excessive concentrations of inhibitory pollutants in a target area. While the pollutants themselves do not directly cause environmental pollution, their physical or chemical properties may interfere with key processes such as catalyst nucleation, cloud droplet condensation, and ice crystal formation, leading to failure or significant reduction in rain enhancement efficiency. This refers to the final comprehensive impact result derived from the fusion of the "agricultural pollutant impact type" and the "pathway pollutant impact type," which is used to guide whether to conduct rain enhancement operations. For example, if the pathway pollutant impact type is "no impact on the path" and the agricultural pollutant impact type is "no impact on the field," the pollutant impact type is "no impact." If the agricultural pollutant impact type is "no impact on the field" and the path pollutant impact type is "pathway rain enhancement pollution," the pollutant impact type is "rain enhancement pollution," and the source-monitored pollutant is designated as the dominant pollutant. If the agricultural pollutant impact type is "agricultural rain enhancement pollution" and the path pollutant impact type is "pathway suppression rain enhancement," the pollutant impact type is prioritized, determined as "rain enhancement pollution," and the agricultural monitored pollutant is designated as the dominant pollutant. The dominant pollutant refers to the pollutant type and parameter object that are prioritized when determining the source of pollutant risk, determining treatment methods (such as the type of neutralizer), and generating specific instructions when the pollutant impact type is "rainfall pollution" or "rainfall suppression." This indicates whether the pollution risk primarily originates from the "source" or "farmland." The appropriate neutralizing agent should be selected based on the dominant pollutant type.

[0049] In some embodiments, a preset treatment measure table is queried based on the pollutant impact type and the dominant pollutant, a treatment measure is determined, and a treatment instruction is generated. The preset treatment measure table includes the pollutant impact type, the dominant pollutant, and the corresponding treatment measure. If the pollutant impact type is no impact, a rain enhancement preparation instruction is generated and sent to the operation dispatch terminal. If the pollutant impact type is rain enhancement pollution, a first treatment instruction is generated and sent to the operation dispatch terminal. If the pollutant impact type is rain enhancement suppression, a second treatment instruction is generated and sent to the operation dispatch terminal. The operation dispatch terminal can be an operation management platform deployed at a ground command center. Its main functions include task distribution, deployment parameter configuration, operation equipment control, and operation process monitoring. Furthermore, when the survey aircraft is not performing rain enhancement operations, its control terminal also establishes a communication connection with the operation dispatch terminal to send instructions to the operation dispatch terminal. If the pollutant impact type is no impact, the test results indicate a clean and safe environment; the control terminal issues a rain enhancement preparation instruction (e.g., "normal rain enhancement preparation"). If the pollutant impact type is rain enhancement pollution, it indicates that a pollutant-type pollutant exceeds the standard. The first processing instruction could be to deploy a neutralizer corresponding to the dominant pollutant, such as "deploy a sulfur dioxide neutralizer." If the pollutant's impact is rain-increasing suppression, indicating the presence of an inhibitory pollutant and a high risk of catalytic ineffectiveness or failure, the second processing instruction could be to deploy a chemical agent corresponding to the dominant pollutant, such as "deploy a PM2.5 chemical agent."

[0050] Through these implementations, operational reliability and accuracy have been improved. Specifically, by collecting pollutant information from farmland and source points, and combining it with historical meteorological data for diffusion simulation, an accurate assessment of the impact of pollution before artificial rainmaking operations has been achieved. By distinguishing between polluting and inhibiting pollutants, it is determined whether they diffuse into farmland areas, and by combining the measured concentrations with the simulation results, the type of pollutant impact in the target area is determined. The system generates corresponding processing instructions based on the judgment results, realizing intelligent decision-making and dynamic regulation before operations. This solution improves the environmental safety and targeted nature of rainmaking operations, avoids acid rain or catalytic failure caused by excessive pollutants, and enhances the accuracy and reliability of artificial rainmaking operations.

[0051] In some embodiments, to further address the third technical problem described in the background technology section, namely, "existing technologies typically use fixed thresholds to judge pollutants, and are unable to combine historical rainfall data to provide personalized early warnings of pollutant response trends, resulting in delayed risk identification. In addition, there is a lack of scientific evaluation methods for the effects of rain enhancement operations, and it is impossible to dynamically feedback the effectiveness of operations or modify decision parameters based on pollutant change trends, thereby limiting the refined management and continuous optimization capabilities of artificial rain enhancement operations," in some embodiments of the present invention, a multi-source data fusion artificial rain enhancement operation control method of the present invention further includes: Step 1: Obtain historical rainfall data for each farmland pollutant and historical rainfall data for each source pollutant; determine the warning threshold for each farmland pollutant based on the historical rainfall data for each farmland pollutant and the corresponding safety threshold, and construct the corresponding farmland pollutant warning interval; determine the warning threshold for each source pollutant based on the historical rainfall data for each source pollutant and the corresponding safety threshold, and construct the corresponding source pollutant warning interval; In some embodiments, the control terminal establishes a communication connection with the environmental monitoring station in the target monitoring area to obtain historical rainfall data for each farmland pollutant and historical rainfall data for each source pollutant. Historical rainfall data refers to the concentration records of a certain pollutant before and after artificial rainmaking or natural rainfall events. These typically include: pre-rainfall concentration, post-rainfall concentration, time intervals (e.g., 2 hours before and 2 hours after rain), and multiple records (for trend modeling). Based on this, the warning thresholds for each farmland pollutant and each source pollutant are calculated using the following formula:

[0052] in, is the warning threshold, is the safety threshold, The range of pollutant concentration changes before and after rainfall. The safety threshold is used as the upper limit of the warning interval, and the warning threshold is used as the lower limit of the warning interval, thus deriving the warning interval for farmland pollutants and the threshold interval for source pollutants. The warning interval is used to determine whether a pollutant is at a "critical risk" but has not yet exceeded the standard.

[0053] Step 2: Update the farmland pollutant impact type and the path pollutant impact type based on the farmland pollutant warning interval and the source pollutant threshold interval to obtain the updated farmland pollutant impact type and the updated path pollutant impact type, and determine the updated pollutant impact type corresponding to the target monitoring area; In some embodiments, in the process of determining the farmland pollutant impact type and the path pollutant impact type, the farmland pollutant warning interval and the source pollutant threshold interval are introduced to replace the safety threshold corresponding to each farmland pollutant and the safety threshold corresponding to each source pollutant, respectively. The farmland pollutant impact type and the path pollutant impact type are re-determined to obtain updated farmland pollutant impact type and updated path pollutant impact type, and further determine the updated pollutant impact type corresponding to the target monitoring area. Among them, the updated pollutant impact type is the final regional judgment result obtained after the "updated farmland pollutant impact type" and the "updated path pollutant impact type" are integrated and judged. Among them, the updated pollutant impact type is updated no impact, updated rain enhancement pollution, and updated rain enhancement suppression.

[0054] Step three: if the updated pollutant impact type is updated with no impact, multiple real-time concentrations corresponding to each pollutant in the rain enhancement process are obtained; and after the rain enhancement is completed, multiple real-time concentrations of each pollutant are analyzed to obtain the concentration change trend of each pollutant, which is increasing or decreasing; if the concentration change trend is decreasing, the rain enhancement operation is marked as a successful rain enhancement event; if the concentration change trend is increasing, it is determined whether the rain enhancement operation has rain enhancement pollution or inhibits rain enhancement; if so, the safety threshold and warning threshold of the corresponding pollutant are adjusted, and the updated warning interval is obtained; if there is no rain enhancement pollution or rain enhancement is inhibited, the selection of multiple path points in the diffusion path is corrected.

[0055] In some embodiments, if the updated pollutant impact type is set to "no impact," during the rain enhancement process, a concentration sensor is used to periodically collect real-time pollutant concentrations and transmit them to the control terminal. The control terminal receives and stores multiple real-time concentrations, each including the pollutant type, corresponding concentration value, and timestamp. After the rain enhancement operation is completed, the multiple real-time concentrations are sorted and analyzed chronologically to determine the concentration trend of each pollutant. The concentration trend is either increasing or decreasing. If the concentration trend is decreasing, the rain enhancement operation is marked as a successful rain enhancement event. If the concentration trend is increasing, a determination is made as to whether the rain enhancement operation presents rain enhancement pollution or rain enhancement inhibition. If so, this indicates a pollution risk or rain enhancement inhibition risk, and the current warning threshold or safety threshold is too high. The warning threshold or safety threshold is then lowered, and the corresponding warning interval is reconstructed to obtain an updated warning interval. If no rain enhancement pollution or rain enhancement inhibition risk is present, this indicates a deviation in the diffusion path modeling. The selection of multiple path points in the diffusion path is then revised, such as by adding key locations, refining the path spacing, and introducing a new sensor layout strategy.

[0056] These examples improve the accuracy of risk identification and the scientific and safe nature of cloud seeding operations. Specifically, by analyzing the differences in pollutant responses before and after historical cloud seeding, personalized "warning thresholds" and "warning intervals" are generated, enhancing the ability to proactively identify potential risks. Farmland pollutants and source pollutants are clearly modeled and judged separately. Through bidirectional determination and fusion, the "updated pollutant impact type" for the target monitoring area is refined. Using real-time pollutant concentration sequences, post-operation analysis of their changing trends is used to assess the effectiveness of cloud seeding operations and determine whether to adjust parameters or correct the path. If the concentration rises abnormally and meets pollution or suppression characteristics, the warning threshold or safety threshold is dynamically lowered. If the concentration rises but does not indicate pollution, model deviation is inferred and the diffusion path point settings are further adjusted. By establishing pollutant warning intervals, updating pollutant impact types, and providing feedback on operation results, the accuracy of risk identification and the scientific and safe nature of cloud seeding operations are effectively improved.

[0057] The above descriptions are merely some preferred embodiments of the present invention and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A multi-source data fusion artificial rainmaking operation control method, characterized in that: include: Acquire cloud data of a target cloud, wherein the cloud data includes cloud height, radar reflectivity, multiple temperature layers, and temperature intervals and boundaries corresponding to each temperature layer; A cloud type determination step is performed based on the cloud data: determining whether a target cloud has a preset temperature layer based on the multiple temperature layers and the temperature intervals corresponding to each temperature layer; if not, determining the corresponding target cloud as a non-operational cloud; and if so, comparing the cloud height with a preset cloud height threshold; if the cloud height is less than the preset cloud height threshold, determining the corresponding target cloud as a non-operational cloud. If the cloud height is greater than or equal to a preset cloud height threshold, multiple fluctuation quantities corresponding to the target cloud are determined based on the cloud height, radar reflectivity, multiple temperature layers, and the temperature range corresponding to each temperature layer. The cloud type of the target cloud is determined based on the multiple fluctuation quantities, and the cloud type is determined as the first target cloud or the second target cloud. For the first target cloud body, a corresponding rain enhancement instruction is sent to the execution device; for the second target cloud body, cloud body data corresponding to the second target cloud body is collected, and the cloud body type determination step is performed to determine the final cloud body type of the second target cloud body; If the final cloud type is the first target cloud, the corresponding rain enhancement instruction is sent to the execution device; If the final cloud body type is the second target cloud body, the corresponding target cloud body is determined as a non-operating cloud body.

2. The multi-source data fusion artificial rainfall control method according to claim 1 is characterized in that: The plurality of fluctuations include an altitude fluctuation, a temperature fluctuation and a radar reflectivity fluctuation; as well as The step of determining the cloud type of the target cloud body according to the plurality of fluctuation quantities, wherein the cloud type is the first target cloud body or the second target cloud body, includes: If the multiple fluctuation amounts are all greater than or equal to the corresponding preset thresholds, the corresponding target cloud body is determined as the first target cloud body; if any of the multiple fluctuation amounts is less than the corresponding preset threshold, the target cloud body is determined as the second target cloud body.

3. The multi-source data fusion artificial rainfall control method according to claim 2 is characterized in that: The step of sending a corresponding rain enhancement instruction to the execution device for the first target cloud body includes: According to the boundary of the preset temperature layer, the corresponding thickness is determined; if the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is greater than or equal to the corresponding threshold, the first flight plan of the execution device is determined; if the ratio of the thickness corresponding to the preset temperature layer to the detection range of the execution device is less than the corresponding threshold, the second flight plan of the execution device is determined.

4. The multi-source data fusion artificial rainfall control method according to claim 3 is characterized in that: The step of sending the corresponding rain enhancement instruction to the execution device for the first target cloud body further includes: During the execution of the first flight plan or the second flight plan by the execution device, multiple meteorological data of a preset temperature layer are received, and each of the multiple meteorological data includes meteorological data and a corresponding execution device number; for each meteorological data, it is determined whether the meteorological data meets the delivery standards; if so, a rain enhancement instruction is generated and sent to the corresponding execution device.

5. The multi-source data fusion artificial rainfall control method according to claim 4 is characterized in that: Before acquiring the cloud data of the target cloud, the following steps are included: Obtain historical meteorological data of the source points in the target monitoring area, pollutant information of the source points, and pollutant information of the farmland points, wherein the pollutant information of the farmland points includes the coordinates of the farmland points, multiple farmland pollutants, the farmland pollutant concentration corresponding to each farmland pollutant, the type of farmland pollutant, and the safety threshold; the pollutant information of the source points includes the coordinates of the source points, multiple source pollutants, the type of source pollutant corresponding to each source pollutant, the source pollutant concentration, the emission rate, the emission height, the lateral diffusion coefficient, the vertical diffusion coefficient, and the safety threshold; Based on the pollutant information of the farmland point, the farmland pollutant impact type corresponding to the farmland point is determined; based on the historical meteorological data, a historical wind rose diagram of the source point is generated; based on the historical wind rose diagram, the diffusion direction of the factory pollutants is determined; based on the coordinates of the farmland point and the coordinates of the source point, the relative position of the farmland point is determined; based on the relative position and diffusion direction of the farmland point, it is judged whether the source pollutants of the source point diffuse to the farmland point; if it does not diffuse to the farmland point, the farmland pollutant impact type is used as the pollutant impact type corresponding to the target monitoring area.

6. The multi-source data fusion artificial rainfall control method according to claim 5, characterized in that: Before acquiring the cloud data of the target cloud, the method further includes: If the wind spreads to farmland points, determine the diffusion path and select multiple path points; for each path point, obtain the average wind speed, and determine the coordinates of each path point with the source point as the origin; Determine source monitoring pollutants based on multiple source pollutants and the corresponding source pollutant concentrations of each source pollutant; Based on the emission rate, emission height, diffusion coefficient, coordinates of each path point and average wind speed corresponding to the source monitoring pollutants, the pollutant concentration of the source monitoring pollutants at each path point is determined by the following formula: in, The emission rate corresponding to the pollutant monitored at the source, is the average wind speed at each path point, The lateral diffusion coefficient and vertical diffusion coefficient corresponding to the source monitoring pollutants are is the coordinate of each path point determined with the source point as the origin, Monitor the pollutant concentration of each route point at the source of the pollutant. Emission height corresponding to pollutants monitored at the source; According to the path pollutant concentration of the source monitoring pollutants at each path point, the path pollutant concentration sequence of the diffusion path is obtained; according to the path pollutant concentration sequence of the diffusion path, the path pollutant impact type corresponding to the diffusion path is determined, and the path pollutant impact type is path rainfall pollution, path rainfall inhibition or path no impact; according to the farmland pollutant impact type and the path pollutant impact type, the pollutant impact type corresponding to the target monitoring area is determined, and the corresponding processing instructions are generated and sent to the job scheduling end, wherein the pollutant impact type is no impact, rainfall pollution or rainfall inhibition.

7. The multi-source data fusion artificial rainfall control method according to claim 6, characterized in that: Determining the farmland pollutant impact type corresponding to the farmland point based on the farmland point pollutant information includes: The farmland pollutant concentrations corresponding to each farmland pollutant are sorted in descending order to obtain a farmland pollutant concentration sequence; the farmland pollutant corresponding to the highest farmland pollutant concentration is selected as the farmland monitoring pollutant, and according to the type of farmland pollutant corresponding to the farmland monitoring pollutant, the farmland pollutant impact type corresponding to the farmland point is determined, and the farmland pollutant impact type is farmland rain-increasing pollution, farmland rain-increasing inhibition or farmland no impact.

8. The multi-source data fusion artificial rainfall control method according to claim 7, characterized in that: Determining the farmland pollutant impact type corresponding to the farmland point based on the farmland pollutant type corresponding to the farmland monitoring pollutant includes: According to the type of farmland pollutants corresponding to the farmland monitoring pollutants, the corresponding pollutant type is determined, wherein the pollutant type is a pollution-type pollutant or an inhibition-type pollutant; if the pollutant type of the farmland monitoring pollutants is a pollution-type pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, then the farmland pollutant impact type is determined to be farmland rainmaking pollution; if the pollutant type of the farmland monitoring pollutants is a pollution-type pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, then the farmland pollutant impact type is determined to be no impact on farmland; If the pollutant type of the farmland monitoring pollutant is an inhibitory pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant impact type is determined to be farmland inhibition of rain enhancement; if the pollutant type of the farmland monitoring pollutant is an inhibitory pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant impact type is determined to be no impact on farmland.

9. The multi-source data fusion artificial rainfall control method according to claim 8, characterized in that: The method of determining the pollutant impact type corresponding to the target monitoring area according to the farmland pollutant impact type and the path pollutant impact type, generating a corresponding processing instruction and sending it to the job scheduling end includes: If the pollutant impact type is no impact, a rain enhancement preparation instruction is generated and sent to the job scheduling end; if the pollutant impact type is rain enhancement pollution, a first processing instruction is generated and sent to the job scheduling end; if the pollutant impact type is rain enhancement inhibition, a second processing instruction is generated and sent to the job scheduling end.

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