A multi-source data fusion artificial rain enhancement operation control method

Abstract

The application relates to the technical field of weather modification, and discloses a multi-source data fusion artificial rain enhancement operation control method, which comprises the following steps: obtaining cloud body data of a target cloud body, including cloud body height, radar reflectivity, and temperature intervals and boundaries of multiple temperature layers, and performing cloud body type judgment. Firstly, it is judged whether a preset temperature layer exists, if not or the cloud body height is lower than a preset threshold, the cloud body is determined as a non-operation cloud body; if the preset temperature layer exists and the height meets the condition, the fluctuation amount of the height, temperature and reflectivity is calculated, and according to the fluctuation amount, the cloud body is divided into a first target cloud body or a second target cloud body. If the cloud body is the first target cloud body, a rain enhancement instruction is directly issued; if the cloud body is the second target cloud body, secondary data acquisition and judgment are carried out, if the cloud body is still the second target cloud body, the operation is abandoned. Therefore, the scientific nature and precision of the rain enhancement operation are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial weather modification, and particularly relates to a multi-source data fusion artificial rain enhancement operation control method. BACKGROUND

[0002] As a main means of artificial weather modification, artificial rain enhancement has been widely applied in the fields of agricultural yield increase, water resource regulation and ecological restoration. The traditional artificial rain enhancement method mainly relies on the operation personnel to select the catalysis time and target cloud body according to the weather forecast, radar echo map and experience judgment, and to throw the catalyst such as silver iodide and liquid nitrogen into the cloud through rockets, cannonballs or airplanes, so as to promote the cloud drop condensation and form precipitation. With the development of remote sensing technology, meteorological data acquisition technology and unmanned aerial vehicle platform, the artificial rain enhancement operation gradually evolves towards intelligence and refinement, and certain progress has been made in operation path planning and catalysis window identification. However, in the existing artificial rain enhancement process, the following technical problems often exist:

[0003] Firstly, the existing artificial rain enhancement method cannot effectively and reliably judge whether the target cloud body has operation value under complex meteorological conditions, which leads to inaccurate identification and low success rate of catalysis operation and resource waste.

[0004] Secondly, the existing artificial rain enhancement operation does not consider the influence of pollutants on the rain enhancement operation, and it is difficult to guarantee the environmental safety and operation reliability of the rain enhancement process. In addition, the rain enhancement operation is single and extensive, lacks pertinence, and reduces the precision of artificial rain enhancement operation.

[0005] Thirdly, the existing technology usually uses fixed threshold to judge the pollutants, cannot make individualized early warning on the response trend of the pollutants combined with historical rainfall data, leads to lag of risk identification, and in addition, the effect after the rain enhancement operation lacks scientific evaluation means, and cannot dynamically feedback the operation effectiveness or correct the decision parameters according to the change trend of the pollutants, thereby limiting the fine control and continuous optimization ability of the artificial rain enhancement operation. SUMMARY

[0006] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed technology, nor is it intended to be used to limit the scope of the claimed technology.

[0007] The present application provides a multi-source data fusion artificial rain enhancement operation control method to solve one or more of the technical problems mentioned in the background section.

[0008] 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;

[0009] 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.

[0010] 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.

[0011] 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.

[0012] Optionally, the plurality of fluctuations include altitude fluctuation, temperature fluctuation and radar reflectivity fluctuation; and

[0013] 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:

[0014] 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.

[0015] Optionally, for the first target cloud body, sending a corresponding rain enhancement instruction to an execution device includes:

[0016] 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.

[0017] Optionally, for the first target cloud body, the corresponding rain enhancement instruction is sent to the execution device, and the method further comprises:

[0018] In the process of executing the first flight plan or the second flight plan by the execution device, a plurality of meteorological data of the preset temperature layer is received, each meteorological data of the plurality of meteorological data comprises meteorological data and a corresponding execution device number; for each meteorological data, it is judged whether the meteorological data meets the release standard; if yes, a rain enhancement instruction is generated and sent to the corresponding execution device.

[0019] Optionally, before the cloud body data of the target cloud body is acquired, the method further comprises:

[0020] The historical meteorological data of the source point of the target monitoring area, the pollutant information of the source point and the pollutant information of the farmland point are acquired, the pollutant information of the farmland point comprises the coordinates of the farmland point, a plurality of farmland pollutants, the farmland pollutant concentration corresponding to each farmland pollutant, the farmland pollutant type and the safety threshold, and the pollutant information of the source point comprises the coordinates of the source point, a plurality of source pollutants, the source pollutant type corresponding to each source pollutant, the source pollutant concentration, the emission rate, the emission height, the horizontal diffusion coefficient, the vertical diffusion coefficient and the safety threshold;

[0021] According to the pollutant information of the farmland point, the farmland pollutant influence type corresponding to the farmland point is determined; according to the historical meteorological data, the historical wind rose of the source point is generated; according to the historical wind rose, the diffusion direction of the factory pollutant is determined; according to the coordinates of the farmland point and the coordinates of the source point, the relative position of the farmland point is determined; according to the relative position of the farmland point and the diffusion direction, it is judged whether the source pollutant of the source point diffuses to the farmland point; if not, the farmland pollutant influence type is taken as the pollutant influence type corresponding to the target monitoring area.

[0022] Optionally, before the cloud body data of the target cloud body is acquired, the method further comprises:

[0023] If the source pollutant diffuses to the farmland point, the diffusion path is determined and a plurality of path points are selected; for each path point, the average wind speed is acquired, and the coordinates of each path point are determined with the source point as the origin;

[0024] According to the plurality of source pollutants and the source pollutant concentration corresponding to each source pollutant, the source monitoring pollutant is determined;

[0025] According to the emission rate, the emission height, the diffusion coefficient corresponding to the source monitoring pollutant, the coordinates of each path point and the average wind speed, the pollutant concentration of the source monitoring pollutant of each path point is determined by the following formula:

[0026]

[0027] wherein, is an emission rate of the source monitoring pollutant, is an average wind speed of each path point, is a horizontal diffusion coefficient and a vertical diffusion coefficient of the source monitoring pollutant determined at each path point, is a coordinate of each path point determined with the source point as the origin, is a path pollutant concentration of the source monitoring pollutant at each path point, is an emission height of the source monitoring pollutant;

[0028] According to the path pollutant concentration of the source monitoring pollutant at each path point, a path pollutant concentration sequence of the diffusion path is obtained; according to the path pollutant concentration sequence of the diffusion path, a path pollutant influence type corresponding to the diffusion path is determined, and the path pollutant influence type is a path rain-increasing pollution, a path rain-inhibiting, or a path no influence; according to the farmland pollutant influence type and the path pollutant influence type, a pollutant influence type corresponding to the target monitoring area is determined, and a corresponding processing instruction is generated and sent to the operation scheduling end, wherein the pollutant influence type is no influence, rain-increasing pollution, or rain-inhibiting.

[0029] Optionally, according to the pollutant information of the farmland point, a farmland pollutant influence type corresponding to the farmland point is determined, including:

[0030] 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 a farmland monitoring pollutant, and according to the farmland pollutant type corresponding to the farmland monitoring pollutant, a farmland pollutant influence type corresponding to the farmland point is determined, and the farmland pollutant influence type is farmland rain-increasing pollution, farmland rain-inhibiting, or farmland no influence.

[0031] Optionally, according to the farmland pollutant type corresponding to the farmland monitoring pollutant, a farmland pollutant influence type corresponding to the farmland point is determined, including:

[0032] According to the farmland pollutant type corresponding to the farmland monitoring pollutant, a 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 pollutant is a pollution-type pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant influence type is determined as farmland rain-increasing pollution; if the pollutant type of the farmland monitoring pollutant is a pollution-type pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant influence type is determined as farmland no influence.

[0033] If the 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 influence type is determined as farmland inhibitory rain enhancement; if the 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 influence type is determined as farmland no influence.

[0034] Optionally, according to the farmland pollutant influence type and the path pollutant influence type, the corresponding pollutant influence type of the target monitoring area is determined, and the corresponding processing instruction is generated and sent to the operation scheduling end, including:

[0035] If the pollutant influence type is no influence, a rain enhancement preparation instruction is generated and sent to the operation scheduling end; if the pollutant influence type is rain enhancement pollution, a first processing instruction is generated and sent to the operation scheduling end; if the pollutant influence type is inhibitory rain enhancement, a second processing instruction is generated and sent to the operation scheduling end.

[0036] The present application has the following beneficial effects:

[0037] 1. Improve the success rate and resource utilization rate of catalytic operation. Specifically, by mounting a multi-source sensor on the surveying machine to obtain key structural information such as the height of the cloud body, radar reflectivity and multiple temperature layers, and introducing a fluctuation index based on the ratio of standard deviation to mean value to quantitatively judge the fluctuation of the cloud body structure, the target cloud body with catalytic potential can be accurately identified. By setting a first target cloud body and a second target cloud body classification mechanism, and performing secondary sampling and re-judgment process on the second target cloud body, a closed-loop judgment logic of "primary judgment-rejudgment-execution" is constructed, which effectively improves the identification accuracy under boundary conditions. Combined with the matching result of temperature layer thickness and detection ability of the execution equipment, the flight plan is dynamically formulated to ensure that the catalyst release area covers the key supercooled water area, enhances the operation efficiency and catalytic effect. According to the real-time meteorological data, it is judged whether the release standard is met, and the catalyst release time and position are finely controlled to avoid resource waste and invalid operation, so as to realize accurate identification and reliable decision-making before artificial rain enhancement operation, and improve the success rate and resource utilization efficiency of catalytic operation.

[0038] 2. Improved operation reliability and precision. Specifically, by collecting pollutant information from farmland and source points, and combining historical weather data for diffusion simulation, the precise assessment of pollution influence before artificial rain enhancement operation is realized. By distinguishing between pollution and inhibition type pollutants, it is determined whether they diffuse to the farmland area, and combined with the measured concentration and simulation results, the pollutant influence type of the target area is determined. The system generates corresponding processing instructions according to the judgment result, realizes intelligent decision-making and dynamic control before operation. This scheme improves the environmental safety and operation pertinence of artificial rain enhancement, avoids acid rain or catalytic failure caused by excessive pollutants, and enhances the precision and reliability of artificial rain enhancement operation.

[0039] 3. Improved risk identification accuracy and scientificity and safety of artificial rain enhancement operation. Specifically, by analyzing the response difference of pollutants before and after historical rain enhancement, personalized "warning threshold" and "warning interval" are generated to enhance the pre-identification ability of potential risks. The farmland pollutants and source pollutants are modeled and judged respectively, and the "updated pollutant influence type" of the target monitoring area is determined by bidirectional judgment and fusion. Using the real-time collected pollutant concentration sequence, the change trend after operation is analyzed, and the effectiveness of artificial rain enhancement operation is judged, and whether to adjust the parameters or correct the path is decided. If the concentration abnormally rises and meets the pollution or inhibition characteristics, the warning threshold or safety threshold is dynamically reduced; if the concentration rises but has no pollution characteristics, the model deviation is further adjusted, the diffusion path point setting is adjusted, and the pollutant warning interval, updated pollutant influence type and operation result feedback mechanism are constructed, thereby effectively improving the risk identification accuracy and scientificity and safety of artificial rain enhancement operation. BRIEF DESCRIPTION OF DRAWINGS

[0040] The above and other features, advantages, and aspects of embodiments of the present application will become more apparent with reference to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals are used to represent the same or similar elements. It is to be understood that the drawings are schematically showing the elements and elements are not necessarily drawn to scale.

[0041] Figure 1 is a flowchart of a multi-source data fusion artificial rain enhancement operation control method of the present application;

[0042] Figure 2 is a surveying machine surveying schematic diagram of a multi-source data fusion artificial rain enhancement operation control method of the present application;

[0043] Figure 3 is a temperature and humidity change schematic diagram of a multi-source data fusion artificial rain enhancement operation control method of the present application. DETAILED DESCRIPTION

[0044] The present application will be described in more detail with reference to the drawings. Although some embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0045] In addition, it should be further noted that only parts related to the present application are shown in the drawings for ease of description. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0046] It should be noted that the concepts of "first", "second", etc. mentioned in the present application 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.

[0047] It should be noted that the adjectives "one" and "multiple" mentioned in the present application are illustrative and not limiting, and those skilled in the art should understand that, unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0048] The names of the messages or information exchanged between the devices of the present application are only for illustrative purposes, and are not used to limit the scope of the messages or information.

[0049] The present application will be described in more detail with reference to the drawings. Although some embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0050] As shown in Figure 1 , a flow chart of a multi-source data fusion artificial rain enhancement operation control method of the present application is shown, which specifically includes the following steps:

[0051] Step 101, obtaining cloud body data of a target cloud body, the cloud body data including cloud body height, radar reflectivity, multiple temperature layers, and temperature intervals and boundaries corresponding to each temperature layer;

[0052] In some embodiments, the execution subject of the multi-source data fusion artificial rain enhancement operation control method of the present application is a control terminal deployed on a surveying machine, and the control terminal can be a background server. The surveying machine is a high-altitude aircraft equipped with special detection sensors, data processing modules and communication modules, which can provide real-time cloud body structure information and environmental parameters for rain enhancement operations, such as an airplane. The special detection sensors include a radiosonde, a temperature and humidity probe, an LWC detector, a millimeter wave radar, a laser radar, etc. Among them, after the surveying machine takes off from the airport, it first climbs to the top of the target cloud body, and then spirals down 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.

[0053] 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.

[0054] 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.

[0055] In some embodiments, after obtaining the cloud body data, the control end performs a cloud body type judgment step: matching the temperature interval of the preset temperature layer with the temperature interval of each temperature layer in the plurality of temperature layers. If the matching fails, it means that the target cloud body does not have the preset temperature layer, and the corresponding target cloud body is marked as a non-operation cloud body. The preset temperature layer refers to the key temperature interval required for artificial precipitation enhancement operation. The non-operation cloud body refers to a cloud body that does not meet the catalytic condition and is not suitable for precipitation enhancement operation (such as a cloud body that is too thin and lacks supercooled water layer). If the matching is successful, it means that the target cloud body has the preset temperature layer. As an example, the target cloud body has temperature layer 1, temperature layer 2 and temperature layer 3, the temperature interval of temperature layer 1 is 0℃ to -5℃, the temperature interval of temperature layer 2 is -5℃ to -10℃, and the temperature interval of temperature layer 3 is -10℃ to -15℃. The preset temperature layer can be -5℃ to -15℃, and by traversing the three temperature layers, it is found that the temperature interval of temperature layer 2 and temperature layer 3 is within the temperature interval corresponding to the preset temperature layer, so it means that there is a preset temperature layer. On this basis, it is judged whether the cloud body height of the target cloud body is greater than a preset cloud body height threshold. The preset cloud body height threshold refers to the minimum allowed cloud body vertical height value set by the system. If the height of the target cloud body is lower than the value, it is considered that the cloud body is not vertically developed enough and the target cloud body is marked as a non-operation cloud body, which is not suitable for artificial precipitation enhancement operation. If the height of the target cloud body is greater than or equal to the value, it is considered that the cloud body is suitable for artificial precipitation enhancement operation. The next step is to judge according to the cloud body height, radar reflectivity, a plurality of temperature layers and the temperature interval corresponding to each temperature layer, and calculate the height fluctuation, temperature fluctuation and radar reflectivity fluctuation of the target cloud body respectively through the following formula:

[0056]

[0057] wherein, is the standard deviation of the random variable , which is used to reflect the fluctuation intensity of the random variable . is the mathematical expectation value of the random variable , which is used to reflect the average level of the random variable . is the fluctuation of the random variable , which is used to measure the relative fluctuation degree of the random variable . The larger the value, the more significant the non-uniformity or spatial or temporal fluctuation of the variable.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] wherein the plurality of fluctuation amounts comprises a height fluctuation amount, a temperature fluctuation amount, and a radar reflectivity fluctuation amount; and

[0062] According to the plurality of fluctuation amounts, determining a cloud type of the target cloud, the cloud type being a first target cloud or a second target cloud, comprises:

[0063] If each of the plurality of fluctuation amounts is greater than or equal to a corresponding preset threshold, the corresponding target cloud is determined as the first target cloud; if any of the plurality of fluctuation amounts is less than a corresponding preset threshold, the target cloud is determined as the second target cloud.

[0064] In some embodiments, the height fluctuation amount is a measure of the structural fluctuation degree of the cloud in the vertical direction (i.e. the height difference fluctuation of the upper and lower layers of the cloud), representing the vertical development activity of the cloud. The temperature fluctuation amount is a measure of whether the temperature of each temperature layer changes significantly, reflecting whether the thermal structure is conducive to the formation of condensation nuclei and the growth of cloud droplets. The radar reflectivity fluctuation amount is a measure of the spatial difference of radar reflectivity signal strength, representing the degree of change in cloud droplet density / size, and indirectly indicating the aggregation of supercooled water or ice crystals. As an example, the preset thresholds corresponding to the height fluctuation amount, the temperature fluctuation amount, and the radar reflectivity fluctuation amount are 0.25, 0.20, and 0.30 respectively. If the calculated height fluctuation amount is 0.32, the temperature fluctuation amount is 0.28, and the radar reflectivity fluctuation amount is 0.40, all of the three fluctuation amounts are greater than the corresponding preset thresholds, and the target cloud is determined as the first target cloud. If the calculated height fluctuation amount is 0.21, the temperature fluctuation amount is 0.28, and the radar reflectivity fluctuation amount is 0.40, among which the height fluctuation amount is less than its corresponding preset threshold, the target cloud is determined as the second target cloud.

[0065] wherein, for the first target cloud, sending a corresponding rain enhancement instruction to the execution device comprises:

[0066] Step one, determining a corresponding thickness according to the boundary of the preset temperature layer; if the ratio of the thickness of the preset temperature layer to the detection range of the execution device is greater than or equal to a corresponding threshold, determining a first flight plan of the execution device; if the ratio of the thickness of the preset temperature layer to the detection range of the execution device is less than a corresponding threshold, determining a second flight plan of the execution device;

[0067] Step two, receiving a plurality of meteorological data of the preset temperature layer during the execution of the first flight plan or the second flight plan of the execution device, each meteorological data in the plurality of meteorological data comprising meteorological data and a corresponding execution device number; for each meteorological data, determining whether the meteorological data meets the release standard; if so, generating a rain enhancement instruction and sending it to the corresponding execution device.

[0068] 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 according to the target cloud type, weather conditions and pollution assessment results, which is used to trigger the execution equipment to carry out catalyst release operation, usually containing specific flight parameters, trigger instructions and operation area information. The execution equipment number refers to the unique identification code allocated to each execution artificial rain enhancement equipment (such as unmanned aerial vehicle), which is used for device-level operation instruction distribution, data matching and process tracking between the control end and multiple operation units.

[0069] In these embodiments, the success rate and resource utilization rate of catalytic operation are improved. Specifically, by using the surveying machine to carry multiple source sensors to obtain the height, radar reflectivity and multiple temperature layers and other key structural information of the cloud, and introducing the fluctuation index based on the ratio of standard deviation to mean value to quantitatively judge the fluctuation of the cloud structure, the target cloud with catalytic potential can be accurately identified. By setting the first target cloud and the second target cloud classification mechanism, and performing secondary sampling and rejudgment process on the second target cloud, a closed-loop judgment logic of "primary judgment-rejudgment-execution" is constructed, which effectively improves the identification accuracy under boundary conditions. Combined with the matching results of temperature layer thickness and execution equipment detection capability, the flight plan is dynamically formulated to ensure that the catalyst release area covers the key supercooled water area, and the operation efficiency and catalytic effect are enhanced. According to the real-time weather data, it is judged whether the release standard is met, and the catalyst release time and position are finely controlled to avoid resource waste and invalid operation, so as to realize the accurate identification and reliable decision before artificial rain enhancement operation, and improve the success rate and resource utilization efficiency of catalytic operation.

[0070] In some embodiments, in order to further solve the technical problem two described in the background section, i.e. "the existing artificial rain enhancement operation does not consider the influence of pollutants on rain enhancement operation, and it is difficult to guarantee the environmental safety and operation reliability of rain enhancement process, and the rain enhancement operation is single and extensive, lacking of pertinence, reducing the accuracy of artificial rain enhancement operation", in some embodiments of the present application, before obtaining the cloud data of the target cloud, the following steps are included:

[0071] Step one, obtaining the historical weather data of the source point of the target monitoring area, the pollution information of the source point and the pollution information of the farmland point, the pollution information of the farmland point including the coordinates of the farmland point, multiple farmland pollutants, the farmland pollution concentration corresponding to each farmland pollutant, the farmland pollutant type and the safety threshold, the pollution information of the source point including the coordinates of the source point, multiple source pollutants, the source pollutant type corresponding to each source pollutant, the source pollutant concentration, the emission rate, the emission height, the horizontal diffusion coefficient, the vertical diffusion coefficient and the safety threshold;

[0072] In some embodiments, the target monitoring area is one of the sampling points in the rain enhancement operation target area, which is a reference point for evaluating whether the influence of the pollutants is acceptable. The target monitoring area includes farmland points and source points. On this basis, historical meteorological data of the source points are extracted from a pre-stored historical meteorological database. The pollutant information of the source points is obtained by synchronizing with the pollution source emission permission system of the ecological environmental department. The pollutant information of the farmland points is obtained by regularly collecting and returning to the control end through the meteorological environment monitoring equipment deployed at the farmland points, which is used for pollutant influence judgment and pre-operation evaluation. The historical meteorological data is the meteorological record of the source points and the surrounding area in the past 3 to 5 years, including wind speed, wind direction, etc., which is 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 pipe), which is the starting point of pollutant diffusion. The pollutant information of the source point refers to the pollutant emission parameters of a specific pollution source (such as a factory, etc.) corresponding to the emission location of the farmland point, which is used to simulate the diffusion of the pollutants under meteorological conditions, including the coordinates of the source point, multiple source pollutants, the source pollutant type corresponding to each source pollutant, the source pollutant concentration, the emission rate, the emission height, the horizontal diffusion coefficient, the vertical diffusion coefficient, and the safety threshold. The coordinates of the source point refer to the latitude and longitude coordinates of the source point. The source pollutant refers to the pollutant monitored by the source point. The source pollutant type refers to the specific chemical or physical component released into the atmosphere by the pollution source, such as sulfur dioxide, nitrogen oxide, etc. The source pollutant concentration refers to the mass or amount of substance of the pollutant per unit volume of air, reflecting the emission intensity of the pollution source. The emission rate refers to the total amount of source pollutants released into the atmosphere from the source per unit time, reflecting the scale of the pollution source. The emission height refers to the initial vertical position of the source pollutant after it is emitted from the source into the atmosphere, affecting the diffusion range and mode. The horizontal diffusion coefficient reflects the horizontal diffusion range. The vertical diffusion coefficient reflects the vertical diffusion range. The pollutant information of the farmland point refers to the data of the pollutant type and concentration actually monitored or obtained at the farmland point, reflecting the possible atmospheric pollution or background pollution state in the current farmland area. The pollutant information of the farmland point includes the coordinates of the farmland point, multiple farmland pollutants, the farmland pollutant concentration corresponding to each farmland pollutant, the farmland pollutant type, and the safety threshold. The farmland point refers to a farmland area with a specific geographic coordinate or spatial range in the target monitoring area. The coordinates of the farmland point can be latitude and longitude coordinates. The farmland pollutant refers to a chemical substance, organism, or physical particle that exists in the farmland ecosystem and may have a negative impact on soil, water, air, or crops. The farmland pollutant concentration refers to the content of the pollutant in a unit volume or unit mass of farmland environmental medium (soil, water, air), which is used to quantify the degree of pollution. The farmland pollutant type refers to the specific chemical or physical component in the atmosphere at the farmland point, such as sulfur dioxide, nitrogen oxide, etc.

[0073] Step two, according to the pollutant information of the farmland point, determine the farmland pollutant influence type corresponding to the farmland point; according to the historical meteorological data, generate the historical wind direction rose diagram of the source point; according to the historical wind direction rose diagram, determine the diffusion direction of the factory pollutant; according to the coordinates of the farmland point and the coordinates of the source point, determine the relative position of the farmland point; according to the relative position of the farmland point and the diffusion direction, judge whether the source pollutant of the source point diffuses to the farmland point; if not, the farmland pollutant influence type is taken as the pollutant influence type corresponding to the target monitoring area;

[0074] In some embodiments, the farmland pollutant concentration corresponding to each farmland pollutant is sorted in descending order to obtain a farmland pollutant concentration sequence. The farmland monitoring pollutant corresponding to the highest farmland pollutant concentration is selected as the farmland monitoring pollutant, and the farmland pollutant influence type corresponding to the farmland point is determined according to the farmland pollutant type corresponding to the farmland monitoring pollutant. The farmland pollutant influence type is farmland rain enhancement pollution, farmland rain inhibition, or no influence on farmland. The farmland pollutant concentration of all farmland pollutants is sorted in descending order to obtain a farmland pollutant concentration sequence. The farmland pollutant concentration sequence refers to an ordered list formed by sorting the concentration values of multiple pollutants monitored by the farmland point in descending order. The farmland pollutant concentration sequence includes the farmland pollutant type and the corresponding concentration value. As an example, the farmland pollutant concentration sequence includes SO2, 80%, NO x60%. The highest concentration value in the concentration sequence of farmland pollutants is selected as the farmland monitoring pollutant, such as SO2 as the farmland monitoring pollutant. Among them, the farmland monitoring pollutant refers to the pollutant with the highest concentration value in the above concentration sequence, which is regarded as the "dominant influence" pollutant in the current farmland point position pollution condition. On this basis, according to the farmland monitoring pollutant corresponding to the farmland pollutant type, the corresponding pollutant type is determined, wherein the pollutant type is a pollution type pollutant or an inhibition type pollutant. According to the farmland pollutant type, the pollutant type table is inquired to determine the pollutant type corresponding to the farmland monitoring pollutant, and the pollutant type table includes the farmland pollutant type, the pollutant type corresponding to each farmland pollutant and the safety threshold. As an example, if the farmland pollutant type is SO2, the corresponding pollutant type is a pollution type pollutant; if the farmland pollutant type is PM2.5, the corresponding pollutant type is a pollution type pollutant and an inhibition type pollutant. Among them, the pollution type pollutant refers to the pollutant that may form acid rain or other environmental pollution by-products after acting with cloud water in the context of artificial precipitation enhancement, which itself has strong environmental hazards and will cause pollution risks of farmland, soil or ecological system, such as acid rain. The inhibition type pollutant refers to the substance that does not directly cause acid pollution, but will interfere with the formation of cloud droplets, ice crystal growth and catalyst mechanism, which will significantly reduce the catalytic efficiency of artificial precipitation enhancement or completely fail.

[0075] In some embodiments, if the pollution type of the farmland monitoring pollutant is a pollution-type pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant influence type is determined to be farmland rain enhancement pollution; if the pollution type of the farmland monitoring pollutant is a pollution-type pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant influence type is determined to be farmland no influence. If the pollution type of the farmland monitoring pollutant is an inhibition-type pollutant, and the corresponding farmland pollutant concentration is greater than or equal to the corresponding safety threshold, the farmland pollutant influence type is determined to be farmland rain inhibition; if the pollution type of the farmland monitoring pollutant is an inhibition-type pollutant, and the corresponding farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant influence type is determined to be farmland no influence. The safety threshold refers to the maximum acceptable upper limit of concentration allowed at the farmland point or the pollution diffusion path point for a specific pollutant. When the measured or simulated concentration of the pollutant exceeds this threshold, it is considered that there is an environmental risk or operation inhibition risk, and the corresponding intervention strategy needs to be triggered. The threshold can be adjusted and set as needed, with reference to the national environmental standards. On this basis, if the pollution type of the farmland monitoring pollutant 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 influence 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 influence type corresponding to the farmland point is determined to be farmland no influence. Farmland rain enhancement pollution means that the monitored pollutant 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 pollution type of the farmland monitoring pollutant 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 influence type corresponding to the farmland point is determined to be farmland rain inhibition. If the farmland pollutant concentration is less than the corresponding safety threshold, the farmland pollutant influence type corresponding to the farmland point is determined to be farmland no influence. Farmland rain inhibition means that the monitored pollutant at the farmland point is an inhibition-type pollutant, and its actual concentration is greater than or equal to the corresponding safety threshold. Such pollutants do not directly pollute the environment, but can significantly reduce the nucleation efficiency of the catalyst or damage the cloud structure, resulting in failure or insignificant effect of the rain enhancement operation. Farmland no influence means that the concentration of all monitored pollutants at the farmland point is lower than the corresponding safety threshold, or the monitored pollutant type does not belong to pollution-type or inhibition-type, which does not cause environmental pollution risk or operation inhibition risk to artificial rain enhancement operations.

[0076] In some embodiments, the wind speed and wind direction are extracted from historical meteorological data, and a historical wind rose of the source point is plotted using a preset tool (such as Python): 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. The graph is plotted: the frequency is represented by the sector length, and the wind speed is represented by the color bar. The historical wind rose is a meteorological data visualization tool that represents the statistical distribution of wind direction and speed at a specific location over a historical period (such as the past 3-5 years). The dominant wind direction and frequency distribution in a specific season (e.g., similar time period in previous years) or specific weather pattern (e.g., similar wind direction, similar cloud distribution) are analyzed through the historical wind rose. The dominant wind direction and its frequency distribution are obtained from the historical wind rose. The dominant wind direction refers to the wind direction that appears most frequently in a specific time period (such as a year, a season, or a month) in a certain area. The dominant wind direction in the historical wind rose is converted to the diffusion direction, which can be the direction angle. Among them, the wind direction is the direction of the wind, for example, the east wind (90 。 ): the wind blows from the east, meaning the wind is moving westward. The west wind (270 。 ): the wind blows from the west, meaning the wind is moving eastward. The diffusion direction is consistent with the direction of the wind, for example, the dominant direction is the east wind, and the diffusion direction is westward. On this basis, the latitude and longitude coordinates of the farmland point and the latitude and longitude coordinates of the source point are converted into spherical coordinates respectively, and the initial azimuth is calculated, the angle is mapped to 16 standard directions, and the relative position of the farmland point is obtained. 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, such as the farmland point being located in the north direction (0 。 ) of the source point, the relative position can be the direction angle. If the angle difference between the direction angle of the diffusion direction and the direction angle of the relative position of the farmland point is greater than a preset angle difference value, the source pollutant of the source point will not diffuse to the farmland point, and the farmland pollutant influence type is taken as the pollutant influence type. For example, the preset angle difference value is 30 。 , the diffusion direction direction angle is 270 。 , and the direction angle of the farmland point is (0 。 ), the angle difference is 270 。 , which is greater than 30 。 , indicating that the source pollutant of the source point will not diffuse to the farmland point, and the corresponding farmland pollutant influence type is taken as the pollutant influence type of the target monitoring area. If the diffusion direction direction angle is 75 。 , and the direction angle of the farmland point is 60 。 , it indicates that the source pollutant of the source point will diffuse to the farmland point.

[0077] Step three, if spreading to farmland points, determine the spreading 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;

[0078] In some embodiments, if spreading to farmland points, a straight-line spreading path is established along the spreading direction from the source point, which simulates the route of the pollutant spreading to the farmland points under the action of wind. Multiple path points are divided on this path. The path points can be 5 or 10, and the specific number is set according to the simulation accuracy requirement; each path point represents a position in the pollutant propagation process. For example, for path points, the following factors are selected: turning points where the pollutant concentration changes significantly (such as steep drops, peak values), high concentration superposition or potential accumulation areas (such as building-intensive areas, valleys, basins), potential sensitive area front (such as near agricultural irrigation areas, schools, drinking water sources), wind direction change areas (such as intersections, rotation areas), terrain forced wind channels or obstacles (such as straits, ventilation corridors). For each path point, a communication connection is established with the anemometer collecting real-time wind speed, so as to obtain the real-time wind speed in the past time period (such as the last 30 minutes of 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, and the coordinates here are three-dimensional coordinates.

[0079] Step four, determine the source monitoring pollutant according to the multiple source pollutants and the source pollutant concentration corresponding to each source pollutant;

[0080] Step five, according to the emission rate, emission height, diffusion coefficient corresponding to the source monitoring pollutant, the coordinates of each path point and the average wind speed, the pollutant concentration of the source monitoring pollutant at each path point is determined by the following formula:

[0081] wherein, is the emission rate corresponding to the source monitoring pollutant, is the average wind speed of each path point, is the horizontal and vertical diffusion coefficient corresponding to the source monitoring pollutant, is the coordinates of each path point determined with the source point as the origin, is the path pollutant concentration of the source monitoring pollutant at each path point, is the emission height corresponding to the source monitoring pollutant;

[0082] In some embodiments, among the multiple source pollutants, the one that has the most significant impact on the farmland or the highest concentration is selected as the source monitoring pollutant. On this basis, the emission rate, emission height, diffusion coefficient, coordinates of each path point, and average wind speed of 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:

[0083]

[0084] wherein, is the emission rate of the source monitoring pollutant. is the average wind speed at each path point. is the horizontal and vertical diffusion coefficients of the source monitoring pollutant. is the coordinates of each path point determined with the source point as the origin. is the path pollutant concentration of the source monitoring pollutant at each path point. is the emission height of the source monitoring pollutant. The path pollutant concentration refers to the estimated or simulated pollutant concentration value at multiple path points on the diffusion path during the propagation of the pollutant from the source point to the farmland point along the diffusion path.

[0085] Step six, according to the path pollutant concentration of the source monitoring pollutant at each path point, a 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, the path pollutant impact type being path rain-increasing pollution, path rain-suppressing, 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 instruction is generated and sent to the operation scheduling end, wherein the pollutant impact type is no impact, rain-increasing pollution, or rain-suppressing.

[0086] In some embodiments, the simulated concentration of the source monitoring pollutant at each path point on the diffusion path is recorded in sequence to form an ordered list, i.e., a path pollutant concentration sequence. The concentration value of each path point in the path pollutant concentration sequence is compared with the safety threshold of the source monitoring pollutant. If the concentration value of any point in the sequence is greater than or equal to the safety threshold, and the pollutant type of the source monitoring pollutant is a pollution-type pollutant, then the path pollutant impact type is path rain enhancement pollution, and the pollutant type of the source monitoring pollutant is an inhibition-type pollutant, then the path pollutant impact type is path inhibition rain enhancement. If the concentration of all points in the sequence is lower than the threshold, then the path pollutant impact type is path no impact. The path pollutant impact type refers to the type of comprehensive judgment on whether the pollutant may have an impact on the rain enhancement operation of the farmland point based on the concentration level of each point in the path pollutant concentration sequence and the pollutant attribute. Path rain enhancement pollution means that at least one path point of the pollutant diffusion path has a concentration of pollution-type pollutant greater than or equal to the safety threshold. Such pollutants may co-deposit with rainwater during the rain enhancement process, causing acid rain or farmland pollution. Path inhibition rain enhancement means that at least one path point of the pollutant diffusion path has a concentration of inhibition-type pollutant exceeding the safety threshold. Such pollutants will inhibit cloud droplet formation or interfere with catalyst activity, thereby reducing rain enhancement efficiency. Path no impact means that the simulated concentration of all path points on the pollutant diffusion path is lower than the corresponding safety threshold, i.e., the current pollution source diffusion direction does not form an observable risk or interference to the farmland point.

[0087] In some embodiments, according to the agricultural field pollutant influence type and the path pollutant influence type, combined with a preset rule, the pollutant influence type corresponding to the target monitoring area is determined, and the dominant pollutant is determined. The pollutant influence type is no influence, rain-increasing pollution, or rain-inhibiting. The pollutant influence type refers to the final classification result obtained after comprehensive evaluation of the measured pollutant influence of the agricultural field point (agricultural field pollutant influence type) and the pollutant influence in the diffusion path of the pollution source to the agricultural field (path pollutant influence type), reflecting whether the target area is suitable for performing artificial rain enhancement operation under the current environmental conditions. No influence means that the measured pollutant concentration at the current agricultural field point is lower than the corresponding safety threshold, and there is no area with concentration exceeding the standard simulated in the diffusion path of the pollution source, that is, it is determined that there is no environmental pollution risk or catalytic inhibition risk in the region, and the rain enhancement operation can be directly carried out. Rain-increasing pollution means that the target area is detected to have a situation of exceeding the standard of pollution-type pollutants (which may be derived from the measured agricultural field point or the path diffusion simulation), and if artificial rain enhancement operation is performed at this time, it may cause the pollutants to fall into the farmland with the precipitation, forming the risk of acid rain or secondary environmental pollution. Rain-inhibiting means that the target area is detected to have a situation of exceeding the standard of inhibition-type pollutants, and the pollutants themselves do not directly cause environmental pollution, but their physical or chemical properties may interfere with the key processes such as catalyst nucleation, cloud droplet condensation, ice crystal generation, etc., resulting in failure or significant reduction of efficiency of artificial rain enhancement catalysis. Wherein, the final comprehensive influence result obtained by fusing the "agricultural field pollutant influence type" and the "path pollutant influence type" is used to guide whether to perform rain enhancement operation. As an example, if the path pollutant influence type is path no influence, and the agricultural field pollutant influence type is agricultural field no influence, the pollutant influence type is no influence. If the agricultural field pollutant influence type is agricultural field no influence, and the path pollutant influence type is path rain-increasing pollution, the pollutant influence type is rain-increasing pollution, and the source monitoring pollutant is the dominant pollutant. If the agricultural field pollutant influence type is agricultural field rain-increasing pollution, and the path pollutant influence type is path rain-inhibiting, the pollutant influence type is rain-increasing pollution with priority to the agricultural field pollutant influence type, and the agricultural field monitoring pollutant is the dominant pollutant. The dominant pollutant refers to the pollutant type and parameter object that is preferentially referenced in the case of "rain-increasing pollution" or "rain-inhibiting" of the pollutant influence type, for indicating the source of the pollution risk, determining the operation processing method (such as the type of neutralizing agent), and generating specific instruction content. It is explained that the pollution risk mainly comes from the "source" or the "agricultural field". According to the type of the dominant pollutant, an appropriate neutralizing agent is selected.

[0088] In some embodiments, a preset treatment measure table is queried according to the pollutant influence type and the dominant pollutant, a treatment measure is determined, and a treatment instruction is generated. The preset treatment measure table includes the pollutant influence type, the dominant pollutant, and the corresponding treatment measure. If the pollutant influence type is no influence, a rain enhancement preparation instruction is generated and sent to the job scheduling end; if the pollutant influence type is rain enhancement pollution, a first treatment instruction is generated and sent to the job scheduling end; and if the pollutant influence type is rain inhibition, a second treatment instruction is generated and sent to the job scheduling end. The job scheduling end can be a job management platform deployed in a ground command center, and its main functions include task distribution, launch parameter configuration, job equipment control, and job process monitoring. On this basis, the surveying machine also establishes a communication connection with the job scheduling terminal when it is not performing the rain enhancement job, so as to send an instruction to the job scheduling end. If the pollutant influence type is no influence, it means that the detection result shows that the environment is clean and safe; the control end issues a rain enhancement preparation instruction (such as “normal rain enhancement preparation”). If the pollutant influence type is rain enhancement pollution, it means that there is a pollution-type pollutant exceeding the standard. The first treatment instruction can be a neutralizing agent corresponding to the dominant pollutant type, such as “launching a neutralizing agent for sulfur dioxide”. If the pollutant influence type is rain inhibition, it means that there is an inhibition-type pollutant, and the catalytic invalidation or failure risk is high. The second treatment instruction can be a chemical agent corresponding to the dominant pollutant type, such as “launching a chemical agent for PM2.5”.

[0089] In these implementations, the job reliability and accuracy are improved. Specifically, by collecting pollutant information of farmland and source points, and combining historical meteorological data for diffusion simulation, the accurate evaluation of pollution influence before artificial rain enhancement job is realized. By distinguishing between pollution-type and inhibition-type pollutants, it is determined whether they diffuse to the farmland area, and the pollutant influence type of the target area is determined by combining the measured concentration and the simulation result. The system generates corresponding treatment instructions according to the judgment result, realizes intelligent decision-making and dynamic regulation and control before the job. This scheme improves the environmental safety and job pertinence of rain enhancement job, avoids acid rain or catalytic failure caused by excessive pollutants, and enhances the accuracy and reliability of artificial rain enhancement job.

[0090] In some embodiments, to further solve the technical problem three described in the background section, i.e., the existing technology usually uses a fixed threshold to judge the pollutants, cannot make individualized early warning on the response trend of the pollutants combined with historical rainfall data, leads to lagging risk identification, and in addition, the effect after the rain enhancement job lacks scientific evaluation means, cannot dynamically feedback the job effectiveness or correct the decision parameters according to the change trend of the pollutants, thereby limiting the fine management and control and continuous optimization ability of artificial rain enhancement job, some embodiments of the present application further include:

[0091] Step one, obtain historical rainfall data of each farmland pollutant and historical rainfall data of each source pollutant; determine the early warning threshold of each farmland pollutant according to the historical rainfall data of each farmland pollutant and the corresponding safety threshold, and construct the corresponding farmland pollutant early warning interval; determine the early warning threshold of each source pollutant according to the historical rainfall data of each source pollutant and the corresponding safety threshold, and construct the corresponding source pollutant early warning interval;

[0092] In some embodiments, the control end establishes a communication connection with the environmental monitoring station of the target monitoring area, thereby obtaining the historical rainfall data of each farmland pollutant and the historical rainfall data of each source pollutant. The historical rainfall data refers to the concentration record of a certain pollutant before and after the artificial precipitation or natural rainfall event. It usually includes: pre-rainfall concentration, post-rainfall concentration, time interval (such as 2 hours before rain and 2 hours after rain), and multiple records (for trend modeling). On this basis, the early warning threshold of each farmland pollutant and the early warning threshold of each source pollutant are calculated by the following formula:

[0093]

[0094] wherein, is the early warning threshold, is the safety threshold, is the change range of pollutant concentration before and after rainfall. The safety threshold is taken as the upper limit of the early warning interval, and the early warning threshold is taken as the lower limit of the early warning interval, thereby obtaining the farmland pollutant early warning interval and the source pollutant threshold interval. The early warning interval is used to judge that the pollutant is in a "critical risk" state but has not exceeded the standard.

[0095] Step two, update the farmland pollutant influence type and the path pollutant influence type according to the farmland pollutant early warning interval and the source pollutant threshold interval, obtain the updated farmland pollutant influence type and the updated path pollutant influence type, and determine the corresponding updated pollutant influence type of the target monitoring area;

[0096] In some embodiments, the farmland pollutant early warning interval and the source pollutant threshold interval are introduced in the process of determining the farmland pollutant influence type and the path pollutant influence type, respectively replacing the corresponding safety threshold of each farmland pollutant and the corresponding safety threshold of each source pollutant, to re-determine the farmland pollutant influence type and the path pollutant influence type, obtain the updated farmland pollutant influence type and the updated path pollutant influence type, and further determine the corresponding updated pollutant influence type of the target monitoring area. The updated pollutant influence type is the final regional judgment result obtained by fusing the "updated farmland pollutant influence type" and the "updated path pollutant influence type", wherein the updated pollutant influence type is updated no influence, updated rainfall pollution and updated inhibition of rainfall.

[0097] In step three, if the update pollutant impact type is update no impact, the multiple real-time concentrations of each pollutant during the rain enhancement process are obtained; and the multiple real-time concentrations of each pollutant after the rain enhancement is completed are analyzed to obtain the concentration change trend of each pollutant, which is an increase or a decrease; if the concentration change trend is a decrease, the rain enhancement operation is marked as a successful rain enhancement event; if the concentration change trend is an increase, it is determined whether the rain enhancement operation has rain enhancement pollution or rain suppression; if so, the safety threshold and the early warning threshold of the corresponding pollutant are adjusted, and an updated early warning interval is obtained; if not, the selection of the multiple path points in the diffusion path is corrected.

[0098] In some embodiments, if the update pollutant impact type is update no impact, during the rain enhancement process, the real-time concentration of the pollutant is periodically collected by the concentration sensor and sent to the control end, and the control end receives and stores the multiple real-time concentrations, wherein the multiple real-time concentrations include the type of the pollutant, the corresponding concentration value and the time stamp. After the rain enhancement is completed, the multiple real-time concentrations are sorted according to the time before and after and analyzed to obtain the concentration change trend of each pollutant. Wherein, the concentration change trend is an increase or a decrease. If the concentration change trend is a decrease, the rain enhancement operation is marked as a successful rain enhancement event. If the concentration change trend is an increase, it is determined whether the rain enhancement operation has rain enhancement pollution or rain suppression. If so, it means that the rain enhancement has pollution risk and suppression risk, and the current early warning threshold or safety threshold is too high, the early warning threshold or safety threshold is reduced, and the corresponding early warning interval is rebuilt to obtain an updated early warning interval. If there is no rain enhancement pollution or rain suppression, it means that the diffusion path modeling has deviation, and the selection of the multiple path points in the diffusion path is corrected, such as increasing the key position points, refining the path interval and introducing a new sensor layout strategy.

[0099] In these embodiments, the risk identification accuracy and the scientificity and safety of artificial rainmaking operation are improved. Specifically, by analyzing the response difference of pollutants before and after historical artificial rainmaking, personalized "early warning threshold" and "early warning interval" are generated to enhance the pre-identification ability of potential risks. The farmland pollutants and source pollutants are modeled and judged respectively, and the "updated pollutant influence type" of the target monitoring area is determined by bidirectional judgment and fusion. The concentration sequence of pollutants collected in real time is used to analyze the change trend after the operation, and the effectiveness of artificial rainmaking operation is judged according to the change trend, and it is decided whether to adjust the parameters or correct the path. If the concentration abnormally rises and meets the pollution or inhibition characteristics, the early warning threshold or the safety threshold is dynamically reduced; if the concentration rises but has no pollution characteristics, the model deviation is presumed, and the diffusion path point setting is further adjusted, and the pollution early warning interval, the updated pollutant influence type and the operation result feedback mechanism are constructed, so that the risk identification accuracy and the scientificity and safety of artificial rainmaking operation are effectively improved.

[0100] The above description is only some of the preferred embodiments of the present application and the explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

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 the preset cloud height threshold, multiple fluctuations 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. This includes calculating the target cloud height fluctuation, temperature fluctuation, and radar reflectivity fluctuation using the following formulas: 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 The average level, is a random variable The fluctuation of the random variable The relative fluctuation degree of the variable is determined, where a larger value indicates more significant heterogeneity or spatial or temporal fluctuation of the variable; determining the cloud type of the target cloud body according to the multiple fluctuation quantities, where the cloud type is either a first target cloud body or a second target cloud body, including: if the multiple fluctuation quantities are all greater than or equal to corresponding preset thresholds, determining the corresponding target cloud body as the first target cloud body; if any fluctuation quantity among the multiple fluctuation quantities is less than the corresponding preset threshold, determining the target cloud body as the second target cloud body; 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.

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.

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, 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.

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 pollutant information of the farmland point 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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