Method for controlling aircraft rain enhancement and electronic device therefor

Abstract

The application discloses an aircraft rain enhancement control method and electronic equipment thereof, and the method comprises the following steps: when it is determined to start rain enhancement operation based on NDVI time series data and soil moisture content of a target area, the soil moisture content, effective root layer depth and evaporation loss compensation coefficient of the target area are processed based on an SCS-CN inverse model, and the required precipitation of the target area is calculated; the required target seeding dose of the target area is calculated based on the precipitation, and the aircraft is controlled to perform rain enhancement operation on the target area at the target seeding dose. Through real-time capture of the soil moisture content of the target area on the ground, the SCS-CN inverse model is used to solve the theoretical water requirement, and the aircraft flight route and the catalyst seeding amount are automatically generated, so that the automatic closed loop from ground monitoring to airborne execution is realized. Compared with the existing scheme which depends on artificial experience, the target area can be accurately seeded in a fixed point and a fixed quantity, the rain enhancement operation effect is ensured, and catalyst waste is avoided.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of aerial rain enhancement technology, and in particular to an aircraft rain enhancement control method and its electronic equipment. Background Technology

[0002] Aircraft-based artificial rain enhancement is a crucial engineering method for alleviating drought and water scarcity and restoring fragile ecosystems. In arid and semi-arid regions, the accuracy of artificial rain enhancement operations directly determines the efficiency of water resource utilization. However, existing aircraft-based rain enhancement operations mostly adopt an open-loop control mode, where operators formulate flight plans based on macroscopic atmospheric detection data such as weather radar echo intensity, cloud top temperature, and liquid water content. This mode focuses on assessing the suitability of meteorological conditions and lacks a real-time perception and feedback mechanism for the actual water demand of the ground ecosystem. Furthermore, since the plans are manually formulated, analytical errors exist, making ineffective operations prone to occur during vegetation dormancy or when soil moisture is saturated. There are also instances where operational parameters do not match the ecological needs of the target area, affecting the effectiveness of rain enhancement operations and causing catalyst waste. Summary of the Invention

[0003] This application provides an aircraft rain enhancement control method and its electronic equipment, which can achieve precise seeding of target areas at fixed points and in fixed quantities, ensuring the effectiveness of rain enhancement operations while avoiding catalyst waste.

[0004] In a first aspect, embodiments of this application provide an aircraft rain enhancement control method, comprising:

[0005] Determine whether to initiate rain enhancement operations based on NDVI time-series data and soil moisture content of the target area;

[0006] Once it is determined that rain enhancement operations will be initiated, the soil moisture content, effective root layer depth, and evaporation loss compensation coefficient of the target area are processed based on the SCS-CN inverse model to calculate the required precipitation for the target area.

[0007] Calculate the target seeding dose required for the target area based on the rainfall, and control the aircraft to carry out rain enhancement operations in the target area with the target seeding dose.

[0008] In some embodiments, a soil moisture sensor is installed in the target area, the target area is associated with target vegetation, and the NDVI time-series data includes the real-time normalized vegetation index of the target vegetation. Determining whether to initiate rain enhancement operations based on the NDVI time-series data and soil moisture content of the target area includes:

[0009] The soil moisture content received from the soil moisture sensor is subjected to moving average filtering.

[0010] Multiply the field water holding capacity corresponding to the target area by a preset coefficient to obtain a reference value;

[0011] If the current normalized vegetation index is greater than the index threshold, the phenological stage of the current target vegetation is between the greening stage and the grain-filling stage, and the soil moisture content is less than the reference value, then it is determined to initiate rain enhancement operations.

[0012] In some embodiments, the soil moisture content, effective root layer depth, and evaporation loss compensation coefficient of the target area are processed based on the SCS-CN inverse model to calculate the required precipitation for the target area, including:

[0013] The target supplementary infiltration amount is calculated based on the field water holding capacity of the target area, the soil moisture content, the effective root layer depth, and the evaporation loss compensation coefficient. The target supplementary infiltration amount is used to indicate the amount of additional water required for the soil moisture content of the target area to reach the field water holding capacity, excluding natural evaporation loss.

[0014] The precipitation amount is calculated based on the SCS-CN inverse model and the target supplementary infiltration amount.

[0015] In some embodiments, calculating the target seeding dose required for the target area based on the precipitation includes:

[0016] The target area is divided into multiple task grids, wherein each task grid has the same reference area.

[0017] The target seeding dose required for each operational grid is calculated based on the precipitation conversion efficiency coefficient, cloud liquid water content, nucleation rate, the reference area, and the precipitation amount.

[0018] In some embodiments, controlling an aircraft to perform rain enhancement operations on a target area at the target seeding dose includes:

[0019] A flight path optimization model is constructed based on all the operational grids and precipitation data in the target area;

[0020] The target rain enhancement operation route is obtained by solving the route optimization model based on a preset genetic algorithm.

[0021] The aircraft is controlled to carry out rain enhancement operations in the target area according to the target rain enhancement operation route and the target seeding dose.

[0022] In some embodiments, the route optimization model corresponds to a fitness function, the expression of which is:

[0023] ;

[0024] in, The value of the fitness function. For weight values, i Waypoint number, M The total number of grids in all the aforementioned task grids. The ecological water demand weight is the value of the operational grid corresponding to the i-th waypoint, and the ecological water demand weight is obtained based on the normalized precipitation. For the first i Each waypoint has a corresponding seeding identifier. When the aircraft passes the i-th waypoint and performs a seeding operation... The value is 1, indicating that the aircraft has not passed the i-th waypoint or the aircraft has passed the i-th waypoint but has not performed a seeding operation. The value is 0. For adjacent waypoints and waypoints The Euclidean distance between them;

[0025] The constraints corresponding to the fitness function include:

[0026] ;

[0027] in, For the range threshold, The rotational speed of the aircraft is [value missing]. For the speed threshold, This is a no-fly zone within the target area. Let i be the i-th waypoint.

[0028] In some embodiments, the target rain enhancement operation route is obtained by solving the route optimization model based on a preset genetic algorithm, including:

[0029] Multiple first chromosomes with different gene arrangements are randomly generated. Each first chromosome includes multiple genes. The number of genes in a first chromosome is the same as the number of grids. Each gene corresponds to a different job grid. Each first chromosome represents a candidate rain enhancement job route.

[0030] Based on the fitness function and the preset evolutionary operator, iterative optimization is performed on all the first chromosomes to output the corresponding second chromosomes;

[0031] The second chromosome with the highest fitness function value is determined as the reference chromosome;

[0032] When the growth rate of the fitness function value corresponding to the reference chromosome within a consecutive number of iterations is less than the growth rate threshold, or when the number of iterations reaches the number threshold, the current reference chromosome is determined as the target chromosome;

[0033] The target chromosome is decoded to obtain the target rain enhancement operation route, which includes a waypoint path sequence.

[0034] In some embodiments, controlling the aircraft to perform rain enhancement operations in the target area according to the target rain enhancement operation route and the target seeding dose includes:

[0035] The waypoint path sequence and target dispersal dose are encapsulated into an encrypted data packet, and the encrypted data packet is sent to the aircraft's onboard terminal via a ground-to-air data link;

[0036] During the flight of the aircraft, the onboard terminal performs reverse decryption and integrity verification on the encrypted data packet. When the verification passes, the flight command associated with the encrypted data packet is sent to the PLC controller of the aircraft.

[0037] The PLC controller compares the aircraft's GNSS positioning data with the coordinates of each waypoint in the waypoint path sequence in real time. When it is determined that the aircraft has passed through any of the waypoint coordinates, it controls the opening of the aircraft's dispersal valve to disperse the target catalyst at the target dispersal dose.

[0038] In some embodiments, after controlling an aircraft to conduct rain enhancement operations on the target area at the target seeding dose, the method further includes:

[0039] After completing a seeding operation, the PLC controller reads the first feedback value of the loop current sensor and the second feedback value of the liquid nitrogen flow meter in real time.

[0040] When the first feedback value is detected to be greater than the current threshold, or the second feedback value is detected to be greater than the spreading amount threshold, the spreading identifier is determined to be a successful spreading, and the spreading identifier, the first feedback value, and the second feedback value are transmitted back to the ground.

[0041] When the first feedback value is detected to be less than or equal to the current threshold, or the second feedback value is detected to be less than or equal to the dissemination amount threshold, the dissemination identifier is determined to be a dissemination failure, and an alarm message is generated based on the dissemination identifier, the first feedback value, and the second feedback value, and the alarm message is sent to the ground.

[0042] In a second aspect, embodiments of this application provide an electronic device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the aircraft rain enhancement control method as described in the first aspect.

[0043] This application provides an aircraft-based rain enhancement control method and its electronic equipment. The method includes: determining whether to initiate rain enhancement operations based on NDVI time-series data and soil moisture content of a target area; when initiating rain enhancement operations, processing the soil moisture content, effective root layer depth, and evaporation loss compensation coefficient of the target area using the SCS-CN inverse model to calculate the required precipitation for the target area; calculating the required target seeding dose for the target area based on the precipitation, and controlling the aircraft to perform rain enhancement operations on the target area with the target seeding dose. According to the scheme provided by this application, by capturing the soil moisture content of the target area in real time, using the SCS-CN inverse model to calculate the theoretical water requirement, and automatically generating the aircraft's flight path and corresponding catalyst seeding amount, an automated closed loop from ground monitoring to airborne execution is achieved. Compared with existing schemes that rely on manual experience, this method enables precise seeding of the target area at fixed points and in fixed quantities, ensuring the effectiveness of rain enhancement operations while avoiding catalyst waste. Attached Figure Description

[0044] Figure 1 This is a flowchart of the steps of an aircraft rain enhancement control method provided in one embodiment of this application;

[0045] Figure 2 This is a structural diagram of an electronic device provided in another embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] It is understandable that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0048] Aircraft-based artificial rain enhancement is a crucial engineering method for alleviating drought and water scarcity and restoring fragile ecosystems. In arid and semi-arid regions, the accuracy of artificial rain enhancement operations directly determines the efficiency of water resource utilization. However, existing aircraft-based rain enhancement operations mostly adopt an open-loop control mode, where operators formulate flight plans based on macroscopic atmospheric detection data such as weather radar echo intensity, cloud top temperature, and liquid water content. This mode focuses on assessing the suitability of meteorological conditions and lacks a real-time perception and feedback mechanism for the actual water demand of the ground ecosystem. Furthermore, since the plans are manually formulated, analytical errors exist, making ineffective operations prone to occur during vegetation dormancy or when soil moisture is saturated. There are also instances where operational parameters do not match the ecological needs of the target area, affecting the effectiveness of rain enhancement operations and causing catalyst waste.

[0049] To address the aforementioned problems, this application provides an aircraft-based rain enhancement control method and its electronic equipment. The method includes: determining whether to initiate rain enhancement operations based on NDVI time-series data and soil moisture content of a target area; when initiating rain enhancement operations, processing the soil moisture content, effective root layer depth, and evaporation loss compensation coefficient of the target area using the SCS-CN inverse model to calculate the required precipitation for the target area; calculating the required target seeding dose for the target area based on the precipitation, and controlling the aircraft to perform rain enhancement operations on the target area using the target seeding dose. According to the solution provided in this application, by capturing the soil moisture content of the target area in real time, calculating the theoretical water requirement using the SCS-CN inverse model, and automatically generating the aircraft's flight path and corresponding catalyst seeding amount, an automated closed loop from ground monitoring to airborne execution is achieved. Compared to existing solutions relying on manual experience, this method enables precise seeding of the target area at fixed points and in fixed quantities, ensuring the effectiveness of rain enhancement operations while avoiding catalyst waste.

[0050] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0051] refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of an aircraft rain enhancement control method according to an embodiment of this application. This application provides an aircraft rain enhancement control method, which includes, but is not limited to, the following steps:

[0052] Step S10: Determine whether to initiate rain enhancement operations based on the NDVI time-series data and soil moisture content of the target area.

[0053] It is understood that the aircraft rain enhancement control method of this application embodiment is applied to an aircraft rain enhancement control system, whose system architecture is divided into the following three layers: a ground perception layer, an edge decision layer, and an airborne execution layer. The ground perception layer is used to monitor soil moisture and low-altitude phenology in the target area, i.e., to collect soil moisture content and canopy spectral data of the target area. The canopy spectral data is used to calculate NDVI time-series data, thereby providing an effective data basis for determining the timing of initiating rain enhancement operations. The edge decision layer includes a data processing server and a communication base station deployed in the ground command center, capable of receiving data collected by the ground perception layer and interacting with the airborne execution layer for data exchange and operational decision-making.

[0054] Specifically, the ground perception layer in this embodiment includes a soil moisture sensor with time-domain reflectometry, a communication module, and a drone equipped with a 5-band multispectral camera. The soil moisture sensor is deployed in the target area in a 5km×5km work grid, and can collect the volumetric water content of three soil layers (10cm, 20cm, and 40cm) as well as soil temperature. The drone performs a patrol once every 3-5 days at a preset cycle to acquire canopy spectral data with a resolution greater than 0.5m.

[0055] Specifically, in this embodiment, the data processing server of the edge decision layer is a high-performance industrial control computer (configured with dual GPUs for model solving) deployed in the ground command center. The server is connected to the Beidou short message terminal through an RS-232 / 422 serial port to realize the physical layer transmission of data. The communication base station of the edge decision layer includes a LoRa gateway (for receiving ground data) and a Beidou-3 short message command machine (for sending air-to-ground commands).

[0056] Specifically, the airborne execution layer of this embodiment includes the aircraft's airborne terminal and PLC controller. The aircraft execution mechanism corresponding to the rain enhancement operation includes a flame spreader and a liquid nitrogen spreader. The flame spreader consists of two sets mounted on each wing of the aircraft, each set containing 12 silver iodide flames (i.e., the target catalyst in this embodiment), and is ignited by a relay. The liquid nitrogen spreader is mounted on the belly of the aircraft, and the nozzle opening is controlled by a solenoid valve.

[0057] Specifically, in some embodiments, a soil moisture sensor is installed in the target area, the target area is associated with target vegetation, and the NDVI time-series data includes the real-time normalized vegetation index of the target vegetation. Figure 1 Step S10 includes, but is not limited to, the following steps:

[0058] Step S11: Perform moving average filtering on the soil moisture content received from the soil moisture sensor.

[0059] Step S12: Multiply the field water holding capacity corresponding to the target area by a preset coefficient to obtain a reference value;

[0060] Step S13: If the current normalized vegetation index is greater than the index threshold, the phenological stage of the current target vegetation is between the greening stage and the grain-filling stage, and the soil moisture content is less than the reference value, then determine to start the rain enhancement operation.

[0061] It is understood that the soil moisture content in this embodiment is data collected periodically by a soil moisture sensor (the period can be 10 minutes, which can be determined by those skilled in the art according to the actual situation), and the phenological period of the target vegetation is determined by using the SG filtering algorithm based on soil moisture content and NDVI time series data.

[0062] It is understood that the method for determining the start time of aircraft-based rain enhancement operations in this embodiment includes: performing moving average filtering on the soil moisture content received from the soil moisture sensor, and adjusting the field capacity of the target area. Multiply by a preset coefficient (0.6 in this embodiment) to obtain a reference value. If the current normalized vegetation index is greater than the index threshold (0.3 in this embodiment), and the phenological stage of the current target vegetation is between the greening stage and the grain-filling stage, a flag is set. Simultaneously, if the soil moisture content is lower than the reference value, the rain enhancement operation is initiated. In other words, the triggering conditions for aircraft-based rain enhancement operations are determined according to the following formula:

[0063] ;

[0064] in, Field holding capacity This refers to soil moisture content. When... When true, the inversion calculation module is activated, which triggers the execution of step S20.

[0065] Step S20: When it is determined to start the rain enhancement operation, the soil moisture content, effective root layer depth and evaporation loss compensation coefficient of the target area are processed based on the SCS-CN inverse model to calculate the required precipitation for the target area.

[0066] Specifically, in some embodiments, Figure 1 Step S20 includes, but is not limited to, the following steps:

[0067] Step S21: Calculate the target supplementary infiltration amount based on the field capacity, soil moisture content, effective root layer depth and evaporation loss compensation coefficient of the target area. The target supplementary infiltration amount is used to indicate the amount of additional water required for the soil moisture content to reach the field capacity in the target area, excluding natural evaporation loss.

[0068] Step S22: Solve for precipitation based on the SCS-CN inverse model and the target supplementary infiltration amount.

[0069] Understandably, this embodiment employs reverse thinking, deriving the required theoretical precipitation from the soil moisture content of the target area. First, the target replenishment infiltration is calculated. This value is jointly determined by the soil moisture content, the field capacity of the target area, soil moisture content, effective root layer depth, and evaporation loss compensation coefficient. The SCS-CN inverse model is then introduced for reverse solution. Specifically, after calculating the target replenishment infiltration, the CN value corresponding to the soil type in the target area, the potential maximum retention capacity S, and the initial loss coefficient are combined to calculate the theoretical total precipitation required to satisfy this infiltration through Newton's iteration method. This process realizes the physical quantity conversion from ecological water demand to meteorological precipitation, providing an effective data foundation for subsequent precise rain enhancement operations.

[0070] It should be noted that the target replenishment infiltration amount is calculated based on the field water holding capacity, soil moisture content, effective root layer depth, and evaporation loss compensation coefficient of the target area, using the following formula:

[0071] ;

[0072] in, To replenish the infiltration volume to the target, The target moisture content is calculated from the field capacity, and in this embodiment, it is set to... ×0.8, The effective root layer depth of the target vegetation (e.g., 400 mm when the target vegetation is sandy shrubs). The evaporation loss compensation coefficient is used in this embodiment, and its value ranges from 1.1 to 1.2.

[0073] It should be noted that the precipitation is calculated based on the SCS-CN inverse model and the target replenishment infiltration amount, using the following formula:

[0074] ;

[0075] ;

[0076] in, For precipitation, As surface runoff, CN represents the potential maximum retention capacity, and CN represents the runoff curve number. This is the initial loss coefficient. It is understood that in this embodiment, a high-precision soil type and land use database for the target area is pre-configured in the server of the edge decision layer. During calculation, the algorithm retrieves this database based on the latitude and longitude coordinates of the current work grid, automatically matches the soil texture (such as aeolian sandy soil, chestnut soil) and land cover type of the location, and dynamically calls the corresponding CN value (e.g., for a moderately covered aeolian sandy soil area, CN=68 is automatically extracted), thereby ensuring the geographical specificity and accuracy of the calculation parameters for each grid, providing effective support for subsequent precise rain enhancement operations.

[0077] Step S30: Calculate the target seeding dose required for the target area based on the precipitation, and control the aircraft to carry out rain enhancement operations on the target area with the target seeding dose.

[0078] Specifically, in some embodiments, Figure 1 Step S30, which calculates the target application dose required for the target area based on precipitation, includes, but is not limited to, the following steps:

[0079] Step S31: Divide the target area to obtain multiple task grids, wherein each task grid has the same reference area;

[0080] Step S32: Calculate the target seeding dose required for each work grid based on the precipitation conversion efficiency coefficient, cloud liquid water content, nucleation rate, reference area, and precipitation.

[0081] It should be noted that the target seeding dose required for each operational grid in this embodiment is calculated based on the precipitation conversion efficiency coefficient, cloud liquid water content, nucleation rate, reference area, and precipitation amount, and is obtained according to the following formula:

[0082] ;

[0083] in, This refers to the target application dose of the target catalyst (silver iodide) in this embodiment, expressed in grams. For reference area ( ), Nucleation rate , Cloud liquid water content (obtained by airborne radiometer or radar inversion) This is an empirical coefficient for precipitation conversion efficiency.

[0084] Furthermore, the target application dose is quantified into the number of flames (catalyst dose), obtained using the following formula:

[0085] ；

[0086] in, For catalyst dosage, This refers to the amount of silver iodide contained in a single silver iodide flame.

[0087] Specifically, in some embodiments, Figure 1 Step S30 involves controlling the aircraft to conduct rain enhancement operations on the target area at the target seeding dose, including but not limited to the following steps:

[0088] Step S33: Construct a flight path optimization model based on all operational grids and precipitation data for the target area;

[0089] Step S34: Solve the flight path optimization model based on the preset genetic algorithm to obtain the target rain enhancement operation flight path;

[0090] Step S35: Control the aircraft to carry out rain enhancement operations in the target area according to the target rain enhancement operation route and the target seeding dose.

[0091] It should be noted that the route optimization model has a corresponding fitness function, the expression of which is:

[0092] ;

[0093] in, The value of the fitness function. For weight values, i Waypoint number, M This represents the total number of grid cells in all job grids. Let be the ecological water demand weight for the operational grid corresponding to the i-th waypoint. The ecological water demand weight is obtained based on precipitation normalization. For the first i Each waypoint has a corresponding seeding marker. When the aircraft passes the i-th waypoint and performs a seeding operation... The value is 1, indicating that the aircraft has not passed the i-th waypoint or the aircraft has passed the i-th waypoint but has not performed a seeding operation. The value is 0. For adjacent waypoints and waypoints The Euclidean distance between them;

[0094] The constraints corresponding to the fitness function include:

[0095] ;

[0096] in, For the range threshold, This is the aircraft's rotational speed. For the speed threshold, This is a no-fly zone within the target area. Let i be the i-th waypoint.

[0097] Specifically, the range threshold in the constraints represents the effective operating range supported by the aircraft's maximum fuel load, while the speed threshold represents the maximum turning rate limit determined by the aircraft's performance (used to prevent the flight path from being too convoluted).

[0098] It is understood that, in this embodiment, the fitness function corresponding to the route optimization model is a logical bridge connecting ground ecological needs and air flight maneuvering. It can quantify the abstract ecological water demand urgency and flight energy consumption constraints into a mathematical extreme value problem. By maximizing the function value, the system automatically plans an optimal flight trajectory that takes into account both operational ecological effectiveness and execution economy, and generates control commands accordingly to drive the aircraft to execute.

[0099] Furthermore, the fitness function in this embodiment is essentially a "pay-cost" game model. This indicates the water scarcity situation in the area the plane flies over; the corresponding item is the revenue item. The higher the value, the more withered and yellow the target vegetation and the drier the soil. When the aircraft passes over this waypoint, the marker will be released. 1. The benefit score, which guides the aircraft to fly towards arid areas. And... The corresponding factor is the penalty, which constrains the aircraft to avoid detours and instead take the shortest path. The weight of this factor can be adjusted based on the actual situation; in disaster relief mode, it can be increased. The aircraft, disregarding fuel consumption, will cover all points. In routine patrol mode, the altitude will be increased... For aircraft, fuel efficiency is the priority.

[0100] Specifically, step S34 includes, but is not limited to, the following steps:

[0101] Step S341: Randomly generate multiple first chromosomes with different gene arrangement orders. Each first chromosome includes multiple genes. The number of genes in a first chromosome is the same as the number of grids. Each gene corresponds to a different job grid. Each first chromosome represents a candidate rain enhancement job route.

[0102] Step S342: Based on the fitness function and the preset evolutionary operator, perform iterative optimization on all first chromosomes and output the corresponding second chromosomes;

[0103] Step S343: The second chromosome with the highest fitness function value is determined as the reference chromosome;

[0104] Step S344: When the growth rate of the fitness function value corresponding to the reference chromosome within a consecutive number of iterations is less than the growth rate threshold, or when the number of iterations reaches the number threshold, the current reference chromosome is determined as the target chromosome.

[0105] Step S345: Decode the target chromosome to obtain the target rain enhancement operation route, which includes a waypoint path sequence.

[0106] It is understood that in this embodiment, one chromosome (the first chromosome or the second chromosome) represents a complete waypoint path sequence, expressed as follows: , Number the center point of the task grid.

[0107] Specifically, the preset evolution operators in this embodiment include selection operators, crossover operators, and mutation operators.

[0108] It is understood that, in this embodiment, the method for obtaining the target rain enhancement operation route based on solving the route optimization model using a preset genetic algorithm includes:

[0109] (1) Chromosome encoding and population initialization: Multiple first chromosomes with different gene arrangements are randomly generated. Each first chromosome contains multiple genes, and the number of genes in a first chromosome is the same as the number of grids. Each gene corresponds to a different job grid (the center point number of the job grid corresponding to the waypoint). Each first chromosome represents a candidate rain enhancement operation route. The gene arrangement order in the first chromosome represents the order in which the aircraft visits each job grid in the target area. Example: If there are 5 job grids in the target area, the first chromosome... The flight path is represented as: [Job Grid 3, Job Grid 1, Job Grid 5, Job Grid 2, Job Grid 4]; Initialization: Randomly generated. One (e.g.) The first chromosome, with a different gene arrangement, constitutes the initial population. At this point, the number of genes within each chromosome is... Furthermore, the task grid corresponding to each gene is different.

[0110] (2) Mapping and calculation of fitness function. The fitness function is the sole criterion for evaluating the quality of an individual. Its calculation process is the process of mapping gene sequences to physical parameters. The mapping logic is as follows: for any first chromosome... The system reads each gene The corresponding database attributes are used to retrieve the ecological water demand weight of the corresponding task grid. Read the spatial coordinates of the task grid. Substitute into the fitness calculation formula:

[0111] ;

[0112] in, This represents the spatial Euclidean distance between two adjacent genes. The evaluation mechanism is as follows: the fitness function value (J value) of each first chromosome is stored in the evaluation matrix. The larger the value, the shorter the total flight distance while covering areas with high water scarcity.

[0113] (3) Evolutionary operators and iterative optimization process: In this embodiment, the following operators are used to transfer and optimize the superior gene segments of each first chromosome between generations:

[0114] Selection operator: Tournament strategy; execution logic is to randomly select from the current population. One (e.g.) Individuals are compared in fitness, and the one with the highest fitness is retained and placed in the "mating pool." This step is repeated until the mating pool is full. This ensures that superior individuals have a higher probability of being selected, while maintaining population diversity.

[0115] Crossover operator: Sequential crossover (OX1) preserves the best local sub-path structure of the parent generation and reorganizes it to generate a new path. The execution logic is as follows: Let the two parent individuals be... and ,exist Randomly extract a segment of gene sequence (e.g., index) arrive ), directly copied to the same location in the offspring. Remaining gene filling: in Remove genes that have already been copied to offspring. The remaining genes are arranged according to their position in The original relative order is used to fill the vacant positions of the offspring (usually starting from the end of the truncated segment and filling cyclically). This allows the offspring to inherit... Local connectivity (geographical proximity) and The relative order relationship generates a new path that is different from any parent.

[0116] Mutation operator: reverse mutation; the execution logic is to perform a mutation with a preset low probability (e.g., Randomly select two cutoff points on a chromosome and reverse the gene sequences between these two points. Example: The technical effect is that by introducing random perturbations, the algorithm is prevented from getting trapped in local optima and new regions in the solution space are explored.

[0117] The system executes a cyclical iteration of "evaluation-selection-crossover-mutation" to terminate the iteration and output the optimal solution. The termination condition is set as follows: the number of iterations reaches a threshold (e.g., ...). Alternatively, the growth rate of the fitness function value corresponding to the reference chromosome within a consecutive number of iterations (20 in this example) is less than the growth rate threshold. .

[0118] When any of the above conditions are met, the current reference chromosome is determined as the target chromosome. The target chromosome is the gene sequence represented by the individual with the highest fitness in the current population. It is decoded into a target rain enhancement operation route that includes waypoint path sequences (i.e., 4D waypoint sequences containing latitude and longitude coordinates), which serves as the final flight operation command.

[0119] Understandably, the waypoint path sequence output by the algorithm for the target rain enhancement operation includes not only spatial coordinates (3D) but also temporal sequence and action instructions (the fourth dimension), namely (longitude, latitude, altitude, time / action). The system encapsulates this into a standard JSON format file and uploads it via BeiDou short message service or air-to-ground data link, providing an effective data foundation for subsequent rain enhancement operations.

[0120] Specifically, step S35 includes, but is not limited to, the following steps:

[0121] Step S351: Encapsulate the waypoint path sequence and target dispersal dose into an encrypted data packet, and send the encrypted data packet to the aircraft's onboard terminal via the air-to-ground data link;

[0122] Step S352: During the flight of the aircraft, the airborne terminal performs reverse decryption and integrity verification on the encrypted data packet. When the verification is successful, the flight command associated with the encrypted data packet is sent to the aircraft's PLC controller.

[0123] In step S353, the PLC controller compares the aircraft's GNSS positioning data with the coordinates of each waypoint in the waypoint path sequence in real time. When it is determined that the aircraft has passed through any waypoint coordinates, it controls the opening of the aircraft's dispersal valve to disperse the target catalyst at the target dispersal dose.

[0124] It is understood that, in this embodiment, the steps of controlling the aircraft to conduct rain enhancement operations in the target area according to the target rain enhancement operation route and the target seeding dose include: (1) encapsulating the waypoint path sequence and the target seeding dose into an encrypted data packet, and sending the encrypted data packet to the aircraft's onboard terminal through the air-to-ground data link. Specifically, the edge decision layer encapsulates the generated waypoint path sequence, the target seeding dose, and the action parameters into an encrypted data packet. To ensure the integrity and tamper-proof of the instructions during transmission, AES encryption and CRC verification mechanisms are used.

[0125] Let the original command payload be The encryption key is The encrypted data packet generated by the sending end satisfy:

[0126] ;

[0127] After receiving the data, the airborne terminal performs reverse decryption and integrity verification. When the verification is successful, the flight command associated with the encrypted data packet is sent to the aircraft's PLC controller, that is, the command is loaded into the task stack of the PLC controller, and the system enters the standby state. (2) Spatial trigger judgment model: The PLC controller compares the aircraft's GNSS positioning data with the coordinates of each waypoint in the waypoint path sequence in real time. That is, it reads the GNSS positioning data at a frequency of 10Hz and calculates the spatial deviation between the aircraft and the preset operation waypoint in real time. When it is determined that the aircraft has passed any waypoint coordinate, it controls the opening of the aircraft's dispersal valve to disperse the target catalyst of the target dispersal dose. Specifically, let The real-time position vector of the aircraft is , No. The coordinate vectors of the preset task waypoints are The controller uses a spatial Euclidean distance determination function. Calculate the deviation:

[0128]

[0129] in, The average radius of the Earth is 6371 km.

[0130] System setting trigger logic status bit :

[0131] ;

[0132] in, The effective spatial window for the operation is defined by a preset trigger radius threshold (200 meters in this embodiment); This flag marks the execution status of the waypoint, with an initial value of 0 (incomplete / pending execution). The flag flips to 1 (executed successfully) only after the PLC controller issues an action command and receives a physical feedback signal from the actuator (such as ignition circuit current or valve flow feedback). At that time, and interlocked flag When the value is 0, the operating conditions are deemed met, and the PLC sets the trigger signal. For quantitative control of the actuator, the PLC controller converts the trigger signal into voltage pulses that drive the relay, thus achieving quantitative catalyst dispensing.

[0133] Suppose the instruction specifies the first The action at each waypoint is to ignite Each flame strip has an ignition duration of [number] ignition times. (0.5 seconds in this example). Voltage control function of PLC output port. for:

[0134] ;

[0135] in, The high-level voltage (DC 24V) is used to drive the relay to close. The trigger time. This ensures that the relay closing time strictly corresponds to the required number of flame strips, achieving precise physical output of the working dosage.

[0136] Additionally, in some embodiments, during execution Figure 1 After step S30, the aircraft rain enhancement control method of this application embodiment also includes, but is not limited to, the following steps:

[0137] Step S41: After completing one spreading operation, the PLC controller reads the first feedback value of the loop current sensor and the second feedback value of the liquid nitrogen flow meter in real time.

[0138] Step S42: When the first feedback value is detected to be greater than the current threshold or the second feedback value is detected to be greater than the seeding amount threshold, the seeding flag is determined to be successful, and the seeding flag, the first feedback value and the second feedback value are transmitted back to the ground.

[0139] Step S43: When the first feedback value is detected to be less than or equal to the current threshold, or the second feedback value is detected to be less than or equal to the spreading amount threshold, the spreading flag is determined to be a spreading failure, and an alarm message is generated based on the spreading flag, the first feedback value, and the second feedback value, and the alarm message is sent to the ground.

[0140] Understandably, after the actuator operates, the PLC controller reads the feedback values ​​from the loop current sensor or liquid nitrogen flow meter, namely the first feedback value and the second feedback value. If a change in effective current or flow is detected, a flag is set. =1, which means that the seeding is confirmed as successful and the seeding, first feedback value and second feedback value are sent back to the ground. That is, an execution confirmation message (ACK) is generated and sent back to the ground through the Beidou link. When the first feedback value is detected to be less than or equal to the current threshold, or the second feedback value is detected to be less than or equal to the seeding amount threshold, the seeding is confirmed as failed and an alarm message is generated based on the seeding, first feedback value and second feedback value. The alarm message is sent to the ground to complete the closed-loop control of the whole process.

[0141] In summary, this embodiment provides an aircraft-based rain enhancement system integrating a ground-based soil TDR sensor network, an edge computing server, and an airborne PLC controller. It constructs an automated data closed-loop link from ground ecological water shortage signal acquisition to the execution of airborne seeding equipment actions. Simultaneously, it provides a dosage calculation method based on hydrological model inverse deriving. This involves constructing a precipitation-infiltration inverse equation using the SCS-CN model, deriving theoretical precipitation based on a set target soil moisture content, and further converting this into a catalyst seeding dosage using cloud physics parameters. Furthermore, it uses the ground-gridized soil moisture deficit as a weighting factor, employs a genetic algorithm to plan a nonlinear flight trajectory that prioritizes coverage of high-weight areas, and embeds matching seeding control command codes into the waypoint data. Additionally, it discloses an automatic triggering control method based on GNSS space fencing. The PLC controller uses the spatial Euclidean distance formula to calculate the three-dimensional deviation between the aircraft and the target waypoint. When the deviation is less than a preset threshold... Based on the dosage parameters in the instruction packet, the relay closing duration is controlled via pulse width modulation (PWM) or timed level output to achieve automatic triggering and precise quantification of the operation. In this way, by capturing the soil moisture content of the target area in real time, the theoretical water requirement is calculated using the SCS-CN inverse model, and the aircraft's flight path and corresponding catalyst application rate are automatically generated. This achieves an automated closed loop from ground monitoring to airborne execution. Compared to existing solutions that rely on manual experience, this method enables precise, targeted, and quantitative application of catalyst to the target area, ensuring the effectiveness of rain enhancement operations while avoiding catalyst waste.

[0142] like Figure 2 As shown, Figure 2 This is a structural diagram of an electronic device provided in one embodiment of this application. The present invention also provides an electronic device 200, comprising:

[0143] The processor 210 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0144] The memory 220 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 220 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 220 and called and executed by the processor 210 to execute the aircraft rain enhancement control method of the embodiments of this application.

[0145] Input / output interface 230 is used to implement information input and output;

[0146] The communication interface 240 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0147] Bus 250 transmits information between various components of the device (e.g., processor 210, memory 220, input / output interface 230, and communication interface 240);

[0148] The processor 210, memory 220, input / output interface 230 and communication interface 240 are connected to each other within the device via bus 250.

[0149] In addition, this application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described aircraft rain enhancement control method.

[0150] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0152] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for controlling aircraft rain enhancement, characterized in that, include: Determine whether to initiate rain enhancement operations based on NDVI time-series data and soil moisture content of the target area; When it is determined to start rain enhancement operations, the soil moisture content, effective root layer depth and evaporation loss compensation coefficient of the target area are processed based on the SCS-CN inverse model to calculate the required precipitation for the target area. The target area is equipped with a soil moisture sensor and is associated with target vegetation. The NDVI time series data includes the real-time normalized vegetation index of the target vegetation. Calculate the target seeding dose required for the target area based on the rainfall, and control the aircraft to carry out rain enhancement operations in the target area with the target seeding dose; The process of determining whether to initiate rain enhancement operations based on NDVI time-series data and soil moisture content of the target area includes: performing a moving average filtering process on the soil moisture content received from the soil moisture sensor; multiplying the field capacity corresponding to the target area by a preset coefficient to obtain a reference value; and determining to initiate rain enhancement operations when the current normalized vegetation index is greater than the index threshold, the current phenological stage of the target vegetation is between the greening stage and the grain-filling stage, and the soil moisture content is less than the reference value. Specifically, the soil moisture content, effective root layer depth, and evaporation loss compensation coefficient of the target area are processed based on the SCS-CN inverse model to calculate the required precipitation for the target area. This includes: calculating the target supplementary infiltration amount based on the field capacity of the target area, the soil moisture content, the effective root layer depth, and the evaporation loss compensation coefficient. The target supplementary infiltration amount indicates the amount of additional water required for the soil moisture content in the target area to reach the field capacity, excluding natural evaporation loss; and solving for the precipitation amount based on the SCS-CN inverse model and the target supplementary infiltration amount. The calculation of the target seeding dose required for the target area based on the precipitation includes: dividing the target area into multiple operational grids, wherein each operational grid has the same reference area; and calculating the target seeding dose required for each operational grid based on the precipitation conversion efficiency coefficient, cloud liquid water content, nucleation rate, the reference area, and the precipitation.

2. The aircraft rain enhancement control method according to claim 1, characterized in that, Controlling an aircraft to conduct rain enhancement operations on the target area at the target seeding dose includes: A flight path optimization model is constructed based on all the operational grids and precipitation data in the target area; The target rain enhancement operation route is obtained by solving the route optimization model based on a preset genetic algorithm. The aircraft is controlled to carry out rain enhancement operations in the target area according to the target rain enhancement operation route and the target seeding dose.

3. The aircraft rain enhancement control method according to claim 2, characterized in that, The route optimization model corresponds to a fitness function, the expression of which is: ； in, The value of the fitness function. For weight values, i Waypoint number, M The total number of grids in all the aforementioned task grids. The ecological water demand weight is the value of the operational grid corresponding to the i-th waypoint, and the ecological water demand weight is obtained based on the normalized precipitation. For the first i Each waypoint has a corresponding seeding identifier. When the aircraft passes the i-th waypoint and performs a seeding operation... The value is 1, indicating that the aircraft has not passed the i-th waypoint or the aircraft has passed the i-th waypoint but has not performed a seeding operation. The value is 0. For adjacent waypoints and waypoints The Euclidean distance between them; The constraints corresponding to the fitness function include: ； in, For the range threshold, The rotational speed of the aircraft is [value missing]. For the speed threshold, This is a no-fly zone within the target area. Let i be the i-th waypoint.

4. The aircraft rain enhancement control method according to claim 3, characterized in that, The target rain enhancement operation route is obtained by solving the route optimization model based on a preset genetic algorithm, including: Multiple first chromosomes with different gene arrangements are randomly generated. Each first chromosome includes multiple genes. The number of genes in a first chromosome is the same as the number of grids. Each gene corresponds to a different job grid. Each first chromosome represents a candidate rain enhancement job route. Based on the fitness function and the preset evolutionary operator, iterative optimization is performed on all the first chromosomes to output the corresponding second chromosomes; The second chromosome with the highest fitness function value is determined as the reference chromosome; When the growth rate of the fitness function value corresponding to the reference chromosome within a consecutive number of iterations is less than the growth rate threshold, or when the number of iterations reaches the number threshold, the current reference chromosome is determined as the target chromosome; The target chromosome is decoded to obtain the target rain enhancement operation route, which includes a waypoint path sequence.

5. The aircraft rain enhancement control method according to claim 4, characterized in that, Controlling the aircraft to conduct rain enhancement operations in the target area according to the target rain enhancement operation route and the target seeding dose includes: The waypoint path sequence and target dispersal dose are encapsulated into an encrypted data packet, and the encrypted data packet is sent to the aircraft's onboard terminal via a ground-to-air data link; During the flight of the aircraft, the onboard terminal performs reverse decryption and integrity verification on the encrypted data packet. When the verification passes, the flight command associated with the encrypted data packet is sent to the PLC controller of the aircraft. The PLC controller compares the aircraft's GNSS positioning data with the coordinates of each waypoint in the waypoint path sequence in real time. When it is determined that the aircraft has passed through any of the waypoint coordinates, it controls the opening of the aircraft's dispersal valve to disperse the target catalyst at the target dispersal dose.

6. The aircraft rain enhancement control method according to claim 5, characterized in that, After controlling the aircraft to conduct rain enhancement operations on the target area at the target seeding dose, the method further includes: After completing a seeding operation, the PLC controller reads the first feedback value of the loop current sensor and the second feedback value of the liquid nitrogen flow meter in real time. When the first feedback value is detected to be greater than the current threshold, or the second feedback value is detected to be greater than the spreading amount threshold, the spreading identifier is determined to be a successful spreading, and the spreading identifier, the first feedback value, and the second feedback value are transmitted back to the ground. When the first feedback value is detected to be less than or equal to the current threshold, or the second feedback value is detected to be less than or equal to the dissemination amount threshold, the dissemination identifier is determined to be a dissemination failure, and an alarm message is generated based on the dissemination identifier, the first feedback value, and the second feedback value, and the alarm message is sent to the ground.

7. An electronic device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the aircraft rain enhancement control method as described in any one of claims 1 to 6.

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