Large unmanned aerial vehicle agriculture and forestry spraying system control platform

Abstract

The invention discloses a large unmanned aerial vehicle agriculture and forestry spraying system control platform comprising an agriculture and forestry collection module used for collecting agriculture and forestry information; the unmanned aerial vehicle acquisition module is used for acquiring unmanned aerial vehicle information; the environment acquisition module is used for acquiring environment information; the control platform processes the agriculture and forestry information to generate first management and control information, processes the unmanned aerial vehicle information to generate second management and control information, processes the environment information to generate third management and control information, and after the first management and control information, the second management and control information and the third management and control information are generated, the unmanned aerial vehicle information is processed. And the control platform sends the first management and control information, the second management and control information and the third management and control information to corresponding receiving terminals. According to the invention, agriculture and forestry, unmanned aerial vehicle and environment data are integrated, more comprehensive control of a large-scale unmanned aerial vehicle agriculture and forestry spraying system is realized, and the operation efficiency of unmanned aerial vehicle agriculture and forestry spraying can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) control, and in particular to a control platform for a large-scale UAV agricultural and forestry spraying system. Background Art

[0002] With the acceleration of rural land transfer and the rise of new agricultural operators such as family farms and large-scale planters, the low efficiency and high costs of traditional manual or ground-based spraying have become increasingly prominent. For example, manual spraying can only cover 40 mu (approximately 16 acres) of land per day, while drones can increase this to 400-700 mu (approximately 160 acres). Furthermore, the migration of rural labor to cities has led to seasonal labor shortages, forcing a shift in agricultural production towards mechanization and intelligent technology.

[0003] In recent years, advances in drone hardware (such as multi-rotor and fuel-powered models), sensors (such as infrared and multi-spectral) and communication technologies (such as 5G and satellite positioning) have provided a technical foundation for building an intelligent control platform that integrates multi-source data. Therefore, the use of drones for agricultural and forestry spraying has gradually become mainstream. In the process of controlling drone systems for agricultural and forestry spraying, the drone agricultural and forestry spraying system control platform will be used.

[0004] The existing control platform has poor effect in controlling UAVs to perform safe operations and has a low level of intelligence, which has brought certain impacts on the use of the control platform. Therefore, a control platform for a large-scale UAV agricultural and forestry spraying system is proposed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: how to solve the problem that the existing safety protection system has a single protection type, resulting in poor protection effect and bringing certain impacts on the use of the safety protection system. A large-scale UAV agricultural and forestry spraying system control platform is provided.

[0006] The present invention solves the above-mentioned technical problems through the following technical solutions, which include:

[0007] Agriculture and forestry collection module, used to collect agriculture and forestry information;

[0008] UAV collection module, used to collect UAV information;

[0009] Environmental collection module, used to collect environmental information;

[0010] The control platform processes agricultural and forestry information to generate first control information, processes drone information to generate second control information, and processes environmental information to generate third control information;

[0011] After the first control information, the second control information and the third control information are generated, the control platform sends the first control information, the second control information and the third control information to the corresponding receiving terminals.

[0012] Furthermore, the specific process of the agriculture and forestry collection module collecting agriculture and forestry information is as follows:

[0013] An agricultural and forestry information collection drone is set up. The agricultural and forestry information collection drone is a small drone with infrared image collection function. It collects agricultural and forestry images before the large drone starts spraying, that is, the images before spraying;

[0014] The large drones will take off in advance before the spraying begins, and real-time images of agriculture and forestry will be collected after the spraying drones take off, that is, images of the spraying process;

[0015] That is, the collected agricultural and forestry information includes images before spraying and images during the spraying process.

[0016] Furthermore, the specific process of processing the agriculture and forestry information to generate the first management and control information is as follows:

[0017] Extract the images before spraying from the agricultural and forestry information. The images before spraying are infrared images collected by the agricultural and forestry information collection drone.

[0018] Import the human infrared model into the infrared image collected by the drone to analyze whether there is a human body;

[0019] When a human body is found in the infrared image collected by the drone, the human body's position is extracted and analyzed to see if it is on the preset spraying path. If the human body is on the preset spraying path, the first control information is generated to control the agricultural and forestry information collection drone to play a prompt message, prompting people in the agricultural and forestry areas to leave the spraying area.

[0020] The image of the spraying process is extracted and the preset target object is identified in the image of the spraying process. When the preset target object is abnormal, the first control information is generated. At this time, control information is sent to the spraying drone to control the spraying drone to slow down the spraying speed or change the spraying route.

[0021] Furthermore, the preset target object recognition process and abnormality determination process are as follows:

[0022] Import abnormal flying object models into the images of the spraying process. The abnormal flying object models include insect models and bird models.

[0023] When an abnormal flying object model is identified in the image of the spraying process, its flight altitude is monitored and marked as H;

[0024] When the number of abnormal flying objects that fly above the warning altitude exceeds the preset value, it means that the preset target object is abnormal;

[0025] At the same time, the number of abnormal flying objects within the unit range is collected. When the number of abnormal flying objects exceeds the warning number, it means that there is an abnormality in the preset target object.

[0026] Furthermore, a behavior abnormality determination is performed on a preset target object at the same time, and when the preset target object has abnormal behavior, first control information is generated;

[0027] The process of determining whether the preset target object's behavior is abnormal is as follows:

[0028] Monitor abnormal flying objects. When the number of abnormal flying objects within the range exceeds the preset value, the abnormal flying objects will be identified as an abnormal group.

[0029] Abnormal groups include bird and insect groups;

[0030] Extract the center position a1 of the abnormal group, then extract the position a2 of the spraying drone. Then connect a1 and a2 to obtain the evaluation line. If the position of a2 does not change, but the evaluation line gradually shrinks, it is determined that the preset target has abnormal behavior, and the first control information is generated.

[0031] When the abnormal group is a flock of birds, a1 is monitored in real time. When a1 flies toward the sprayed area, it is determined that the preset target object has abnormal behavior and the first control information is generated;

[0032] When the abnormal group is an insect group, a1 is monitored in real time. When the position of a1 continues to move toward the edge of the spraying area, it is determined that the preset target object has abnormal behavior and the first control information is generated.

[0033] Furthermore, the specific process of processing the drone information to generate the second control information is as follows:

[0034] Extract drone information, including the real-time remaining amount of liquid in the liquid tank, spray flow rate, drone battery information, and drone location information;

[0035] Processing the real-time remaining amount of liquid in the liquid tank and the spraying flow rate to obtain a first parameter;

[0036] Process the drone battery information and obtain the second parameter;

[0037] Process the drone's location information to obtain the third parameter;

[0038] When any one of the first parameter, the second parameter and the third parameter is abnormal, the second control information is generated.

[0039] Furthermore, the process of obtaining the first parameter and determining an abnormality is as follows:

[0040] Extract the real-time remaining amount of liquid in the liquid tank and the spraying flow rate, then collect the spraying duration of the drone and mark it as t, mark the real-time remaining amount of liquid in the liquid tank as Z1, mark the spraying flow rate as Z2, and then collect the liquid volume when the drone takes off and mark it as Z3;

[0041] The first parameter Zz is obtained by the formula (Z3-Z1)-Z2*t=Zz, where t is the correction value, 0.95≤t≤1.05, and t is proportional to Z3;

[0042] When the first parameter Zz exceeds the preset range, it means that the first parameter is abnormal;

[0043] The acquisition and abnormality determination process of the second parameter is as follows: extracting the drone battery information, which is real-time power information, and then collecting the drone's power consumption per unit time and the estimated task execution time;

[0044] The real-time power information is marked as W1, the drone's power consumption per unit time is marked as W2, and the estimated task execution time is marked as R;

[0045] The second parameter Ww is obtained by the formula W2*R*α-W1=Ww. When the second parameter Ww is less than the preset value, the second parameter Ww is abnormal. α is the correction value, 1.01≤α≤1.1;

[0046] The acquisition process of the third parameter and the abnormality judgment process are as follows: the location information of the UAV is extracted, and a preset collection time point is set. The location information of the UAV is collected once every preset collection time point. The collected UAV position is marked as Ji, where i is the number of collections and Ji(xi, yi, zi) is the location coordinate of the UAV;

[0047] Extract the standard position Bi(xi, yi, zi) of each preset acquisition time point;

[0048] Calculate the coordinate difference between Ji(xi, yi, zi) and Bi(xi, yi, zi) and obtain the coordinate difference of a single position (xi 差 , yi 差 ,zi 差 ), when xi 差 , yi 差 With Zi 差 If any one of them exceeds the preset range, it means that the position is abnormal. Then, the number of position abnormalities is extracted, that is, the third parameter is obtained. When the third parameter is greater than i / 3, it means that the third parameter is abnormal.

[0049] Furthermore, the specific process of processing the environmental information to generate the third control information is as follows:

[0050] Extract environmental information, including wind speed information, ambient temperature information, ambient humidity information and light intensity information;

[0051] Process the wind speed information to obtain wind speed assessment parameters. If the wind speed assessment parameters are abnormal, the third control information is generated;

[0052] Process the ambient temperature information to obtain temperature assessment parameters. If the temperature assessment parameters are abnormal, third control information is generated;

[0053] Process the ambient humidity information to obtain humidity assessment parameters. If the humidity assessment parameters are abnormal, third control information is generated.

[0054] The light intensity information is processed to obtain light evaluation parameters. If the light evaluation parameters are abnormal, the third control information is generated;

[0055] When there are no abnormalities in the wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters and light assessment parameters, the wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters and light assessment parameters are processed to obtain comprehensive assessment parameters. When the comprehensive assessment parameters are abnormal, the third control information is generated.

[0056] Furthermore, the process of obtaining the wind speed evaluation parameter, the temperature evaluation parameter, the humidity evaluation parameter, and the light evaluation parameter and the abnormality judgment process are as follows: the ratio of the wind speed information to the standard wind speed is calculated, that is, the wind speed evaluation parameter D1 is obtained. When the wind speed evaluation D1 exceeds the preset range, it indicates that there is an abnormality;

[0057] Calculate the ratio of the ambient temperature information to the standard temperature, that is, obtain the temperature evaluation parameter D2. When the temperature evaluation parameter D2 exceeds the preset range, it indicates that there is an abnormality;

[0058] Calculate the ratio of ambient humidity to standard humidity, that is, obtain humidity assessment parameter D3. When humidity assessment parameter D3 exceeds a preset range, it indicates that there is an abnormality.

[0059] The ratio of the light intensity information to the standard light intensity is calculated to obtain the light evaluation parameter D4. When the light evaluation parameter D4 exceeds a preset range, it indicates that an abnormality exists.

[0060] Furthermore, the process of obtaining comprehensive evaluation parameters and abnormality determination is as follows:

[0061] Assign D1 a correction value of β1, assign D2 a correction value of β2, D3 a correction value of β3, and D4 a correction value of β4;

[0062] The comprehensive evaluation parameter Dd is obtained through the formula D1*β1+D2*β2+D2*β3+D4*β4=Dd. When the comprehensive evaluation parameter Dd is less than the preset value, it indicates that there is an abnormality.

[0063] The present invention offers the following advantages over existing technologies: The control platform for the large-scale UAV agricultural and forestry spraying system, through infrared images collected by agricultural and forestry information drones, can detect in advance whether there are human bodies within the spraying area. If a person is detected within the preset spraying path, prompt messages are played to guide the person away, preventing harm from the spraying operation. In terms of operational accuracy and adaptability, the system uses preset target recognition in images of the spraying process to promptly detect unusual flying objects such as insects and birds. Based on these conditions, the spraying drone can be controlled to slow down or change its route, making the spraying operation more targeted. Furthermore, the system can evaluate and adjust the drone's spraying operation based on environmental information such as wind speed, temperature, humidity, and light intensity, ensuring that spraying can be carried out effectively under various environmental conditions. In terms of drone status monitoring, the system processes and monitors the drone's remaining liquid level, battery status, and location information. If any of this information is abnormal, control information is generated promptly to ensure the drone completes its spraying mission safely and stably, improving operational efficiency and reliability, and achieving intelligent control of the large-scale UAV agricultural and forestry spraying system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a structural block diagram of the present invention. DETAILED DESCRIPTION

[0065] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0066] like Figure 1 As shown, this embodiment provides a technical solution: a large-scale UAV agricultural and forestry spraying system control platform, including:

[0067] Agriculture and forestry collection module, used to collect agriculture and forestry information;

[0068] UAV collection module, used to collect UAV information;

[0069] Environmental collection module, used to collect environmental information;

[0070] The control platform processes agricultural and forestry information to generate first control information, processes drone information to generate second control information, and processes environmental information to generate third control information;

[0071] After the first control information, the second control information and the third control information are generated, the control platform sends the first control information, the second control information and the third control information to the corresponding receiving terminals.

[0072] Furthermore, the specific process of the agriculture and forestry collection module collecting agriculture and forestry information is as follows:

[0073] An agricultural and forestry information collection drone is set up. The agricultural and forestry information collection drone is a small drone with infrared image collection function. It collects agricultural and forestry images before the large drone starts spraying, that is, the images before spraying;

[0074] The large drones will take off in advance before the spraying begins, and real-time images of agriculture and forestry will be collected after the spraying drones take off, that is, images of the spraying process;

[0075] That is, the collected agricultural and forestry information includes images before spraying and images during the spraying process.

[0076] Furthermore, the specific process of processing the agriculture and forestry information to generate the first management and control information is as follows:

[0077] Extract the images before spraying from the agricultural and forestry information. The images before spraying are infrared images collected by the agricultural and forestry information collection drone.

[0078] Import the human infrared model into the infrared image collected by the drone to analyze whether there is a human body;

[0079] When a human body is found in the infrared image collected by the drone, the human body's position is extracted and analyzed to see if it is on the preset spraying path. If the human body is on the preset spraying path, the first control information is generated to control the agricultural and forestry information collection drone to play a prompt message, prompting people in the agricultural and forestry areas to leave the spraying area.

[0080] Extract images of the spraying process and perform preset target object recognition on the images of the spraying process. When the preset target object is abnormal, the first control information is generated. At this time, control information is sent to the spraying drone to control the spraying drone to slow down the spraying speed or change the spraying route;

[0081] Agricultural and forestry information collection drones take off in advance to capture infrared images before spraying, enabling detection of personnel within the spraying area before operations begin. Using a human infrared model to analyze images, they accurately locate human positions. If a person is detected within the pre-set spraying path, a primary control message is immediately generated, prompting personnel to evacuate, preventing unwitting exposure to the spraying agent and effectively preventing accidents.

[0082] During the spraying process, real-time image capture and preset target identification are performed, enabling timely detection of unusual targets such as insects and birds, and identifying potential hazards to the spraying drone. When an anomaly is detected, primary control information is generated to slow the drone down or change its route, ensuring precise application of the pesticide to the target area. This prevents spray deviations caused by external interference, improves pesticide application efficiency, reduces pesticide waste and environmental pollution, and better ensures flight safety, preventing damage to the drone due to bird strikes and other conditions.

[0083] By collecting images before and during spraying in stages, the control platform can fully grasp the dynamic changes in the agricultural and forestry operation environment, and adjust the management and control strategies in a timely manner according to the information at different stages, so that the entire agricultural and forestry spraying system can flexibly respond to complex and changing operation scenarios, and improve the system's adaptability and reliability.

[0084] The preset target object recognition process and abnormality determination process are as follows:

[0085] Import abnormal flying object models into the images of the spraying process. The abnormal flying object models include insect models and bird models.

[0086] When an abnormal flying object model is identified in the image of the spraying process, its flight altitude is monitored and marked as H;

[0087] When the number of abnormal flying objects that fly above the warning altitude exceeds the preset value, it means that the preset target object is abnormal;

[0088] At the same time, the number of abnormal flying objects within the unit range is collected. When the number of abnormal flying objects exceeds the warning number, it means that there is an abnormality in the preset target object;

[0089] By incorporating models of unusual flying objects like insects and birds, the system can quickly and accurately identify potential threats, such as large-scale insect migrations or bird gatherings. Combining the dual criteria of flight altitude and number, it can quantitatively assess the risk of pest outbreaks, enabling more targeted pesticide spraying, improving control effectiveness, and reducing damage to agricultural and forestry crops.

[0090] By monitoring the height and number of abnormal flying objects in real time, the system can adjust spraying strategies accordingly. For example, if the number of abnormal flying objects at high altitude exceeds a threshold, it may indicate the spread of pests and diseases. The system can adjust the drone's flight path and spraying dosage accordingly, achieving dynamic and intelligent operations and avoiding the inadequate prevention and control measures or waste of resources caused by mechanical execution of fixed procedures.

[0091] Reduce ecological impact: Avoid blind spraying of pesticides, reduce coverage of pesticides in non-target areas, reduce the impact on the surrounding ecological environment and beneficial organisms, and practice the concept of green agriculture. At the same time, precise spraying operations can reduce pesticide residues, ensure the quality and safety of agricultural products, and improve the overall benefits of agricultural production;

[0092] If birds gather, the spraying drone needs to be controlled to slow down and wait for the flock to disperse before spraying. This not only ensures the safety of the spraying drone, but also reduces the impact of the sprayed drugs on the birds.

[0093] If insects gather, the spraying drone needs to be controlled to change the spraying route, giving priority to flying to the gathering places for spraying, so as to achieve precise killing and better ensure agricultural and forestry safety;

[0094] At the same time, the preset target object is judged to have abnormal behavior. When the preset target object has abnormal behavior, the first control information is generated;

[0095] The process of determining whether the preset target object's behavior is abnormal is as follows:

[0096] Monitor abnormal flying objects. When the number of abnormal flying objects within the range exceeds the preset value, the abnormal flying objects will be identified as an abnormal group.

[0097] Abnormal groups include bird and insect groups;

[0098] Extract the center position a1 of the abnormal group, then extract the position a2 of the spraying drone. Then connect a1 and a2 to obtain the evaluation line. If the position of a2 does not change, but the evaluation line gradually shrinks, it is determined that the preset target has abnormal behavior and the first control information is generated. The shrinking evaluation line indicates that the flock of birds may be stimulated to fly towards the spraying drone. Therefore, it is necessary to control the accompanying drone to disperse the flock of birds to ensure the safety of the spraying drone.

[0099] When the abnormal group is a flock of birds, a1 is monitored in real time. When a1 flies towards an area that has been sprayed, it is determined that the preset target object has abnormal behavior and the first control information is generated. The pesticide concentration in the sprayed area is high and may affect the birds. At this time, the accompanying drone flock is controlled to drive the birds away from the spraying area to ensure their safety.

[0100] When the abnormal group is an insect group, a1 is monitored in real time. When the position of a1 continues to move toward the edge of the spraying area, it is determined that the preset target object has abnormal behavior and the first control information is generated. When the abnormal group is an insect group, the position of a1 continues to move toward the edge of the spraying area, which means that the insect group wants to escape. It may fly back to the target farmland and forest after the pesticide spraying is completed. At this time, the first control information is generated to drive the insect group to the area where the spraying process has already occurred to achieve better pest control effect.

[0101] The specific process of processing the drone information to generate the second control information is as follows:

[0102] Extract drone information, including the real-time remaining amount of liquid in the liquid tank, spray flow rate, drone battery information, and drone location information;

[0103] Processing the real-time remaining amount of liquid in the liquid tank and the spraying flow rate to obtain a first parameter;

[0104] Process the drone battery information and obtain the second parameter;

[0105] Process the drone's location information to obtain the third parameter;

[0106] When any one of the first parameter, the second parameter and the third parameter is abnormal, the second control information is generated.

[0107] Furthermore, the process of obtaining the first parameter and determining an abnormality is as follows:

[0108] Extract the real-time remaining amount of liquid in the liquid tank and the spraying flow rate, then collect the drone's spraying time, mark it as t, mark the real-time remaining amount of liquid in the liquid tank as Z1, mark the spraying flow rate as Z2, and then collect the liquid volume when the drone takes off, mark it as Z3;

[0109] The first parameter Zz is obtained by the formula (Z3-Z1)-Z2*t=Zz, where t is the correction value, 0.95≤t≤1.05, and t is proportional to Z3;

[0110] When the first parameter Zz exceeds the preset range, it means that the first parameter is abnormal;

[0111] The acquisition and abnormality determination process of the second parameter is as follows: extracting the drone battery information, which is real-time power information, and then collecting the drone's unit time power consumption and estimated task execution time;

[0112] The real-time power information is marked as W1, the drone's power consumption per unit time is marked as W2, and the estimated task execution time is marked as R;

[0113] The second parameter Ww is obtained by the formula W2*R*α-W1=Ww. When the second parameter Ww is less than the preset value, the second parameter Ww is abnormal. α is the correction value, 1.01≤α≤1.1;

[0114] The acquisition process of the third parameter and the abnormality judgment process are as follows: the location information of the UAV is extracted, and a preset collection time point is set. The location information of the UAV is collected once every preset collection time point. The collected UAV position is marked as Ji, where i is the number of collections and Ji(xi, yi, zi) is the location coordinate of the UAV;

[0115] Extract the standard position Bi(xi, yi, zi) of each preset acquisition time point;

[0116] Calculate the coordinate difference between Ji(xi, yi, zi) and Bi(xi, yi, zi) and obtain the coordinate difference of a single position (xi 差 , yi 差 ,zi 差 ), when xi 差 , yi 差 With Zi 差 If any one of them exceeds the preset range, it means that the position is abnormal. Then the number of abnormal positions is extracted, that is, the third parameter is obtained. When the third parameter is greater than i / 3, it means that the third parameter is abnormal.

[0117] By real-time monitoring of the remaining tank volume, spray flow rate, battery charge, and location, problems such as depletion of spray liquid, low battery, or flight deviation can be predicted in advance. For example, if the remaining spray volume calculated based on the formula is abnormal, the drone can be dispatched to return for replenishment, avoiding interruptions due to depletion and ensuring the continuity of the spraying mission.

[0118] Accurately analyzing battery information, combining power consumption per unit time with estimated mission execution duration, effectively prevents drone crashes due to low battery. Furthermore, real-time location monitoring ensures that if a drone strays beyond its preset route, control information is immediately generated to correct the situation. This reduces the risk of collisions or disorientation, ensuring the safety of the device and its surroundings.

[0119] Based on accurate parameter calculation and anomaly detection, the system can rationally plan the drone's operating rhythm and path. For example, when sufficient liquid medicine is remaining, the spraying speed can be appropriately increased; when the battery is low, the system prioritizes completing the operation in the nearest area, reducing ineffective flights, optimizing the allocation of resources such as medicine and electricity, and reducing operating costs.

[0120] Through quantitative parameter calculation and standardized anomaly judgment rules, the system can quickly and objectively evaluate the status of drones and generate control instructions, reducing manual intervention, improving the timeliness and accuracy of control, and promoting the development of agricultural and forestry spraying operations towards intelligence and automation.

[0121] The specific process of processing environmental information and generating third-party control information is as follows:

[0122] Extract environmental information, including wind speed information, ambient temperature information, ambient humidity information and light intensity information;

[0123] Process the wind speed information to obtain wind speed assessment parameters. If the wind speed assessment parameters are abnormal, the third control information is generated;

[0124] Process the ambient temperature information to obtain temperature assessment parameters. If the temperature assessment parameters are abnormal, third control information is generated;

[0125] Process the ambient humidity information to obtain humidity assessment parameters. If the humidity assessment parameters are abnormal, third control information is generated.

[0126] The light intensity information is processed to obtain light evaluation parameters. If the light evaluation parameters are abnormal, the third control information is generated;

[0127] When the wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters, and light assessment parameters are all normal, the wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters, and light assessment parameters are processed to obtain a comprehensive assessment parameter. When the comprehensive assessment parameter is abnormal, the third control information is generated;

[0128] Single environmental factors such as wind speed, temperature, humidity, and light intensity are evaluated separately. When one factor is abnormal, such as excessive wind speed that may affect the stability of the drone, the system can promptly generate third-party control information and take measures such as lowering the flight altitude and suspending operations to avoid accidents such as drone loss of control and crashes due to harsh environments, thereby ensuring the safe operation of the equipment.

[0129] Adjust the spraying strategy according to parameters such as ambient temperature and humidity. For example, in a high-temperature and dry environment, appropriately reduce the spraying speed or increase the spraying volume to prevent the liquid from evaporating too quickly and affecting the efficacy of the medicine; in a high-humidity environment, choose a more appropriate time to spray to avoid dilution or washing of the liquid, thereby improving the utilization rate of pesticides and the effectiveness of pest control.

[0130] By comprehensively evaluating various environmental factors, even if a single environmental parameter does not reach the abnormal threshold, the combined effect may affect the operation. At this time, the system can still generate third-party control information and flexibly adjust the operation plan, so that the drone can find the best operation mode under different weather and environmental conditions, ensuring that agricultural and forestry spraying work is carried out continuously and efficiently.

[0131] Accurate environmental assessments avoid ineffective or duplicated operations due to unsuitable environments, reducing the waste of resources like pesticides and electricity. They also effectively mitigate the risk of equipment damage caused by environmental factors, lower maintenance costs, and optimize operating costs in multiple ways.

[0132] The process of obtaining wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters, and light assessment parameters and the abnormality judgment process are as follows: the ratio of wind speed information to standard wind speed is calculated, that is, the wind speed assessment parameter D1 is obtained. When the wind speed assessment D1 exceeds the preset range, it indicates that there is an abnormality;

[0133] Calculate the ratio of the ambient temperature information to the standard temperature, that is, obtain the temperature evaluation parameter D2. When the temperature evaluation parameter D2 exceeds the preset range, it indicates that there is an abnormality;

[0134] Calculate the ratio of ambient humidity to standard humidity, that is, obtain humidity assessment parameter D3. When humidity assessment parameter D3 exceeds a preset range, it indicates that there is an abnormality.

[0135] The ratio of the light intensity information to the standard light intensity is calculated to obtain the light evaluation parameter D4. When the light evaluation parameter D4 exceeds a preset range, it indicates that an abnormality exists.

[0136] The process of obtaining comprehensive evaluation parameters and determining abnormalities is as follows:

[0137] Assign D1 a correction value of β1, assign D2 a correction value of β2, D3 a correction value of β3, and D4 a correction value of β4;

[0138] β1+β2+β3+β4=1, β1>β2>β3>β4;

[0139] The comprehensive evaluation parameter Dd is obtained through the formula D1*β1+D2*β2+D2*β3+D4*β4=Dd. When the comprehensive evaluation parameter Dd is less than the preset value, it indicates that there is an abnormality.

[0140] Environmental factors such as wind speed, temperature, humidity, and light intensity are converted into specific ratio parameters. By comparing them with preset standard ranges, it can intuitively and accurately determine whether a single environmental factor is abnormal. For example, when the wind speed assessment parameter is out of range, the potential threat of strong winds to drone flight and pesticide dispersion can be immediately identified, avoiding subjective judgment errors and providing a reliable basis for risk warning.

[0141] Comprehensive assessment parameters combine individual parameters and apply weighted corrections to fully account for the varying weights of environmental factors affecting operations. Even if individual parameters do not reach abnormal thresholds, the combined presence of multiple factors can create risks. In these cases, a comprehensive assessment can identify potential threats, such as the combination of high temperatures, low humidity, and strong winds, which can accelerate the evaporation of liquid medicine. The system then generates control information based on this information, enabling more comprehensive and intelligent decision-making.

[0142] Based on real-time evaluation of quantitative parameters, the system can dynamically adjust its operation strategy based on environmental changes. For example, if temperature assessment parameters are abnormal, the system automatically adjusts the spraying height and speed. If comprehensive assessment parameters are abnormal, the system pauses or replans the operation. This ensures that the drone can always operate safely and efficiently in complex environments, minimizing the negative impact of the environment on operational performance.

[0143] Standardized parameter calculation and anomaly determination rules make the environmental assessment process standardized and replicable, reducing operation interruptions, equipment losses, and pesticide waste caused by environmental factors, effectively controlling operating costs, ensuring that agricultural and forestry spraying tasks proceed as planned, and improving overall operation reliability and economic benefits.

[0144] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0145] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0146] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A large-scale UAV agricultural and forestry spraying system control platform, characterized by: include: Agriculture and forestry collection module, used to collect agriculture and forestry information; UAV collection module, used to collect UAV information; Environmental collection module, used to collect environmental information; The control platform processes agricultural and forestry information to generate first control information, processes drone information to generate second control information, and processes environmental information to generate third control information; After the first control information, the second control information and the third control information are generated, the control platform sends the first control information, the second control information and the third control information to the corresponding receiving terminals.

2. A large-scale UAV agricultural and forestry spraying system control platform according to claim 1, characterized in that: The specific process of collecting agricultural and forestry information by the agricultural and forestry collection module is as follows: An agricultural and forestry information collection drone is set up. The agricultural and forestry information collection drone is a small drone with infrared image collection function. It collects agricultural and forestry images before the large drone starts spraying, that is, the images before spraying; The large drones will take off in advance before the spraying begins, and real-time images of agriculture and forestry will be collected after the spraying drones take off, that is, images of the spraying process; That is, the collected agricultural and forestry information includes images before spraying and images during the spraying process.

3. A large-scale UAV agricultural and forestry spraying system control platform according to claim 2, characterized in that: The specific process of processing the agricultural and forestry information to generate the first management and control information is as follows: Extract the images before spraying from the agricultural and forestry information. The images before spraying are infrared images collected by the agricultural and forestry information collection drone. Import the human infrared model into the infrared image collected by the drone to analyze whether there is a human body; When a human body is found in the infrared image collected by the drone, the human body's position is extracted and analyzed to see if it is on the preset spraying path. If the human body is on the preset spraying path, the first control information is generated to control the agricultural and forestry information collection drone to play a prompt message, prompting people in the agricultural and forestry areas to leave the spraying area. The image of the spraying process is extracted and the preset target object is identified in the image of the spraying process. When the preset target object is abnormal, the first control information is generated. At this time, control information is sent to the spraying drone to control the spraying drone to slow down the spraying speed or change the spraying route.

4. A large-scale UAV agricultural and forestry spraying system control platform according to claim 3, characterized in that: The preset target object recognition process and abnormality determination process are as follows: Import abnormal flying object models into the images of the spraying process. The abnormal flying object models include insect models and bird models. When an abnormal flying object model is identified in the image of the spraying process, its flight altitude is monitored and marked as H; When the number of abnormal flying objects that fly above the warning altitude exceeds the preset value, it means that the preset target object is abnormal; At the same time, the number of abnormal flying objects within the unit range is collected. When the number of abnormal flying objects exceeds the warning number, it means that there is an abnormality in the preset target object.

5. A large-scale UAV agricultural and forestry spraying system control platform according to claim 4, characterized in that: At the same time, the preset target object is judged to have abnormal behavior. When the preset target object has abnormal behavior, the first control information is generated; The process of determining whether the preset target object's behavior is abnormal is as follows: Monitor abnormal flying objects. When the number of abnormal flying objects within the range exceeds the preset value, the abnormal flying objects will be identified as an abnormal group. Abnormal groups include bird and insect groups; Extract the center position a1 of the abnormal group, then extract the position a2 of the spraying drone. Then connect a1 and a2 to obtain the evaluation line. If the position of a2 does not change, but the evaluation line gradually shrinks, it is determined that the preset target has abnormal behavior, and the first control information is generated. When the abnormal group is a flock of birds, a1 is monitored in real time. When a1 flies toward the sprayed area, it is determined that the preset target object has abnormal behavior and the first control information is generated; When the abnormal group is an insect group, a1 is monitored in real time. When the position of a1 continues to move toward the edge of the spraying area, it is determined that the preset target object has abnormal behavior and the first control information is generated.

6. The large-scale UAV agricultural and forestry spraying system control platform according to claim 1, characterized in that: The specific process of processing the drone information to generate the second control information is as follows: Extract drone information, including the real-time remaining amount of liquid in the liquid tank, spray flow rate, drone battery information, and drone location information; Processing the real-time remaining amount of liquid in the liquid tank and the spraying flow rate to obtain a first parameter; Process the drone battery information and obtain the second parameter; Process the drone's location information to obtain the third parameter; When any one of the first parameter, the second parameter and the third parameter is abnormal, the second control information is generated.

7. The large-scale UAV agricultural and forestry spraying system control platform according to claim 1, characterized in that: The specific process of processing environmental information and generating third-party control information is as follows: Extract environmental information, including wind speed information, ambient temperature information, ambient humidity information and light intensity information; Process the wind speed information to obtain wind speed assessment parameters. If the wind speed assessment parameters are abnormal, the third control information is generated; Process the ambient temperature information to obtain temperature assessment parameters. If the temperature assessment parameters are abnormal, third control information is generated; Process the ambient humidity information to obtain humidity assessment parameters. If the humidity assessment parameters are abnormal, third control information is generated. The light intensity information is processed to obtain light evaluation parameters. If the light evaluation parameters are abnormal, the third control information is generated; When there are no abnormalities in the wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters and light assessment parameters, the wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters and light assessment parameters are processed to obtain comprehensive assessment parameters. When the comprehensive assessment parameters are abnormal, the third control information is generated.

8. The large-scale UAV agricultural and forestry spraying system control platform according to claim 7, characterized in that: The process of obtaining wind speed assessment parameters, temperature assessment parameters, humidity assessment parameters, and light assessment parameters and the abnormality judgment process are as follows: the ratio of wind speed information to standard wind speed is calculated, that is, the wind speed assessment parameter D1 is obtained. When the wind speed assessment D1 exceeds the preset range, it indicates that there is an abnormality; Calculate the ratio of the ambient temperature information to the standard temperature, that is, obtain the temperature evaluation parameter D2. When the temperature evaluation parameter D2 exceeds the preset range, it indicates that there is an abnormality; Calculate the ratio of ambient humidity to standard humidity, that is, obtain humidity assessment parameter D3. When humidity assessment parameter D3 exceeds a preset range, it indicates that there is an abnormality. The ratio of the light intensity information to the standard light intensity is calculated to obtain the light evaluation parameter D4. When the light evaluation parameter D4 exceeds a preset range, it indicates that an abnormality exists.

9. A large-scale UAV agricultural and forestry spraying system control platform according to claim 8, characterized in that: The process of obtaining comprehensive evaluation parameters and determining abnormalities is as follows: Assign D1 a correction value of β1, assign D2 a correction value of β2, D3 a correction value of β3, and D4 a correction value of β4; The comprehensive evaluation parameter Dd is obtained through the formula D1*β1+D2*β2+D2*β3+D4*β4=Dd. When the comprehensive evaluation parameter Dd is less than the preset value, it indicates that there is an abnormality.

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