Intelligent mosquito eradication system, control method, device and medium

Abstract

This application discloses an intelligent mosquito control system, control method, device, and medium. The control method of the intelligent mosquito control system includes: in response to a user-sent spray location, acquiring a target image of the spray location; determining spray characteristics based on the target image; determining spray operating parameters based on the spray characteristics; and controlling a mobile spraying device to perform spraying operations based on the spray operating parameters. This application can effectively improve mosquito control efficiency.

Description

Technical Field

[0001] This application relates to, but is not limited to, intelligent mosquito control systems, and particularly to an intelligent mosquito control system, control method, device, and medium. Background Technology

[0002] Mosquitoes are major vectors for many diseases, including dengue fever, malaria, and chikungunya, posing a serious threat to public health. Traditional mosquito control methods mainly include chemical spraying, physical trapping, and environmental remediation. Chemical spraying (such as vehicle-mounted or manual spraying) often has limited coverage and suffers from problems such as indiscriminate use, environmental pollution, and mosquito resistance. Physical trapping devices, such as mosquito lamps and high-voltage electric grid mosquito killers, are usually deployed in fixed locations, passively waiting for mosquitoes to approach, and are of limited effectiveness in controlling large-scale, dynamically changing mosquito breeding grounds. Environmental remediation relies heavily on manual patrols, which is inefficient and difficult to sustain. Summary of the Invention

[0003] This application provides an intelligent mosquito control system, control method, device, and medium that can effectively improve mosquito control efficiency.

[0004] In a first aspect, embodiments of this application provide a control method for an intelligent mosquito control system, including: In response to the user-sent location to be sprayed, acquire a target image of the location to be sprayed; Based on the target image, the spray characteristics are determined; Based on the spray characteristics, spray operating parameters are determined, and the mobile spraying equipment is controlled to perform spraying operations based on the spray operating parameters.

[0005] The control method of the intelligent mosquito control system according to the first aspect of this application has at least the following beneficial effects: by responding to the location to be sprayed sent by the user, acquiring the target image, determining the spray characteristics, and controlling the spraying equipment accordingly, precise mosquito control is achieved, which improves the accuracy and efficiency of mosquito control, reduces the waste of pesticides and the risk of environmental pollution, and improves mosquito control efficiency.

[0006] According to some embodiments of the first aspect of this application, determining the spray features based on the target image includes: Based on the target image, the characteristics of water accumulation, plant quantity, and personnel at the location to be sprayed are determined.

[0007] According to some embodiments of the first aspect of this application, determining the spray operating parameters based on the spray characteristics includes: Based on the characteristics of the water accumulation and the characteristics of the plant quantity, determine the concentration of the pesticide solution and the number of sprays; Based on the described personnel characteristics, the type of medicine was determined; The concentration of the drug solution, the number of sprays, and the type of drug solution are used as spraying operation parameters.

[0008] According to some embodiments of the first aspect of this application, determining the water accumulation characteristics, plant quantity characteristics, and personnel characteristics of the location to be sprayed based on the target image includes: Based on the target image and the preset recognition model, the characteristics of water accumulation, plant quantity, and personnel at the location to be sprayed are determined.

[0009] According to some embodiments of the first aspect of this application, the control method further includes: In response to multiple spray locations sent by the user, acquire target images of all spray locations; Based on the target image, determine the spray characteristics of all the locations to be sprayed; Based on the spray characteristics, the spray operating parameters are determined; The spray priority is obtained by prioritizing all the spray locations based on their spray characteristics. The mobile spraying equipment is controlled to perform spraying operations based on the spraying priority and the spraying operation parameters of all the locations to be sprayed.

[0010] According to some embodiments of the first aspect of this application, controlling the mobile spraying device to perform spraying operations based on the spraying priority and spraying operating parameters of all the locations to be sprayed includes: The spray route is determined based on the spray priority and all the locations to be sprayed; The mobile spraying equipment is controlled to perform spraying operations based on the spraying route and the spraying operation parameters of all the locations to be sprayed.

[0011] According to some embodiments of the first aspect of this application, the spraying features include water accumulation features, plant quantity features, and personnel features. The step of prioritizing the spraying features of all the locations to be sprayed to obtain a spraying priority includes: The spray weight is calculated based on the water accumulation characteristics, the plant quantity characteristics, and the personnel characteristics; The spray priority is determined based on the spray weight of all the locations to be sprayed.

[0012] Secondly, embodiments of this application provide an operation control device for implementing the control method of the intelligent mosquito-killing system as provided in the first aspect embodiment.

[0013] Thirdly, embodiments of this application provide an intelligent mosquito control system, including the operation control device provided in the second aspect of the embodiments above.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the control method of the intelligent mosquito-killing system as described in the first aspect embodiment above.

[0015] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 A detailed flowchart of the control method for the intelligent mosquito control system provided in the embodiments of this application; Figure 2 A detailed flowchart of the control method for an intelligent mosquito control system provided in another embodiment of this application; Figure 3 A block diagram of the operation control device provided in the embodiments of this application. Detailed Implementation

[0018] 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.

[0019] 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.

[0020] Mosquitoes are major vectors for many diseases, including dengue fever, malaria, and chikungunya, posing a serious threat to public health. Traditional mosquito control methods mainly include chemical spraying, physical trapping, and environmental remediation. Chemical spraying (such as vehicle-mounted or manual spraying) often has limited coverage and suffers from problems such as indiscriminate use, environmental pollution, and mosquito resistance. Physical trapping devices, such as mosquito lamps and high-voltage electric grid mosquito killers, are usually deployed in fixed locations, passively waiting for mosquitoes to approach, and are of limited effectiveness in controlling large-scale, dynamically changing mosquito breeding grounds. Environmental remediation relies heavily on manual patrols, which is inefficient and difficult to sustain.

[0021] Based on this, embodiments of this application provide an intelligent mosquito control system, control method, device, and medium. By responding to the location to be sprayed sent by the user, the system acquires a target image, determines the spray characteristics, and controls the spraying equipment accordingly, thereby achieving precise mosquito control. This improves the accuracy and efficiency of mosquito control, reduces pesticide waste and environmental pollution risks, and increases mosquito control efficiency.

[0022] Firstly, referring to Figure 1 , Figure 1 A detailed flowchart of the control method for the intelligent mosquito control system provided in this application embodiment includes, but is not limited to, the following steps: Step S100: In response to the user sending the location to be sprayed, obtain the target image of the location to be sprayed; Step S200: Determine the spray characteristics based on the target image; Step S300: Determine the spray operating parameters based on the spray characteristics and control the mobile spraying equipment to perform spraying operations based on the spray operating parameters.

[0023] Understandably, responding to a user-sent spray location and acquiring a target image of that location ensures targeted spraying operations and provides raw data for subsequent spray feature analysis. For example, a user can manually input geographic coordinates to specify a spray location. Upon receiving these coordinates, the system can dispatch a drone or a mobile robot equipped with a camera to that area and capture a target image of the spray location. Determining spray features from the target image allows for the extraction of key information related to mosquito breeding environments based on the raw image data (target image), providing a basis for developing spraying strategies. For instance, it can identify potential mosquito breeding environment factors in the image, such as the presence of puddles or dense vegetation, and record the identification results to obtain spray features. Furthermore, based on the spray features, spraying parameters are determined, and the mobile spraying equipment is controlled accordingly. This enables dynamic adjustments to the spraying strategy based on environmental characteristics, achieving precise and efficient mosquito control. Specifically, spraying parameters can be determined based on a comparison table of spray characteristics and preset parameters. For example, if the spray characteristics indicate water accumulation, the pesticide concentration is set to medium; if there are many plants, the number of sprays is set to twice. Based on the determined spray characteristics, the preset parameter comparison table is consulted to determine the pesticide type, concentration, spray volume, and number of sprays. After determining the spraying parameters, the mobile spraying equipment is controlled to perform spraying operations using these parameters. The mobile spraying equipment can be a spraying drone, which can improve the efficiency of pesticide spraying.

[0024] It should be noted that image recognition technology can be used to identify the presence of puddles, ditches, or other water-filled areas in the target image, assessing the area and distribution of these water-filled areas to determine water accumulation characteristics. It can also identify the species, density, and coverage of plants in the area to be sprayed, such as the presence of dense shrubs or tall trees, to determine plant quantity characteristics. Furthermore, image analysis can determine the presence of human activity in the spraying area, thus identifying human characteristics. Based on these spray characteristics, the accuracy of spraying parameters can be effectively improved. For example, if the water accumulation characteristics indicate a large area of ​​water accumulation, and the plant quantity characteristics indicate dense vegetation, it can be determined that the area has a high risk of mosquito breeding, allowing for the setting of a higher pesticide concentration and multiple sprays. If the human characteristics indicate the presence of children in the area, biological agents with lower impact on humans and the environment can be prioritized. Determining spraying parameters by combining multiple spray characteristics can improve mosquito control efficiency while enhancing the safety of pesticide application.

[0025] Specifically, the acquisition of water accumulation characteristics can be achieved by analyzing target images to identify and quantify the presence and extent of water bodies in the area to be sprayed. For example, image segmentation technology can be used to identify water bodies in the image and quantify them based on parameters such as area and shape. Alternatively, multispectral image analysis can be combined to identify water accumulation areas by analyzing the absorption characteristics of water bodies to specific wavelengths of light.

[0026] Specifically, obtaining plant quantity characteristics can involve analyzing target images to assess the vegetation cover and density of the area to be sprayed. For example, plant quantity characteristics can be obtained by using color histogram analysis or texture feature extraction to distinguish between vegetated and non-vegetated areas and calculate vegetation cover; alternatively, machine learning classifiers can be used to classify image pixels, identify plant pixels, and count their quantity or density.

[0027] Specifically, personnel characteristics can be obtained by analyzing target images to identify the presence, approximate location, and activity status of personnel within the spraying area. In spraying operations, identifying the presence of personnel is crucial for ensuring operational safety and preventing the pesticide from harming the human body. For example, algorithms based on feature point detection and tracking can be used to identify human contours or key points in images to determine the presence and number of personnel; alternatively, motion detection technology can be combined to analyze dynamic changes in image sequences to identify moving personnel.

[0028] It should be noted that in some embodiments, the characteristics of water accumulation, plant quantity, and personnel at the spraying location can also be determined based on the target image and a preset recognition model. The preset recognition model refers to a pre-trained computational model capable of analyzing and understanding image data. This model learns from a large number of image samples, mastering the ability to recognize specific visual patterns. This preset recognition model can be a deep learning-based convolutional neural network (CNN) model, such as ResNet, VGG, or Inception, which performs well in tasks such as image classification, object detection, and semantic segmentation. Alternatively, the preset recognition model can be a model based on traditional machine learning algorithms, such as Support Vector Machines (SVM) combined with feature extractors (e.g., SIFT, HOG), or decision trees, random forests, etc., which complete the recognition task by analyzing the texture, color, shape, and other features of the image. The characteristics of water accumulation, plant quantity, and personnel at the spraying location are determined. Water accumulation directly relates to mosquito breeding grounds, plant quantity affects pesticide diffusion and coverage, and personnel characteristics are related to pesticide safety and pesticide selection. The preset recognition model can employ object detection technology to select and identify waterlogged areas, individual plants, and people in the target image, and count the plants. Alternatively, the preset recognition model can employ image semantic segmentation technology to classify each pixel in the target image as waterlogged, plants, people, or background, thereby accurately delineating the range and quantity of these features.

[0029] Reference Figure 2 , Figure 2 A detailed flowchart of the control method for an intelligent mosquito control system provided in another embodiment of this application includes, but is not limited to, the following steps: Step S400: In response to multiple locations to be sprayed sent by the user, acquire target images of all locations to be sprayed; Step S500: Based on the target image, determine the spray characteristics of all locations to be sprayed; Step S600: Determine the spray operating parameters based on the spray characteristics; Step S700: Sort the spraying priorities according to the spraying characteristics of all locations to be sprayed to obtain the spraying priorities; Step S800: Control the mobile spraying equipment to perform spraying operations according to the spraying priority and the spraying operation parameters of all locations to be sprayed.

[0030] It's understandable that users might simultaneously submit multiple spray locations, each with different spray characteristics. Therefore, when multiple locations exist, priority can be assigned based on their spray characteristics. This allows for a more efficient scheduling of spray tasks based on actual needs and urgency, optimizing resource allocation and improving overall mosquito control effectiveness. For example, different weights can be assigned to different spray characteristics (water accumulation area, plant density, population density, etc.), and a comprehensive priority score can be calculated for each location. These scores can then be used to rank the locations. For instance, areas with large water accumulation areas might be given higher priority to address mosquito breeding grounds, while densely populated areas might receive less priority to ensure personnel safety. By comprehensively considering factors such as the urgency of mosquito control, pesticide consumption, environmental impact, and equipment scheduling efficiency, all spray locations can be evaluated to generate an optimal spray sequence, thereby improving mosquito control efficiency.

[0031] Specifically, there are three areas to be sprayed: Area A (park lakeside, with large areas of standing water and dense vegetation, no people), Area B (residential green belt, with small amounts of standing water and sparse vegetation, and a small number of people), and Area C (edge ​​of school playground, no standing water, with dense vegetation, and a large number of people). After receiving information about these three spray locations, target images of these areas are acquired via drones or pre-set cameras. Image recognition technology is then used to analyze these images and determine the spraying characteristics of each area: Area A is identified as "high standing water, high vegetation density, low population density"; Area B is identified as "low standing water, low vegetation density, low population density"; and Area C is identified as "no standing water, high vegetation density, high population density". Based on these characteristics, the spraying parameters for each area are determined: Area A may be set to "high concentration insecticide, two sprays, specific insecticide"; Area B may be set to "low concentration insecticide, one spray, common insecticide"; and Area C may be set to "medium concentration insecticide, one spray, biological agent safe for humans". Then, the spraying characteristics of each area are prioritized. For example, considering that stagnant water is the main cause of mosquito breeding, area A has the highest priority; considering the population density, area C has the next highest priority; and area B has the lowest priority. Finally, based on this spraying priority (A->C->B) and the spraying parameters of each area, the mobile spraying equipment is controlled to spray in the order of A, C, B, and to spray precisely in each area according to the spraying parameters.

[0032] It should be noted that unreasonable path planning may occur during multi-point spraying operations, resulting in ineffective movement of the equipment between different spray locations. Therefore, a spraying route can be determined based on spray priority and all spray locations. Then, the moving spraying equipment can be controlled to perform spraying operations based on the spraying route and the spraying parameters of all spray locations. For example, an A* algorithm or genetic algorithm can be used to combine the spatial coordinates of all spray locations with the spray priority. The spray priority provides constraints on the access order of the algorithm, while the location information provides constraints on spatial distribution. This allows for the calculation of a spraying route with the shortest total movement distance or the least total operation time, thereby optimizing the equipment's movement trajectory and improving the mosquito control efficiency of the intelligent mosquito control system.

[0033] It's important to note that stagnant water is a primary breeding ground for mosquitoes. The presence, size, and depth of stagnant water directly impact mosquito density; therefore, the stagnant water characteristic refers to the presence and extent of stagnant water at the spraying location. Dense vegetation provides mosquitoes with habitats and breeding grounds; therefore, the vegetation quantity characteristic refers to the vegetation cover and density at the spraying location. Areas with human activity require consideration of the impact of spraying on human health, potentially necessitating adjustments to the type of insecticide or spraying time; this refers to the presence and density of human activity at the spraying location. Based on the stagnant water, vegetation quantity, and human characteristics, a spray weight is calculated. This quantifies and integrates these multiple spray characteristics into a comprehensive index to reflect the mosquito risk level and urgency of spraying at the location. This provides a quantitative basis for subsequent prioritization. The spray weight can be calculated using a weighted summation model. For example, the weight of the stagnant water characteristic can be higher than that of the vegetation quantity characteristic; alternatively, a machine learning model can be used to learn the mapping relationship between features and risk levels by training on historical data, thereby outputting the spray weight.

[0034] Specifically, assuming a user sends three locations A, B, and C to be sprayed, target images of these three locations are first acquired. For location A, image analysis identifies a large area of ​​standing water, dense shrubs, and no human activity. For location B, it identifies a small area of ​​standing water, sparse grass, and a small number of people. For location C, it identifies no standing water, tall trees, and a large number of people. When calculating the spray weights, the weight coefficients for the standing water feature can be set to 0.5, the plant quantity feature to 0.3, and the human activity feature to 0.2. Specifically, the standing water feature scores are: 10 points for a large area of ​​standing water, 5 points for a small area of ​​standing water, and 0 points for no standing water. The plant quantity feature scores are: 8 points for dense shrubs, 6 points for tall trees, and 3 points for sparse grass. The human activity feature scores are: 1 point for a large number of people, 3 points for a small number of people, and 5 points for no people. The spray weights for each location are then calculated as follows: The spray weight at position A = (10 * 0.5) + (8 * 0.3) + (5 * 0.2) = 52.4 + 1 = 8.4; The spray weight at position B = (5*0.5) + (3*0.3) + (3*0.2) = 2.5 + 0.9 + 0.6 = 4.0; The spray weight at position C = (0*0.5) + (6*0.3) + (1*0.2) = 0 + 1.8 + 0.2 = 2.0; Based on the calculated spray weights, the spray priorities are sorted from high to low as follows: Position A > Position B > Position C.

[0035] Secondly, referring to Figure 3 This invention provides an operation control device 300, including a memory 310, a processor 320, and a computer program stored in the memory 310 and executable on the processor 320. The processor 320 executes the program to implement the control method of the intelligent mosquito-killing system as described in the first aspect embodiment above, for example, executing... Figure 1 Method steps S100 to S300 and Figure 2 Method steps S400 to S800.

[0036] The memory 310, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, including the control method of the intelligent mosquito-killing system in the above embodiments of the present invention. The processor 320 implements the control method of the intelligent mosquito-killing system in the above embodiments of the present invention by running the non-transitory software programs and instructions stored in the memory 310.

[0037] The memory 310 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data required for executing the control method of the intelligent mosquito control system in the above embodiments. Furthermore, the memory 310 may include a high-speed random access memory 310, and may also include non-transitory memory 310, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. It should be noted that the memory 310 may include remotely located memories 310 relative to the processor 320, and these remote memories 310 can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0038] Thirdly, embodiments of the present invention provide an intelligent mosquito control system. The battery includes an operation control device 300 as described in the second aspect embodiment. By responding to the location to be sprayed sent by the user, the system acquires a target image, determines the spray characteristics, and controls the spraying equipment accordingly. This achieves precise mosquito control, improves the accuracy and efficiency of mosquito control, reduces pesticide waste and environmental pollution risks, and increases mosquito control efficiency.

[0039] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the control method of the intelligent mosquito control system as described in the first aspect embodiment above, for example, executing... Figure 1 Method steps S100 to S300 and Figure 2 The method steps S400 to S800 are described above. Those skilled in the art will understand that all or some of the steps in the methods and systems disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all physical components can be implemented as processors, such as central processing units, digital signal processors, or microprocessors executing software, or as hardware, or as integrated circuits, such as application-specific integrated circuits. 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, 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 by a computer. Furthermore, as is known to those skilled in the art, communication media typically contain 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 transmission medium.

[0040] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0041] The above is a detailed description of the preferred embodiments of this application. However, this application 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 this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A control method for an intelligent mosquito control system, characterized in that, include: In response to the user-sent location to be sprayed, acquire a target image of the location to be sprayed; Based on the target image, the spray characteristics are determined; Based on the spray characteristics, spray operating parameters are determined, and the mobile spraying equipment is controlled to perform spraying operations based on the spray operating parameters.

2. The control method according to claim 1, characterized by, Determining the spray features based on the target image includes: Based on the target image, the characteristics of water accumulation, plant quantity, and personnel at the location to be sprayed are determined.

3. The control method according to claim 2, characterized by, The step of determining the spray operating parameters based on the spray characteristics includes: Based on the characteristics of the water accumulation and the characteristics of the plant quantity, determine the concentration of the pesticide solution and the number of sprays; Based on the described personnel characteristics, the type of medicine was determined; The concentration of the drug solution, the number of sprays, and the type of drug solution are used as spraying operation parameters.

4. The control method according to claim 2, characterized by, The step of determining the water accumulation characteristics, plant quantity characteristics, and personnel characteristics at the location to be sprayed based on the target image includes: Based on the target image and the preset recognition model, the characteristics of water accumulation, plant quantity, and personnel at the location to be sprayed are determined.

5. The control method according to claim 1, characterized in that, The control method further includes: In response to multiple spray locations sent by the user, acquire target images of all spray locations; Based on the target image, determine the spray characteristics of all the locations to be sprayed; Based on the spray characteristics, the spray operating parameters are determined; The spray priority is obtained by prioritizing all the spray locations based on their spray characteristics. The mobile spraying equipment is controlled to perform spraying operations based on the spraying priority and the spraying operation parameters of all the locations to be sprayed.

6. The control method according to claim 5, characterized in that, The step of controlling the mobile spraying device to perform spraying operations based on the spraying priority and the spraying operation parameters of all the locations to be sprayed includes: The spray route is determined based on the spray priority and all the locations to be sprayed; The mobile spraying equipment is controlled to perform spraying operations based on the spraying route and the spraying operation parameters of all the locations to be sprayed.

7. The control method according to claim 5, characterized in that, The spray characteristics include water accumulation characteristics, plant quantity characteristics, and personnel characteristics. The process of prioritizing all spray characteristics at the locations to be sprayed to obtain spray priority includes: The spray weight is calculated based on the water accumulation characteristics, the plant quantity characteristics, and the personnel characteristics; The spray priority is determined based on the spray weight of all the locations to be sprayed.

8. An operation control device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the control method of the intelligent mosquito control system as described in any one of claims 1 to 7.

9. An intelligent mosquito control system, characterized in that, Includes the operation control device as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the control method of the intelligent mosquito control system as described in any one of claims 1 to 7.

Images