Collaborative insect trapping method and system and computer equipment

Abstract

The invention provides a collaborative insect trapping method and system and computer equipment. The collaborative insect trapping method comprises the following steps: acquiring environment data from a plurality of insect killing devices; predicting insect distribution and activity trend according to the environment data and the insect activity model; according to the insect distribution and activity trend, a control instruction is generated, and the control instruction is used for indicating working parameters of the deinsectization device.

Description

Technical Field

[0001] The present application relates to the technical field of insect control, and in particular to a collaborative insect trapping method, system and computer device. Background Art

[0002] The insecticide device is a device widely used in agricultural pest control. Traditional insecticide devices usually work individually, and each insecticide device operates independently, so the trapping range and effect of each insecticide device are limited, it is difficult to cover a large area, and the insecticide efficiency is low. Summary of the invention

[0003] A technical problem to be solved by the present application is to improve the insect killing efficiency of the insect killing device.

[0004] In order to solve the above-mentioned technical problems, the embodiments of the present application provide a collaborative insect trapping method, system and computer device.

[0005] An embodiment of the present application provides a collaborative insect trapping method, including: acquiring environmental data from multiple insect killing devices; predicting insect distribution and activity trends based on the environmental data and an insect activity model; and generating control instructions based on the insect distribution and activity trends, the control instructions being used to indicate working parameters of the insect killing devices.

[0006] In some embodiments, the insect killing device includes an insect attractant lamp and an insect killer; the working parameters include the light intensity and flashing frequency of the insect attractant lamp.

[0007] In some embodiments, the method also includes: dividing multiple insect killing devices into different light intensity areas according to the sensitivity of insects to different light intensities; correspondingly, generating control instructions according to the insect distribution and activity trends includes: calculating the light intensity of each light intensity area according to the insect distribution and activity trends; generating control instructions according to the light intensity of each light area.

[0008] In some embodiments, the method also includes: dividing multiple insect killing devices into different frequency regions according to the phototropism characteristics of different insect species; correspondingly, generating control instructions according to the insect distribution and activity trends includes: calculating the flashing frequency of the insect killing device in each frequency region, and generating control instructions according to the flashing frequency of each frequency region.

[0009] In some embodiments, each frequency region includes a plurality of insect-killing devices arranged at intervals; the flashing frequencies of two adjacent insect-killing devices have a phase difference.

[0010] In some embodiments, the insect extermination device also includes a power generation device and a power supply device, and the power generation device is connected to the power supply device; the method also includes: obtaining the power generation of the power generation device and the storage capacity of the power supply device from each insect extermination device; calculating the working time of the insect extermination device based on the power generation and the storage capacity; calculating the working time of each insect extermination device based on the working time of the insect extermination device, weather forecast data and a preset insect activity model.

[0011] In some embodiments, the method further includes: obtaining the number of insects caught by each insect killing device; and optimizing the insect activity model according to the environmental data and the number of insects caught by each insect killing device.

[0012] An embodiment of the present application also provides a collaborative insect trapping system, including: multiple insect extermination devices and a server connected to the multiple insect extermination devices; the server is configured to: obtain environmental data from the multiple insect extermination devices; predict insect distribution and activity trends based on the environmental data and insect activity models; generate control instructions based on the insect distribution and activity trends, and the control instructions are used to indicate the working parameters of the insect extermination devices.

[0013] In some embodiments, the insect extermination device includes an environmental sensor, an insect trap light, and an insect killer; the environmental sensor is used to detect the environmental data of the insect trap light; the insect trap light is used to attract insects into the insecticidal range of the insect killer; and the insect killer is used to capture insects.

[0014] An embodiment of the present application provides a computer device, including a processor and a memory storing a computer program. When the processor executes the program, the collaborative insect trapping method provided in the above embodiment is implemented.

[0015] An embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs any of the above-mentioned steps of collaboratively trapping insects.

[0016] Through the above technical solution, the application provides a network system that connects multiple pest control devices through a wireless communication network to form a collaborative network system. The server can dynamically adjust the working parameters of each pest control device in the network according to the pest activity patterns and environmental factors, significantly improving the trapping effect and trapping efficiency; and dynamically adjust the trapping strategy according to the pest activity patterns and distribution conditions to achieve precise prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the structure of the cooperative insect trapping system disclosed in the embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of the structure of the insecticide device disclosed in the embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of the process of the collaborative insect trapping method disclosed in the embodiment of the present application;

[0021] Figure 4 is a flow chart of a collaborative insect trapping system method disclosed in yet another embodiment of the present application;

[0022] Figure 5 is a schematic diagram of light intensity coordinated control disclosed in an embodiment of the present application;

[0023] Figure 6 It is a schematic diagram of frequency coordination control disclosed in an embodiment of the present application;

[0024] Figure 7 is a schematic diagram of spatial collaborative control disclosed in an embodiment of the present application;

[0025] Figure 8 A schematic diagram of the structure of a computer device disclosed in an embodiment of the present application.

[0026] Description of reference numerals:

[0027] 100, insect killing device; 110, environmental sensor; 120, insect attractant lamp; 130, insect killer; 140, power generation device; 150, power supply device; 160, controller; 170, communication module; 200, server; 501, processor; 502, communication interface; 503, memory; 504, communication bus. DETAILED DESCRIPTION

[0028] The following is a further detailed description of the implementation methods of the present application in conjunction with the accompanying drawings and examples. The detailed descriptions of the following examples and the accompanying drawings are used to exemplarily illustrate the principles of the present application, but cannot be used to limit the scope of the present application. The present application can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

[0029] The present application provides these embodiments to make the present application thorough and complete, and to fully express the scope of the present application to those skilled in the art. It should be noted that unless otherwise specifically stated, the relative arrangement of the parts and steps, the composition of the materials, the numerical expressions and the numerical values ​​set forth in these embodiments should be interpreted as being merely exemplary, and not as limiting.

[0030] In addition, words such as “include” or “comprises” and the like used in the present application mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of also including other elements.

[0031] It should also be noted that in the description of this application, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific circumstances. When a specific device is described as being located between a first device and a second device, there may or may not be an intermediate device between the specific device and the first device or the second device.

[0032] All terms used in this application have the same meaning as those understood by those of ordinary skill in the art to which this application belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries, such as general dictionaries, should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined herein.

[0033] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0034] Pest control devices are widely used in agricultural pest control. Traditional solar insect control devices usually work alone, which has the following problems:

[0035] 1. Limited trapping effect: The trapping range and effect of a single insecticide device are limited, and it is difficult to cover a large area. 2. Low energy efficiency: Each insecticide device requires independent solar panels and batteries, resulting in low energy efficiency. 3. Lack of flexibility: It is impossible to dynamically adjust the trapping strategy according to the activity patterns and distribution of pests. 4. Difficult management and maintenance: A large number of scattered insecticide devices increase the difficulty and cost of management and maintenance.

[0036] To this end, an embodiment of the present application provides a collaborative insect trapping system, which includes: a plurality of insect extermination devices 100 and a server 200 connected to the plurality of insect extermination devices 100.

[0037] The network is used to provide a medium for communication links between the multiple pest control devices 100 and the server 200. The network may include various connection types, such as wired or wireless communication links or optical fiber cables, etc. The embodiments of the present application are not limited here.

[0038] The server 200 may be an independent physical server 200, or a server 200 cluster or distributed system composed of multiple physical servers 200, or a cloud server 200 that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The server 200 and the pest control device 100 can communicate directly or indirectly through the network, and the embodiments of the present application are not limited here. For example, the server 200 can directly send a control instruction to the pest control device 100 through the network.

[0039] It should be understood that Figure 1 The number of networks and servers 200 in the embodiment is only for illustration. According to the implementation requirements, there may be any suitable number of insect killing devices 100, networks and servers 200.

[0040] In some specific embodiments, Figure 2 As shown, the insect extermination device 100 includes an environment sensor 110 , an insect attracting light 120 and an insect killer 130 .

[0041] The environmental sensor 110 is used to detect the environmental data of the insect trap light 120, such as collecting environmental data such as temperature, humidity, and light of the environment in which the insect trap light 120 is located, so that the server 200 can predict the distribution and activity trend of insects through the environmental data.

[0042] The insect-attracting lamp 120 can emit light of a specific wavelength to attract insects, such as light with a wavelength in the ultraviolet range. Since many insects are very sensitive to such light, insects can be attracted to the vicinity of the insect-attracting lamp 120 .

[0043] The insect killer 130 is arranged around the insect trap light 120. When the insect trap light 120 attracts insects to the insecticidal range of the insect killer 130, the insect killer 130 can capture the insects. For example, the insect killer 130 may include at least one electric grid. When the insects approach the electric shock range of the electric grid, the insects are given electric shocks, thereby killing them. Alternatively, the insecticide includes a sticky material. The insects are attracted by the insect trap light 120 and approach the sticky material and are captured by the sticky material, thereby achieving the effect of killing insects.

[0044] In some specific embodiments, the insect extermination device 100 further includes a power generation device 140 and a power supply device 150 , and the power generation device 140 is connected to the power supply device 150 .

[0045] Exemplarily, the power generation device 140 may be a solar panel, through which the collected solar energy can be converted into electrical energy, and the electrical energy can be stored in the power supply device 150 to supply the insect attractant lamp 120 and other electronic components.

[0046] The power supply device 150 may be a storage battery that can store the solar energy collected by the solar panel during the day, and is usually a rechargeable battery to ensure normal operation at night or on cloudy days.

[0047] In some specific embodiments, the insect killing device 100 further includes a controller 160 and a communication module 170, and the controller 160 communicates with other electronic components in the insect killing device 100 through the communication module 170. Exemplarily, the controller 160 can adjust the switch of the insect attracting light 120 to ensure that it is automatically turned on and off when needed (such as at night).

[0048] The present application embodiment provides a method for cooperatively trapping insects, such as Figure 3 As shown, the method can be applied to the above-mentioned server 200, and the collaborative insect trapping method includes: S10: acquiring environmental data from multiple insect killing devices 100; S20: predicting insect distribution and activity trends based on environmental data and insect activity models; S30: generating control instructions based on insect distribution and activity trends, and the control instructions are used to indicate the working parameters of the insect killing device 100.

[0049] In this embodiment, multiple pest control devices 100 are connected through a wireless communication network to form a collaborative network system. The server 200 can dynamically adjust the working parameters of each pest control device 100 in the network according to the pest activity patterns and environmental factors, significantly improving the trapping effect and trapping efficiency; and dynamically adjust the trapping strategy according to the pest activity patterns and distribution conditions to achieve precise prevention and control.

[0050] The present application embodiment also provides a method for cooperatively trapping insects, which can be applied to the above-mentioned cooperatively trapping insect system, such as Figure 4 As shown, the collaborative insect trapping method includes the following steps:

[0051] S11: The server 200 obtains environmental data from a plurality of insect extermination devices 100 .

[0052] In this embodiment, each insect killing device 100 collects environmental data through the environmental sensor 110 and uploads the data to the server 200. For example, the environmental data such as temperature, humidity, and light of the environment where the insect trap light 120 is located are collected so that the server 200 can predict the distribution and activity trend of insects through the environmental data.

[0053] S21: The server 200 predicts insect distribution and activity trends based on environmental data and insect activity models.

[0054] In this embodiment, the server 200 can pre-model the insect activity model based on the insect's response to environmental factors (such as temperature, humidity and light), so as to predict the insect distribution, habitat selection and activity trend based on the environmental data of the environment in which the insect killing device 100 is located.

[0055] S31: The server 200 generates a control instruction according to the insect distribution and activity trend, and the control instruction is used to indicate the working parameters of the insect killing device 100. The working parameters include the light intensity and flashing frequency of the insect attracting lamp 120.

[0056] In some specific embodiments, Figure 5 As shown, the server 200 divides the insect-killing device 100 into different light intensity areas according to the sensitivity of the pests to different light intensities. For example, the insect-killing device 100 is divided into light intensity area 1-light intensity area 9. The insect-killing device 100 in the light intensity area 5 located in the center is set to the first light intensity (high light intensity), the light intensity area 2, light intensity area 4, light intensity area 6 and light intensity area 8 located around the light intensity area 5 are set to the second light intensity (medium light intensity), and the light intensity area 1, light intensity area 3, light intensity area 7 and light intensity area 9 located in the periphery are set to the third light intensity (weak light intensity), that is, the first light intensity is greater than the second light intensity, and the second light intensity is greater than the third light intensity, thereby forming a light intensity gradient. It can guide insects to gather in the central area. For example, the first light intensity can be set to 500-1000 lux (lx), and the first light intensity can attract insects that are active during the day, such as bees and some butterflies. Strong light can stimulate the activity of insects and increase the capture rate. The second light intensity is set to 200-500 lux (lx), which attracts a variety of insects, including some diurnal and nocturnal species. The third light intensity is set to 50-200 lux (lx), which can attract nocturnal insects, such as moths and some beetles, and is suitable for use at dusk or at night, imitating the low light conditions of the natural environment, and can effectively attract insects active under low light intensity.

[0057] In specific implementation, the server 200 calculates the optimal light intensity distribution according to the light data collected by the environmental sensor 110 and the preset pest activity model. Then, the server 200 sends different light intensity setting instructions to the controller 160 of each insect killing device 100. The controller 160 achieves different light intensity outputs by adjusting the input current of the insect attractant lamp 120.

[0058] In some other specific embodiments, Figure 6As shown, the server 200 can divide the insect-killing device 100 into different frequency regions according to the phototropism characteristics of different types of pests. Thus, by controlling the flashing frequency of the insect-attracting lamps 120 of different insect-killing devices 100, a frequency synergy effect is formed. For example, the insect-killing device 100 is divided into frequency region 1-frequency region 9. The insect-killing device 100 in the frequency region 5 located in the center is set to a high frequency, the frequency region 2, frequency region 4, frequency region 6 and frequency region 8 located around the frequency region 5 are set to a medium frequency, and the frequency region 1, frequency region 3, frequency region 7 and frequency region 9 located in the periphery are set to a low frequency, thereby forming a frequency gradient and a frequency synergy effect.

[0059] In some specific embodiments, the server 200 can set the flashing frequencies of adjacent insect-killing devices 100 to present a certain phase difference, so as to produce a "wave" effect and enhance the attraction to pests. For example, for certain noctuid pests, the base frequency can be set to 2 Hz, and the phase difference between adjacent insect-attracting lamps 120 can be set to 45°. The server 200 sends these frequency and phase information to the controller 160 of each insect-killing device 100, and the controller 160 achieves the desired flashing effect by accurately controlling the on and off time of the insect-attracting lamp 120.

[0060] It should be noted that in order to facilitate the server 200 to manage multiple insect killing devices 100, in some embodiments, the server 200 can divide the multiple insect killing devices 100 into multiple areas, and control the light intensity and flashing frequency of the insect killing devices 100 in each area according to the insect distribution and activity trends. There is no need to divide the light intensity area and frequency area separately, thereby simplifying the management process.

[0061] In some other specific embodiments, Figure 7 As shown, the server 200 can also optimize the working state of the pest killing device 100 according to the field geographical features and the distribution of pests. For example, the number of working pest killing devices 100 is increased in areas with higher pest density, and the number of working pest killing devices 100 is reduced in areas with lower density, so as to achieve spatial collaborative trapping.

[0062] In specific implementation, the server 200 comprehensively analyzes the data collected by the environmental sensor 110 and the historical insect trapping data to generate a pest distribution density map. Based on this density map, the server 200 determines the number and location of the insect killing devices 100 that need to be turned on in each area. Then, the server 200 sends an opening instruction to the selected insect killing device 100 and a standby instruction to other units. In this way, energy can be saved to the maximum extent while ensuring the trapping effect.

[0063] S41: Each insect extermination device 100 adjusts the brightness and frequency of the insect attracting lamp 120 and the working state of the high-voltage power grid according to the received control instruction to achieve coordinated trapping.

[0064] S51: The server 200 evaluates the collaborative trapping effect and performs strategy optimization according to the actual number of insects caught by each insect killing device 100 and the new environmental data.

[0065] In this embodiment, the environmental sensor 110 continuously collects environmental data such as temperature, humidity, and light, and records the number of insects caught by each insect killing device 100. The server 200 can use machine learning algorithms to analyze the relationship between these data and pest activities, and continuously optimize the insect activity model and trapping strategy.

[0066] In a specific embodiment, a random forest model can be used to establish an insect activity model. Exemplarily, first, an original data set is obtained, and then different data subsets are obtained based on the original data set. The original data set includes multiple environmental data and insect capture data of each insect killing device 100 corresponding to each environmental data. The environmental data includes temperature, humidity, and light data of the environment in which the insect killing device 100 is located. The data subset is a subset of data randomly extracted from the original data set; the sub-model is trained according to different data subsets to obtain an insect activity model. That is, each sub-model may be trained using different data samples.

[0067] In the process of training each sub-model, the data is split according to the features of each data in the data subset until the stopping condition is met (all data belong to the same category or there are no more features to split). For example, the insect capture data of each insect killing device 100 corresponding to different temperatures can be divided into one category, and the insect capture data of each insect killing device 100 corresponding to different humidity can be divided into another category. In this way, the sub-model is trained according to the split data, which can reduce the correlation between each sub-model to improve the diversity of the insect activity model. Finally, by voting on the prediction results of each sub-model, the category with the highest probability of occurrence is selected as the final result.

[0068] It should be noted that in the embodiment of the present application, each data in the data subset can be split according to the selected features based on actual needs. For example, each data in the data subset can also be split according to the species of insects, which is not limited in this embodiment.

[0069] In this way, by integrating multiple sub-models, higher prediction accuracy can be provided than that of a single model. Moreover, by splitting each data in the data subset, the impact of each environmental factor on insect activity can be evaluated, which helps to improve the trapping effect of different insect species in different environments, thereby improving insect catching efficiency.

[0070] In another specific embodiment, a machine learning method such as a neural network can be used to establish an insect activity model. Exemplarily, first, an original data set is obtained, and then the original data set is preprocessed. The original data set includes multiple environmental data and insect capture data of each insect killing device 100 corresponding to each environmental data. The environmental data includes the temperature, humidity, and light data of the environment in which the insect killing device 100 is located. Preprocessing can include converting category data (such as insect species) using One-Hot Encoding or Label Encoding. Then, the nonlinear relationship between environmental data and insect activity is captured by a multi-layer fully connected network in a multilayer perceptron (MLP), thereby obtaining an insect activity model.

[0071] It should be noted that the neural network model in the embodiment of the present application can be adjusted according to the data in the original data set. For example, when the insect capture data of each insect killing device 100 includes the number of insects captured in different time periods, the preprocessing can also include using methods such as sliding windows to generate features to characterize the correlation between the number of insects captured and time. Neural network models such as Recurrent Neural Network (RNN) and Long Short-term Memory Networks (LSTM) are used to study the impact of dynamic changes in time series (such as daily changes and seasonal changes) on insects.

[0072] Insect activities are affected by a variety of environmental factors (such as temperature, humidity, light, etc.), and there are usually complex nonlinear relationships between these factors. The use of neural networks can more accurately establish insect activity models. For seasonal and temporal insect activities, the use of recurrent neural networks (RNN) or long short-term memory networks (LSTM) can better capture the impact of time, environment and other factors on insect activities, thereby having higher prediction accuracy and better robustness.

[0073] Thus, by constructing this insect activity model and taking environmental factors (such as temperature, humidity, light, etc.) as input, the insect activity in the next few days can be predicted. Based on this prediction result, the server 200 can adjust the trapping strategy in advance, such as increasing the working intensity of the insect killing device 100 during the peak period of pest activity.

[0074] In some other specific embodiments, the server 200 can also analyze the effects of different collaborative strategies through long-term data accumulation, for example, comparing the trapping effects of different light intensity distributions and frequency combinations on different insect species, thereby continuously optimizing the collaborative trapping strategy.

[0075] It is understandable that the cooperative insect trapping system provided in the embodiment of the present application can increase or decrease the number of insect killing devices 100 according to actual needs. When a new insect killing device 100 is added, it only needs to be connected to the network, and the server 200 can automatically identify it and include it in the cooperative control range. Similarly, when an insect killing device 100 fails or is removed, the server 200 can also automatically adjust the strategy to ensure the normal operation of the entire cooperative insect trapping system.

[0076] In addition, the system can also be integrated with other agricultural IoT systems, such as weather stations, farmland monitoring systems, etc., to further improve the accuracy of prediction and control.

[0077] It should be noted that the cooperative insect trapping system in the embodiment of the present application is not limited to the functions provided in the above embodiment, and those skilled in the art can increase the functions that the cooperative insect trapping system can achieve according to actual needs. For example, in some embodiments, in order to ensure the security and reliability of the system, the communication between the insect killing device 100 and the server 200 adopts an encryption protocol to prevent data from being stolen or tampered with.

[0078] In other embodiments, the controller 160 of each insect killer device 100 has a self-diagnosis function, which can detect and report faults in time. For example, when the controller 160 detects that the insect trap lamp 120 or the insect killer 130 is short-circuited, the faulty device can be turned off in time, thereby reducing safety hazards.

[0079] In other embodiments, the server 200 and the key insect killing device 100 in the collaborative insect trapping system are equipped with backup power supplies, thereby ensuring that the collaborative insect trapping system can still operate normally under extreme weather conditions.

[0080] In other embodiments, the collaborative insect trapping system can periodically back up the data stored in the server 200 to prevent data loss. The stored data can be environmental data and insect killing quantity light data sent by the insect killing device 100, or data related to the insect activity model in the server 200, which is not limited in the embodiments of the present application.

[0081] In a specific embodiment, it was found through testing that the collaborative insect trapping system provided in the embodiment of the present application has a 40%-60% improvement in trapping effect and significantly reduces the number of pests compared to traditional independently working insect killing devices; the energy utilization efficiency is increased by about 30%, extending the working time of the system; the management and maintenance costs are reduced by about 50%, greatly reducing manpower investment; and the strategy can be flexibly adjusted according to actual conditions, with strong adaptability.

[0082] Therefore, the collaborative insect trapping system provided in the embodiment of the present application significantly improves the trapping effect by realizing the light intensity coordination, frequency coordination and spatial coordination of multiple insect killing devices. At the same time, through intelligent energy management and data analysis, energy utilization efficiency is improved and management and maintenance costs are reduced. This method provides an efficient, environmentally friendly and intelligent solution for agricultural pest control and has broad application prospects.

[0083] Based on the above embodiments, the present application further provides a computer device, Figure 8 A schematic diagram of a computer device structure provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, it includes: a processor 501 , a communication interface 502 , a memory 503 and a communication bus 504 , wherein the processor 501 , the communication interface 502 , and the memory 503 communicate with each other via the communication bus 504 .

[0084] The memory 503 stores a computer program. When the program is executed by the processor 501, the processor 501 performs the following steps:

[0085] S10: Acquire environmental data from multiple insecticide devices;

[0086] S20: Predict insect distribution and activity trends based on environmental data and insect activity models;

[0087] S30: Generate control instructions based on insect distribution and activity trends, where the control instructions are used to indicate working parameters of the insect killing device.

[0088] The communication bus mentioned in the above computer device can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0089] The communication interface 502 is used for communication between the above-mentioned computer device and other devices.

[0090] The memory may include RAM (Random Access Memory) or NVM (Non-Volatile Memory), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0091] The above-mentioned processor can be a general-purpose processor, including a central processing unit, NP (Network Processor), etc.; it can also be a DSP (Digital Signal Processing), an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc.

[0092] Based on the above embodiments, an embodiment of the present application provides a computer-readable storage medium, in which a computer program executable by a computer device is stored. When the program is run on the computer device, the computer device implements the following steps when executing:

[0093] S10: Acquire environmental data from multiple insecticide devices;

[0094] S20: Predict insect distribution and activity trends based on environmental data and insect activity models;

[0095] S30: Generate control instructions based on insect distribution and activity trends, where the control instructions are used to indicate working parameters of the insect killing device.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0097] So far, various embodiments of the present application have been described in detail. In order to avoid obscuring the concept of the present application, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed herein.

[0098] Although some specific embodiments of the present application have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present application. It should be understood by those skilled in the art that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and spirit of the present application. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no structural conflict.

Claims

1. A collaborative insect trapping method, characterized in that: include: Obtaining environmental data from multiple pest control devices; Predicting insect distribution and activity trends based on the environmental data and insect activity models; A control instruction is generated according to the insect distribution and activity trend, and the control instruction is used to indicate the working parameters of the insect killing device.

2. The method according to claim 1, characterized in that The insect killing device includes an insect attracting lamp and an insect killer; The working parameters include the light intensity and flashing frequency of the insect attractant lamp.

3. The method according to claim 2, characterized in that The method further comprises: Dividing the plurality of insect killing devices into different light intensity areas according to the sensitivity of insects to different light intensities; Accordingly, according to the insect distribution and activity trend, the control instructions generated include: Calculating the light intensity of each light intensity area according to the insect distribution and activity trend; A control instruction is generated according to the light intensity of each light intensity area.

4. The method according to claim 1, characterized in that The method further comprises: According to the phototropism characteristics of different insect species, the plurality of insect-killing devices are divided into different frequency regions; Accordingly, according to the insect distribution and activity trend, the control instructions generated include: The flashing frequency of the insect extermination device in each of the frequency regions is calculated, and a control instruction is generated according to the flashing frequency of each of the frequency regions.

5. The method according to claim 4, characterized in that Each frequency region includes a plurality of insect-killing devices arranged at intervals; The flashing frequencies of two adjacent insect-killing devices have a phase difference.

6. The method according to any one of claims 1 to 5, characterized in that The insect extermination device further comprises a power generation device and a power supply device, wherein the power generation device is connected to the power supply device; The method further comprises: Obtaining the power generation amount from the power generation device of each insect extermination device and the power storage amount of the power supply device; Calculating the working time of the insecticide device according to the power generation and the power storage; The working time of each of the insect extermination devices is calculated according to the working time of the insect extermination devices, weather forecast data and a preset insect activity model.

7. The method according to claim 6, characterized in that The method further comprises: Obtain the number of insects caught by each insect killing device; The insect activity model is optimized according to the environmental data of each insect killing device and the corresponding number of insects caught.

8. A collaborative insect trapping system, characterized in that: include: A plurality of pest control devices and a server connected to the plurality of pest control devices; The server is configured to: Obtaining environmental data from multiple pest control devices; Predicting insect distribution and activity trends based on the environmental data and insect activity models; A control instruction is generated according to the insect distribution and activity trend, and the control instruction is used to indicate the working parameters of the insect killing device.

9. The system according to claim 8, characterized in that The insect extermination device includes an environmental sensor, an insect attracting lamp and an insect killer; The environmental sensor is used to detect the environmental data of the insect attractant lamp; The insect attracting lamp is used to attract insects into the insecticidal range of the insect killer; The insect killer is used to capture insects.

10. A computer device comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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