Energy consumption optimization method and device for insecticidal lamp IoT based on edge computing

Abstract

The present invention provides an edge computing-based Internet of Things (IoT) energy consumption optimization method and device for insecticidal lamps. The method includes: dividing the insecticidal time into multiple time slots according to a preset time period, and obtaining the number of insecticidal lamps required to be in an operating state within the specified area of each time slot; obtaining an edge computing-based pest distribution index model, and determining the scheduling priority of each time slot based on the pest distribution index, and selecting the optimal time slot for scheduling based on the scheduling priority; and arranging the energy status of all insecticidal lamps in descending order, and selecting the insecticidal lamp that meets the required number of insecticidal lamps as the operating insecticidal lamp in the specified area based on the arrangement order. This invention can effectively reduce the energy consumption of insecticidal lamps, achieving more efficient energy utilization and pest control.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things energy consumption optimization method for insecticidal lamps based on edge computing. Background Art

[0002] Solar insecticide lamps are a bioprotection technology used in agricultural pest control, utilizing solar power systems for energy. They effectively reduce the impact of chemical pesticides on agricultural product safety and the farm environment, offer a one-time investment for long-term use, and are safe and efficient. They also feature automatic control, energy savings, and environmental friendliness. As a new energy source for insecticides, solar insecticide lamps are poised for widespread adoption in many emerging agricultural sectors, including smart farms. In recent years, the Internet of Insecticide Lamp (IoT), a new, large-scale, and organized approach to solar insecticide lamps, has seen continued growth and application in pest control and extermination.

[0003] Prior art methods typically improve the insecticidal efficiency of insecticidal lamps per unit energy consumption by controlling their on / off function, ensuring that the lamp's effective operating time matches pest activity as closely as possible, thereby reducing energy waste. However, current models for managing insecticidal lamp operating hours primarily utilize remote centralized control, with the system pre-programming several operating modes to adjust lamp operating hours based on climate and seasonal pest distribution. These modes neither enable the timely activation of solar-powered insecticidal lamps to kill pests during sudden outbreaks, nor do they enable the timely deactivation of insecticidal lamps during periods of low pest activity to conserve energy. Consequently, in the absence of sufficient energy, it is difficult to ensure the lamps operate normally during peak pest activity, and the insecticidal efficiency per unit energy consumption is difficult to guarantee. Summary of the Invention

[0004] The present invention provides an edge computing-based Internet of Things (IoT) energy consumption optimization method and device for insecticidal lamps, which are used to solve the defects of the existing technology that it is difficult to ensure insecticidal efficiency and reduce energy consumption, and to achieve energy optimization of insecticidal lamps.

[0005] The present invention provides an edge computing-based method for optimizing energy consumption of an insecticidal lamp Internet of Things, comprising:

[0006] According to the preset time period, the insecticide time is divided into multiple time slots, and the number of insecticide lamps that need to be in working state in the specified area of each time slot is obtained;

[0007] Obtaining an edge computing-based pest distribution index model, obtaining a scheduling priority for each of the time slots according to the pest distribution index, and selecting an optimal time slot for scheduling according to the scheduling priority;

[0008] The energy states of all the insecticidal lamps are arranged in descending order, and insecticidal lamps that meet the number of insecticidal lamps are selected as working insecticidal lamps in the specified area according to the arrangement order.

[0009] According to the edge computing-based insecticidal lamp Internet of Things energy consumption optimization method provided by the present invention, obtaining the number of insecticidal lamps that need to be in a working state within a specified area of each time slot specifically includes:

[0010] The number of insecticidal lamps that need to be in working state in each time slot under different weather conditions is obtained; wherein the weather conditions include: sunny days, rainy days, and days with humidity greater than a preset threshold but no rain.

[0011] According to the method for optimizing energy consumption of insecticidal lamps in the Internet of Things based on edge computing provided by the present invention, after selecting insecticidal lamps that meet the number of insecticidal lamps as working insecticidal lamps in the specified area according to the arrangement order, the method further includes:

[0012] The working insecticidal lamps are screened according to the time slots, coverage areas, and energy states of the insecticidal lamps to obtain a screened insecticidal lamp set.

[0013] According to the present invention, a method for optimizing energy consumption of insecticidal lamps in the Internet of Things based on edge computing is provided. The method comprises screening the working insecticidal lamps according to their time slots, coverage areas, and energy states to obtain a screened insecticidal lamp set, specifically comprising:

[0014] When confirming that the energy states of the two insecticidal lamps are the same, selecting the insecticidal lamp with the earlier time slot;

[0015] Select the second insecticidal lamp based on the position of the first insecticidal lamp and the coverage area of the two adjacent insecticidal lamps;

[0016] According to the energy state of the insecticidal lamp, preferably an insecticidal lamp having an energy state higher than a preset threshold;

[0017] When it is confirmed that the number of the insecticidal lamps is lower than a preset threshold, insecticidal lamps in an area adjacent to the specified area are selected as working insecticidal lamps.

[0018] According to the edge computing-based insecticidal lamp IoT energy consumption optimization method provided by the present invention, obtaining the pest distribution index specifically includes:

[0019] Based on edge computing, a pest distribution index model is established; wherein the pest distribution index model is obtained through multiple iterative updates;

[0020] The pest distribution conditional factors are input into the pest distribution index model to obtain the pest distribution index under different weather conditions.

[0021] According to the edge computing-based insecticidal lamp Internet of Things energy consumption optimization method provided by the present invention, after obtaining the number of insecticidal lamps that need to be in a working state within a specified area of each time slot, the method further includes:

[0022] Establishing an energy replenishment model for the insecticidal lamp to obtain the energy stored in the insecticidal lamp during a charging cycle;

[0023] An energy consumption model of an insecticidal lamp is established to obtain the energy loss of the insecticidal lamp in a certain time slot. If it is confirmed that the energy loss is higher than a preset threshold, it is determined to be a non-priority working insecticidal lamp.

[0024] The present invention also provides an edge computing-based insecticidal lamp IoT energy consumption optimization device, comprising:

[0025] The insecticidal lamp number acquisition module is used to divide the insecticidal time into multiple time slots according to the preset time period, and obtain the number of insecticidal lamps that need to be in working state in the specified area of each time slot;

[0026] An optimal time slot acquisition module is used to obtain a pest distribution index model based on edge computing, obtain the scheduling priority of each time slot according to the pest distribution index, and select the optimal time slot for scheduling according to the scheduling priority;

[0027] The working insecticidal lamp selection module is used to arrange the energy states of all the insecticidal lamps in descending order, and select insecticidal lamps that meet the number of insecticidal lamps as working insecticidal lamps in the specified area according to the arrangement order.

[0028] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the steps of the method for optimizing energy consumption of an insecticidal lamp Internet of Things based on edge computing as described above are implemented.

[0029] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing energy consumption of an insecticidal lamp Internet of Things based on edge computing as described above are implemented.

[0030] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for optimizing energy consumption of insect-killing lamps in the Internet of Things based on edge computing.

[0031] The edge computing-based Internet of Things energy consumption optimization method and device for insecticidal lamps provided by the present invention obtain the pest index distribution model based on edge computing to obtain the activity cycle of the pests, and determine the insecticidal lamps that need to be in a working state according to the energy state of the insecticidal lamps, thereby maximizing the energy utilization of the insecticidal lamps. The insecticidal lamps can be timely turned on and off according to changes in the pest situation, thereby extending the working time of the insecticidal lamps, effectively reducing the energy consumed by the insecticidal lamps, and achieving more efficient energy utilization and pest control effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 This is one of the flow charts of the method for optimizing energy consumption of insect-killing lamp IoT based on edge computing provided by the present invention;

[0034] Figure 2 This is the second flow chart of the method for optimizing energy consumption of insecticidal lamp Internet of Things based on edge computing provided by the present invention;

[0035] Figure 3 This is one of the experimental simulation diagrams of the insecticidal lamp Internet of Things energy consumption optimization method based on edge computing provided by the present invention;

[0036] Figure 4 This is the second experimental simulation diagram of the insecticidal lamp Internet of Things energy consumption optimization method based on edge computing provided by the present invention;

[0037] Figure 5 This is the third experimental simulation diagram of the energy consumption optimization method of the insecticidal lamp Internet of Things based on edge computing provided by the present invention;

[0038] Figure 6 This is a schematic diagram of the structure of the edge computing-based insecticidal lamp IoT energy consumption optimization device provided by the present invention;

[0039] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0041] The following combination Figure 1-Figure 5 The present invention describes an edge computing-based energy consumption optimization method for insecticidal lamps in the Internet of Things.

[0042] Reference Figure 1 The energy consumption optimization method of the insecticidal lamp Internet of Things based on edge computing provided by the present invention includes the following steps:

[0043] Step 110: Divide the insecticide time into multiple time slots according to a preset time period, and obtain the number of insecticide lamps that need to be in a working state within the specified area of each time slot;

[0044] Specifically, in this embodiment, 24 hours of a day are divided into multiple time slots according to a preset time period. Optionally, the length of the preset time period can be selected as 15 minutes, that is, the 24 hours of a day are divided into 96 time slots with each time slot being 15 minutes.

[0045] According to the divided time slots, the number of insecticidal lamps that need to be in working state in each time slot is obtained. In actual situations, the number of insecticidal lamps that need to be in working state needs to be determined based on multiple factors, such as weather, time and pest distribution.

[0046] Step 120: Obtain an edge computing-based pest distribution index model, obtain a scheduling priority for each of the time slots based on the pest distribution index, and select an optimal time slot for scheduling based on the scheduling priority;

[0047] Specifically, this embodiment matches the time slots divided in step 110 with the pest distribution index, that is, matches the pest control time with the pest distribution. The pest distribution index is obtained based on an established pest distribution index model and indicates the specific activity data of pests under the conditions of multiple factors.

[0048] This embodiment determines the scheduling priority of each time slot by judging the size of the pest index, selects an optimal time slot from multiple time slots for scheduling, and uses the optimal time slot for pest control, which can match the activity time of the pests, thereby achieving the effect of improving pest control efficiency and saving pest control time.

[0049] Step 130: Arrange the energy states of all the insecticidal lamps in descending order, and select insecticidal lamps that meet the number of insecticidal lamps as working insecticidal lamps in the specified area according to the arrangement order.

[0050] Specifically, in this embodiment, qualified insecticidal lamps are selected from all insecticidal lamps within a specified area for operation. The principle of selection is to prioritize insecticidal lamps with higher energy levels to prevent battery shortages that could cause insecticide operation to cease. Insecticidal lamps that meet the number of lamps specified in step 110 are selected for insecticide operation, while the remaining lamps are placed in a dormant or charging state. This allows insecticide control to be achieved with fewer lamps, thereby reducing energy loss.

[0051] The edge computing-based IoT energy consumption optimization method and device for insecticidal lamps provided in this embodiment obtain the pest index distribution to obtain the pest activity cycle, and determine the insecticidal lamps that need to be in working state according to the energy state of the insecticidal lamps, thereby maximizing the energy utilization of the insecticidal lamps. The insecticidal lamps can be timely turned on and off according to changes in pest conditions, thereby extending the working time of the insecticidal lamps and achieving more efficient energy utilization and pest control effects.

[0052] Based on the above embodiment, obtaining the number of insecticidal lamps that need to be in working state within the specified area of each time slot specifically includes:

[0053] The number of insecticidal lamps that need to be in working state in each time slot under different weather conditions is obtained; wherein the weather conditions include: sunny days, rainy days, and days with humidity greater than a preset threshold but no rain.

[0054] In this embodiment, the number of insecticidal lamps is determined by the weather, so it is necessary to obtain the number of insecticidal lamps under different weather conditions.

[0055] Historical pest activity data shows that pests are often active at night. Therefore, the time slots are divided into charging cycles and pest control cycles, corresponding to daytime and nighttime, respectively. Obtaining the number of insecticidal lamps required to be active in each time slot is equivalent to obtaining the number of insecticidal lamps required to be active during the pest control cycle.

[0056] make is the area A in time slot t k The number of insecticidal lamps that need to be in working condition, The upper limit of an insecticidal lamp's insecticidal capacity without affecting its continuous and stable use. and N upp A at time t k The ratio of the expected number of active pests in the time period to the theoretical maximum number of active pests is A at time t. k Pest distribution index in and

[0057] A. On a clear night

[0058] On clear nights, the priority is to kill as many pests as possible, considering that pests not killed in the previous time slot are likely to continue to be active in the next time slot. The calculation is as follows.

[0059]

[0060] B. Rainy night

[0061] Consider the first ρ time slots, as the number of active pests continues to decline rapidly over time. The calculation is as follows, count t is the number of time slots during which the current weather conditions persist, and ρ is the number of effective time slots during which pest activities persist on rainy nights.

[0062]

[0063] C. Humidity greater than 95% RH but no rain at night

[0064] When the humidity is greater than 95% RH, there is a risk of damage to the components of the insecticidal lamp, and the probability of pests increasing over time. The calculation is as follows:

[0065]

[0066] Where γ is the growth coefficient of active pests on nights with humidity greater than 95% RH but no rain.

[0067] Based on the above embodiment, after selecting the insecticidal lamps that meet the number of insecticidal lamps as the working insecticidal lamps in the specified area according to the arrangement order, the method further includes:

[0068] The working insecticidal lamps are screened according to the time slots, coverage areas, and energy states of the insecticidal lamps to obtain a screened insecticidal lamp set.

[0069] Specifically, since the working insecticidal lamps have different time slots, coverage areas, and energy states, it is difficult to achieve an ideal insecticidal effect with fewer insecticidal lamps. Therefore, this embodiment screens the working insecticidal lamps selected according to the energy state to obtain an insecticidal lamp set with a better insecticidal effect.

[0070] In this embodiment, since the working insecticidal lamps need to be screened, the number of insecticidal lamps selected according to the arrangement order needs to be greater than the number of insecticidal lamps in working state.

[0071] Based on the above embodiment, the working insecticidal lamps are screened according to their time slots, coverage areas, and energy states to obtain a screened insecticidal lamp set, specifically including:

[0072] When confirming that the energy states of the two insecticidal lamps are the same, selecting the insecticidal lamp with the earlier time slot;

[0073] Select the second insecticidal lamp based on the position of the first insecticidal lamp and the coverage area of the two adjacent insecticidal lamps;

[0074] According to the energy state of the insecticidal lamp, preferably an insecticidal lamp having an energy state higher than a preset threshold;

[0075] When it is confirmed that the number of the insecticidal lamps is lower than a preset threshold, insecticidal lamps in an area adjacent to the specified area are selected as working insecticidal lamps.

[0076] Specifically, when two insecticidal lamps have the same energy level, the lamp that was operating in the previous time slot is prioritized to avoid frequent switching on and off. To ensure maximum coverage of the entire area, the lamp that is as far away from the previously selected active lamp is prioritized. Furthermore, to prevent deep discharge, lamps with an energy level below 20% are minimized from being selected as the active lamp. If there are insufficient insecticidal lamps in an area to complete the pest control task, lamps from adjacent areas are selected for collaborative work, if feasible.

[0077] In addition, considering the impact of weather changes, it is necessary to judge the weather conditions at the beginning of each time slot and update the corresponding parameters according to the weather changes before scheduling the insecticide lamp switch.

[0078] Based on the above embodiment, obtaining the pest distribution index specifically includes:

[0079] Based on edge computing, a pest distribution index model is established; wherein the pest distribution index model is obtained through multiple iterative updates;

[0080] The pest distribution conditional factors are input into the pest distribution index model to obtain the pest distribution index under different weather conditions.

[0081] Specifically, the pest distribution index under three weather conditions is calculated as follows:

[0082] sunny:

[0083]

[0084] rain:

[0085]

[0086] Humidity greater than 95% RH but no rain:

[0087]

[0088] Among them, count t is the number of time slots when the current weather conditions persist, β is the attenuation coefficient of active pests on rainy nights, ρ is the number of effective time slots when pest activity persists on rainy nights, γ is the growth coefficient of active pests on nights with humidity greater than 95% RH but no rain, and τ is the number of time slots when growth persists. This is a theoretical prediction calculated based on historical pest activity data. The pest distribution index on clear nights is roughly the same. On rainy nights, most pests remain active for a short period of time. Therefore, on rainy nights, the main focus is on the effect of rain duration on pest activity. The pest distribution index is calculated as shown in formula (5). When the humidity is greater than 95% RH but it is not raining, when the air humidity does not drop after rain, and when dew begins to condense, many pests are very active. However, this type of weather generally does not last long, so it is important to monitor weather changes promptly. The pest distribution index is calculated as shown in formula (6).

[0089] Based on edge computing technology, the number of active pests monitored at the end of time slot t can be quickly calculated based on the monitoring data of the sensor definition and are the insecticidal lamp and area A in time slot t respectively k The actual number of insecticidal lamps. It can be calculated using the following formula.

[0090]

[0091] It can be updated according to (5), and Updates can also be made in the same way.

[0092]

[0093] Where α is the update coefficient of the pest activity index on a clear night.

[0094] Based on this embodiment, a pest distribution index model based on edge computing is constructed, and through multiple rounds of iterative updates, a pest distribution index model that reflects the latest situation is obtained, providing a basis for the scheduling of insecticidal lamps.

[0095] Based on the above embodiment, refer to Figure 2 After obtaining the number of insecticidal lamps that need to be in working state within the specified area of each time slot, the method further includes the following steps:

[0096] Step 210: Establishing an energy replenishment model for the insecticidal lamp to obtain the energy stored in the insecticidal lamp during a charging cycle;

[0097] Step 220: Establish an energy consumption model for the insecticidal lamp, obtain the energy loss of the insecticidal lamp in a certain time slot, and determine that the insecticidal lamp is a non-priority working insecticidal lamp if the energy loss is higher than a preset threshold.

[0098] In this embodiment, the energy replenishment model of the insecticidal lamp is first established, and the definition and The energy storage efficiency of the insecticidal lamp on sunny and rainy days, and the charging cycle T s They are also defined as and The energy that insect killer lamp can store in one day It can be calculated using the following formula.

[0099]

[0100] definition and They are respectively the charging cycle T s The battery energy status of the insecticide lamp at the beginning and end i,k represents the battery capacity of the insecticidal lamp, then we can get

[0101]

[0102] In addition, this embodiment establishes an energy consumption model for insecticidal lamps. The energy consumed by the insecticidal lamps in time slot t is expressed as in Consists of three parts: Energy consumption of insect trap lamp High-voltage pulse insecticide power grid energy consumption and other components' energy consumption

[0103]

[0104] in, is the high voltage pulse power, and remains essentially unchanged within time slot t. We can get:

[0105]

[0106] Obviously, the energy consumption of insecticide lamps is Follow The battery energy state of the insect killer lamp starts at time t It can be calculated by the following formula.

[0107]

[0108] when When 80% of the battery's energy is consumed, it means that the battery is undergoing a deep discharge process, which has an impact on the battery life and needs to be avoided as much as possible when scheduling the insecticide lamp switch.

[0109] In addition, the present invention also provides an insecticidal lamp switch scheduling model:

[0110] The energy-optimized insecticidal lamp on / off scheduling model provided by this invention essentially aims to kill as many pests as possible while avoiding energy waste. In other words, it's necessary to consider both energy consumption and insecticide efficiency per unit of energy consumed to maximize the optimal energy utilization of the insecticidal lamp. The energy-optimized insecticidal lamp on / off scheduling model is constructed as follows.

[0111]

[0112] In order to simplify the model, it is assumed that each insecticidal lamp is of the same type. Will be a constant. Define SIL i,k The switch state in time slot t is 0 represents off and 1 represents on, so the simplified scheduling model is as follows.

[0113]

[0114] Based on the above model, three principles are considered during the scheduling process: the set of working insecticidal lamps derived from the pest distribution can theoretically kill most pests; the fewer working insecticidal lamps, the better; and the operating time slots of the insecticidal lamps are as consistent as possible with the peak activity time slots of the pests.

[0115] The following simulation experiment is conducted on the energy consumption optimization method of the insect-killing lamp IoT based on edge computing provided by the present invention:

[0116] Assume that there are four pest control areas, with 25 insecticidal lamps deployed in each area. The insecticidal lamps all use insect traps with a wavelength of 300-680nm and a double-layer high-voltage pulse insecticidal power grid with a voltage level of 5000V. The insecticidal lamp solar panel has a power of 60W and a battery DC12V / 60AH. In addition to the method of the present invention, two strategies are also simulated: (1) US strategy: Based on the statistical distribution of pests, the working hours of the insecticidal lamps are uniformly set to 7:00pm-1:00am and 4:00am-6:00am, because under normal circumstances, the pest activity index is highest in these two time periods; (2) PWTMS strategy: The insecticidal lamp time is pre-set according to the pest prediction situation. The working hours of the insecticidal lamps are not necessarily the same, which conforms to the basic pest control law. In addition, an extended strategy of the method of the present invention is also simulated, the difference is that the scheduling time slot length is set to 1 hour.

[0117] Reference Figure 3 , Figure 3 The comparison of the four strategies' insecticide efficiency per unit energy consumption over a complete insecticide cycle demonstrates that the proposed strategy significantly kills more pests with higher efficiency, achieving significant energy optimization even within a one-hour scheduling window.

[0118] Reference Figure 4 , Figure 4 The study compares the total energy consumption of the pest control area under the four strategies over a complete pest control cycle. The proposed strategy reduces overall energy consumption by 20%, even reaching 25%, achieving significant energy savings. This is because the proposed method adjusts the number of insecticidal lamps operating based on pest distribution, significantly reducing unnecessary lamp operation and conserving lamp energy.

[0119] Reference Figure 5 , Figure 5 The paper shows a comparison of the percentage of total energy consumption for the high-voltage grid used for pest control under four strategies over a complete pest control cycle. The proposed strategy achieves the highest percentage, allowing the insecticidal lamp to use up all available energy for pest control rather than being idle, achieving more efficient energy utilization and better pest control results.

[0120] The following describes the insecticidal lamp Internet of Things energy consumption optimization device based on edge computing provided by the present invention. The insecticidal lamp Internet of Things energy consumption optimization device based on edge computing described below and the insecticidal lamp Internet of Things energy consumption optimization method based on edge computing described above can be referenced to each other.

[0121] Reference Figure 6 The embodiment of the present invention provides an edge computing-based insect-killing lamp IoT energy consumption optimization device, which includes the following modules:

[0122] The insecticidal lamp number acquisition module 610 is used to divide the insecticidal time into multiple time slots according to a preset time period, and obtain the number of insecticidal lamps that need to be in a working state within the specified area of each time slot;

[0123] An optimal time slot acquisition module 620 is configured to obtain a pest distribution index model based on edge computing, obtain a scheduling priority for each time slot based on the pest distribution index, and select an optimal time slot for scheduling based on the scheduling priority;

[0124] The working insecticidal lamp selection module 630 is used to arrange the energy states of all the insecticidal lamps in descending order, and select insecticidal lamps that meet the number of insecticidal lamps as the working insecticidal lamps in the specified area according to the arrangement order.

[0125] Optionally, the insecticidal lamp number acquisition module is specifically used to: acquire the number of insecticidal lamps that need to be in working state in each time slot under different weather conditions; wherein the weather conditions include: sunny days, rainy days, and days with humidity greater than a preset threshold but no rain.

[0126] Optionally, the edge computing-based insecticidal lamp IoT energy consumption optimization device provided in this embodiment further includes the following modules:

[0127] The screening module is used to screen the working insecticidal lamps according to the time slots, coverage areas and energy states of the insecticidal lamps to obtain a screened insecticidal lamp set.

[0128] Optionally, the screening module is specifically used to:

[0129] When confirming that the energy states of the two insecticidal lamps are the same, selecting the insecticidal lamp with the earlier time slot;

[0130] Select the second insecticidal lamp based on the position of the first insecticidal lamp and the coverage area of the two adjacent insecticidal lamps;

[0131] According to the energy state of the insecticidal lamp, preferably an insecticidal lamp having an energy state higher than a preset threshold;

[0132] When it is confirmed that the number of the insecticidal lamps is lower than a preset threshold, insecticidal lamps in an area adjacent to the specified area are selected as working insecticidal lamps.

[0133] Optionally, the optimal time slot acquisition module is specifically configured to:

[0134] Based on edge computing, a pest distribution index model is established; wherein the pest distribution index model is obtained through multiple iterative updates;

[0135] The pest distribution conditional factors are input into the pest distribution index model to obtain the pest distribution index under different weather conditions.

[0136] Optionally, the edge computing-based insecticidal lamp IoT energy consumption optimization device provided in this embodiment further includes the following modules:

[0137] A replenishment module is used to establish an energy replenishment model for the insecticidal lamp and obtain the energy stored in the insecticidal lamp during a charging cycle;

[0138] The consumption module is used to establish an energy consumption model of the insecticidal lamp, obtain the energy loss of the insecticidal lamp in a certain time slot, and determine it as a non-priority insecticidal lamp when it is confirmed that the energy loss is higher than a preset threshold.

[0139] Figure 7 An example of a physical structure diagram of an electronic device is shown below. Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the insecticidal lamp IoT energy consumption optimization method based on edge computing, which includes:

[0140] According to the preset time period, the insecticide time is divided into multiple time slots, and the number of insecticide lamps that need to be in working state in the specified area of each time slot is obtained;

[0141] Obtaining an edge computing-based pest distribution index model, obtaining a scheduling priority for each of the time slots according to the pest distribution index, and selecting an optimal time slot for scheduling according to the scheduling priority;

[0142] The energy states of all the insecticidal lamps are arranged in descending order, and insecticidal lamps that meet the number of insecticidal lamps are selected as working insecticidal lamps in the specified area according to the arrangement order.

[0143] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0144] On the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the edge computing-based insecticidal lamp Internet of Things energy consumption optimization method provided by the above methods, which includes:

[0145] According to the preset time period, the insecticide time is divided into multiple time slots, and the number of insecticide lamps that need to be in working state in the specified area of each time slot is obtained;

[0146] Obtaining an edge computing-based pest distribution index model, obtaining a scheduling priority for each of the time slots according to the pest distribution index, and selecting an optimal time slot for scheduling according to the scheduling priority;

[0147] The energy states of all the insecticidal lamps are arranged in descending order, and insecticidal lamps that meet the number of insecticidal lamps are selected as working insecticidal lamps in the specified area according to the arrangement order.

[0148] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing energy consumption of an insecticidal lamp Internet of Things based on edge computing provided by the above methods is implemented. The method includes:

[0149] According to the preset time period, the insecticide time is divided into multiple time slots, and the number of insecticide lamps that need to be in working state in the specified area of each time slot is obtained;

[0150] Obtaining an edge computing-based pest distribution index model, obtaining a scheduling priority for each of the time slots according to the pest distribution index, and selecting an optimal time slot for scheduling according to the scheduling priority;

[0151] The energy states of all the insecticidal lamps are arranged in descending order, and insecticidal lamps that meet the number of insecticidal lamps are selected as working insecticidal lamps in the specified area according to the arrangement order.

[0152] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0153] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing energy consumption of insecticidal lamp Internet of Things based on edge computing, characterized in that: include: According to the preset time period, the insecticide time is divided into multiple time slots, and the number of insecticide lamps that need to be in working state in the specified area of each time slot is obtained; Obtaining an edge computing-based pest distribution index model, obtaining a scheduling priority for each of the time slots based on the size of the pest distribution index, and selecting an optimal time slot for scheduling based on the scheduling priority; Arrange the energy states of all the insecticidal lamps in descending order, and select the insecticidal lamps that meet the number of insecticidal lamps as the working insecticidal lamps in the specified area according to the arrangement order; Wherein, obtaining the pest distribution index model specifically includes: Based on edge computing, a pest distribution index model is established; wherein the pest distribution index model is obtained through multiple iterative updates; The pest distribution conditional factors are input into the pest distribution index model to obtain the pest distribution index under different weather conditions.

2. The method for optimizing energy consumption of insecticidal lamp Internet of Things based on edge computing according to claim 1 is characterized in that: The obtaining of the number of insecticidal lamps that need to be in working state within the specified area of each time slot specifically includes: The number of insecticidal lamps that need to be in working state in each time slot under different weather conditions is obtained; wherein the weather conditions include: sunny days, rainy days, and days with humidity greater than a preset threshold but no rain.

3. The method for optimizing energy consumption of insecticidal lamp Internet of Things based on edge computing according to claim 1 is characterized in that: After selecting the insecticidal lamps that meet the number of insecticidal lamps according to the arrangement order as the working insecticidal lamps in the specified area, the method further includes: The working insecticidal lamps are screened according to the time slots, coverage areas, and energy states of the insecticidal lamps to obtain a screened insecticidal lamp set.

4. The method for optimizing energy consumption of insecticidal lamp Internet of Things based on edge computing according to claim 3 is characterized in that: The screening of the working insecticidal lamps according to the time slots, coverage areas, and energy states of the insecticidal lamps to obtain a screened insecticidal lamp set specifically includes: When confirming that the energy states of the two insecticidal lamps are the same, selecting the insecticidal lamp with the earlier time slot; Select the second insecticidal lamp based on the position of the first insecticidal lamp and the coverage area of the two adjacent insecticidal lamps; According to the energy status of the insecticidal lamp, selecting an insecticidal lamp with an energy status higher than a preset threshold; When it is confirmed that the number of the insecticidal lamps is lower than a preset threshold, insecticidal lamps in an area adjacent to the specified area are selected as working insecticidal lamps.

5. The method for optimizing energy consumption of insecticidal lamp Internet of Things based on edge computing according to claim 1 is characterized in that: After obtaining the number of insecticidal lamps that need to be in a working state within the specified area of each time slot, the method further includes: Establishing an energy replenishment model for the insecticidal lamp to obtain the energy stored in the insecticidal lamp during a charging cycle; An energy consumption model of an insecticidal lamp is established to obtain the energy loss of the insecticidal lamp in a certain time slot. If it is confirmed that the energy loss is higher than a preset threshold, it is determined to be a non-priority working insecticidal lamp.

6. An edge computing-based insecticidal lamp IoT energy consumption optimization device, characterized in that: include: The insecticidal lamp number acquisition module is used to divide the insecticidal time into multiple time slots according to the preset time period, and obtain the number of insecticidal lamps that need to be in working state in the specified area of each time slot; An optimal time slot acquisition module is used to obtain a pest distribution index model based on edge computing, obtain the scheduling priority of each time slot according to the size of the pest distribution index, and select the optimal time slot for scheduling according to the scheduling priority; a working insecticidal lamp selection module, configured to arrange the energy states of all the insecticidal lamps in descending order, and select insecticidal lamps that meet the number of insecticidal lamps as the working insecticidal lamps in the specified area according to the arrangement order; Wherein, the device is also used for: Based on edge computing, a pest distribution index model is established; wherein the pest distribution index model is obtained through multiple iterative updates; The pest distribution conditional factors are input into the pest distribution index model to obtain the pest distribution index under different weather conditions.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for optimizing energy consumption of an insect-killing lamp Internet of Things based on edge computing are implemented as described in any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing energy consumption of an insect-killing lamp Internet of Things based on edge computing as described in any one of claims 1 to 6 are implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for optimizing energy consumption of an insect-killing lamp Internet of Things based on edge computing as described in any one of claims 1 to 6 are implemented.

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