A control system for a mosquito eradication lamp and a control method thereof

Abstract

The application relates to the technical field of automatic control, in particular to a control system for mosquito-killing lamps and a control method thereof, wherein the control system comprises the following modules: a mosquito behavior analysis module, a spectrum efficiency analysis module, a mosquito-trapping lamp parameter configuration module, a voltage parameter adjustment module and an automatic counting module; the application identifies the activity mode of mosquitoes by analyzing the relationship between the number of mosquito activities and various environmental conditions, optimizes the start-stop parameters and spectrum band selection of mosquito-killing lamps in combination with real-time monitoring of the environmental conditions, improves the adaptability and trapping effect of mosquito-trapping lamps, reduces the invalid working time, realizes real-time parameter adjustment of a high-voltage power grid by analyzing the voltage parameters required by various mosquito killing, optimizes the working efficiency of the power grid and reduces the energy consumption, counts the number of trapped mosquitoes in real time by analyzing the current change, evaluates the trapping efficiency, dynamically optimizes the working parameters of the lamps and the power grid according to the results, and improves the working efficiency and resource utilization efficiency.

Description

A control system and control method for a mosquito-killing lamp. Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to a control system and control method for a mosquito-killing lamp. Background Technology

[0002] The field of automatic control technology involves the design, development, and implementation of systems, including sensors for monitoring environmental or equipment status, controllers for processing the detected information, and actuators for executing control commands to adjust operations. The aim is to improve efficiency, accuracy, and safety, reduce human intervention, and is applied in various industries such as industrial automation, robotics, aerospace, transportation, energy management, and home automation. By enhancing the intelligence level of the system, enabling it to autonomously learn and adapt to constantly changing environmental conditions, and combining the Internet of Things and artificial intelligence, real-time data analysis and processing are achieved, improving response speed and operational intelligence.

[0003] The control system for mosquito-killing lamps is designed for automatic control, enabling intelligent management of the lamps' operation. This includes automatically monitoring mosquito activity and adjusting the lamp's operation based on the monitoring data to achieve effective mosquito control. Intelligent algorithms analyze mosquito activity data, automatically turning on the lamps during peak activity periods and turning them off when not in use, optimizing energy consumption and improving the mosquito-killing effect. The system also adjusts the light wave band of the mosquito-attracting lamp according to the type and activity pattern of the mosquitoes to enhance its ability to attract and kill them.

[0004] Traditional mosquito-killing lamps rely on fixed preset programs to control their on / off states. This lack of real-time dynamic adjustment to adapt to varying mosquito activity levels and temperature / humidity conditions prevents them from reaching their optimal effectiveness during peak hours. Furthermore, the absence of intelligent response mechanisms hinders precise adjustments based on mosquito activity levels and energy consumption optimization, leading to energy waste. In high-voltage grid control, a single voltage setting is used, neglecting mosquito activity periods and further wasting electricity. Finally, the evaluation of the lamp's trapping efficiency relies on manual statistics, making automatic feedback and parameter optimization difficult. Summary of the Invention

[0005] The technical problem this invention aims to solve is to provide a control system and method for mosquito-killing lamps. This system analyzes the relationship between mosquito activity and various environmental conditions to identify mosquito activity patterns. By combining real-time monitoring of environmental conditions, it optimizes the start-up and shutdown parameters and spectral band selection of the mosquito-killing lamp, improving its adaptability and trapping effect, reducing ineffective working time, and optimizing energy use. Furthermore, by analyzing the voltage parameters required for mosquito killing, it achieves real-time parameter adjustment of the high-voltage power grid, optimizing grid efficiency and reducing energy consumption. Finally, by analyzing current changes, it counts the number of trapped mosquitoes in real time, assesses trapping efficiency, and dynamically optimizes the operating parameters of the lamp and power grid based on the results, thereby improving work efficiency and resource utilization.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A control system for a mosquito-killing lamp, the system comprising:

[0008] The mosquito behavior analysis module is based on mosquito activity monitoring data. It collects temperature, humidity and light data in real time, analyzes the number of mosquitoes under various environmental conditions, assesses the intensity and frequency of mosquito activity under various conditions, and generates activity pattern prediction results.

[0009] Based on the activity pattern prediction results, the spectral efficiency analysis module analyzes the attractiveness of various spectral bands to mosquitoes, considers the energy consumption of the insect-attracting lamp in various spectral bands, identifies the optimal spectral bands for attracting and killing various mosquitoes, and generates an insect-attracting spectrum matching list.

[0010] The insect-attracting lamp parameter configuration module adjusts the operating parameters of the insect-attracting lamp based on the insect-attracting spectrum matching list and combined with real-time environmental monitoring data, including start-stop time and spectral band selection, and generates real-time adjustment control parameters.

[0011] The voltage parameter adjustment module, based on the real-time adjustment control parameters and considering the activity time periods of various mosquitoes, adjusts the voltage parameters of the high-voltage power grid in multiple time periods, generating power grid parameter optimization results.

[0012] The automatic counting module monitors the current data of the high-voltage power grid in real time based on the power grid parameter optimization results. By analyzing the current changes, it counts the number of mosquitoes trapped, analyzes the trapping efficiency, optimizes the working parameters of the insect-attracting lamp and the high-voltage power grid, and generates a trapping efficiency evaluation result.

[0013] The following are further optimizations of the above technical solution by the present invention:

[0014] The activity pattern prediction results include predicted mosquito activity quantity records, predicted activity intensity change information, and real-time environmental monitoring information. The insect-attracting spectrum matching list includes spectral band attraction score, optimal spectral band, and spectral band operating energy consumption data. The real-time adjustment control parameters include the start-up and stop time of the insect-attracting lamp, the selected spectral band, and operating parameter adjustment values ​​based on environmental conditions. The power grid parameter optimization results include high-voltage power grid voltage parameters, power grid operating time period, and high-voltage power grid energy consumption data. The trapping efficiency evaluation results include the number of mosquitoes trapped, the trend of trapping efficiency changes, and operating parameter adjustment records.

[0015] Further optimization: The mosquito behavior analysis module includes:

[0016] The environmental data acquisition submodule uses mosquito activity monitoring data and temperature and humidity sensors and light sensors to collect temperature, humidity and light data in real time and generate an environmental monitoring dataset.

[0017] The activity count analysis submodule, based on the environmental monitoring dataset, analyzes the active number of mosquitoes under various environmental conditions, calculates the correlation between mosquito activity and various environmental conditions, and generates mosquito activity analysis results.

[0018] The active time prediction submodule analyzes and predicts the active periods of mosquitoes under various environmental conditions based on the mosquito activity analysis results, and generates activity pattern prediction results.

[0019] Further optimization: The specific formula for calculating the correlation between mosquito activity and various environmental conditions is as follows:

[0020] ;

[0021] in, This represents the correlation coefficient between mosquito activity and environmental conditions. Representative at the Environmental condition values ​​at each point in time, including temperature, humidity, and light intensity. Representative at the Mosquito activity levels at specific time points. Represents the average value of environmental conditions. The average value representing mosquito activity. This represents the total number of data points, i.e., the number of observation points in time. An index representing a point in time.

[0022] Further optimization: The spectral efficiency analysis module includes:

[0023] Based on the activity mode prediction results, the working energy consumption analysis submodule collects energy consumption data of the insect-attracting lamps working in multiple spectral bands and generates an insect-attracting lamp energy consumption dataset.

[0024] The attractiveness assessment submodule, based on the energy consumption dataset of the insect-attracting lamp, identifies the influence of multiple spectral bands on the number of active mosquitoes, calculates the attractiveness of multiple spectral bands to multiple mosquitoes, and generates spectral attractiveness assessment data.

[0025] Based on the spectral attractiveness assessment data and considering operating energy consumption, the spectral band identification submodule identifies the optimal spectral bands for trapping various mosquitoes and generates a list of insect-attracting spectral matches.

[0026] Further optimization: The specific formula for calculating the attractiveness of multiple spectral bands to various mosquitoes is as follows:

[0027] ;

[0028] in, This represents an attractiveness score for mosquitoes in a specific spectral band. This represents the number of active mosquitoes observed in the current spectral band. This represents the maximum number of mosquitoes observed across all tested spectral bands. This represents the duration of mosquito activity in this wavelength range. Represents the total observation time. This represents the flight frequency of mosquitoes in this wavelength band. This represents the maximum observed flight frequency across all test bands. This represents the average distance between mosquitoes and the light source in this wavelength range. This represents the maximum measurement distance within the observation area.

[0029] Further optimization: The insect-attracting lamp parameter configuration module includes:

[0030] The environmental parameter analysis submodule, based on the insect-attracting spectrum matching list and real-time environmental monitoring data, including temperature, humidity and light intensity, predicts and identifies the activity time periods of various mosquitoes and generates real-time environmental analysis results.

[0031] Based on the real-time environmental analysis results, the working time configuration submodule identifies the working time period required by the insect-attracting lamp according to the active periods of various mosquitoes and generates the start and stop parameter configuration of the insect-attracting lamp.

[0032] The control parameter setting submodule, based on the start / stop parameters of the insect-attracting lamp, adjusts the working spectral band of the insect-attracting lamp at the target time according to the active mosquito species during the target period, and generates real-time adjustment control parameters.

[0033] Further optimization: The voltage parameter adjustment module includes:

[0034] The voltage demand analysis submodule analyzes the voltage intensity required to kill various mosquitoes based on the real-time adjustment control parameters and generates voltage demand analysis data.

[0035] Based on the voltage demand analysis data, the power grid parameter configuration submodule adjusts the voltage operating parameters of the high-voltage power grid in multiple time periods according to the active time periods of various mosquitoes, and generates the operating voltage parameter configuration.

[0036] The power grid energy consumption recording submodule is configured based on the operating voltage parameters, monitors the operating energy consumption of the high-voltage power grid in real time, records energy consumption data, and generates power grid parameter optimization results.

[0037] Further optimization: The automatic counting module includes:

[0038] Based on the power grid parameter optimization results, the current change analysis submodule analyzes the current changes corresponding to the high-voltage power grid when killing mosquitoes, identifies the current characteristics of various mosquitoes, and generates current characteristic analysis results.

[0039] Based on the current characteristic analysis results, the real-time current monitoring submodule monitors the current data of the high-voltage power grid in real time, identifies current changes and records the number of various mosquitoes killed, and generates current change monitoring data.

[0040] The trapping efficiency optimization submodule uses the current change monitoring data to analyze the trapping efficiency of the mosquito-killing lamp, adjust the operating parameters of the insect-attracting lamp and the high-voltage power grid, and generate trapping efficiency evaluation results.

[0041] The present invention also provides a control method for a mosquito-killing lamp, the method being based on the aforementioned control system for a mosquito-killing lamp, the method comprising:

[0042] S1: Based on mosquito activity monitoring data, real-time data on ambient temperature, humidity and light intensity are collected. By analyzing the number of mosquitoes under various environmental conditions, the activity intensity and frequency of various mosquitoes are identified, and activity pattern prediction results are generated.

[0043] S2: Based on the activity pattern prediction results, evaluate the attractiveness and energy consumption of multiple spectral bands to mosquitoes, identify the optimal spectral band of the insect-attracting lamp, and generate an insect-attracting spectrum matching list.

[0044] S3: Using the insect-attracting spectrum matching list and real-time environmental monitoring data, adjust the start-stop time and spectral band of the insect-attracting lamp to generate real-time adjustment control parameters;

[0045] S4: Based on the real-time adjustment control parameters, considering the working energy consumption of the high-voltage power grid according to various mosquito activity time periods, adjust the working voltage parameters of the high-voltage power grid, and generate power grid parameter optimization results;

[0046] S5: Based on the power grid parameter optimization results, analyze the current changes corresponding to the high-voltage power grid when killing mosquitoes, record the types and quantities of mosquitoes killed, and generate mosquito trapping data records;

[0047] S6: Using the recorded mosquito trapping data, evaluate the trapping efficiency, optimize the operating parameters of the insect-attracting lamp and the high-voltage power grid, and generate trapping efficiency evaluation results.

[0048] This invention employs the above-mentioned technical solution, which is ingeniously conceived. By analyzing the relationship between the number of mosquitoes and various environmental conditions, it identifies the activity patterns of mosquitoes. Combined with real-time monitoring of environmental conditions, it optimizes the start-stop parameters and spectral band selection of mosquito-killing lamps, improving the adaptability and trapping effect of the insect-attracting lamps, reducing ineffective working time, and optimizing energy use. By analyzing the voltage parameters required for the killing of various mosquitoes, it realizes real-time parameter adjustment of the high-voltage power grid, optimizes the power grid's working efficiency, and reduces energy consumption. By analyzing current changes, it counts the number of mosquitoes trapped in real time, evaluates the trapping efficiency, and dynamically optimizes the working parameters of the lamps and the power grid based on the results, thereby improving work efficiency and resource utilization efficiency.

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0050] Figure 1 is a system flowchart of an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of the system framework in an embodiment of the present invention;

[0052] Figure 3 is a flowchart of the mosquito behavior analysis module in an embodiment of the present invention;

[0053] Figure 4 is a flowchart of the spectral efficiency analysis module in an embodiment of the present invention;

[0054] Figure 5 is a flowchart of the insect-attracting lamp parameter configuration module in an embodiment of the present invention;

[0055] Figure 6 is a flowchart of the voltage parameter adjustment module in an embodiment of the present invention;

[0056] Figure 7 is a flowchart of the automatic counting module in an embodiment of the present invention;

[0057] Figure 8 is a schematic diagram of the method steps in an embodiment of the present invention. Detailed Implementation

[0058] As shown in Figure 1-7, a control system for a mosquito-killing lamp includes the following modules in its processing flow:

[0059] The mosquito behavior analysis module is based on mosquito activity monitoring data. It collects temperature, humidity and light data in real time, analyzes the number of mosquitoes under various environmental conditions, assesses the intensity and frequency of mosquito activity under various conditions, and generates activity pattern prediction results.

[0060] The spectral efficiency analysis module analyzes the attractiveness of various spectral bands to mosquitoes based on the activity pattern prediction results, takes into account the energy consumption of the insect-attracting lamp in various spectral bands, identifies the optimal spectral bands for attracting and killing various mosquitoes, and generates an insect-attracting spectrum matching list.

[0061] The insect-attracting lamp parameter configuration module adjusts the operating parameters of the insect-attracting lamp based on the insect-attracting spectrum matching list and combined with real-time environmental monitoring data, including start-stop time and spectral band selection, and generates real-time adjustment control parameters.

[0062] The voltage parameter adjustment module adjusts the voltage parameters of the high-voltage grid in multiple time periods based on real-time adjustment control parameters, taking into account the active time periods of various mosquitoes and the energy consumption of the power grid, and generates optimized grid parameters.

[0063] The automatic counting module monitors the current data of the high-voltage power grid in real time based on the power grid parameter optimization results. By analyzing the current changes, it counts the number of mosquitoes trapped, analyzes the trapping efficiency, optimizes the working parameters of the insect-attracting lamp and the high-voltage power grid, and generates trapping efficiency evaluation results.

[0064] The activity pattern prediction results include predicted mosquito activity data, predicted activity intensity changes, and real-time environmental monitoring information. The insect attraction spectrum matching list includes spectral band attraction scores, optimal spectral bands, and spectral band energy consumption data. Real-time control parameter adjustment includes the start and stop times of the insect attraction lamps, the selected spectral bands, and adjustment values ​​of working parameters based on environmental conditions. The power grid parameter optimization results include high-voltage power grid voltage parameters, power grid operating time periods, and high-voltage power grid energy consumption data. The trapping efficiency evaluation results include the number of mosquitoes trapped, the trend of trapping efficiency changes, and working parameter adjustment records.

[0065] Please refer to Figures 2 and 3. The mosquito behavior analysis module includes:

[0066] The environmental data acquisition submodule uses mosquito activity monitoring data and temperature and humidity sensors and light sensors to collect temperature, humidity and light data in real time and generate an environmental monitoring dataset.

[0067] In the environmental data acquisition submodule, based on mosquito activity monitoring data, real-time acquisition of temperature, humidity, and light data is performed. Temperature and humidity sensors collect environmental temperature and humidity information. Target data is output as analog or digital signals, and after signal conditioning, it is converted into a readable digital format, including analog-to-digital conversion. Light sensors measure the light intensity in the environment, use the photoelectric effect to convert the light signal into an electrical signal, and perform signal conditioning. The collected data is transmitted to the central data processing unit wirelessly or via wired means. The data is formatted and stored in the environmental monitoring dataset, providing raw input for subsequent analysis. The data synchronization and time stamping technologies involved in the process ensure the integrity of the dataset and the accuracy of time series analysis.

[0068] The activity count analysis submodule is based on the environmental monitoring dataset. It analyzes the number of active mosquitoes under various environmental conditions, calculates the correlation between mosquito activity and various environmental conditions, and generates mosquito activity analysis results.

[0069] The specific formula for calculating the correlation between mosquito activity and various environmental conditions is as follows:

[0070] ;

[0071] in, This represents the correlation coefficient between mosquito activity and environmental conditions. Representative at the Environmental condition values ​​at each point in time, including temperature, humidity, and light intensity. Representative at the Mosquito activity levels at specific time points. Represents the average value of environmental conditions. The average value representing mosquito activity. This represents the total number of data points, i.e., the number of observation points in time. An index representing a point in time.

[0072] formula: ;

[0073] Detailed explanation of the formula and its calculation derivation: The formula is used to calculate the correlation coefficient between mosquito activity and environmental conditions, and to identify the degree of correlation between mosquito activity and environmental conditions;

[0074] Parameter meanings and settings: Representative at the Environmental condition values ​​at various time points, assuming temperature data was collected 5 times within one hour. ;

[0075] Representative at the Mosquito activity at five different time points, assuming that the number of mosquitoes measured at the same five time points are 2, 5, 3, 4, and 6 respectively, i.e. ;

[0076] Represents the average value of environmental conditions. ;

[0077] The average value representing mosquito activity. ;

[0078] Substitute the parameters into the formula to calculate:

[0079] Calculate the numerator:

[0080] ;

[0081] ;

[0082] Calculate the denominator:

[0083] ;

[0084] ;

[0085] Calculate the correlation coefficient:

[0086] ;

[0087] result This indicates a strong positive correlation between mosquito activity and environmental conditions, with temperature having a significant impact on mosquito activity. The numerical values ​​provide data support for adjusting and optimizing mosquito killer lamp parameters.

[0088] The active time prediction submodule analyzes and predicts the active periods of mosquitoes under various environmental conditions based on the mosquito activity analysis results, and generates activity pattern prediction results.

[0089] In the active time prediction submodule, based on the mosquito activity analysis results, an autoregressive moving average model is used to predict the active periods of mosquitoes under various environmental conditions. Based on the time series data of mosquito activity, the stationarity test (ADF test) is applied to determine the stationarity of the data. Combined with differencing, the model parameters, including the number of lag terms, are determined through autocorrelation and partial autocorrelation function graph analysis. Statistical software, such as the `forecast` package in R, is used for model fitting, training, and validation. Based on the predicted data output by the model, the high-activity periods of mosquitoes are determined, helping to optimize the timeliness and efficiency of mosquito control measures. The generated activity pattern prediction results describe in detail the future activity trends of mosquitoes at different times.

[0090] Please refer to Figures 2 and 4. The spectral efficiency analysis module includes:

[0091] The working energy consumption analysis submodule collects energy consumption data of insect-attracting lamps operating in multiple spectral bands based on the activity mode prediction results, and generates an insect-attracting lamp energy consumption dataset.

[0092] In the energy consumption analysis submodule, based on the activity mode prediction results, energy consumption data of the insect-attracting lamp is collected under different spectral bands. Data is collected by setting up an energy consumption monitoring system. The energy consumption of each spectral band is continuously monitored by a current sensor and a power meter. The data acquisition system includes multiple sensors, each corresponding to a spectral band, and transmits power consumption data to the central processing unit in real time. The collected energy consumption data undergoes preliminary filtering and cleaning to remove abnormal data points caused by equipment failure or external interference. The data is sent to a cloud server via a wireless network for storage and analysis. The technologies used include data compression and encryption to ensure the security and integrity of the data during transmission. The collected and processed data form an insect-attracting lamp energy consumption dataset, which records the energy consumption under each spectral band, providing accurate basic data for subsequent attractiveness assessment.

[0093] The attractiveness assessment submodule is based on the energy consumption dataset of insect-attracting lamps. By identifying the impact of multiple spectral bands on the number of active mosquitoes, it calculates the attractiveness of multiple spectral bands to multiple mosquitoes and generates spectral attractiveness assessment data.

[0094] The specific formula for calculating the attractiveness of multiple spectral bands to various mosquitoes is as follows:

[0095] ;

[0096] in, This represents an attractiveness score for mosquitoes in a specific spectral band. This represents the number of active mosquitoes observed in the current spectral band. This represents the maximum number of mosquitoes observed across all tested spectral bands. This represents the duration of mosquito activity in this wavelength range. Represents the total observation time. This represents the flight frequency of mosquitoes in this wavelength band. This represents the maximum observed flight frequency across all test bands. This represents the average distance between mosquitoes and the light source in this wavelength range. This represents the maximum measurement distance within the observation area.

[0097] formula:

[0098] ;

[0099] Detailed explanation of the formula and its calculation derivation:

[0100] The formula is used to calculate the attractiveness of different spectral bands to mosquitoes and to evaluate the attractiveness of multiple spectral bands to multiple mosquitoes.

[0101] Parameter meanings and settings:

[0102] Based on laboratory monitoring data: This represents the number of active mosquitoes observed in the current spectral band. For example, if the infrared sensor detects 82 active target mosquito species at a wavelength of 520 nm.

[0103] This represents the maximum number of mosquitoes observed across all tested spectral bands, assuming a maximum of 95 mosquitoes monitored across all bands.

[0104] This represents the duration of continuous activity of mosquitoes in this wavelength band. For example, it is assumed that mosquitoes were continuously active in the 520nm wavelength band for 38 minutes through time-series video recording.

[0105] This represents the total observation time, assuming the observation time is 60 minutes;

[0106] High-speed camera statistics show that the average number of flight times of the target mosquito in the 520nm band is 42 times / minute.

[0107] This represents the maximum flight frequency observed across all test bands, assuming the maximum flight frequency monitored across all bands is 55 times / minute.

[0108] This represents the average distance between the mosquito and the light source in this wavelength band. It is assumed that the average distance of the target mosquito in the 520nm wavelength band is 0.4 meters.

[0109] This represents the maximum measurement distance in the observation area, assuming an observation radius of 2 meters.

[0110] Substitute the parameters into the formula to calculate:

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] The result of 0.382 indicates that the 520nm band has a moderate attraction to the target mosquitoes. The value is used to evaluate the attraction of multiple spectral bands to mosquitoes and select the optimal operating parameters for the mosquito-attracting lamp.

[0116] The spectral band identification submodule identifies the optimal spectral bands for trapping various mosquitoes based on spectral attractiveness assessment data and takes into account working energy consumption, and generates a list of insect-attracting spectral matches.

[0117] In the spectral band identification submodule, the optimal spectral band is identified based on spectral attractiveness assessment data. Using a decision support system, combined with attractiveness assessment data and energy consumption data, the best spectral band is determined through cost-benefit analysis. This includes setting multiple evaluation indicators, such as attractiveness efficiency, energy cost, and durability. A weighted scoring method is used to calculate a comprehensive score for each spectral band. During the scoring process, the weight allocation is based on historical data and expert experience, and is adjusted through optimization algorithms, such as genetic algorithms, to achieve the optimal weight allocation. Through target analysis and decision-making processes, an insect-attracting spectrum matching list is generated. The list details the optimal spectral bands derived from the analysis of attractiveness and energy consumption data, providing precise guidance for the actual configuration of insect-attracting lamps.

[0118] Please refer to Figures 2 and 5. The insect-attracting lamp parameter configuration module includes:

[0119] The environmental parameter analysis submodule, based on the insect attraction spectrum matching list and real-time environmental monitoring data, including temperature, humidity and light intensity, predicts and identifies the activity time periods of various mosquitoes and generates real-time environmental analysis results.

[0120] In the environmental parameter analysis submodule, based on the insect-attracting spectral matching list, predictive analysis of temperature, humidity, and light intensity data is performed. Data prediction techniques, including a random forest model from machine learning, are used to process real-time environmental monitoring data. Real-time data on temperature, humidity, and light intensity are collected from environmental sensor systems. The target data undergoes preliminary cleaning and formatting to adapt to the input requirements of the prediction model. During preprocessing, outliers in the data are identified and corrected, and the data is normalized to reduce the impact of different measurement ranges and units. The random forest algorithm is used to analyze the data. Model training relies on historical monitoring data and known mosquito activity patterns to identify environmental factors related to mosquito activity intensity, predict how each environmental factor affects the activity period of mosquitoes, and generate real-time environmental analysis results. The results illustrate the activity period of mosquitoes under current and predicted environmental conditions, providing a basis for adjusting insect-attracting lamp strategies.

[0121] The working period configuration submodule identifies the required working period of the insect-attracting lamp based on the real-time environmental analysis results and the active periods of various insects, and generates the start and stop parameter configuration of the insect-attracting lamp.

[0122] In the working period configuration submodule, the working period is configured based on the real-time environmental analysis results. A linear programming model is used to optimize the start-up and shutdown times of the insect-attracting lamps. Based on the predicted mosquito activity periods from the real-time environmental analysis, potential working time windows for the lamps are set. The model divides these time windows into multiple smaller time periods, each assigned a decision variable for starting or stopping. This maximizes mosquito trapping efficiency while minimizing energy consumption. Constraints include equipment operation limitations and safety standards. Using historical data and the predicted mosquito activity intensity provided by the model, the optimal working period is determined by solving the linear programming model. The optimization software CPLEX or Gurobi is used for calculation. The generated lamp start-up and shutdown parameter configuration lists the working status of the lamps for each time period, ensuring they start during the most active mosquito periods to improve energy efficiency and trapping effect.

[0123] The control parameter setting submodule is based on the start and stop parameters of the insect-attracting lamp. According to the active mosquito species in the target time period, it adjusts the working spectrum band of the insect-attracting lamp at the target time and generates real-time adjustment control parameters.

[0124] In the control parameter setting submodule, based on the start / stop parameter configuration of the insect-attracting lamp, the working spectral band of the insect-attracting lamp is adjusted. A dynamic programming method is used to optimize the selection of spectral bands. According to the active mosquito species in the target time period and their response to different spectral bands, the optimal spectral band to be used by the insect-attracting lamp in each time period is set. The dynamic programming algorithm considers multi-dimensional data such as spectral efficiency, energy consumption and mosquito attraction. By constructing state transition equations and decision equations, the most suitable spectral setting for each time period is determined step by step. Each decision step is based on the optimal solution of the previous time period and the environmental conditions of the current time period to ensure the continuity and efficiency of the overall strategy. The generated real-time adjustment control parameters specify in detail the spectral band to be used by the insect-attracting lamp in each time period to achieve the most efficient trapping of specific mosquito species.

[0125] Please refer to Figures 2 and 6. The voltage parameter adjustment module includes:

[0126] The voltage demand analysis submodule analyzes the voltage intensity required to kill various mosquitoes based on real-time adjustment of control parameters and generates voltage demand analysis data.

[0127] In the voltage demand analysis submodule, voltage demand analysis is performed based on real-time adjustment of control parameters. Using electrical engineering analysis and load demand prediction models, the operating status of the insect-attracting lamps under different operating conditions and spectral bands is analyzed to determine the current demand under each condition. The steps involve detailed measurements of the insect-attracting lamps' electrical characteristics, including resistance, power factor, and maximum load capacity. A power analyzer is used, and load prediction models, such as time series analysis, are employed to predict the total current demand in each time period. Considering the start-up and shutdown cycles and expected operating intensity of the insect-attracting lamps, the model is adjusted based on historical power usage data and the lamps' operation logs to ensure prediction accuracy. The analysis results generate voltage demand analysis data, which details the voltage and current demands of the insect-attracting lamps during different operating periods, providing a basis for adjusting grid parameters.

[0128] The power grid parameter configuration submodule, based on voltage demand analysis data and according to the active periods of various mosquitoes, adjusts the voltage operating parameters of the high-voltage power grid in multiple time periods to generate operating voltage parameter configurations.

[0129] In the power grid parameter configuration submodule, based on voltage demand analysis data, the voltage operating parameters of the high-voltage power grid are adjusted. Dynamic adjustment technology from the power grid management system is used to adjust voltage parameters and set target voltage levels according to the voltage demand analysis data, ensuring sufficient power supply to meet the operational needs of the insect-attracting lamps in each time period. During this process, the load management system is used to monitor and adjust the high-voltage power grid in real time, including peak shaving and valley filling strategies to optimize power resource utilization. Predictive control algorithms are used to predict and respond to changes in grid load, ensuring the timeliness and accuracy of voltage adjustments. Through a comprehensive control and management strategy, the operating voltage parameter configuration is generated, specifying the voltage settings of the high-voltage power grid in each time period, providing the necessary power support to ensure the effective operation of the insect-attracting lamps.

[0130] The power grid energy consumption recording submodule is configured based on the working voltage parameters, monitors the working energy consumption of the high-voltage power grid in real time, records energy consumption data, and generates power grid parameter optimization results.

[0131] In the power grid energy consumption recording submodule, based on the configured operating voltage parameters, energy consumption monitoring and recording of the high-voltage power grid are performed. The process uses an energy consumption monitoring system, including power meters and data acquisition equipment, to record the power grid's energy consumption in real time. The collection of energy consumption data involves multiple detection points, including main transmission lines and distribution branches, to ensure the comprehensiveness and representativeness of the data. After data collection, the data is processed and analyzed through a data analysis platform, including energy consumption trend analysis and abnormal consumption detection. An energy efficiency assessment model is used to compare energy consumption efficiency under different periods and voltage settings, helping to identify energy waste and potential energy-saving measures. Through a rigorous monitoring and analysis process, power grid parameter optimization results are generated. The target results show in detail the power grid's energy consumption and optimization potential in actual operation, providing a scientific basis for energy efficiency improvement and cost control.

[0132] Please refer to Figures 2 and 7. The automatic counting module includes:

[0133] The current change analysis submodule is based on the power grid parameter optimization results. By analyzing the current changes corresponding to the high-voltage power grid when killing mosquitoes, it identifies the current characteristics of various mosquitoes and generates current characteristic analysis results.

[0134] In the current variation analysis submodule, based on the power grid parameter optimization results, current variation analysis is performed during mosquito control on the high-voltage power grid. Fourier transform analysis is used to identify and analyze the characteristic frequencies in the current waveform. Current changes associated with mosquito killing activities are recorded in detail. Current data is collected from the power grid monitoring system, including time-series records of the current and current fluctuations during power grid operation. The current signal in the time domain is converted into a frequency domain signal using FFT to identify specific current fluctuation patterns. Each mosquito type forms unique frequency components in the current waveform due to its different body size and resistance. The identified frequency components are used to distinguish different types of mosquitoes and classify the current characteristics of each mosquito. The generated current feature analysis results provide detailed information on the current changes corresponding to different mosquitoes, which is crucial for the optimization of subsequent monitoring and control activities.

[0135] The real-time current monitoring submodule monitors the current data of the high-voltage power grid in real time based on the current characteristic analysis results, identifies current changes and records the number of various mosquitoes killed, and generates current change monitoring data.

[0136] In the real-time current monitoring submodule, based on the current characteristic analysis results, real-time monitoring of the current data of the high-voltage power grid is performed. The process uses current sensors and a real-time data acquisition system to monitor the current changes of the entire power grid. The monitoring system periodically receives data from the sensors and records the current readings using data acquisition software. The acquired current data is analyzed using pattern recognition technology and support vector machines to identify waveforms that match known mosquito current characteristics. The killing events of different mosquitoes are recorded and classified in real time. The generated current change monitoring data reflects the current status of the power grid, provides immediate feedback on mosquito control efficiency, and provides data support for adjusting strategies and optimizing energy consumption.

[0137] The trapping efficiency optimization submodule uses current change monitoring data to analyze the trapping efficiency of the mosquito-killing lamp, adjusts the operating parameters of the insect-attracting lamp and the high-voltage power grid, and generates trapping efficiency evaluation results.

[0138] In the trapping efficiency optimization submodule, current change monitoring data is used to analyze and adjust the trapping efficiency of the insect-attracting lamps. Statistical analysis methods, such as regression analysis, are employed to evaluate the insect-attracting efficiency under different spectral bands and current settings. Data obtained from the real-time current monitoring module is used to calculate the insect-attracting success rate under each setting, and this is combined with energy consumption data for cost-benefit analysis. The analysis process uses Tableau to graphically present the relationship between trapping rate and energy consumption, helping decision-makers understand the efficiency of different configurations. Based on the target analysis results, the parameters of the insect-attracting lamps and the high-voltage power grid are adjusted to optimize the light intensity and grid voltage to achieve the best energy efficiency ratio. The generated trapping efficiency evaluation results describe in detail the trapping efficiency under various parameter configurations, providing a scientific basis for optimizing insect-attracting strategies.

[0139] Please refer to Figure 8. A control method for a mosquito-killing lamp is provided. The control method for the mosquito-killing lamp is used to execute the aforementioned control system for the mosquito-killing lamp. The method includes:

[0140] S1: Based on mosquito activity monitoring data, real-time data on ambient temperature, humidity and light intensity are collected. By analyzing the number of mosquitoes under various environmental conditions, the activity intensity and frequency of various mosquitoes are identified, and activity pattern prediction results are generated.

[0141] S2: Based on the activity pattern prediction results, evaluate the attractiveness and energy consumption of multiple spectral bands to mosquitoes, identify the optimal spectral band of the insect-attracting lamp, and generate an insect-attracting spectrum matching list.

[0142] S3: Using the insect-attracting spectrum matching list and real-time environmental monitoring data, adjust the start-stop time and spectral band of the insect-attracting lamp to generate real-time adjustment control parameters;

[0143] S4: Based on real-time adjustment of control parameters, considering various mosquito activity periods and the working energy consumption of the high-voltage power grid, adjust the working voltage parameters of the high-voltage power grid and generate power grid parameter optimization results;

[0144] S5: Based on the results of power grid parameter optimization, analyze the current changes corresponding to the high-voltage power grid when killing mosquitoes, record the types and quantities of mosquitoes killed, and generate mosquito trapping data records;

[0145] S6: Utilize mosquito trapping data records to evaluate trapping efficiency, optimize the operating parameters of the insect-attracting lamps and high-voltage power grid, and generate trapping efficiency evaluation results.

[0146] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0147] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0148] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0149] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0150] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0151] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0152] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0155] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0156] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control system for a mosquito-killing lamp, characterized in that: The system includes: The mosquito behavior analysis module, based on mosquito activity monitoring data, collects real-time temperature, humidity, and light data. By analyzing the number of mosquitoes under various environmental conditions, it assesses the intensity and frequency of mosquito activity under these conditions and generates activity pattern prediction results. The spectral efficiency analysis module, based on the activity pattern prediction results, analyzes the attractiveness of various spectral bands to mosquitoes, considers the energy consumption of the insect-attracting lamp in various spectral bands, identifies the optimal spectral bands for attracting and killing various mosquitoes, and generates an insect-attracting spectral matching list. The insect-attracting lamp parameter configuration module, based on the insect-attracting spectral matching list and combined with real-time environmental monitoring data, adjusts the operating parameters of the insect-attracting lamp, including start-up and shutdown times and spectral band selection, and generates real-time adjustment control parameters. The voltage parameter adjustment module, based on the real-time adjustment control parameters and considering the energy consumption of the power grid, adjusts the voltage parameters of the high-voltage power grid in multiple time periods, generating power grid parameter optimization results. The automatic counting module, based on the power grid parameter optimization results, monitors the current data of the high-voltage power grid in real-time, analyzes current changes, counts the number of mosquitoes trapped, analyzes the trapping efficiency, optimizes the operating parameters of the insect-attracting lamp and the high-voltage power grid, and generates a trapping efficiency evaluation result. The spectral efficiency analysis module includes: an energy consumption analysis submodule, which, based on the activity mode prediction results, collects energy consumption data of the insect-attracting lamp operating in multiple spectral bands and generates an insect-attracting lamp energy consumption dataset; an attraction evaluation submodule, based on the insect-attracting lamp energy consumption dataset, calculates the attraction of multiple spectral bands to multiple mosquitoes by identifying the impact of multiple spectral bands on the number of active mosquitoes, and generates spectral attraction evaluation data; and a spectral band identification submodule, based on the spectral attraction evaluation data and considering energy consumption, identifies the optimal spectral bands for trapping multiple mosquitoes and generates an insect-attracting spectral matching list; the specific formula for calculating the attraction of multiple spectral bands to multiple mosquitoes is as follows: ;in, This represents an attractiveness score for mosquitoes in a specific spectral band. This represents the number of active mosquitoes observed in the current spectral band. This represents the maximum number of mosquitoes observed across all tested spectral bands. This represents the duration of mosquito activity in this wavelength range. Represents the total observation time. This represents the flight frequency of mosquitoes in this wavelength band. This represents the maximum observed flight frequency across all test bands. This represents the average distance between mosquitoes and the light source in this wavelength range. This represents the maximum measurement distance within the observation area.

2. The control system for a mosquito-killing lamp according to claim 1, characterized in that: The activity pattern prediction results include predicted mosquito activity quantity records, predicted activity intensity change information, and real-time environmental monitoring information. The insect-attracting spectrum matching list includes spectral band attraction score, optimal spectral band, and spectral band operating energy consumption data. The real-time adjustment control parameters include the start-up and stop time of the insect-attracting lamp, the selected spectral band, and operating parameter adjustment values ​​based on environmental conditions. The power grid parameter optimization results include high-voltage power grid voltage parameters, power grid operating time period, and high-voltage power grid energy consumption data. The trapping efficiency evaluation results include the number of mosquitoes trapped, the trend of trapping efficiency changes, and operating parameter adjustment records.

3. The control system for a mosquito-killing lamp according to claim 1, characterized in that: The mosquito behavior analysis module includes: an environmental data acquisition submodule that, based on mosquito activity monitoring data, uses temperature and humidity sensors and light sensors to collect temperature, humidity, and light data in real time, generating an environmental monitoring dataset; an activity count analysis submodule that, based on the environmental monitoring dataset, analyzes the number of active mosquitoes under various environmental conditions, calculates the correlation between mosquito activity and various environmental conditions, and generates mosquito activity analysis results; and an activity time prediction submodule that, based on the mosquito activity analysis results, analyzes and predicts the active periods of mosquitoes under various environmental conditions, generating activity pattern prediction results.

4. The control system for a mosquito-killing lamp according to claim 3, characterized in that: The specific formula for calculating the correlation between mosquito activity and various environmental conditions is as follows: ;in, This represents the correlation coefficient between mosquito activity and environmental conditions. Representative at the Environmental condition values ​​at each point in time, including temperature, humidity, and light intensity. Representative at the Mosquito activity levels at specific time points. Represents the average value of environmental conditions. The average value representing mosquito activity. This represents the total number of data points, i.e., the number of observation points in time. An index representing a point in time.

5. The control system for a mosquito-killing lamp according to claim 1, characterized in that: The insect-attracting lamp parameter configuration module includes: an environmental parameter analysis submodule, which, based on the insect-attracting spectrum matching list and real-time environmental monitoring data including temperature, humidity, and light intensity, predicts and identifies the activity periods of various mosquitoes and generates real-time environmental analysis results; a working period configuration submodule, based on the real-time environmental analysis results and the activity periods of various mosquitoes, identifies the required working period of the insect-attracting lamp and generates insect-attracting lamp start-stop parameter configurations; and a control parameter setting submodule, based on the insect-attracting lamp start-stop parameter configurations and the active mosquito species during the target time, adjusts the working spectral band of the insect-attracting lamp at the target time and generates real-time adjustment control parameters.

6. The control system for a mosquito-killing lamp according to claim 1, characterized in that: The voltage parameter adjustment module includes: a voltage demand analysis submodule, which analyzes the voltage intensity required to kill various mosquitoes based on the real-time adjustment control parameters and generates voltage demand analysis data; a power grid parameter configuration submodule, which adjusts the voltage operating parameters of the high-voltage power grid in multiple time periods based on the voltage demand analysis data and the active time periods of various mosquitoes, and generates operating voltage parameter configuration; and a power grid energy consumption recording submodule, which monitors the operating energy consumption of the high-voltage power grid in real time based on the operating voltage parameter configuration, records the energy consumption data, and generates power grid parameter optimization results.

7. The control system for a mosquito-killing lamp according to claim 1, characterized in that: The automatic counting module includes: a current change analysis submodule, which, based on the power grid parameter optimization results, analyzes the current changes corresponding to the high-voltage power grid when killing mosquitoes, identifies the current characteristics corresponding to various mosquitoes, and generates current characteristic analysis results; a real-time current monitoring submodule, based on the current characteristic analysis results, monitors the current data of the high-voltage power grid in real time, identifies current changes, records the number of various mosquitoes killed, and generates current change monitoring data; and a trapping efficiency optimization submodule, which uses the current change monitoring data to analyze the trapping efficiency of the mosquito-killing lamp, adjusts the operating parameters of the insect-attracting lamp and the high-voltage power grid, and generates trapping efficiency evaluation results.

8. A control method for a mosquito-killing lamp, based on the control system for a mosquito-killing lamp according to any one of claims 1-7, characterized in that: The method includes: based on mosquito activity monitoring data, real-time acquisition of ambient temperature, humidity, and light intensity data; analysis of mosquito activity under various environmental conditions to identify the activity intensity and frequency of various mosquitoes, generating activity pattern prediction results; based on the activity pattern prediction results, evaluation of the attractiveness and energy consumption of various spectral bands to mosquitoes, identification of the optimal spectral band for the insect-attracting lamp, and generation of an insect-attracting spectral matching list; using the insect-attracting spectral matching list, combined with real-time environmental monitoring data, adjusting the start-up and stop times and spectral bands of the insect-attracting lamp, generating real-time adjustment control parameters; based on the real-time adjustment control parameters, considering the energy consumption of the high-voltage power grid and the various mosquito activity time periods, adjusting the operating voltage parameters of the high-voltage power grid, generating power grid parameter optimization results; based on the power grid parameter optimization results, analyzing the current changes corresponding to the high-voltage power grid killing mosquitoes, recording the types and quantities of mosquitoes killed, and generating mosquito trapping data records; using the mosquito trapping data records, evaluating the trapping efficiency, optimizing the operating parameters of the insect-attracting lamp and the high-voltage power grid, and generating trapping efficiency evaluation results.

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