Machine learning-based pesticide spraying management system for disinfection and killing equipment

Abstract

The invention relates to the technical field of sterilization equipment control, in particular to a sterilization equipment spraying management system based on machine learning, which comprises an equipment data acquisition module for acquiring sterilization equipment data and a target area image in a monitoring period, a real-time data acquisition module for acquiring sterilization target positioning data and target area environment data in real time, and a processing module for processing the sterilization target positioning data and the target area environment data. The area grade analysis module is used for dividing a target area to obtain disinfection areas and carrying out grade division on the disinfection areas, and is also used for constructing disinfection spraying indexes; the environment monitoring module is used for analyzing volatilization abnormity of the disinfection areas; the disinfection target analysis module is used for analyzing target density abnormity of the disinfection areas; the pesticide spraying management module is used for generating a pesticide spraying management scheme of each disinfection and killing area, and the pesticide effect monitoring module is used for optimizing the pesticide spraying management scheme of each disinfection and killing area. The pesticide spraying disinfection efficiency of the disinfection equipment is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pest control equipment, and in particular to a pest control equipment spray management system based on machine learning. Background Art

[0002] Traditional pest control methods usually rely on manual experience to set fixed spraying parameters, such as dosage, frequency, and time. However, this method is difficult to adapt to complex and changing environmental conditions (such as weather changes and different stages of crop growth), and can easily lead to waste of resources, environmental pollution, and impacts on non-target organisms. With the development of intelligent technology and data analysis capabilities, it has become possible to use machine learning models to predict the best spraying strategy, which can improve efficiency and reduce side effects.

[0003] Chinese patent publication number CN115779113A discloses a terminal disinfection robot and terminal disinfection system, which belongs to the field of disinfection technology. The terminal disinfection robot includes a central control module, a spray disinfection module, an atomization disinfection module, a drug preparation module, an environmental perception module, an intelligent analysis module, a path planning module, a power supply module and a mobile module; wherein the environmental perception module, the intelligent analysis module and the drug preparation module cooperate to realize automatic detection of indoor space and objects in automatic mode, automatic calculation of the spatial volume and surface area required for disinfection, calculation of the required amount of drugs and automatic preparation of drugs, and then use the spray disinfection module and the atomization disinfection module to achieve surface disinfection and air disinfection; it can be seen that the disinfection process of the invention is to spray drugs evenly to achieve the disinfection effect, but the distribution of the actual disinfection target is not analyzed, and accurate and efficient disinfection cannot be achieved, and there is a problem of low disinfection efficiency of the disinfection equipment. Summary of the invention

[0004] The object of the present invention is to provide a pest control equipment spray management system based on machine learning to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A pest control equipment spray management system based on machine learning, characterized by comprising:

[0007] Equipment data acquisition module, used to obtain disinfection equipment data and target area images within the monitoring period;

[0008] Real-time data collection module, used to collect the location data of the disinfection target and the environmental data of the target area in real time;

[0009] The regional grade analysis module is used to divide the target area according to the target area image to obtain various disinfection areas, and to grade each disinfection area according to the number of stored items in each disinfection area, and to construct a disinfection spraying index based on the grade division results;

[0010] Environmental monitoring module, used to analyze the volatility anomaly of each pest control area based on the target area environmental data and pest control spray index within the monitoring period;

[0011] The pest control target analysis module is used to analyze the abnormality of target density in each pest control area based on the pest control target positioning data and the pest control spray index;

[0012] A spraying management module is used to generate a spraying management plan for each disinfection area based on the volatility anomaly analysis results and target density anomaly analysis results of each disinfection area;

[0013] The drug efficacy monitoring module is used to optimize the spraying management plan for each disinfection area according to the activity frequency of the disinfection targets in each disinfection area after the spraying cycle.

[0014] Further, the region level analysis module includes a region division unit, and the region division unit is used to calculate the minimum longitudinal line segment length Lx and the minimum transverse line segment length Wx;

[0015] The area division unit divides the target area image using a rectangle composed of a longitudinal minimum line segment length Lx and a transverse minimum line segment length Wx as a division unit to obtain each disinfection area D(i).

[0016] Furthermore, the area grade analysis module is also provided with a grade division unit, which is used to calculate the storage density ρ(i) of each disinfection area according to the number of stored items in each disinfection area, and grade each disinfection area according to ρ(i), and then construct a disinfection spraying index according to the grade division result: if ρ(i)<N1, the grade division unit divides the disinfection area into three levels, and sets the disinfection spraying index to P1; if N1≤ρ(i)<N2, the grade division unit divides the disinfection area into two levels, and sets the disinfection spraying index to P2; if ρ(i)≥N2, the grade division unit divides the disinfection area into one level, and sets the disinfection spraying index to P3; wherein N1 is the first preset storage density, and N2 is the second preset storage density.

[0017] Furthermore, the environmental monitoring module includes a temperature anomaly analysis unit, which is used to compare the temperature t(i) of each disinfection area during the monitoring period with the preset evaporation temperature T to determine the temperature abnormality of each disinfection area. The temperature abnormality of each disinfection area includes normal and abnormal. When the temperature of each disinfection area is normal, the temperature anomaly coefficient is set to WD1(i); when the temperature of each disinfection area is abnormal, the temperature anomaly coefficient is set to WD2(i).

[0018] Furthermore, the environmental monitoring module also includes a humidity anomaly analysis unit, which is used to compare the humidity s(i) of each disinfection area during the monitoring period with each preset evaporation humidity to determine the humidity abnormality of each disinfection area. The humidity abnormality of each disinfection area includes normal and abnormal. When the humidity in the disinfection area is abnormal, the humidity abnormality coefficient is set to SD1(i); when the humidity in the disinfection area is normal, the humidity abnormality coefficient is set to SD2(i).

[0019] Furthermore, the environmental monitoring module also includes a volatile anomaly analysis unit, which is used to analyze the volatile anomaly of each disinfection area according to the temperature anomaly analysis results, humidity anomaly analysis results and disinfection spray index construction results of each disinfection area during the monitoring period. The analysis results of the volatile anomaly of each disinfection area include normal and abnormal.

[0020] Furthermore, the disinfection target analysis module includes a density anomaly analysis unit, which is used to calculate the target density ρm(i) of each disinfection area according to the disinfection target positioning data, and compare ρm(i) with the preset target density P to determine the target density abnormality of each disinfection area. The target density abnormality of each disinfection area includes normal target density and abnormal target density.

[0021] Furthermore, the pest control target analysis module also includes a drug resistance analysis unit, which is used to analyze the drug resistance of the pest control targets in each pest control area according to the adult insect proportion μ(i) of the pest control targets in each pest control area. The drug resistance of the pest control targets in each pest control area includes normal and abnormal. When the drug resistance in the pest control area is abnormal, the preset target density is adjusted to P(i).

[0022] Furthermore, the spray management module is used to generate a spray management plan for each disinfection area according to the volatility abnormality analysis results and target density abnormality analysis results of each disinfection area within the monitoring period:

[0023] If the target density of the disinfection area is normal, the spray management module determines that the active disinfection target of the disinfection area is normal, sets the spraying cycle of the disinfection area to XT1(i), sets XT1(i)=XT0, sets the spraying amount of the disinfection area to PY1, and sets PY1(i)=PY0×[Lx×Wx];

[0024] If the volatilization of the target area is abnormal and the target density of the disinfection area is abnormal, the spraying management module determines that the disinfection of the disinfection area is abnormal, and sets the disinfection period of the disinfection area to XT2(i), sets XT2(i)=XT0×{1-[a1×temperature anomaly coefficient+a2×humidity anomaly coefficient-B×disinfection spraying index] / B×disinfection spraying index}, sets the spraying amount of the disinfection area to PY2(i), and sets PY2(i)=PY0×[Lx×Wx]×exp[P×disinfection spraying index-ρm(i)];

[0025] If the volatilization of the target area is normal and the target density of the disinfection area is abnormal, the spray management module determines that the active disinfection target of the disinfection area is abnormal, and sets the disinfection period of the disinfection area to XT3(i), sets XT3(i)=XT0, sets the spraying amount of the disinfection area to PY3(i), and sets PY3(i)=PY0×exp[P×disinfection spray index-ρm(i)];

[0026] Among them, XT0 is the initial disinfection cycle and PY0 is the initial spraying density.

[0027] Furthermore, the drug efficacy monitoring module is used to compare the activity frequency v(i) of the disinfection target in each disinfection area after the spraying cycle with the activity frequency V of the preset disinfection target to determine the abnormality of the activity frequency of the disinfection target in the disinfection area. The abnormality of the activity frequency of the disinfection target in the disinfection area includes normal and abnormal. When the activity frequency of the disinfection target in the disinfection area is abnormal, the spraying amount of the disinfection area is optimized to PYd(i)', where d is a digital subscript and d=1,2,3.

[0028] Compared with the prior art, the beneficial effects of the present invention are: by dividing the target area into regions, a specific analysis is conducted on the volatility abnormality and the abnormality of the disinfection target density according to the division results to determine whether the medicine liquid is affected by the environment, and the differences in medicine requirements for each disinfection area are analyzed, and then the spraying process of the disinfection equipment is managed according to the analysis results to achieve accurate and efficient disinfection of the disinfection equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 This is a structural diagram of the pesticide spraying management system for disinfection equipment based on machine learning in this embodiment.

[0031] Figure 2 Schematic diagram of the structure of the regional level analysis module of this embodiment.

[0032] Figure 3 Schematic diagram of the structure of the environmental monitoring module of this embodiment.

[0033] Figure 4 This is a schematic diagram of the structure of the target elimination analysis module of this embodiment. DETAILED DESCRIPTION

[0034] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0035] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0036] Specifically, the machine learning-based pest control equipment spray management system described in this embodiment is applied to the spray management of pest control robots in food circulation areas; the pest control robots described in this embodiment specifically target small or medium-sized insects such as cockroaches; the even-numbered pest control robots in this embodiment have the function of real-time image recognition of small or medium-sized insects such as cockroaches.

[0037] See also Figure 1 As shown, it is a structural diagram of the pest control equipment spray management system based on machine learning in this embodiment, including:

[0038] The equipment data acquisition module is used to obtain the pest control equipment data and target area image within the monitoring period; the pest control equipment data specifically includes the initial pest control period and initial spraying density of the pest control equipment; the target area image is a grayscale image of the target area.

[0039] Specifically, the present embodiment does not make any specific limitation on the value of the duration of the monitoring period, and those skilled in the art can set it freely, as long as the requirements for the value of the duration of the monitoring period are met. In this implementation, the value of the duration of the monitoring period is 10 minutes; the present embodiment does not make any specific limitation on the method for obtaining the data of the disinfection equipment, and those skilled in the art can set it freely, as long as the requirements for obtaining the data of the disinfection equipment are met. In this embodiment, the data is obtained through user interactive input. In this embodiment, multiple cameras are set on the ceiling of the target area to capture multiple grayscale images, and the multiple grayscale images are combined into a target area image; it is worth noting that the frequency of obtaining the target area image in this embodiment is periodically obtained according to the duration of the monitoring period.

[0040] Please continue reading Figure 1 As shown, the system also includes a real-time data acquisition module, which is used to collect the pest control target positioning data and the target area environmental data in real time; the pest control target positioning data includes the pest control target positioning coordinates and the number of pest control targets, and the pest control targets are specifically small and medium-sized insects such as cockroaches; the target area environmental data includes environmental humidity data, environmental temperature data and the activity frequency of the pest control targets.

[0041] Specifically, this embodiment does not specifically limit the collection process of the positioning data of the disinfection target. Those skilled in the art can set it freely as long as the collection requirements of the positioning data of the disinfection target are met. In this embodiment, the disinfection target is identified in the target area image through YOLOv5 technology, and the positioning coordinates of the disinfection target in the target area image are recorded. The activity frequency of the disinfection target is also counted according to the recording results. In this embodiment, the method for collecting the environmental data of the target area is to collect environmental humidity data and environmental temperature data in real time through intelligent sensors.

[0042] Please continue reading Figure 1 As shown, the system also includes a regional grade analysis module, which is connected to the equipment number acquisition module. The regional grade analysis module is used to divide the target area according to the target area image to obtain various disinfection areas, and grade each disinfection area according to the number of stored items in each disinfection area, and construct a disinfection spray index based on the grade division results.

[0043] See also Figure 2 As shown, the region level analysis module includes a region division unit, and the region division unit is used to divide the target region according to the target region image;

[0044] The area division unit divides the target area image into minimum rectangles, and the analysis process of the minimum rectangle is as follows: the area division unit projects the target area image into a plane rectangular coordinate system, and calculates the minimum longitudinal line segment length Lx and the minimum transverse line segment length Wx, and sets Lx=min{L(y)}, Wx=min{W(x)}; the area division unit divides the target area image into each disinfection area D(i) using a rectangle composed of the minimum longitudinal line segment length Lx and the minimum transverse line segment length Wx as a division unit, where L(y) represents the length of the yth longitudinal line segment, W(x) represents the length of the xth transverse line segment, D(i) represents the i-th disinfection area, i=1,2,3...N, and N is the number of divisions of the disinfection area; the division unit is obtained by analyzing the length and width of the minimum rectangle of the target area image, and the target area is divided to achieve accurate division of the disinfection area, thereby improving the accuracy of spraying management of each disinfection area in the target area.

[0045] Specifically, the "projecting the target area image into a plane rectangular coordinate system" described in this embodiment is specifically: taking the lower left vertex of the image as the coordinate origin, from left to right as the positive direction of the horizontal coordinate, from bottom to top as the positive direction of the vertical coordinate, and establishing a plane rectangular coordinate system with pixels as the unit length.

[0046] Please continue reading Figure 2 As shown, the regional level analysis module is further provided with a level division unit, which is connected to the regional division unit, and the level division unit is used to calculate the storage density ρ(i) of each disinfection area according to the number of stored items in each disinfection area, and set ρ(i)=N(i) / [Lx×Wx];

[0047] The grading unit grades each disinfection area according to the calculation result of the storage density, and constructs a disinfection spraying index according to the grading result to quantify the disinfection difficulty of each disinfection area: if ρ(i)<N1, the grading unit divides the disinfection area into three levels, and sets the disinfection spraying index to P1, setting P1=exp[ρ(i)-N1]; if N1≤ρ(i)<N2, the grading unit divides the disinfection area into two levels, and sets it to P2, setting P2=1; if ρ(i)≥ N2, the level division unit divides the disinfection area into one level, and sets the disinfection spraying index to P3, setting P3=ln{e+[ρ(i)-N2] / N2}; wherein N1 is the first preset storage density, N2 is the second preset storage density, N1<N2, and e is the natural logarithm; by comparing and analyzing the storage quantity of each disinfection area, the disinfection difficulty level of each disinfection area is judged, and then the disinfection spraying index is set to quantify the difficulty, thereby achieving accurate judgment of the environmental abnormality and target density abnormality of each disinfection area.

[0048] Specifically, this embodiment does not impose any specific restrictions on the values ​​of the first preset storage density N1 and the second preset storage density N2. Those skilled in the art can freely set them as long as the value requirements of the first preset storage density N1 and the second preset storage density N2 are met. In this embodiment, the first preset storage density N1 can be set to 2 pieces / square meter, and the second preset storage density N2 can be set to 5 pieces / square meter.

[0049] Please continue reading Figure 1 As shown, the system also includes an environmental monitoring module, which is used to analyze the volatility abnormality of each disinfection area based on the target area environmental data and disinfection spray index within the monitoring period.

[0050] See also Figure 3 As shown, the environmental monitoring module includes a temperature anomaly analysis unit, which is used to compare the temperature t(i) of each disinfection area during the monitoring period with the preset evaporation temperature T, and analyze the temperature anomaly of each disinfection area according to the comparison result to determine the influence of temperature on the efficacy of the sprayed medicine, and construct a temperature anomaly coefficient: if t(i)<T, the temperature anomaly analysis unit determines that the temperature of each disinfection area during the monitoring period is normal, and sets the temperature anomaly coefficient to WD1(i), setting WD1(i)=0; if t(i)≥T, the temperature anomaly analysis unit determines that the temperature of each disinfection area during the monitoring period is abnormal, and sets the temperature anomaly coefficient to WD2(i), setting WD2(i)=[t(i)-T] / T; wherein t(i) is the temperature of the i-th disinfection area during the monitoring period; by judging the local temperature of each disinfection area, the temperature anomaly of each minimum spraying area is analyzed, and then the situation of the reduction of the effective amount of medicine due to temperature is judged, thereby improving the accuracy of the spraying management of each disinfection area in the target area.

[0051] Specifically, this embodiment does not specifically limit the value of the preset evaporation temperature T, and those skilled in the art can freely set it as long as the value requirement of the preset evaporation temperature T is met. In this embodiment, the value of the preset evaporation temperature T is 30°C.

[0052] Please continue reading Figure 3As shown, the environmental monitoring module also includes a humidity anomaly analysis unit, which is used to compare the humidity s(i) of each disinfection area during the monitoring period with each preset evaporation humidity, and analyze the humidity anomaly of each disinfection area according to the comparison result, and construct a humidity anomaly coefficient: if s(i)<S1 or s(i)≥S2, the humidity anomaly analysis unit determines that the humidity of the disinfection area during the monitoring period is abnormal, and sets the humidity anomaly coefficient to SD1(i), setting SD1(i)=|s(i)-[S1+S2] / 2| / {[S1+S2] / 2}; if S 1≤s(i)<S2, the humidity anomaly analysis unit determines that the humidity of the disinfection area is normal during the monitoring period, and sets the humidity anomaly coefficient to SD2(i), and sets SD2(i)=0; wherein, S1 is the first preset evaporation humidity, S2 is the second preset evaporation humidity, S1<S2; by comparing and judging the local humidity of each disinfection area, the effect of the medicine in each disinfection area is analyzed, and then the situation of reduced effective medicine concentration or less attached medicine due to humidity is analyzed, thereby improving the accuracy of spraying management of each disinfection area in the target area.

[0053] Specifically, the values ​​of the first preset evaporation humidity S1 and the second preset evaporation humidity S2 in this embodiment depend on the optimal efficacy environment humidity for spraying medicines. Those skilled in the art can freely set the first preset evaporation humidity S1 and the second preset evaporation humidity S2 according to the optimal efficacy environment humidity. It is only necessary to meet the value requirements of the first preset evaporation humidity S1 and the second preset evaporation humidity S2. In this embodiment, the first preset evaporation humidity S1 is set to the optimal efficacy environment humidity for spraying medicines × 70%, and the second preset evaporation humidity S2 is set to the optimal efficacy environment humidity for spraying medicines × 120%.

[0054] Specifically, the environmental monitoring module also includes a volatile anomaly analysis unit, which is connected to the temperature anomaly analysis unit and the humidity anomaly analysis unit. The volatile anomaly analysis unit is used to analyze the volatility anomaly of each disinfection area according to the temperature anomaly analysis results, humidity anomaly analysis results and disinfection spray index construction results of each disinfection area during the monitoring period: if a1×temperature anomaly coefficient+a2×humidity anomaly coefficient<B×disinfection spray index, the volatile anomaly analysis unit determines that the volatility of the disinfection area during the monitoring period is normal; if a1×temperature anomaly coefficient+a2×humidity anomaly coefficient≥B×disinfection spray index, the volatile anomaly analysis unit determines that the volatility of the disinfection area during the monitoring period is abnormal; wherein B is a preset volatile anomaly index, a1 is a temperature weight, a2 is a humidity weight, and a1+a2=1; the volatile anomaly analysis unit comprehensively analyzes the volatility anomaly of each disinfection area based on the temperature and humidity that have the greatest impact on the volatilization of the drug, thereby improving the accuracy of the analysis of the volatility anomaly of the drug in each disinfection area.

[0055] Specifically, this embodiment does not specifically limit the value of the preset volatility abnormality index B, and those skilled in the art can freely set it as long as the value requirement of the preset volatility abnormality index B is met. In this embodiment, the preset volatility abnormality index B is set to 0.5.

[0056] Please continue reading Figure 1 As shown, the system also includes a pest control target analysis module, which is connected to the real-time data acquisition module and the area level analysis module. The pest control target analysis module is used to analyze the target density abnormality of each pest control area according to the pest control target positioning data, and adjust the analysis process of the target density abnormality of each pest control area according to the adult insect proportion of the pest control target.

[0057] See also Figure 4 As shown, the disinfecting target analysis module includes a density anomaly analysis unit, which is used to calculate the target density ρm(i) of each disinfecting area according to the disinfecting target positioning data, and set ρm(i)=mn(i) / [Lx×Wx], where mn(i) is the number of disinfecting targets in the i-th disinfecting area;

[0058] The density anomaly analysis unit is used to compare ρm(i) with the preset target density P, and analyze the target density anomaly of each disinfection area according to the comparison result, so as to realize accurate comparison and judgment of the density of disinfection targets in each disinfection area: if ρm(i)≥P×disinfection spray index, the density anomaly analysis unit determines that the target density of the disinfection area is abnormal; if ρm(i)<P×disinfection spray index, the density anomaly analysis unit determines that the target density of the disinfection area is normal; by judging the disinfection target density of each disinfection area within the monitoring period, it is determined whether the number of disinfection targets in each disinfection area meets the disinfection standard, thereby improving the accuracy of spraying management of each disinfection area in the target area; it can be understood that the method of obtaining the number mn(i) of disinfection targets in the i-th disinfection area in this embodiment is to judge whether the disinfection target positioning coordinates belong to the i-th disinfection area, and count the number of disinfection target positioning coordinates belonging to the disinfection target in the i-th disinfection area as mn(i).

[0059] Specifically, this embodiment does not impose any specific limitation on the value of the preset target density P, and those skilled in the art can freely set it as long as the value requirement of the preset target density P is met. For example, the preset target density P can be set to 4 pieces / square meter.

[0060] Please continue reading Figure 4As shown, the pest control target analysis module also includes a drug resistance analysis unit, which is connected to the volatility abnormality analysis unit. The drug resistance analysis unit is used to analyze the drug resistance of the pest control targets in each pest control area according to the adult insect proportion μ(i) of the pest control targets in each pest control area, and adjust the analysis process of the target density abnormality according to the analysis result: if μ(i)<U, the drug resistance analysis unit determines that the drug resistance of the pest control area is normal and no adjustment is made; if μ(i)≥U, the drug resistance analysis unit determines that the drug resistance of the pest control area is abnormal, and adjusts the preset target density to P(i), setting P(i)=P×[μ(i)-U] / U; wherein U is the preset pest control target adult proportion; by comparing the values ​​of the adult proportions in each pest control area to analyze the influence of the adult proportion on whether the normal use of drugs in each pest control area can achieve the pest control target, the accuracy of the analysis of the target density abnormality is improved, thereby improving the accuracy of the spraying management of each pest control area in the target area.

[0061] Specifically, this embodiment does not impose any specific limitation on the value of the preset target adult insect disinfecting ratio U. Those skilled in the art can set it freely as long as the value requirement of the preset target adult insect disinfecting ratio U is met. This embodiment sets the preset target adult insect disinfecting ratio U to 0.4.

[0062] Please continue reading Figure 1 As shown, the system also includes a spraying management module, which is connected to the environmental monitoring module and the disinfection target analysis module. The spraying management module is used to generate a spraying management plan for each disinfection area according to the volatility abnormality analysis results and the target density abnormality analysis results of each disinfection area within the monitoring period:

[0063] If the target density of the disinfection area is normal, the spray management module determines that the active disinfection target of the disinfection area is normal, sets the spraying cycle of the disinfection area to XT1(i), sets XT1(i)=XT0, sets the spraying amount of the disinfection area to PY1, and sets PY1(i)=PY0×[Lx×Wx];

[0064] If the volatilization of the target area is abnormal and the target density of the disinfection area is abnormal, the spraying management module determines that the disinfection of the disinfection area is abnormal, and sets the disinfection period of the disinfection area to XT2(i), sets XT2(i)=XT0×{1-[a1×temperature anomaly coefficient+a2×humidity anomaly coefficient-B×disinfection spraying index] / B×disinfection spraying index}, sets the spraying amount of the disinfection area to PY2(i), and sets PY2(i)=PY0×[Lx×Wx]×exp[P×disinfection spraying index-ρm(i)];

[0065] If the volatilization of the target area is normal and the target density of the disinfection area is abnormal, the spray management module determines that the active disinfection target of the disinfection area is abnormal, and sets the disinfection period of the disinfection area to XT3(i), sets XT3(i)=XT0, sets the spraying amount of the disinfection area to PY3(i), and sets PY3(i)=PY0×exp[P×disinfection spray index-ρm(i)];

[0066] Among them, XT0 is the initial disinfection cycle, and PY0 is the initial spraying density;

[0067] The spray management module is also used to output the disinfection cycle and spraying amount of each disinfection area to the user as a spray management plan for each disinfection area; through a comprehensive analysis of the environment and disinfection targets, it lists and judges the impact of various abnormal conditions on the spraying process of the disinfection equipment, and gives corresponding spray plans, thereby improving the accuracy of spray management of each disinfection area in the target area.

[0068] Specifically, this embodiment does not make specific limitations on the values ​​of the initial disinfection period XT0 and the initial spraying density PY0. Those skilled in the art can limit them freely as long as the value requirements of the initial disinfection period XT0 and the initial spraying density PY0 are met. In this embodiment, the initial disinfection period XT0 and the initial spraying density PY0 are obtained by user interactive input.

[0069] Please continue reading Figure 1 As shown, the system also includes a drug efficacy monitoring module, which is used to optimize the spraying management plan of each disinfection area according to the activity frequency v(i) of the disinfection target in each disinfection area after the spraying cycle, so as to realize the continuous optimization of the disinfection equipment: if v(i)<V, the drug efficacy monitoring module determines that the activity frequency of the disinfection target in the disinfection area is normal and does not perform optimization; if v(i)≥V, the drug efficacy monitoring module determines that the activity frequency of the disinfection target in the disinfection area is abnormal, and optimizes the spraying amount of the disinfection area to PYd(i)', and sets PYd(i)'=PYd(i)×exp{[v(i)-V] / V};

[0070] Among them, V is the activity frequency of the preset disinfection target; by analyzing the spraying effect of the disinfection equipment, the spraying management plan of each disinfection area is optimized, which improves the accuracy of the analysis of the spraying management plan of each disinfection area, and then improves the accuracy of the spraying management of each disinfection area in the target area.

[0071] Specifically, this embodiment does not limit the value of the activity frequency V of the preset disinfection target. Technical personnel in this field can set it freely, and it only needs to meet the value requirements of the activity frequency V of the preset disinfection target. In this embodiment, the value of the activity frequency V of the preset disinfection target is 0.1 times / minute.

[0072] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A pesticide spraying management system based on machine learning, characterized in that: include: Equipment data acquisition module, used to obtain disinfection equipment data and target area images within the monitoring period; Real-time data collection module, used to collect the location data of the disinfection target and the environmental data of the target area in real time; The regional grade analysis module is used to divide the target area according to the target area image to obtain various disinfection areas, and to grade each disinfection area according to the number of stored items in each disinfection area, and to construct a disinfection spraying index based on the grade division results; Environmental monitoring module, used to analyze the volatility anomaly of each pest control area based on the target area environmental data and pest control spray index within the monitoring period; The pest control target analysis module is used to analyze the abnormality of target density in each pest control area based on the pest control target positioning data and the pest control spray index; A spraying management module is used to generate a spraying management plan for each disinfection area based on the volatility anomaly analysis results and target density anomaly analysis results of each disinfection area; The drug efficacy monitoring module is used to optimize the spraying management plan for each disinfection area according to the activity frequency of the disinfection targets in each disinfection area after the spraying cycle.

2. The machine learning-based pesticide spraying management system for pest control equipment according to claim 1, characterized in that: The region level analysis module includes a region division unit, and the region division unit is used to calculate the minimum longitudinal line segment length Lx and the minimum transverse line segment length Wx; The area division unit divides the target area image using a rectangle composed of a longitudinal minimum line segment length Lx and a transverse minimum line segment length Wx as a division unit to obtain each disinfection area D(i).

3. The machine learning-based pesticide spraying management system for pest control equipment according to claim 2 is characterized in that: The regional grade analysis module is also provided with a grade division unit, which is used to calculate the storage density ρ(i) of each disinfection area according to the number of stored items in each disinfection area, and grade each disinfection area according to ρ(i), and then construct a disinfection spray index according to the grade division result: if ρ(i)<N1, the grade division unit divides the disinfection area into three levels, and sets the disinfection spray index to P1; if N1≤ρ(i)<N2, the grade division unit divides the disinfection area into two levels, and sets the disinfection spray index to P2; if ρ(i)≥N2, the grade division unit divides the disinfection area into one level, and sets the disinfection spray index to P3; wherein N1 is the first preset storage density, and N2 is the second preset storage density.

4. The machine learning-based pesticide spraying management system for pest control equipment according to claim 3 is characterized in that: The environmental monitoring module includes a temperature anomaly analysis unit, which is used to compare the temperature t(i) of each disinfection area during the monitoring period with the preset evaporation temperature T to determine the temperature abnormality of each disinfection area. The temperature abnormality of each disinfection area includes normal and abnormal. When the temperature of each disinfection area is normal, the temperature anomaly coefficient is set to WD1(i); when the temperature of each disinfection area is abnormal, the temperature anomaly coefficient is set to WD2(i).

5. The machine learning-based pesticide spraying management system for pest control equipment according to claim 4 is characterized in that: The environmental monitoring module also includes a humidity anomaly analysis unit, which is used to compare the humidity s(i) of each disinfection area during the monitoring period with each preset evaporation humidity to determine the humidity abnormality of each disinfection area. The humidity abnormality of each disinfection area includes normal and abnormal. When the humidity in the disinfection area is abnormal, the humidity abnormality coefficient is set to SD1(i); when the humidity in the disinfection area is normal, the humidity abnormality coefficient is set to SD2(i).

6. The machine learning-based pesticide spraying management system for pest control equipment according to claim 5, characterized in that: The environmental monitoring module also includes a volatile anomaly analysis unit, which is used to analyze the volatile anomaly of each disinfection area according to the temperature anomaly analysis results, humidity anomaly analysis results and disinfection spray index construction results of each disinfection area during the monitoring period. The analysis results of the volatile anomaly of each disinfection area include normal and abnormal.

7. The machine learning-based pesticide spraying management system for pest control equipment according to claim 6, characterized in that: The disinfection target analysis module includes a density anomaly analysis unit, which is used to calculate the target density ρm(i) of each disinfection area according to the disinfection target positioning data, and compare ρm(i) with the preset target density P to determine the target density abnormality of each disinfection area. The target density abnormality of each disinfection area includes normal target density and abnormal target density.

8. The machine learning-based pesticide spraying management system for pest control equipment according to claim 7, characterized in that: The pest control target analysis module also includes a drug resistance analysis unit, which is used to analyze the drug resistance of the pest control targets in each pest control area according to the adult insect proportion μ(i) of the pest control targets in each pest control area. The drug resistance of the pest control targets in each pest control area includes normal and abnormal. When the drug resistance in the pest control area is abnormal, the preset target density is adjusted to P(i).

9. The machine learning-based pesticide spraying management system for pest control equipment according to claim 8, characterized in that: The spray management module is used to generate a spray management plan for each disinfection area according to the volatility abnormality analysis results and target density abnormality analysis results of each disinfection area within the monitoring period: If the target density of the disinfection area is normal, the spray management module determines that the active disinfection target of the disinfection area is normal, sets the spraying cycle of the disinfection area to XT1(i), sets XT1(i)=XT0, sets the spraying amount of the disinfection area to PY1, and sets PY1(i)=PY0×[Lx×Wx]; If the volatilization of the target area is abnormal and the target density of the disinfection area is abnormal, the spraying management module determines that the disinfection of the disinfection area is abnormal, and sets the disinfection period of the disinfection area to XT2(i), sets XT2(i)=XT0×{1-[a1×temperature anomaly coefficient+a2×humidity anomaly coefficient-B×disinfection spraying index] / B×disinfection spraying index}, sets the spraying amount of the disinfection area to PY2(i), and sets PY2(i)=PY0×[Lx×Wx]×exp[P×disinfection spraying index-ρm(i)]; If the volatilization of the target area is normal and the target density of the disinfection area is abnormal, the spray management module determines that the active disinfection target of the disinfection area is abnormal, and sets the disinfection period of the disinfection area to XT3(i), sets XT3(i)=XT0, sets the spraying amount of the disinfection area to PY3(i), and sets PY3(i)=PY0×exp[P×disinfection spray index-ρm(i)]; Among them, XT0 is the initial disinfection cycle and PY0 is the initial spraying density.

10. The machine learning-based pesticide spraying management system for pest control equipment according to claim 9, characterized in that: The drug efficacy monitoring module is used to compare the activity frequency v(i) of the disinfection target in each disinfection area after the spraying cycle with the activity frequency V of the preset disinfection target to determine the abnormality of the activity frequency of the disinfection target in the disinfection area. The abnormality of the activity frequency of the disinfection target in the disinfection area includes normal and abnormal. When the activity frequency of the disinfection target in the disinfection area is abnormal, the spraying amount of the disinfection area is optimized to PYd(i)', where d is a digital subscript and d=1, 2, 3.

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