Disinfection and killing equipment control system based on artificial intelligence

Abstract

The invention relates to the technical field of equipment control, in particular to an artificial intelligence-based disinfection and killing equipment control system, which comprises an operation area acquisition module for acquiring planar graph information of an operation area of disinfection and killing equipment; the plane coordinate construction module is used for constructing a work area coordinate system; the inspection information acquisition module is used for acquiring the inspection information and the operation information of the killing cancelling equipment; the routing inspection path analysis module is used for analyzing the routing inspection demand area and further used for analyzing routing inspection area parameters; the disinfection and killing target analysis module is used for analyzing the types of the disinfection and killing target area and the inspection area; the inspection collision analysis module is used for analyzing the path collision type; the inspection path optimization module is used for optimizing the inspection path of the disinfection equipment; and the equipment inspection control module is used for controlling the disinfecting and killing equipment to perform disinfecting and killing operation in the inspection demand area according to the inspection path. According to the invention, accurate control of the running track of the disinfection equipment is realized.

Description

Technical Field

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

[0002] Pest control technology is developed based on advanced technologies such as the Internet of Things, big data, and artificial intelligence. Through intelligent identification and automated control, this technology achieves efficient and safe pest management, reduces human and material input, protects the ecological environment, and provides protection for life and business activities.

[0003] Chinese Patent Publication No.: CN114474091A discloses a robot disinfection method, disinfection robot, disinfection equipment and storage medium, including: obtaining environmental feature information of a target area, marking the target area according to the environmental feature information to obtain multiple sub-areas, each sub-area including a feature tag, and disinfecting the target area according to the feature tag. This invention controls the disinfection equipment by detecting environmental changes in each area, but does not realize comprehensive analysis and control of the inspection trajectory of the comprehensive disinfection equipment's inspection data, surrounding environment data and images captured by the disinfection equipment. There are problems such as low efficiency in analyzing the running data of the disinfection equipment during operation and inaccurate optimization of the disinfection equipment's inspection trajectory. Summary of the invention

[0004] The object of the present invention is to provide a pest control system based on artificial intelligence 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 disinfection equipment control system based on artificial intelligence, comprising:

[0007] The operation area collection module is used to collect the plan information of the operation area of ​​the disinfection equipment;

[0008] A plane coordinate construction module is used to construct a coordinate system of the operation area according to the plane map information;

[0009] Inspection information acquisition module, used to obtain inspection information and operation information of the disinfection equipment according to the coordinate system of the operation area;

[0010] The inspection path analysis module is used to analyze the inspection requirement area according to the inspection information of the disinfection equipment and the coordinate system of the operation area, and is also used to analyze the inspection area parameters according to the inspection requirement area and the inspection information;

[0011] The disinfection target analysis module is used to analyze the disinfection target area and inspection area type according to the operation information of the disinfection equipment;

[0012] Inspection collision analysis module, used to divide the straight path according to the inspection information, and analyze the path collision type according to the straight path, operation information and plan view information;

[0013] Inspection path optimization module, used to optimize the inspection path of the disinfection equipment according to the path collision type, operation information, inspection area parameters and inspection area type;

[0014] The equipment inspection control module is used to control the disinfection equipment to perform disinfection operations in the inspection required area according to the inspection path.

[0015] Furthermore, the inspection path analysis module is provided with a demand area analysis unit, which is used to extract the room type label corresponding to each coordinate point in the inspection path as the inspection demand area, and respectively count the number of each room type label corresponding to the coordinate point in the inspection path as the number of inspection areas.

[0016] Furthermore, the inspection path analysis module is also provided with an area parameter analysis unit, which is used to analyze the inspection area parameters according to the number of inspection areas NW(j), the inspection requirement area and the inspection path to obtain the inspection area parameters W(i).

[0017] Furthermore, the disinfection target analysis module inputs the grayscale image into the target detection model to identify the disinfection target area in the grayscale image, and analyzes the inspection area type according to the disinfection target area and the inspection information. If the disinfection device is inspecting the coordinate area corresponding to the current area marking, there is a disinfection target area in the grayscale image it captures, the disinfection target analysis module determines that the inspection area type marked in the current area is Class I. If the disinfection device is inspecting the coordinate area corresponding to the current area marking, there is no disinfection target area in the grayscale image it captures, the disinfection target analysis module determines that the inspection area type marked in the current area is Class II.

[0018] Furthermore, the inspection collision analysis module is provided with a straight path division unit, which is used to analyze the path slope parameter according to the inspection path to obtain the path slope parameter k(m), and divide the straight path according to the path slope parameter. If x(m)≠x(m-1) and k(m)=k(m-1), the straight path division unit divides the coordinate points of the current analysis inspection path and the coordinate points of the previous inspection path into the same straight path, and numbers the straight paths according to the inspection order, and sets the straight path number to u, u∈N +, if x(m)=x(m-1), the straight path division unit divides the coordinate point of the current analysis inspection path and the coordinate point of the previous inspection path into the same straight path; if x(m)≠x(m-1) and k(m)≠k(m-1), the straight path division unit uses the coordinate point of the current analysis inspection path as the starting point of a new straight path, where (x(m), y(m)) represents the coordinate point in the inspection path, m represents the sequence number of the inspection path, and m∈N + .

[0019] Furthermore, the inspection collision analysis module is also provided with an inspection collision analysis unit, which is used to analyze the collision parameters according to the straight path, the collision distance M1(u,θ) and the plane diagram ratio ω to obtain the collision parameter A(u), and compare the collision parameter with the distance threshold a, and analyze the path collision type according to the comparison result to obtain the path collision type, and the path collision type includes one category and two categories.

[0020] Furthermore, the inspection path optimization module is provided with an optimization parameter analysis unit, which is used to set the initial value of the iteration parameter to h. When the path collision type is one type, the path optimization parameter is analyzed according to the collision distance and the iteration parameter to obtain the path optimization parameter F(m,h).

[0021] Furthermore, the inspection path optimization module is also provided with an inspection area analysis unit, which is used to adjust the analysis process of the path optimization parameters according to the inspection area parameters and the inspection area type. When the inspection area type is type one, the inspection area analysis unit adjusts the analysis process of the path optimization parameters, and the adjusted path optimization parameters are F1(m,h).

[0022] Furthermore, the inspection path optimization module is also provided with an inspection image analysis unit, which is used to extract the area with a temperature greater than t in the thermal imaging image as the heat area when the path collision type is one type, and calculate the average temperature in the heat area as the heat average value Q(m), compare the heat average value with the heat threshold q, and optimize the adjustment process of the path optimization parameters according to the comparison result. When the heat average value meets the threshold, the adjustment process of the path optimization parameters is optimized, and the optimized path optimization parameters are F2(m).

[0023] Furthermore, the inspection path optimization module is also provided with an inspection path optimization unit, which is used to compare the path optimization parameter F(m,h) with the path optimization threshold f, and optimize the inspection path of the disinfection equipment according to the comparison result.

[0024] The beneficial effects of the present invention are as follows: through the collection of plan information, inspection information and operation information of the disinfection equipment by the operation area acquisition module and the inspection information acquisition module, and the comprehensive analysis of the collected data by other modules, the optimized analysis of the inspection trajectory of the disinfection equipment is achieved based on the comprehensive disinfection equipment operation information and the images taken during its operation and the plan data of the disinfection equipment operation area, thereby realizing the control of the disinfection equipment inspection, and then improving the system's analysis efficiency of the operation data of the disinfection equipment during operation, and improving the accuracy of the optimization of the disinfection equipment inspection trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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.

[0026] Figure 1 This is a schematic diagram of the structure of the artificial intelligence-based disinfection equipment control system of this embodiment.

[0027] Figure 2 Schematic diagram of the structure of the inspection path analysis module of this embodiment.

[0028] Figure 3 Schematic diagram of the structure of the inspection collision analysis module of this embodiment.

[0029] Figure 4 This is a schematic diagram of the structure of the inspection path optimization module of this embodiment. DETAILED DESCRIPTION

[0030] 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.

[0031] 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.

[0032] See also Figure 1 As shown, it is a disinfection equipment control system based on artificial intelligence in this embodiment, including:

[0033] The operation area acquisition module is used to collect the floor plan information of the operation area of ​​the disinfection equipment. The floor plan information includes the house floor plan, area markings and floor plan ratio. The floor plan ratio is the ratio of the house area to the number of pixels in the house floor plan. The area markings are the usage plans of the areas marked in the house floor plan. The area markings include but are not limited to wall position markings, home position markings and room type markings, etc. The room types include bedrooms, bathrooms and kitchens, etc. The floor plan information is obtained through user interactive input.

[0034] Specifically, this embodiment is applied to the control system cloud of the pest control equipment, and optimizes the inspection path by real-time collection of operating data and surrounding environment data of the pest control equipment during the inspection operation. The pest control equipment is a device for performing pest control operations on cockroaches.

[0035] The plane coordinate construction module is used to construct the operation area coordinate system according to the plane map information, and the plane coordinate construction module is connected to the operation area acquisition module.

[0036] Specifically, the plane coordinate construction module described in this embodiment takes the lower left pixel point in the house floor plan as the coordinate origin, and the two sides adjacent to the coordinate origin as the x-axis and y-axis respectively. The x-axis increases from left to right, and the y-axis increases from bottom to top. The unit length is 1 pixel. A plane rectangular coordinate system is established, and the position of each pixel point in the house floor plan is represented by coordinate points. The coordinate points are corresponded to their corresponding area labels, and the floor plan information represented by the coordinate points is used as the working area coordinate system.

[0037] It can be understood that the present embodiment does not specifically limit the process of constructing the work area coordinate system, and technical personnel in this field can freely set it. For example, when the house floor plan is an irregular image, its circumscribed rectangle can be constructed, and then the work area coordinate system can be constructed according to the sides and corners of the circumscribed rectangle.

[0038] Specifically, in this embodiment, the plane coordinate construction module analyzes the plan view information to construct a coordinate system of the operating area, and uses the coordinate points in the coordinate system to represent the operating area of ​​the disinfection equipment, making the location data of the operating area more intuitive, thereby improving the system's efficiency in analyzing the operating data of the disinfection equipment during operation and improving the accuracy of optimizing the inspection trajectory of the disinfection equipment.

[0039] Please continue reading Figure 1 As shown, the artificial intelligence-based disinfection equipment control system also includes:

[0040] The inspection information acquisition module is used to obtain the inspection information and operation information of the disinfection equipment according to the coordinate system of the working area. The inspection information includes the inspection path and the inspection cycle. The inspection path is a series of coordinate points passed by the disinfection equipment during inspection in the working area of ​​the disinfection equipment. Each item in the series represents a coordinate point in the coordinate system of the working area passed by the disinfection equipment during inspection. The inspection path is an initial inspection path set by the user to ensure that the inspection area of ​​the disinfection equipment meets user needs. The operation information includes operating speed, thermal imaging image, grayscale image and collision distance. The thermal imaging image is a thermal imaging image taken by a thermal imager installed on the disinfection equipment in the running direction of the disinfection equipment. The grayscale image is a grayscale image taken by a camera installed on the disinfection equipment in the running direction of the disinfection equipment. The collision distance is the distance between the disinfection equipment and its surrounding objects measured by a laser radar installed on the disinfection equipment. The inspection information acquisition module is connected to the plane coordinate construction module.

[0041] Please continue reading Figure 1 As shown, the artificial intelligence-based disinfection equipment control system also includes:

[0042] The inspection path analysis module is used to analyze the inspection requirement area according to the inspection information of the disinfection equipment and the coordinate system of the operation area, and is also used to analyze the inspection area parameters according to the inspection requirement area and the inspection information. The inspection path analysis module is connected to the inspection information acquisition module.

[0043] See also Figure 2 As shown, the inspection path analysis module includes:

[0044] The demand area analysis unit is used to analyze the inspection demand area and the number of inspection areas according to the inspection path of the disinfection equipment and the coordinate system of the operating area.

[0045] Specifically, the demand area analysis unit in this embodiment extracts the room type label corresponding to each coordinate point in the inspection path as the inspection demand area, and counts the number of each room type label corresponding to the coordinate point in the inspection path as the number of inspection areas.

[0046] Specifically, in this embodiment, the inspection path and the operating area coordinate system of the disinfection equipment are analyzed by the demand area analysis unit to analyze the inspection demand area and the number of inspection areas. The inspection demand area is used to represent the summary of indoor areas that the disinfection robot needs to inspect during operation, and the number of inspection areas is used to represent the proportion of the inspection time of the inspection robot in each indoor area, thereby increasing the diversity of system analysis, thereby improving the system's analysis efficiency of the operating data of the disinfection equipment during operation, and improving the accuracy of optimizing the disinfection equipment inspection trajectory.

[0047] Please continue reading Figure 2 As shown, the inspection path analysis module also includes:

[0048] The area parameter analysis unit is used to analyze the inspection area parameters according to the number of inspection areas, the inspection demand areas and the inspection paths. The area parameter analysis unit is connected to the demand area analysis unit.

[0049] Specifically, the regional parameter analysis unit in this embodiment analyzes the inspection area parameters according to the number of inspection areas, the inspection required area and the inspection path, and sets the inspection area parameters as W(i). Where NW(j) represents the number of inspection areas, j represents the room type label number, NP(j) represents the number of different room type labels in the inspection requirement area, and NS(i) represents the number of coordinate points in the inspection path. The room type label number is defined as the number used to distinguish different room type labels. For example, j=1 can be used to represent the room type label as the kitchen, j=2 can be used to represent the room type label as the living room, etc.

[0050] Specifically, in this embodiment, the area parameter analysis unit analyzes the number of inspection areas, inspection demand areas and inspection paths to analyze the inspection area parameters, and uses the inspection area parameters to represent the characteristic relationship between the proportion of inspection stay time of the disinfection equipment in each plane area, so as to realize the judgment of the diversity of the inspection areas, thereby improving the system's analysis efficiency of the operating data of the disinfection equipment during operation and improving the accuracy of optimizing the inspection trajectory of the disinfection equipment.

[0051] Please continue reading Figure 1 As shown, the artificial intelligence-based disinfection equipment control system also includes:

[0052] The disinfection target analysis module is used to analyze the disinfection target area and the inspection area type according to the operation information of the disinfection equipment. The disinfection target analysis module is connected to the inspection information acquisition module.

[0053] Specifically, the disinfection target analysis module in this embodiment inputs the grayscale image into the target detection model to identify the disinfection target area in the grayscale image, and analyzes the inspection area type according to the disinfection target area and the inspection information. If the disinfection device is inspecting the coordinate area corresponding to the current area marking, there is a disinfection target area in the grayscale image it captures, the disinfection target analysis module determines that the inspection area type marked in the current area is Class I. If the disinfection device is inspecting the coordinate area corresponding to the current area marking, there is no disinfection target area in the grayscale image it captures, the disinfection target analysis module determines that the inspection area type marked in the current area is Class II.

[0054] Specifically, the target detection model described in this embodiment is an OpenCV target detection model, and the OpenCV target detection model is trained using an insect detection data set to achieve recognition of the target area of ​​​​the disinfecting target in the grayscale image. It can be understood that the choice of the target detection model is not specifically limited in this embodiment, and those skilled in the art can freely set it, such as using a target detection algorithm based on Yo L OV8 to achieve recognition of the disinfecting target.

[0055] Specifically, in this embodiment, the operation information of the disinfection equipment is analyzed by the disinfection target analysis module to analyze the disinfection target area and the inspection area type, so as to realize the detection and identification of disinfection targets during the inspection of disinfection equipment, and divide the current area into two categories according to whether there is a disinfection target in the current area, so as to realize the division of each area type in the plan view, thereby improving the system's analysis efficiency of the operation data of the disinfection equipment during operation, and improving the accuracy of optimizing the inspection trajectory of the disinfection equipment.

[0056] Please continue reading Figure 1 As shown, the artificial intelligence-based disinfection equipment control system also includes:

[0057] The inspection collision analysis module is used to divide the straight path according to the inspection information, and analyze the path collision type according to the straight path, operation information and plane map information. The inspection collision analysis module is connected to the disinfection target analysis module.

[0058] See also Figure 3 As shown, the inspection collision analysis module includes:

[0059] The straight path division unit is used to divide the straight path according to the inspection information. The straight path is used to represent the inspection of the disinfection equipment, and the inspection path is a straight-line path.

[0060] Specifically, the straight path division unit described in this embodiment analyzes the path slope parameter according to the inspection path, sets the path slope parameter to k(m), sets k(m)=[y(m)-y(m-1)] / [x(m)-x(m-1)], x(m)≠x(m-1), and divides the straight path according to the path slope parameter. If x(m)≠x(m-1) and k(m)=k(m-1), the straight path division unit divides the coordinate points of the currently analyzed inspection path and the coordinate points of the previous inspection path into the same straight path, and numbers the straight paths according to the inspection order, and sets the straight path number to u, u∈N +, if x(m)=x(m-1), the straight path division unit divides the coordinate point of the current analysis inspection path and the coordinate point of the previous inspection path into the same straight path; if x(m)≠x(m-1) and k(m)≠k(m-1), the straight path division unit uses the coordinate point of the current analysis inspection path as the starting point of a new straight path, where (x(m), y(m)) represents the coordinate point in the inspection path, m represents the sequence number of the inspection path, and m∈N + The sequence number defining the inspection path is a number that distinguishes the item in the inspection path, such as m=2, which indicates the second item in the inspection path.

[0061] Specifically, in this embodiment, the straight path division unit analyzes the inspection path to analyze the path slope parameters, and uses the path slope parameters to represent the travel direction of the disinfection equipment, thereby dividing the continuous paths traveling in the same direction into the same straight path, thereby realizing the segmentation of the disinfection equipment inspection path, thereby improving the system's analysis efficiency of the operating data of the disinfection equipment during operation, and improving the accuracy of optimizing the disinfection equipment inspection trajectory.

[0062] Please continue reading Figure 3 As shown, the inspection collision analysis module also includes:

[0063] The inspection collision analysis unit is used to analyze the path collision type according to the straight path, the collision distance and the plane map information, and the inspection collision analysis unit is connected to the straight path division unit.

[0064] Specifically, the inspection collision analysis unit in this embodiment analyzes the collision parameters according to the straight line path, the collision distance and the plane diagram ratio, sets the collision parameter to A(u), and sets The collision parameters are compared with the distance threshold, and the path collision type is analyzed according to the comparison result. If A(u)≤a, the inspection collision analysis unit determines that the path collision type is Class I; if A(u)>a, the inspection collision analysis unit determines that the path collision type is Class II; wherein, (x1(u), y1(u)) represents the starting point coordinates of the straight line path, (x2(u), y2(u)) represents the end point coordinates of the straight line path, ω represents the plane diagram ratio, M1(u,θ) represents the collision distance of the starting point of the straight line path, θ represents the deflection angle, a represents the distance threshold, 0≤a≤0.2. The starting point of the straight path is defined as the coordinate point corresponding to the first item of the straight path, the end point of the straight path is defined as the coordinate point corresponding to the last item of the straight path, the deflection angle is defined as the angle between the measured collision distance position and the front of the disinfection equipment, a positive deflection angle indicates the angle in the clockwise direction in front of the disinfection equipment, and a negative deflection angle indicates the angle in the counterclockwise direction in front of the disinfection equipment; the inspection collision analysis unit analyzes the straight path, collision distance and plane diagram ratio to analyze the collision parameters, and uses the collision parameters to represent the distance between the disinfection equipment and the obstacle in front on the inspection path, thereby dividing the path collision type into two categories according to the distance between the end point of each straight path and the obstacle, thereby improving the system's analysis efficiency of the operating data of the disinfection equipment during operation and improving the accuracy of optimizing the inspection trajectory of the disinfection equipment; it can be understood that in this embodiment, the value of the distance threshold is not specifically limited, and technical personnel in this field can set it freely, as long as it meets the analysis of the path collision type. The optimal value of the distance threshold is: a=0.1.

[0065] Please continue reading Figure 1 As shown, the artificial intelligence-based disinfection equipment control system also includes:

[0066] The inspection path optimization module is used to optimize the inspection path of the disinfection equipment according to the path collision type, operation information, inspection area parameters and inspection area type optimization parameters. The inspection path optimization module is connected to the inspection path analysis module and the inspection collision analysis module.

[0067] See also Figure 4 As shown, the inspection path optimization module includes:

[0068] The optimization parameter analysis unit is used to analyze the path optimization parameters according to the path collision type and operation information.

[0069] Specifically, the optimization parameter analysis unit in this embodiment sets the initial value of the iteration parameter to h, and analyzes the path optimization parameter according to the path collision type, collision distance and iteration parameter. If the path collision type is a type, the optimization parameter analysis unit analyzes the path optimization parameter and sets the path optimization parameter to F(m,h). Among them, M(m,θ) represents the collision distance of the disinfection equipment in the current inspection path, max{} represents the maximum value of the data, 2≤h≤5.

[0070] Specifically, in this implementation, the optimization parameter analysis unit analyzes the path collision type and operation information to analyze the path optimization parameters. The path optimization parameters are used to represent the possible collision distance between the disinfection robot and the obstacle in front after turning at a certain angle when there may be a collision in front of the disinfection robot during operation. This improves the system's analysis efficiency of the operation data of the disinfection equipment during operation and improves the accuracy of optimizing the inspection trajectory of the disinfection equipment.

[0071] Please continue reading Figure 4 As shown, the inspection path optimization module also includes:

[0072] The inspection area analysis unit is used to adjust the analysis process of the path optimization parameters according to the inspection area parameters and the inspection area type, so that the adjusted path optimization parameters are related to whether there are disinfection targets in the current area and the inspection path characteristics of the current area. The inspection area analysis unit is connected to the optimization parameter analysis unit.

[0073] Specifically, the inspection area analysis unit in this embodiment adjusts the analysis process of the path optimization parameters according to the inspection area parameters and the inspection area type. If the inspection area type is type one, the inspection area analysis unit adjusts the analysis process of the path optimization parameters, and the adjusted path optimization parameters are F1(m,h), and F1(m,h)=F(m,h)×W(i); if the inspection area type is type two, the inspection area analysis unit does not adjust the analysis process of the path optimization parameters.

[0074] Specifically, in this embodiment, the inspection area analysis unit analyzes the inspection area parameters and the inspection area type of the toxic area to adjust the analysis process of the path optimization parameters. When there is a disinfection target in the current area, the path optimization parameters are adjusted to ensure the accuracy of the disinfection equipment in detecting disinfection targets in the current area, thereby improving the system's analysis efficiency of the operating data of the disinfection equipment during operation and improving the accuracy of optimizing the disinfection equipment inspection trajectory.

[0075] Please continue reading Figure 4 As shown, the inspection path optimization module also includes:

[0076] The inspection image analysis unit is used to optimize the adjustment process of the path optimization parameters according to the path collision type and the thermal imaging image, so that the optimized path optimization parameters are related to the temperature characteristics in the thermal imaging image taken by the disinfection equipment during the inspection process. The inspection image analysis unit is connected to the inspection area analysis unit.

[0077] Specifically, when the path collision type is one, the inspection image analysis unit in this embodiment extracts the area with a temperature greater than t in the thermal imaging image as the heat area, calculates the average temperature in the heat area as the heat average, compares the heat average with the heat threshold, and optimizes the adjustment process of the path optimization parameters according to the comparison result. If Q(m) 2 / [t×T(m) max ]<q, the inspection image analysis unit determines that the average heat value does not meet the threshold value, and does not optimize the adjustment process of the path optimization parameters. If Q(m) 2 / [t×T(m) max ]≥q, the inspection image analysis unit determines that the heat average meets the threshold, optimizes the adjustment process of the path optimization parameter, and the optimized path optimization parameter is F2(m), and F2(m,h)=F1(m,h)×Q(m) is set 2 / [t×T(m) max ]; where Q(m) represents the average heat value, t represents the temperature extraction parameter, 20≤t≤30, T(m) max Represents the maximum temperature in the heat area, q represents the heat threshold, 0.7≤q≤0.9; the inspection image analysis unit analyzes the path collision type and the temperature of each area in the thermal imaging image to calculate the average temperature of the heat area, so as to judge whether the obstacle blocking the inspection path is a person or a pet, and realize the influence of the obstacle on the optimization of the inspection path, thereby improving the system's analysis efficiency of the operating data of the disinfection equipment during operation, and improving the accuracy of optimizing the inspection trajectory of the disinfection equipment; it can be understood that in this embodiment, the values ​​of the temperature extraction parameters and the heat threshold are not specifically limited, and those skilled in the art can set them freely, as long as the optimization of the path optimization parameter adjustment process is satisfied. The optimal values ​​of the temperature extraction parameters and the heat threshold are: t=25, q=0.7.

[0078] Please continue reading Figure 4 As shown, the inspection path optimization module also includes:

[0079] The inspection path optimization unit is used to optimize the inspection path of the disinfection equipment according to the path optimization parameters.

[0080] Specifically, the inspection path optimization unit described in this embodiment compares the path optimization parameters with the path optimization threshold, and optimizes the inspection path of the disinfection equipment according to the comparison result. If F(m,h)>f and M(m,θ+h)>M(m,θ-h), the inspection path optimization unit optimizes the disinfection equipment to rotate h degrees clockwise at the current inspection path coordinate point to operate; if F(m,h)>f and M(m,θ+h)≤M(m,θ-h), the inspection path optimization unit optimizes the disinfection equipment to rotate h degrees counterclockwise at the current inspection path coordinate point to operate; if F(m,h)≤f, the inspection path optimization unit adds c to the value of the iterative parameter and iterates the analysis process of the path optimization parameter; wherein, f represents the path optimization threshold, f=M(m,θ) / cosθ, c represents the iterative increment parameter, 2≤c≤5. It is understandable that the value of the iteration increment parameter is not specifically limited in this embodiment, and those skilled in the art can freely set it, as long as it satisfies the iteration of the analysis process of the path optimization parameters. The best value of the iteration increment parameter is: c=3.

[0081] Specifically, in this embodiment, the inspection path optimization unit analyzes the path optimization parameters to optimize the inspection path of the disinfection equipment, ensuring that the inspection path of the disinfection equipment meets user needs and that the inspection road is wide during disinfection operations, thereby improving the system's analysis efficiency of the operating data of the disinfection equipment during operation and improving the accuracy of optimizing the disinfection equipment's inspection trajectory.

[0082] Please continue reading Figure 1 As shown, the artificial intelligence-based disinfection equipment control system also includes:

[0083] The equipment inspection control module is used to control the disinfection equipment to perform disinfection operations in the inspection requirement area according to the inspection path. The equipment inspection control module is connected to the inspection path optimization module.

[0084] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A disinfection equipment control system based on artificial intelligence, characterized in that: include: The operation area collection module is used to collect the plan information of the operation area of ​​the disinfection equipment; A plane coordinate construction module is used to construct a coordinate system of the operation area according to the plane map information; Inspection information acquisition module, used to obtain inspection information and operation information of the disinfection equipment according to the coordinate system of the operation area; The inspection path analysis module is used to analyze the inspection requirement area according to the inspection information of the disinfection equipment and the coordinate system of the operation area, and is also used to analyze the inspection area parameters according to the inspection requirement area and the inspection information; The disinfection target analysis module is used to analyze the disinfection target area and inspection area type according to the operation information of the disinfection equipment; Inspection collision analysis module, used to divide the straight path according to the inspection information, and analyze the path collision type according to the straight path, operation information and plan view information; Inspection path optimization module, used to optimize the inspection path of the disinfection equipment according to the path collision type, operation information, inspection area parameters and inspection area type; The equipment inspection control module is used to control the disinfection equipment to perform disinfection operations in the inspection required area according to the inspection path.

2. The artificial intelligence-based disinfection equipment control system according to claim 1 is characterized in that: The inspection path analysis module is provided with a demand area analysis unit, which is used to extract the room type label corresponding to each coordinate point in the inspection path as the inspection demand area, and respectively count the number of each room type label corresponding to the coordinate point in the inspection path as the inspection area number.

3. The artificial intelligence-based disinfection equipment control system according to claim 2 is characterized in that: The inspection path analysis module is also provided with an area parameter analysis unit, which is used to analyze the inspection area parameters according to the number of inspection areas NW(j), the inspection requirement area and the inspection path to obtain the inspection area parameters W(i).

4. The artificial intelligence-based disinfection equipment control system according to claim 3 is characterized in that: The disinfection target analysis module inputs the grayscale image into the target detection model to identify the disinfection target area in the grayscale image, and analyzes the inspection area type according to the disinfection target area and the inspection information. If the disinfection device is inspecting the coordinate area corresponding to the current area marking, there is a disinfection target area in the grayscale image it captures, the disinfection target analysis module determines that the inspection area type marked in the current area is Class I. If the disinfection device is inspecting the coordinate area corresponding to the current area marking, there is no disinfection target area in the grayscale image it captures, the disinfection target analysis module determines that the inspection area type marked in the current area is Class II.

5. The artificial intelligence-based disinfection equipment control system according to claim 4 is characterized in that: The inspection collision analysis module is provided with a straight path division unit, which is used to analyze the path slope parameter according to the inspection path to obtain the path slope parameter k(m), and divide the straight path according to the path slope parameter. If x(m)≠x(m-1) and k(m)=k(m-1), the straight path division unit divides the coordinate points of the current analysis inspection path and the coordinate points of the previous inspection path into the same straight path, and numbers the straight paths according to the inspection order, and sets the straight path number to u, u∈N + , if x(m)=x(m-1), the straight path division unit divides the coordinate point of the current analysis inspection path and the coordinate point of the previous inspection path into the same straight path; if x(m)≠x(m-1) and k(m)≠k(m-1), the straight path division unit uses the coordinate point of the current analysis inspection path as the starting point of a new straight path, where (x(m), y(m)) represents the coordinate point in the inspection path, m represents the sequence number of the inspection path, and m∈N + .

6. The artificial intelligence-based disinfection equipment control system according to claim 5 is characterized in that: The inspection collision analysis module is also provided with an inspection collision analysis unit, which is used to analyze the collision parameters according to the straight path, the collision distance M1(u,θ) and the plane diagram ratio ω to obtain the collision parameter A(u), and compare the collision parameter with the distance threshold a, and analyze the path collision type according to the comparison result to obtain the path collision type, which includes one category and two categories.

7. The artificial intelligence-based disinfection equipment control system according to claim 6 is characterized in that: The inspection path optimization module is provided with an optimization parameter analysis unit, which is used to set the initial value of the iteration parameter to h. When the path collision type is one type, the path optimization parameter is analyzed according to the collision distance and the iteration parameter to obtain the path optimization parameter F(m,h).

8. The artificial intelligence-based disinfection equipment control system according to claim 7 is characterized in that: The inspection path optimization module is also provided with an inspection area analysis unit, which is used to adjust the analysis process of the path optimization parameters according to the inspection area parameters and the inspection area type. When the inspection area type is type one, the inspection area analysis unit adjusts the analysis process of the path optimization parameters, and the adjusted path optimization parameters are F1(m,h).

9. The artificial intelligence-based disinfection equipment control system according to claim 8, characterized in that: The inspection path optimization module is also provided with an inspection image analysis unit, which is used to extract the area with a temperature greater than t in the thermal imaging image as the heat area when the path collision type is one type, and calculate the average temperature in the heat area as the heat average value Q(m), compare the heat average value with the heat threshold q, and optimize the adjustment process of the path optimization parameters according to the comparison result. When the heat average value meets the threshold, the adjustment process of the path optimization parameters is optimized, and the optimized path optimization parameters are F2(m).

10. The artificial intelligence-based disinfection equipment control system according to claim 9, characterized in that: The inspection path optimization module is also provided with an inspection path optimization unit, which is used to compare the path optimization parameter F(m,h) with the path optimization threshold f, and optimize the inspection path of the disinfection equipment according to the comparison result.

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