Intelligent disinfection robot based on 222nm ultraviolet rays and control method thereof

Abstract

The invention discloses an intelligent disinfection robot based on 222nm ultraviolet rays and a control method thereof, and belongs to the technical field of intelligent disinfection equipment. The robot comprises a robot body, a core control unit, a UVC-LED array cabin and an omnidirectional wheel chassis, 48 lamp beads with the wavelength of 222nm are arranged in the UVC-LED array cabin and are arranged in a hexagon shape, the inclination angle of a single lamp bead is 15 degrees, and a quartz glass protective cover capable of blocking the wavelength of 230nm or above is arranged on the periphery of the UVC-LED array cabin. Environmental information is detected in real time through a multi-sensor fusion technology, an operation area is divided into an instant disinfection area, a reservation disinfection area and a safe isolation area by adopting a dynamic partition disinfection algorithm, and safe and efficient disinfection of man-machine coexistence is realized. The technical problem that traditional ultraviolet disinfection equipment cannot work in real time in a personnel activity area is solved, and the ultraviolet disinfection equipment is suitable for disinfection requirements of public places such as hospitals, schools and public transportation.

Description

Technical Field

[0001] This invention relates to the field of intelligent disinfection equipment technology, and in particular to an intelligent mobile disinfection robot based on 222nm far-ultraviolet light and its control method. Background Technology

[0002] In the field of public health disinfection, ultraviolet (UV) technology has become an important alternative to chemical disinfectants due to its non-contact and residue-free characteristics. Among existing technologies, low-pressure mercury lamps with a wavelength of 254nm have become the mainstream choice for UV disinfection due to their highly efficient ability to destroy microbial DNA. However, medical research shows that UV radiation in this band can penetrate the stratum corneum of the human epidermis, and long-term exposure can lead to adverse reactions such as corneal damage and skin erythema, forcing disinfection operations to be carried out at night in enclosed, unoccupied locations. This limitation makes it difficult to achieve real-time protection in frequently visited places such as hospital outpatient departments and school classrooms. While autonomous mobile disinfection robots have improved spatial coverage through mechanical automation in recent years, they are still limited by the inherent biological risks of 254nm UV radiation, failing to overcome the technical bottleneck of "human-machine incompatibility." Traditional chemical disinfection methods suffer from problems such as corrosiveness, residual toxicity, and low efficiency of manual operation, while conventional 254nm UV disinfection equipment, due to the significant risk of damage to human tissue, must be used in unoccupied environments, severely restricting its application value in dynamic scenarios.

[0003] A 2018 study published in *Nature* by a Columbia University team first confirmed that 222nm far-ultraviolet (Far-UVC) light can effectively inactivate airborne pathogens, while being strongly absorbed by the stratum corneum of the skin and unable to reach the living cell layer. This discovery provides a theoretical basis for developing real-time disinfection systems for human use, but three major technical obstacles remain in engineering applications: First, 222nm light sources are bulky and consume a lot of power, making it difficult to integrate existing excimer lamps into mobile platforms; second, there is a lack of precise methods for dynamic control of radiation dose, making it difficult to balance sterilization efficiency and safety thresholds; and finally, real-time biometric technology in complex environments is still immature, and existing infrared or laser sensors are susceptible to environmental interference and misjudgments.

[0004] Currently available disinfection robots generally suffer from insufficient safety or low disinfection efficiency. To address these technical issues, this invention utilizes an innovative modular light source design, multimodal sensor fusion, and intelligent decision-making algorithms. By combining 222nm far-ultraviolet sterilization technology with an autonomous navigation robot system, it creatively solves the technical challenge of real-time disinfection in human-robot coexistence environments. This invention can be widely applied to pathogen inactivation operations in densely populated areas such as medical institutions, educational facilities, and public transportation. Summary of the Invention

[0005] This invention proposes an intelligent disinfection robot based on 222nm ultraviolet light and its control method. It combines the sterilization characteristics of 222nm far-ultraviolet light with an intelligent mobile platform, and adopts a compact 222nm UVC-LED array module, a dynamic target tracking algorithm based on multi-sensor fusion, and a dynamic zoning disinfection algorithm to achieve safe and efficient real-time ultraviolet disinfection in human activity environments. It solves the safety hazards and low efficiency problems of traditional disinfection methods. Furthermore, through intelligent design, it greatly improves the convenience and reliability of disinfection management, meets the disinfection needs of public spaces such as medical institutions, public transportation, and educational venues, and has outstanding technical advantages and broad market prospects.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses an intelligent disinfection robot based on 222nm ultraviolet light. The intelligent disinfection robot includes a robot body, a core control unit, a UVC-LED array chamber, an omnidirectional wheel chassis, and a core control unit. The robot body is used to sense environmental information and fix the UVC-LED array cabin, the omnidirectional wheel chassis, and the core control unit. The core control unit is located in the middle of the robot body and is used to receive and process environmental information sent by the sensor tower, obtain a first control signal and send it to the omnidirectional wheel chassis, and obtain a second control signal and send it to the UVC-LED array cabin. The UVC-LED array chamber is located on the top of the robot body and is used to receive and process the second control signal and emit 222nm ultraviolet light based on the second control signal; The omnidirectional wheel chassis is located at the bottom of the robot body and is used to receive and process the first control signal to control the movement of the intelligent disinfection robot. The core control unit is electrically connected to the robot body and the UVC-LED array compartment.

[0007] As an optional implementation, in the first aspect of the present invention, the robot body includes a body shell, a battery, and a sensor tower; The sensor tower is located at the rear of the fuselage housing and is connected to the fuselage housing via a rotating drive mechanism that can rotate continuously 360 degrees in the horizontal plane. It is used to collect environmental information from all directions. The sensor tower integrates sensors, millimeter-wave radar, infrared night vision device, infrared camera, infrared thermal imager and RGB-D camera. The battery is used to provide power to the intelligent disinfection robot.

[0008] As an optional implementation, in the first aspect of the present invention, the UVC-LED array cabin includes an array of LED beads, LED units, a graphene composite heat sink, and a microlens array waveguide panel. The lamp array consists of 48 222nm wavelength lamp beads, which are arranged in a hexagonal honeycomb pattern with a single bead tilt angle of 15 degrees. The outer periphery is provided with a quartz glass protective cover that can block wavelengths above 230nm. The LED unit uses a copper substrate-ceramic encapsulation-sapphire lens combination material, with a photoelectric conversion efficiency of not less than 35%. The graphene composite material heat sink adopts a fin structure with optimized fluid dynamics design. The microlens array waveguide panel has microlenses with a diameter of 2mm densely distributed on its surface to achieve uniform diffusion of ultraviolet light.

[0009] As an optional implementation, in the first aspect of the present invention, the core control unit adopts an edge computing and cloud computing collaborative architecture, including: a signal processing module and a cloud management platform; The signal processing module is equipped with an NVIDIA-Jetson module for real-time processing of 12 sensor signals; The cloud management platform constructs a virtual disinfection model using digital twin technology; The signal processing module and the cloud management platform interact with each other via the MQTT protocol.

[0010] As an optional implementation, in the first aspect of the present invention, a dynamic zoning disinfection strategy module is also included; the dynamic zoning disinfection strategy module constructs a three-dimensional point cloud map using the SLAM algorithm to divide the work area into an immediate disinfection area, a scheduled disinfection area, and a safe isolation area; The instant disinfection zone is an area that can be continuously irradiated. The scheduled disinfection area can intelligently adjust the irradiation dose according to the time people leave; The safety isolation zone is a circular area with a radius of 1.5 meters centered on the human body, set up when the infrared thermal imager detects the surface temperature of the human body.

[0011] As an optional implementation, in the first aspect of the present invention, the dynamic partition disinfection strategy module includes a target recognition unit, a driving circuit unit, and an optical filter film. The target recognition unit is used to identify high-frequency contact surfaces using a deep learning-based target recognition algorithm; The driving circuit unit is used to drive the output of ultraviolet light using a dual-mode drive of pulse width modulation and current regulation. The output power of the ultraviolet light is steplessly adjustable within the range of 5-100%, and the response time is less than 0.1 seconds. The optical filter film is used to block secondary radiation with wavelengths greater than 230 nm.

[0012] A second aspect of this invention discloses a control method for an intelligent disinfection robot, the control method comprising: S1, perform trajectory planning and area division for the intelligent disinfection robot's movement route to obtain the robot's movement trajectory information and area division information; S2, the intelligent disinfection robot moves according to the disinfection robot's trajectory information and acquires sensor signal data; S3, the core control unit performs fusion processing on the sensor signal data to obtain the sensor fused signal: S4, based on the region division information, the core control unit performs feature-level fusion processing on the sensor fusion signal to obtain a first control signal and a second control signal; S5, using the omnidirectional wheel chassis to receive and process the first control signal, control the movement of the intelligent disinfection robot; S6, using the UVC-LED array chamber to receive and process the second control signal, emits ultraviolet light into the surrounding environment to disinfect it.

[0013] As an optional implementation, in a second aspect of the present invention, the core control unit performs fusion processing on the sensor signals to obtain a sensor fused signal, including: S31 uses the core control unit to process millimeter-wave radar signal data to obtain target motion data; The target motion data includes target distance data and target speed data; The expression for acquiring and processing the target motion information is: Where R represents the target distance data; c represents the speed of light; τ represents the radar echo delay; K1 represents the phase compensation coefficient; Δθ represents the echo phase deviation; V represents the target velocity data; β represents the radar wavelength; f d Indicates Doppler frequency shift; M represents the smoothed frame number; m represents the smoothed frame index; ω m This represents the confidence weight of the m-th smoothed frame; f d,m This represents the measured Doppler frequency shift value of the m-th smoothed frame; S(fd,m ) Represents a symbolic function; S32, The core control unit is used to process the infrared thermal imaging data to obtain the target contour data; S33, The core control unit is used to process the RGB-D camera data to obtain three-dimensional scene data; S34, the core control unit is used to fuse the target motion data, the target contour data and the three-dimensional scene data to obtain the sensor fusion signal.

[0014] As an optional implementation, in a second aspect of the present invention, the process of using a core control unit to process infrared thermal imaging data to obtain target contour data includes: S321, The core control unit is used to preprocess the infrared thermal imaging data to obtain preprocessed infrared thermal imaging data; S322, The core control unit is used to segment the preprocessed infrared thermal imaging data to obtain separated infrared thermal imaging data; S323, The core control unit is used to extract and process the separated infrared thermal imaging data to obtain the target contour edge data; S324, The core control unit is used to post-process the target contour edge data to obtain the target contour data.

[0015] As an optional implementation, in a second aspect of the present invention, the core control unit performs feature-level fusion processing on the sensor fusion signal based on the region division information to obtain a first control signal and a second control signal, including: S41, Obtain robot coordinate position information; S42, Perform feature recognition processing on the sensor fused signal to obtain obstacle information and obstacle motion data; S43, based on the obstacle motion information, determine whether the obstacle is a moving obstacle, and obtain the obstacle type determination result; If the obstacle type determination result is yes, execute S44; If the obstacle type determination result is negative, execute S45; S44, Based on the obstacle motion information, process the robot coordinate position information and the obstacle information to obtain a first control signal; S45, based on the obstacle information and the region division information, process the robot coordinate position information and the obstacle information to determine the second control signal.

[0016] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention develops a compact 222nm UVC-LED array module. This module employs a unique hexagonal honeycomb arrangement structure, with each LED unit precisely installed at a 15-degree tilt angle. Combined with a specially designed sapphire lens and optical filter layer, it ensures both a uniform distribution of the radiation field and effective control of stray radiation. Experimental verification shows that this design achieves an ultraviolet radiation intensity uniformity of over 90%, while simultaneously controlling secondary radiation intensity above 230nm to below the safe threshold of 1%.

[0017] Secondly, a multi-sensor fusion environmental recognition system was constructed. Through the collaborative work of millimeter-wave radar, infrared thermal imager, and RGB-D camera, the system can accurately identify personnel activities and object distribution in the environment in real time. In particular, the developed dynamic target tracking algorithm can accurately distinguish between moving personnel and static obstacles, with an accuracy rate of over 99.7%, providing a reliable guarantee for safe disinfection. Through the above design, the sensor tower described in this invention not only achieves 360-degree physical field of view coverage, but more importantly, through its controllable and perceptible rotation capability, combined with the dynamic decision-making of the core control unit, it achieves optimal allocation of sensing resources. Compared with fixed or simple multi-sensor stacking solutions, this achieves the same or even better all-round environmental perception capability with lower cost and a more compact structure, solving the technical contradiction of mobile robots needing to both comprehensively perceive and focus on tracking in complex dynamic environments.

[0018] Third, a dynamic zoning disinfection method based on real-time environmental perception data is proposed. This method intelligently divides the disinfection area into immediate disinfection zones, scheduled disinfection zones, and safe isolation zones, and dynamically adjusts the disinfection strategy according to personnel activity. Tests show that the algorithm has a response time of less than 0.1 seconds and can achieve a disinfection coverage rate of over 99.5% while ensuring personnel safety.

[0019] The technical solution of this invention demonstrates significant advantages in practical applications. Actual test data from a hospital outpatient hall shows that the system can complete the comprehensive disinfection of an 80-square-meter area within 18 minutes, achieving a 99.9% kill rate of bacteria on object surfaces. More importantly, even if personnel accidentally enter the work area during the disinfection process, the system can respond within 0.08 seconds and shut off the ultraviolet radiation in the corresponding area, fully ensuring safe use. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the structure of the intelligent disinfection robot based on 222nm ultraviolet light disclosed in Embodiment 1 of the present invention; Figure 2 This is the overall chassis assembly drawing of the intelligent disinfection robot based on 222nm ultraviolet light disclosed in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the sensor tower structure of the intelligent disinfection robot based on 222nm ultraviolet light disclosed in Embodiment 1 of the present invention; Figure 4 This is a schematic flowchart of the intelligent disinfection robot control method based on 222nm ultraviolet light disclosed in Embodiment 2 of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] It should be noted that when a component is referred to as "fixed to," "placed," "equipped with," "provided with," "arranged on," or "connected to" another component, it can be directly on the other component or may have an intervening component. When a component is considered to be "connected" to another component, it can be directly connected to the other component or may have an intervening component present.

[0025] It should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0029] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0030] Example 1 Please refer to Figures 1 to 2 To better understand the technical solution of the present invention. An intelligent disinfection robot based on 222nm ultraviolet light, the intelligent disinfection robot comprising: a robot body 100, a core control unit, a UVC-LED array chamber 200, and an omnidirectional wheel chassis 300; The robot body 100 is used to sense environmental information and fix the UVC-LED array cabin 200, the omnidirectional wheel chassis 300, and the core control unit; The core control unit is located in the middle of the robot body 100. It is used to receive and process environmental information sent by the sensor tower, obtain a first control signal and send it to the omnidirectional wheel chassis 300, and obtain a second control signal and send it to the UVC-LED array cabin 200. The UVC-LED array chamber 200 is disposed on the top of the robot body 100 and is used to receive and process the second control signal and emit 222nm ultraviolet light based on the second control signal. The omnidirectional wheel chassis is located at the bottom of the robot body 100 and is used to receive and process the first control signal to control the movement of the intelligent disinfection robot. The core control unit is electrically connected to the robot body 100 and the UVC-LED array compartment.

[0031] Optionally, the robot body 100 includes: a body shell 101, a battery, and a sensor tower 102; like Figure 2 As shown, the sensor tower 102 is located at the rear of the fuselage housing 101 and is connected to the fuselage housing 101 via a rotary drive mechanism capable of continuous 360-degree rotation in the horizontal plane, for collecting environmental information from all directions; as Figure 3 As shown, the sensor tower 102 integrates a sensor 201, a millimeter-wave radar 202, an infrared night vision device 203, an infrared camera 204, an infrared thermal imager 206, and an RGB-D camera 207. The battery is used to provide power to the intelligent disinfection robot.

[0032] It should be noted that, through the coordinated operation of the sensors, the millimeter-wave radar, the infrared night vision device, the infrared camera, the infrared thermal imager, and the RGB-D camera, the sensor tower system can accurately identify human activities and object distribution in the environment in real time. In particular, the developed dynamic target tracking algorithm can accurately distinguish between moving people and static obstacles, with an accuracy rate of over 99.7%, providing a reliable guarantee for safe disinfection.

[0033] Optional, such as Figure 3 As shown, the rotary drive mechanism includes a stepper motor, a reduction gear set 205, and an absolute encoder. The stepper motor is fixed inside the housing via a motor mount. The reduction gear set 205 is connected to the output shaft of the stepper motor, and its final gear meshes with a driven gear ring fixed to the bottom of the sensor tower, thereby transmitting the rotational motion of the motor to the sensor tower and increasing the torque. The absolute encoder is used to detect and provide feedback on the rotation angle of the sensor tower relative to the housing in real time.

[0034] Optionally, the core control unit, the stepper motor, and the absolute encoder are electrically connected; the core control unit is configured to control the sensor tower to perform at least one of the following motion modes: continuous scanning mode, controlling the stepper motor to rotate at a constant speed, driving the sensor tower to perform a 360-degree continuous scan, realizing the acquisition of panoramic information of the surrounding environment without blind spots; directional staring mode, based on the preliminary detection results of the millimeter-wave radar or infrared thermal imager, when a suspicious target is detected in a specific direction, controlling the sensor tower to rotate to that direction and perform a stationary or small-angle swing, using an RGB-D camera for more refined identification and tracking; variable speed scanning mode, using a faster rotation speed in open areas to improve efficiency, and a slower rotation speed in densely populated or complex areas to improve data acquisition quality and target tracking stability.

[0035] Optionally, the UVC-LED array compartment includes: an array of LED beads, LED units, a graphene composite heat sink, and a microlens array waveguide panel. The lamp array consists of 48 222nm wavelength lamp beads, which are arranged in a hexagonal honeycomb pattern with a single bead tilt angle of 15 degrees. The outer periphery is provided with a quartz glass protective cover that can block wavelengths above 230nm. The LED unit uses a copper substrate-ceramic encapsulation-sapphire lens combination material, with a photoelectric conversion efficiency of not less than 35%. The graphene composite material heat sink adopts a fin structure with optimized fluid dynamics design. The microlens array waveguide panel is used to achieve uniform diffusion of ultraviolet light, and its surface is densely covered with microlenses with a diameter of 2mm.

[0036] It should be noted that the UVC-LED array chamber adopts a unique hexagonal honeycomb arrangement structure, with each LED unit precisely installed at a 15-degree tilt angle. Combined with a specially made sapphire lens and optical filter film, this ensures both a uniform distribution of the radiation field and effective control of stray radiation. Experimental verification shows that this design achieves a uniformity of ultraviolet radiation intensity distribution of over 90%, while controlling secondary radiation intensity above 230nm to below the safety threshold of 1%.

[0037] Optionally, the core control unit adopts an edge computing and cloud computing collaborative architecture, including a signal processing module and a cloud management platform; The signal processing module is equipped with an NVIDIA-Jetson module for real-time processing of 12 sensor signals; The cloud management platform constructs a virtual disinfection model using digital twin technology; The signal processing module and the cloud management platform interact with each other via the MQTT protocol.

[0038] Optionally, the intelligent disinfection robot also includes a dynamic zoning disinfection strategy module; the dynamic zoning disinfection strategy module constructs a three-dimensional point cloud map using the SLAM algorithm to divide the work area into an immediate disinfection zone, a scheduled disinfection zone, and a safe isolation zone; The instant disinfection zone is an area that can be continuously irradiated. The scheduled disinfection area can intelligently adjust the irradiation dose according to the time people leave; The safety isolation zone is a circular area with a radius of 1.5 meters centered on the human body, set up when the infrared thermal imager detects the surface temperature of the human body.

[0039] It should be noted that the dynamic zoning disinfection strategy module utilizes a dynamic zoning disinfection algorithm to intelligently divide the disinfection area into immediate disinfection zones, scheduled disinfection zones, and safe isolation zones based on real-time environmental perception data, and dynamically adjusts the disinfection strategy according to personnel activity. Tests show that the algorithm's response time is less than 0.1 seconds, and while ensuring personnel safety, the disinfection coverage rate can reach over 99.5%. Optionally, the dynamic partition disinfection strategy module includes a target recognition unit, a driving circuit unit, and an optical filter film. The target recognition unit is used to identify high-frequency contact surfaces using a deep learning-based target recognition algorithm; It should be noted that the high-frequency contact surfaces include, but are not limited to, door handles and elevator buttons; The driving circuit unit is used to drive the output of ultraviolet light using a dual-mode drive of pulse width modulation and current regulation. The output power of the ultraviolet light is steplessly adjustable within the range of 5-100%, and the response time is less than 0.1 seconds. The optical filter film is used to block secondary radiation with wavelengths greater than 230 nm.

[0040] Optionally, the intelligent disinfection robot also includes a human-computer interaction system, which includes: a cartoon voice prompt module, a risk avoidance demonstration animation interface, and a real-time radiation monitoring display screen; The cartoon voice prompt module is used to broadcast safety reminders to people in the surrounding area; The risk avoidance demonstration animation interface is displayed on the top screen; The real-time radiation monitoring display screen is used to display the current ultraviolet intensity using dynamic color blocks.

[0041] Optionally, the intelligent disinfection robot also includes a modular maintenance system; the modular maintenance system includes: a battery quick replacement module, a UVC-LED array quick replacement module, a main control box quick replacement module, a sensor tower replacement module, a walking mechanism replacement module, a shell panel replacement module, and an expansion interface replacement module; The battery quick-change module is designed with a handle; The UVC-LED array quick-change module is equipped with a quick-release interface; The main control box quick-change module uses a transparent hatch. The sensor tower replacement module uses a magnetic base. The walking mechanism replacement module adopts a modular drive wheel design; The outer panel replacement module is fixed by a snap-fit ​​method; The expansion interface replacement module uses a standardized aviation connector.

[0042] Optionally, the intelligent disinfection robot also includes a formaldehyde detection probe and a spray disinfection module; The formaldehyde detection probe is used to monitor TVOC concentration during disinfection; The spray disinfection module works in conjunction with ultraviolet light to create a photochemical composite disinfection effect. The formaldehyde detection probe and the spray disinfection module are connected to the robot body via a standardized aviation connector.

[0043] As can be seen, the intelligent disinfection robot provided in this embodiment combines the sterilization characteristics of 222nm far-ultraviolet light with an intelligent mobile platform. It adopts a compact 222nm UVC-LED array module, a dynamic target tracking algorithm based on multi-sensor fusion, and a dynamic zoning disinfection algorithm to achieve safe and efficient real-time ultraviolet disinfection in environments with human activity. It solves the safety hazards and low efficiency problems of traditional disinfection methods. Furthermore, through intelligent design, it greatly improves the convenience and reliability of disinfection management, meets the disinfection needs of public spaces such as medical institutions, public transportation, and educational venues, and has outstanding technical advantages and broad market prospects.

[0044] Example 2 Please see Figure 4 , Figure 4 This is a flowchart illustrating a control method for an intelligent disinfection robot based on 222nm ultraviolet light, as disclosed in an embodiment of the present invention. Wherein, Figure 4The described device is capable of the intelligent disinfection robot based on 222nm ultraviolet light described in Embodiment 1. For example... Figure 4 As shown, the control method includes: S1, perform trajectory planning and area division for the intelligent disinfection robot's movement route to obtain the robot's movement trajectory information and area division information; It should be noted that the area division information includes information on immediate disinfection areas, appointment-based disinfection areas, and safe isolation areas; It should be noted that the trajectory planning refers to planning the movement route of the intelligent disinfection robot based on the scene information and using a route map to obtain the movement trajectory information of the disinfection robot. It should be noted that the area division refers to the division of the work area into the immediate disinfection area, the scheduled disinfection area, and the safe isolation area by the dynamic zoning disinfection strategy module constructing a three-dimensional point cloud map using the SLAM algorithm. S2, the intelligent disinfection robot moves according to the disinfection robot's trajectory information and acquires sensor signal data; It should be noted that the acquisition of sensor signal data refers to the millimeter-wave radar signal data, infrared thermal imager data, and RGB-D camera data detected and acquired by the intelligent disinfection robot during its movement. S3, the core control unit performs fusion processing on the sensor signals to obtain the sensor fused signal: S4, based on the region division information, the core control unit performs feature-level fusion processing on the sensor fusion signal to obtain a first control signal and a second control signal; S5, the omnidirectional wheel chassis receives and processes the first control signal to control the movement of the intelligent disinfection robot; S6, the UVC-LED array chamber receives and processes the second control signal, emits ultraviolet light into the surrounding environment, and disinfects the surrounding environment with ultraviolet light.

[0045] Optionally, the step of using the core control unit to perform fusion processing on the sensor signals to obtain a sensor fused signal includes: S31 uses the core control unit to process millimeter-wave radar signal data to obtain target motion data; It should be noted that the process of processing millimeter-wave radar signals to obtain target motion data means that the millimeter-wave radar signals are subjected to noise reduction, automatic gain control, down-conversion processing, and target motion information acquisition processing to obtain target motion data. The target motion data includes target distance data and target speed data; It should be noted that the adaptive filter LMS is used to suppress clutter and perform noise reduction, providing a clean first pre-processed radar signal for subsequent processing; It should be noted that the automatic gain control process adjusts the echo signal amplitude to avoid the signal from distant targets being too weak or the signal from nearby targets being saturated; the down-conversion process converts the high-frequency echo signal into a baseband signal, reducing the computational load of subsequent processing. It should be noted that the expression for acquiring and processing the target motion information is as follows: Where R represents the target distance data; c represents the speed of light; τ represents the radar echo delay; K1 represents the phase compensation coefficient; Δθ represents the echo phase deviation; V represents the target velocity data; β represents the radar wavelength; f d Indicates Doppler frequency shift; M represents the smoothed frame number; m represents the smoothed frame index; ω m This represents the confidence weight of the m-th smoothed frame; f d,m This represents the measured Doppler frequency shift value of the m-th smoothed frame; S(f d,m ) Represents a symbolic function; It should be noted that by adding a multi-frame smoothing term, the impact of single-frame noise on the speed measurement results is reduced; confidence weights are introduced to reduce the weight of frame data with low signal-to-noise ratio, further improving accuracy; and a sign function is introduced to ensure that the velocity direction is not lost. It should be noted that in this embodiment, the number of smoothing frames is set to 5; It should be noted that in this embodiment, when f d,m When the sign function is greater than 0, i.e., when the target is close to the millimeter radar, the sign function takes the value of 1; when f d,m When the value is less than 0, that is, when the target is far away from the millimeter radar, the sign function takes the value of -1; S32, The core control unit is used to process the infrared thermal imaging data to obtain the target contour data; S33, The core control unit is used to process the RGB-D camera data to obtain three-dimensional scene data; It should be noted that the processing of RGB-D camera data means using the RGB-D camera information to create a model and obtain three-dimensional scene data; S34, The core control unit is used to fuse the target motion data, the target contour data and the three-dimensional scene data to obtain a sensor fusion signal; It should be noted that the fusion process refers to arranging the target motion data, the target contour data, and the three-dimensional scene data in sequence to obtain a sensor fusion signal.

[0046] Optionally, the step of processing the infrared thermal imaging data using the core control unit to obtain target contour data includes: S321, The core control unit is used to preprocess the infrared thermal imaging data to obtain preprocessed infrared thermal imaging data; It should be noted that the preprocessing includes noise reduction and contrast enhancement. It should be noted that Gaussian filtering is used to denoise the infrared thermal imaging data to remove Gaussian noise and salt-and-pepper noise, resulting in filtered infrared thermal imaging data; histogram equalization is used to amplify the grayscale difference between the human body and the background, and contrast enhancement is performed on the filtered infrared thermal imaging data to obtain preprocessed infrared thermal imaging data. S322, The core control unit is used to segment the preprocessed infrared thermal imaging data to obtain separated infrared thermal imaging data; It should be noted that, taking advantage of the characteristic that "human body temperature (36~37℃) is significantly higher than that of the environment (usually <30℃)," the image is divided into "human body area (foreground)" and "background area" to obtain separated infrared thermal imaging data; S323, The core control unit is used to extract and process the separated infrared thermal imaging data to obtain the target contour edge data; It should be noted that the Canny edge detection algorithm is used for edge extraction to obtain the target contour edge data; S324, The core control unit is used to post-process the target contour edge data to obtain the target contour data.

[0047] Optionally, the step of post-processing the target contour edge data using the core control unit to obtain target contour data includes: S3241, The core control unit is used to smooth the human body contour data to obtain smoothed human body contour data; It should be noted that the smoothing process refers to Gaussian smoothing. S3242, The core control unit is used to perform breakpoint connection processing on the human body contour smoothing data to obtain human body contour information; It should be noted that the breakpoint connection processing means that when the distance between any two data points of the human body contour smoothing data is less than the maximum allowable connection distance, the two data points are connected by a Bézier curve. It should be noted that by post-processing the human body contour data, "breakpoints" and "burrs" in the human body contour can be eliminated, thereby improving the accuracy of human posture estimation.

[0048] Optionally, based on the region division information, the core control unit performs feature-level fusion processing on the sensor fusion signals to obtain a first control signal and a second control signal, including: S41, Obtain robot coordinate position information; S42, Perform feature recognition processing on the sensor fused signal to obtain obstacle information and obstacle motion data; It should be noted that the obstacle motion data includes obstacle distance data and obstacle speed data; S43, based on the obstacle motion information, determine whether the obstacle is a moving obstacle, and obtain the obstacle type determination result; It should be noted that the step of determining whether an obstacle is a moving obstacle based on the obstacle's motion information and obtaining an obstacle type determination result means that the determination of whether an obstacle is a moving obstacle is based on the magnitude of the obstacle's speed data. If the obstacle type determination result is yes, execute S44; If the obstacle type determination result is negative, execute S45; S44, Based on the obstacle motion information, process the robot coordinate position information and the obstacle information to obtain a first control signal; S45, based on the obstacle information and the region division information, process the robot coordinate position information and the obstacle information to determine the second control signal.

[0049] Optionally, the step of performing feature recognition processing on the sensor fused signals to obtain obstacle information and obstacle motion information includes: S421, The sensor fusion signal is analyzed and processed to obtain obstacle motion data, obstacle type data and obstacle parameter data; It should be noted that the parsing process refers to extracting information according to field type to obtain the corresponding obstacle motion data, obstacle type data, and obstacle parameter data; The obstacle parameter information includes obstacle position parameter data and obstacle size parameter data; S422, using a feature recognition processing model, the obstacle parameter data is processed to obtain obstacle position data and obstacle size data; The expression for the feature recognition processing model is: Among them, (x z y z , z z ) represents the coordinates of the merged obstacle; S Z Indicates the size of the merged obstacle; (x A y A , z A (x) represents the coordinates of the obstacle measured by millimeter-wave radar; B y B , z B (x) represents the coordinates of the obstacle measured by the infrared thermal imager; c y c , z c ) represents the obstacle coordinates measured by the RGB-D camera; S A This indicates the size of the obstacle as measured by millimeter-wave radar; S B Indicates the size of the obstacle as measured by the infrared thermal imager; S C This represents the obstacle size measured by the RGB-D camera; ω A ω represents the weight of the first confidence level. B ω represents the weight of the second confidence level. C σ represents the third confidence level weight; R T represents the standard deviation of radar range measurements. O T represents the temperature of an obstacle as measured by an infrared thermal imager. b T represents the average background temperature of an infrared image. max Indicates the maximum temperature measurement range of the infrared thermal imager; σ e E represents the standard deviation of grayscale values ​​at the edge of the RGB-D camera outline; E represents the average grayscale value at the edge of the camera outline. It should be noted that the first confidence weight ω A The confidence weight of the millimeter-wave radar signal; the second confidence weight ω B The third confidence weight is the confidence weight of the infrared thermal imager signal; C The confidence weights of the RGB-D camera signal; It should be noted that the ω A ω B and ω C The confidence weight changes dynamically with the corresponding signal quality. For example, the closer the radar is, the greater the temperature difference between the infrared target and the background, and the clearer the camera outline, the higher the corresponding confidence weight, thus avoiding the fusion bias of fixed weights. It should be noted that, in this embodiment, the T max Set to -40~150℃; S423, perform structured processing on the obstacle type data, the obstacle location data, and the obstacle size data to obtain obstacle information; It should be noted that the structured processing refers to arranging the obstacle type data, obstacle location data, and obstacle size data in the order of type, location, and size. Optionally, the step of processing the robot's coordinate position information and the obstacle information based on the obstacle motion information to obtain the first control signal includes: S441, set the first control signal to stop; S442, The obstacle information is parsed and processed to obtain obstacle coordinate position data; S443, Based on the robot's coordinate position information and the obstacle's coordinate position data, determine the obstacle distance data and obstacle direction data; It should be noted that the obstacle distance data and the obstacle direction data are calculated and obtained from the robot's position information and the obstacle position data; When the obstacle distance data is less than or equal to a preset distance threshold and the obstacle direction data is within the preset direction threshold range, the first control signal is set to stop, and S5 is executed; It should be noted that if the obstacle distance data is less than or equal to a preset distance threshold and the obstacle direction data is within a preset direction threshold range, it means that the obstacle is within the movement radius of the robot's forward direction. When the obstacle distance data is greater than a preset distance threshold and the obstacle direction data is within a preset direction threshold range, the first control signal is set to start, and S5 is executed.

[0050] Optionally, the step of processing the robot's coordinate position information and the obstacle information based on the obstacle information and the region division information to determine the second control signal includes: S451, set the second control signal to off; S452, determine whether the robot's coordinate position information belongs to the instant disinfection zone information of the obstacle information, and obtain the first area judgment result; When the first area determination result is yes, the second control signal is set to irradiation, and S6 is executed; If the result of the first region determination is negative, execute S453; S453, determine whether the robot's coordinate position information belongs to the reservation disinfection area information of the obstacle information, and obtain the second area judgment result; If the result of the second region determination is yes, execute S454; If the result of the second region determination is negative, execute S455; S454, determine whether the current time has reached the reservation time, and obtain the time reservation judgment result; When the time reservation determination result is yes, the second control signal is set to irradiation, and S6 is executed; If the time reservation determination result is negative, execute S451; S455, The obstacle information is parsed and processed to obtain obstacle type information; Determine whether the obstacle type information is equal to a first type preset value to obtain the first obstacle type determination result; It should be noted that the first type has a preset value of 1, indicating that the obstacle type is a person; When the first obstacle type determination result is yes, the second control signal will be set to weak illumination, and S451 will be executed; It should be noted that the term "weak irradiation" means that the dynamic zoning disinfection strategy module marks a radius of 1.5 meters as a safe isolation zone and does not irradiate it. If the first obstacle type determination result is negative, execute S456; S456, determine whether the obstacle type information is equal to the second type preset value, and obtain the second obstacle type determination result; It should be noted that the second type has a preset value of 2, indicating that the obstacle type is a high-frequency contact surface; When the second obstacle type determination result is yes, the second control signal is set to strong illumination, and S6 is executed; If the result of the second obstacle type determination is negative, execute S1.

[0051] Optionally, the intelligent disinfection robot control method also includes software upgrades and maintenance: A dual-partition backup mechanism is adopted. OTA update packages are first installed and verified on the backup partition, and then switched to the primary partition after passing a 72-hour continuous stress test. Set up anti-tampering protection for key parameters, and require dual authentication when modifying them; The fault diagnosis subsystem distinguishes between hardware anomalies and software errors; When archiving historical data, the original data containing images of people is anonymized.

[0052] It is evident that implementing the intelligent disinfection robot control method described in this embodiment enables the software upgrade and maintenance of the intelligent disinfection robot control method, ensuring safe and efficient real-time ultraviolet disinfection in environments with human activity. It solves the safety hazards and inefficiencies of traditional disinfection methods, and significantly improves the convenience and reliability of disinfection management through intelligent design. It meets the disinfection needs of public spaces such as medical institutions, public transportation, and educational venues, and has outstanding technical advantages and broad market prospects.

[0053] Example 3 This invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps in the intelligent disinfection robot control method described in Embodiment 2.

[0054] Example 4 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the intelligent disinfection robot control method described in Embodiment 2.

[0055] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0056] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), once programmable read-only memory (OTPROM), electronically erasable rewritable read-only memory (EEPROM), read-only optical disc (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0057] It should be noted that all calculation expressions or mathematical functions in the embodiments of the present invention have undergone dimensionless processing of the variables involved before calculation.

[0058] It should be noted that in all the calculation expressions or mathematical functions in the embodiments of the present invention, the values ​​of the input independent variables all meet the reasonable requirements of the input value range of the calculation expression or mathematical function, and can ensure that the calculation expression or mathematical function can be calculated smoothly without violating physical laws or mathematical rules.

[0059] Finally, it should be noted that the intelligent mobile disinfection robot and its control method based on 222nm far-ultraviolet light disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A 222nm ultraviolet-based intelligent disinfection robot, characterized in that, The intelligent disinfection robot comprises a robot body, a core control unit, a UVC-LED array cabin and an omnidirectional wheel chassis; The robot body is used for sensing environmental information, fixing the UVC-LED array cabin, the omnidirectional wheel chassis and the core control unit; The core control unit is arranged in the middle of the robot body and is used for receiving and processing the environmental information sent by the sensor tower, obtaining a first control signal and sending it to the omnidirectional wheel chassis and obtaining a second control signal and sending it to the UVC-LED array cabin; The UVC-LED array cabin is arranged at the top of the robot body and is used for receiving and processing the second control signal and emitting 222nm ultraviolet light based on the second control signal; The omnidirectional wheel chassis is arranged at the bottom of the robot body and is used for receiving and processing the first control signal and controlling the intelligent disinfection robot to move; The core control unit is electrically connected with the robot body and the UVC-LED array cabin.

2. The intelligent disinfection robot of claim 1, wherein, The robot body comprises a body shell, a battery and a sensor tower; The sensor tower is arranged at the tail of the body shell and is connected with the body shell through a rotating driving mechanism capable of continuously rotating by 360 degrees in the horizontal plane and is used for collecting environmental information in all directions; the sensor tower is integrated with a sensor, a millimeter wave radar, an infrared night vision instrument, an infrared camera, an infrared thermal imager and an RGB-D camera; The battery is used for providing power supply for the intelligent disinfection robot.

3. The intelligent disinfection robot of claim 1, wherein, The UVC-LED array cabin comprises a lamp bead array, an LED unit, a graphene composite material heat sink and a microlens array waveguide panel; The lamp bead array is 48 222nm wavelength lamp beads, the lamp beads are arranged in a hexagonal honeycomb shape and have a uniform inclination angle of 15 degrees, and a quartz glass protective cover capable of blocking wavelengths above 230nm is arranged on the periphery; The LED unit adopts a combination material of copper substrate-ceramic packaging-sapphire lens, and the photoelectric conversion efficiency is not less than 35%; The graphene composite material heat sink adopts a fin structure optimized by fluid dynamics; The microlens array waveguide panel is used for realizing uniform diffusion of ultraviolet light, and the surface is densely covered with microlenses with a diameter of 2mm.

4. The intelligent disinfection robot of claim 1, wherein, The core control unit adopts an edge computing and cloud computing collaborative architecture and comprises a signal processing module and a cloud management platform; The signal processing module is loaded with an NVIDIA-Jetson module and is used for processing 12 sensor signals in real time; The cloud management platform constructs a virtual disinfection model through digital twinning technology; The signal processing module and the cloud management platform interact with each other through an MQTT protocol.

5. The intelligent disinfection robot of claim 1, wherein, The intelligent disinfection robot further comprises a dynamic partition disinfection strategy module; the dynamic partition disinfection strategy module constructs a three-dimensional point cloud map through an SLAM algorithm and divides the working area into an instant disinfection area, a pre-booking disinfection area and a safety isolation area; The instant disinfection area is an area where continuous irradiation can be performed; The pre-booking disinfection area can intelligently adjust the irradiation dose according to the personnel departure time; The safe isolation area is a circular area with a human body as the center and a radius of 1.5 meters, which is set when the infrared thermal imager detects the surface temperature of the human body.

6. The intelligent disinfection robot of claim 5, wherein, The dynamic partition disinfection strategy module includes a target recognition unit, a driving circuit unit and an optical filter film layer. The target recognition unit is configured to recognize the high-frequency contact surface by using a target recognition algorithm based on deep learning. The driving circuit unit is configured to drive the output of ultraviolet light by using pulse width modulation and current adjustment dual-mode driving, the ultraviolet light output power is steplessly adjusted in the range of 5-100%, and the response time is less than 0.1 second. The optical filter film layer is configured to block secondary radiation with a wavelength greater than 230 nm. 7.A control method of a 222nm ultraviolet-based intelligent disinfection robot, characterized in that, The control method is applied to the intelligent disinfection robot of any one of claims 1-6, and the control method comprises: S1, trajectory planning and region division are performed on the travel route of the intelligent disinfection robot to obtain travel trajectory information and region division information of the disinfection robot; S2, the intelligent disinfection robot travels according to the travel trajectory information of the disinfection robot to obtain sensor signal data; S3, the sensor signal data is fused by using a core control unit to obtain a sensor fusion signal; S4, based on the region division information, the sensor fusion signal is processed by using the core control unit to obtain a first control signal and a second control signal; S5, the first control signal is received and processed by using an omnidirectional wheel chassis to control the intelligent disinfection robot to travel; S6, the second control signal is received and processed by using a UVC-LED array cabin to emit ultraviolet light to the surrounding environment for ultraviolet disinfection of the surrounding environment.

8. The 222 nm ultraviolet-based intelligent disinfection robot control method according to claim 7, wherein, The sensor signal is fused by using the core control unit to obtain a sensor fusion signal, which comprises: S31, target motion data is obtained by processing millimeter wave radar signal data by using the core control unit; The target motion data includes target distance data and target speed data; The target motion information acquisition and processing expression is: Wherein, R represents the target distance data; c represents the speed of light; τ represents the radar echo time delay; K1 represents the phase compensation coefficient; △θ represents the echo phase deviation; V represents the target speed data; β represents the radar wavelength; f d represents the Doppler shift; M represents the number of smoothing frames; m represents the smoothing frame index; ω m represents the confidence weight of the mth smoothing frame. f d,m represents the Doppler shift measurement value of the mth smoothing frame. S(f d,m ) represents the sign function. S32, target contour data is obtained by processing infrared thermal imaging data by using the core control unit; S33, three-dimensional scene data is obtained by processing RGB-D camera data by using the core control unit; S34, the sensor fusion signal is obtained by fusing the target motion data, the target contour data and the three-dimensional scene data by using the core control unit. 9.The 222 nm ultraviolet-based intelligent disinfection robot control method according to claim 8, wherein, The target contour data is obtained by processing infrared thermal imaging data by using the core control unit, which comprises: S321, preprocessed infrared thermal imaging data is obtained by preprocessing infrared thermal imaging data by using the core control unit; S322, separated infrared thermal imaging data is obtained by segmenting the preprocessed infrared thermal imaging data by using the core control unit; S323, target contour edge data is obtained by extracting the separated infrared thermal imaging data by using the core control unit; S324, the target contour data is obtained by post-processing the target contour edge data by using the core control unit.

10. The 222 nm ultraviolet-based intelligent disinfection robot control method according to claim 7, wherein, The core control unit is used for performing feature-level fusion processing on the sensor fusion signal based on the region division information to obtain a first control signal and a second control signal, including: S41, obtaining robot coordinate position information; S42, performing feature recognition processing on the sensor fusion signal to obtain obstacle information and obstacle motion data; S43, judging whether the obstacle is a moving obstacle based on the obstacle motion information to obtain an obstacle type judgment result; When the obstacle type judgment result is yes, S44 is performed; When the obstacle type judgment result is no, S45 is performed; S44, processing the robot coordinate position information and the obstacle information based on the obstacle motion information to obtain the first control signal; S45, processing the robot coordinate position information and the obstacle information based on the obstacle information and the region division information to determine the second control signal.

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