Crawler-type bed surface sterilization robot adaptive speed control method, system and platform based on multi-sensor fusion and storage medium

Abstract

The invention discloses a self-adaptive speed control method, a self-adaptive speed control system and a self-adaptive speed control platform for a crawler-type bed surface sterilization robot based on multi-sensor fusion, and a storage medium. And intelligent operation control of the crawler-type bed surface sterilization robot in a complex bed surface environment is realized. The multi-dimensional sensor is adopted to sense the material characteristics and the friction state of the bed surface in real time, and dynamic modeling and a compound control algorithm are combined to realize accurate speed self-adaptive adjustment, so that the problems of slipping, out-of-control speed and the like easily occurring on a smooth or soft bed surface in a traditional method are effectively solved. Meanwhile, by introducing a sterilization effect closed-loop verification mechanism based on infrared thermal imaging, the comprehensiveness and reliability of sterilization operation are ensured; the movement stability, the sterilization coverage rate and the operation safety of the robot on bed sheets and mattresses made of different materials are improved, and meanwhile the energy efficiency performance of the system is optimized.

Description

Technical Field

[0001] This invention belongs to the field of smart home cleaning equipment technology, specifically relating to an adaptive speed control method, system, platform, and storage medium for a tracked bed surface sterilization robot based on multi-sensor fusion. Background Technology

[0002] With the rapid development of smart home technology, bed sterilization robots, as an important component of intelligent cleaning equipment, are widely used in hotels, hospitals, homes and other scenarios.

[0003] In existing technologies, when tracked sterilization robots operate on beds, the stability of their movement is poor due to differences in mattress firmness (such as memory foam and latex mattresses) and the smoothness of bed sheet materials (such as silk and pure cotton sheets). When the bed sheet is too slippery, the friction between the track and the bed surface is insufficient, causing the drive motor to idle and severely affecting the sterilization effect; when the mattress is too soft, the robot sinks in, reducing resistance and making it easy for the movement speed to exceed the safety threshold, resulting in the ultraviolet or ultrasonic sterilization module not being able to fully cover the target area.

[0004] Furthermore, traditional PID control algorithms suffer from response lag, making it difficult to adapt to changes in ground conditions in real time. Frequent start-stop adjustments are necessary, increasing energy consumption and accelerating mechanical wear. Moreover, existing systems cannot verify sterilization effectiveness in real time or make dynamic adjustments based on actual sterilization coverage. They also lack effective overcurrent protection and emergency braking mechanisms, posing safety hazards.

[0005] Therefore, in order to address the technical problems and deficiencies mentioned above, there is an urgent need to design and develop an adaptive speed control method, system, platform, and storage medium for a tracked bed sterilization robot based on multi-sensor fusion. Summary of the Invention

[0006] To overcome the shortcomings and difficulties of the existing technology, the present invention aims to provide an adaptive speed control method, system, platform and storage medium for a tracked bed sterilization robot based on multi-sensor fusion, which can adapt to different bed conditions, has high control accuracy, has sterilization effect verification function and is safe and reliable.

[0007] The first objective of this invention is to provide an adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion; the second objective of this invention is to provide an adaptive speed control system for a tracked bed sterilization robot based on multi-sensor fusion; the third objective of this invention is to provide an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion; and the fourth objective of this invention is to provide a computer-readable storage medium.

[0008] The first objective of this invention is achieved as follows: the method comprises: First data corresponding to the tracked bed surface sterilization robot is created and acquired. Based on the first data and combined with preset surface scanning data, a first model corresponding to the tracked bed surface sterilization robot is constructed. The first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data, and infrared thermal imaging data. The first model is a dynamic friction coefficient model of the bed surface. Based on the first data, second data corresponding to the tracked bed sterilization robot is calculated and generated. Based on the second data and combined with the composite controller, the moving speed of the tracked bed sterilization robot is adaptively adjusted. The second data is the deviation between the robot's actual angular velocity and its theoretical angular velocity. Based on the infrared thermal imaging data in the first data, the temperature distribution characteristics of the bed surface are analyzed and processed, and corresponding speed adjustment or local supplementary killing operations are triggered according to the analysis results.

[0009] Furthermore, the step of creating and acquiring first data corresponding to the tracked bed surface sterilization robot, and constructing a first model corresponding to the tracked bed surface sterilization robot based on the first data and in combination with preset surface scanning data, further includes: The compression ratio relative to the mattress is calculated based on the data from the pressure sensor array. The roughness parameters corresponding to the surface of the bed sheet are generated and acquired by scanning with a visual sensor or lidar. Calculate the dynamic friction coefficient corresponding to the bed surface; the calculation formula is as follows: ; In the formula, This is the baseline value for mattress compression rate; This serves as the reference value for surface roughness. and These are the weighting coefficients calibrated through experiments; For mattress compression ratio; This refers to the surface roughness parameter of the bed sheet.

[0010] Furthermore, the step of calculating and generating second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjusting the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller, further includes: Based on the second data, and combined with the fuzzy rule base, the proportional factor, integral factor and derivative factor of the PID controller are adjusted in real time; Based on the adjusted PID parameters, the corresponding PWM duty cycle adjustment signal is calculated and generated; the calculation expression is as follows: ; In the formula, This is the PWM duty cycle adjustment signal; It is a scaling factor; It is the integrating factor; The differential factor; The preset attenuation coefficient; It is a symbolic function.

[0011] Furthermore, the step of calculating and generating second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjusting the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller, further includes: A sliding mode controller is used to compensate for abrupt changes in the ground friction characteristics; wherein, the sliding surface of the sliding mode controller is set as follows: ; In the formula, It is a sliding surface; For speed tracking error; The sliding surface coefficient; The control law is set as follows: ; In the formula, For equivalent control quantity; To switch the gain; It is a saturation function; Boundary layer thickness; Let be the gain coefficient of the sliding mode observer.

[0012] Furthermore, the step of analyzing and processing the bed surface temperature distribution characteristics based on the infrared thermal imaging data in the first data, and triggering corresponding speed adjustment or local supplementary killing operations according to the analysis results, also includes: Based on the infrared thermal imaging data, the temperature rise rate and temperature distribution uniformity corresponding to the preset area of ​​the bed surface are calculated and generated. Based on the pre-screening threshold, slippage or uneven coverage is determined. Specifically, when the temperature rise rate of a certain area is detected to be lower than the first preset threshold, slippage is determined to have occurred in that area, and a first-level deceleration command is triggered. When the uniformity of the bed surface temperature distribution is detected to be lower than the second preset threshold, uneven sterilization coverage is determined, and a local supplementary sterilization mode is activated. In this mode, the robot's movement speed in the corresponding area is increased to 1.2 to 1.8 times the base speed.

[0013] Furthermore, the method further includes: generating and acquiring third data corresponding to the monitored drive motor, and determining whether the third data exceeds a third preset threshold; wherein the third data is the operating current of the drive motor; and the third preset threshold is a preset duration threshold. A fourth set of data corresponding to the tracked bed sterilization robot is generated and acquired, and a fourth preset threshold is determined based on the fourth set of data; wherein, the fourth set of data is the monitoring of continuously occurring angular velocity deviation exceeding the limit event; and the fourth preset threshold is the number of times within a preset time period.

[0014] The second objective of this invention is achieved as follows: the system is applied to the adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion, the system comprising: The data model building unit is used to create and acquire first data corresponding to the tracked bed surface sterilization robot, and based on the first data and combined with preset surface scanning data, construct a first model corresponding to the tracked bed surface sterilization robot; wherein, the first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data and infrared thermal imaging data; the first model is a bed surface dynamic friction coefficient model; An adaptive adjustment unit is used to calculate and generate second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjust the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller; wherein, the second data is the deviation between the robot's actual angular velocity and theoretical angular velocity; The first data processing unit is used to analyze and process the temperature distribution characteristics of the bed surface based on the infrared thermal imaging data in the first data, and trigger corresponding speed adjustment or local supplementary killing operations according to the analysis results.

[0015] Furthermore, the data model construction unit also includes: The first generation module is used to calculate and generate a compression ratio relative to the mattress based on the pressure sensor array data; The second generation module is used to generate and acquire roughness parameters corresponding to the surface of the bed sheet by scanning with a visual sensor or lidar. The third generation module is used to calculate the dynamic friction coefficient corresponding to the bed surface; the calculation formula is as follows: ; In the formula, This is the baseline value for mattress compression rate; This serves as the reference value for surface roughness. and These are the weighting coefficients calibrated through experiments; For mattress compression ratio; This refers to the surface roughness parameter of the bed sheet; And / or, the adaptive adjustment unit further includes: The first processing module is used to adjust the proportional factor, integral factor and derivative factor of the PID controller in real time based on the second data and in combination with the fuzzy rule base. The fourth generation module is used to calculate and generate the corresponding PWM duty cycle adjustment signal based on the adjusted PID parameters; the calculation expression is as follows: ; In the formula, This is the PWM duty cycle adjustment signal; It is a scaling factor; It is the integrating factor; The differential factor; The preset attenuation coefficient; It is a symbolic function; The second processing module is used to compensate for abrupt changes in ground friction characteristics using a sliding mode controller; wherein the sliding surface of the sliding mode controller is set as follows: ; In the formula, It is a sliding surface; For speed tracking error; The sliding surface coefficient; The control law is set as follows: ; In the formula, For equivalent control quantity; To switch the gain; It is a saturation function; Boundary layer thickness; is the gain coefficient of the sliding mode observer.

[0016] And / or, the first data processing unit further includes: The fifth generation module is used to calculate and generate the temperature rise rate and temperature distribution uniformity corresponding to the preset area of ​​the bed surface based on the infrared thermal imaging data. The first judgment module is used to determine slippage or uneven coverage based on the pre-screening threshold. Specifically, when the temperature rise rate of a certain area is detected to be lower than the first preset threshold, it is determined that slippage has occurred in that area, and a first-level deceleration command is triggered. When the uniformity of the bed surface temperature distribution is detected to be lower than the second preset threshold, it is determined that the sterilization coverage is uneven, and a local supplementary sterilization mode is activated. In this mode, the robot's moving speed in the corresponding area is increased to 1.2 to 1.8 times the base speed. The system also includes: The second determination module is used to generate and acquire third data corresponding to the monitored drive motor, and determine whether the third data exceeds a third preset threshold; wherein, the third data is the operating current of the drive motor; and the third preset threshold is a preset duration threshold. The third determination module is used to generate and acquire fourth data corresponding to the tracked bed sterilization robot, and determine whether the fourth data exceeds the fourth preset threshold; wherein, the fourth data is the monitoring of continuously occurring angular velocity deviation exceeding the limit event; the fourth preset threshold is the number of times within a preset time period.

[0017] The third objective of this invention is achieved as follows: it includes a processor, a memory, and a control program for an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion; wherein the control program for the adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion is executed in the processor, and the control program for the adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion is stored in the memory; the control program for the adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion implements the adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion.

[0018] The fourth objective of this invention is achieved as follows: the computer-readable storage medium stores a control program for an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion, and the control program for the adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion implements the adaptive speed control method for the tracked bed sterilization robot based on multi-sensor fusion.

[0019] This invention creates and acquires first data corresponding to a tracked bed sterilization robot through a method, and constructs a first model corresponding to the tracked bed sterilization robot based on the first data and preset surface scanning data. The first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data, and infrared thermal imaging data. The first model is a dynamic friction coefficient model of the bed surface. Based on the first data, second data corresponding to the tracked bed sterilization robot is calculated and generated. Based on the second data and combined with a composite controller, the moving speed of the tracked bed sterilization robot is adaptively adjusted. The second data is the deviation between the robot's actual angular velocity and its theoretical angular velocity. Based on the infrared thermal imaging data in the first data, the temperature distribution characteristics of the bed surface are analyzed and processed, and corresponding speed adjustment or local supplementary sterilization operations are triggered according to the analysis results. The invention also includes a corresponding system, platform, and storage medium, effectively solving the problems of poor bed surface adaptability, low control accuracy, and lack of effect verification in the prior art. It features fast response, high accuracy, and high reliability.

[0020] In other words, this invention achieves intelligent operation control of a tracked bed sterilization robot in complex bed environments through an innovative combination of multi-sensor data fusion and adaptive control technology. By employing multi-dimensional sensors to perceive the material characteristics and friction state of the bed surface in real time, and combining dynamic modeling and composite control algorithms to achieve precise adaptive speed adjustment, it effectively overcomes the problems of slippage and speed loss that easily occur on smooth or soft bed surfaces using traditional methods. Simultaneously, by introducing a closed-loop verification mechanism for sterilization effect based on infrared thermal imaging, it ensures the comprehensiveness and reliability of sterilization operations; improves the robot's movement stability, sterilization coverage, and operational safety on different types of sheets and mattresses; and optimizes system energy efficiency, providing a highly efficient and reliable intelligent sterilization solution for diverse application scenarios such as hotels, hospitals, and homes. Attached Figure Description

[0021] 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.

[0022] Figure 1 This is a schematic diagram of the process steps of an adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion according to the present invention. Figure 2 A schematic diagram of the adaptive speed control system architecture for a tracked bed sterilization robot based on multi-sensor fusion is provided in this invention. Figure 3 This is a schematic diagram of the adaptive speed control platform architecture for a tracked bed sterilization robot based on multi-sensor fusion according to the present invention. Figure 4 This is a schematic diagram of a computer-readable storage medium architecture in one embodiment of the present invention. Detailed Implementation

[0023] To facilitate a clearer understanding of the objectives, technical solutions, and advantages of this invention, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of this invention from the content disclosed in this specification.

[0024] This invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of this invention.

[0025] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0026] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0027] Preferably, the adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion of the present invention is applied in one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0028] The terminal can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal can interact with the customer via a keyboard, mouse, remote control, touchpad, or voice control device.

[0029] This invention provides an adaptive speed control method, system, platform, and storage medium for a tracked bed sterilization robot based on multi-sensor fusion.

[0030] like Figure 1 The diagram shown is a flowchart of an adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion, provided in an embodiment of the present invention.

[0031] In this embodiment, the adaptive speed control method for the tracked bed sterilization robot based on multi-sensor fusion can be applied to a terminal with display function or a fixed terminal. The terminal is not limited to personal computers, smartphones, tablets, desktop computers or all-in-one computers with cameras, etc.

[0032] The adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion in this embodiment can be executed by the server, by the terminal, or by both the server and the terminal.

[0033] For example, for a tracked bed sterilization robot requiring adaptive speed control based on multi-sensor fusion, the adaptive speed control function based on multi-sensor fusion provided by the method of this invention can be directly integrated into the terminal, or a client for implementing the method of this invention can be installed. Alternatively, the method provided by this invention can also run on servers or other devices in the form of a Software Development Kit (SDK), providing an interface for the adaptive speed control function of the tracked bed sterilization robot based on multi-sensor fusion. Terminals or other devices can then implement the adaptive speed control function of the tracked bed sterilization robot based on multi-sensor fusion through the provided interface. The invention will be further described below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, this invention provides an adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion. The method includes the following steps: S01. Create and acquire first data corresponding to the tracked bed surface sterilization robot, and based on the first data and combined with preset surface scanning data, construct a first model corresponding to the tracked bed surface sterilization robot; wherein, the first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data and infrared thermal imaging data; the first model is a dynamic friction coefficient model of the bed surface. S02. Based on the first data, calculate and generate second data corresponding to the tracked bed sterilization robot, and based on the second data, combined with the composite controller, adaptively adjust the moving speed of the tracked bed sterilization robot; wherein, the second data is the deviation between the robot's actual angular velocity and theoretical angular velocity; S03. Based on the infrared thermal imaging data in the first data, analyze and process the temperature distribution characteristics of the bed surface, and trigger corresponding speed adjustment or local supplementary killing operations according to the analysis results.

[0035] The step of creating and acquiring first data corresponding to the tracked bed surface sterilization robot, and constructing a first model corresponding to the tracked bed surface sterilization robot based on the first data and in combination with preset surface scanning data, further includes: S011. Calculate and generate the compression ratio relative to the mattress based on the pressure sensor array data; S012. Generate and acquire roughness parameters corresponding to the surface of the bed sheet by scanning with a visual sensor or lidar. S013. Calculate the dynamic friction coefficient corresponding to the bed surface; the calculation formula is as follows: ; In the formula, This is the baseline value for mattress compression rate; This serves as the reference value for surface roughness. and These are the weighting coefficients calibrated through experiments; For mattress compression ratio; This refers to the surface roughness parameter of the bed sheet.

[0036] The step of calculating and generating second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjusting the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller, further includes: S021. Based on the second data and combined with the fuzzy rule base, adjust the proportional factor, integral factor and derivative factor of the PID controller in real time. S022. Based on the adjusted PID parameters, calculate and generate the corresponding PWM duty cycle adjustment signal; where the calculation expression is: ; In the formula, This is the PWM duty cycle adjustment signal; It is a scaling factor; It is the integrating factor; The differential factor; The preset attenuation coefficient; It is a symbolic function.

[0037] The step of calculating and generating second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjusting the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller, further includes: S023. A sliding mode controller is used to compensate for abrupt changes in the ground friction characteristics; wherein, the sliding surface of the sliding mode controller is set as follows: ; In the formula, It is a sliding surface; For speed tracking error; The sliding surface coefficient; The control law is set as follows: ; In the formula, For equivalent control quantity; To switch the gain; It is a saturation function; Boundary layer thickness; is the gain coefficient of the sliding mode observer.

[0038] The step of analyzing and processing the bed surface temperature distribution characteristics based on the infrared thermal imaging data in the first data, and triggering corresponding speed adjustment or local supplementary killing operations according to the analysis results, also includes: S031. Based on the infrared thermal imaging data, calculate and generate the temperature rise rate and temperature distribution uniformity corresponding to the preset area of ​​the bed surface; S032. Based on the pre-screening threshold, slippage or uneven coverage is determined respectively; when the temperature rise rate of a certain area is detected to be lower than the first preset threshold, it is determined that slippage has occurred in the area and the first-level deceleration command is triggered; when the temperature distribution uniformity of the bed surface is detected to be lower than the second preset threshold, it is determined that the sterilization coverage is uneven and the local supplementary sterilization mode is started. In this mode, the robot's moving speed in the corresponding area is increased to 1.2 to 1.8 times the base speed.

[0039] The method further includes: S041, generating and acquiring third data corresponding to the monitored drive motor, and determining whether the third data exceeds a third preset threshold; wherein, the third data is the operating current of the drive motor; and the third preset threshold is a preset duration threshold. S042. Generate and acquire fourth data corresponding to the tracked bed sterilization robot, and determine whether the fourth data exceeds the fourth preset threshold; wherein, the fourth data is the monitoring of continuously occurring angular velocity deviation exceeding the limit event; the fourth preset threshold is the number of times within a preset time period.

[0040] Specifically, in this embodiment of the invention, an adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion is provided, comprising the following steps: a sensor data acquisition step, which involves real-time acquisition of gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data, and infrared thermal imaging data; a dynamic modeling step, which establishes a dynamic friction coefficient model of the bed surface based on the pressure sensor array data and preset surface scanning data; a deviation calculation step, which calculates the deviation between the actual angular velocity and the theoretical angular velocity of the tracked sterilization robot based on the gyroscope and encoder data; an adaptive control step, which uses a composite controller to dynamically adjust the PWM control signal output to the drive motor based on the deviation, thereby achieving adaptive adjustment of the robot's movement speed; and an effect verification step, which analyzes the temperature distribution characteristics of the bed surface based on the infrared thermal imaging data, and triggers corresponding speed adjustment or local supplementary sterilization operations according to the analysis results.

[0041] The specific process of establishing a dynamic friction coefficient model for the bed surface in the dynamic modeling step includes: calculating the mattress compression rate based on the pressure sensor array data. ; Obtain the surface roughness parameters of the bed sheet through visual sensors or lidar scanning. The dynamic friction coefficient is calculated using the following mathematical model: ; In the formula, This is the baseline value for mattress compression rate; This serves as the reference value for surface roughness. and These are the weighting coefficients calibrated through experiments; For mattress compression ratio; This refers to the surface roughness parameter of the bed sheet.

[0042] In the adaptive control step, the control process of the composite controller includes: a fuzzy inference sub-step, using the deviation... As input, the scaling factor of the PID controller is adjusted in real time using a fuzzy rule base. Integral factor and differential factors The PID control sub-step generates a PWM duty cycle adjustment signal based on the adjusted PID parameters. Its expression is: ; In the formula, This is the PWM duty cycle adjustment signal; It is a scaling factor; It is the integrating factor; The differential factor; The preset attenuation coefficient; It is a symbolic function.

[0043] The effect verification steps include: a temperature analysis sub-step, which calculates the temperature rise rate and temperature distribution uniformity of a preset area on the bed surface based on the infrared thermal imaging data; and a slippage detection and response sub-step, which determines whether the temperature rise rate of a certain area is lower than a first preset threshold. When slippage occurs in the area, a first-level deceleration command is triggered; in the uneven coverage determination and response sub-step, when the uniformity of bed surface temperature distribution is detected to be lower than a second preset threshold... If uneven sterilization coverage is detected, a localized supplementary sterilization mode is activated. In this mode, the robot's movement speed in the corresponding area is increased to 1.2 to 1.8 times the base speed.

[0044] It also includes a safety monitoring step, which includes: a current monitoring sub-step to monitor the operating current of the drive motor in real time; and an overcurrent protection sub-step to protect the motor when the operating current continuously exceeds 120% of the rated current for a first preset time. At that time, the motor power supply is cut off; the slip monitoring sub-step monitors continuously occurring angular velocity deviation exceeding the limit events, where a single event is defined as... Emergency braking sub-step, during the second preset duration When three or more angular velocity deviation exceeding the limit events are detected consecutively within the internal monitoring system, the electromagnetic braking device is activated.

[0045] Before the deviation calculation step, a data fusion step is also included, in which the angular velocity data collected by the gyroscope and the linear velocity data collected by the encoder are fused using a Kalman filter algorithm to eliminate sensor noise and improve the accuracy of motion state estimation.

[0046] The adaptive control step further includes a sliding mode compensation sub-step, which uses a sliding mode controller to compensate for abrupt changes in the ground friction characteristics; wherein, the sliding surface of the sliding mode controller... Designed as follows: ; In the formula, It is a sliding surface; For speed tracking error; The sliding surface coefficient; The control law is designed as follows: ; In the formula, For equivalent control quantity; To switch the gain; It is a saturation function; Boundary layer thickness; This is the gain coefficient of the sliding mode observer, used to suppress high-frequency chattering.

[0047] In the fuzzy inference sub-step, the parameters of the PID controller , , The online tuning is achieved using an improved particle swarm optimization algorithm. This improved algorithm introduces adaptive inertia weights into the velocity update formula of the standard particle swarm optimization algorithm and performs particle mutation operations based on historical information of the global optimal solution in each iteration. In the sensor data acquisition step: the sampling frequency of the gyroscope is not less than 100Hz; the resolution of the encoder is not less than 1000PPR; and the pressure sensor array adopts a 16-point distributed layout.

[0048] The method further includes an initialization step, which includes: controlling the drive motor to start at a preset maximum start-up speed; synchronously starting the gyroscope, encoder, pressure sensor array, and infrared thermal imaging module; and calibrating the initial parameters of the PID controller through a step response experiment. , , Calibrate sliding mode observer parameters: boundary layer thickness Sliding mode observer gain coefficient ; Example 1: Application Implementation Plan for Hotel Guest Room Scenarios Scene characteristics analysis: Bed sheet material is mostly high-count cotton, linen, or chemical fiber blends, with a wide range of surface friction coefficients; Mattress type is mainly spring mattresses, with some high-end rooms equipped with latex mattresses; Frequency of use: daily cleaning, requiring rapid sterilization; Safety requirements: avoid damaging the bed sheet fabric and ensure no areas are missed during sterilization.

[0049] Specific implementation steps: Step 1: Pre-scanning and parameter setting; After the robot is placed on the bed, it first performs a 3-second rapid pre-scan: The vision sensor scans along the diagonal of the bed to obtain the texture feature parameters of the bed sheet. The pressure sensor array acquires the pressure distribution at 16 points using a sampling frequency of 10Hz; the initial bed surface friction coefficient is calculated. The system matches the optimal initial PID parameters based on the historical database. If the material is identified as silk or other highly slippery material, the initial speed is set to 0.8 m / s. If the material is identified as cotton, the initial speed is set to 1.2 m / s.

[0050] Step 2: Adaptive cleaning path planning. Based on the pre-scan results, the cleaning path is dynamically planned. High-friction areas (sheet folds, mattress edges): a dense grid path is used, and the speed is reduced to 0.6m / s; flat areas: a zigzag path is used, and the speed is increased to 1.0m / s; mattress indentation areas: the compression rate is detected by pressure sensors. The area is automatically marked as a "soft zone" and the speed is reduced to 0.5 m / s.

[0051] Step 3: Real-time dynamic adjustment. During the cleaning process, a control loop is executed every 50ms; the gyroscope collects angular velocity data at a frequency of 100Hz, and the encoder collects linear velocity data; a Kalman filter is used for fusion processing to remove motor vibration noise; when detected... Upon that time, the fuzzy inference module is immediately triggered; the fuzzy controller outputs the adjustment factor. , , The PID controller outputs an updated PWM duty cycle to adjust the motor speed.

[0052] Step 4: Real-time verification of sterilization effect. The infrared thermal imaging module collects the temperature distribution of the bed surface at a frequency of 2 frames / second; calculates the temperature gradient of each 0.1m × 0.1m grid; if the temperature rise rate of a certain grid is... The area was determined to have insufficient UV irradiation and marked as requiring further sterilization. After completing the overall cleaning, the robot automatically returned to the area requiring further sterilization and performed a second sterilization at a low speed of 0.4 m / s. Hotel scenario optimized parameter configuration: safe current threshold, 115% of rated current (considering frequent start-stop conditions); emergency braking condition: two consecutive slips and... Maximum operating speed: 1.2m / s (ensuring effective UV irradiation time ≥ 0.5 seconds / point); Energy consumption optimization: Start-stop acceleration adopts S-curve acceleration and deceleration algorithm to reduce current surge.

[0053] In hotel scenario testing, the cleaning time for a standard double room was reduced from 45 minutes to 28 minutes; the bed sheet damage rate decreased from 0.3% to 0.05%; and the customer satisfaction score increased from 8.2 to 9.6 (out of 10).

[0054] Example 2: Implementation plan for deep cleaning of household mattresses Scene characteristics analysis: Bed sheet materials are diverse, ranging from silk to flannel; Mattress types include a mix of materials such as memory foam, latex, and springs; Usage habits may result in localized indentations or body pressure marks; Space constraints mean there may be furniture around the bed, requiring precise boundary identification.

[0055] Specific implementation steps: Step 1, Personalized bed surface modeling: Start "learning mode" and the robot runs around the edge of the bed surface for a full circle; build a 3D model of the bed surface and identify raised and recessed areas; establish a mattress firmness distribution map through multiple pressure tests; record the texture features of the bed sheet and store them in the user's personalized configuration file; identify the bed surface boundary to avoid the risk of falling.

[0056] Step 2, Intelligent Zoning Cleaning Strategy: Divide the bed surface into multiple functional zones; Head area, usually with pillow indentations, set the speed to 0.6m / s and increase the number of passes; Main body area: Based on historical usage data, focus on cleaning key areas at a speed of 0.8m / s; Mattress edge area: Areas prone to dust accumulation, speed 0.5m / s, extend the dwell time; Special stain areas are visually identified and marked for subsequent focused treatment; Step 3, Long-term performance optimization: Based on historical cleaning data, analyze cleaning efficiency data weekly and automatically optimize path planning; adjust control parameters according to seasonal changes (e.g., thinner sheets in summer, thicker sheets in winter); learn users' wake-up times and intelligently schedule cleaning periods; predict mattress aging trends and adjust pressure compensation parameters in advance.

[0057] Step 4, Enhanced Home Safety: Child safety mode automatically shuts off when a child is detected approaching; pet recognition uses a camera to locate pets and prevent collisions; silent night mode reduces motor speed and noise; anti-tangling design with special track patterns prevents sheets from getting caught in the motor. Optimized Parameter Configuration for Home Scenarios: Working modes include Quick (30 minutes), Standard (60 minutes), and Deep (90 minutes); Energy Consumption Optimization: Intelligently adjusts power based on electricity prices and time of day; User Interaction: Supports real-time monitoring and parameter adjustment via a mobile app; Maintenance Reminders: Automatically reminds users to replace UV lamps and clean sensors based on usage time.

[0058] Home environment test: Dust mite removal rate increased from 88% to 99%; user operation complexity decreased by 70%; annual maintenance cost decreased by 45%.

[0059] Based on the present invention and the above embodiments, the performance test data of the present invention are as follows: Table 1: Comparison of Speed ​​Control Accuracy in Different Scenarios

[0060] Table 2: Data on sterilization efficacy verification

[0061] Table 3: Energy Efficiency Comparison Data

[0062] To achieve the above objectives, the present invention also provides an adaptive speed control system for a tracked bed sterilization robot based on multi-sensor fusion, such as... Figure 2 As shown, the system is applied to the adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion, and the system includes: The data model building unit is used to create and acquire first data corresponding to the tracked bed surface sterilization robot, and based on the first data and combined with preset surface scanning data, construct a first model corresponding to the tracked bed surface sterilization robot; wherein, the first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data and infrared thermal imaging data; the first model is a bed surface dynamic friction coefficient model; An adaptive adjustment unit is used to calculate and generate second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjust the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller; wherein, the second data is the deviation between the robot's actual angular velocity and theoretical angular velocity; The first data processing unit is used to analyze and process the temperature distribution characteristics of the bed surface based on the infrared thermal imaging data in the first data, and trigger corresponding speed adjustment or local supplementary killing operations according to the analysis results.

[0063] The data model construction unit further includes: a first generation module for calculating and generating a compression ratio relative to the mattress based on the pressure sensor array data; a second generation module for generating and acquiring roughness parameters corresponding to the surface of the bed sheet through visual sensors or lidar scanning; and a third generation module for calculating and generating a dynamic friction coefficient corresponding to the bed surface; wherein the calculation formula is: ; In the formula, This is the baseline value for mattress compression rate; This serves as the reference value for surface roughness. and These are the weighting coefficients calibrated through experiments; For mattress compression ratio; This refers to the surface roughness parameter of the bed sheet; And / or, the adaptive adjustment unit further includes: a first processing module, used to adjust the proportional factor, integral factor, and derivative factor of the PID controller in real time based on the second data and in conjunction with a fuzzy rule base; and a fourth generation module, used to calculate and generate a corresponding PWM duty cycle adjustment signal based on the adjusted PID parameters; wherein the calculation expression is: ; In the formula, This is the PWM duty cycle adjustment signal; It is a scaling factor; It is the integrating factor; The differential factor; The preset attenuation coefficient; It is a symbolic function; The second processing module is used to compensate for abrupt changes in ground friction characteristics using a sliding mode controller; wherein the sliding surface of the sliding mode controller is set as follows: ; In the formula, It is a sliding surface; For speed tracking error; The sliding surface coefficient; The control law is set as follows: ; In the formula, For equivalent control quantity; To switch the gain; It is a saturation function; Boundary layer thickness; is the gain coefficient of the sliding mode observer.

[0064] And / or, the first data processing unit further includes: a fifth generation module, used to calculate and generate a temperature rise rate and temperature distribution uniformity corresponding to a preset area of ​​the bed surface based on the infrared thermal imaging data; a first determination module, used to perform slippage determination or uneven coverage determination based on a pre-screening threshold; wherein, when the temperature rise rate of a certain area is detected to be lower than the first preset threshold, it is determined that the area has slipped and a first-level deceleration command is triggered; when the temperature distribution uniformity of the bed surface is detected to be lower than the second preset threshold, it is determined that the sterilization coverage is uneven and a local supplementary sterilization mode is activated, in which the robot's moving speed in the corresponding area is increased to 1.2 to 1.8 times the base speed; The system further includes: a second determination module, used to generate and acquire third data corresponding to the monitored drive motor, and determine whether the third data exceeds a third preset threshold; wherein the third data is the operating current of the drive motor; and the third preset threshold is a preset duration threshold; and a third determination module, used to generate and acquire fourth data corresponding to the tracked bed sterilization robot, and determine whether the fourth data exceeds a fourth preset threshold; wherein the fourth data is the monitored continuously occurring angular velocity deviation exceeding the limit event; and the fourth preset threshold is a threshold for the number of occurrences within a preset duration.

[0065] In the system solution embodiment of the present invention, the specific details of the method steps involved in the adaptive speed control of a tracked bed sterilization robot based on multi-sensor fusion have been described above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be repeated here.

[0066] To achieve the above objectives, the present invention also provides an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion, such as... Figure 3 As shown, the system includes a processor, a memory, and a control program for an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion. The processor executes the control program, which is stored in the memory. This control program implements the steps of the adaptive speed control method for the tracked bed sterilization robot based on multi-sensor fusion.

[0067] The specific details of the steps have been explained above and will not be repeated here.

[0068] In this embodiment of the invention, the built-in processor of the adaptive speed control platform for the tracked bed sterilization robot based on multi-sensor fusion can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or multiple integrated circuits packaged with the same or different functions. This includes combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor connects to various components using various interfaces and lines, and executes programs or units stored in the memory, as well as calling data stored in the memory, to perform various functions of adaptive speed control for the tracked bed sterilization robot based on multi-sensor fusion and to process data. The memory, used to store program code and various data, is installed in the adaptive speed control platform of the tracked bed sterilization robot based on multi-sensor fusion, and enables high-speed and automatic access to programs or data during operation. The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0069] To achieve the above objectives, the present invention also provides a computer-readable storage medium, such as... Figure 4 As shown, the computer-readable storage medium stores a control program for an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion. The control program implements the steps of the adaptive speed control method for the tracked bed sterilization robot based on multi-sensor fusion. The specific details of the steps have been described above and will not be repeated here.

[0070] In the description of embodiments of the present invention, it should be noted that any process or method description in the flowcharts or otherwise described herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0071] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, a “computer-readable medium” can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0072] This invention creates and acquires first data corresponding to a tracked bed sterilization robot through a method, and constructs a first model corresponding to the tracked bed sterilization robot based on the first data and preset surface scanning data. The first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data, and infrared thermal imaging data. The first model is a dynamic friction coefficient model of the bed surface. Based on the first data, second data corresponding to the tracked bed sterilization robot is calculated and generated. Based on the second data and combined with a composite controller, the moving speed of the tracked bed sterilization robot is adaptively adjusted. The second data is the deviation between the robot's actual angular velocity and its theoretical angular velocity. Based on the infrared thermal imaging data in the first data, the temperature distribution characteristics of the bed surface are analyzed and processed, and corresponding speed adjustment or local supplementary sterilization operations are triggered according to the analysis results. The invention also includes a corresponding system, platform, and storage medium, effectively solving the problems of poor bed surface adaptability, low control accuracy, and lack of effect verification in the prior art. It features fast response, high accuracy, and high reliability.

[0073] In other words, this invention achieves intelligent operation control of a tracked bed sterilization robot in complex bed environments through an innovative combination of multi-sensor data fusion and adaptive control technology. By employing multi-dimensional sensors to perceive the material characteristics and friction state of the bed surface in real time, and combining dynamic modeling and composite control algorithms to achieve precise adaptive speed adjustment, it effectively overcomes the problems of slippage and speed loss that easily occur on smooth or soft bed surfaces using traditional methods. Simultaneously, by introducing a closed-loop verification mechanism for sterilization effect based on infrared thermal imaging, it ensures the comprehensiveness and reliability of sterilization operations; improves the robot's movement stability, sterilization coverage, and operational safety on different types of sheets and mattresses; and optimizes system energy efficiency, providing a highly efficient and reliable intelligent sterilization solution for diverse application scenarios such as hotels, hospitals, and homes.

[0074] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An adaptive speed control method for a tracked bed-type sterilization robot based on multi-sensor fusion, characterized in that, The method includes: First data corresponding to the tracked bed surface sterilization robot is created and acquired. Based on the first data and combined with preset surface scanning data, a first model corresponding to the tracked bed surface sterilization robot is constructed. The first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data, and infrared thermal imaging data. The first model is a dynamic friction coefficient model of the bed surface. Based on the first data, second data corresponding to the tracked bed sterilization robot is calculated and generated. Based on the second data and combined with the composite controller, the moving speed of the tracked bed sterilization robot is adaptively adjusted. The second data is the deviation between the robot's actual angular velocity and its theoretical angular velocity. Based on the infrared thermal imaging data in the first data, the temperature distribution characteristics of the bed surface are analyzed and processed, and corresponding speed adjustment or local supplementary killing operations are triggered according to the analysis results.

2. The adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion according to claim 1, characterized in that, The step of creating and acquiring first data corresponding to the tracked bed surface sterilization robot, and constructing a first model corresponding to the tracked bed surface sterilization robot based on the first data and in combination with preset surface scanning data, further includes: The compression ratio relative to the mattress is calculated based on the data from the pressure sensor array. The roughness parameters corresponding to the surface of the bed sheet are generated and acquired by scanning with a visual sensor or lidar. Calculate the dynamic friction coefficient corresponding to the bed surface; the calculation formula is as follows: ， In the formula, This is the baseline value for mattress compression rate; This serves as the reference value for surface roughness. and These are the weighting coefficients calibrated through experiments; For mattress compression ratio; This refers to the surface roughness parameter of the bed sheet.

3. The adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion according to claim 1 or 2, characterized in that, The step of calculating and generating second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjusting the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller, further includes: Based on the second data, and combined with the fuzzy rule base, the proportional factor, integral factor and derivative factor of the PID controller are adjusted in real time; Based on the adjusted PID parameters, the corresponding PWM duty cycle adjustment signal is calculated and generated; the calculation expression is as follows: ， In the formula, This is the PWM duty cycle adjustment signal; It is a scaling factor; It is the integrating factor; The differential factor; The preset attenuation coefficient; It is a symbolic function.

4. The adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion according to claim 3, characterized in that, The step of calculating and generating second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjusting the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller, further includes: A sliding mode controller is used to compensate for abrupt changes in the ground friction characteristics; wherein, the sliding surface of the sliding mode controller is set as follows: ， In the formula, It is a sliding surface; For speed tracking error; The sliding surface coefficient; ， In the formula, For equivalent control quantity; To switch the gain; It is a saturation function; Boundary layer thickness; is the gain coefficient of the sliding mode observer.

5. The adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion according to claim 1, characterized in that, The step of analyzing and processing the bed surface temperature distribution characteristics based on the infrared thermal imaging data in the first data, and triggering corresponding speed adjustment or local supplementary killing operations according to the analysis results, also includes: Based on the infrared thermal imaging data, the temperature rise rate and temperature distribution uniformity corresponding to the preset area of ​​the bed surface are calculated and generated. Based on the pre-screening threshold, slippage or uneven coverage is determined respectively; when the temperature rise rate of a certain area is detected to be lower than the first preset threshold, it is determined that the area has slipped and the first-level deceleration command is triggered; when the temperature distribution uniformity of the bed surface is detected to be lower than the second preset threshold, it is determined that the sterilization coverage is uneven and the local supplementary sterilization mode is activated.

6. The adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion according to claim 1, characterized in that, The method further includes: Generate and acquire third data corresponding to the monitored drive motor, and determine whether the third data exceeds a third preset threshold; wherein, the third data is the operating current of the drive motor; and the third preset threshold is a preset duration threshold. A fourth set of data corresponding to the tracked bed sterilization robot is generated and acquired, and a fourth preset threshold is determined based on the fourth set of data; wherein, the fourth set of data is the monitoring of continuously occurring angular velocity deviation exceeding the limit event; and the fourth preset threshold is the number of times within a preset time period.

7. An adaptive speed control system for a tracked bed sterilization robot based on multi-sensor fusion, characterized in that, The system is applied to the adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion as described in any one of claims 1 to 6, and the system comprises: The data model building unit is used to create and acquire first data corresponding to the tracked bed surface sterilization robot, and based on the first data and combined with preset surface scanning data, construct a first model corresponding to the tracked bed surface sterilization robot; wherein, the first data includes gyroscope angular velocity data, encoder linear velocity data, pressure sensor array data and infrared thermal imaging data; the first model is a bed surface dynamic friction coefficient model; An adaptive adjustment unit is used to calculate and generate second data corresponding to the tracked bed sterilization robot based on the first data, and adaptively adjust the moving speed of the tracked bed sterilization robot based on the second data and in conjunction with the composite controller; wherein, the second data is the deviation between the robot's actual angular velocity and theoretical angular velocity; The first data processing unit is used to analyze and process the temperature distribution characteristics of the bed surface based on the infrared thermal imaging data in the first data, and trigger corresponding speed adjustment or local supplementary killing operations according to the analysis results.

8. The adaptive speed control system for a tracked bed sterilization robot based on multi-sensor fusion according to claim 7, characterized in that, The data model construction unit further includes: The first generation module is used to calculate and generate a compression ratio relative to the mattress based on the pressure sensor array data; The second generation module is used to generate and acquire roughness parameters corresponding to the surface of the bed sheet by scanning with a visual sensor or lidar. The third generation module is used to calculate the dynamic friction coefficient corresponding to the bed surface; the calculation formula is as follows: ， In the formula, This is the baseline value for mattress compression rate; This serves as the reference value for surface roughness. and These are the weighting coefficients calibrated through experiments; For mattress compression ratio; This refers to the surface roughness parameter of the bed sheet; And / or, the adaptive adjustment unit further includes: The first processing module is used to adjust the proportional factor, integral factor and derivative factor of the PID controller in real time based on the second data and in combination with the fuzzy rule base. The fourth generation module is used to calculate and generate the corresponding PWM duty cycle adjustment signal based on the adjusted PID parameters; the calculation expression is as follows: ， In the formula, This is the PWM duty cycle adjustment signal; It is a scaling factor; It is the integrating factor; The differential factor; The preset attenuation coefficient; It is a symbolic function; The second processing module is used to compensate for abrupt changes in ground friction characteristics using a sliding mode controller; wherein the sliding surface of the sliding mode controller is set as follows: ， In the formula, It is a sliding surface; For speed tracking error; The sliding surface coefficient; The control law is set as follows: ， In the formula, For equivalent control quantity; To switch the gain; It is a saturation function; Boundary layer thickness; This represents the gain coefficient of the sliding mode observer; And / or, the first data processing unit further includes: The fifth generation module is used to calculate and generate the temperature rise rate and temperature distribution uniformity corresponding to the preset area of ​​the bed surface based on the infrared thermal imaging data. The first judgment module is used to determine slippage or uneven coverage based on the pre-screening threshold. Specifically, when the temperature rise rate of a certain area is detected to be lower than the first preset threshold, it is determined that slippage has occurred in that area, and a first-level deceleration command is triggered. When the uniformity of the bed surface temperature distribution is detected to be lower than the second preset threshold, it is determined that the sterilization coverage is uneven, and a local supplementary sterilization mode is activated. The system also includes: The second determination module is used to generate and acquire third data corresponding to the monitored drive motor, and determine whether the third data exceeds a third preset threshold; wherein, the third data is the operating current of the drive motor; and the third preset threshold is a preset duration threshold. The third determination module is used to generate and acquire fourth data corresponding to the tracked bed sterilization robot, and determine whether the fourth data exceeds the fourth preset threshold; wherein, the fourth data is the monitoring of continuously occurring angular velocity deviation exceeding the limit event; the fourth preset threshold is the number of times within a preset time period.

9. An adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion, characterized in that, The system includes a processor, a memory, and a control program for an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion. The processor executes the control program, which is stored in the memory. This control program implements the adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a control program for an adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion. The control program for the adaptive speed control platform for a tracked bed sterilization robot based on multi-sensor fusion implements the adaptive speed control method for a tracked bed sterilization robot based on multi-sensor fusion as described in any one of claims 1 to 6.

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