Multi-scene integrated system and method for robot supervision and logistics service in health care place

Abstract

The invention discloses a health care place robot supervision and logistics service multi-scene integration system and method, and relates to the technical field of robots, and the method comprises the steps: obtaining the service time of a historical service record of a robot, and extracting a target time period in the service record according to a microphone and a panoramic camera installed on the robot; establishing a three-dimensional model of the health care place, extracting a moving track and a facility area, and extracting a target area in the facility area; installing audio pickups at facility points of the target areas to obtain a spectrogram of the noise source, obtaining historical adjustment records, and establishing an adjustment function corresponding to each target area; and obtaining set interaction parameters of the robot, obtaining an early warning value of the target area, and carrying out early warning prompt according to the early warning value. According to the method, whether the interaction parameters of the current robot are reasonable or not is judged by analyzing the historical service records and the adjustment records, the experience satisfaction degree of the user can be effectively improved, and the service accuracy of a health care place is enhanced.

Description

Technical Field

[0001] This invention relates to the field of robotics technology, specifically to an integrated system and method for multi-scenario robot supervision and logistical services in health and wellness facilities. Background Technology

[0002] Wellness and health facilities refer to those that provide health and wellness services. The use of robots in wellness and health facilities has become an important trend to improve service efficiency and optimize user experience. Robots in wellness and health facilities typically include multiple service functions to achieve integrated integration across multiple scenarios, such as environmental hygiene monitoring, security management, reception services, and logistics management. In environmental hygiene monitoring, robots can detect issues such as piled-up items in corridors, disorderly placement of items, and litter on the ground. In security management, robots can provide early warnings of fighting and abnormal behavior of elderly people in visible areas. In reception services, robots can provide consultation and guidance and check attendance at work posts. In logistics management, robots can provide services such as locating people, delivering items, and managing smart home interconnection. By having robots handle multiple tasks, the workload of human staff can be reduced. In the above scenarios, robots usually interact with users. However, currently, in human-computer interaction, staff usually set the interaction parameters between the robot and the user based on experience. However, due to the subjectivity of manual settings, and the fact that the interaction parameters between the robot and the user can affect the user's experience in different areas, if the interaction parameters can be intelligently judged based on different regional characteristics and conditions, it can effectively improve user satisfaction and enhance the supervision and service accuracy of health and wellness facilities. Summary of the Invention

[0003] The purpose of this invention is to provide an integrated system and method for multi-scenario robot supervision and logistical services in health and wellness facilities, in order to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A multi-scenario integrated approach for robot supervision and logistical services in health and wellness facilities includes the following steps: Step S100: Obtain the service time corresponding to the robot's historical service records; obtain the target range corresponding to each service time based on the microphone installed on the robot; obtain the local area of ​​the moving object in the monitoring video based on the panoramic camera installed on the robot; and then extract the target time period corresponding to the service record based on the target range and the local area. Step S200: Establish a 3D model of the health and wellness facility, extract the robot's movement trajectory during the target time period, obtain the facility area based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness facility, and mark the movement trajectory and facility area in the 3D model; divide the facility area into several sub-areas based on the movement trajectory, and extract the target area in the facility area. Step S300: Install audio pickups at the facility points in the target area to obtain the spectrum diagram corresponding to the noise source in the environment; obtain the adjustment records of the user's historical adjustment of the robot's interaction parameters; and establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter values ​​of the robot after adjustment. Step S400: Obtain the currently set robot interaction parameters, obtain the warning value corresponding to each target area based on the robot's inspection route and the established adjustment function, issue warning prompts to relevant personnel based on the warning values, and reset the robot's interaction parameters.

[0005] Furthermore, step S100 includes: Step S110: Obtain historical service records. Service records are records of corresponding service events captured by the robot when it monitors the health and wellness facility based on instructions pre-set by staff. Extract the service time of each service record. The service time is the period from the start of the event detected by the robot to the end of the event handling. Install a panoramic camera on top of the robot in advance and install microphones that can collect audio on multiple parts of the robot body, and number each microphone. Step S120: Based on the start time T1 and end time T2 corresponding to a certain service record R, extract a certain time period D from the start time T1 and end time T2, obtain the audio collected by each microphone at a certain time T0 in the time period D, obtain the microphone corresponding to the maximum decibel value of the audio, and take the position of the microphone at time T0 as the starting point, and take the fan-shaped range with radius r1 and central angle θ forward along the direction of the microphone itself as the target range of time T0; Step S130: Obtain the local region LR of a moving object within the target range, and obtain the values ​​at time T0 and the previous time T0. -1 The grayscale image of the local region LR is used to establish time T based on the grayscale value of each pixel. -1 The grayscale histogram h corresponding to time T0 -1 Calculate the histogram similarity between the two grayscale histograms and h0. If the histogram similarity is less than the preset similarity threshold, then mark time T0. If the number of marked times in time period D is greater than the preset first time period threshold, then time period D is taken as the target time period, and then all target time periods are extracted.

[0006] Since most people in health and wellness facilities have mobility impairments, it is essential to install microphones at different locations on robots deployed there to receive requests for help from various directions. Whether a user is communicating with others or making a phone call during the target time period, it indicates that the location corresponding to that time period is a high-frequency area for crowd gathering. These high-frequency areas are also the areas of high-frequency interaction between people and robots. Therefore, analyzing the target time period is more meaningful and reliable for this application compared to time periods corresponding to low-frequency areas of interaction between people and robots.

[0007] Furthermore, step S200 includes: Step S210: Establish a three-dimensional model of the health and wellness site, pre-deploy positioning sensors on the robot, extract the robot's movement trajectory in each target time period, and, based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness site, designate the area with the location corresponding to the facility point as the center and a radius of r2 as the facility area, and mark all movement trajectories and facility areas in the three-dimensional model. Step S220: Extract all movement trajectories within a facility area FR, obtain several sub-regions into which the facility area FR is divided based on the movement trajectories, and determine the area of ​​each sub-region and the total area S of the facility area FR. FR Given the number of sub-regions N, the target value for facility area FR is: If the target value is less than the preset target threshold and the number N is greater than the preset sub-region number threshold, then the facility region FR will be taken as the target region, and thus all target regions will be obtained.

[0008] The purpose of obtaining the target area here is to make the interaction function established below more reasonable. This is because, compared to the concentration of people in a certain area of ​​the facility area, the dispersion of people is more representative and reasonable for measuring the relationship between the adjustment distance and the adjustment value within the facility area. Therefore, in this application, it is necessary to divide the facility area into several sub-areas by the movement trajectory and calculate the variance between the areas of each sub-area in order to determine whether the facility area is the target area.

[0009] Furthermore, step S300 includes: Step S310: Obtain the user's historical adjustment of the robot's interaction parameters, and the adjustment records when the position is within the target area. The interaction parameter is the voice volume. Use the parameter value after the interaction parameter is adjusted as the adjustment value. An audio pickup is installed at the facility point corresponding to a target area P. When the noise source emits noise, the audio detected by the audio pickup is converted into a spectrum diagram, which is then used as the spectrum diagram SD0 of the noise source. Step S320: Obtain a certain adjustment record Q corresponding to the target region P, and extract the adjustment time T corresponding to the adjustment record Q. Q To obtain the adjustment time T Q Starting from point D, moving forward through time period D Q Inside, the audio pickup monitors the audio and obtains the spectrum at each moment, then selects a specific moment T. g The corresponding spectrum is used as SD g If the spectrum SD g If the difference in amplitude between each corresponding frequency in the spectrum diagram SD0 is not less than 0, then time T... g Mark the time period D. Q If the number of marked moments within the time frame is greater than the preset second time frame threshold, the adjustment record Q will be marked, and the distance between the position of the adjustment record Q during adjustment and the position of the noise source will be used as the adjustment distance. Then, an adjustment function is established corresponding to the target region P, in which the adjustment value changes with the adjustment distance, and the adjustment distance and adjustment value corresponding to several marked adjustment records are marked in the adjustment function.

[0010] Furthermore, step S400 includes: Step S410: Obtain the interaction parameters of the robot in each target area according to the current inspection route of the robot. Take the shortest distance between the noise source location and the inspection route in the target area P as L0. Convert the audio detected by the audio pickup corresponding to the target area P at the current time into the corresponding spectrum diagram. Determine whether the current time is the marked time based on the spectrum diagram of the noise source. Step S420: If it is the marked time, obtain the adjustment values ​​corresponding to the M adjustment distances closest to L0 in the adjustment function corresponding to the target region P, and take the average value among the adjustment values ​​as the target value V of the target region P. P Then, based on the parameter value V0 of the interaction parameter set in the target area P, the warning value of the target area P is obtained as follows: ; This allows for the generation of warning values ​​for each target area. If a warning value exceeds a preset warning threshold, a warning is issued to the relevant personnel, and the robot's interaction parameters are reset.

[0011] The integrated system for robot supervision and logistics services in health and wellness facilities includes a target time period extraction module, a target area extraction module, an adjustment function establishment module, and an early warning module. Target Time Period Extraction Module: This module is used to obtain the service time corresponding to the robot's historical service records. Based on the microphone installed on the robot, it obtains the target range corresponding to each service time. Based on the panoramic camera installed on the robot, it obtains the local area of ​​the moving object in the monitoring video. Then, based on the target range and the local area, it extracts the target time period corresponding to the service record. Target area extraction module: used to build a 3D model of the health and wellness site, extract the robot's movement trajectory during the target time period, obtain the facility area based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness site, and mark the movement trajectory and facility area in the 3D model; based on the movement trajectory, the facility area is divided into several sub-regions, and the target area in the facility area is extracted. The adjustment function establishment module is used to install audio pickups at facility points in the target area to obtain the spectrum diagram of the noise source in the environment; to obtain the adjustment record of the user's historical adjustment of the robot's interaction parameters; and to establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter value of the robot after adjustment. Early warning module: It is used to obtain the currently set interaction parameters of the robot, obtain the early warning value corresponding to each target area based on the robot's inspection route and the established adjustment function, and issue early warning prompts to relevant personnel based on the early warning values, and reset the robot's interaction parameters.

[0012] Furthermore, the target time period extraction module includes a service record acquisition unit, a target range division unit, and a target time period extraction unit; Service record acquisition unit: used to acquire historical service records, extract the service time of each service record, pre-install a panoramic camera on the top of the robot, install microphones that can collect audio on multiple parts of the robot body, and number each microphone; Target range segmentation unit: used to extract the audio collected by the microphone within the service time according to the service time corresponding to the service record, and to obtain the target range corresponding to each service time according to the orientation of the microphone itself; Target time period extraction unit: used to obtain local areas of mobile objects within the target range, obtain grayscale images corresponding to each service time, and then determine the marked time; and obtain the target time period based on the marked time.

[0013] Furthermore, the regulation function establishment module includes a regulation record analysis unit and a regulation function establishment unit; Adjustment Record Analysis Unit: Used to acquire historical user interaction parameters adjustment records of the robot, and the adjustment location within the target area, and use the parameter values ​​after the interaction parameters are adjusted as the adjustment values; install audio pickups at facility points in the target area to obtain the spectrum diagrams corresponding to noise sources in the environment; The adjustment function establishment unit is used to obtain the adjustment records corresponding to the target area, extract the adjustment time corresponding to the adjustment records, and establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter values ​​of the robot after adjustment.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides an integrated system and method for multi-scenario robot supervision and logistical services in health and wellness facilities, including: acquiring the service time of historical service records of the robot; extracting the target time period from the service records based on the microphone and panoramic camera installed on the robot; establishing a three-dimensional model of the health and wellness facility, extracting the movement trajectory and facility areas, and extracting the target area within the facility area; installing audio pickups at the facility points in the target area to obtain the spectrum diagram of the noise source, acquiring historical adjustment records, and establishing an adjustment function corresponding to each target area; acquiring the set interaction parameters of the robot, obtaining the warning value of the target area, and providing warning prompts based on the warning value. This invention, by analyzing historical service records and adjustment records, determines whether the current robot interaction parameters are reasonable, effectively improving user experience satisfaction and enhancing the service accuracy of health and wellness facilities. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the multi-scenario integrated method for robot supervision and logistical services in health and wellness facilities according to the present invention. Figure 2 This is a structural diagram of the multi-scenario integrated system for robot supervision and logistics services in health and wellness facilities according to the present invention. Detailed Implementation

[0016] 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.

[0017] Example: Figure 1 As shown, this invention provides a technical solution for a multi-scenario integrated method for robot supervision and logistical services in elderly care facilities, including the following steps: Step S100: Obtain the service time corresponding to the robot's historical service records; obtain the target range corresponding to each service time based on the microphone installed on the robot; obtain the local area of ​​the moving object in the monitoring video based on the panoramic camera installed on the robot; and then extract the target time period corresponding to the service record based on the target range and the local area. Step S110: Obtain historical service records. Service records are records of corresponding service events captured by the robot when it monitors the health and wellness facility based on instructions pre-set by staff. Extract the service time of each service record. The service time is the period from the start of the event detected by the robot to the end of the event handling. Install a panoramic camera on top of the robot in advance and install microphones that can collect audio on multiple parts of the robot body, and number each microphone. Step S120: Based on the start time T1 and end time T2 corresponding to a certain service record R, extract a certain time period D from the start time T1 and end time T2, obtain the audio collected by each microphone at a certain time T0 in the time period D, obtain the microphone corresponding to the maximum decibel value of the audio, and take the position of the microphone at time T0 as the starting point, and take the fan-shaped range with radius r1 and central angle θ forward along the direction of the microphone itself as the target range of time T0; Step S130: Obtain the local region LR of a moving object within the target range, and obtain the values ​​at time T0 and the previous time T0. -1 The grayscale image of the local region LR is used to establish time T based on the grayscale value of each pixel. -1 The grayscale histogram h corresponding to time T0 -1 Calculate the histogram similarity between the two grayscale histograms and h0. If the histogram similarity is less than the preset similarity threshold, then mark time T0. If the number of marked times in time period D is greater than the preset first time period threshold, then time period D is taken as the target time period, and then all target time periods are extracted.

[0018] In this embodiment, the moving object is the human body, and the local area is the mouth area. Based on the gray value of each pixel in the grayscale image of the local area LR, the same gray values ​​are aggregated to obtain the proportion of each gray value, and then a corresponding grayscale histogram is established. In this embodiment, the technique for calculating the histogram similarity is cosine similarity. Therefore, the smaller the histogram similarity, the less similar the two grayscale histograms are. When a human is speaking, the grayscale distribution of the mouth area is constantly changing, that is, the grayscale histograms of the previous moment and the next moment are less similar. Therefore, when the histogram similarity is less than the preset similarity threshold, it means that a person is speaking. Therefore, time T0 is marked to obtain the target time period of communication between people.

[0019] Step S200: Establish a 3D model of the health and wellness facility, extract the robot's movement trajectory during the target time period, obtain the facility area based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness facility, and mark the movement trajectory and facility area in the 3D model; divide the facility area into several sub-areas based on the movement trajectory, and extract the target area in the facility area. Step S210: Establish a three-dimensional model of the health and wellness site, pre-deploy positioning sensors on the robot, extract the robot's movement trajectory in each target time period, and, based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness site, designate the area with the location corresponding to the facility point as the center and a radius of r2 as the facility area, and mark all movement trajectories and facility areas in the three-dimensional model. Step S220: Extract all movement trajectories within a facility area FR, obtain several sub-regions into which the facility area FR is divided based on the movement trajectories, and determine the area of ​​each sub-region and the total area S of the facility area FR. FR Given the number of sub-regions N, the target value for facility area FR is: If the target value is less than the preset target threshold and the number N is greater than the preset sub-region number threshold, then the facility region FR will be taken as the target region, and thus all target regions will be obtained.

[0020] Step S300: Install audio pickups at the facility points in the target area to obtain the spectrum diagram corresponding to the noise source in the environment; obtain the adjustment records of the user's historical adjustment of the robot's interaction parameters; and establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter values ​​of the robot after adjustment. Step S310: Obtain the user's historical adjustment of the robot's interaction parameters, and the adjustment records when the position is within the target area. The interaction parameter is the voice volume. Use the parameter value after the interaction parameter is adjusted as the adjustment value. An audio pickup is installed at the facility point corresponding to a target area P. When the noise source emits noise, the audio detected by the audio pickup is converted into a spectrum diagram, which is then used as the spectrum diagram SD0 of the noise source. Step S320: Obtain a certain adjustment record Q corresponding to the target region P, and extract the adjustment time T corresponding to the adjustment record Q. Q To obtain the adjustment time T Q Starting from point D, moving forward through time period D Q Inside, the audio pickup monitors the audio and obtains the spectrum at each moment, then selects a specific moment T. g The corresponding spectrum is used as SD g If the spectrum SD g If the difference in amplitude between each corresponding frequency in the spectrum diagram SD0 is not less than 0, then time T... g Mark the time period D. Q If the number of marked moments within the time frame is greater than the preset second time frame threshold, the adjustment record Q will be marked, and the distance between the position of the adjustment record Q during adjustment and the position of the noise source will be used as the adjustment distance. Then, an adjustment function is established corresponding to the target region P, in which the adjustment value changes with the adjustment distance, and the adjustment distance and adjustment value corresponding to several marked adjustment records are marked in the adjustment function.

[0021] The decibel level of a noise source decreases with increasing distance, so the sound of the noise source will be different for people at different locations. Therefore, different adjustments are needed. This principle is used here to establish a corresponding adjustment function, and then to determine whether the manually set interactive parameters are reasonable.

[0022] Step S400: Obtain the currently set robot interaction parameters, obtain the warning value corresponding to each target area based on the robot's inspection route and the established adjustment function, issue warning prompts to relevant personnel based on the warning values, and reset the robot's interaction parameters.

[0023] Step S410: Obtain the interaction parameters of the robot in each target area according to the current inspection route of the robot. Take the shortest distance between the noise source location and the inspection route in the target area P as L0. Convert the audio detected by the audio pickup corresponding to the target area P at the current time into the corresponding spectrum diagram. Determine whether the current time is the marked time based on the spectrum diagram of the noise source. Step S420: If it is the marked time, obtain the adjustment values ​​corresponding to the M adjustment distances closest to L0 in the adjustment function corresponding to the target region P, and take the average value among the adjustment values ​​as the target value V of the target region P. P Then, based on the parameter value V0 of the interaction parameter set in the target area P, the warning value of the target area P is obtained as follows: ; In the formula y=1-e -x In the equation, y increases as x increases, and when x > 0, y takes values ​​from 0 to 1; since |V P The larger the value of -V0|, the greater the deviation between the manually set interactive parameter value and the expected value, and the higher the warning value should be. Therefore, in this solution, |V P The larger the value of -V0|, the higher the warning value Y. P The larger the value, the more reasonable and reliable the formula design is.

[0024] This allows for the generation of warning values ​​for each target area. If a warning value exceeds a preset warning threshold, a warning is issued to the relevant personnel, and the robot's interaction parameters are reset.

[0025] This invention also provides an integrated system for multi-scenario robot supervision and logistical services in health and wellness facilities, such as... Figure 2 As shown, it includes: Target Time Period Extraction Module: This module is used to obtain the service time corresponding to the robot's historical service records. Based on the microphone installed on the robot, it obtains the target range corresponding to each service time. Based on the panoramic camera installed on the robot, it obtains the local area of ​​the moving object in the monitoring video. Then, based on the target range and the local area, it extracts the target time period corresponding to the service record. Target area extraction module: used to build a 3D model of the health and wellness site, extract the robot's movement trajectory during the target time period, obtain the facility area based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness site, and mark the movement trajectory and facility area in the 3D model; based on the movement trajectory, the facility area is divided into several sub-regions, and the target area in the facility area is extracted. The adjustment function establishment module is used to install audio pickups at facility points in the target area to obtain the spectrum diagram of the noise source in the environment; to obtain the adjustment record of the user's historical adjustment of the robot's interaction parameters; and to establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter value of the robot after adjustment. Early warning module: It is used to obtain the currently set interaction parameters of the robot, obtain the early warning value corresponding to each target area based on the robot's inspection route and the established adjustment function, and issue early warning prompts to relevant personnel based on the early warning values, and reset the robot's interaction parameters.

[0026] The target time period extraction module includes a service record acquisition unit, a target range division unit, and a target time period extraction unit; Service record acquisition unit: used to acquire historical service records, extract the service time of each service record, pre-install a panoramic camera on the top of the robot, install microphones that can collect audio on multiple parts of the robot body, and number each microphone; Target range segmentation unit: used to extract the audio collected by the microphone within the service time according to the service time corresponding to the service record, and to obtain the target range corresponding to each service time according to the orientation of the microphone itself; Target time period extraction unit: used to obtain local areas of mobile objects within the target range, obtain grayscale images corresponding to each service time, and then determine the marked time; and obtain the target time period based on the marked time.

[0027] The regulation function establishment module includes a regulation record analysis unit and a regulation function establishment unit; Adjustment Record Analysis Unit: Used to acquire historical user interaction parameters adjustment records of the robot, and the adjustment location within the target area, and use the parameter values ​​after the interaction parameters are adjusted as the adjustment values; install audio pickups at facility points in the target area to obtain the spectrum diagrams corresponding to noise sources in the environment; The adjustment function establishment unit is used to obtain the adjustment records corresponding to the target area, extract the adjustment time corresponding to the adjustment records, and establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter values ​​of the robot after adjustment.

[0028] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A multi-scenario integrated method for robot supervision and logistical services in health and wellness facilities, characterized in that: Includes the following steps: Step S100: Obtain the service time corresponding to the robot's historical service records; obtain the target range corresponding to each service time based on the microphone installed on the robot; obtain the local area of ​​the moving object in the monitoring video based on the panoramic camera installed on the robot; and then extract the target time period corresponding to the service record based on the target range and the local area. Step S200: Establish a 3D model of the health and wellness facility, extract the robot's movement trajectory during the target time period, obtain the facility area based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness facility, and mark the movement trajectory and facility area in the 3D model; divide the facility area into several sub-areas based on the movement trajectory, and extract the target area in the facility area. Step S300: Install audio pickups at the facility points in the target area to obtain the spectrum diagram corresponding to the noise source in the environment; obtain the adjustment records of the user's historical adjustment of the robot's interaction parameters; and establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter values ​​of the robot after adjustment. Step S400: Obtain the currently set robot interaction parameters, obtain the warning value corresponding to each target area based on the robot's inspection route and the established adjustment function, issue warning prompts to relevant personnel based on the warning values, and reset the robot's interaction parameters.

2. The integrated method for multi-scenario supervision and logistical services of robots in health and wellness facilities according to claim 1, characterized in that, Step S100 includes: Step S110: Obtain historical service records, which are records of corresponding service events captured by the robot when it monitors the health and wellness facility based on instructions pre-set by staff; extract the service time of each service record, which is the time period from the start of the robot's discovery of the event to the completion of the event; install a panoramic camera on top of the robot in advance, and install microphones capable of collecting audio on multiple parts of the robot body, and number each microphone. Step S120: Based on the start time T1 and end time T2 corresponding to a certain service record R, extract a certain time period D from the start time T1 and end time T2, obtain the audio collected by each microphone at a certain time T0 in the time period D, obtain the microphone corresponding to the maximum decibel value of the audio, and take the position of the microphone at time T0 as the starting point, and take the fan-shaped range with radius r1 and central angle θ along the orientation of the microphone itself as the target range of time T0; Step S130: Obtain the local region LR of a moving object within the target range, and obtain the values ​​at time T0 and the previous time T0. -1 The grayscale image of the local region LR is used to establish time T based on the grayscale value of each pixel. -1 The grayscale histogram h corresponding to time T0 -1 Calculate the histogram similarity between the two grayscale histograms and h0. If the histogram similarity is less than a preset similarity threshold, then mark the time T0. If the number of marked times in time period D is greater than a preset first time period threshold, then time period D is taken as the target time period, and then all target time periods are extracted.

3. The integrated method for multi-scenario supervision and logistical services of robots in health and wellness facilities according to claim 1, characterized in that, Step S200 includes: Step S210: Establish a three-dimensional model of the health and wellness site, pre-deploy positioning sensors on the robot, extract the robot's movement trajectory in each target time period, and, based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness site, designate the area with the location corresponding to the facility point as the center and a radius of r2 as the facility area, and mark all movement trajectories and facility areas in the three-dimensional model. Step S220: Extract all movement trajectories within a facility area FR, obtain several sub-regions into which the facility area FR is divided based on the movement trajectories, and determine the area of ​​each sub-region and the total area S of the facility area FR. FR Given the number of sub-regions N, the target value for facility area FR is: If the target value is less than the preset target threshold and the number N is greater than the preset sub-region number threshold, then the facility area FR is taken as the target area, and thus all target areas are obtained.

4. The integrated method for multi-scenario supervision and logistical services of robots in health and wellness facilities according to claim 1, characterized in that, Step S300 includes: Step S310: Obtain the user's historical adjustment of the robot's interaction parameters, and the adjustment records when the position is within the target area. The interaction parameter is the voice volume. The parameter value after the interaction parameter is adjusted is used as the adjustment value. An audio pickup is installed at a facility point corresponding to a target area P. When a noise source emits noise, the audio detected by the audio pickup is converted into a spectrum diagram, which is then used as the spectrum diagram SD0 of the noise source. Step S320: Obtain a certain adjustment record Q corresponding to the target region P, and extract the adjustment time T corresponding to the adjustment record Q. Q To obtain the adjustment time T Q Starting from point D, moving forward through time period D Q Inside, the audio pickup monitors the audio and obtains the spectrum at each moment, then selects a specific moment T. g The corresponding spectrum is used as SD g If the spectrum SD g If the difference in amplitude between the frequency and the corresponding frequency in the spectrum diagram SD0 is not less than 0, then the time T is... g Mark the time period D. Q If the number of marked moments within the time frame is greater than the preset second time frame threshold, then the adjustment record Q will be marked, and the distance between the position of the adjustment record Q during adjustment and the position of the noise source will be used as the adjustment distance. Then, an adjustment function is established corresponding to the target region P, in which the adjustment value changes with the adjustment distance, and the adjustment distance and adjustment value corresponding to several marked adjustment records are marked in the adjustment function.

5. The method for integrating robot supervision and logistical services in health and wellness facilities across multiple scenarios according to claim 4, characterized in that, Step S400 includes: Step S410: Obtain the interaction parameters of the robot in each target area according to the current inspection route of the robot. Take the shortest distance between the noise source location and the inspection route in the target area P as L0. Convert the audio detected by the audio pickup corresponding to the target area P at the current time into the corresponding spectrum diagram. Determine whether the current time is the marked time based on the spectrum diagram of the noise source. Step S420: If it is a marked time, obtain the adjustment values ​​corresponding to the M adjustment distances closest to the distance L0 in the adjustment function corresponding to the target region P, and take the average value among the adjustment values ​​as the target value V of the target region P. P Then, based on the parameter value V0 of the interaction parameters set in the target area P, the warning value of the target area P is obtained as follows: ; This allows for the generation of warning values ​​for each target area. If a warning value exceeds a preset warning threshold, a warning is issued to the relevant personnel, and the robot's interaction parameters are reset.

6. A multi-scenario integrated system for robot supervision and logistical services in health and wellness facilities, used to execute the multi-scenario integrated method for robot supervision and logistical services in health and wellness facilities as described in any one of claims 1-5, characterized in that, The system includes a target time period extraction module, a target area extraction module, an adjustment function establishment module, and an early warning module. Target Time Period Extraction Module: This module is used to obtain the service time corresponding to the robot's historical service records. Based on the microphone installed on the robot, it obtains the target range corresponding to each service time. Based on the panoramic camera installed on the robot, it obtains the local area of ​​the moving object in the monitoring video. Then, based on the target range and the local area, it extracts the target time period corresponding to the service record. Target area extraction module: used to build a 3D model of the health and wellness site, extract the robot's movement trajectory during the target time period, obtain the facility area based on the facility points corresponding to the fixed and unchanging facilities in the health and wellness site, and mark the movement trajectory and facility area in the 3D model; based on the movement trajectory, the facility area is divided into several sub-regions, and the target area in the facility area is extracted. The adjustment function establishment module is used to install audio pickups at facility points in the target area to obtain the spectrum diagram of the noise source in the environment; to obtain the adjustment record of the user's historical adjustment of the robot's interaction parameters; and to establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter value of the robot after adjustment. Early warning module: It is used to obtain the currently set interaction parameters of the robot, obtain the early warning value corresponding to each target area based on the robot's inspection route and the established adjustment function, and issue early warning prompts to relevant personnel based on the early warning values, and reset the robot's interaction parameters.

7. The integrated system for multi-scenario supervision and logistical services of robots in health and wellness facilities according to claim 6, characterized in that, The target time period extraction module includes a service record acquisition unit, a target range division unit, and a target time period extraction unit. Service record acquisition unit: used to acquire historical service records, extract the service time of each service record, pre-install a panoramic camera on the top of the robot, install microphones that can collect audio on multiple parts of the robot body, and number each microphone; Target range segmentation unit: used to extract the audio collected by the microphone within the service time according to the service time corresponding to the service record, and to obtain the target range corresponding to each service time according to the orientation of the microphone itself; Target time period extraction unit: used to obtain local areas of mobile objects within the target range, obtain grayscale images corresponding to each service time, and then determine the marked time; and obtain the target time period based on the marked time.

8. The integrated system for multi-scenario supervision and logistical services of robots in health and wellness facilities according to claim 6, characterized in that, The regulation function establishment module includes a regulation record analysis unit and a regulation function establishment unit; Adjustment Record Analysis Unit: Used to acquire historical user interaction parameters adjustment records of the robot, and the adjustment location within the target area, and use the parameter values ​​after the interaction parameters are adjusted as the adjustment values; install audio pickups at facility points in the target area to obtain the spectrum diagrams corresponding to noise sources in the environment; The adjustment function establishment unit is used to obtain the adjustment records corresponding to the target area, extract the adjustment time corresponding to the adjustment records, and establish the adjustment function corresponding to each target area based on the distance between the robot and the noise source during adjustment and the parameter values ​​of the robot after adjustment.

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