Intelligent lifting control system and method based on contactless posture recognition

Abstract

The application discloses an intelligent lifting control system and method based on contactless posture recognition, relates to the technical field of hand washing facility lifting control, and comprises determining the initial height of a hand washing table surface in a hand washing facility and initially adjusting the height of the hand washing table surface; analyzing the deviation degree of the initial height of the hand washing table surface and a user posture to obtain target posture data; analyzing the time sequence correlation degree between the height posture of the user in a period and the matching error of the hand washing table surface, analyzing the adjustment intention of the user to the height of the hand washing table surface, determining the adjustment intention of the user to the hand washing table surface, analyzing the adjustment direction intention of the user to the hand washing table surface, and obtaining adjustment intention data; and according to the adjustment intention data, the height of the hand washing table surface in the hand washing facility is controlled to be lifted or lowered, and the height of the hand washing table surface is adjusted, so that the shortcomings that the hand washing facility based on contactless posture recognition is not clear about the intention of the user and the lifting of the hand washing table surface is prone to be miscontrolled are completely solved.

Description

Technical Field

[0001] This invention relates to the field of handwashing facility lifting control technology, specifically to an intelligent lifting control system and method based on contactless posture recognition. Background Technology

[0002] As healthcare institutions continue to raise their infection control requirements, the demand for contactless, intelligent handwashing facilities is also increasing. Currently, handwashing facilities that can be adjusted to height automatically have appeared on the market. These facilities use millimeter-wave radar to detect the user's height and automatically adjust the height of the handwashing surface.

[0003] While current handwashing facilities can detect users' height and standing height in real time and adjust the height of the washbasin accordingly, users' postures are not static during actual use. Users may bend over to varying degrees due to personal habits, or turn sideways to grab hand soap, engage in brief conversations, or adjust clothing. These changes in posture and position make it difficult for the handwashing facility's lifting control system to effectively correlate the user's intention to move up or down with these changes in posture. This can lead to misinterpretations of the user's intention, not only interfering with the normal handwashing process and causing discomfort, but also prompting users to operate the facilities via touch-sensitive knobs, thus contradicting the original intention of contactless design. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent lifting control system and method based on contactless posture recognition to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent lifting control method based on contactless posture recognition, the method comprising: Step S1: Obtain the initial height record of the user collected by the handwashing facility, determine the initial height of the handwashing countertop in the handwashing facility, and make initial adjustments to the height of the handwashing countertop; Step S2: Obtain the user's body posture record after the initial adjustment of the washbasin surface, analyze the deviation between the initial height of the washbasin surface and the user's posture, and obtain the target posture data; Step S3: Based on the target posture data, analyze the temporal correlation between the user's height posture and the matching error of the washbasin surface in the current period, analyze the user's intention to adjust the height of the washbasin surface, determine the user's intention to adjust the washbasin surface, analyze the user's intention to adjust the direction of the washbasin surface, and obtain the adjustment intention data. Step S4: Based on the adjustment intention data, adjust the height of the handwashing countertop in the handwashing facility by raising and lowering it.

[0006] Furthermore, step S3 includes: Obtain the user's target pose data within the current period, calculate the height change value and matching error change value for each marked time window, set the time delay variable τ and the time delay window threshold g, obtain each integer between -g and +g, and use it as the value range τ´ of the time delay variable τ; Set the active intent variable K, calculate the correlation coefficient J(τ) between the user's height change and the matching error change within the range τ´ of the time delay variable τ, and obtain the maximum value J of the correlation coefficient. max Set the correlation coefficient threshold J´, when J max When J' > J', the correlation coefficient J(τ) is obtained as the maximum value J. max When the time delay variable τ takes the value τ*, if τ*>0, it is determined that the user has the intention to actively adjust the height of the washbasin surface, and K is set to 1; otherwise, it is determined that the user does not have the intention to actively adjust the height of the washbasin surface, and K is set to 0. K takes the value of 1 or 0. When J max When J' ≤ J', set the short-term sampling value d and the long-term sampling value L' of the user's height, and obtain the maximum value E of each short-term attitude energy in the y-th marked time window within the current period. (short,max) and the maximum value E of the energy of each long-term attitude (long,max) Calculate the user's pose energy ratio η=E in the y-th labeled time window. (short,max) / E (long,max) ; Calculate the proportion of attitude transitions ζ in the y-th marked time window. y Calculate the user's intention value U for adjusting the height of the washbasin surface within the y-th marked time window. y Obtain the maximum value U of the user's intention to adjust the height of the washbasin surface within each marked time window. max Set the adjustment intent threshold U, when U max If the value is >U, it is determined that the user has the intention to actively adjust the height of the washbasin surface in the current cycle, and K is set to 1; otherwise, it is determined that the user does not have the intention to actively adjust the height of the washbasin surface, and K is set to 0. Set the adjustment direction variable P. When the user is determined to have an active intention to adjust the washbasin surface, obtain the mean value R´ of the effective matching error within each marked time window. When R´>0, it is determined that the washbasin surface is too low, and P=1. Otherwise, it is determined that the washbasin surface is too high, and P=-1. P takes the value of -1 or 1. The adjustment direction variable P and the active intention variable K of the handwashing facility in the current cycle are obtained and aggregated to obtain the lifting and lowering adjustment data of the handwashing facility in the current cycle; The above steps, by obtaining the time delay variable corresponding to the maximum value of the correlation coefficient, completely eliminate the shortcomings of handwashing facilities causing erroneous lifting and lowering caused by common passive postures such as bending over and standing up. Furthermore, by calculating the posture energy ratio, high-frequency, high-energy active gestures are separated from low-frequency, smooth passive bending from the intrinsic characteristics of the signal, thereby effectively distinguishing the differences brought about by different movement modes. Finally, by obtaining the adjustment direction variable and active intention variable of the handwashing facility in the current cycle, the shortcomings of traditional methods in accurately identifying the user's adjustment intention are solved, thus improving the accuracy of the lifting and lowering control of the handwashing countertop.

[0007] Furthermore, step S2 includes: Once the height of the handwashing station is initially adjusted, the handwashing station records the user's posture after the initial adjustment, thus obtaining a record of the body posture after the initial adjustment of the handwashing station. The user height is obtained from each sampling point in the body posture record, the duration of the time window is set, and the user height set G for a certain time window is obtained. Calculate the difference Δh between the maximum and minimum user heights in the user height set G. w Set the height change threshold h δ When △h w h δ If the user's posture is unstable within a certain time window, then that time window is removed. When Δh w ≤h δ If the user's posture is stable within a certain time window, then that time window is recorded as the marked time window. Obtain the user height set for each marked time window in the body posture record, and obtain the initial height h of the handwashing station surface in the handwashing facility. base Set the posture physiological offset distance L, obtain the average user height h[e] of each sampling point in the e-th marked time window of the height and posture record, and calculate the effective matching error R of the e-th marked time window. e ; Set an effective matching error threshold R, when |R| e If |>R, it is determined that there is a deviation between the user's posture and the initial height of the handwashing station in the handwashing facility during the e-th marked time window; otherwise, no processing is performed on the e-th marked time window. Calculate the deviation value F between the initial height of the washbasin surface and the user's posture, and set a deviation threshold f. When F ≤ f, it is determined that there is a deviation between the user's posture and the initial height of the washbasin surface. Obtain the user's height set and effective matching error from the marked time window in the user's body posture record and aggregate them to obtain the target posture data. Otherwise, it is determined that there is no deviation between the user's posture and the initial height of the washbasin surface, and the height of the washbasin surface in the handwashing facility is not adjusted.

[0008] Furthermore, step S1 includes: Set the feature sampling point value 'a' and the unit duration. When the handwashing facility detects the user within the preset detection area for 'a' consecutive sampling points, the user's height signal is collected, and the user's height signal within the first unit duration after the collection begins is recorded to obtain the user's initial height record. The user heights were obtained from the initial height records, and the user heights were sampled at various sampling points for each handwashing facility. These samples were then aggregated to obtain the initial height dataset H. The median absolute deviation h of the initial height dataset H was then obtained. △ ; Calculate the height deviation threshold B', when B C When B > B', it is determined that there is a data deviation between the c-th sampling point and the initial height dataset H, and the c-th sampling point is recorded as an outlier sampling point. C If the value is less than or equal to B', then the c-th sampling point will not be processed. Remove all outlier sampling points from the initial height dataset H to obtain dataset H'. Obtain the average height of the user corresponding to each sampling point in dataset H' and record it as the initial height h. Calculate the initial height h of the handwashing station countertop based on the initial height h. base Get the current height h of the handwashing station surface in the handwashing facility. start Calculate the initial lifting height of the handwashing facility Δh = h start -h base ; Based on the initial lifting height △h, adjust the height of the handwashing station. The specific adjustment process is as follows: when △h≥0, control the handwashing facility to raise the handwashing station by △h; when △h<0, control the handwashing facility to lower the handwashing station by |△h|.

[0009] Furthermore, step S4 includes: Obtain the lifting and lowering adjustment data of the handwashing facilities in the current cycle, and extract the adjustment direction variable P and the active intention variable K of the handwashing facilities in the current cycle from the lifting and lowering adjustment data; Based on the height adjustment data, the height of the handwashing countertop in the handwashing facility is controlled by raising and lowering. The specific process is as follows: When K=0, it is determined that the user does not have the intention to actively adjust the washbasin surface, and the height of the washbasin surface of the handwashing facility is not adjusted. When K=1, it is determined that the user has the intention to actively adjust the washbasin surface. Set the minimum lifting height △h min When P=-1, the handwashing station in the handwashing facility is lowered by Δh each time. min The height adjustment of the handwashing facility is completed when the effective matching error between the handwashing countertop and the user within a unit of time is less than the effective matching error threshold R. When P=1, the handwashing station surface in the handwashing facility is raised, with each rise being Δh. min The height adjustment of the handwashing station is completed when the effective matching error between the handwashing station and the user within a unit of time is less than the effective matching error threshold R.

[0010] To better implement the above method, an intelligent lifting control system is also proposed, which includes a height initial adjustment module, a deviation analysis module, an adjustment intention analysis module, and a lifting control module. The initial height adjustment module is used to acquire the initial height records of users collected by the handwashing facility, determine the initial height of the handwashing countertop in the handwashing facility, and perform initial adjustment of the height of the handwashing countertop; The deviation analysis module is used to acquire the user's body posture record after the initial adjustment of the washbasin surface, analyze the degree of deviation between the initial height of the washbasin surface and the user's posture, and obtain the target posture data. The adjustment intention analysis module is used to analyze the user's intention to adjust the height of the washbasin surface and the user's intention to adjust the direction of the washbasin surface, and obtain adjustment intention data. The lifting control module is used to control the height of the handwashing countertop in the handwashing facility based on the adjustment intention data.

[0011] Furthermore, the initial height adjustment module includes an initial height determination unit and an initial height adjustment unit; The initial height determination unit is used to collect the user's height signal, obtain the user's initial height record, and determine the initial height of the handwashing station surface in the handwashing facility; The initial height adjustment unit is used to adjust the initial height of the handwashing station surface in the handwashing facility according to the initial height of the handwashing station surface.

[0012] Furthermore, the deviation analysis module includes an attitude recording acquisition unit and a deviation analysis unit; The posture recording and acquisition unit is used to record the user's posture after the initial adjustment of the washbasin height, and obtain the user's body posture record after the initial adjustment of the washbasin. The deviation analysis unit is used to calculate the deviation between the initial height of the washbasin surface and the user's posture based on the user's body posture record, and to analyze the degree of deviation between the initial height of the washbasin surface and the user's posture to obtain the target posture data.

[0013] Furthermore, the intention analysis module includes an intention analysis unit and a direction analysis unit; The intention analysis unit is used to analyze the temporal correlation between the user's height and posture and the matching error of the washbasin surface in the current period based on the target posture data, and to analyze the user's intention to adjust the height of the washbasin surface and determine the user's intention to adjust the height of the washbasin surface. The adjustment direction analysis unit is used to analyze the user's intention to adjust the height of the washbasin surface, and obtain adjustment intention data.

[0014] Furthermore, the lifting control module includes a lifting control unit; The lifting control unit is used to acquire the lifting adjustment data of the handwashing facility in the current cycle, obtain the adjustment direction variable and active intention variable of the handwashing facility in the current cycle from the lifting adjustment data, and control the lifting of the handwashing countertop in the handwashing facility according to the lifting adjustment data.

[0015] Compared with existing technologies, the advantages of this invention are as follows: Existing contactless handwashing facilities cannot distinguish between the user's active intention to raise or lower the handwashing surface and passive posture changes such as bending over, standing up, or turning to the side, which can easily lead to frequent erroneous raising and lowering of the countertop and interfere with the normal handwashing process. However, this invention sets the initial height of the handwashing surface and collects the user's posture record in front of the handwashing surface after the initial height is set, thereby determining whether there is a deviation between the initially set height of the handwashing surface and the user's posture, and accurately identifying the user's intention to adjust the handwashing surface. After determining the adjustment intention, the specific adjustment direction of the handwashing surface is also clarified. This completely solves the shortcomings of contactless posture recognition handwashing facilities that are unclear about the user's intention and are prone to erroneous control of the handwashing surface raising and lowering, and realizes intelligent control of the raising and lowering of the handwashing surface in a truly meaningful sense. Attached Figure Description

[0016] Figure 1 This is a flowchart of the adjustment intention determination process for the intelligent lifting control method based on non-contact posture recognition according to the present invention. Figure 2 This is a flowchart of the modules of an intelligent lifting control system according to the present invention. Detailed Implementation

[0017] 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.

[0018] Example: Figures 1-2 As shown, the present invention provides a technical solution, an intelligent lifting control method based on contactless posture recognition, the method comprising: Step S1: Obtain the initial height record of the user collected by the handwashing facility, determine the initial height of the handwashing countertop in the handwashing facility, and make initial adjustments to the height of the handwashing countertop; Step S1 includes: Set the feature sampling point value 'a' and the unit duration. When the handwashing facility detects the user within the preset detection area for 'a' consecutive sampling points, the user's height signal is collected, and the user's height signal within the first unit duration after the collection begins is recorded to obtain the user's initial height record. For example, the sampling rate for handwashing facilities to sample users' height is a preset value, which in a specific embodiment is 20Hz, that is, sampling once every 0.05 seconds; The user heights were obtained from the initial height records, and the user heights were sampled at various sampling points for each handwashing facility. These samples were then aggregated to obtain the initial height dataset H. The median absolute deviation h of the initial height dataset H was then obtained. △ ; For example, the median absolute deviation h △ The specific acquisition process is as follows: Obtain the median h´ of the user heights at each sampling point in the initial height dataset H. Then, obtain the median of the absolute values ​​of the user heights at each sampling point in the initial height dataset H minus the median h´, and denote this as the median absolute deviation h of the initial height dataset H. △ ; Calculate the height deviation threshold B', when B C When B > B', it is determined that there is a data deviation between the c-th sampling point and the initial height dataset H, and the c-th sampling point is recorded as an outlier sampling point. C If the value is less than or equal to B', then the c-th sampling point will not be processed. For example, the specific calculation process for the high deviation threshold B' is as follows: Set the deviation coefficient α, and obtain the absolute value of the difference between the c-th sampling point in the initial height dataset H and the median absolute deviation H´, to obtain the data deviation B of the c-th sampling point from the initial height dataset H. C Calculate the height deviation threshold B´=α×h△ ; Remove all outlier sampling points from the initial height dataset H to obtain dataset H'. Obtain the average height of the user corresponding to each sampling point in dataset H' and record it as the initial height h. Calculate the initial height h of the handwashing station countertop based on the initial height h. base Get the current height h of the handwashing station surface in the handwashing facility. start Calculate the initial lifting height of the handwashing facility Δh = h start -h base ; For example, calculate the initial height h of the handwashing station countertop based on the initial height h. base The specific calculation process is as follows: Obtain the preset first weight coefficient, second weight coefficient, and third weight coefficient β1, β2, and β3 for height; obtain the preset first threshold, second threshold, and third threshold h1, h2, and h3 for height. Obtain the first and second baseline height values ​​γ1 and γ2, where β1>β2>β3>0, 0 Calculate the initial height h of the handwashing countertop in the handwashing facility. base : , Based on the initial lifting height △h, adjust the height of the handwashing station. The specific adjustment process is as follows: when △h≥0, control the handwashing facility to raise the handwashing station by △h; when △h<0, control the handwashing facility to lower the handwashing station by |△h|.

[0019] Step S2: Obtain the user's body posture record after the initial adjustment of the washbasin surface, analyze the deviation between the initial height of the washbasin surface and the user's posture, and obtain the target posture data; Step S2 includes: Once the height of the handwashing station is initially adjusted, the handwashing station records the user's posture after the initial adjustment, thus obtaining a record of the body posture after the initial adjustment of the handwashing station. The user height is obtained from each sampling point in the body posture record, the duration of the time window is set, and the user height set G for a certain time window is obtained. For example, the specific process of obtaining the user's height set G is as follows: The body posture record is divided into time windows, and the user heights of each sampling point within a certain time window are obtained and aggregated to obtain the user height set G for a certain time window. Calculate the difference Δh between the maximum and minimum user heights in the user height set G.​w Set the height change threshold h δ When △h w h δ If the user's posture is unstable within a certain time window, then that time window is removed. When Δh w ≤h δ If the user's posture is stable within a certain time window, then that time window is recorded as the marked time window. Obtain the user height set for each marked time window in the body posture record, and obtain the initial height h of the handwashing station surface in the handwashing facility. base Set the posture physiological offset distance L, obtain the average user height h[e] of each sampling point in the e-th marked time window of the height and posture record, and calculate the effective matching error R of the e-th marked time window. e ; For example, the effective matching error R e The specific calculation formula is as follows: , Set an effective matching error threshold R, when |R| e If |>R, it is determined that there is a deviation between the user's posture and the initial height of the handwashing station in the handwashing facility during the e-th marked time window; otherwise, no processing is performed on the e-th marked time window. Calculate the deviation value F between the initial height of the washbasin and the user's posture, set the deviation threshold f, and when F≤f, it is determined that there is a deviation between the user's posture and the initial height of the washbasin. Obtain the user's height set and effective matching error in the marked time window of the user's body posture record and collect them to obtain the target posture data. Otherwise, it is determined that there is no deviation between the user's posture and the initial height of the washbasin, and the height of the washbasin in the handwashing facility is not adjusted. For example, the specific calculation process for the deviation value F is as follows: Obtain the total number n´ of the marked time windows where the user's posture is determined to deviate from the initial height of the washbasin surface, obtain the total number n of each marked time window, and calculate the deviation value F=n´ / n between the initial height of the washbasin surface and the user's posture.

[0020] Step S3: Based on the target posture data, analyze the temporal correlation between the user's height posture and the matching error of the washbasin surface in the current period, analyze the user's intention to adjust the height of the washbasin surface, determine the user's intention to adjust the washbasin surface, analyze the user's intention to adjust the direction of the washbasin surface, and obtain the adjustment intention data. Step S3 includes: Obtain the user's target pose data within the current period, calculate the height change value and matching error change value for each marked time window, set the time delay variable τ and the time delay window threshold g, obtain each integer between -g and +g, and use it as the value range τ´ of the time delay variable τ; For example, the specific formula for calculating the height change value h´[q] in the q-th marked time window is as follows: , Where h[q] is the average height of users in the user height set within the q-th labeled time window; h[q+1] is the average height of users in the user height set within the (q+1)-th labeled time window; For example, the specific formula for calculating the matching error change value R´[q] of the q-th marking time window is as follows: , Where R[q] is the effective matching error of the q-th marking time window; R[q+1] is the effective matching error of the (q+1)-th marking time window; Set the active intent variable K, calculate the correlation coefficient J(τ) between the user's height change and the matching error change within the range τ´ of the time delay variable τ, and obtain the maximum value J of the correlation coefficient. max Set the correlation coefficient threshold J´, when J max When J' > J', the correlation coefficient J(τ) is obtained as the maximum value J. max When the time delay variable τ takes the value τ*, if τ*>0, it is determined that the user has the intention to actively adjust the height of the washbasin surface, and K is set to 1; otherwise, it is determined that the user does not have the intention to actively adjust the height of the washbasin surface, and K is set to 0. K takes the value of 1 or 0. For example, the specific formula for calculating the correlation coefficient J(τ) is: , in, h´[i] is the average height change value of the user in each marked time window within the current period; h´[i] is the height change value in the i-th marked time window; R'[i] is the average value of the matching error change of the user in each marked time window within the current period; R'[i] is the matching error change value of the i-th marked time window; n is the total number of marked time windows in the current period. When J max When J' ≤ J', set the short-term sampling value d and the long-term sampling value L' of the user's height, and obtain the maximum value E of each short-term attitude energy in the y-th marked time window within the current period. (short,max) and the maximum value E of the energy of each long-term attitude (long,max) Calculate the user's pose energy ratio η=E in the y-th labeled time window.(short,max) / E (long,max) ; For example, the specific process for obtaining the short-time attitude energy of the y-th labeled time window is as follows: The height change value from the first sampling point to the dth sampling point in the y-th marked time window within the current period is squared and accumulated to obtain the first short-term attitude energy of the y-th marked time window. In chronological order, each short-term attitude energy of the y-th marked time window is obtained sequentially. For example, the specific process for obtaining the long-term pose energy of the y-th labeled time window is as follows: The height change value from the first sampling point to the L´th sampling point in the y-th marked time window within the current period is squared and accumulated to obtain the first long-term attitude energy of the y-th marked time window. In chronological order, each long-term attitude energy of the y-th marked time window is obtained sequentially. Calculate the proportion of attitude transitions ζ in the y-th marked time window. y Calculate the user's intention value U for adjusting the height of the washbasin surface within the y-th marked time window. y Obtain the maximum value U of the user's intention to adjust the height of the washbasin surface within each marked time window. max Set the adjustment intent threshold U, when U max If the value is >U, it is determined that the user has the intention to actively adjust the height of the washbasin surface in the current cycle, and K is set to 1; otherwise, it is determined that the user does not have the intention to actively adjust the height of the washbasin surface, and K is set to 0. For example, the difference between the user's height at the (z+1)th sampling point and the user's height at the zth sampling point in the yth marking time window is divided by the user's height at the zth sampling point in the yth marking time window to obtain the pose change ratio at the zth sampling point; The attitude change rate of the z-th sampling point is obtained by performing arctangent processing on the attitude change ratio of the z-th sampling point. The attitude phase of the z-th sampling point is obtained by subtracting the attitude phase of the z-th sampling point from the attitude phase of the (z+1)-th sampling point. If the attitude phase change rate of the z-th sampling point is greater than π / 2, then the user attitude at the z-th sampling point is determined to have changed abruptly. Obtain the total number N' of sampling points where the user pose changes abruptly within the y-th labeled time window, obtain the total number N of each sampling point within the y-th labeled time window, and calculate the pose jump percentage ζ within the y-th labeled time window. y : , For example, calculating the user's intention to adjust the height of the washbasin surface within the y-th marked time window. y The specific calculation formula is as follows: , Set the adjustment direction variable P. When the user is determined to have an active intention to adjust the washbasin surface, obtain the mean value R´ of the effective matching error within each marked time window. When R´>0, it is determined that the washbasin surface is too low, and P=1. Otherwise, it is determined that the washbasin surface is too high, and P=-1. P takes the value of -1 or 1. The adjustment direction variable P and the active intention variable K of the handwashing facility in the current cycle are obtained and aggregated to obtain the lifting and lowering adjustment data of the handwashing facility in the current cycle.

[0021] Step S4: Based on the adjustment intention data, adjust the height of the handwashing countertop in the handwashing facility by raising and lowering it. Step S4 includes: Obtain the lifting and lowering adjustment data of the handwashing facilities in the current cycle, and extract the adjustment direction variable P and the active intention variable K of the handwashing facilities in the current cycle from the lifting and lowering adjustment data; Based on the height adjustment data, the height of the handwashing countertop in the handwashing facility is controlled by raising and lowering. The specific process is as follows: When K=0, it is determined that the user does not have the intention to actively adjust the washbasin surface, and the height of the washbasin surface of the handwashing facility is not adjusted. When K=1, it is determined that the user has the intention to actively adjust the washbasin surface. Set the minimum lifting height △h min When P=-1, the handwashing station in the handwashing facility is lowered by Δh each time. min The height adjustment of the handwashing facility is completed when the effective matching error between the handwashing countertop and the user within a unit of time is less than the effective matching error threshold R. When P=1, the handwashing station surface in the handwashing facility is raised, with each rise being Δh. min The height adjustment of the handwashing station is completed when the effective matching error between the handwashing station and the user within a unit of time is less than the effective matching error threshold R.

[0022] To better implement the above method, an intelligent lifting control system is also proposed, which includes a height initial adjustment module, a deviation analysis module, an adjustment intention analysis module, and a lifting control module. The initial height adjustment module is used to acquire the initial height records of users collected by the handwashing facility, determine the initial height of the handwashing countertop in the handwashing facility, and perform initial adjustment of the height of the handwashing countertop; The deviation analysis module is used to acquire the user's body posture record after the initial adjustment of the washbasin surface, analyze the degree of deviation between the initial height of the washbasin surface and the user's posture, and obtain the target posture data. The adjustment intention analysis module is used to analyze the user's intention to adjust the height of the washbasin surface and the user's intention to adjust the direction of the washbasin surface, and obtain adjustment intention data. The lifting control module is used to control the height of the handwashing countertop in the handwashing facility based on the adjustment intention data.

[0023] The initial height adjustment module includes an initial height determination unit and an initial height adjustment unit; The initial height determination unit is used to collect the user's height signal, obtain the user's initial height record, and determine the initial height of the handwashing station surface in the handwashing facility; The initial height adjustment unit is used to adjust the initial height of the handwashing station surface in the handwashing facility according to the initial height of the handwashing station surface.

[0024] The deviation analysis module includes an attitude recording acquisition unit and a deviation analysis unit. The posture recording and acquisition unit is used to record the user's posture after the initial adjustment of the washbasin height, and obtain the user's body posture record after the initial adjustment of the washbasin. The deviation analysis unit is used to calculate the deviation between the initial height of the washbasin surface and the user's posture based on the user's body posture record, and to analyze the degree of deviation between the initial height of the washbasin surface and the user's posture to obtain the target posture data.

[0025] The adjustment intention analysis module includes an adjustment intention analysis unit and an adjustment direction analysis unit; The intention analysis unit is used to analyze the temporal correlation between the user's height and posture and the matching error of the washbasin surface in the current period based on the target posture data, and to analyze the user's intention to adjust the height of the washbasin surface and determine the user's intention to adjust the height of the washbasin surface. The adjustment direction analysis unit is used to analyze the user's intention to adjust the height of the washbasin surface, and obtain adjustment intention data.

[0026] The lifting control module includes a lifting control unit; The lifting control unit is used to acquire the lifting adjustment data of the handwashing facility in the current cycle, obtain the adjustment direction variable and active intention variable of the handwashing facility in the current cycle from the lifting adjustment data, and control the lifting of the handwashing countertop in the handwashing facility according to the lifting adjustment data.

[0027] 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. An intelligent lifting control method based on contactless posture recognition, characterized in that, The method includes: Step S1: Obtain the initial height record of the user collected by the handwashing facility, determine the initial height of the handwashing countertop in the handwashing facility, and make initial adjustments to the height of the handwashing countertop; Step S2: Obtain the user's body posture record after the initial adjustment of the washbasin surface, analyze the deviation between the initial height of the washbasin surface and the user's posture, and obtain the target posture data; Step S3: Based on the target posture data, analyze the temporal correlation between the user's height posture and the matching error of the washbasin surface in the current period, analyze the user's intention to adjust the height of the washbasin surface, determine the user's intention to adjust the washbasin surface, analyze the user's intention to adjust the direction of the washbasin surface, and obtain the adjustment intention data. Step S4: Based on the adjustment intention data, adjust the height of the handwashing countertop in the handwashing facility by raising and lowering it. Step S3 includes: Obtain the user's target pose data within the current period, calculate the height change value and matching error change value for each marked time window, set the time delay variable τ and the time delay window threshold g, obtain each integer between -g and +g, and use it as the value range τ´ of the time delay variable τ; Set the active intent variable K, calculate the correlation coefficient J(τ) between the user's height change and the matching error change within the range τ´ of the time delay variable τ, and obtain the maximum value J of the correlation coefficient. max Set the correlation coefficient threshold J´, when J max When J' > J', the correlation coefficient J(τ) is obtained as the maximum value J. max When the time delay variable τ takes the value τ*, if τ*>0, it is determined that the user has the intention to actively adjust the height of the washbasin surface, and K is set to 1; otherwise, it is determined that the user does not have the intention to actively adjust the height of the washbasin surface, and K is set to 0. K takes the value of 1 or 0. When J max When J' ≤ J', set the short-term sampling value d and the long-term sampling value L' of the user's height, and obtain the maximum value E of each short-term attitude energy in the y-th marked time window within the current period. (short,max) and the maximum value E of the energy of each long-term attitude (long,max) Calculate the user's pose energy ratio η=E in the y-th labeled time window. (short,max) / E (long,max) ; Calculate the proportion of attitude transitions ζ in the y-th marked time window. y Calculate the user's intention value U for adjusting the height of the washbasin surface within the y-th marked time window. y Obtain the maximum value U of the user's intention to adjust the height of the washbasin surface within each marked time window. max Set the adjustment intent threshold U. When U max If the value is >U, it is determined that the user has the intention to actively adjust the height of the washbasin surface in the current cycle, and K is set to 1; otherwise, it is determined that the user does not have the intention to actively adjust the height of the washbasin surface, and K is set to 0. Set the adjustment direction variable P. When the user is determined to have an active intention to adjust the washbasin surface, obtain the mean value R´ of the effective matching error within each marked time window. When R´>0, it is determined that the washbasin surface is too low, and P=1. Otherwise, it is determined that the washbasin surface is too high, and P=-1. P takes the value of -1 or 1. The adjustment direction variable P and the active intention variable K of the handwashing facility in the current cycle are obtained and aggregated to obtain the lifting and lowering adjustment data of the handwashing facility in the current cycle.

2. The intelligent lifting control method based on contactless posture recognition according to claim 1, characterized in that, Step S2 includes: Once the height of the handwashing station in the handwashing facility is initially adjusted, the handwashing facility records the user's posture after the initial adjustment of the handwashing station height, thus obtaining a record of the body posture after the initial adjustment of the handwashing station. The user height is obtained from each sampling point in the body posture record, the duration of the time window is set, and the user height set G for a certain time window is obtained. Calculate the difference Δh between the maximum and minimum user heights in the user height set G. w Set the height change threshold h δ When △h w h δ If the user's posture is unstable within a certain time window, then that time window is removed. When Δh w ≤h δ If the user's posture is stable within a certain time window, then that time window is recorded as the marked time window. Obtain the user height set for each marked time window in the body posture record, and obtain the initial height h of the handwashing station surface in the handwashing facility. base Set the posture physiological offset distance L, obtain the average user height h[e] of each sampling point in the e-th marked time window of the height and posture record, and calculate the effective matching error R of the e-th marked time window. e ; Set an effective matching error threshold R, when |R| e If |>R, it is determined that there is a deviation between the user's posture and the initial height of the handwashing station in the handwashing facility during the e-th marked time window; otherwise, no processing is performed on the e-th marked time window. Calculate the deviation value F between the initial height of the washbasin surface and the user's posture, and set a deviation threshold f. When F ≤ f, it is determined that there is a deviation between the user's posture and the initial height of the washbasin surface. Obtain the user's height set and effective matching error from the marked time window in the user's body posture record and aggregate them to obtain the target posture data. Otherwise, it is determined that there is no deviation between the user's posture and the initial height of the washbasin surface, and the height of the washbasin surface in the handwashing facility is not adjusted.

3. The intelligent lifting control method based on contactless posture recognition according to claim 1, characterized in that, Step S1 includes: Set the feature sampling point value 'a' and the unit duration. When the handwashing facility detects the user within the preset detection area for 'a' consecutive sampling points, the user's height signal is collected, and the user's height signal within the first unit duration after the collection begins is recorded to obtain the user's initial height record. The user heights sampled at various sampling points for handwashing facilities are obtained from the initial height records and aggregated to obtain the initial height dataset H. The median absolute deviation h of the initial height dataset H is then obtained. △ ; Calculate the height deviation threshold B', when B C When B > B', it is determined that there is a data deviation between the c-th sampling point and the initial height dataset H, and the c-th sampling point is recorded as an outlier sampling point. C If the value is less than or equal to B', then the c-th sampling point will not be processed. Remove all outlier sampling points from the initial height dataset H to obtain dataset H'. Obtain the average height of the user corresponding to each sampling point in dataset H' and record it as the initial height h. Calculate the initial height h of the handwashing station countertop based on the initial height h. base Get the current height h of the handwashing station surface in the handwashing facility. start Calculate the initial lifting height of the handwashing facility Δh = h start -h base ; Based on the initial lifting height △h, adjust the height of the handwashing station. The specific adjustment process is as follows: when △h≥0, control the handwashing facility to raise the handwashing station by △h; when △h<0, control the handwashing facility to lower the handwashing station by |△h|.

4. The intelligent lifting control method based on contactless posture recognition according to claim 1, characterized in that, Step S4 includes: Obtain the lifting and lowering adjustment data of the handwashing facilities in the current cycle, and extract the adjustment direction variable P and the active intention variable K of the handwashing facilities in the current cycle from the lifting and lowering adjustment data; Based on the height adjustment data, the height of the handwashing countertop in the handwashing facility is controlled by raising and lowering. The specific process is as follows: When K=0, it is determined that the user does not have the intention to actively adjust the washbasin surface, and the height of the washbasin surface of the handwashing facility is not adjusted. When K=1, it is determined that the user has the intention to actively adjust the washbasin surface. Set the minimum lifting height △h min When P=-1, the handwashing station in the handwashing facility is lowered by Δh each time. min The height adjustment of the handwashing facility is completed when the effective matching error between the handwashing countertop and the user within a unit of time is less than the effective matching error threshold R. When P=1, the handwashing station surface in the handwashing facility is raised, with each rise being Δh. min The height adjustment of the handwashing station is completed when the effective matching error between the handwashing station and the user within a unit of time is less than the effective matching error threshold R.

5. An intelligent lifting control system, used to execute the intelligent lifting control method based on non-contact posture recognition as described in any one of claims 1-4, characterized in that, The system includes an initial height adjustment module, a deviation analysis module, an adjustment intention analysis module, and a lifting control module. The initial height adjustment module is used to acquire the initial height record of the user collected by the handwashing facility, determine the initial height of the handwashing countertop in the handwashing facility, and perform initial adjustment of the height of the handwashing countertop. The deviation analysis module is used to acquire the user's body posture record after the initial adjustment of the washbasin surface, analyze the degree of deviation between the initial height of the washbasin surface and the user's posture, and obtain the target posture data. The adjustment intention analysis module is used to analyze the user's intention to adjust the height of the washbasin surface and the user's intention to adjust the direction of the washbasin surface, and to obtain adjustment intention data. The lifting control module is used to control the height of the handwashing countertop in the handwashing facility according to the adjustment intention data.

6. The intelligent lifting control system according to claim 5, characterized in that, The initial height adjustment module includes an initial height determination unit and an initial height adjustment unit; The initial height determination unit is used to collect the user's height signal, obtain the user's initial height record, and determine the initial height of the handwashing countertop in the handwashing facility. The initial height adjustment unit is used to adjust the initial height of the handwashing countertop in the handwashing facility according to the initial height of the countertop.

7. The intelligent lifting control system according to claim 5, characterized in that, The deviation analysis module includes an attitude recording acquisition unit and a deviation analysis unit; The posture recording and acquisition unit is used to record the user's posture after the initial adjustment of the washbasin height, and obtain the user's body posture record after the initial adjustment of the washbasin. The deviation analysis unit is used to calculate the deviation between the initial height of the washbasin surface and the user's posture based on the user's body posture record, and to analyze the degree of deviation between the initial height of the washbasin surface and the user's posture to obtain target posture data.

8. The intelligent lifting control system according to claim 5, characterized in that, The adjustment intention analysis module includes an adjustment intention analysis unit and an adjustment direction analysis unit; The adjustment intention analysis unit is used to analyze the temporal correlation between the user's height and posture and the matching error of the washbasin surface in the current period based on the target posture data, and to analyze the user's intention to adjust the height of the washbasin surface and determine the user's intention to adjust the height of the washbasin surface. The adjustment direction analysis unit is used to analyze the user's intention to adjust the height of the washbasin surface, and obtain adjustment intention data.

9. The intelligent lifting control system according to claim 5, characterized in that, The lifting control module includes a lifting control unit; The lifting control unit is used to acquire the lifting adjustment data of the handwashing facility in the current cycle, obtain the adjustment direction variable and active intention variable of the handwashing facility in the current cycle from the lifting adjustment data, and control the lifting of the handwashing countertop in the handwashing facility according to the lifting adjustment data.

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