Eye fumigation therapeutic apparatus supervision method based on data fusion analysis

Abstract

The invention relates to the technical field of data processing, in particular to an eye fumigation therapeutic apparatus supervision method based on data fusion analysis, and the method comprises the steps: obtaining a multi-dimensional historical feedback parameter data sequence of a user, and multi-dimensional treatment parameters and multi-dimensional feedback parameters of the user at each monitoring moment in the process of using an eye fumigation therapeutic apparatus; acquiring the safety degree of any one treatment parameter according to the difference between any one treatment parameter in the multi-dimensional treatment parameters at the current monitoring moment and the normal fluctuation range of the treatment parameter; acquiring a treatment parameter data sequence, and acquiring the total confidence coefficient of any treatment parameter according to the data fluctuation characteristics in the treatment parameter data sequence and the data difference between the multi-dimensional feedback parameter at the current monitoring moment and the multi-dimensional historical feedback parameter data sequence; the safety degree and the total confidence degree of each treatment parameter in the multi-dimensional treatment parameters at the current monitoring moment are utilized to obtain the comprehensive safety index, the eye fumigation therapeutic apparatus is supervised, and the use effect of a user is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for monitoring an eye fumigation therapy device based on data fusion analysis. Background Technology

[0002] Eye steam therapy devices, as a new type of ophthalmic treatment equipment, primarily use warm steam to fumigate the eyes, relieving eye fatigue, dryness, and promoting blood circulation. With changes in modern lifestyles and the widespread use of electronic devices, more and more people are facing problems such as eye fatigue and dryness, leading to a growing demand for eye steam therapy devices. However, the effectiveness of eye steam therapy devices is closely related to treatment parameters (such as temperature, humidity, and air pressure). Precise control and real-time monitoring of these parameters are crucial to ensuring treatment effectiveness and equipment safety.

[0003] Existing eye fumigation therapy devices rely on single sensors for monitoring, such as temperature, humidity, and air pressure, and are monitored based on preset fixed thresholds. However, the use of eye fumigation therapy involves the sensitive area of ​​the eyes, making safety paramount. While these sensors can provide operational information in certain aspects, the interaction of multiple factors during actual use means that a single data source cannot comprehensively reflect the true state of the eye fumigation therapy device. Furthermore, preset fixed threshold monitoring cannot adapt to individual differences and environmental changes, resulting in ineffective monitoring of user safety during treatment and impacting the user experience.

[0004] Therefore, how to obtain the true state of the eye fumigation therapy device and improve the user's experience during the treatment process has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a monitoring method for an eye fumigation therapy device based on data fusion analysis, in order to solve the problem of how to obtain the real status of the eye fumigation therapy device and improve the user's experience during the treatment process.

[0006] This invention provides a method for monitoring an eye fumigation therapy device based on data fusion analysis, the method comprising the following steps: Before any user uses the eye fumigation therapy device for treatment, a multidimensional historical feedback parameter data sequence of the user's use of the eye fumigation therapy device is obtained. During the user's use of the eye fumigation therapy device, multidimensional treatment parameters and multidimensional feedback parameters of the user are obtained at each monitoring moment. For any treatment parameter in the multidimensional treatment parameters at the current monitoring time, obtain the normal fluctuation range of the treatment parameter, and obtain the safety level of the treatment parameter based on the difference between the treatment parameter and the normal fluctuation range. Within a preset historical period prior to the current monitoring time, treatment parameters belonging to the same dimension as any of the treatment parameters, along with any of the treatment parameters, are combined to form a treatment parameter data sequence. Based on the data fluctuation characteristics within the treatment parameter data sequence and the data differences between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence, the total confidence level of any of the treatment parameters is obtained. The safety level and overall confidence level of each treatment parameter in the multidimensional treatment parameters at the current monitoring time are obtained. Based on the safety level and overall confidence level of each treatment parameter, the comprehensive safety index of the eye fumigation therapy device at the current monitoring time is obtained, and the eye fumigation therapy device at the current monitoring time is monitored.

[0007] Preferably, obtaining the safety level of any treatment parameter based on the difference between any treatment parameter and the normal fluctuation range includes: If any of the treatment parameters is outside the normal fluctuation range, then the safety level of any of the treatment parameters is set to 0; If any of the treatment parameters is within the normal fluctuation range, then the maximum value in the normal fluctuation range is recorded as the maximum fluctuation value, and the minimum value in the normal fluctuation range is recorded as the minimum fluctuation value. The difference between any treatment parameter and the maximum fluctuation value is obtained to get the first difference. The difference between any treatment parameter and the minimum fluctuation value is obtained to get the second difference. The product between constant 4, the first difference, and the second difference is obtained to get the first product result. The product of constant 3 and the square of the minimum fluctuation value is obtained to get the first squared value. The product of constant 2, the maximum fluctuation value and the minimum fluctuation value is obtained to get the second product result. The square of the maximum fluctuation value is obtained to get the second squared value. The sum of the first squared value and the second product result is obtained to get the first sum result. The difference between the first sum result and the second squared value is obtained to get the third difference. The safety level of any treatment parameter is obtained by comparing the ratio of the first product result to the third difference.

[0008] Preferably, obtaining the total confidence level of any treatment parameter based on the data fluctuation characteristics within the treatment parameter data sequence and the data difference between the multidimensional feedback parameter at the current monitoring time and the multidimensional historical feedback parameter data sequence includes: Based on the data fluctuation characteristics within the treatment parameter data sequence, the safety confidence level of any one of the treatment parameters is obtained; Based on the data differences between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence, the user's security confidence at the current monitoring time is obtained; The total confidence level of any treatment parameter is obtained by multiplying the security confidence level and the user security confidence level.

[0009] Preferably, obtaining the safety confidence level of any treatment parameter based on the data fluctuation characteristics within the treatment parameter data sequence includes: Obtain the standard deviation of the treatment parameter data sequence, normalize the standard deviation to obtain the normalized standard deviation, and obtain the reciprocal of the normalized standard deviation to obtain the first stability level. The following steps are performed: First, obtain peak data from the treatment parameter data sequence and obtain the corresponding mean peak data. Then, normalize the mean peak data to obtain a normalized mean peak data. Next, obtain valley data from the treatment parameter data sequence and obtain the corresponding mean valley data. Normalize the mean valley data to obtain a normalized mean valley data. Then, obtain the difference between the normalized mean peak data and the normalized mean valley data to obtain the average fluctuation value. Finally, obtain the rate of change of each peak data in the treatment parameter data sequence and obtain the corresponding mean peak rate of change. Normalize the mean peak rate of change to obtain the normalized mean peak rate of change. Then, obtain the reciprocal of the product of the average fluctuation value and the normalized mean peak rate of change to obtain the second stability level. If there are no peak or valley data in the treatment parameter data sequence, the second stability level is set to a constant 1. The theoretical rate of change of any of the treatment parameters is obtained. The treatment process of the eye fumigation therapy device is divided into at least two working stages. The working stage at the current monitoring time is obtained and recorded as the current working stage. The treatment parameters belonging to the current working stage in the treatment parameter data sequence are formed into a subsequence. The subsequence is fitted to obtain a first-order fitting function. The slope of the first-order fitting function is obtained and recorded as the actual rate of change. The reciprocal of the absolute value of the difference between the theoretical rate of change and the actual rate of change is obtained to obtain the third stability degree. In the first fitting function, the fitted value of each treatment parameter in the subsequence is obtained, and the reciprocal of the absolute value of the difference between each treatment parameter and its fitted value is accumulated to obtain the fourth stability level. The first safety confidence level is obtained by adding the first stability level and the second stability level. The second safety confidence level is obtained by multiplying the third stability level and the fourth stability level. The product of the first safety confidence level and the second safety confidence level is normalized to obtain the safety confidence level of any treatment parameter.

[0010] Preferably, obtaining the rate of change of each peak data in the treatment parameter data sequence includes: For any peak data in the treatment parameter data sequence, the absolute value of the difference between the peak data and its left adjacent treatment parameter is obtained to obtain the left rate of change. The absolute value of the difference between the peak data and its right adjacent treatment parameter is obtained to obtain the right rate of change. The mean between the left rate of change and the right rate of change is obtained to obtain the rate of change of the peak data.

[0011] Preferably, obtaining the user security confidence level at the current monitoring time based on the data difference between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence includes: The multidimensional feedback parameters include head movement frequency and blink frequency. In the multidimensional historical feedback parameter data sequence, the multidimensional historical feedback parameters corresponding to the multidimensional feedback parameters at the current monitoring time are obtained, and the absolute value of the difference between the blink frequency at the current monitoring time and its corresponding historical blink frequency is obtained to obtain the blink frequency difference value. The skin sensitivity of any user is obtained. The product of the head movement frequency, the difference in blink frequency, and the skin sensitivity at the current monitoring time is normalized to obtain the normalization result. The difference between the constant 1 and the normalization result is obtained to obtain the user's safety confidence at the current monitoring time.

[0012] Preferably, the method for obtaining the multidimensional feedback parameters at the current monitoring time includes: The treatment process of the eye fumigation therapy device is divided into at least two working stages. The working stage at the current monitoring time is obtained and recorded as the current working stage. The ratio of the number of head movements of any user in the current working stage to the working time of the current working stage is obtained to obtain the head movement frequency at the current monitoring time. The ratio of the number of blinks of any user in the current working stage to the working time of the current working stage is obtained to obtain the blink frequency at the current monitoring time.

[0013] Preferably, the step of obtaining the comprehensive safety index of the eye fumigation therapy device at the current monitoring time based on the safety level and overall confidence level of each treatment parameter includes: The safety index of each treatment parameter is obtained by multiplying the safety level and the total confidence level of each treatment parameter. The mean of the safety index of each treatment parameter is obtained to obtain the comprehensive safety index of the eye fumigation therapy device at the current monitoring time.

[0014] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: In this invention, firstly, the safety level of the treatment parameters in the multidimensional treatment parameters at the current monitoring time is obtained, which reflects the working status of the eye fumigation therapy device at the current monitoring time. The higher the safety level, the more normal the working status. Then, the total confidence level of the treatment parameters in the multidimensional treatment parameters at the current monitoring time is obtained, which reflects the reliability of the safety level of the treatment parameters. The higher the total confidence level, the more reliable the safety level, and the more reliable the eye fumigation therapy device is in a normal working state at the current monitoring time, the higher the safety. Finally, the comprehensive safety index of the eye fumigation therapy device at the current monitoring time is obtained to monitor the eye fumigation therapy device used by the user in real time, ensure the safety of the use process, and improve the user's experience. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for monitoring an eye fumigation therapy device based on data fusion analysis, provided in Embodiment 1 of the present invention. Detailed Implementation

[0017] Embodiments of this disclosure are described in detail below, with examples of these embodiments illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting it.

[0018] It should be noted that the terms "first," "second," etc., used in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure.

[0019] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0020] See Figure 1 This is a flowchart of a method for monitoring an eye fumigation therapy device based on data fusion analysis, as provided in Embodiment 1 of the present invention. Figure 1 As shown, the method may include: Step S101: For any user, before using the eye fumigation therapy device for treatment, obtain the multidimensional historical feedback parameter data sequence of the user's use of the eye fumigation therapy device. During the user's use of the eye fumigation therapy device, obtain the multidimensional treatment parameters and the user's multidimensional feedback parameters at each monitoring moment.

[0021] As a new type of ophthalmic treatment device, the eye fumigation therapy device mainly uses warm steam to fumigate the eyes, which can relieve eye fatigue, dry eyes, and promote blood circulation in the eyes.

[0022] Existing eye fumigation therapy devices rely on single sensors such as temperature, humidity, and air pressure to monitor various parameters and conduct safety supervision based on preset fixed thresholds. However, the use of eye fumigation therapy involves the sensitive area of ​​the eyes, making safety paramount. While these sensors can provide operational information in certain aspects, the interaction of multiple factors during actual use means that a single data source cannot comprehensively reflect the true state of the eye fumigation therapy device. Furthermore, preset fixed threshold monitoring cannot adapt to individual differences and environmental changes, resulting in ineffective monitoring of user safety during treatment and impacting the user experience.

[0023] Therefore, in this embodiment of the invention, the safety level of the treatment parameters of the eye fumigation therapy device during the treatment process is first obtained to reflect the working status of the eye fumigation therapy device. Then, the total confidence level of the treatment parameters is obtained to reflect the reliability of the safety level of the treatment parameters. Finally, the safety level of the treatment parameters and the total confidence level are combined to obtain the comprehensive safety index of the eye fumigation therapy device during the treatment process. This allows for real-time monitoring of the eye fumigation therapy device used by the user, ensuring the safety of the use process and improving the user's experience.

[0024] The eye fumigation therapy device is applied to the user's eye area. Once started, it will begin treatment according to a preset mode. During the treatment, built-in sensors will collect relevant data in real time at a fixed frequency: a temperature sensor collects temperature data, a humidity sensor collects humidity data, and an air pressure sensor collects air pressure data. At the same time, the inertial measurement unit (IMU) acquires the number of times the user's head moves, and the electrooculogram sensor acquires the number of times the user blinks. These data are used to analyze the comprehensive safety indicators of the eye fumigation therapy device during the treatment process and to monitor the user's use of the eye fumigation therapy device in real time.

[0025] In this embodiment, the built-in sensor of the eye fumigation therapy device is set to a data acquisition frequency of once per second. This is not limited and can be set according to the specific implementation scenario. Taking any user as an example, during the use of the eye fumigation therapy device, the built-in sensor acquires temperature, humidity, and air pressure data at each monitoring moment to form multi-dimensional treatment parameters for that moment. Simultaneously, the head movement frequency and blinking frequency of any user are calculated to form multi-dimensional feedback parameters for that user, enabling monitoring and analysis of the eye fumigation therapy device.

[0026] For example, taking the current monitoring moment, the method for obtaining the head movement frequency and blinking frequency at the current monitoring moment during any user's use of the eye steaming therapy device is as follows: The treatment process of the eye fumigation therapy device is divided into at least two working stages, such as heating, humidification, pressurization, heat preservation, moisturization, pressure preservation, cooling, dehumidification, and pressure reduction. The working stage at the current monitoring moment is recorded as the current working stage. For example, if the current monitoring moment is in the heating working stage, the period from the first monitoring moment of the heating working stage to the current monitoring moment is recorded as the current working stage. The ratio of the number of head movements of any user in the current working stage to the working duration of the current working stage is obtained to obtain the head movement frequency at the current monitoring moment. The ratio of the number of blinks of any user in the current working stage to the working duration of the current working stage is obtained to obtain the blink frequency at the current monitoring moment.

[0027] To better match the user's condition when using the eye steaming device and to effectively monitor its safety, each user needs to undergo a treatment experience before their first use of the eye steaming therapy device. The multidimensional feedback parameters obtained during this experience form a multidimensional historical feedback parameter data sequence for each user's use of the eye steaming therapy device. This data is used for analysis in conjunction with the user's historical condition. During the experience, to ensure the user's safety and comfort, relevant staff should be assigned to monitor the user's condition. After the user's experience, the user's skin sensitivity level also needs to be obtained (in this embodiment, the skin sensitivity level is set to a range of 1-10, where 1 is the lowest and 10 is the highest. Users should assess their own skin sensitivity and fill in the data; for example, if a user's skin is easily affected by the external environment in daily life, their skin sensitivity level can be considered high). The user's multidimensional historical feedback parameter data sequence and skin sensitivity level will be saved and can be directly retrieved during subsequent treatments without needing to undergo another experience.

[0028] Step S102: For any treatment parameter in the multidimensional treatment parameters at the current monitoring time, obtain the normal fluctuation range of the treatment parameter, and obtain the safety level of the treatment parameter based on the difference between the treatment parameter and the normal fluctuation range.

[0029] During operation, all treatment parameters of an eye fumigation therapy device should fall within a specified normal fluctuation range, i.e., a safe range. Although some fluctuations may occur, these parameters should remain within the normal range. When a treatment parameter exceeds the normal fluctuation range, it indicates that the eye fumigation therapy device may be operating in an unsafe condition. Therefore, for any treatment parameter among the multidimensional treatment parameters at the current monitoring time, obtain the normal fluctuation range of that parameter from the product manual or the device's label. Based on the difference between any treatment parameter and the normal fluctuation range, determine the safety level of that parameter. This safety level reflects the operating status of the eye fumigation therapy device at the current monitoring time; a higher safety level indicates a more normal operating status.

[0030] The method for determining the safety level of any treatment parameter based on its difference from the normal fluctuation range is as follows: If any of the treatment parameters is outside the normal fluctuation range, it means that any of the treatment parameters has exceeded the safe operating range of the eye fumigation therapy device. Therefore, the safety level of any of the treatment parameters is set to 0. If any of the treatment parameters is within the normal fluctuation range, then the maximum value in the normal fluctuation range is recorded as the maximum fluctuation value, and the minimum value in the normal fluctuation range is recorded as the minimum fluctuation value. The difference between any treatment parameter and the maximum fluctuation value is obtained to get the first difference. The difference between any treatment parameter and the minimum fluctuation value is obtained to get the second difference. The product between constant 4, the first difference, and the second difference is obtained to get the first product result. The product of constant 3 and the square of the minimum fluctuation value is obtained to get the first squared value. The product of constant 2, the maximum fluctuation value and the minimum fluctuation value is obtained to get the second product result. The square of the maximum fluctuation value is obtained to get the second squared value. The sum of the first squared value and the second product result is obtained to get the first sum result. The difference between the first sum result and the second squared value is obtained to get the third difference. The safety level of any treatment parameter is obtained by comparing the ratio of the first product result to the third difference.

[0031] In one embodiment, taking the j-th treatment parameter among the multidimensional treatment parameters at the current monitoring time as an example, the formula for calculating the safety level of the j-th treatment parameter is:

[0032] in, The safety level of the j-th treatment parameter among the multidimensional treatment parameters at the current monitoring time; This refers to the j-th treatment parameter in the multidimensional treatment parameters at the current monitoring time. This represents the maximum fluctuation value. This is the minimum fluctuation value.

[0033] It should be noted that when When the safety level of the j-th treatment parameter is calculated, the formula is a parabolic equation consisting of three coordinates: (Hmin, 0), (Hmax, 0), and ((Hmax-Hmin) / 2, 1). The closer the j-th treatment parameter is to the center value of the normal fluctuation range, the higher the safety level; conversely, the closer the j-th treatment parameter is to the maximum or minimum fluctuation value of the normal fluctuation range, the lower the safety level. After simplification, it becomes To ensure the formula is meaningful, within the obtained normal fluctuation range, if Hmax = 3Hmin or Hmax = -Hmin, then Hmin is reduced by 0.001 as the minimum fluctuation value of the normal fluctuation range.

[0034] At this point, the safety level of any one of the multidimensional treatment parameters at the current monitoring moment is obtained.

[0035] Step S103: Within a preset historical period prior to the current monitoring time, treatment parameters belonging to the same dimension as the treatment parameter and the treatment parameter itself are combined to form a treatment parameter data sequence. Based on the data fluctuation characteristics within the treatment parameter data sequence and the data differences between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence, the total confidence level of the treatment parameter is obtained.

[0036] Since the various treatment parameters of the eye fumigation therapy device should change steadily during normal operation, a treatment parameter data sequence is formed within a preset historical period prior to the current monitoring time, combining treatment parameters belonging to the same dimension as any given treatment parameter with any given treatment parameter. This sequence is used to analyze the data fluctuation characteristics of the treatment parameters. Furthermore, the eye fumigation therapy device has corresponding operating states at different stages of its operation, such as heating, humidification, pressurization, heat preservation, moisturization, pressure preservation, cooling, dehumidification, and pressure reduction. The changing trends of the treatment parameters differ at each stage.

[0037] In this embodiment, the preset historical time period before the current monitoring time is set to 59 seconds. That is, the treatment parameter data sequence includes the treatment parameters within one minute of the current monitoring time. This is not limited and can be set according to the specific implementation scenario. To ensure that there is a sufficiently long historical time period before the current monitoring time, the eye fumigation therapy device will start running one minute before the user's formal treatment.

[0038] Secondly, since eye steaming therapy devices are generally applied to the user's eyes, and the user inevitably moves during use (for example, the steam suddenly becomes cold or hot; or the steam output stops), the position of the eye steaming therapy device may shift, resulting in abnormal fluctuations in related data. At the same time, eye steaming therapy should be a comfortable process during normal use, and the user should not blink frequently. Moreover, different users have different skin sensitivities, and the same treatment parameters may elicit different reactions from different users.

[0039] Therefore, this embodiment obtains the total confidence level of any treatment parameter based on the data fluctuation characteristics within the treatment parameter data sequence and the data differences between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence. This total confidence level reflects the reliability of the safety of any treatment parameter. The higher the total confidence level, the higher the reliability of the safety, and the higher the safety of the treatment parameter in the eye fumigation therapy device.

[0040] The method for obtaining the total confidence level of any treatment parameter based on the data fluctuation characteristics within the treatment parameter data sequence and the data differences between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequences is as follows: (1) Obtain the safety confidence level of any treatment parameter based on the data fluctuation characteristics within the treatment parameter data sequence.

[0041] Specifically, the standard deviation of the treatment parameter data sequence is obtained, the standard deviation is normalized by a sliding window to obtain the normalized standard deviation, and the reciprocal of the normalized standard deviation is obtained to obtain the first stability level. The AMPD algorithm is used to obtain peak data in the treatment parameter data sequence, and the mean of the peak data is obtained. The mean of the peak data is then normalized by a sliding window to obtain a normalized mean of the peak data. The derivative method is used to obtain valley data in the treatment parameter data sequence, and the mean of the valley data is obtained. The mean of the valley data is then normalized by a sliding window to obtain a normalized mean of the valley data. The difference between the normalized mean of the peak data and the mean of the normalized valley data is obtained to obtain the average fluctuation value. The rate of change of each peak data in the treatment parameter data sequence is obtained, and the mean of the peak rate of change is obtained. The mean of the peak rate of change is then normalized by a sliding window to obtain a normalized mean of the peak rate of change. The reciprocal of the product of the average fluctuation value and the mean of the normalized peak rate of change is obtained to obtain the second stability level. If there is no peak data or valley data in the treatment parameter data sequence, the second stability level is set to a constant 1. The AMPD algorithm, the derivative method, and the sliding window normalization process are existing technologies and will not be described in detail here. In the preset operating program of the eye fumigation therapy device, the theoretical rate of change of any treatment parameter in the current monitoring time's operating phase is obtained. For example, in the heating phase, the eye fumigation therapy device should raise the temperature by 10 degrees Celsius within 10 minutes (i.e., 600 seconds). Therefore, the theoretical rate of change of the temperature data in the heating phase is... The current working stage at the current monitoring time is obtained and denoted as the current working stage. The treatment parameters belonging to the current working stage in the treatment parameter data sequence are grouped into a subsequence. The subsequence is fitted using the least squares method to obtain a first-order fitting function. The slope of the first-order fitting function is obtained and denoted as the actual rate of change. The reciprocal of the absolute value of the difference between the theoretical rate of change and the actual rate of change is obtained to obtain the third stability. The least squares method is an existing technology and will not be described in detail here. In the first fitting function, the fitted value of each treatment parameter in the subsequence is obtained, and the reciprocal of the absolute value of the difference between each treatment parameter and its fitted value is accumulated to obtain the fourth stability level. The first safety confidence level is obtained by adding the first stability level and the second stability level. The second safety confidence level is obtained by multiplying the third stability level and the fourth stability level. The product of the first safety confidence level and the second safety confidence level is normalized to obtain the safety confidence level of any treatment parameter.

[0042] In one embodiment, taking the j-th treatment parameter among the multidimensional treatment parameters at the current monitoring time as an example, the formula for calculating the safety confidence of the j-th treatment parameter is:

[0043] in, The safety confidence level of the j-th treatment parameter among the multidimensional treatment parameters at the current monitoring time; is the normalized standard deviation; F is the normalized mean peak value; G is the normalized mean trough value; B is the normalized mean peak value change rate; Kj is the theoretical change rate of the j-th treatment parameter; Hj represents the actual rate of change of the j-th treatment parameter; Hm represents the m-th treatment parameter in the subsequence. is the fitted value of the m-th treatment parameter in the subsequence; M is the number of treatment parameters in the subsequence; It is the absolute value symbol; This is the normalization function.

[0044] It should be noted that, For the first level of stability, The smaller the value, the higher the stability of the treatment parameter data sequence, and the more consistent it is with the stable changes in treatment parameters of an eye fumigation therapy device during normal operation. The larger it is; For the second level of stability, The smaller the value of B, the smoother the fluctuation of the treatment parameter data sequence, which better matches the stable changes in treatment parameters of an eye fumigation therapy device during normal operation. The larger it is; As the third level of stability, As the fourth level of stability, and The smaller the value, the higher the consistency between the treatment parameters in the subsequence and their theoretical changes, and the higher the degree of agreement between the changes and the data, indicating greater data reliability. The larger it is.

[0045] The method for obtaining the rate of change of each peak data in the treatment parameter data sequence is as follows: For any peak data in the treatment parameter data sequence, in the treatment parameter data sequence, obtain the absolute value of the difference between the any peak data and its left adjacent treatment parameter to obtain the left rate of change; obtain the absolute value of the difference between the any peak data and its right adjacent treatment parameter to obtain the right rate of change; obtain the mean between the left rate of change and the right rate of change to obtain the rate of change of the any peak data.

[0046] (2) Based on the data difference between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence, obtain the user safety confidence level at the current monitoring time.

[0047] Specifically, the multidimensional feedback parameters include head movement frequency and blink frequency. In the multidimensional historical feedback parameter data sequence, the multidimensional historical feedback parameters corresponding to the multidimensional feedback parameters at the current monitoring time are obtained. Since the experience process before treatment is the same as the experience process of a complete treatment process, the time of the experience process is the same as the time of the normal treatment process. Therefore, the multidimensional historical feedback parameter data sequence is composed of the multidimensional historical feedback parameters of a complete treatment process. Thus, the data at each monitoring moment in the current treatment process can find the corresponding historical data in the multidimensional historical feedback parameter data sequence (for example, if the current monitoring moment is the 60th second in the current treatment process, then the multidimensional feedback parameters at the current monitoring moment correspond to the multidimensional historical feedback parameters at the 60th second in the experience process). The absolute value of the difference between the blink frequency at the current monitoring moment and its corresponding historical blink frequency is obtained to obtain the blink frequency difference value. The skin sensitivity of any user is obtained. The product of the head movement frequency, the difference in blink frequency, and the skin sensitivity at the current monitoring time is normalized to obtain the normalization result. The difference between the constant 1 and the normalization result is obtained to obtain the user's safety confidence at the current monitoring time.

[0048] In one implementation, the formula for calculating the user's security confidence at the current monitoring time is:

[0049] Where W represents the user's security confidence at the current monitoring time; T represents the head movement frequency at the current monitoring time; and Z represents the blinking frequency at the current monitoring time. The current blink frequency corresponds to the historical blink frequency at the current monitoring moment; N represents the skin sensitivity of any user. It is the absolute value symbol; This is the normalization function.

[0050] It should be noted that the smaller T is, the lower the probability of abnormal treatment parameters at the current monitoring time, and the larger W is. This represents the difference in blink frequency. The smaller the value of N, the smaller the difference between the user's blinking frequency at the current monitoring time and the historical blinking frequency, and the higher the user's comfort level; the larger the value of N, the higher the user's tolerance for fluctuations in various treatment parameters of the eye steaming therapy device, and the higher the value of W.

[0051] (3) The total confidence level of any treatment parameter is obtained by multiplying the security confidence level and the user security confidence level.

[0052] In one embodiment, taking the j-th treatment parameter among the multidimensional treatment parameters at the current monitoring time as an example, the formula for calculating the total confidence of the j-th treatment parameter is:

[0053] in, The total confidence level of the j-th treatment parameter among the multidimensional treatment parameters at the current monitoring time; is the safety confidence level of the j-th treatment parameter in the multidimensional treatment parameters at the current monitoring time; W is the user safety confidence level at the current monitoring time.

[0054] At this point, the total confidence level of any one of the multidimensional treatment parameters at the current monitoring time is obtained.

[0055] Step S104: Obtain the safety level and total confidence level of each treatment parameter in the multidimensional treatment parameters at the current monitoring time. Based on the safety level and total confidence level of each treatment parameter, obtain the comprehensive safety index of the eye fumigation therapy device at the current monitoring time, and supervise the eye fumigation therapy device at the current monitoring time.

[0056] Following the method described above for obtaining the safety level and total confidence level of any treatment parameter among the multidimensional treatment parameters at the current monitoring time, the safety level and total confidence level of each treatment parameter among the multidimensional treatment parameters at the current monitoring time are obtained. Then, based on the safety level and total confidence level of each treatment parameter, the comprehensive safety index of the eye fumigation therapy device at the current monitoring time is obtained, which is used to supervise the eye fumigation therapy device at the current monitoring time.

[0057] The method for obtaining the comprehensive safety index of the eye fumigation therapy device at the current monitoring time based on the safety level and overall confidence level of each treatment parameter is as follows: The safety index of each treatment parameter is obtained by multiplying the safety level and the total confidence level of each treatment parameter. The mean of the safety index of each treatment parameter is obtained to obtain the comprehensive safety index of the eye fumigation therapy device at the current monitoring time.

[0058] In one embodiment, the formula for calculating the comprehensive safety index of the eye fumigation therapy device at the current monitoring time is:

[0059] Where X represents the comprehensive safety index of the eye fumigation therapy device at the current monitoring time; The safety level of the j-th treatment parameter among the multidimensional treatment parameters at the current monitoring time; is the total confidence level of the j-th treatment parameter in the multidimensional treatment parameters at the current monitoring time; J is the number of treatment parameters in the multidimensional treatment parameters at the current monitoring time.

[0060] It should be noted that the higher the safety level of each treatment parameter at the current monitoring time, the higher the safety of the eye fumigation therapy device at the current monitoring time. The higher the overall confidence level of the treatment parameters among the multidimensional treatment parameters at the current monitoring time, the more guaranteed the safety of the eye fumigation therapy device at the current monitoring time, and the larger X will be.

[0061] After obtaining the comprehensive safety index of the eye fumigation therapy device at the current monitoring time, if the comprehensive safety index is lower than the comprehensive safety index threshold, an alarm will be issued directly, the power will be cut off, and a message will be sent indicating that the safety of the eye fumigation therapy device is too low, notifying relevant personnel to further investigate the cause. Simultaneously, to prevent accidents, if the comprehensive safety index of the eye fumigation therapy device is not lower than the comprehensive safety index threshold at the current monitoring time, but the comprehensive safety index continues to decline within a continuous preset time including the current monitoring time, the eye fumigation therapy device will also issue a warning, reminding relevant personnel to check the working status of the eye fumigation therapy device in a timely manner to avoid safety accidents. The comprehensive safety index threshold needs to be set with different alarm scores according to different models of eye fumigation therapy devices. In this embodiment, the comprehensive safety index threshold is set to 0.5, and the continuous preset time including the current monitoring time is set to 10 seconds. This is not limited here and can be set according to the specific implementation scenario.

[0062] In summary, in this embodiment of the invention, firstly, the safety level of the treatment parameters in the multidimensional treatment parameters at the current monitoring time is obtained to reflect the working status of the eye fumigation therapy device at the current monitoring time; the higher the safety level, the more normal the working status. Then, the total confidence level of the treatment parameters in the multidimensional treatment parameters at the current monitoring time is obtained to reflect the reliability of the safety level of the treatment parameters; the higher the total confidence level, the more reliable the safety level, and the greater the reliability that the eye fumigation therapy device is in normal working status at the current monitoring time, thus increasing its safety. Finally, the comprehensive safety index of the eye fumigation therapy device at the current monitoring time is obtained to monitor the eye fumigation therapy device used by the user in real time, ensuring the safety of the usage process and improving the user's experience.

[0063] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for monitoring eye fumigation therapy devices based on data fusion analysis, characterized in that, The aforementioned method for monitoring eye fumigation therapy devices based on data fusion analysis includes: Before any user uses the eye fumigation therapy device for treatment, a multidimensional historical feedback parameter data sequence of the user's use of the eye fumigation therapy device is obtained. During the user's use of the eye fumigation therapy device, multidimensional treatment parameters and multidimensional feedback parameters of the user are obtained at each monitoring moment. For any treatment parameter in the multidimensional treatment parameters at the current monitoring time, obtain the normal fluctuation range of the treatment parameter, and obtain the safety level of the treatment parameter based on the difference between the treatment parameter and the normal fluctuation range. Within a preset historical period prior to the current monitoring time, treatment parameters belonging to the same dimension as any of the treatment parameters, along with any of the treatment parameters, are combined to form a treatment parameter data sequence. Based on the data fluctuation characteristics within the treatment parameter data sequence and the data differences between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence, the total confidence level of any of the treatment parameters is obtained. The safety level and overall confidence level of each treatment parameter in the multidimensional treatment parameters at the current monitoring time are obtained. Based on the safety level and overall confidence level of each treatment parameter, the comprehensive safety index of the eye fumigation therapy device at the current monitoring time is obtained, and the eye fumigation therapy device at the current monitoring time is monitored.

2. The method for monitoring an eye fumigation therapy device based on data fusion analysis according to claim 1, characterized in that, The step of obtaining the safety level of any treatment parameter based on the difference between any treatment parameter and the normal fluctuation range includes: If any of the treatment parameters is outside the normal fluctuation range, then the safety level of any of the treatment parameters is set to 0; If any of the treatment parameters is within the normal fluctuation range, then the maximum value in the normal fluctuation range is recorded as the maximum fluctuation value, and the minimum value in the normal fluctuation range is recorded as the minimum fluctuation value. The difference between any treatment parameter and the maximum fluctuation value is obtained to get the first difference. The difference between any treatment parameter and the minimum fluctuation value is obtained to get the second difference. The product between constant 4, the first difference, and the second difference is obtained to get the first product result. The product of constant 3 and the square of the minimum fluctuation value is obtained to get the first squared value. The product of constant 2, the maximum fluctuation value and the minimum fluctuation value is obtained to get the second product result. The square of the maximum fluctuation value is obtained to get the second squared value. The sum of the first squared value and the second product result is obtained to get the first sum result. The difference between the first sum result and the second squared value is obtained to get the third difference. The safety level of any treatment parameter is obtained by comparing the ratio of the first product result to the third difference.

3. The method for monitoring an eye fumigation therapy device based on data fusion analysis according to claim 1, characterized in that, The step of obtaining the total confidence level of any treatment parameter based on the data fluctuation characteristics within the treatment parameter data sequence and the data difference between the multidimensional feedback parameter at the current monitoring time and the multidimensional historical feedback parameter data sequence includes: Based on the data fluctuation characteristics within the treatment parameter data sequence, the safety confidence level of any one of the treatment parameters is obtained; Based on the data differences between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence, the user's security confidence at the current monitoring time is obtained; The total confidence level of any treatment parameter is obtained by multiplying the security confidence level and the user security confidence level.

4. The monitoring method for an eye fumigation therapy device based on data fusion analysis according to claim 3, characterized in that, The step of obtaining the safety confidence level of any treatment parameter based on the data fluctuation characteristics within the treatment parameter data sequence includes: Obtain the standard deviation of the treatment parameter data sequence, normalize the standard deviation to obtain the normalized standard deviation, and obtain the reciprocal of the normalized standard deviation to obtain the first stability level. The following steps are performed: First, obtain peak data from the treatment parameter data sequence and obtain the corresponding mean peak data. Then, normalize the mean peak data to obtain a normalized mean peak data. Next, obtain valley data from the treatment parameter data sequence and obtain the corresponding mean valley data. Normalize the mean valley data to obtain a normalized mean valley data. Then, obtain the difference between the normalized mean peak data and the normalized mean valley data to obtain the average fluctuation value. Finally, obtain the rate of change of each peak data in the treatment parameter data sequence and obtain the corresponding mean peak rate of change. Normalize the mean peak rate of change to obtain the normalized mean peak rate of change. Then, obtain the reciprocal of the product of the average fluctuation value and the normalized mean peak rate of change to obtain the second stability level. If there are no peak or valley data in the treatment parameter data sequence, the second stability level is set to a constant 1. The theoretical rate of change of any of the treatment parameters is obtained. The treatment process of the eye fumigation therapy device is divided into at least two working stages. The working stage at the current monitoring time is obtained and recorded as the current working stage. The treatment parameters belonging to the current working stage in the treatment parameter data sequence are formed into a subsequence. The subsequence is fitted to obtain a first-order fitting function. The slope of the first-order fitting function is obtained and recorded as the actual rate of change. The reciprocal of the absolute value of the difference between the theoretical rate of change and the actual rate of change is obtained to obtain the third stability degree. In the first fitting function, the fitted value of each treatment parameter in the subsequence is obtained, and the reciprocal of the absolute value of the difference between each treatment parameter and its fitted value is accumulated to obtain the fourth stability level. The first safety confidence level is obtained by adding the first stability level and the second stability level. The second safety confidence level is obtained by multiplying the third stability level and the fourth stability level. The product of the first safety confidence level and the second safety confidence level is normalized to obtain the safety confidence level of any treatment parameter.

5. A method for monitoring an eye fumigation therapy device based on data fusion analysis according to claim 4, characterized in that, The step of obtaining the rate of change of each peak data in the treatment parameter data sequence includes: For any peak data in the treatment parameter data sequence, the absolute value of the difference between the peak data and its left adjacent treatment parameter is obtained to obtain the left rate of change. The absolute value of the difference between the peak data and its right adjacent treatment parameter is obtained to obtain the right rate of change. The mean between the left rate of change and the right rate of change is obtained to obtain the rate of change of the peak data.

6. The method for monitoring an eye fumigation therapy device based on data fusion analysis according to claim 3, characterized in that, The step of obtaining the user's security confidence level at the current monitoring time based on the data difference between the multidimensional feedback parameters at the current monitoring time and the multidimensional historical feedback parameter data sequence includes: The multidimensional feedback parameters include head movement frequency and blink frequency. In the multidimensional historical feedback parameter data sequence, the multidimensional historical feedback parameters corresponding to the multidimensional feedback parameters at the current monitoring time are obtained, and the absolute value of the difference between the blink frequency at the current monitoring time and its corresponding historical blink frequency is obtained to obtain the blink frequency difference value. The skin sensitivity of any user is obtained. The product of the head movement frequency, the difference in blink frequency, and the skin sensitivity at the current monitoring time is normalized to obtain the normalization result. The difference between the constant 1 and the normalization result is obtained to obtain the user's safety confidence at the current monitoring time.

7. A method for monitoring an eye fumigation therapy device based on data fusion analysis according to claim 6, characterized in that, The method for obtaining the multidimensional feedback parameters at the current monitoring time includes: The treatment process of the eye fumigation therapy device is divided into at least two working stages. The working stage at the current monitoring time is obtained and recorded as the current working stage. The ratio of the number of head movements of any user in the current working stage to the working time of the current working stage is obtained to obtain the head movement frequency at the current monitoring time. The ratio of the number of blinks of any user in the current working stage to the working time of the current working stage is obtained to obtain the blink frequency at the current monitoring time.

8. The method for monitoring an eye fumigation therapy device based on data fusion analysis according to claim 1, characterized in that, The comprehensive safety index of the eye fumigation therapy device at the current monitoring time is obtained based on the safety level and overall confidence level of each treatment parameter, including: The safety index of each treatment parameter is obtained by multiplying the safety level and the total confidence level of each treatment parameter. The mean of the safety index of each treatment parameter is obtained, and the comprehensive safety index of the eye fumigation therapy device at the current monitoring time is obtained.

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