An intelligent analysis method for the disinfection quality of dental equipment

Abstract

The present invention relates to the field of data processing technology, and specifically to an intelligent analysis method for the disinfection quality of dental equipment, including: collecting a temperature change curve when the dental equipment is sterilized with high-pressure steam; obtaining a first possibility and a key temperature value of the temperature value through a neighborhood range of the temperature value in the temperature change curve; obtaining a number of segmentation points of the temperature change curve; obtaining a sampling interval of the temperature change curve according to the segmentation points; and then obtaining a sampling error of each sampling interval value in the sampling interval; obtaining a sampling frequency of the temperature change curve; and finally evaluating the disinfection quality of the dental equipment according to the similarity and sampling frequency between the sampling sequences. The present invention improves the efficiency of intelligent analysis of the disinfection quality of dental equipment by sampling the temperature change curve to be analyzed and comparing it with a standard sampling sequence.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent analysis method for the disinfection quality of dental equipment. Background Art

[0002] Dental equipment may come into contact with the air and may contain certain bacteria. Therefore, the equipment must be disinfected before use to ensure that patients are not harmed by equipment infection during treatment.

[0003] When testing the disinfection quality of dental equipment, it is usually judged whether the disinfection temperature of the equipment to be tested is normal based on the difference between the temperature change data of the equipment that meets the disinfection specifications and the temperature change data of the equipment to be tested, so as to judge whether the disinfection quality of the equipment to be tested meets the standards. Due to the influence of the environment during the disinfection process of the equipment to be tested and the different types and quantities of bacteria on the equipment to be tested, the disinfection temperatures of different equipment are different. The traditional Euclidean distance has a large error in temperature comparison. Therefore, the DTW algorithm is used to analyze the trend similarity of the temperature change data. However, the DTW algorithm has a high complexity in the calculation process, resulting in a lot of time and resources spent on analyzing and judging the temperature change data of the equipment to be tested. Summary of the invention

[0004] In order to solve the above problems, the present invention provides an intelligent analysis method for the disinfection quality of dental equipment.

[0005] The intelligent analysis method for the disinfection quality of dental equipment of the present invention adopts the following technical scheme:

[0006] An embodiment of the present invention provides an intelligent analysis method for the disinfection quality of dental equipment, the method comprising the following steps:

[0007] Obtain temperature change curves of several identical dental instruments during high pressure steam sterilization;

[0008] Preset the left neighborhood range and the right neighborhood range of each temperature value in each temperature change curve; obtain the first possibility of each temperature value in each temperature change curve according to the temperature change curve, the left neighborhood range and the right neighborhood range; obtain several key temperature values ​​of each temperature change curve according to the size of the first possibility;

[0009] According to the left neighborhood range, the right neighborhood range, the key temperature value and the first possibility, the final segmentation possibility of each temperature value in each temperature change curve is obtained; according to the size of the final segmentation possibility, a number of segmentation points of each temperature change curve are obtained;

[0010] The sampling interval of each temperature change curve is obtained according to the segmentation point of the temperature change curve; the sampling error of each acquisition interval value in the sampling interval is obtained according to the sampling interval and the segmentation point; the evaluation index of each acquisition interval value in the sampling interval is obtained according to the sampling error; the sampling frequency of each temperature change curve is obtained according to the size of the evaluation index;

[0011] According to the sampling frequency, the similarity between the sampling sequence of each temperature change curve and the sampling sequences of different temperature change curves is obtained; and the disinfection quality of dental equipment is evaluated according to the similarity between the sampling sequences and the sampling frequency.

[0012] Furthermore, the preset left neighborhood range and right neighborhood range of each temperature value in each temperature change curve includes the following specific steps:

[0013] Any temperature change curve is recorded as the target temperature change curve. The temperature value is the center, The temperature value adjacent to the left The range determined by the temperature value is used as the target temperature change curve. The left neighborhood of the temperature value is The temperature value adjacent to the right The range determined by the temperature value is used as the target temperature change curve. The right neighborhood range of the temperature value, is a preset first value.

[0014] Furthermore, the step of obtaining the first possibility of each temperature value in each temperature change curve according to the temperature change curve, the left neighborhood range and the right neighborhood range includes the following specific steps:

[0015] The target temperature change curve minus the temperature value The difference between the temperature values ​​is used as the target temperature change curve. The range of change of temperature value;

[0016]

[0017] In the formula, The target temperature change curve The temperature change range, The target temperature change curve The temperature change range is To take the absolute value, The target temperature change curve The mean of the change range of all temperature values ​​in the left neighborhood of a temperature value, The target temperature change curve The mean of the variation range of all temperature values ​​in the right neighborhood of a temperature value; The target temperature change curve The number of temperature values ​​in the right neighborhood of a temperature value, The target temperature change curve The right neighborhood of the temperature value The temperature change range is The target temperature change curve The number of temperature values ​​in the left neighborhood of a temperature value, The target temperature change curve The left neighborhood of the temperature value The temperature change range is The target temperature change curve The first possibility for a temperature value.

[0018] Furthermore, the method of obtaining the final possibility of segmentation of each temperature value in each temperature change curve according to the left neighborhood range, the right neighborhood range, the key temperature value and the first possibility includes the following specific steps:

[0019] The same When the dental instruments to be tested are sterilized by high pressure steam The set of temperature change curves is called curve set. is a preset second value; The left neighborhood range and right neighborhood range of the temperature value are used as the target temperature change curve. The total neighborhood range of temperature values;

[0020]

[0021] In the formula, is the number of temperature change curves in the curve set, is the first The temperature change curve The first possibility for the temperature value, The specific method of obtaining is as follows: Get the first The temperature change curve The first probability of each temperature value within the total neighborhood of the temperature value is The time when the maximum value of the first possibility of all temperature values ​​in the total neighborhood of a temperature value corresponds to the temperature value is recorded as ; The specific method for obtaining is as follows: Get the first temperature change curve in each temperature change curve in the curve set. The maximum value among the first possibilities of all temperature values ​​within the total neighborhood of the temperature value is recorded as the first maximum value, and the average value of the time corresponding to all the first maximum values ​​of the temperature value is recorded as , is the number of temperature values ​​within the total neighborhood of any temperature value in any temperature change curve, is the first The temperature change curve The total neighborhood of the temperature value The first possibility for the temperature value, is the first The temperature change curve The total neighborhood of the temperature value The first possibility for the temperature value, is the first The temperature change curve The total neighborhood of the temperature value Temperature value, is the first The temperature change curve The total neighborhood of the temperature value Temperature value, To take the absolute value, The target temperature change curve The initial probability of segmentation for each temperature value;

[0022] According to the initial segmentation possibility and the key temperature value, the final segmentation possibility of each temperature value in each temperature change curve is obtained.

[0023] Furthermore, the method of obtaining the final segmentation possibility of each temperature value in each temperature change curve according to the initial segmentation possibility and the key temperature value includes the following specific steps:

[0024] Fitting all key temperature values ​​of the target temperature change curve to obtain a fitting curve of the target temperature change curve; the fitting curve of the target temperature change curve includes a plurality of fitting temperature values;

[0025]

[0026] In the formula, The target temperature change curve The initial probability of segmentation for each temperature value is is the fitting curve of all temperature change curves in the curve set. The variance of the fitted temperature values, is the temperature change curve of all the curves in the curve set. The variance of the temperature values, is the number of temperature change curves in the curve set, is the first The temperature change curve Temperature value, is the first The fitting curve of the temperature change curve The fitting temperature value, To take the absolute value, The target temperature change curve The split factor for each temperature value;

[0027] The segmentation factor of each temperature value in the target temperature change curve is obtained, and the segmentation factors of all temperature values ​​in the target temperature change curve are linearly normalized, and the obtained result is used as the final segmentation possibility of each temperature value in the target temperature change curve.

[0028] Furthermore, the step of obtaining the sampling interval of each temperature change curve according to the segmentation points of the temperature change curve includes the following specific steps:

[0029] Get the time interval between adjacent segmentation points in the target temperature change curve, and record the maximum value of the time interval between adjacent segmentation points in the target temperature change curve as ,Will The determined interval is recorded as the sampling interval of the target temperature change curve, and the sampling interval of the target temperature change curve includes a plurality of sampling interval values.

[0030] Furthermore, the sampling error of each acquisition interval value in the sampling interval is obtained according to the sampling interval and the segmentation point, and the specific steps include the following:

[0031] Record any sampling interval value in the sampling interval of the target temperature change curve as the target sampling interval value, record the ratio of the target sampling interval value to the time corresponding to the first segmentation point in the target temperature change curve as a ratio coefficient of the target sampling interval value, obtain the ratio of the target sampling interval value to the time corresponding to each segmentation point in the target temperature change curve, and obtain several ratio coefficients of the target sampling interval value, record the product of the first ratio coefficient of the target sampling interval value and the target sampling interval value, and the absolute value of the difference between the time corresponding to the first segmentation point in the target temperature change curve, as the error parameter of the first ratio coefficient of the target sampling interval value, record the product of the second ratio coefficient of the target sampling interval value and the target sampling interval value, and the absolute value of the difference between the time corresponding to the second segmentation point in the target temperature change curve, as the error parameter of the second ratio coefficient of the target sampling interval value, obtain the error parameter of each ratio coefficient of the target sampling interval value, and take the minimum value of the error parameters of all ratio coefficients of the target sampling interval value as the sampling error of the target sampling interval value.

[0032] Furthermore, the step of obtaining the evaluation index of each acquisition interval value in the sampling interval according to the sampling error includes the following specific steps:

[0033] Arrange all the ratio coefficients of the target sampling interval value in the order of the segmentation points in the target temperature change curve to obtain a sequence, which is recorded as the ratio coefficient sequence of the target sampling interval value. The number of ratio coefficients contained in the ratio coefficient sequence of the target sampling interval value is recorded as , the ratio coefficient sequence of the target sampling interval value is The vector determined in the dimensional space is recorded as the ratio coefficient vector of the target sampling interval value, and the ratio of the modulus of the ratio coefficient vector of the target sampling interval value to the sampling error of the target sampling interval value is used as the evaluation index of the target sampling interval value.

[0034] Furthermore, the method of obtaining the similarity between the sampling sequence of each temperature change curve and the sampling sequences of different temperature change curves according to the sampling frequency includes the following specific steps:

[0035] The target temperature change curve is sampled according to the sampling frequency of the target temperature change curve to obtain a plurality of sampled temperature values ​​of the target temperature change curve, and a sequence consisting of all the sampled temperature values ​​of the target temperature change curve is recorded as a sampling sequence of the target temperature change curve;

[0036] Get the The sampling sequence of the temperature change curve is the same as that of the The DTW distance of the sampling sequence of the temperature change curve; The sampling sequence of the temperature change curve is the same as that of the The DTW distance of the sampling sequence of the temperature change curve is taken as the The sampling sequence of the temperature change curve is the same as that of the The similarity between the sampling sequences of the temperature change curves.

[0037] Furthermore, the method of evaluating the disinfection quality of dental equipment according to the similarity between sampling sequences and the sampling frequency includes the following specific steps:

[0038] The set consisting of the sampling sequences of all temperature change curves is recorded as a sampling sequence set, any sampling sequence in the sampling sequence set is recorded as a target sampling sequence, the similarity between the target sampling sequence and each sampling sequence in the sampling sequence set is obtained, the accumulated value of the similarity between the target sampling sequence and each sampling sequence in the sampling sequence set is used as the standard parameter of the target sampling sequence, the standard parameter of each sampling sequence in the sampling sequence set is obtained, and the sampling sequence corresponding to the maximum value of the standard parameter is recorded as the standard sampling sequence;

[0039] Recollect a temperature change curve of a new oral device to be tested during high-pressure steam sterilization, and record it as the temperature change curve to be analyzed; take the sampling frequency of the target temperature change curve as the sampling frequency of the temperature change curve to be analyzed, and obtain a sampling sequence of the temperature change curve to be analyzed, and record it as the sampling sequence to be analyzed;

[0040] The DTW distance between the sampling sequence to be analyzed and the standard sampling sequence is obtained, and the DTW distance between the sampling sequence to be analyzed and the standard sampling sequence is used as the similarity between the sampling sequence to be analyzed and the standard sampling sequence; the minimum value of the similarity between different sampling sequences in the sampling sequence set is recorded as the similarity threshold; if the similarity between the sampling sequence to be analyzed and the standard sampling sequence is greater than or equal to the similarity threshold, the disinfection quality of the dental equipment corresponding to the sampling sequence to be analyzed is qualified; if the similarity between the sampling sequence to be analyzed and the standard sampling sequence is less than the similarity threshold, the disinfection quality of the dental equipment corresponding to the sampling sequence to be analyzed is unqualified.

[0041] The beneficial effect of the technical solution of the present invention is: after collecting multiple temperature change curves when oral equipment is sterilized with high-pressure steam, the present invention determines the left neighborhood range and the right neighborhood range of each temperature value in the temperature change curve, and obtains the possibility of each temperature value in each temperature change curve being a key temperature value, that is, the first possibility, based on the temperature change curve, the left neighborhood range and the right neighborhood range. The key temperature value represents the temperature value containing important information in the temperature change curve, and then obtains several key temperature values ​​of each temperature change curve. By analyzing the possibility of the neighborhood range and the temperature value as the key temperature value, the final possibility of each temperature value in each temperature change curve being a segmentation point is obtained, that is, The final possibility is divided, and several segmentation points of each temperature change curve are obtained. The determination of the segmentation points makes the sampling frequency more accurate when the sampling frequency of the temperature change curve is obtained later, and the complexity of the temperature change curve is reduced. The sampling interval of each temperature change curve is obtained through the segmentation points of the temperature change curve, and the sampling error of each acquisition interval value in the sampling interval is analyzed to reflect the sampling effect. The sampling frequency of each temperature change curve is obtained by constructing an evaluation index, and finally the disinfection quality of dental equipment is evaluated through the similarity and sampling frequency between the sampling sequences, which improves the efficiency of analyzing the temperature change data of the equipment to be tested, and thus the disinfection quality of dental equipment is analyzed more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flowchart of the steps of an intelligent analysis method for the disinfection quality of dental equipment provided by one embodiment of the present invention;

[0044] Figure 2 A flow chart for evaluating the sterilization quality of dental instruments provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent analysis method for the disinfection quality of dental equipment proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The specific scheme of the intelligent analysis method for the disinfection quality of dental equipment provided by the present invention is described in detail below with reference to the accompanying drawings.

[0048] See also Figure 1 , which shows a flowchart of a method for intelligent analysis of the disinfection quality of dental equipment provided by an embodiment of the present invention, the method comprising the following steps:

[0049] Step S001, obtaining temperature change curves of a plurality of identical dental instruments when sterilized by high pressure steam.

[0050] It should be noted that the purpose of this embodiment is to shorten the detection time and improve the detection efficiency of the disinfection quality of dental equipment by segmenting the temperature change data of the equipment to be detected and sampling the temperature change data. Data is collected before starting the analysis.

[0051] Specifically, the temperature recorder is used to collect the same When high pressure steam sterilization is performed on dental instruments Temperature variation curve, is a preset second value. It should be noted that each dental equipment to be tested corresponds to a temperature change curve, and each temperature change curve is a two-dimensional curve, the horizontal axis of the temperature change curve is time, and the vertical axis is the temperature value corresponding to the time.

[0052] Thus, the temperature variation curve of the dental instruments during high pressure steam sterilization is obtained.

[0053] Step S002, presetting the left neighborhood range and the right neighborhood range of each temperature value in each temperature change curve; obtaining the first possibility of each temperature value in each temperature change curve according to the temperature change curve, the left neighborhood range and the right neighborhood range; and obtaining several key temperature values ​​of each temperature change curve according to the size of the first possibility.

[0054] It should be noted that in the process of disinfecting dental equipment, different disinfection stages require different temperature values ​​and corresponding temperature control. By selecting representative temperature values ​​as the key temperature values ​​of the temperature change curve, the changing trend of the entire temperature change curve is reflected, and the number of temperature values ​​participating in dynamic time warping (DTW) and the computational complexity of the detection process are reduced.

[0055] It should be noted that the possibility of determining the temperature value in the temperature change curve as the key temperature value is determined by analyzing the change amplitude of the temperature change curve. Due to the noise interference in the disinfection process of dental equipment, the temperature change curve also fluctuates to a certain extent. Therefore, the temperature fluctuation in the neighborhood and the difference in the change amplitude are used to determine the possibility of the temperature value in the temperature change curve as the key temperature value.

[0056] Specifically, the left neighborhood range and the right neighborhood range of each temperature value in each temperature change curve are preset. As an embodiment, the specific calculation method is as follows:

[0057] Any temperature change curve is recorded as the target temperature change curve. The temperature value is the center, The temperature value adjacent to the left The range determined by the temperature value is used as the target temperature change curve. The left neighborhood of the temperature value is The temperature value adjacent to the right The range determined by the temperature value is used as the target temperature change curve. The right neighborhood range of the temperature value, is a preset first value. It should be noted that The temperature value adjacent to the right The specific temperature values ​​are: Temperature value, Temperature values... and temperature values, the left adjacent The temperature values ​​are in descending order. temperature values, no further details will be given; when obtaining the left neighboring range or the right neighboring range, if there is no If the temperature value is not present, the neighborhood range will not be obtained. In the subsequent analysis and calculation involving the left neighborhood range or the right neighborhood range, it will not participate in the analysis and calculation, and it has no neighborhood range.

[0058] Furthermore, according to the temperature change curve, the left neighborhood range and the right neighborhood range, the first possibility of each temperature value in each temperature change curve is obtained. As an embodiment, the specific calculation method is as follows:

[0059] The target temperature change curve minus the temperature value The difference between the temperature values ​​is used as the target temperature change curve. The range of temperature change.

[0060]

[0061] In the formula, The target temperature change curve The temperature change range is The target temperature change curve The temperature change range is To take the absolute value, The target temperature change curve The mean of the change range of all temperature values ​​in the left neighborhood of a temperature value, The target temperature change curve The mean of the variation range of all temperature values ​​in the right neighborhood of a temperature value; The target temperature change curve The number of temperature values ​​in the right neighborhood of a temperature value, The target temperature change curve The right neighborhood of the temperature value The temperature change range is The target temperature change curve The number of temperature values ​​in the left neighborhood of a temperature value, The target temperature change curve The left neighborhood of the temperature value The temperature change range is The target temperature change curve The first possibility for a temperature value.

[0062] It should be noted that the first possibility of obtaining the temperature value in the target temperature change curve above indicates the possibility of the temperature value being the key temperature value, wherein Indicates The difference in the change range before and after the temperature value. The larger the value, the greater the possibility of a sudden change in temperature here, and the greater the possibility of it being the key temperature value of the target temperature change curve; Indicates The difference between the change range of the temperature value in the neighborhood to the right of the temperature value and the mean, Indicates The difference between the change range of the temperature value in the neighborhood on the left side of the temperature value and the mean, the smaller the difference between the left and right sides, the smaller the difference between the The greater the possibility that the temperature value is used as the key temperature value of the target temperature change curve; since the target temperature change curve maintains a stable change, it will also lead to a small change range in the left and right neighborhoods. Therefore, the mean difference of the change range of the temperature values ​​in the left and right neighborhoods is used. , judge the possibility of the temperature value as the key temperature value of the target temperature change curve. The larger the value, the different temperature change trends in the left and right neighborhoods. The greater the possibility that a temperature value is the key temperature value, the greater the first possibility.

[0063] Furthermore, according to the size of the first possibility, several key temperature values ​​of each temperature change curve are obtained, as follows:

[0064] The first possibility of each temperature value in the target temperature change curve is obtained, and the first possibilities of all temperature values ​​in the target temperature change curve are linearly normalized. The obtained result is used as the final possibility of each temperature value in the target temperature change curve as the key temperature value. A first threshold is preset. In this embodiment, the first threshold is described as 0.8. The temperature values ​​corresponding to all final possibilities whose final possibilities of the temperature value in the target temperature change curve as the key temperature value are greater than the first threshold are used as the key temperature value of the target temperature change curve.

[0065] At this point, several key temperature values ​​of each temperature variation curve are obtained.

[0066] Step S003, according to the left neighborhood range, the right neighborhood range, the key temperature value and the first possibility, obtain the final possibility of segmentation of each temperature value in each temperature change curve; according to the size of the final possibility of segmentation, obtain a number of segmentation points of each temperature change curve.

[0067] It should be noted that, since the temperature change curves of the same equipment used in the dental department are similar when the quality monitoring of disinfection meets the standard, there is a certain offset in time between the key temperature values ​​of different temperature change curves. Therefore, the possibility of determining the temperature value as the segmentation point of the temperature change curve is determined based on the possibility of the neighborhood key temperature value and the difference in time between the key temperature values. The sampling frequency of the temperature change curve is determined according to the frequency of the segmentation point, and the temperature change curve is segmented by combining the key temperature values ​​near the segmentation point. The most representative temperature value is determined by comparing the similarity of each curve. Set an appropriate sampling frequency to sample the temperature change curve, reduce the amount of data, and retain the temperature change trend during the disinfection process as much as possible.

[0068] It should be noted that if only the key temperature value is used as the dividing point of the temperature change curve, the temperature change of the key temperature value itself will be ignored, and the temperature values ​​with large differences will have higher similarity. Therefore, it is necessary to use the relationship between the key temperature values ​​in time series to determine the sampling frequency; there is a certain deviation between the key temperature values ​​represented by different temperature change curves. If the key temperature values ​​of all temperature change curves are considered, the difference in the key temperature values ​​in time series will be too small, making the sampling frequency too dense, and it cannot effectively achieve the purpose of reducing the amount of data and thus reducing the small detection time in the detection process and improving the detection efficiency.

[0069] It should be further explained that the possibility of a temperature value being used as a segmentation point is determined by using the similarity of the key temperature values ​​of different temperature change curves in time series. The greater the possibility of a key temperature value in a neighborhood, the greater the possibility of using it as a segmentation point; the greater the similarity of the data in the neighborhood, the greater the possibility of using it as a segmentation point; and the smaller the difference in time series between key temperature values, the greater the possibility of using it as a segmentation point.

[0070] Specifically, according to the left neighborhood range, the right neighborhood range and the first possibility, the initial possibility of segmentation of each temperature value in each temperature change curve is obtained. As an embodiment, the specific calculation method is as follows:

[0071] The same When the dental instruments to be tested are sterilized by high pressure steam The set of temperature change curves is called curve set. is a preset second value; The left neighborhood range and right neighborhood range of the temperature value are used as the target temperature change curve. The total neighborhood range of temperature values.

[0072]

[0073] In the formula, is the number of temperature change curves in the curve set, is the first The temperature change curve The first possibility for the temperature value, The specific method of obtaining is as follows: Get the first The temperature change curve The first probability of each temperature value within the total neighborhood of the temperature value is The time when the maximum value of the first possibility of all temperature values ​​in the total neighborhood of a temperature value corresponds to the temperature value is recorded as ; The specific method for obtaining is as follows: Get the first temperature change curve in each temperature change curve in the curve set. The maximum value among the first possibilities of all temperature values ​​within the total neighborhood of the temperature value is recorded as the first maximum value, and the average value of the time corresponding to all the first maximum values ​​of the temperature value is recorded as ; is the number of temperature values ​​in the total neighborhood of any temperature value in any temperature change curve, that is, the 5 on the left side of 10 plus the 5 on the right side, is the first The temperature change curve The total neighborhood of the temperature value The first possibility for the temperature value, is the first The temperature change curve The total neighborhood of the temperature value The first possibility for the temperature value, is the first The temperature change curve The total neighborhood of the temperature value Temperature value, is the first The temperature change curve The total neighborhood of the temperature value Temperature value, To take the absolute value, The target temperature change curve The initial probability of segmentation for each temperature value.

[0074] It should be noted that the temperature change curve The performance of each temperature value in the neighborhood is the probability of the key point and the difference in temperature values ​​determines the initial probability of the segmentation point; It indicates the difference in temperature data expressed by different temperature change curves at the same time. The smaller the difference, the greater the initial possibility of the temperature value as the segmentation point. However, there may be a greater possibility of being a non-critical temperature value. Therefore, the possibility of the temperature value neighborhood expressing the critical temperature value is used to determine the initial possibility of the temperature value as the segmentation point. Reflects the The greater the value, the greater the probability that the temperature value in the neighborhood of the temperature value is the key temperature value. The greater the initial possibility of a temperature value as a segmentation point; the initial possibility of the temperature value as a segmentation point is determined by combining the time difference of the key points in the neighborhood. Indicates the time difference of the corresponding temperature value. The smaller the difference, the The greater the initial probability of a temperature value being used as a segmentation point, the greater the initial probability of segmentation.

[0075] It should be noted that the key temperature value is the temperature value at which the temperature change curve suddenly changes. Only using the neighborhood information of the temperature value will ignore the global change of the temperature change curve. Therefore, it is necessary to use the difference between the entire temperature change curve and the key fitting temperature fitting curve to correct the initial possibility of the temperature value as the segmentation point.

[0076] Specifically, according to the initial segmentation possibility and the key temperature value, the final segmentation possibility of each temperature value in each temperature change curve is obtained. As an embodiment, the specific calculation method is as follows:

[0077] All key temperature values ​​of the target temperature change curve are fitted to obtain a fitting curve of the target temperature change curve; it should be noted that the fitting curve contains several fitting temperature values. In this embodiment, the least squares method is used to fit all key temperature values ​​of the target temperature change curve, wherein the fitted curve is a quintic polynomial curve. The specific fitting is an existing method and will not be repeated in this embodiment.

[0078]

[0079] In the formula, The target temperature change curve The initial probability of segmentation for each temperature value is is the fitting curve of all temperature change curves in the curve set. The variance of the fitted temperature values, is the temperature change curve of all the curves in the curve set. The variance of the temperature values, is the number of temperature change curves in the curve set, is the first The temperature change curve Temperature value, is the first The fitting curve of the temperature change curve The fitting temperature value, To take the absolute value, The target temperature change curve The division factor for each temperature value.

[0080] The segmentation factor of each temperature value in the target temperature change curve is obtained, and the segmentation factors of all temperature values ​​in the target temperature change curve are linearly normalized, and the obtained result is used as the final segmentation possibility of each temperature value in the target temperature change curve.

[0081] It should be noted that Indicates the fitting error. The larger the error, the more likely it is to be noise, and the less likely the temperature value is to be used as a split point. The larger the The greater the difference between the change trends corresponding to the temperature values, the greater the possibility of being affected by noise, and the less likely it is to be used as a segmentation point; Indicates The larger the temperature value is, the more likely it is to be affected by noise, and the smaller the possibility of it being used as a segmentation point, that is, the smaller the possibility of the final segmentation.

[0082] Furthermore, according to the size of the final segmentation possibility, several segmentation points of each temperature change curve are obtained, as follows:

[0083] A second threshold is preset. This embodiment is described with the second threshold being 0.8. The temperature value for which the final probability of segmentation of the temperature value in the target temperature change curve is greater than the second threshold is recorded as the first temperature value of the target temperature change curve. The time corresponding to each first temperature value of the target temperature change curve is obtained, and the coordinate point determined by each first temperature value and the corresponding time in the target temperature change curve is used as the segmentation point of the target temperature change curve. It should be noted that the horizontal axis of the temperature change curve is time, and the vertical axis is the temperature value corresponding to the time. Therefore, the coordinate point can be determined according to each first temperature value and the corresponding time in the target temperature change curve.

[0084] Step SOO4, obtain the sampling interval of each temperature change curve according to the segmentation point of the temperature change curve; obtain the sampling error of each acquisition interval value in the sampling interval according to the sampling interval and the segmentation point; obtain the evaluation index of each acquisition interval value in the sampling interval according to the sampling error; obtain the sampling frequency of each temperature change curve according to the size of the evaluation index.

[0085] Specifically, the sampling interval of each temperature change curve is obtained according to the segmentation point of the temperature change curve, as follows:

[0086] Get the time interval between adjacent segmentation points in the target temperature change curve, and record the maximum value of the time interval between adjacent segmentation points in the target temperature change curve as ,Will The determined interval is recorded as the sampling interval of the target temperature change curve, wherein the sampling interval includes a number of sampling interval values; it should be noted that the sampling interval value is a positive integer.

[0087] Furthermore, according to the sampling interval and the split point, the sampling error of each acquisition interval value in the sampling interval is obtained, as follows:

[0088] Record any sampling interval value in the sampling interval of the target temperature change curve as the target sampling interval value, record the ratio of the target sampling interval value to the time corresponding to the first segmentation point in the target temperature change curve as a ratio coefficient of the target sampling interval value, obtain the ratio of the target sampling interval value to the time corresponding to each segmentation point in the target temperature change curve, and obtain several ratio coefficients of the target sampling interval value, record the product of the first ratio coefficient of the target sampling interval value and the target sampling interval value, and the absolute value of the difference between the time corresponding to the first segmentation point in the target temperature change curve, as the error parameter of the first ratio coefficient of the target sampling interval value, record the product of the second ratio coefficient of the target sampling interval value and the target sampling interval value, and the absolute value of the difference between the time corresponding to the second segmentation point in the target temperature change curve, as the error parameter of the second ratio coefficient of the target sampling interval value, obtain the error parameter of each ratio coefficient of the target sampling interval value, and take the minimum value of the error parameters of all ratio coefficients of the target sampling interval value as the sampling error of the target sampling interval value.

[0089] It should be noted that the error parameter represents the error between the target sampling interval value and the segmentation point. The smaller the error, the better the target sampling interval value. Therefore, the minimum value of the error parameter is selected as the sampling error of the target sampling interval value.

[0090] It should be noted that when the sampling interval is 1, it is obviously better to include all the segmentation points, and the error is smaller at this time. Therefore, it is necessary to use the multiple relationship between the sampling interval and the segmentation point, that is, the ratio coefficient, to limit the sampling interval, so that the sampling interval can include the segmentation points as much as possible. The evaluation of the sampling interval can be expressed by the ratio coefficient. The larger the ratio coefficient, the better the evaluation index.

[0091] Specifically, the evaluation index of each sampling interval value in the sampling interval is obtained according to the sampling error, as follows:

[0092] Arrange all the ratio coefficients of the target sampling interval value in the order of the segmentation points in the target temperature change curve to obtain a sequence, which is recorded as the ratio coefficient sequence of the target sampling interval value. The number of ratio coefficients contained in the ratio coefficient sequence of the target sampling interval value is recorded as , the ratio coefficient sequence of the target sampling interval value is The vector determined in the dimensional space is recorded as the ratio coefficient vector of the target sampling interval value, and the ratio of the modulus of the ratio coefficient vector of the target sampling interval value to the sampling error of the target sampling interval value is used as the evaluation index of the target sampling interval value.

[0093] Furthermore, the sampling frequency of each temperature change curve is obtained according to the size of the evaluation index, as follows:

[0094] The sampling interval value corresponding to the maximum value of the evaluation index of the collection interval value in the sampling interval of the target temperature change curve is used as the sampling frequency of the target temperature change curve.

[0095] So far, the sampling frequency of each temperature change curve.

[0096] Step S005: Obtain the similarity between the sampling sequence of each temperature change curve and the sampling sequences of different temperature change curves according to the sampling frequency; and evaluate the disinfection quality of the dental equipment according to the similarity between the sampling sequences and the sampling frequency.

[0097] Specifically, according to the sampling frequency, the similarity between the sampling sequence of each temperature change curve and the sampling sequences of different temperature change curves is obtained, as follows:

[0098] The target temperature change curve is sampled according to the sampling frequency of the target temperature change curve to obtain a number of sampled temperature values ​​of the target temperature change curve, and a sequence consisting of all the sampled temperature values ​​of the target temperature change curve is recorded as the sampling sequence of the target temperature change curve.

[0099] Get the The sampling sequence of the temperature change curve is the same as that of the The DTW distance of the sampling sequence of the temperature change curve is obtained; it should be noted that obtaining the DTW distance of different sampling sequences is an existing method, which will not be repeated in this embodiment; The sampling sequence of the temperature change curve is the same as that of the The DTW distance of the sampling sequence of the temperature change curve is taken as the The sampling sequence of the temperature change curve is the same as that of the The similarity between the sampling sequences of the temperature change curves.

[0100] It should be noted that if any sampling sequence has greater similarity with other sampling sequences, the possibility that any sampling sequence is used as the standard temperature sampling sequence is greater.

[0101] Furthermore, the disinfection quality of dental instruments was evaluated based on the similarity between sampling sequences and sampling frequency, as follows:

[0102] The set consisting of the sampling sequences of all temperature change curves is recorded as the sampling sequence set, any sampling sequence in the sampling sequence set is recorded as the target sampling sequence, the similarity between the target sampling sequence and each sampling sequence in the sampling sequence set is obtained, the accumulated value of the similarity between the target sampling sequence and each sampling sequence in the sampling sequence set is used as the standard parameter of the target sampling sequence, the standard parameter of each sampling sequence in the sampling sequence set is obtained, and the sampling sequence corresponding to the maximum value of the standard parameter is recorded as the standard sampling sequence; a new temperature change curve of the oral equipment to be tested during high-pressure steam sterilization is re-collected and recorded as the temperature change curve to be analyzed; it should be noted that the re-collection of a new oral equipment to be tested and the temperature change curve collected in step S001 are The dental instruments corresponding to the temperature change curves are the same dental instruments; the sampling frequency of the target temperature change curve is used as the sampling frequency of the temperature change curve to be analyzed to obtain a sampling sequence of the temperature change curve to be analyzed, which is recorded as the sampling sequence to be analyzed; it should be noted that the temperature change curve to be analyzed and the target temperature change curve are not the same curve.

[0103] Get the DTW distance between the sampling sequence to be analyzed and the standard sampling sequence, and use the DTW distance between the sampling sequence to be analyzed and the standard sampling sequence as the similarity between the sampling sequence to be analyzed and the standard sampling sequence; record the minimum value of the similarity between different sampling sequences in the sampling sequence set as the similarity threshold. If the similarity between the sampling sequence to be analyzed and the standard sampling sequence is greater than or equal to the similarity threshold, the disinfection quality of the dental equipment corresponding to the sampling sequence to be analyzed is qualified; if the similarity between the sampling sequence to be analyzed and the standard sampling sequence is less than the similarity threshold, the disinfection quality of the dental equipment corresponding to the sampling sequence to be analyzed is unqualified; please participate Figure 2 , Figure 2 This is a flow chart for evaluating the sterilization quality of dental instruments provided in this embodiment.

[0104] At this point, the intelligent analysis of the disinfection quality of dental equipment is completed by sampling the temperature change curve to be analyzed and comparing it with the standard sampling sequence.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent analysis method for the disinfection quality of dental equipment, characterized in that: The method comprises the following steps: Obtain temperature change curves of several identical dental instruments during high pressure steam sterilization; Preset the left neighborhood range and the right neighborhood range of each temperature value in each temperature change curve; obtain the first possibility of each temperature value in each temperature change curve according to the temperature change curve, the left neighborhood range and the right neighborhood range; obtain several key temperature values ​​of each temperature change curve according to the size of the first possibility; According to the left neighborhood range, the right neighborhood range, the key temperature value and the first possibility, the final segmentation possibility of each temperature value in each temperature change curve is obtained; according to the size of the final segmentation possibility, a number of segmentation points of each temperature change curve are obtained; The sampling interval of each temperature change curve is obtained according to the segmentation point of the temperature change curve; the sampling error of each acquisition interval value in the sampling interval is obtained according to the sampling interval and the segmentation point; the evaluation index of each acquisition interval value in the sampling interval is obtained according to the sampling error; the sampling frequency of each temperature change curve is obtained according to the size of the evaluation index; According to the sampling frequency, the similarity between the sampling sequence of each temperature change curve and the sampling sequences of different temperature change curves is obtained; the disinfection quality of dental equipment is evaluated according to the similarity between the sampling sequences and the sampling frequency; The left neighborhood range and the right neighborhood range of each temperature value in each temperature change curve are preset, including: Any temperature change curve is recorded as the target temperature change curve. The temperature value is the center, The temperature value adjacent to the left The range determined by the temperature value is used as the target temperature change curve. The left neighborhood of the temperature value is The temperature value to the right of The range determined by the temperature value is used as the target temperature change curve. The right neighborhood range of the temperature value, is a preset first value.

2. The intelligent analysis method for the disinfection quality of dental equipment according to claim 1, characterized in that: The method of obtaining the first possibility of each temperature value in each temperature change curve according to the temperature change curve, the left neighborhood range and the right neighborhood range includes the following specific steps: The target temperature change curve minus the temperature value The difference between the temperature values ​​is used as the target temperature change curve. The range of change of temperature value; In the formula, The target temperature change curve The temperature change range, The target temperature change curve The temperature change range is To take the absolute value, The target temperature change curve The mean of the change range of all temperature values ​​in the left neighborhood of a temperature value, The target temperature change curve The mean of the variation range of all temperature values ​​in the right neighborhood of a temperature value; The target temperature change curve The number of temperature values ​​in the right neighborhood of a temperature value, The target temperature change curve The right neighborhood of the temperature value The temperature change range is The target temperature change curve The number of temperature values ​​in the left neighborhood of a temperature value, The target temperature change curve The left neighborhood of the temperature value The temperature change range, The target temperature change curve The first possibility for a temperature value.

3. The intelligent analysis method for the disinfection quality of dental equipment according to claim 1, characterized in that: The method of obtaining the final possibility of segmentation of each temperature value in each temperature change curve according to the left neighborhood range, the right neighborhood range, the key temperature value and the first possibility includes the following specific steps: The same When the dental instruments to be tested are sterilized by high pressure steam The set of temperature change curves is called curve set. is a preset second value; The left neighborhood range and right neighborhood range of the temperature value are used as the target temperature change curve. The total neighborhood range of temperature values; In the formula, is the number of temperature change curves in the curve set, is the first The temperature change curve The first possibility for the temperature value, The specific method of obtaining is as follows: Get the first The temperature change curve The first probability of each temperature value within the total neighborhood of the temperature value is The time when the maximum value of the first possibility of all temperature values ​​in the total neighborhood of a temperature value corresponds to the temperature value is recorded as ; The specific method for obtaining is as follows: Get the first temperature change curve in each temperature change curve in the curve set. The maximum value among the first possibilities of all temperature values ​​within the total neighborhood of the temperature value is recorded as the first maximum value, and the average value of the time corresponding to all the first maximum values ​​of the temperature value is recorded as , is the number of temperature values ​​within the total neighborhood of any temperature value in any temperature change curve, is the first The temperature change curve The total neighborhood of the temperature value The first possibility for the temperature value, is the first The temperature change curve The total neighborhood of the temperature value The first possibility for the temperature value, is the first The temperature change curve The total neighborhood of the temperature value Temperature value, is the first The temperature change curve The total neighborhood of the temperature value Temperature value, To take the absolute value, The target temperature change curve The initial probability of segmentation for each temperature value; According to the initial segmentation possibility and the key temperature value, the final segmentation possibility of each temperature value in each temperature change curve is obtained.

4. The intelligent analysis method for the disinfection quality of dental equipment according to claim 3, characterized in that: The method of obtaining the final segmentation possibility of each temperature value in each temperature change curve according to the initial segmentation possibility and the key temperature value includes the following specific steps: Fitting all key temperature values ​​of the target temperature change curve to obtain a fitting curve of the target temperature change curve; the fitting curve of the target temperature change curve includes a plurality of fitting temperature values; In the formula, The target temperature change curve The initial probability of segmentation for each temperature value is is the fitting curve of all temperature change curves in the curve set. The variance of the fitted temperature values, is the temperature change curve of all the curves in the curve set. The variance of the temperature values, is the number of temperature change curves in the curve set, is the first The temperature change curve Temperature value, is the first The fitting curve of the temperature change curve The fitting temperature value, To take the absolute value, The target temperature change curve The split factor for each temperature value; The segmentation factor of each temperature value in the target temperature change curve is obtained, and the segmentation factors of all temperature values ​​in the target temperature change curve are linearly normalized, and the obtained result is used as the final segmentation possibility of each temperature value in the target temperature change curve.

5. The intelligent analysis method for the disinfection quality of dental equipment according to claim 1, characterized in that: The specific steps of obtaining the sampling interval of each temperature change curve according to the segmentation points of the temperature change curve are as follows: Get the time interval between adjacent segmentation points in the target temperature change curve, and record the maximum value of the time interval between adjacent segmentation points in the target temperature change curve as ,Will The determined interval is recorded as the sampling interval of the target temperature change curve, and the sampling interval of the target temperature change curve includes a plurality of sampling interval values.

6. The intelligent analysis method for the disinfection quality of dental equipment according to claim 1, characterized in that: The step of obtaining the sampling error of each acquisition interval value in the sampling interval according to the sampling interval and the segmentation point includes the following specific steps: Record any sampling interval value in the sampling interval of the target temperature change curve as the target sampling interval value, record the ratio of the target sampling interval value to the time corresponding to the first segmentation point in the target temperature change curve as a ratio coefficient of the target sampling interval value, obtain the ratio of the target sampling interval value to the time corresponding to each segmentation point in the target temperature change curve, and obtain several ratio coefficients of the target sampling interval value, record the product of the first ratio coefficient of the target sampling interval value and the target sampling interval value, and the absolute value of the difference between the time corresponding to the first segmentation point in the target temperature change curve, as the error parameter of the first ratio coefficient of the target sampling interval value, record the product of the second ratio coefficient of the target sampling interval value and the target sampling interval value, and the absolute value of the difference between the time corresponding to the second segmentation point in the target temperature change curve, as the error parameter of the second ratio coefficient of the target sampling interval value, obtain the error parameter of each ratio coefficient of the target sampling interval value, and take the minimum value of the error parameters of all ratio coefficients of the target sampling interval value as the sampling error of the target sampling interval value.

7. The intelligent analysis method for the disinfection quality of dental equipment according to claim 6, characterized in that: The specific steps of obtaining the evaluation index of each acquisition interval value in the sampling interval according to the sampling error are as follows: Arrange all the ratio coefficients of the target sampling interval value in the order of the segmentation points in the target temperature change curve to obtain a sequence, which is recorded as the ratio coefficient sequence of the target sampling interval value. The number of ratio coefficients contained in the ratio coefficient sequence of the target sampling interval value is recorded as , the ratio coefficient sequence of the target sampling interval value is The vector determined in the dimensional space is recorded as the ratio coefficient vector of the target sampling interval value, and the ratio of the modulus of the ratio coefficient vector of the target sampling interval value to the sampling error of the target sampling interval value is used as the evaluation index of the target sampling interval value.

8. The intelligent analysis method for the disinfection quality of dental equipment according to claim 1, characterized in that: The method of obtaining the similarity between the sampling sequence of each temperature change curve and the sampling sequences of different temperature change curves according to the sampling frequency includes the following specific steps: The target temperature change curve is sampled according to the sampling frequency of the target temperature change curve to obtain a plurality of sampled temperature values ​​of the target temperature change curve, and a sequence consisting of all the sampled temperature values ​​of the target temperature change curve is recorded as a sampling sequence of the target temperature change curve; Get the The sampling sequence of the temperature change curve is the same as that of the The DTW distance of the sampling sequence of the temperature change curve; The sampling sequence of the temperature change curve is the same as that of the The DTW distance of the sampling sequence of the temperature change curve is taken as the The sampling sequence of the temperature change curve is the same as that of the The similarity between the sampling sequences of the temperature change curves.

9. The intelligent analysis method for the disinfection quality of dental equipment according to claim 1, characterized in that: The method of evaluating the disinfection quality of dental instruments based on the similarity between sampling sequences and the sampling frequency includes the following specific steps: The set consisting of the sampling sequences of all temperature change curves is recorded as a sampling sequence set, any sampling sequence in the sampling sequence set is recorded as a target sampling sequence, the similarity between the target sampling sequence and each sampling sequence in the sampling sequence set is obtained, the accumulated value of the similarity between the target sampling sequence and each sampling sequence in the sampling sequence set is used as the standard parameter of the target sampling sequence, the standard parameter of each sampling sequence in the sampling sequence set is obtained, and the sampling sequence corresponding to the maximum value of the standard parameter is recorded as the standard sampling sequence; Recollect a temperature change curve of a new oral device to be tested during high-pressure steam sterilization, and record it as the temperature change curve to be analyzed; take the sampling frequency of the target temperature change curve as the sampling frequency of the temperature change curve to be analyzed, and obtain a sampling sequence of the temperature change curve to be analyzed, and record it as the sampling sequence to be analyzed; Obtaining the DTW distance between the sampling sequence to be analyzed and the standard sampling sequence, and using the DTW distance between the sampling sequence to be analyzed and the standard sampling sequence as the similarity between the sampling sequence to be analyzed and the standard sampling sequence; The minimum value of the similarity between different sampling sequences in the sampling sequence set is recorded as the similarity threshold. If the similarity between the sampling sequence to be analyzed and the standard sampling sequence is greater than or equal to the similarity threshold, the disinfection quality of the dental equipment corresponding to the sampling sequence to be analyzed is qualified. If the similarity between the sampling sequence to be analyzed and the standard sampling sequence is less than the similarity threshold, the disinfection quality of the dental equipment corresponding to the sampling sequence to be analyzed is unqualified.

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