Data processing method and system of thulium laser treatment device

Abstract

The invention relates to the field of data processing, in particular to a data processing method and system of a thulium laser treatment device. The method comprises the following steps: acquiring a laser data time sequence and a biological reflection data time sequence; dividing the laser data time sequence and the biological reflection data time sequence into a plurality of laser data segments and a plurality of biological reflection data segments respectively; setting a plurality of preset layer numbers, setting the decomposition layer number of a wavelet filtering algorithm as the preset layer number, and carrying out filtering processing on the laser data segment to obtain a denoised data segment; the denoising credibility of the preset number of layers is calculated, and the denoising credibility is positively correlated with the signal-to-noise splitting degree and negatively correlated with the information loss degree; and selecting the denoised data segments obtained by the preset number of layers with the maximum denoising credibility as final denoised data segments, and splicing all the final denoised data segments into a final denoised time sequence. The denoising accuracy is improved by setting a proper number of decomposition layers.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data processing method and system for a thulium laser treatment device. Background Art

[0002] As a laser treatment method, thulium laser therapy has the advantages of efficient tissue vaporization and cutting ability, excellent hemostatic performance and shallow thermal damage depth. It is widely used in the medical field.

[0003] During treatment, thulium laser therapy devices collect data, including laser data and the patient's biological response data. This data serves as the basis for adjusting the patient's treatment plan and improving the thulium laser therapy device. Therefore, the accuracy of this data affects the results of subsequent data analysis. Due to interference from other signals in the cyclic environment or defects in the sensor itself, the collected data may contain noise. To improve the accuracy of subsequent analysis, the collected data must be denoised before analysis.

[0004] Wavelet filtering algorithms are often used to denoise data. Before performing this analysis, the algorithm must first set the number of decomposition levels. This number of levels determines how well the algorithm separates real information from noise in the data. Setting a low number of levels makes it difficult to separate real information from noise, resulting in an inability to accurately remove noise. Setting a high number of levels results in significant information loss. Therefore, determining an appropriate number of levels for wavelet filtering algorithms is the focus of this study.

[0005] The patent application document with publication number CN103908243A discloses an algorithm combining lifting wavelet and median filtering. This method is a filtering method that combines wavelet filtering and median filtering. In this method, the number of decomposition layers is set artificially based on experience, so this method cannot solve the above-mentioned problem. Summary of the Invention

[0006] In order to solve the problem of how to accurately set the number of decomposition layers, the present invention provides a data processing method and system for a thulium laser treatment device.

[0007] In a first aspect, the present invention provides a data processing method for a thulium laser treatment device, which adopts the following technical solution: A data processing method for a thulium laser treatment device comprises the following steps: Acquire a laser data time series sequence of a thulium laser treatment device and a biological response data time series sequence of a patient in the same time period; divide the laser data time series sequence and the biological response data time series sequence into a plurality of laser data segments and a plurality of biological response data segments respectively; Setting a plurality of preset numbers of layers, setting the number of decomposition layers of the wavelet filtering algorithm to the preset number of layers, and filtering the laser data segments to obtain denoised data segments; calculating the denoising credibility of the preset number of layers, wherein the denoising credibility is positively correlated with the degree of signal-noise splitting and negatively correlated with the degree of information loss; selecting the denoised data segment obtained by the preset number of layers with the maximum denoising credibility as the final denoised data segment, and splicing all the final denoised data segments into a final denoised time series sequence; The signal-to-noise splitting degree satisfies the relationship: , obtaining the components corresponding to the wavelet coefficients decomposed in the filtering process, and recording the components corresponding to the wavelet coefficients belonging to the preset distribution interval and those not belonging to the preset distribution interval as signal components and noise components respectively; , denote the autocorrelation mean of all signal components and the autocorrelation mean of all noise components, respectively. Represents the mean value of the correlation between all signal components and the biological response data segment in the same time period, It represents the mean correlation between all noise components and the biological response data segments in the same time period.

[0008] The present invention selects the decomposition layer number corresponding to the best denoising effect by analyzing the denoising effect of the filtering process based on each value of the decomposition layer number, thereby making it possible to remove noise information more comprehensively while minimizing the loss of useful information. Furthermore, when analyzing the denoising effect of the filtering process based on each value of the decomposition layer number, taking into account whether the appropriateness of the decomposition layer number setting will affect the segmentation of noise and useful information, an accurate evaluation of the denoising effect is achieved by analyzing the segmentation of noise and useful information obtained by the filtering process based on each value of the decomposition layer number, thereby assisting in selecting a more appropriate decomposition layer number; at the same time, taking into account whether the appropriateness of the decomposition layer number setting will affect the information loss, an accurate evaluation of the denoising effect is achieved by analyzing the loss of useful information obtained by the filtering process based on each value of the decomposition layer number, thereby assisting in selecting a more appropriate decomposition layer number. Furthermore, when analyzing the segmentation of noise and useful information, the characteristics of low correlation between noise information and biological response information and high correlation between useful information and biological response information are taken into account. By analyzing the correlation between noise information and biological response information filtered by the wavelet filtering algorithm, as well as the correlation between useful information and biological response information filtered by the wavelet filtering algorithm, the segmentation of noise and useful information is more accurately evaluated. Furthermore, when analyzing the segmentation of noise and useful information, the characteristics of poor regularity of noise information and strong regularity of useful information are also taken into account. Therefore, by analyzing the regularity of noise information filtered by the wavelet filtering algorithm, as well as the regularity of useful information filtered by the wavelet filtering algorithm, the segmentation of noise and useful information is more accurately evaluated.

[0009] Preferably, the method for obtaining the degree of information loss includes: ; Among them, the data segment reconstructed using all wavelet coefficients is recorded as the reconstructed laser data segment. represents the autocorrelation of the white noise sequence, represents the reconstructed laser data segment, Represents the laser data segment, Indicates the modulo length symbol, Indicates the degree of information loss.

[0010] When calculating the degree of information loss, the present invention considers that the appropriateness of the number of decomposition levels affects data loss. Therefore, by analyzing the information loss before and after decomposition and reconstruction of the laser data segment, a more accurate assessment of the useful information loss of filtering performed at that decomposition level is achieved. Furthermore, when calculating the degree of information loss, the present invention considers that the appropriateness of the number of decomposition levels affects the amount of useful information contained in the filtered noise. Therefore, by analyzing the amount of useful information contained in the filtered noise, a more accurate assessment of the useful information loss of filtering performed at that decomposition level is achieved.

[0011] Preferably, the denoising credibility is equal to the ratio of the signal-to-noise splitting degree to the information loss degree.

[0012] The present invention takes into account that the suitability of the setting of the number of decomposition layers not only affects the segmentation effect of useful information and noise information, but also affects the loss of useful information. Therefore, when analyzing the denoising credibility of each value of the decomposition layer, two indicators, the degree of signal-noise splitting and the degree of information loss, are considered at the same time, thereby ensuring that the denoising credibility calculated in this way can more comprehensively evaluate the availability of each value of the decomposition layer, thereby assisting in better selecting the appropriate number of decomposition layers.

[0013] Preferably, the method for acquiring the laser data segment includes: The laser data time series is divided into several unit segments, the frequency complexity of each unit segment is calculated, all the unit segments are clustered according to the frequency complexity to obtain several categories, and the data segment composed of all the unit segments in a category is used as the laser data segment.

[0014] The present invention takes into account the different regular complexities of each time period in the laser data time series sequence. Using the same number of decomposition layers will lead to over-decomposition in some time periods and under-decomposition in some time periods in the laser data time series sequence, resulting in incomplete denoising or large information loss. Therefore, by segmenting data with similar regular complexities together and data with different regular complexities separately, a basis is provided for the subsequent accurate setting of the number of decomposition layers.

[0015] Preferably, the method for obtaining frequency complexity includes: Performing Fourier transform on the unit segment to obtain spectrum data of the unit segment, and recording the information entropy of all frequencies in the spectrum data as frequency complexity.

[0016] The present invention utilizes frequency complexity to more accurately reflect the regular complexity of each time period, providing a basis for subsequent accurate segmentation.

[0017] Preferably, the method for obtaining the biological response data segment includes: According to the time period corresponding to the laser data segment, the biological response data time series sequence is divided into several data segments recorded as biological response data segments.

[0018] Preferably, the correlation is equal to the absolute value of the Pearson correlation coefficient.

[0019] In a second aspect, the present invention provides a data processing system for a thulium laser treatment device, which adopts the following technical solution: A data processing system for a thulium laser treatment device comprises a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the data processing method for the thulium laser treatment device is implemented.

[0020] By adopting the above technical solution, the data processing method of the above-mentioned thulium laser treatment device is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is made based on the memory and the processor for easy use.

[0021] The present invention has the following technical effects: The present invention analyzes the denoising effect of the filtering processing based on the number of decomposition layers of each value, and selects the decomposition layer number corresponding to the best denoising effect, so that the denoising processing can not only remove the noise information more comprehensively, but also reduce the damage to the useful information.

[0022] Furthermore, when analyzing the denoising effect of the filtering processing based on the decomposition layer number of each value, taking into account that the suitability of the decomposition layer number setting will affect the segmentation of noise and useful information, the denoising effect is accurately evaluated by analyzing the segmentation of noise and useful information obtained by the filtering processing based on the decomposition layer number of each value, thereby assisting in selecting a more appropriate decomposition layer number; at the same time, taking into account that the suitability of the decomposition layer number setting will affect the information loss, the denoising effect is accurately evaluated by analyzing the useful information loss obtained by the filtering processing based on the decomposition layer number of each value, thereby assisting in selecting a more appropriate decomposition layer number.

[0023] Furthermore, when analyzing the segmentation of noise and useful information, taking into account the characteristics that noise information has little correlation with biological response information, while useful information has a high correlation with biological response information, the segmentation of noise information and useful information is evaluated more accurately by analyzing the correlation between the noise information and biological response information filtered out by the wavelet filtering algorithm and the correlation between the useful information and biological response information filtered out by the wavelet filtering algorithm.

[0024] Furthermore, when analyzing the segmentation of noise information and useful information, the characteristics of poor regularity of noise information and strong regularity of useful information are also taken into consideration. Therefore, by analyzing the regularity of noise information filtered out by the wavelet filtering algorithm and the regularity of useful information filtered out by the wavelet filtering algorithm, the segmentation of noise information and useful information can be evaluated more accurately.

[0025] Furthermore, when calculating the degree of information loss, the appropriateness of the decomposition layer setting is taken into consideration, which will affect the data information loss. Therefore, the information loss before and after the decomposition and reconstruction of the laser data segment is analyzed to more accurately evaluate the useful information loss of the filtering performed at the decomposition layer.

[0026] Furthermore, when calculating the degree of information loss, it is taken into account that the appropriateness of the number of decomposition layers will affect the content of useful information contained in the filtered noise information. Therefore, by analyzing the situation in which useful information is contained in the filtered noise information, the useful information loss of the filtering performed under the decomposition layer is more accurately evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding numbers represent the same or corresponding parts.

[0028] Figure 1 The present invention is a flowchart of a method for processing data of a thulium laser treatment device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0030] It should be understood that when the terms "first," "second," and the like are used in the claims, description, and drawings of the present invention, they are merely used to distinguish between different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0031] The embodiment of the present invention discloses a data processing method for a thulium laser treatment device, referring to Figure 1 , including steps S1 to S4: S1: Acquire the laser data time series of the thulium laser treatment device and the biological response data time series of the patient in the same time period.

[0032] Specifically, the laser data of the thulium laser treatment device at each moment during the patient treatment process is collected, and the laser data at all moments during the patient treatment process are arranged in time sequence to obtain a laser data time series sequence; in this embodiment, the laser data can be any one of the following types of data: laser wavelength, power data, pulse frequency, duration of each pulse, and energy density.

[0033] The bioreflection data of the patient at each moment in the same period are collected, and the bioreflection data of the patient at all moments collected are arranged in time sequence to obtain a bioreflection data time series sequence; in this embodiment, the bioreflection data can be any of the following types of data: surface temperature of the treatment area, redness and swelling area, blood pressure or heart rate, skin electrical response.

[0034] S2: dividing the laser data time series sequence and the biological response data time series sequence into a plurality of laser data segments and a plurality of biological response data segments respectively.

[0035] It should be noted that the number of decomposition layers determines how to segment data with different patterns. However, because the data patterns in each time period of the laser data time series vary in complexity, setting the same number of decomposition layers for each time period in the laser data time series will result in some time periods being over-segmented and others being under-segmented. Over-segmentation unnecessarily increases the risk of information loss in that time period. Under-segmentation, for example, fails to separate noise from useful information, making it impossible to completely remove noise.

[0036] Optionally, as an example, the laser data time series sequence and the biological response data time series sequence are divided into a plurality of laser data segments and a plurality of biological response data segments, respectively, including: The laser data time series is evenly divided into N data segments, which are recorded as laser data segments. N represents a preset number. In this embodiment, N is 20 as an example. Other embodiments may use other values, and this embodiment does not specifically limit this.

[0037] According to the time period corresponding to the laser data segment, the biological response data time series sequence is divided into several data segments recorded as biological response data segments.

[0038] It can be seen that by artificially setting the number of segments and segmenting the laser data time series, although it can separate the noise data of different complexity levels to a certain extent, this segmentation method cannot accurately segment data with similar complexity levels or separate data with different complexity levels when analyzing the complexity of the data patterns in each time period in the laser data time series.

[0039] It should be noted that frequency information can, to a certain extent, reflect the regularity of each time period in the laser data time series. When the regularity information is complex, the frequency information distribution is also relatively complex, and when the regularity information is simple, the frequency information distribution is also relatively simple. Therefore, the complexity of the frequency information distribution of each time period in the laser data time series can be used to control data segmentation.

[0040] Preferably, as an example, the laser data time series sequence and the biological response data time series sequence are divided into a plurality of laser data segments and a plurality of biological response data segments, respectively, including: The laser data time series sequence is evenly divided into several unit segments of length L, where L represents a preset length. This embodiment is described using L as 10 as an example. Other embodiments may take other values, and this embodiment does not impose specific restrictions. The frequency complexity of each unit segment is calculated. Based on the frequency complexity, all unit segments are clustered using the DBSCAN clustering algorithm to obtain several categories, and the data segment consisting of all unit segments in a category is used as the laser data segment.

[0041] According to the time period corresponding to the laser data segment, the biological response data time series sequence is divided into several data segments recorded as biological response data segments.

[0042] It can be understood that by segmenting data with similar frequency complexity into a single laser data segment and data with different frequency complexities into different laser data segments, data with similar regular complexity can be segmented into a single laser data segment and data with similar regular complexity can be segmented into different laser data segments. Therefore, the number of decomposition levels required for the laser data segments is similar. Processing the laser data segments with the same number of decomposition levels can effectively separate the information with different regularities in the laser data segments, preventing over-segmentation and under-segmentation in some regions, thereby improving the accuracy of the denoising analysis.

[0043] The above embodiments involve frequency complexity, and a method for determining frequency complexity will be described below.

[0044] Preferably, as an example, the method for obtaining frequency complexity includes: Performing Fourier transform on the unit segment to obtain spectrum data of the unit segment, and recording the information entropy of all frequencies in the spectrum data as frequency complexity.

[0045] S3: setting a plurality of preset layers, setting the decomposition layer number of the wavelet filtering algorithm to the preset layer number, and performing filtering processing on the laser data segment to obtain a denoised data segment.

[0046] It should be noted that based on experience, the number of decomposition layers is generally set between 2 and 10. When the number of decomposition layers is less than 2, the data regularity segmentation effect will be poor, resulting in poor denoising effect. When the number of decomposition layers is greater than 10, not only will there be severe information loss, but also low denoising efficiency. Therefore, the number of decomposition layers is generally set between 2 and 10.

[0047] Preferably, as an example, a plurality of preset layers are set, the decomposition layer number of the wavelet filtering algorithm is set to the preset layer number, and the laser data segment is filtered to obtain a denoised data segment, including: A number of preset layers are set. In this embodiment, the preset layers are integers between 2 and 10. Other embodiments may take other values, and this embodiment does not impose any specific restrictions.

[0048] The preset number of layers is used as the decomposition number of the wavelet filtering algorithm, and the wavelet filtering algorithm is used to filter the laser data segment to obtain a denoised data segment.

[0049] S4: Calculate the denoising credibility of the preset number of layers, where the denoising credibility is positively correlated with the degree of signal-noise splitting and negatively correlated with the degree of information loss; select the denoised data segment obtained by the preset number of layers with the maximum denoising credibility as the final denoised data segment, and splice all the final denoised data segments into the final denoised time series sequence.

[0050] S40: Calculating the denoising credibility of the preset number of layers.

[0051] It should be noted that in order to set the appropriate number of decomposition levels, it is necessary to evaluate the denoising performance of the filtered data obtained by filtering at each decomposition level. Setting a larger number of decomposition levels results in significant information loss in the data filtered by the wavelet filter algorithm, while setting a smaller number of decomposition levels results in poor segmentation of noise and useful information. Therefore, the appropriateness of the number of decomposition levels can be evaluated by using information loss and the segmentation of noise and useful information.

[0052] Preferably, as an example, calculating the denoising credibility of the preset number of layers includes: The ratio of the signal-to-noise separation degree to the information loss degree is taken as the denoising credibility.

[0053] It should be noted that when a wavelet filter is used to filter data, it decomposes the data into a series of wavelet coefficients, each of which describes different regular characteristics. The wavelet filter algorithm compares the wavelet coefficients with a preset threshold and sets the wavelet coefficients that exceed the threshold to zero, thereby achieving denoising.

[0054] It's important to further clarify that, because noise information exhibits poor regularity while useful information exhibits strong regularity, a well-defined number of decomposition layers results in less regularity in the removed noise and more regularity in the retained useful information. Furthermore, different laser intensities produce different physiological responses, leading to correlations between the laser data and the physiological response data. Since noise information in laser data segments has no correlation with physiological responses, the correlation between noise information in laser data segments and physiological response segments is poor, while the correlation between useful information and physiological response segments is strong. Therefore, the degree of signal-to-noise separation can be assessed based on these characteristics.

[0055] Among them, the degree of signal-to-noise separation satisfies the following relationship:

[0056] In the acquisition filtering process, all wavelet coefficients of the laser data segment are decomposed, one wavelet coefficient is retained, and the other wavelet coefficients are set to zero. The components corresponding to the wavelet coefficients are reconstructed using the Mallat algorithm, and the components corresponding to the wavelet coefficients belonging to the preset distribution interval and those not belonging to the preset distribution interval are recorded as signal components and noise components respectively. The signal component is the useful information in the laser data segment decomposed by the wavelet filtering algorithm, and the noise component is the noise information in the laser data segment decomposed by the wavelet filtering algorithm. Represents the autocorrelation mean of all signal components, which reflects the regularity of the signal components. It represents the mean of the autocorrelation of all noise components. This value reflects the regularity of the noise components. Since the regularity of useful information in the laser data segment is strong and the regularity of noise information is poor, when the segmentation effect of noise information and useful information is good, Should be larger, Represents the mean value of the correlation between all signal components and the biological response data segment in the same time period, It represents the mean value of the correlation between all noise components and the biological response data segments in the same time period. Since the correlation between noise information and biological response data segments is poor, and the correlation between useful information and biological response data segments is strong, when the noise information and useful information segmentation effect is good, Should be larger. Indicates the degree of signal-to-noise splitting for the preset number of layers. Get the variance of all wavelet coefficients and the mean of all wavelet decimals In this embodiment, the preset distribution interval is set to , other embodiments may set other values, and this embodiment does not impose any specific limitations.

[0057] It should be noted that the autocorrelation of the signal component is the autocorrelation value obtained by autocorrelation detection of the signal component. Autocorrelation detection is a conventional technique and will not be further described here. The correlation between the noise component and the biological response data segment in the same time period is the absolute value of the Pearson correlation coefficient between the noise component and the biological response data segment in the same time period. The correlation between the signal component and the biological response data segment in the same time period is the absolute value of the Pearson correlation coefficient between the signal component and the biological response data segment in the same time period.

[0058] It should be noted that information loss occurs during the wavelet filtering algorithm's decomposition and reconstruction of laser data segments. The greater the number of decomposition layers, the greater the information loss. Therefore, the information loss associated with the set number of decomposition layers can be evaluated by analyzing the information loss of the data before and after decomposition and reconstruction. At the same time, an inappropriate number of decomposition layers can lead to poor noise-signal segmentation, potentially removing some useful information from the noise. Therefore, it's necessary to analyze the presence of useful information in the noise signal to determine the appropriate information loss associated with the set number of decomposition layers.

[0059] In addition, the degree of information loss satisfies the relationship: ; The data segment reconstructed using all wavelet coefficients is recorded as the reconstructed laser data segment. The reconstructed laser data segment is not filtered to remove some wavelet coefficients. Therefore, the information loss of the reconstructed laser data segment compared with the original laser data segment is only caused by decomposition and reconstruction. represents the reconstructed laser data segment, Represents the laser data segment, Indicates the modulo length symbol, It reflects the information difference between the reconstructed laser data segment and the original laser data segment. The larger the value, the greater the information loss caused by decomposition and reconstruction. Represents the autocorrelation of the white noise sequence. This value reflects the regularity of the white noise sequence. White noise is a common noise in the signal, so it can be used as a comparison object to analyze the regularity difference between the filtered noise data and the white noise. Represents the autocorrelation mean of all noise components. This value reflects the regularity of the noise components. When the denoising effect is more accurate, the noise components contain less useful information. When the denoising effect is more accurate, This value should be smaller. This value reflects the difference between the regularity of the noise component and the regularity of white noise. The larger the value, the more regular information the noise component contains compared to white noise. Therefore, more useful information is wrapped in the noise and filtered out, resulting in a greater loss of useful information. Indicates the degree of information loss.

[0060] It should be added that the white noise sequence is a sequence of white noise data generated by a white noise generator, and the length of the white noise sequence is the same as the length of the laser data segment. The white noise generator generating white noise is a prior art and will not be described in detail here.

[0061] S41: selecting the denoised data segments obtained by the preset number of layers with the maximum denoising credibility as the final denoised data segments, and concatenating all the final denoised data segments into a final denoised time series sequence.

[0062] An embodiment of the present invention further discloses a data processing system for a thulium laser treatment device, comprising a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a data processing method for a thulium laser treatment device according to the present invention is implemented.

[0063] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0064] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory, dynamic random access memory, static random access memory, enhanced dynamic random access memory, high bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of, accessible to, or connectable to the device.

[0065] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0066] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data processing method for a thulium laser treatment device, characterized in that: Includes steps: Acquire a laser data timing sequence of a thulium laser treatment device and a biological response data timing sequence of a patient in the same time period; divide the laser data timing sequence and the biological response data timing sequence into a plurality of laser data segments and a plurality of biological response data segments respectively; Setting a plurality of preset layers, setting the decomposition layer number of the wavelet filter algorithm to the preset layer number, and filtering the laser data segment to obtain a denoised data segment; Calculating the denoising credibility of the preset number of layers, wherein the denoising credibility is positively correlated with the degree of signal-noise splitting and negatively correlated with the degree of information loss; selecting the denoised data segment obtained by the preset number of layers with the maximum denoising credibility as the final denoised data segment, and splicing all the final denoised data segments into a final denoised time series sequence; The signal-to-noise splitting degree satisfies the relationship: , obtaining the components corresponding to each wavelet coefficient decomposed in the filtering process, and recording the components corresponding to the wavelet coefficients belonging to the preset distribution interval and not belonging to the preset distribution interval as signal components and noise components respectively; , denote the autocorrelation mean of all signal components and the autocorrelation mean of all noise components, respectively. Represents the mean value of the correlation between all signal components and the biological response data segment in the same time period, It represents the mean correlation between all noise components and the biological response data segment in the same time period.

2. The data processing method of a thulium laser treatment device according to claim 1, characterized in that: The method for obtaining the degree of information loss includes: ； Among them, the data segment reconstructed using all wavelet coefficients is recorded as the reconstructed laser data segment. represents the autocorrelation of the white noise sequence, represents the reconstructed laser data segment, represents the laser data segment, Indicates the modulo length symbol, Indicates the degree of information loss.

3. The data processing method of a thulium laser treatment device according to claim 1, characterized in that: The denoising credibility is equal to the ratio of the signal-to-noise splitting degree to the information loss degree.

4. The data processing method of a thulium laser treatment device according to claim 1, characterized in that: The method for acquiring the laser data segment comprises: The laser data time series is divided into several unit segments, the frequency complexity of each unit segment is calculated, all the unit segments are clustered according to the frequency complexity to obtain several categories, and the data segment composed of all the unit segments in a category is used as the laser data segment.

5. The data processing method of a thulium laser treatment device according to claim 4, characterized in that: The method for obtaining frequency complexity comprises: The unit segment is subjected to Fourier transformation to obtain the frequency spectrum data of the unit segment, and the information entropy of all frequencies in the frequency spectrum data is recorded as frequency complexity.

6. The data processing method of a thulium laser treatment device according to claim 1, characterized in that: The method for obtaining the biological response data segment includes: According to the time period corresponding to the laser data segment, the biological response data time series sequence is divided into several data segments recorded as biological response data segments.

7. The data processing method of a thulium laser treatment device according to claim 1, characterized in that: The correlation is equal to the absolute value of the Pearson correlation coefficient.

8. A data processing system for a thulium laser treatment device, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a data processing method for a thulium laser treatment device according to any one of claims 1-7 is implemented.

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