Data processing method and system for intervening PI central mechanism based on three-part massage method

Abstract

The invention provides a data processing method and system for intervening a PI central mechanism based on a three-part massage method, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: S1, data consistency evaluation; s2, data preprocessing; and S3, performing data complexity evaluation. According to the method, the data consistency evaluation value is obtained through the collected initial brain region data of each channel, whether the data is collected again is judged based on the data consistency evaluation value and the data consistency threshold value, and then the initial brain region data and the corresponding timestamp are integrated and preprocessed to obtain the brain region data of the patient to be monitored. And finally, obtaining a data complexity evaluation value according to the network data and the brain region data, and judging whether to perform dimension reduction on the brain region data based on the data complexity evaluation value and a data complexity evaluation value threshold, thereby improving the data processing efficiency. The problem that in the prior art, the data processing efficiency is low in the process of intervening a PI central mechanism through a three-part massage method is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) signal processing, and in particular to a data processing method and system for intervening in a PI central mechanism based on a three-step massage method. Background Art

[0002] Primary Insomnia (PI) is a common sleep disorder that not only affects the patient's quality of life, but may also lead to complications such as depression and cardiovascular disease. With the in-depth study of the pathophysiological mechanism of insomnia, it has been found that insomnia is related to dysfunction of the central nervous system. At present, Western medicine is addictive in the treatment of PI, and cognitive behavioral therapy is also limited. In contrast, massage, as a typical non-drug representative of traditional Chinese medicine for the treatment of PI, is simple, effective and inexpensive. The three-part massage method is a traditional treatment technique of traditional Chinese medicine massage, which has been increasingly used in the treatment of central nervous system-related diseases such as insomnia.

[0003] In the prior art, different treatment methods are implemented on subjects in different experimental groups, and the treatment data of the subjects in different experimental groups are collected. Then, the treatment data of the subjects in different experimental groups are compared to evaluate the effect of massage therapy, thereby realizing the evaluation of the effectiveness of the three-step massage treatment plan.

[0004] However, the inventors of the present invention found that the above technology has at least the following technical problems in the process of implementing the technical solution of the embodiments of the present invention:

[0005] In the prior art, the data in the massage intervention process is usually multimodal, and multimodal data usually involves massive amounts of data. Therefore, problems such as data information loss may occur during data collection and processing. There is a problem of low data processing efficiency in the process of intervening in the PI central mechanism through the three-step massage method. Summary of the invention

[0006] The embodiment of the present invention solves the problem of low data processing efficiency in the process of intervening in the PI central mechanism through the three-step massage method in the prior art by providing a data processing method and system based on the three-step massage method to intervene in the PI central mechanism, thereby achieving improved data processing efficiency.

[0007] The embodiment of the present invention provides a data processing method for intervening the PI central mechanism based on the three-step massage method, comprising the following steps:

[0008] S1, the initial brain area data of the patient to be monitored is collected by a data acquisition device, and the initial brain area data of each channel collected are compared to obtain a data consistency evaluation value, and whether to re-collect the initial brain area data is determined based on the data consistency evaluation value and the data consistency threshold, wherein the data acquisition device includes a positron emission tomography imager, a functional magnetic resonance imaging device and an electroencephalograph; S2, the collected initial brain area data and the corresponding timestamps are integrated and preprocessed to obtain the brain area data of the patient to be monitored, and the brain area data of the patient to be monitored is classified according to the collection method and encrypted and stored; S3, the data complexity is evaluated according to the acquired network data and brain area data to obtain a data complexity evaluation value, and whether to reduce the dimension of the brain area data is determined based on the data complexity evaluation value and the data complexity evaluation value threshold.

[0009] Optionally, the initial brain area data includes initial electroencephalogram data, initial functional magnetic resonance imaging data and initial positron emission tomography data; the initial electroencephalogram data is acquired through the configured first backup electrode, second backup electrode and main electrode of the electroencephalograph; the initial functional magnetic resonance imaging data is acquired according to the preset magnetic resonance imaging resolution and preset scanning repetition frequency of the functional magnetic resonance imaging device; the initial positron emission tomography data is acquired according to the preset scanning imaging resolution and preset scanning frequency of the positron emission tomography imager.

[0010] Optionally, the specific method of comparing the collected initial brain area data of each channel to obtain the data consistency evaluation value is as follows: Pearson correlation coefficients are calculated for each pair of the initial EEG data collected by the first backup electrode, the second backup electrode and the main electrode to obtain the EEG Figure 1 The imaging resolution and scanning repetition frequency in the acquired initial functional magnetic resonance imaging data are obtained, and the ratio of the acquired magnetic resonance imaging resolution to the preset magnetic resonance imaging resolution and the ratio of the scanning repetition frequency to the preset scanning repetition frequency are summed to obtain the magnetic resonance imaging consistency coefficient; the imaging resolution and scanning frequency in the acquired initial positron emission tomography data are obtained, and the ratio of the acquired scanning imaging resolution to the preset scanning imaging resolution and the ratio of the scanning frequency to the preset scanning frequency are summed to obtain the positron emission tomography consistency coefficient; the acquired EEG data are summed to obtain the positron emission tomography consistency coefficient. Figure 1 The consistency coefficient is subjected to a hyperbolic tangent function operation to obtain the EEG hyperbolic tangent function value; the obtained magnetic resonance imaging consistency coefficient is subjected to a hyperbolic secant function operation to obtain the magnetic resonance hyperbolic secant function value; the obtained positron emission tomography consistency coefficient is subjected to a hyperbolic secant function operation to obtain the positron hyperbolic secant function value; the sum of the EEG hyperbolic tangent function value, the magnetic resonance hyperbolic secant function value and the positron hyperbolic secant function value is subjected to an exponential operation to obtain the data consistency evaluation value.

[0011] Optionally, the specific process of judging whether to re-collect the initial brain area data based on the data consistency evaluation value and the data consistency threshold is as follows: A1, judging whether the data consistency evaluation value is greater than the data consistency threshold; if the data consistency evaluation value is not greater than the data consistency threshold, the initial brain area data is not re-collected, otherwise A2 is executed; A2, adjusting the noise level of the electroencephalograph and re-collecting the initial electroencephalogram data, judging whether the data consistency evaluation value obtained after the adjustment of the electroencephalograph is greater than the data consistency threshold; if the data consistency evaluation value is not greater than the data consistency threshold, the initial functional magnetic resonance imaging data and the initial positron emission tomography data are not re-collected, otherwise A3 is executed; A3, adjusting the noise level of the functional magnetic resonance imaging device After adjusting the magnetic field strength in the central area of ​​the scanning cavity of the positron emission tomography imager, the initial functional magnetic resonance imaging data is re-collected to determine whether the data consistency evaluation value obtained after the functional magnetic resonance imaging imager is adjusted is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, the initial positron emission tomography data is not re-collected, otherwise A4 is executed; A4, the initial positron emission tomography data is re-collected after adjusting the photomultiplier tube gain of the positron emission tomography imager to determine whether the data consistency evaluation value obtained after the positron emission tomography imager is adjusted is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, no feedback is given, otherwise feedback is given.

[0012] Optionally, the specific method for acquiring the brain region data is as follows: B1, integrating the collected initial brain region data with the corresponding timestamp; B2, performing a first preprocessing on the integrated first electroencephalogram data to obtain electroencephalogram data, the first preprocessing including artifact removal, filtering and baseline correction; B3, performing a second preprocessing on the integrated initial functional magnetic resonance imaging data to obtain functional magnetic resonance imaging data, the second preprocessing including motion correction, spatial alignment, standardization and spatial smoothing; B4, performing a third preprocessing on the integrated initial positron emission tomography data to obtain positron emission tomography data, the third preprocessing including motion correction, spatial alignment and standardization, the brain region data including electroencephalogram data, functional magnetic resonance imaging data and positron emission tomography data.

[0013] Optionally, the specific process of classifying the brain area data of the patient to be monitored according to the acquisition method and then encrypting and storing it is as follows: the preprocessed brain area data is transmitted to the database, and the transmission process of the brain area data is encrypted using the transport layer security protocol; the brain area data is divided into brain area classification data according to the acquisition device, and the brain area classification data is encrypted using a key generated by a symmetric encryption algorithm; and the encrypted brain area classification data is stored in a preset area in the database.

[0014] Optionally, the specific method of evaluating the data complexity according to the acquired network data and brain area data to obtain the data complexity evaluation value is as follows: obtaining preset complexity evaluation data from a preset database, the preset complexity evaluation data including preset electroencephalogram data volume, preset electroencephalogram acquisition frequency, preset functional magnetic resonance imaging data volume, preset positron emission tomography data volume, preset CPU utilization, preset memory occupancy and preset hard disk read and write rate; evaluating the data complexity according to the acquired network data and brain area data to obtain the data complexity evaluation value, the network data including the data volume of brain area data, the number of data features, CPU utilization, memory occupancy and hard disk read and write rate; the specific limiting expression of the data complexity evaluation value is:

[0015]

[0016] Where i is the number of the patient to be monitored, i = 1, 2, ..., N, N is the total number of patients to be monitored, FZ i represents the data complexity evaluation value of the i-th patient to be monitored, ND i represents the complexity of the EEG of the i-th patient to be monitored, CG i represents the complexity of the magnetic resonance imaging of the i-th patient to be monitored, ZD i NSU represents the complexity of the positron emission tomography scan of the i-th patient to be monitored. i represents the amount of EEG data of the i-th patient to be monitored, SF i represents the EEG acquisition frequency of the i-th patient to be monitored, CSU i represents the amount of functional magnetic resonance imaging data of the i-th patient to be monitored, TE i represents the imaging resolution of the functional magnetic resonance imaging device of the i-th patient to be monitored, ZSU i represents the amount of positron emission tomography data of the i-th patient to be monitored, SA i represents the scanning imaging resolution of the positron emission tomography imager of the i-th patient to be monitored, CA i represents the scanning repetition frequency of the functional magnetic resonance imaging device of the i-th patient to be monitored, CM i represents the scanning frequency of the positron emission tomography imager of the i-th patient to be monitored, CPU i represents the CPU utilization rate of the data processing of the i-th patient to be monitored, NE i represents the memory usage of the data processing of the i-th patient to be monitored, DU irepresents the hard disk read and write rate of data processing of the i-th patient to be monitored, NSU0 represents the preset EEG data volume, SF0 represents the preset EEG acquisition frequency, CSU0 represents the preset functional magnetic resonance imaging data volume, TE0 represents the preset imaging resolution of the functional magnetic resonance imaging apparatus, CA0 represents the preset scanning repetition frequency of the functional magnetic resonance imaging apparatus, ZSU0 represents the preset positron emission tomography data volume, SA0 represents the preset scanning imaging resolution of the positron emission tomography imaging apparatus, CM0 represents the preset scanning frequency of the positron emission tomography imaging apparatus, CPU0 represents the preset CPU utilization, NE0 represents the preset memory usage, and DU0 represents the preset hard disk read and write rate.

[0017] Optionally, the specific process of judging whether to perform dimensionality reduction on brain area data based on the data complexity assessment value and the data complexity assessment value threshold is as follows: compare the data complexity assessment value with the data complexity assessment value threshold: if the data complexity assessment value is greater than the data complexity assessment value threshold, perform dimensionality reduction on the brain area data; if the data complexity assessment value is not greater than the data complexity assessment value threshold, do not perform dimensionality reduction on the brain area data; the dimensionality reduction includes principal component analysis method, spatial downsampling and temporal downsampling.

[0018] Optionally, the spatial downsampling includes reducing the imaging resolution of a functional magnetic resonance imager and the scanning imaging resolution of a positron emission tomography scanner; the temporal downsampling includes reducing the acquisition frequency of an electroencephalograph, the scanning repetition frequency of a functional magnetic resonance imager and the scanning frequency of a positron emission tomography scanner.

[0019] An embodiment of the present invention provides a data processing system for intervening in the central mechanism of PI based on the three-part massage method, comprising a data consistency judgment module, a data preprocessing module and a data complexity judgment module; wherein the data consistency judgment module is used to collect the initial brain area data of the patient to be monitored through the data acquisition device, compare the collected initial brain area data of each channel to obtain a data consistency evaluation value, and judge whether to re-collect the initial brain area data based on the data consistency evaluation value and the data consistency threshold, and the data acquisition device includes a positron emission tomography imager, a functional magnetic resonance imaging device and an electroencephalograph; the data preprocessing module is used to integrate and preprocess the collected initial brain area data with the corresponding timestamp to obtain the brain area data of the patient to be monitored, and classify the brain area data of the patient to be monitored according to the acquisition method and then encrypt and store it; the data complexity judgment module is used to evaluate the data complexity according to the acquired network data and brain area data to obtain a data complexity evaluation value, and judge whether to reduce the dimension of the brain area data based on the data complexity evaluation value and the data complexity evaluation value threshold.

[0020] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0021] 1. The data consistency evaluation value is obtained by collecting the initial brain area data of each channel, and it is determined whether to re-collect the data based on the data consistency evaluation value and the data consistency threshold. Then, the data complexity evaluation value is obtained according to the acquired network data and brain area data. Finally, it is determined whether to reduce the dimension of the brain area data based on the data complexity evaluation value and the data complexity evaluation value threshold, thereby effectively evaluating the complexity of the data, thereby achieving an improvement in data processing efficiency, and effectively solving the problem of low data processing efficiency in the process of intervening in the PI central mechanism through the three-step massage method in the prior art.

[0022] 2. By obtaining the EEG complexity, MRI complexity and PET complexity based on the acquired brain area data, and then evaluating the data complexity based on the acquired network data, EEG complexity, MRI complexity and PET complexity to obtain a data complexity assessment value, the complexity of the data is effectively quantitatively evaluated, thereby realizing dynamic monitoring of the data and timely adjustment of the data processing strategy.

[0023] 3. The preprocessed brain region data is transmitted to the database, and the transmission process of the brain region data is encrypted using the transport layer security protocol. The brain region data is then divided into brain region classification data according to the acquisition device, and the brain region classification data is encrypted using the key generated by the symmetric encryption algorithm. Finally, the encrypted brain region classification data is stored in the preset area of ​​the database, thereby preventing data leakage and improving the security of brain region data during transmission and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0025] Figure 1 A flow chart of a data processing method for intervening the PI central mechanism based on the three-step massage method provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of changes in the data complexity evaluation value provided by an embodiment of the present invention;

[0027] Figure 3 A structural schematic diagram of a data processing system for intervening in the PI central mechanism based on a three-step massage method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "one" or "the" do not indicate a quantitative limitation, but indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0030] It should be noted that the terms "up", "down", "left", "right", "front", "back", etc. used in the present invention are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0031] The embodiment of the present invention solves the problem of low data processing efficiency in the process of intervening the PI central mechanism through the three-part massage method in the prior art by providing a data processing method and system based on the three-part massage method to intervene in the PI central mechanism. A data consistency evaluation value is obtained by collecting the initial brain area data of each channel, and whether to re-collect the data is determined based on the data consistency evaluation value and the data consistency threshold. Then, the collected initial brain area data and the corresponding timestamp are integrated and preprocessed to obtain the brain area data of the patient to be monitored. Finally, a data complexity evaluation value is obtained based on the acquired network data and brain area data. Based on the data complexity evaluation value and the data complexity evaluation value threshold, it is determined whether to reduce the dimension of the brain area data, thereby improving the data processing efficiency.

[0032] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0033] like Figure 1As shown, it is a flow chart of a data processing method for intervening in the central mechanism of PI based on the three-step massage method provided by an embodiment of the present invention, the method comprising the following steps: S1, data consistency evaluation: the initial brain area data of the patient to be monitored is collected by a data acquisition device, and the initial brain area data of each channel collected are compared to obtain a data consistency evaluation value, and whether to re-collect the initial brain area data is determined based on the data consistency evaluation value and the data consistency threshold, and the data acquisition device includes a positron emission tomography imager, a functional magnetic resonance imaging device and an electroencephalograph; S2, data preprocessing: the collected initial brain area data and the corresponding timestamp are integrated and preprocessed to obtain the brain area data of the patient to be monitored, and the brain area data of the patient to be monitored is classified according to the collection method and encrypted and stored; S3, data complexity evaluation: the data complexity is evaluated according to the acquired network data and brain area data to obtain a data complexity evaluation value, and whether to reduce the dimension of the brain area data is determined based on the data complexity evaluation value and the data complexity evaluation value threshold.

[0034] In this embodiment, the data acquisition equipment includes a positron emission tomography (PET), a functional magnetic resonance imaging (FMRI) and an electroencephalograph; the positron emission tomography (PET) uses radioactive tracers to display biochemical processes in the body, especially metabolic activities in the brain; the functional magnetic resonance imaging (fMRI) is used to measure changes in blood oxygen levels in the brain, thereby inferring active areas of the brain; the electroencephalogram (EEG) refers to a technology that records brain electrical activity through electrodes, and is often used to study the functional state of the brain; the electroencephalogram (EEG) voltage signal collected by the electroencephalogram is separated by an analog-to-digital converter to obtain an electroencephalogram voltage value, and the electroencephalogram voltage value is recorded as initial electroencephalogram data.

[0035] The initial brain area data is the initial brain area data collected through multiple channels; the network data includes the data volume of the brain area data, CPU utilization, memory occupancy and hard disk read and write rate; the acquisition channels include the main electrode, the first backup electrode and the second backup electrode for collecting the initial electroencephalogram data; the consistency of the data is improved by comparing the data collected by each channel, the confidentiality and integrity of the data are improved through encrypted storage, and the efficiency of data processing is improved by evaluating the complexity of the data and performing dimensionality reduction processing.

[0036] Among them, the initial brain area data includes initial electroencephalogram data, initial functional magnetic resonance imaging data and initial positron emission tomography data; the initial electroencephalogram data is obtained through the configured first backup electrode, second backup electrode and main electrode of the electroencephalograph; the initial functional magnetic resonance imaging data is obtained according to the preset magnetic resonance imaging resolution and preset scanning repetition frequency of the functional magnetic resonance imaging device; the initial positron emission tomography data is obtained according to the preset scanning imaging resolution and preset scanning frequency of the positron emission tomography imager.

[0037] In this embodiment, the main electrode of the electroencephalograph is located in a preset brain area and is responsible for recording the main electrical activities in the preset area; the first backup electrode and the second backup electrode of the electroencephalograph serve as backup electrodes and are located 5 mm away from the main electrode; when the main electrode in the preset brain area fails or fails, the backup electrodes ensure that data can continue to be collected to prevent the loss of critical data.

[0038] The preset magnetic resonance imaging resolution and the preset scanning repetition frequency are obtained from the preset database, the preset magnetic resonance imaging resolution is set to 3x3x3 cubic millimeters, and the preset scanning repetition frequency is set to 2 seconds; the preset scanning imaging resolution and the preset scanning frequency are obtained from the preset database, the preset scanning imaging resolution is set to 4 mm, and the preset scanning frequency is set to 60 seconds per frame; the stability of initial brain area data acquisition is improved.

[0039] The specific method of comparing the collected initial brain area data of each channel to obtain the data consistency evaluation value is as follows: the initial EEG data collected by the first backup electrode, the second backup electrode and the main electrode are calculated by Pearson correlation coefficient to obtain the EEG Figure 1 The imaging resolution and scanning repetition frequency in the acquired initial functional magnetic resonance imaging data are obtained, and the ratio of the acquired magnetic resonance imaging resolution to the preset magnetic resonance imaging resolution and the ratio of the scanning repetition frequency to the preset scanning repetition frequency are summed to obtain the magnetic resonance imaging consistency coefficient; the imaging resolution and scanning frequency in the acquired initial positron emission tomography data are obtained, and the ratio of the acquired scanning imaging resolution to the preset scanning imaging resolution and the ratio of the scanning frequency to the preset scanning frequency are summed to obtain the positron emission tomography consistency coefficient; the acquired EEG data are summed to obtain the positron emission tomography consistency coefficient. Figure 1 The consistency coefficient is subjected to a hyperbolic tangent function operation to obtain the EEG hyperbolic tangent function value; the obtained magnetic resonance imaging consistency coefficient is subjected to a hyperbolic secant function operation to obtain the magnetic resonance hyperbolic secant function value; the obtained positron emission tomography consistency coefficient is subjected to a hyperbolic secant function operation to obtain the positron hyperbolic secant function value; the sum of the EEG hyperbolic tangent function value, the magnetic resonance hyperbolic secant function value and the positron hyperbolic secant function value is subjected to an exponential operation to obtain the data consistency evaluation value.

[0040] The specific restriction expression of the data consistency evaluation value is:

[0041]

[0042]

[0043] Where i is the number of the patient to be monitored, i = 1, 2, ..., N, N is the total number of patients to be monitored, t is the number of the time point to be monitored, t = 1, 2, ..., T, T is the total number of time points to be monitored, YZ i is the data consistency evaluation value of the i-th patient to be monitored, NZ i is the EEG of the i-th patient to be monitored Figure 1 Coefficient of conformity, CZ i is the consistency coefficient of the magnetic resonance imaging of the i-th patient to be monitored, DZ i is the positron emission tomography consistency coefficient of the i-th patient to be monitored, is the first EEG signal of the main electrode and the first backup electrode of the i-th patient to be monitored Figure 1 Coefficient of conformity, is the second EEG signal of the main electrode and the second backup electrode of the i-th patient to be monitored Figure 1 Coefficient of conformity, is the third EEG signal of the first electrode and the second backup electrode of the i-th patient to be monitored Figure 1 Coefficient of conformity, x i.t is the initial EEG data of the i-th patient to be monitored collected by the main electrode at the t-th time point to be monitored, y i.t is the initial EEG data of the i-th patient to be monitored collected by the first backup electrode at the t-th time point to be monitored, z i.t is the initial EEG data of the i-th patient to be monitored collected by the second backup electrode at the t-th time point to be monitored, is the average value of the initial EEG data of the i-th patient to be monitored collected by the main electrode, is the average value of the initial EEG data of the i-th patient to be monitored collected by the first backup electrode, is the average value of the initial EEG data of the i-th patient to be monitored collected by the second backup electrode, TE i represents the imaging resolution of the functional magnetic resonance imaging device of the i-th patient to be monitored, SA i represents the scanning imaging resolution of the positron emission tomography imager of the i-th patient to be monitored, TE0 represents the preset imaging resolution of the functional magnetic resonance imaging device, SA0 represents the preset scanning imaging resolution of the positron emission tomography imager, CA i represents the scanning repetition frequency of the functional magnetic resonance imaging device of the i-th patient to be monitored, CM irepresents the scanning frequency of the positron emission tomography scanner of the i-th patient to be monitored, CA0 represents the preset scanning repetition frequency of the functional magnetic resonance imaging device, and CM0 represents the preset scanning frequency of the positron emission tomography scanner.

[0044] In this embodiment, the data consistency evaluation value is used to comprehensively evaluate the similarity between the brain area data currently collected by each channel of each device; Figure 1 The consistency coefficient is used to evaluate the similarity between the initial EEG data collected by each channel in the electroencephalograph; the magnetic resonance imaging consistency coefficient is used to evaluate the similarity between the initial functional magnetic resonance imaging data collected by each channel in the functional magnetic resonance imaging device; the positron emission tomography consistency coefficient is used to evaluate the similarity between the initial positron emission tomography data collected by each channel in the positron emission tomography imager; the Pearson correlation coefficient is a statistic used to measure the degree of linear correlation between two variables, and its value is between -1 and 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation; it realizes the quantitative evaluation of the similarity between the data collected by each channel.

[0045] To simplify the analysis, we define C i =sech(CZ i -2), where C i is the magnetic resonance coefficient of the i-th patient to be monitored, and D is defined i =sech(DZ i -2), where D i is the positron emission tomography coefficient of the i-th patient to be monitored. Taking the total number of patients to be monitored as 1 as an example, the statistical table of changes in the data consistency evaluation value is shown in Table 1:

[0046] Table 1 Statistics of changes in data consistency evaluation values

[0047]

[0048] As can be seen from Table 1, for Group 1 and Group 2, when EEG Figure 1 When the consistency coefficient and magnetic resonance coefficient remain unchanged, the larger the PET coefficient is, the greater the data consistency evaluation value is. For example, in Group 2 and Group 3, when the EEG Figure 1 When the consistency coefficient and PET coefficient remain unchanged, the larger the MRI coefficient is, the greater the data consistency evaluation value is. For example, in Group 3 and Group 4, when the MRI coefficient and PET coefficient are unchanged, the EEG Figure 1 The larger the consistency coefficient, the greater the data consistency evaluation value; if the EEG Figure 1The instability may affect the magnetic resonance coefficient and positron emission tomography coefficient. For example, the motion artifact caused by the patient's restlessness may affect the reduction of the magnetic resonance coefficient. When collecting EEG data in a nuclear magnetic resonance environment, the magnetic field will produce artifacts on the EEG data recording, making the EEG Figure 1 The coefficient of consistency is reduced; therefore, the data consistency evaluation value is consistent with the EEG Figure 1 The coefficient of conformity, magnetic resonance coefficient and positron emission tomography coefficient are positively correlated.

[0049] The specific process of judging whether to re-collect the initial brain area data based on the data consistency evaluation value and the data consistency threshold is as follows: A1, judging whether the data consistency evaluation value is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, the initial brain area data is not re-collected, otherwise A2 is executed; A2, adjusting the noise level of the electroencephalograph and re-collecting the initial electroencephalogram data, specifically refers to re-collecting the initial electroencephalogram data after reducing the noise level of the electroencephalograph to the standard noise level (if the noise level of the electroencephalograph is equal to the standard noise level, directly executing A3), judging whether the data consistency evaluation value obtained after the adjustment of the electroencephalograph is greater than the data consistency threshold, if the data consistency evaluation value is not greater than the data consistency threshold, the initial functional magnetic resonance imaging data and the initial positron emission tomography data are not re-collected, otherwise A3 is executed; A3, adjusting the magnetic field strength of the central area of ​​the scanning cavity of the functional magnetic resonance imaging device and re-collecting the initial functional magnetic resonance imaging data Specifically, it refers to adjusting the magnetic field strength of the central area of ​​the scanning cavity of the functional magnetic resonance imaging device to the standard magnetic field strength range (if the magnetic field strength of the central area of ​​the scanning cavity of the functional magnetic resonance imaging device is within the standard magnetic field strength range, then continue to adjust it step by step in the form of 0.1 times, 0.2 times, and 0.3 times within the standard magnetic field strength range), re-collecting the initial functional magnetic resonance imaging data, and judging whether the data consistency evaluation value obtained after the adjustment of the functional magnetic resonance imaging device is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, then the initial positron emission tomography data is not re-collected, otherwise A4 is executed; A4, after adjusting the photomultiplier tube gain of the positron emission tomography display device, the initial positron emission tomography data is re-collected, and judging whether the data consistency evaluation value obtained after the adjustment of the positron emission tomography display device is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, then no feedback is performed, otherwise feedback is performed.

[0050] In this embodiment, the data consistency threshold is obtained from a preset database, and the data consistency threshold is set by the maximum value of the data consistency evaluation value in the historical time period; the standard noise level is set to 0.5μV; the standard magnetic field strength range is set according to the nominal magnetic field strength, for example, when the nominal magnetic field strength is greater than 1.0T, the magnetic field strength deviation in the central area of ​​the scanning cavity is within ±2.0% of the nominal value; the gain adjustment coefficient of each photomultiplier tube is obtained by an iterative algorithm, and the hardware circuit is adjusted to achieve precise adjustment of the gain; the historical time period is set to the beginning of last month to the beginning of this month, for example, if it is currently August 11, the historical time period is set to July 1 to August 1; the data consistency evaluation value is used to comprehensively evaluate the consistency of data collected by each device, and the data consistency evaluation value reflects the overall situation and ensures the stability of the overall data; the data consistency between the channels of the same acquisition device is guaranteed, thereby improving the data quality and avoiding the impact of the data on the analysis results.

[0051] Among them, the specific method of acquiring brain area data is as follows: B1, integrating the collected initial brain area data with the corresponding timestamp; B2, performing a first preprocessing on the integrated first EEG data to obtain EEG data, the first preprocessing includes artifact removal, filtering and baseline correction, filtering includes removing low-frequency noise, high-frequency noise and power line interference, artifact removal means removing artifacts caused by eye movement, electromyography and heartbeat, baseline correction means adjusting the baseline value of the initial EEG data to 0; B3, performing a second preprocessing on the integrated initial functional magnetic resonance imaging data to obtain functional magnetic resonance imaging data, the second preprocessing includes motion correction , spatial registration, standardization and spatial smoothing, motion correction means correcting artifacts caused by head movement, spatial registration means registering the initial functional magnetic resonance imaging data to the anatomical structure image; standardization means standardizing the initial functional magnetic resonance imaging data, and spatial smoothing is used to improve the signal-to-noise ratio of the initial functional magnetic resonance imaging data; B4, the integrated initial positron emission tomography data is subjected to a third preprocessing to obtain positron emission tomography data, the third preprocessing includes motion correction, spatial registration and standardization, and the brain region data includes electroencephalogram data, functional magnetic resonance imaging data and positron emission tomography data.

[0052] In this embodiment, in medical imaging, artifacts refer to abnormal phenomena that appear in the image and are inconsistent with the actual anatomical structure or physiological function; the first EEG data is filtered and the artifacts are removed by Minimum Norm Estimate (MNE); the initial functional magnetic resonance imaging data and initial positron emission tomography data are subjected to second and third preprocessing by statistical parametric mapping (SPM) to obtain functional magnetic resonance imaging data and positron emission tomography data; the quality of the EEG data, functional magnetic resonance imaging data, and positron emission tomography data is improved so as to more accurately reflect the activity of the brain.

[0053] Among them, the specific process of classifying the brain area data of the patient to be monitored according to the collection method and then encrypting and storing it is as follows: the preprocessed brain area data is transmitted to the database, and the transmission process of the brain area data is encrypted using the transport layer security protocol; the brain area data is divided into brain area classification data according to the collection device, and the brain area classification data is encrypted using a key generated by a symmetric encryption algorithm; the encrypted brain area classification data is stored in a preset area in the database.

[0054] In this embodiment, Transport Layer Security (TLS) is a security protocol used to provide confidentiality and data integrity between two communicating applications; TLS is often used to ensure that data transmission between a client and a server is encrypted, thereby protecting the data from eavesdropping or tampering; a symmetric encryption algorithm is an algorithm that uses the same key for encryption and decryption, such as Advanced Encryption Standard (AES) and Data Encryption Standard (DES); in this embodiment, the AES algorithm is selected; the brain region classification data includes electroencephalogram data, functional magnetic resonance imaging data, and positron emission tomography data; the encrypted brain region classification data is stored in a preset area in a database, for example, the electroencephalogram data is stored in the electroencephalogram area; the security of the brain region data during transmission and storage is improved, thereby preventing data leakage or illegal access.

[0055] Furthermore, a specific method for evaluating data complexity based on the acquired network data and brain region data to obtain a data complexity assessment value is as follows: obtaining preset complexity assessment data from a preset database, the preset complexity assessment data including a preset EEG data volume, a preset EEG acquisition frequency, a preset functional magnetic resonance imaging data volume, a preset positron emission tomography data volume, a preset CPU utilization rate, a preset memory occupancy rate, and a preset hard disk read and write rate; evaluating data complexity based on the acquired network data and brain region data to obtain a data complexity assessment value, the network data including the data volume of the brain region data, the number of data features, the CPU utilization rate, the memory occupancy rate, and the hard disk read and write rate.

[0056] The specific limiting expression of the data complexity evaluation value is:

[0057]

[0058] Where i is the number of the patient to be monitored, i = 1, 2, ..., N, N is the total number of patients to be monitored, FZ i represents the data complexity evaluation value of the i-th patient to be monitored, ND i represents the complexity of the EEG of the i-th patient to be monitored, CG i represents the complexity of the magnetic resonance imaging of the i-th patient to be monitored, ZD i NSU represents the complexity of the positron emission tomography scan of the i-th patient to be monitored. i represents the amount of EEG data of the i-th patient to be monitored, SF i represents the EEG acquisition frequency of the i-th patient to be monitored, CSU i represents the amount of functional magnetic resonance imaging data of the i-th patient to be monitored, TE i represents the imaging resolution of the functional magnetic resonance imaging device of the i-th patient to be monitored, ZSU i represents the amount of positron emission tomography data of the i-th patient to be monitored, SA i represents the scanning imaging resolution of the positron emission tomography imager of the i-th patient to be monitored, CA i represents the scanning repetition frequency of the functional magnetic resonance imaging device of the i-th patient to be monitored, CM i represents the scanning frequency of the positron emission tomography imager of the i-th patient to be monitored, CPU i represents the CPU utilization rate of the data processing of the i-th patient to be monitored, NE i represents the memory usage of the data processing of the i-th patient to be monitored, DU irepresents the hard disk read and write rate of data processing of the i-th patient to be monitored, NSU0 represents the preset EEG data volume, SF0 represents the preset EEG acquisition frequency, CSU0 represents the preset functional magnetic resonance imaging data volume, TE0 represents the preset imaging resolution of the functional magnetic resonance imaging apparatus, CA0 represents the preset scanning repetition frequency of the functional magnetic resonance imaging apparatus, ZSU0 represents the preset positron emission tomography data volume, SA0 represents the preset scanning imaging resolution of the positron emission tomography imaging apparatus, CM0 represents the preset scanning frequency of the positron emission tomography imaging apparatus, CPU0 represents the preset CPU utilization, NE0 represents the preset memory usage, and DU0 represents the preset hard disk read and write rate.

[0059] In this embodiment, the preset complex evaluation data is obtained from a preset database, the preset EEG data volume is represented by the average value of the EEG data volume in the historical time period; the preset EEG acquisition frequency is represented by 500Hz; the preset functional magnetic resonance imaging data volume is represented by the average value of the functional magnetic resonance imaging data volume in the historical time period; the preset positron emission tomography data volume is represented by the average value of the positron emission tomography data volume in the historical time period; the preset CPU utilization rate is represented by 60%; the preset memory usage is represented by 8GB; the preset hard disk read and write rate is set according to the hard disk type, for example, the preset hard disk read and write rate of the solid state hard disk is set to 500MB / s; the EEG data volume, the functional magnetic resonance imaging data volume, and the positron emission tomography data volume represent the size of the data file; the EEG acquisition frequency is obtained by the minimum norm estimate (Minimum Norm Estimate, MNE); the statistical parameter mapping (StatisticalParametric The imaging resolution and scanning repetition frequency of the functional magnetic resonance imaging instrument and the scanning imaging resolution and scanning frequency of the positron emission tomography imaging instrument were obtained through the SPM (Scanning Mapping, SPM); the CPU utilization and memory usage were obtained through the top monitoring tool; the hard disk read and write rates were obtained through the iostat command; and a quantitative evaluation of the complexity of the data was achieved, thereby realizing dynamic monitoring and adjustment of the data processing strategy.

[0060] For example, the total number of patients to be monitored is 1, the preset CPU utilization is 1, the preset memory usage is 1, and the preset hard disk read and write rate is 1. Figure 2As shown, it is a schematic diagram of the change of the data complexity evaluation value provided by an embodiment of the present invention. When the complexity of EEG, the complexity of MRI, the complexity of PET, the CPU utilization and the memory occupancy rate are 1, the data complexity evaluation value decreases with the increase of the hard disk read and write rate. When the complexity of MRI, the complexity of PET, the CPU utilization, the memory occupancy rate and the hard disk read and write rate are 1, the data complexity evaluation value increases with the increase of EEG complexity. When the complexity of EEG, the complexity of PET, the CPU utilization, the memory occupancy rate and the hard disk read and write rate are 1, the data complexity evaluation value increases with the increase of MRI complexity. When the complexity of EEG, the complexity of MRI, the CPU utilization, the memory When the occupancy rate and the hard disk read and write rate are 1, the data complexity assessment value increases with the increase of the PET complexity. When the EEG complexity, MRI complexity, PET complexity, memory occupancy rate and hard disk read and write rate are 1, the data complexity assessment value increases with the increase of CPU utilization. When the EEG complexity, MRI complexity, PET complexity, CPU utilization and hard disk read and write rate are 1, the data complexity assessment value increases with the increase of memory occupancy. Therefore, the data complexity assessment value is positively correlated with the EEG complexity, MRI complexity, PET complexity, CPU utilization and memory occupancy, and the data complexity assessment value is negatively correlated with the hard disk read and write rate.

[0061] The algorithm of this embodiment combines the complexity of the EEG, the complexity of the MRI, the complexity of the PET scan, the CPU utilization rate of the data processing, the memory usage, and the hard disk read and write rate to obtain the data complexity evaluation value. The various parameters are related to each other and do not exist independently. They are a whole and cannot be obtained by simple combination and addition. For example, an increase in the hard disk read and write rate of the patient to be monitored in the formula may lead to a decrease in the data complexity assessment value, which does not necessarily mean an increase in the data complexity assessment value. At this time, it is also necessary to consider the complexity of the magnetic resonance imaging of the patient to be monitored, the complexity of the positron emission tomography, the CPU utilization of data processing, the memory usage of data processing and the hard disk read and write rate of data processing. These parameters can also determine the data complexity at the next moment. Generally speaking, with the increase in the complexity of EEG, magnetic resonance imaging and positron emission tomography, the consumption of CPU, memory and hard disk resources also increases; an increase in CPU utilization usually means more data processing tasks, and will also increase memory usage and hard disk read and write frequency. The algorithm itself is obtained through comprehensive analysis, which can accurately quantify the complexity of the analysis data, indicating the effect of accurately evaluating the data complexity assessment value. Therefore, accurate analysis is performed by establishing a mathematical form.

[0062] Among them, the specific process of judging whether to perform dimensionality reduction on brain area data based on the data complexity assessment value and the data complexity assessment value threshold is as follows: compare the data complexity assessment value with the data complexity assessment value threshold: if the data complexity assessment value is greater than the data complexity assessment value threshold, the brain area data is reduced in dimension; if the data complexity assessment value is not greater than the data complexity assessment value threshold, the brain area data is not reduced in dimension; dimensionality reduction includes principal component analysis method, spatial downsampling and temporal downsampling.

[0063] In this embodiment, the data complexity evaluation value is used to comprehensively evaluate the overall complexity of the data, avoiding over-reliance on the complexity index of a single signal, thereby reducing unnecessary dimensionality reduction operations, ensuring data integrity, and improving computing efficiency; the electroencephalograph, functional magnetic resonance imaging, and positron emission tomography respectively evaluate brain activities at different levels (electrical activity, functional activity, metabolic activity), and the complexity of the electroencephalogram data may have a certain positive correlation with the complexity of the functional magnetic resonance imaging data, because functional activity is usually related to electrical activity, and there may be a synergistic relationship between the complexity of the functional magnetic resonance imaging data and the positron emission tomography data, because functional activity and metabolic activity usually affect each other; preliminary screening is performed through the data complexity evaluation value, which effectively avoids complexity judgment and dimensionality reduction operations for each signal source, which is conducive to improving data processing efficiency and saving computing resources; through the evaluation of the complexity of the data, it is dynamically determined whether to perform dimensionality reduction processing on the data, ensuring the validity and simplicity of the data, thereby achieving an increase in data processing speed, and then achieving an improvement in data analysis quality.

[0064] It should be understood that the principal component analysis method is used to extract the principal components of brain area activity; spatial downsampling includes reducing the imaging resolution of the functional magnetic resonance imaging device and the scanning imaging resolution of the positron emission tomography device; temporal downsampling includes reducing the acquisition frequency of the electroencephalogram device, the scanning repetition frequency of the functional magnetic resonance imaging device and the scanning frequency of the positron emission tomography device.

[0065] Specifically, dimensionality reduction means simplifying data by reducing its features or dimensions while retaining as much key information as possible; principal component analysis is used to compress data to find the most important activity features of brain regions and eliminate redundant information; principal component analysis obtains the covariance matrix by standardizing brain region data, then obtains eigenvectors and eigenvalues ​​through the covariance matrix, and finally selects the principal component with the largest variance and projects the data onto it; spatial downsampling means reducing the spatial dimension by lowering the scanning resolution; temporal downsampling reduces the temporal dimension by reducing the sampling frequency; this achieves a reduction in data dimension and complexity, thereby reducing computational complexity.

[0066] like Figure 3 As shown, it is a structural schematic diagram of a data processing system for intervening the PI central mechanism based on the three-step massage method provided by an embodiment of the present invention. The data processing system for intervening the PI central mechanism based on the three-step massage method provided by an embodiment of the present invention includes: a data consistency judgment module, a data preprocessing module and a data complexity judgment module; wherein the data consistency judgment module is used to collect the initial brain area data of the patient to be monitored through the data acquisition device, compare the collected initial brain area data of each channel to obtain a data consistency evaluation value, and judge whether to re-collect the initial brain area data based on the data consistency evaluation value and the data consistency threshold. The data acquisition device includes a positron emission tomography imager, a functional The magnetic resonance imaging device and electroencephalograph are used, and the initial brain area data are the initial brain area data collected by multiple channels; the data preprocessing module is used to integrate and preprocess the collected initial brain area data with the corresponding timestamps to obtain the brain area data of the patient to be monitored, and the brain area data of the patient to be monitored is classified according to the collection method and encrypted and stored; the data complexity judgment module is used to evaluate the data complexity according to the acquired network data and brain area data to obtain a data complexity evaluation value, and judge whether to reduce the dimension of the brain area data based on the data complexity evaluation value and the data complexity evaluation value threshold, and the network data includes the data volume of the brain area data, CPU utilization, memory occupancy and hard disk read and write rate.

[0067] In this embodiment, the three-part massage method is used to treat insomnia, focusing on regulating the Qi and calming the mind, acting on the head, abdomen, and back. Through overall adjustment, the Qi is regulated, the Qi circulates and blood flows, the upper and lower parts communicate, the heart and kidney communicate, the Yin and Yang are harmonized, and the mutual transformation of essence, Qi, and spirit is promoted, so as to achieve the purpose of calming the mind above, unblocking the Qi in the middle, settling the essence below, nourishing the heart and spleen, and circulating the Qi and blood. If the mind and spirit are nourished, the patient will be energetic during the day and sleep at night, and insomnia will be eliminated. Primary Insomnia (PI) refers to a type of sleep disorder caused by physical disease, serious mental illness or drug abuse. The patient is dissatisfied with the quality or quantity of sleep, has difficulty falling asleep, wakes up easily, or cannot recover after waking up, thereby damaging social functions to varying degrees.

[0068] The data consistency judgment module is used to judge whether the brain area data collected through different channels is less than the data consistency threshold. If the data collected from each channel (such as different devices in the same brain area) is less than the data consistency threshold, the data needs to be re-collected to ensure the reliability of the data; the data preprocessing module is used to process the collected initial brain area data, classify the data, and encrypt and store them to protect the security of the data; the data complexity judgment module evaluates the complexity of the data based on the collected brain area data and network data. If the data complexity evaluation value is less than the data complexity evaluation value threshold, the system reduces the dimension of the data to reduce computing pressure and resource usage; it avoids erroneous analysis caused by data collection errors, and improves data processing efficiency while maintaining data quality, thereby improving the overall performance of the system.

[0069] In summary, the embodiment of the present invention obtains a data consistency evaluation value by collecting the initial brain area data of each channel, determines whether to re-collect data based on the data consistency evaluation value and the data consistency threshold, and then obtains a data complexity evaluation value based on the acquired network data and brain area data. Finally, based on the data complexity evaluation value and the data complexity evaluation value threshold, it determines whether to reduce the dimension of the brain area data, thereby effectively evaluating the complexity of the data, thereby achieving an improvement in data processing efficiency, and effectively solving the problem of low data processing efficiency in the process of intervening in the PI central mechanism through the three-step massage method in the prior art.

[0070] There are a few points to note:

[0071] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0072] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0073] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0074] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A data processing method based on the three-step massage method to intervene in the central mechanism of PI, characterized in that: The following steps are involved: S1, collecting initial brain area data of the patient to be monitored by a data collection device, comparing the collected initial brain area data of each channel to obtain a data consistency evaluation value, and judging whether to re-collect the initial brain area data based on the data consistency evaluation value and the data consistency threshold, wherein the data collection device includes a positron emission tomography imager, a functional magnetic resonance imaging device, and an electroencephalograph; S2, integrating and preprocessing the collected initial brain region data and the corresponding timestamps to obtain the brain region data of the patient to be monitored, and classifying the brain region data of the patient to be monitored according to the collection method and encrypting and storing them; S3, evaluating the data complexity according to the acquired network data and brain region data to obtain a data complexity evaluation value, and judging whether to perform dimensionality reduction on the brain region data based on the data complexity evaluation value and the data complexity evaluation value threshold.

2. The data processing method based on the three-part massage method for intervening the central mechanism of PI according to claim 1 is characterized in that ,The initial brain region data include initial electroencephalogram data, initial functional magnetic resonance imaging data and initial positron emission tomography data; The initial EEG data is obtained through a first backup electrode, a second backup electrode and a main electrode of the configured EEG instrument; The initial functional magnetic resonance imaging data is acquired according to a preset magnetic resonance imaging resolution and a preset scanning repetition frequency of a functional magnetic resonance imaging apparatus; The initial positron emission tomography data is acquired according to a preset scanning imaging resolution and a preset scanning frequency of the positron emission tomography imager.

3. The data processing method based on the three-part massage method for intervening the central mechanism of PI according to claim 1 is characterized in that ,The specific method of comparing the collected initial brain area data of each channel to obtain the data consistency evaluation value is as follows: The initial EEG data collected by the first backup electrode, the second backup electrode and the main electrode are paired to calculate the Pearson correlation coefficient to obtain the EEG consistency coefficient; Acquiring imaging resolution and scanning repetition frequency in the acquired initial functional magnetic resonance imaging data, and summing the ratio of the acquired magnetic resonance imaging resolution to the preset magnetic resonance imaging resolution and the ratio of the scanning repetition frequency to the preset scanning repetition frequency to obtain a magnetic resonance imaging consistency coefficient; Acquire the imaging resolution and scanning frequency in the collected initial positron emission tomography data, and sum the ratio of the acquired scanning imaging resolution to the preset scanning imaging resolution and the ratio of the scanning frequency to the preset scanning frequency to obtain a positron emission tomography consistency coefficient; Performing a hyperbolic tangent function operation on the obtained EEG consistency coefficient to obtain an EEG hyperbolic tangent function value; Performing a hyperbolic secant function operation on the acquired magnetic resonance imaging consistency coefficient to obtain a magnetic resonance hyperbolic secant function value; Performing a hyperbolic secant function operation on the obtained positron emission tomography consistency coefficient to obtain a positron hyperbolic secant function value; The data consistency evaluation value is obtained by performing exponential operation on the sum of the EEG hyperbolic tangent function value, the magnetic resonance hyperbolic secant function value and the positron hyperbolic secant function value.

4. The data processing method based on the three-part massage method for intervening the central mechanism of PI according to claim 3 is characterized in that ,The specific process of judging whether to re-collect the initial brain area data based on the data consistency evaluation value and the data consistency threshold is as follows: A1, determine whether the data consistency evaluation value is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, the initial brain area data will not be recollected, otherwise A2 will be executed; A2, re-collecting the initial EEG data after adjusting the noise level of the EEG device, and judging whether the data consistency evaluation value obtained after the adjustment of the EEG device is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, the initial functional magnetic resonance imaging data and the initial positron emission tomography data are not re-collected, otherwise A3 is executed; A3, adjusting the magnetic field strength of the central area of ​​the scanning cavity of the functional magnetic resonance imaging apparatus and recollecting the initial functional magnetic resonance imaging data, determining whether the data consistency evaluation value obtained after the functional magnetic resonance imaging apparatus is adjusted is greater than the data consistency threshold, if the data consistency evaluation value is not greater than the data consistency threshold, then the initial positron emission tomography data is not recollected, otherwise executing A4; A4, after adjusting the photomultiplier tube gain of the positron emission tomography imager, the initial positron emission tomography data is collected again, and it is determined whether the data consistency evaluation value obtained after the adjustment of the positron emission tomography imager is greater than the data consistency threshold. If the data consistency evaluation value is not greater than the data consistency threshold, no feedback is given, otherwise feedback is given.

5. The data processing method for intervening the PI central mechanism based on the three-part massage method according to claim 2 is characterized in that ,The specific method of obtaining the brain region data is as follows: B1, integrating the collected initial brain region data with the corresponding timestamps; B2, performing a first preprocessing on the integrated first electroencephalogram data to obtain electroencephalogram data, wherein the first preprocessing includes artifact removal, filtering, and baseline correction; B3, performing a second preprocessing on the integrated initial functional magnetic resonance imaging data to obtain functional magnetic resonance imaging data, wherein the second preprocessing includes motion correction, spatial registration, standardization and spatial smoothing; B4, performing a third preprocessing on the integrated initial positron emission tomography data to obtain positron emission tomography data, wherein the third preprocessing includes motion correction, spatial registration and standardization, and the brain region data includes electroencephalogram data, functional magnetic resonance imaging data and positron emission tomography data.

6. The data processing method for intervening the PI central mechanism based on the three-part massage method according to claim 1 is characterized in that ,The specific process of classifying the brain area data of the patient to be monitored according to the ,acquisition method and then encrypting and storing it is as follows: The pre-processed brain region data is transmitted to the database, and the transmission process of the brain region data is encrypted using the transport layer security protocol; The brain region data are divided into brain region classification data according to the acquisition device, and the brain region classification data are encrypted using a key generated by a symmetric encryption algorithm; The encrypted brain region classification data is stored in a preset area in the database.

7. The data processing method for intervening the central mechanism of PI based on the three-part massage method according to claim 1 is characterized in that ,The specific method of evaluating the data complexity according to the acquired network data and brain area data to obtain the data complexity evaluation value is as follows: Acquire preset complex evaluation data from a preset database, wherein the preset complex evaluation data includes a preset electroencephalogram data volume, a preset electroencephalogram acquisition frequency, a preset functional magnetic resonance imaging data volume, a preset positron emission tomography data volume, a preset CPU utilization rate, a preset memory usage, and a preset hard disk read and write rate; Evaluate the data complexity according to the acquired network data and brain area data to obtain a data complexity evaluation value, wherein the network data includes the data volume of the brain area data, the number of data features, CPU utilization, memory usage, and hard disk read and write rate; The specific limiting expression of the data complexity evaluation value is: Where i is the number of the patient to be monitored, i = 1, 2, ..., N, N is the total number of patients to be monitored, FZ i represents the data complexity evaluation value of the i-th patient to be monitored, ND i represents the complexity of the EEG of the i-th patient to be monitored, CG i represents the complexity of the magnetic resonance imaging of the i-th patient to be monitored, ZD i represents the complexity of the positron emission tomography scan of the i-th patient to be monitored, NSU i represents the amount of EEG data of the i-th patient to be monitored, SF i represents the EEG acquisition frequency of the i-th patient to be monitored, CSU i represents the amount of functional magnetic resonance imaging data of the i-th patient to be monitored, TE i represents the imaging resolution of the functional magnetic resonance imaging device of the i-th patient to be monitored, ZSU i represents the amount of positron emission tomography data of the i-th patient to be monitored, SA i represents the scanning imaging resolution of the positron emission tomography imager of the i-th patient to be monitored, CA i represents the scanning repetition frequency of the functional magnetic resonance imaging device of the i-th patient to be monitored, CM i represents the scanning frequency of the positron emission tomography imager of the i-th patient to be monitored, CPU i represents the CPU utilization rate of the data processing of the i-th patient to be monitored, NE i represents the memory usage of the data processing of the i-th patient to be monitored, DU i represents the hard disk read and write rate of data processing of the i-th patient to be monitored, NSU0 represents the preset EEG data volume, SF0 represents the preset EEG acquisition frequency, CSU0 represents the preset functional magnetic resonance imaging data volume, TE0 represents the preset imaging resolution of the functional magnetic resonance imaging apparatus, CA0 represents the preset scanning repetition frequency of the functional magnetic resonance imaging apparatus, ZSU0 represents the preset positron emission tomography data volume, SA0 represents the preset scanning imaging resolution of the positron emission tomography imaging apparatus, CM0 represents the preset scanning frequency of the positron emission tomography imaging apparatus, CPU0 represents the preset CPU utilization, NE0 represents the preset memory usage, and DU0 represents the preset hard disk read and write rate.

8. The data processing method for intervening the PI central mechanism based on the three-part massage method according to claim 1 is characterized in that ,The specific process of judging whether to reduce the dimension of brain area data based on the data complexity evaluation value and the data complexity evaluation value threshold is as follows: Compare the data complexity estimate to the data complexity estimate threshold: If the data complexity evaluation value is greater than the data complexity evaluation value threshold, the brain region data is reduced in dimension; If the data complexity assessment value is not greater than the data complexity assessment value threshold, the brain region data will not be reduced in dimension; The dimension reduction includes principal component analysis method, spatial downsampling and temporal downsampling.

9. The data processing method based on the three-part massage method for intervening the central mechanism of PI according to claim 8, characterized in that , the spatial downsampling includes reducing the imaging resolution of a functional magnetic resonance imager and the scanning imaging resolution of a positron emission tomography imager; The time downsampling includes reducing the acquisition frequency of the electroencephalograph, the scanning repetition frequency of the functional magnetic resonance imaging device and the scanning frequency of the positron emission tomography device.

10. A data processing system based on the three-step massage method to intervene in the central mechanism of PI, characterized in that: include: Data consistency judgment module, data preprocessing module and data complexity judgment module; The data consistency judgment module is used to collect the initial brain area data of the patient to be monitored through the data acquisition device, compare the collected initial brain area data of each channel to obtain a data consistency evaluation value, and judge whether to re-collect the initial brain area data based on the data consistency evaluation value and the data consistency threshold. The data acquisition device includes a positron emission tomography imager, a functional magnetic resonance imaging device and an electroencephalograph; The data preprocessing module is used to integrate and preprocess the collected initial brain region data with the corresponding timestamp to obtain the brain region data of the patient to be monitored, and to classify the brain region data of the patient to be monitored according to the collection method and then encrypt and store it; The data complexity judgment module is used to evaluate the data complexity according to the acquired network data and brain area data to obtain a data complexity evaluation value, and to judge whether to reduce the dimension of the brain area data based on the data complexity evaluation value and the data complexity evaluation value threshold.

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