Health scanning method and system based on multi-dimensional slow waves and AI

Abstract

The invention provides a health scanning method and system based on multi-dimensional slow waves and AI, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining the distribution data of body movement data in a data analysis time period, and combining the distribution data of a data deviation time period in an adjacent time period of the data analysis time period, determining data credibility coefficients of different data analysis time periods and credible data time periods, acquiring slow wave monitoring data of monitoring equipment of different dimensions in the credible data time periods, and obtaining the data credibility coefficients of the monitoring equipment of different dimensions on the basis of the slow wave monitoring data and the body movement data of the corresponding moments; according to the embodiment of the invention, the preset AI model is utilized to determine the matched scanning results in different credible data periods, and whether the body scanning result of the tester can be output or not is determined on the basis of the matched scanning results in the different credible data periods and the credible coefficient, so that the accuracy of the body scanning result is improved.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a health scanning method and system based on multi-dimensional slow waves and AI. Background Art

[0002] With the rapid economic development, people are pursuing higher and higher levels of sleep health and quality of life. At the same time, with the accelerated pace of life, the number of people with sleep quality disorders is increasing. In order to identify and process people with sleep disorders, in the invention patent application CN202410140388.9 "A near-infrared spectrum physical and mental stress and sleep quality monitoring system", near-infrared spectrum data is used to calculate and reflect the physiological characteristics of the test personnel, and a sleep state recognition model is established in combination with a classification algorithm. At the same time, an analysis model is constructed in combination with frequency domain change technology to analyze and evaluate the physical sign status of the test personnel from multiple dimensions. However, the following technical problems exist through analysis: During the sleep disorder assessment process, when the test person frequently turns over and moves, slow wave data such as EEG signals, ultrasonic echoes, and infrared blood oxygen will inevitably change. Therefore, if the above-mentioned slow wave change data is ignored, the accuracy of the test person's sleep disorder scan results cannot be guaranteed.

[0003] In response to the above technical problems, the present application specifically provides a health scanning method and system based on multi-dimensional slow waves and AI. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: A health scanning method based on multi-dimensional slow waves and AI, specifically including: S1 determines the distribution data of the body motion data at different times based on the analysis results of the body motion data of the test person in different time periods, and divides the time period into a data analysis period and a data deviation period using the distribution data; S2: determining the distribution data of the body motion data in the data analysis period, and combining the distribution data of the data deviation period in the adjacent period of the data analysis period to determine the data credibility coefficient and the credible data period of different data analysis periods; S3 obtains slow wave monitoring data of monitoring devices of different dimensions in the trusted data period, and determines matching scanning results in different trusted data periods using a preset AI model based on the slow wave monitoring data and body motion data at corresponding moments; S4 determines whether the output of the body scan result of the test person can be performed based on the matching scan results in different trusted data periods and the trust coefficient.

[0005] The beneficial effects of the present invention are: Based on the slow wave monitoring data and the body motion data at the corresponding time, the preset AI model is used to determine the matching scanning results in different trusted data time periods. It not only takes into account the difference in the credibility of the slow wave monitoring data caused by the difference in body motion data at different times in the trusted time period, but also realizes the determination of matching scanning results based on the slow wave monitoring data at different times in the trusted time period by using the preset AI model, thereby ensuring the accuracy of body scanning results in different trusted data time periods.

[0006] Based on the matching scan results and the credibility coefficients in different trusted data time periods, it is determined whether the body scan results of the test person can be output, thereby avoiding the technical problem of insufficient reliability of the output results of the body scan of the test person caused by single consideration of the matching scan results of a certain trusted data time period. The output of the body scan results of the test person is achieved from the matching scan results in multiple trusted data time periods, thereby improving the accuracy of the body scan results.

[0007] A further technical solution is that the time periods are divided according to preset unit durations.

[0008] A further technical solution is that the body motion data is determined based on monitoring data of an inertial sensor chip installed in the sleeping chamber.

[0009] A further technical solution is to divide the time period into a data analysis time period and a data deviation time period, specifically including: Determine the time when the body motion data exists in the time period based on the distribution data of the body motion data at different times, and use it as the body motion time; The time period is divided into a data analysis period and a data deviation period according to the proportion of the number of the body movement moments.

[0010] A further technical solution is to divide the time period into a data analysis period and a data deviation period according to the proportion of the number of body movement moments, specifically including: When the proportion of body movement moments in the time period is not greater than a preset proportion, the time period is determined to be a data analysis period; When the proportion of body movement moments in the time period is greater than a preset proportion, the time period is determined to be a data deviation period.

[0011] A further technical solution is to determine whether the output of the body scan result of the test person can be performed, specifically including: Determine the trusted data time periods corresponding to the different types of matching scan results based on the matching scan results in different trusted data time periods; Determining the sum of the credibility coefficients of different types of matching scan results according to the sum of the credibility coefficients of the credibility data periods corresponding to the different types of matching scan results; The output of the body scan result of the test person is determined based on the confidence coefficients of the different types of matching scan results and whether it can be performed.

[0012] A further technical solution is to determine whether the output of the body scan result of the test person can be performed based on the credibility coefficient of different types of matching scan results, specifically including: When there is no matching scan result with a confidence coefficient and a value greater than the confidence coefficient setting value, it is determined that the output of the body scan result of the test person cannot be performed; When there is a matching scan result with a credibility coefficient greater than the credibility coefficient setting value, or when there is only one matching scan result with a credibility coefficient greater than the credibility coefficient setting value, the credibility coefficient and the largest matching scan result are used as the body scan result of the test person; When there are multiple confidence coefficients and matching scan results greater than the confidence coefficient setting value, it is determined that the output of the body scan result of the test person cannot be performed.

[0013] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned health scanning method based on multidimensional slow waves and AI when running the computer program.

[0014] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flow chart of a health scanning method based on multi-dimensional slow waves and AI; Figure 2 is a flow chart of a method for dividing a time period into a data analysis time period and a data deviation time period; Figure 3 is a flow chart of a method for determining a data credibility coefficient during a data analysis period; Figure 4is a flow chart of a method for determining matching scan results in a trusted data period; Figure 5 is a flow chart of a method for determining whether output of a body scan result of the test person can be performed; Figure 6 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0018] When the test person frequently turns over and moves, slow wave data such as EEG signals, ultrasonic echoes, and infrared blood oxygen will inevitably change. Therefore, it is necessary to comprehensively consider the slow wave change data to ensure the accuracy of the test person's sleep disorder scanning results.

[0019] When the proportion of the moments with body movement data is greater than 0.6, the period is determined to be a data deviation period; when the proportion of the moments with body movement data is not greater than 0.6, the period is determined to be a data analysis period.

[0020] The proportion of moments with body movement data in the data analysis period is taken as the proportion of body movement moments, and the proportion of data deviation periods in the adjacent periods of the data analysis period is taken as the proportion of deviation periods. The data credibility coefficient of the data analysis period is determined according to the average of the proportion of body movement moments and the proportion of deviation periods, and the data analysis period with a data credibility coefficient greater than 0.8 is taken as a credible data period.

[0021] The AI ​​model is built using fuzzy neural network and multi-layer neural network algorithm models.

[0022] The matching scan result with the largest confidence coefficient is output as the body scan result of the test person.

[0023] Intelligent algorithms include Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, Random Forests, and K-Nearest Neighbors (KNN).

[0024] Embodiment 1 To solve the above-mentioned problem, according to one aspect of the present invention, Figure 1 As shown, a health scanning method based on multi-dimensional slow waves and AI is provided, which specifically includes: S1 determines the distribution data of the body motion data at different times based on the analysis results of the body motion data of the test person in different time periods, and divides the time period into a data analysis period and a data deviation period using the distribution data; Furthermore, the time periods are divided according to preset unit durations.

[0025] Specifically, the body motion data is determined based on monitoring data of an inertial sensor chip installed in the sleeping chamber.

[0026] It should be noted that if Figure 2 As shown, the time period is divided into a data analysis period and a data deviation period, specifically including: Determine the time when the body motion data exists in the time period based on the distribution data of the body motion data at different times, and use it as the body motion time; The time period is divided into a data analysis period and a data deviation period according to the proportion of the number of the body movement moments.

[0027] Furthermore, the time period is divided into a data analysis period and a data deviation period according to the proportion of the number of body movement moments, specifically including: When the proportion of body movement moments in the time period is not greater than a preset proportion, the time period is determined to be a data analysis period; When the proportion of body movement moments in the time period is greater than a preset proportion, the time period is determined to be a data deviation period.

[0028] Optionally, the time period is divided into a data analysis period and a data deviation period, specifically including: Determine the time when the body motion data exists in the time period based on the distribution data of the body motion data at different times, and use it as the body motion time; Determine the proportion of the number of body movement moments in different unit time periods according to the distribution data of the body movement moments in different unit time periods; The body movement unit time period in the unit time period is determined by using the proportion of the number of body movement moments, and the time period is divided into a data analysis period and a data deviation period by using the number of the body movement unit time periods.

[0029] Specifically, the time period is divided into a data analysis time period and a data deviation time period by using the number of the body movement unit time periods, specifically including: When the number of the body movement unit time periods is greater than the number of preset time periods, determining the time period as a data deviation time period; When the number of the body movement unit time periods is not greater than the number of preset time periods, the time period is determined to be a data analysis time period.

[0030] In another embodiment, the time period is divided into a data analysis period and a data deviation period, specifically including: S11 determines the time when the body motion data exists in the time period based on the distribution data of the body motion data at different times, and uses it as the body motion time; Optionally, the above step S11 includes the following contents: S111 determines the moment when the body movement data exists in the time period based on the distribution data of the body movement data at different moments, and takes it as the body movement moment. When the number of body movement moments in the time period is less than the preset body movement moment number threshold, the process proceeds to step S112. When the number of body movement moments in the time period is not less than the preset body movement moment number threshold, the process proceeds to step S113. S112, based on the interval data between different body movement moments, when it is determined that the number of interval moments between different body movement moments is greater than the preset number of interval moments, the period is determined as the data analysis period, and when there is a body movement moment with the number of interval moments not greater than the preset number of interval moments, the process proceeds to step S12; S113: When the number of body movement moments in the time period is greater than the preset moment number threshold, the time period is determined to be a data deviation period; when the number of body movement moments in the time period is not greater than the preset moment number threshold, the process proceeds to step S12.

[0031] S12 determines the proportion of the number of body movement moments in different unit time periods according to the distribution data of the body movement moments in different unit time periods, and determines the data deviation coefficient of different unit time periods according to the proportion of the number of body movement moments in different unit time periods and the interval data between different body movement moments; Optionally, the above step S12 includes the following contents: S121 determines the proportion of body movement moments in different unit time periods according to the distribution data of body movement moments in different unit time periods, and when there is a unit time period in which the proportion of body movement moments is greater than a preset proportion, the process proceeds to step S122; when there is no unit time period in which the proportion of body movement moments is greater than the preset proportion, the process proceeds to step S123; S122: when the number of body movement moments accounts for more than the number of unit time periods of the preset number ratio, the period is determined to be a data deviation period; when the number of body movement moments accounts for more than the number of unit time periods of the preset number ratio, the process proceeds to step S123; S123 determines the data deviation coefficients of different unit time periods based on the proportion of the number of body movement moments in different unit time periods and the interval data between different body movement moments. When the data deviation coefficients of different unit time periods are all less than the preset deviation coefficient threshold, the process proceeds to step S124. When there is a unit time period whose data deviation coefficient is not less than the preset deviation coefficient threshold, the process proceeds to step S125. S124: when the number of unit time periods with body movement moments is within the preset time period number interval, the time period is determined to be a data analysis time period; when the number of unit time periods with body movement moments is not within the preset time period number interval, the process proceeds to step S13; S125 When the number of unit time periods in which the data deviation coefficient is not less than the preset deviation coefficient threshold is greater than the preset deviation time period number, the time period is determined to be a data deviation time period; when the number of unit time periods in which the data deviation coefficient is not less than the preset deviation coefficient threshold is not greater than the preset deviation time period number, proceed to step S13.

[0032] S13 uses the data deviation coefficients of different unit time periods to determine the comprehensive deviation coefficient, and uses the comprehensive deviation coefficient to divide the time period into a data analysis period and a data deviation period.

[0033] Furthermore, the comprehensive deviation coefficient is determined based on the weighted sum of the data deviation coefficients of different unit time periods.

[0034] S2: determining the distribution data of the body motion data in the data analysis period, and combining the distribution data of the data deviation period in the adjacent period of the data analysis period to determine the data credibility coefficient and the credible data period of different data analysis periods; Specifically, the adjacent time period is a time period whose deviation from the data analysis time period is within a preset time interval.

[0035] Specifically, Figure 3 As shown, the method for determining the data credibility coefficient of the data analysis period is: Determine the time when the body movement data exists in the data analysis period based on the distribution data of the body movement data in the data analysis period, and use it as the body movement moment, and use the proportion of the body movement moments in the data analysis period as the proportion of the body movement moments; Determine the proportion of the number of data deviation periods in the adjacent periods of the data analysis period, and use it as the proportion of the number of deviation periods; The data credibility coefficient of the data analysis period is determined according to the average value of the proportion of the number of body movement moments and the proportion of the number of deviation periods.

[0036] Furthermore, the data credibility coefficient of the data analysis period is determined according to the deviation between a preset value and an average value of the proportion of the number of body movement moments and the proportion of the number of deviation periods.

[0037] It should be noted that the method for determining the data credibility coefficient of the data analysis period is: Determine the time when the body motion data exists in the data analysis period based on the distribution data of the body motion data in the data analysis period, and use it as the body motion time; determining the number of data deviation periods in the adjacent periods of the data analysis period; The data credibility coefficient of the data analysis period is determined according to the number of the body movement moments and the data deviation period.

[0038] Furthermore, the data credibility coefficient of the data analysis period is determined according to the number of the body movement moments and the data deviation periods and the corresponding preset credibility coefficients.

[0039] Furthermore, the data credibility coefficient of the data analysis period ranges from 0 to 1, wherein when the data credibility coefficient of the data analysis period is greater than a preset credibility coefficient threshold, the data analysis period is determined to be a credible data period.

[0040] S3 obtains slow wave monitoring data of monitoring devices of different dimensions in the trusted data period, and determines matching scanning results in different trusted data periods using a preset AI model based on the slow wave monitoring data and body motion data at corresponding moments; Specifically, Figure 4 As shown, the method for determining the matching scan result in the trusted data period is: Based on the body motion data at different times, the preset trustworthy weight coefficients at different times are determined; The slow wave monitoring data of different dimensions at different times are used as input, and the output results of the preset AI model are used to determine the matching scanning results at different times; Based on the matching scan results at different times, the sum of preset trust weight coefficients corresponding to different types of scan results is determined, and the matching scan result in the trusted data period is determined using the sum of the preset trust weight coefficients.

[0041] Furthermore, the preset credible weight coefficient is determined according to whether there is body motion data at the moment.

[0042] Optionally, the slow wave monitoring data includes respiratory data, heartbeat data, electroencephalogram signal data, cerebral blood pressure, cerebral blood oxygen, and ultrasonic echo.

[0043] S4 determines whether the output of the body scan result of the test person can be performed based on the matching scan results in different trusted data periods and the trust coefficient.

[0044] Specifically, Figure 5 As shown, determining whether the output of the test person's body scan result can be performed specifically includes: Determine the trusted data time periods corresponding to the different types of matching scan results based on the matching scan results in different trusted data time periods; Determining the sum of the credibility coefficients of different types of matching scan results according to the sum of the credibility coefficients of the credibility data periods corresponding to the different types of matching scan results; The output of the body scan result of the test person is determined based on the confidence coefficients of the different types of matching scan results and whether it can be performed.

[0045] Further, the output of the body scan result of the test person is determined based on the credibility coefficient of the different types of matching scan results and whether it is possible to perform the output, specifically including: When there is no matching scan result with a confidence coefficient and a value greater than the confidence coefficient setting value, it is determined that the output of the body scan result of the test person cannot be performed; When there is a matching scan result with a credibility coefficient greater than the credibility coefficient setting value, or when there is only one matching scan result with a credibility coefficient greater than the credibility coefficient setting value, the credibility coefficient and the largest matching scan result are used as the body scan result of the test person; When there are multiple confidence coefficients and matching scan results greater than the confidence coefficient setting value, it is determined that the output of the body scan result of the test person cannot be performed.

[0046] Furthermore, the matching scan results include primary sleep disorder, secondary sleep disorder and absence of sleep disorder.

[0047] In another embodiment, determining whether the output of the body scan result of the test person can be performed specifically includes: When it is determined that the matching scan results in different trusted data periods are the same, the matching scan results are used as the body scan results of the test person; When there are multiple types of matching scan results in different trusted data time periods: determining the trusted data time periods corresponding to the different types of matching scan results, and when the number of trusted data time periods corresponding to the different types of matching scan results is less than a preset number threshold, determining that the output of the body scan result of the test person cannot be performed; When there are types of matching scan results whose number of trusted data periods is not less than the preset number threshold: When there is only one type of matching scan result whose number of trusted data periods is not less than a preset number threshold, determining that the output of the body scan result of the test person can be performed; When there are multiple types of matching scan results whose number of credible data periods is not less than a preset number threshold, the sum of credible coefficients of different types of matching scan results is determined according to the sum of credible coefficients of credible data periods corresponding to different types of matching scan results, and when the sum of credible coefficients of different types of matching scan results is less than a preset coefficient setting value, it is determined that the output of the body scan result of the test person cannot be performed; When there is a type of matching scan result with a confidence factor not less than the preset factor setting value: When the type of matching scan result with the largest credibility coefficient and the largest matching scan result is selected as the scan result to be selected, and when there is a matching scan result whose deviation from the credibility coefficient and the matching scan result to be selected is less than the preset credibility coefficient deviation, it is determined that the output of the body scan result of the test person cannot be performed; When there is no matching scan result whose deviation from the sum of the credibility coefficients of the selected scan result is less than the preset credibility coefficient deviation, Based on the credibility coefficient of the candidate scan result and the deviation between the credibility coefficient of other matching scan results and the candidate scan result, a comprehensive credibility coefficient is determined, and the comprehensive credibility coefficient is used to determine whether the output of the test person's body scan result can be performed.

[0048] Furthermore, when the comprehensive credibility coefficient is greater than a credibility coefficient setting threshold, it is determined that the output of the body scan result of the test person can be performed.

[0049] Embodiment 2 The second aspect, as Figure 6As shown, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned health scanning method based on multidimensional slow waves and AI when running the computer program.

[0050] Optionally, the method for determining the data credibility coefficient of the data analysis period is: Based on the distribution data of the body motion data in the data analysis period, the time when the body motion data exists in the data analysis period is determined, and the time is used as the body motion time. When the number of the body motion time is not within the preset time number interval, when it is determined that there is a data deviation period in the adjacent period of the data analysis period, it is determined that the data analysis period does not belong to the credible data period. When there is no data deviation period in the adjacent period of the data analysis period, the data credibility coefficient of the data analysis period is determined by using the number of body movement moments; When the number of the body movement moments is within the preset moment number range: Determine the number of data deviation periods in the adjacent period of the data analysis period, and when the sum of the number of the body movement moments and the number of the data deviation periods does not meet the requirement, determine that the data analysis period does not belong to the credible data period; When the sum of the number of the body movement moments and the data deviation periods meets the requirements: The basic abnormality coefficient of the data analysis period is determined based on the number of body movement moments in the data analysis period and the number of intervals between different body movement moments. When the basic abnormality coefficient is greater than a preset abnormality coefficient threshold: When there is a data deviation period, it is determined that the data analysis period does not belong to a credible data period; When there is no data deviation period, determining the data credibility coefficient of the data analysis period by using the basic abnormality coefficient of the data analysis period; When the basic abnormal coefficient is not greater than the preset abnormal coefficient threshold: Determine the adjacent anomaly coefficient based on the proportion of the number of data deviation periods in the adjacent period and the interval between different data deviation periods and the data analysis period. When the average value of the adjacent anomaly coefficient and the basic anomaly coefficient does not meet the requirements, determine that the data analysis period does not belong to the credible data period. When the average value of the adjacent anomaly coefficient and the basic anomaly coefficient meets the requirements: The data credibility coefficient of the data analysis period is determined based on the adjacent abnormality coefficient and the basic abnormality coefficient.

[0051] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0052] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A health scanning method based on multidimensional slow waves and AI, characterized in that: Specifically include: Determine the distribution data of the body motion data at different times based on the analysis results of the body motion data of the test persons in different time periods, and divide the time period into a data analysis period and a data deviation period using the distribution data; Determine the distribution data of the body motion data in the data analysis period, and determine the data credibility coefficients and credible data periods of different data analysis periods in combination with the distribution data of the data deviation period in the adjacent period of the data analysis period; Acquire slow wave monitoring data of monitoring devices of different dimensions in the trusted data period, and determine matching scanning results in different trusted data periods using a preset AI model based on the slow wave monitoring data and body motion data at the corresponding moment; Based on the matching scan results in different trusted data periods and the trust coefficient, it is determined whether the output of the body scan result of the test person can be performed.

2. The health scanning method based on multidimensional slow waves and AI as claimed in claim 1, characterized in that: The time periods are divided according to preset unit durations.

3. The health scanning method based on multidimensional slow waves and AI as claimed in claim 1, characterized in that: The body motion data is determined based on monitoring data of an inertial sensor chip installed in the sleeping chamber.

4. The health scanning method based on multidimensional slow waves and AI as claimed in claim 1, characterized in that: The time period is divided into a data analysis period and a data deviation period, specifically including: Determine the time when the body motion data exists in the time period based on the distribution data of the body motion data at different times, and use it as the body motion time; The time period is divided into a data analysis period and a data deviation period according to the proportion of the number of the body movement moments.

5. The health scanning method based on multidimensional slow waves and AI as claimed in claim 4, characterized in that: The time period is divided into a data analysis period and a data deviation period according to the proportion of the number of the body movement moments, specifically including: When the proportion of body movement moments in the time period is not greater than a preset proportion, the time period is determined to be a data analysis period; When the proportion of body movement moments in the time period is greater than a preset proportion, the time period is determined to be a data deviation period.

6. The health scanning method based on multidimensional slow waves and AI as claimed in claim 1, characterized in that: The adjacent time period is a time period whose deviation from the data analysis time period is within a preset time interval.

7. The health scanning method based on multidimensional slow waves and AI as claimed in claim 1, characterized in that: The method for determining the data credibility coefficient of the data analysis period is: Determine the time when the body movement data exists in the data analysis period based on the distribution data of the body movement data in the data analysis period, and use it as the body movement moment, and use the proportion of the body movement moments in the data analysis period as the proportion of the body movement moments; Determine the proportion of the number of data deviation periods in the adjacent periods of the data analysis period, and use it as the proportion of the number of deviation periods; The data credibility coefficient of the data analysis period is determined according to the average value of the proportion of the number of body movement moments and the proportion of the number of deviation periods.

8. The health scanning method based on multidimensional slow waves and AI as claimed in claim 1, characterized in that: Determining whether the output of the test person's body scan result can be performed specifically includes: Determine the trusted data time periods corresponding to the different types of matching scan results based on the matching scan results in different trusted data time periods; Determining the sum of the credibility coefficients of different types of matching scan results according to the sum of the credibility coefficients of the credibility data periods corresponding to the different types of matching scan results; The output of the body scan result of the test person is determined based on the confidence coefficients of the different types of matching scan results and whether it can be performed.

9. The health scanning method based on multidimensional slow waves and AI as claimed in claim 8, characterized in that: The output of the body scan result of the test person is determined based on the confidence coefficient of the different types of matching scan results and whether it is possible to perform the body scan result of the test person, specifically including: When there is no matching scan result with a confidence coefficient and a value greater than the confidence coefficient setting value, it is determined that the output of the body scan result of the test person cannot be performed; When there is a matching scan result with a credibility coefficient greater than the credibility coefficient setting value, or when there is only one matching scan result with a credibility coefficient greater than the credibility coefficient setting value, the credibility coefficient and the largest matching scan result are used as the body scan result of the test person; When there are multiple confidence coefficients and matching scan results greater than the confidence coefficient setting value, it is determined that the output of the body scan result of the test person cannot be performed.

10. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a health scanning method based on multidimensional slow waves and AI as described in any one of claims 1-9 is executed.

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