Data collection system and data collection method for physical examination instrument

Abstract

The present invention discloses a data collection system and a data collection method for a physical examination instrument. The present invention relates to the technical field of data collection and solves the problem that the overall accuracy of physical examination data in the data collection process cannot be guaranteed. The present invention performs correlation analysis on the relevant physical examination data collected by the physical examination instrument, determines relevant data segments with relatively similar fluctuation characteristics based on their specific numerical fluctuations, and then identifies the most standard relevant physical examination data from the corresponding data segments. This method of confirming standard physical examination data is used to lock in physical examination data with relatively standard values ​​from several groups of physical examination data. Compared with the physical examination data confirmed by the original method, the confirmed physical examination data has higher numerical accuracy, thereby achieving a better physical examination data determination effect.

Description

Technical Field

[0001] The present invention relates to the technical field of data collection, and in particular to a data collection system and a data collection method for a physical examination instrument. Background Art

[0002] Physical examination instruments play a vital role in modern health management. They assess health status by accurately measuring and analyzing individual physiological indicators.

[0003] The application with publication number CN111192647A discloses a data collection system for a physical examination instrument, including: an input / output port, suitable for connecting to a physical examination instrument via a connecting line; a memory, storing instructions executable by a processor; a processor, connecting the physical examination instrument, an input device and the memory, and the processor is configured to execute the instructions to implement the following steps: identifying the physical examination instrument using preset hardware information; receiving first physical examination data from the physical examination instrument; parsing the first physical examination data to obtain multiple elements; encapsulating the multiple elements into a unified object format to obtain second physical examination data; and saving the second physical examination data to the memory.

[0004] There are different physical examination instruments for different physical examination items. In actual application, the physical examination instruments generally identify relevant changes in the collected data from the relevant collection process based on the collection process associated with the collection object. When the data change is paused, the physical examination data is confirmed from the paused display data. When there are multiple groups of paused collection data or the physical examination data is constantly fluctuating, it will cause relevant accuracy problems in the physical examination data in the corresponding collection process, and the overall accuracy of the data collection process cannot be guaranteed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a data collection system and a data collection method for a physical examination instrument, which solves the problem that the overall accuracy of the physical examination data cannot be guaranteed during the data collection process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A data collection method of a physical examination instrument comprises the following steps:

[0007] S1. Based on the physical examination data collected by the relevant physical examination instruments in the relevant period and based on the fluctuation trend characteristics of the corresponding physical examination data, the associated change value of the corresponding physical examination item is determined in the following manner:

[0008] S11, based on the relevant period T set by the corresponding physical examination instrument, where T is a preset value, confirming several groups of physical examination data monitored within the relevant period T one by one;

[0009] S12, generating a physical examination data change curve of the current related period T based on different physical examination data corresponding to different moments in the current related period T;

[0010] S13, confirm the turning points in the change curve of the physical examination data one by one, and the change trends of the related points before and after the turning points are opposite. Based on the confirmed turning points, the physical examination data corresponding to each turning point is calibrated as J i , where i represents different turning points, from several individual examination data J i Select the maximum value J i max and minimum value J i min, using G = (J i max-J i min)÷X1 to confirm the associated change value G of this physical examination item, where X1 is the preset value, and the associated change value generated by different physical examination personnel is calibrated as G k , where k represents different medical examiners;

[0011] S2. Based on the associated change value confirmed by the corresponding physical examination data, segment analysis is performed on the physical examination data associated with the main physical examination data, and the best analysis segment is selected from the separated data segments, and the standard physical examination data is determined from the best analysis segment. The specific sub-steps are:

[0012] S21, based on the several groups of physical examination data generated within the relevant period T, sort the several groups of physical examination data according to the time when the corresponding physical examination data were generated to determine a data sequence;

[0013] S22, identify whether the change difference between adjacent data in the data sequence is of the same type, identify the change difference between the first group of physical examination data and the second group of physical examination data, the change difference = |previous group of physical examination data-latter group of physical examination data|, if the change difference ≤ G k , mark the first two groups of physical examination data as the same type of data, then identify whether the second group of physical examination data and the third group of physical examination data are the same type of data, and so on, until the confirmed subsequent physical examination data is not the same type of data, and divide the several groups of physical examination data before this physical examination data into the first data segment;

[0014] Starting from this physical examination data, similar values ​​are confirmed in the future, and the processing is carried out step by step, and several groups of subsequent related data are confirmed one by one;

[0015] S23, from the confirmed data segment, determine the time difference Cz between the corresponding time of the initial data and the corresponding time of the end data of the corresponding data segment q , where q represents different data segments, and then confirm the sum of the difference values ​​between different physical examination data in the corresponding data segment and mark it as Zh q, using Tz q =(Cz q ×C1)÷(Zh q ×C2) determine the characteristic value Tz q , where C1 and C2 are preset fixed coefficient factors;

[0016] S24, confirm the characteristic values ​​Tz of different data segments in turn q , from different Tz q The data segment corresponding to the maximum value is selected, and the data segment is marked as the best analysis segment. Several groups of physical examination data corresponding to the best analysis segment are averaged to determine the standard physical examination data of this physical examination item;

[0017] S3, according to the same processing method in step S1 and step S2, the standard physical examination data generated by the same physical examination personnel for other physical examination items are confirmed one by one, and several groups of standard physical examination data confirmed by the corresponding physical examination personnel are displayed;

[0018] S4, confirm the time lines associated with several groups of standard physical examination data confirmed for the same physical examination item, and then confirm the time sequence of the corresponding standard physical examination data based on the time parameters of the time line in the relevant period, and then perform variance analysis on the time sequences of different standard physical examination data to identify whether there is a pattern between the time lines and determine the calibration period. The specific sub-steps are:

[0019] S41, confirm the time lines of several groups of standard physical examination data associated with the same physical examination item, determine the initial time Cs and the end time Ms of the time line, then identify the initial time Ts and the end time Tm of the corresponding time period T, identify the front time parameter QD of the standard physical examination data, and QD=Cs-Ts, then identify the end time parameter MD of the standard physical examination data, and MD=Tm-Ms, and use the determined front time parameter QD and end time parameter MD as the time sequence {QD, MD} of the standard physical examination data;

[0020] S42, performing variance processing on several groups of front-end time parameters QD of the time series of several groups of standard physical examination data identified in this stage, determining a first group of variance values ​​F1, and then performing variance processing on several groups of terminal time parameters MD of the time series of several groups of standard physical examination data identified in this stage, confirming a second group of variance values ​​F2, and performing mean processing on the two groups of variance values ​​associated with the main physical examination items, determining a standard mean value;

[0021] S43, comparing the confirmed standard mean with the preset value Y1: when the standard mean ≤ Y1, recalibrating the time lines of several groups of standard physical examination data, calibrating the initial time of the relevant period T associated with the corresponding time line to 0, thereby obtaining the previous and next times of the corresponding time line; when the standard mean > Y1, no processing is performed;

[0022] Then, the calibrated timeline is cross-confirmed, the common time period with the most cross-continuities is determined, and the time period is calibrated within the relevant period T based on the determined common time period to determine the modified calibration period;

[0023] S5. After the calibration period is determined, when confirming the standard physical examination data for the same physical examination items, the average value is directly confirmed from the physical examination data related to the common time period calibrated within the calibration period during the monitoring process to determine the standard physical examination data in the corresponding physical examination process.

[0024] Preferably, the data collection system of the physical examination instrument comprises:

[0025] The change value confirmation end determines the associated change value of the corresponding physical examination item based on the physical examination data collected by the relevant physical examination instrument in the relevant period and the fluctuation trend characteristics of the corresponding physical examination data;

[0026] The standard physical examination data confirmation terminal performs segmented analysis on the physical examination data associated with the main physical examination data based on the associated change value confirmed by the corresponding physical examination data, selects the best analysis segment from the separated data segments, and determines the standard physical examination data from the best analysis segment;

[0027] The data display terminal displays the standard physical examination data determined by the corresponding physical examination items through the corresponding display terminal;

[0028] The regularity analysis end confirms the timelines associated with several groups of standard physical examination data confirmed for the same physical examination item, and then confirms the time sequence of the corresponding standard physical examination data based on the time parameters of the timeline in the relevant period, and then performs variance analysis on the time sequences of different standard physical examination data to identify whether there is a regularity between their timelines;

[0029] When the relevant execution end confirms the standard physical examination data of the physical examination items with a calibration cycle, the average is directly confirmed from the physical examination data related to the common time period calibrated within the calibration cycle during the monitoring process to determine the standard physical examination data in the corresponding physical examination process.

[0030] The present invention provides a data collection system and a data collection method for a physical examination instrument. Compared with the prior art, it has the following beneficial effects:

[0031] The present invention performs correlation analysis on the relevant physical examination data collected by the physical examination instrument, determines the relevant data segments with relatively similar fluctuation characteristics based on the specific value fluctuations, and then identifies the most standard relevant physical examination data from the corresponding data segments. By adopting this standard physical examination data confirmation method, physical examination data with relatively standard values ​​are locked from a number of groups of physical examination data. Compared with the physical examination data confirmed by the original method, the confirmed physical examination data has higher numerical accuracy, thereby achieving a better physical examination data determination effect;

[0032] In order to identify the corresponding standard physical examination data during the confirmation process, the common time period in which several standard physical examination data exist is confirmed within the associated relevant period. Based on the specific time position of the common time period in the corresponding relevant period, when the subsequent standard physical examination data is confirmed, the physical examination data at the corresponding time position is directly obtained in this relevant period, and then the standard physical examination data is directly confirmed from the obtained physical examination data. This not only can quickly obtain the corresponding standard data, but also synchronously ensure the accuracy of the obtained data, thereby providing relevant guarantees for the overall effect of data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the process of the present invention;

[0034] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments 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.

[0036] Example 1

[0037] See also Figure 1 , the present application provides a data collection method of a physical examination instrument, comprising the following steps:

[0038] S1. Based on the physical examination data collected by the relevant physical examination instruments in the relevant period and the fluctuation trend characteristics of the corresponding physical examination data, the associated change value of the corresponding physical examination item is determined. Specifically, for different physical examination items, when performing specific numerical monitoring, it is generally necessary to measure for a certain period of time. Based on the relevant data generated in this time period, the specific data of the physical examination item is confirmed. However, each different physical examination item has different floating conditions, which will generate different floating values. In order to ensure that the subsequent monitored physical examination data is more accurate, it is necessary to determine the specific change range of the corresponding physical examination item. Among them, the specific method of determining the associated change value of the physical examination item is:

[0039] S11, based on the relevant period T set by the corresponding physical examination instrument, where T is a preset value, the specific value of which is prepared in advance by the relevant operator according to the physical examination item characteristics of the corresponding physical examination instrument, confirming several groups of physical examination data monitored within the relevant period T one by one;

[0040] S12. Based on different physical examination data corresponding to different moments in the relevant period T, a physical examination data change curve of the relevant period T is generated. Since different moments correspond to different data, relevant points can be determined in a two-dimensional coordinate system. Then, the relevant points are interconnected to determine a relevant change curve. The horizontal coordinate axis of the curve is the timeline, and the vertical coordinate axis is the specific physical examination data.

[0041] S13. Confirm the turning points in the physical examination data change curve one by one. The change trends of the related points before and after the turning point are opposite. For example, there are three points, namely A, B and C. The physical examination data corresponding to A is 10, B is 12, and C is also 10. Then the change trend between AB is rising, and the change trend between BC is falling. Then point B is the turning point. Similarly, when A is 12, B is 10, and C is 12, the change trend is also opposite. Then point B is also a turning point. Based on the confirmed turning points, the physical examination data corresponding to each turning point is calibrated as J. i , where i represents different turning points, from several individual examination data J i Select the maximum value J i max and minimum value J i min, using G = (J i max-J i min)÷X1 to confirm the associated change value G of this physical examination item, where X1 is a preset value, and its specific value is determined by the operator based on experience, generally 2, and the associated change value generated by different physical examination personnel is calibrated as G k , where k represents different medical examiners;

[0042] S2. Based on the associated change value confirmed by the corresponding physical examination data, the physical examination data associated with the main physical examination data is segmented and analyzed. From the separated data segments, the best analysis segment is selected, and the standard physical examination data is determined from the best analysis segment. Specifically, the so-called standard physical examination data is the data with the most accurate value in the corresponding physical examination process. In the original data confirmation process, generally through the set monitoring program, when a certain value lasts for several seconds, such value is used as the physical examination data of this type of physical examination personnel. However, this original value confirmation method still has some errors in accuracy, so it is necessary to determine the standard physical examination data with the highest calibration degree;

[0043] Among them, the specific sub-steps of determining the standard physical examination data are:

[0044] S21, based on the several groups of physical examination data generated within the relevant period T, sort the several groups of physical examination data according to the time when the corresponding physical examination data were generated to determine a data sequence;

[0045] S22, identify whether the change difference between adjacent data in the data sequence is of the same type, identify the change difference between the first group of physical examination data and the second group of physical examination data, the change difference = |previous group of physical examination data-latter group of physical examination data|, if the change difference ≤ G k , mark the first two groups of physical examination data as the same type of data, then identify whether the second group of physical examination data and the third group of physical examination data are the same type of data, and so on, until the confirmed subsequent physical examination data is not the same type of data, and divide the several groups of physical examination data before this physical examination data into the first data segment;

[0046] Starting from this physical examination data, similar values ​​are confirmed in the future, and the processing is carried out step by step. The subsequent related groups of data are confirmed one by one. For example, assuming that the generated data sequence is {20, 19, 20, 21, 23, 29, 23, 22, 24, 23}, then the calibrated change difference G k =5, then the difference between the data of 20, 19, 20, 21, 23 is less than 5, which belongs to the first data segment, and the data of 29 does not meet the requirements. The subsequent second data segment is 23, 22, 24, 23;

[0047] S23, from the confirmed data segment, determine the time difference Cz between the corresponding time of the initial data and the corresponding time of the end data of the corresponding data segment q , where q represents different data segments, and then confirm the sum of the difference values ​​between different physical examination data in the corresponding data segment and mark it as Zh q , using Tz q =(Cz q ×C1)÷(Zh q ×C2) determine the characteristic value Tzq , where C1 and C2 are preset fixed coefficient factors, and their specific values ​​are determined by the operator based on experience. q The smaller the value, the larger the time difference, and the larger the resulting eigenvalue will be;

[0048] S24, confirm the characteristic values ​​Tz of different data segments in turn q , from different Tz q The data segment corresponding to the maximum value is selected, and the data segment is marked as the best analysis segment. Several groups of physical examination data corresponding to the best analysis segment are averaged to determine the standard physical examination data of this physical examination item;

[0049] Specifically, by adopting this method of confirming standard physical examination data, physical examination data with relatively standard values ​​are locked from a number of groups of physical examination data. The confirmed physical examination data has higher numerical accuracy than the physical examination data confirmed by the original method, thereby achieving a better physical examination data confirmation effect;

[0050] S3. According to the same processing method in step S1 and step S2, the standard physical examination data generated by the same physical examination personnel for other physical examination items are confirmed one by one, and several groups of standard physical examination data confirmed by the corresponding physical examination personnel are displayed for external relevant personnel to view.

[0051] Example 2

[0052] In the specific implementation process, compared with the above embodiment, the specific difference of this embodiment is that this embodiment mainly identifies whether there is a regularity in the time period associated with the data collected for the same physical examination item;

[0053] Also includes:

[0054] S4. Confirm the time lines associated with several groups of standard physical examination data confirmed by the same physical examination item, and then confirm the time sequence of the corresponding standard physical examination data based on the time parameters of the time line in the relevant period, and then perform variance analysis on the time sequences of different standard physical examination data to identify whether there is a pattern between the time lines. Specifically, after each standard physical examination data is generated, it is determined from the corresponding time period T. If the relevant physical examination instruments complete the relevant confirmation of the corresponding standard physical examination data within a certain period of time during the time analysis process, then such standard physical examination data has a relevant pattern in the confirmation process. Then, for such physical examination items, the numerical confirmation can be directly performed from the time period with the pattern, without going through the corresponding numerical analysis process, and the selected several groups of standard physical examination data are not less than 50 groups;

[0055] The specific sub-steps for identifying whether there is a pattern between the timelines are:

[0056] S41, confirm the time lines of several groups of standard physical examination data associated with the same physical examination item, determine the initial time Cs and the end time Ms of the time line, then identify the initial time Ts and the end time Tm of the corresponding time period T, identify the front time parameter QD of the standard physical examination data, and QD=Cs-Ts, then identify the end time parameter MD of the standard physical examination data, and MD=Tm-Ms, and use the determined front time parameter QD and end time parameter MD as the time sequence {QD, MD} of the standard physical examination data;

[0057] S42, performing variance processing on several groups of front-end moment parameters QD of the moment series of several groups of standard physical examination data identified in this stage, determining the first group of variance values ​​F1, and then performing variance processing on several groups of terminal moment parameters MD of the moment series of several groups of standard physical examination data identified in this stage, confirming the second group of variance values ​​F2, performing mean processing on the two groups of variance values ​​associated with the main physical examination items, and determining the standard mean. Specifically, after the physical examination items associated with the corresponding stage have undergone the relevant data processing process, the corresponding standard physical examination data will be confirmed. The standard physical examination data is determined within the corresponding time period, so the time period is a fixed period, and the time difference corresponding to the relevant moments of the front and rear points can be confirmed. If they are all confirmed within a similar time period, the time difference generated before and after should not be too different, and the determined variance value should be small, so that it can be identified whether there is a relevant law on the relevant time line;

[0058] S43, comparing the confirmed standard mean with the preset value Y1: when the standard mean>Y1, no processing is performed; when the standard mean≤Y1, the time lines of several groups of standard physical examination data are recalibrated, and the initial time of the relevant period T associated with the corresponding time line is calibrated to 0, so as to obtain the previous and next times of the corresponding time line;

[0059] Then, the calibrated timeline is cross-confirmed, and the common time period with the most crossover times is determined (for example, if after calibration, the corresponding time lines are 5-10, 6-11 and 4-9, then the common time period generated by the three time lines is 5-9). Based on the determined common time period, the time period calibration is performed within the relevant cycle T to determine the modified calibration cycle. For example, if the corresponding calibration cycle is 3 minutes, and the common time period generated is 2.1-2.5 minutes, then the relevant calibration of the common time period can be performed within the calibration cycle, that is, 2.1-2.5 minutes within 3 minutes of this calibration cycle belongs to the common time period;

[0060] S5. After the calibration period is determined, when confirming the standard physical examination data for the same physical examination items in the future, the average value is directly confirmed from the physical examination data related to the common time period calibrated in the calibration period during the monitoring process to determine the standard physical examination data in the corresponding physical examination process;

[0061] Example 3

[0062] Combination Figure 2 , the data collection system of the physical examination instrument includes:

[0063] The change value confirmation end determines the associated change value of the corresponding physical examination item based on the physical examination data collected by the relevant physical examination instrument in the relevant period and the fluctuation trend characteristics of the corresponding physical examination data;

[0064] The standard physical examination data confirmation terminal performs segmented analysis on the physical examination data associated with the main physical examination data based on the associated change value confirmed by the corresponding physical examination data, selects the best analysis segment from the separated data segments, and determines the standard physical examination data from the best analysis segment;

[0065] The data display terminal displays the standard physical examination data determined by the corresponding physical examination items through the corresponding display terminal;

[0066] The regularity analysis end confirms the timelines associated with several groups of standard physical examination data confirmed for the same physical examination item, and then confirms the time sequence of the corresponding standard physical examination data based on the time parameters of the timeline in the relevant period, and then performs variance analysis on the time sequences of different standard physical examination data to identify whether there is a regularity between their timelines;

[0067] When the relevant execution end confirms the standard physical examination data of the physical examination items with a calibration cycle, the average is directly confirmed from the physical examination data related to the common time period calibrated within the calibration cycle during the monitoring process to determine the standard physical examination data in the corresponding physical examination process.

[0068] Example 4

[0069] The specific implementation process of this embodiment includes all the implementation processes of the above three groups of embodiments.

[0070] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0071] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for collecting data using a physical examination instrument, characterized in that: The following steps are involved: S1. Based on the physical examination data collected by the relevant physical examination instruments in the relevant period and based on the fluctuation trend characteristics of the corresponding physical examination data, the associated change value of the corresponding physical examination item is determined in the following manner: S11, based on the relevant period T set by the corresponding physical examination instrument, where T is a preset value, confirming several groups of physical examination data monitored within the relevant period T one by one; S12, generating a physical examination data change curve of the current related period T based on different physical examination data corresponding to different moments in the current related period T; S13, confirm the turning points in the change curve of the physical examination data one by one, and the change trends of the related points before and after the turning point are opposite. Based on the confirmed turning points, the physical examination data corresponding to each turning point is calibrated as J i , where i represents different turning points, from several individual examination data J i Select the maximum value J i max and minimum value J i min, using G = (J i max-J i min)÷X1 to confirm the associated change value G of this physical examination item, where X1 is the preset value, and the associated change value generated by different physical examination personnel is calibrated as G k , where k represents different medical examiners; S2. Based on the associated change value confirmed by the corresponding physical examination data, segment analysis is performed on the physical examination data associated with the main physical examination data, and the best analysis segment is selected from the separated data segments, and the standard physical examination data is determined from the best analysis segment. The specific sub-steps are: S21, based on the several groups of physical examination data generated within the relevant period T, sort the several groups of physical examination data according to the time when the corresponding physical examination data were generated to determine a data sequence; S22, identify whether the change difference between adjacent data in the data sequence is of the same type, identify the change difference between the first group of physical examination data and the second group of physical examination data, the change difference = |previous group of physical examination data-latter group of physical examination data|, if the change difference ≤ G k , mark the first two groups of physical examination data as the same type of data, then identify whether the second group of physical examination data and the third group of physical examination data are the same type of data, and so on, until the confirmed subsequent physical examination data is not the same type of data, and divide the several groups of physical examination data before this physical examination data into the first data segment; Starting from this physical examination data, similar values ​​are confirmed in the future, and the processing is carried out step by step, and several groups of subsequent related data are confirmed one by one; S23, from the confirmed data segment, determine the time difference Cz between the corresponding time of the initial data and the corresponding time of the end data of the corresponding data segment q , where q represents different data segments, and then confirm the sum of the difference values ​​between different physical examination data in the corresponding data segment and mark it as Zh q , using Tz q =(Cz q ×C1)÷(Zh q ×C2) determine the characteristic value Tz q , where C1 and C2 are preset fixed coefficient factors; S24, confirm the characteristic values ​​Tz of different data segments in turn q , from different Tz q The data segment corresponding to the maximum value is selected, and the data segment is marked as the best analysis segment. Several groups of physical examination data corresponding to the best analysis segment are averaged to determine the standard physical examination data of this physical examination item; S3, according to the same processing method in step S1 and step S2, the standard physical examination data generated by the same physical examination personnel for other physical examination items are confirmed one by one, and several groups of standard physical examination data confirmed by the corresponding physical examination personnel are displayed; S4. Confirm the time lines associated with several groups of standard physical examination data confirmed for the same physical examination item, and then confirm the time series of the corresponding standard physical examination data based on the time parameters of the time line in this related period, and then perform variance analysis on the time series of different standard physical examination data to identify whether there is a pattern between their time lines and determine the calibration period.

2. The data collection method of the physical examination instrument according to claim 1, characterized in that: In step S4, the specific sub-steps for identifying whether there is a regularity between the time lines are: S41, confirm the time lines of several groups of standard physical examination data associated with the same physical examination item, determine the initial time Cs and the end time Ms of the time line, then identify the initial time Ts and the end time Tm of the corresponding time period T, identify the front time parameter QD of the standard physical examination data, and QD=Cs-Ts, then identify the end time parameter MD of the standard physical examination data, and MD=Tm-Ms, and use the determined front time parameter QD and end time parameter MD as the time sequence {QD, MD} of the standard physical examination data; S42, performing variance processing on several groups of front-end time parameters QD of the time series of several groups of standard physical examination data identified in this stage, determining a first group of variance values ​​F1, and then performing variance processing on several groups of terminal time parameters MD of the time series of several groups of standard physical examination data identified in this stage, confirming a second group of variance values ​​F2, and performing mean processing on the two groups of variance values ​​associated with the main physical examination items, determining a standard mean value; S43, comparing the confirmed standard mean with the preset value Y1: when the standard mean ≤ Y1, recalibrating the time lines of several groups of standard physical examination data, calibrating the initial time of the relevant period T associated with the corresponding time line to 0, thereby obtaining the previous and next times of the corresponding time line; The calibrated timeline is then cross-confirmed for time periods, the common time period with the most crossover times is determined, and time period calibration is performed within the relevant period T based on the determined common time period to determine the modified calibration period.

3. The data collection method of the physical examination instrument according to claim 2, characterized in that: In the step S43, when the standard mean value>Y1, no processing is performed.

4. The data collection method of the physical examination instrument according to claim 2, characterized in that: The following steps are also included: S5. After the calibration period is determined, when confirming the standard physical examination data for the same physical examination items, the average value is directly confirmed from the physical examination data related to the common time period calibrated within the calibration period during the monitoring process to determine the standard physical examination data in the corresponding physical examination process.

5. A data collection system for a physical examination instrument, the collection system being operated according to the data collection method for a physical examination instrument according to any one of claims 1 to 4, characterized in that: include: The change value confirmation end determines the associated change value of the corresponding physical examination item based on the physical examination data collected by the relevant physical examination instrument in the relevant period and the fluctuation trend characteristics of the corresponding physical examination data; The standard physical examination data confirmation terminal performs segmented analysis on the physical examination data associated with the main physical examination data based on the associated change value confirmed by the corresponding physical examination data, selects the best analysis segment from the separated data segments, and determines the standard physical examination data from the best analysis segment; The data display terminal displays the standard physical examination data determined by the corresponding physical examination items through the corresponding display terminal; The regularity analysis end confirms the timelines associated with several groups of standard physical examination data confirmed for the same physical examination item, and then confirms the time sequence of the corresponding standard physical examination data based on the time parameters of the timeline in the relevant period, and then performs variance analysis on the time sequences of different standard physical examination data to identify whether there is a regularity between their timelines; When the relevant execution end confirms the standard physical examination data of the physical examination items with a calibration cycle, the average is directly confirmed from the physical examination data related to the common time period calibrated within the calibration cycle during the monitoring process to determine the standard physical examination data in the corresponding physical examination process.

Images