A real-time detection method and system for fatigue of epidemic prevention personnel

Abstract

The present invention provides a real-time detection method and system for fatigue of epidemic prevention personnel, which relates to the field of data processing. The method includes: collecting and obtaining multiple types of real-time vital sign data of epidemic prevention personnel, obtaining multiple sets of real-time vital sign data as result data; judging whether there is resting state data in the result data, and obtaining a judgment result; if the judgment result is yes, continue to collect and obtain multiple types of real-time vital sign data of epidemic prevention personnel until the time length of the result data is greater than the preset time length; analyze the resting state data and cumulative data in the result data to obtain the analysis result; according to the analysis result, calculate and output the probability value of fatigue of epidemic prevention personnel. The present invention solves the technical problem that there is no method suitable for fatigue state detection of epidemic prevention personnel in the prior art, and there is a poor effect of fatigue state detection of epidemic prevention personnel, and achieves the technical effect of improving the applicability and accuracy of fatigue state detection of epidemic prevention personnel.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a real-time detection method and system for fatigue of epidemic prevention personnel. Background Art

[0002] Fatigue can cause a person's attention to be distracted, reaction to become slower, and physical fitness to become weaker. When performing complex and dangerous tasks such as driving and operating equipment, fatigue can seriously affect work quality and life safety.

[0003] At present, common methods for detecting fatigue status include detecting the user's facial expression and voice intonation based on face / image recognition technology, or directly detecting based on working time, etc. Since epidemic prevention personnel need to work for a long time, the detection of their fatigue level is of great significance to their physical health and the effectiveness of epidemic management.

[0004] Since epidemic prevention personnel wear protective masks and protective clothing, traditional fatigue status detection methods such as facial expressions and voice intonation are not applicable. Fatigue status detection methods based on electrocardiogram, skin electricity, etc. have strong resistance to motion noise. However, there are no relevant methods suitable for fatigue status detection of epidemic prevention personnel in the existing technology, and there is a technical problem that the fatigue status detection effect of epidemic prevention personnel is poor. Summary of the invention

[0005] The present application provides a real-time detection method and system for fatigue status of epidemic prevention personnel, which is used to solve the technical problem that there is no relevant method suitable for fatigue status detection of epidemic prevention personnel in the prior art, and the fatigue status detection effect of epidemic prevention personnel is poor.

[0006] In view of the above problems, the present application provides a real-time detection method and system for fatigue of epidemic prevention personnel.

[0007] In a first aspect of the present application, a real-time detection method for fatigue of epidemic prevention personnel is provided, wherein the method comprises: collecting and acquiring multiple types of real-time vital sign data of epidemic prevention personnel, obtaining multiple sets of real-time vital sign data as result data; judging whether there is resting state data in the result data, and obtaining a judgment result; if the judgment result is no, continuing to collect and acquire multiple types of real-time vital sign data of the epidemic prevention personnel until the time length of the result data is greater than a first preset time length, and deleting the result data; if the judgment result is yes, continuing to collect and acquire multiple types of real-time vital sign data of the epidemic prevention personnel until the time length of the result data is greater than a second preset time length, wherein the second preset time length is less than the first preset time length; analyzing the resting state data and accumulated data in the result data to obtain an analysis result; and according to the analysis result, calculating and outputting the probability value of fatigue of the epidemic prevention personnel.

[0008] In a second aspect of the present application, the present application also provides a real-time detection system for fatigue of epidemic prevention personnel, wherein the system includes: a result data acquisition module, which is used to collect and obtain multiple types of real-time vital sign data of epidemic prevention personnel, and obtain multiple sets of real-time vital sign data as result data; a resting state data judgment module, which is used to judge whether there is resting state data in the result data and obtain a judgment result; a result data processing module, which is used to continue to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel if the judgment result is no, until the time length of the result data is greater than a first preset time length, and delete the result data; a continuous acquisition module, which is used to continue to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel if the judgment result is yes, until the time length of the result data is greater than a second preset time length, wherein the second preset time length is less than the first preset time length; a data analysis module, which is used to analyze the resting state data and accumulated data in the result data to obtain an analysis result; a fatigue probability calculation module, which is used to calculate and output the probability value of fatigue of the epidemic prevention personnel according to the analysis result.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The technical solution provided by this application collects multiple types of real-time vital sign data of epidemic prevention personnel during work, obtains result data, and then determines whether there is resting state data in the result data. If not, it is cleaned up after the result data reaches a certain length of time. If there is resting state data, the resting state data and the cumulative data in the result data are continued to be analyzed and compared. According to the analysis results, the probability of fatigue of the current epidemic prevention personnel is calculated to complete the fatigue state detection of the epidemic prevention personnel. This application detects the fatigue state of epidemic prevention personnel by detecting real-time vital sign data including electrocardiogram, skin electricity and other data. It can avoid the noise of epidemic prevention personnel at work and the influence of epidemic prevention masks and protective clothing, and adapt to the working conditions of epidemic prevention personnel. This application also sets up specific data comparison, analysis and processing methods, which can be processed and analyzed more accurately according to the electrocardiogram and skin electricity data of epidemic prevention personnel, and obtain the probability of fatigue state of epidemic prevention personnel. This application establishes a detection method suitable for fatigue state of epidemic prevention personnel, which fills the gap in the prior art that is not suitable for fatigue detection of epidemic prevention personnel, and achieves the technical effect of improving the applicability and accuracy of fatigue state detection of epidemic prevention personnel.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 A flowchart of a method for real-time detection of fatigue of epidemic prevention personnel provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of a flow chart for calculating a probability value in a real-time detection method for fatigue of epidemic prevention personnel provided in an embodiment of the present application;

[0015] Figure 3 A flowchart of a method for real-time detection of fatigue of epidemic prevention personnel provided in an embodiment of the present application;

[0016] Figure 4 A schematic diagram of the structure of a real-time detection system for fatigue of epidemic prevention personnel provided in an embodiment of the present application;

[0017] Explanation of the accompanying reference numerals: result data acquisition module 11 , resting state data judgment module 12 , result data processing module 13 , continuous acquisition module 14 , data analysis module 15 , fatigue probability calculation module 16 . DETAILED DESCRIPTION

[0018] The present application provides a real-time detection method and system for fatigue of epidemic prevention personnel, aiming to solve the technical problem that there is no relevant method suitable for fatigue status detection of epidemic prevention personnel in the prior art, and the fatigue status detection effect of epidemic prevention personnel is poor.

[0019] 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.

[0020] Embodiment 1

[0021] like Figure 1 As shown, the embodiment of the present application provides a real-time detection method for fatigue of epidemic prevention personnel, and the method specifically includes the following steps:

[0022] Step S100: collecting and acquiring multiple types of real-time vital sign data of epidemic prevention personnel, and obtaining multiple real-time vital sign data sets as result data;

[0023] In the embodiment of the present application, the epidemic prevention personnel are personnel who carry out epidemic prevention and control work, preferably personnel who carry out epidemic prevention work such as nucleic acid testing and vaccine injection.

[0024] Collect multiple types of real-time vital sign data of epidemic prevention personnel to obtain multiple sets of real-time vital sign data. Among them, the real-time vital sign data is preferably human body vital sign data of electrocardiogram and skin electricity. This part of the vital sign data can avoid the influence of protective clothing and protective masks of epidemic prevention personnel, and can collect and obtain more accurate vital sign data that can better reflect the real state of epidemic prevention personnel. After the collection is completed, the multiple real-time vital sign data are collected and saved as result data.

[0025] Step S100 in the method provided in the embodiment of the present application includes:

[0026] S110: Collecting and obtaining electrocardiogram data and skin conduction level data of the epidemic prevention personnel;

[0027] S120: until the time length of the ECG data and the skin conduction level data reaches a third preset time length, extracting features from the ECG data and the skin conduction level data to obtain HR data, HF data, and SCL data;

[0028] S130: taking the HR data, HF data and SCL data as the result data, and clearing the current data cache.

[0029] Specifically, based on the medical detection methods in the prior art, the real-time electrocardiogram data and skin conduction level data of the epidemic prevention personnel are detected.

[0030] The collection is continued until the time length of the ECG data and the skin conduction level data reaches a third preset time length, wherein the third preset time length can be set according to demand, and the ECG data and the skin conduction level data collected and acquired for this time length need to be representative to a certain extent, and avoid the time length being too short. Preferably, the third preset time length is 5 minutes.

[0031] Feature extraction is performed on the collected ECG data and skin conductivity level data. Exemplarily, based on the ECG data analysis means and skin conductivity analysis means in the prior art, HR (Heart rate) data, HF data and SCL (skin conductivity level) data are obtained, wherein HR data can be obtained based on the heart rate data in the ECG data, HF high-frequency data can be obtained based on the heart rate variability analysis method in the prior art by Fourier transforming the ECG data during RR, and SCL data can be obtained based on skin conductivity data.

[0032] The above HR data, HF data and SCL data are used as the current result data, and the time period corresponding to the result data is the third preset time length. In this way, the result data within the next third preset time period is continuously collected, and the current data buffer is cleared, and the current result data is used as the data basis for detecting the fatigue state of the epidemic prevention personnel, and the next step of judgment and analysis is performed.

[0033] Step S200: determining whether there is resting state data in the result data, and obtaining a determination result;

[0034] In the embodiment of the present application, it is determined whether there is resting state data in the aforementioned result data, and the resting state data is the electrocardiogram and skin conduction data of humans in a sober and relaxed state. Based on the determination method in the prior art and the experience of electrocardiogram and skin conduction detection of epidemic prevention personnel, it can be determined whether there is resting state data in the result data to obtain the determination result.

[0035] Step S300: if the judgment result is no, continue to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel until the time length of the result data is greater than a first preset time length, and delete the result data;

[0036] In the embodiment of the present application, if the judgment result in step S200 is no, there is no resting state data in the current result data, which reflects that the current epidemic prevention personnel are not in a fatigue state, and there is no need to perform further analysis and processing based on the result data. The result data is temporarily saved, and the real-time vital sign data of the epidemic prevention personnel continues to be collected and it is determined whether there is resting state data, that is, steps S100-S200 are repeated.

[0037] In this way, multiple types of real-time vital sign data of the epidemic prevention personnel are continuously collected. When the cumulative duration of the result data is greater than the first preset time length, there is no resting state data in the result data of the epidemic prevention personnel within the first preset time length. In order to save memory, the currently stored result data will be deleted.

[0038] The first preset time length is preferably a longer time length, preferably 3 hours.

[0039] Step S400: If the judgment result is yes, continue to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel until the time length of the result data is greater than a second preset time length, wherein the second preset time length is less than the first preset time length;

[0040] If the judgment result in step S200 is yes, and there is resting state data in the result data, the epidemic prevention personnel may be in a fatigue state, and it is necessary to analyze and process the result data to calculate the probability of fatigue among the epidemic prevention personnel.

[0041] Specifically, multiple types of real-time vital sign data of epidemic prevention personnel are continuously collected to increase the data scale until the time length of the accumulated result data is greater than the second preset time length. The second preset time length is less than the first preset time length, and the second preset time length needs to meet the data scale requirement for data analysis. Preferably, the second preset time length is 1 hour.

[0042] Step S500: analyzing the resting state data and the accumulated data in the result data to obtain analysis results;

[0043] Specifically, based on step S400, there is resting state data in the result data, and the resting state data in the result data is extracted to obtain resting state ECG data and resting state skin conductance data. The other data are cumulative ECG data and cumulative skin conductance data. The resting state data and cumulative data in the result data are analyzed as the data basis for detecting whether the epidemic prevention personnel are fatigued.

[0044] Step S500 in the method provided in the embodiment of the present application includes:

[0045] S510: classifying the result data to obtain the resting state data and the accumulated data;

[0046] S520: using the Kruskal-Wallis method to compare multiple data features of the resting state data and the accumulated data to obtain a comparison result;

[0047] S530: Record the result of the comparison. , , , as the analysis result.

[0048] Specifically, the accumulated result data are classified to obtain resting data and accumulated data. The Kruskal-Wallis method is used to compare multiple data features of the resting data and the accumulated data. Specifically, multiple data features of the resting HR data and the accumulated HR data, the resting HF data and the accumulated HF data, and the resting SCL data and the accumulated SCL data are compared to obtain a comparison result.

[0049] In order to avoid interference from data errors, the first 10% and last 10% of the data in multiple data features in the resting state data are deleted before comparison, and then the multiple data features of the resting state data and the cumulative data are compared.

[0050] Based on the comparison result, the probability that the resting HR data and the cumulative HR data in the comparison result are from the same population is recorded. , similarly, obtain , , as the above analysis result.

[0051] The embodiment of the present application obtains resting state data and cumulative data by classifying the result data, and then compares and analyzes the various features of the resting state data and the cumulative data based on the Kruskal-Wallis method to obtain , , , as the data basis for detecting and judging the fatigue of epidemic prevention personnel, it fully utilizes the characteristics of resting data and cumulative data in the result data.

[0052] Step S600: Based on the analysis results, the probability value of fatigue of the epidemic prevention personnel is calculated and outputted.

[0053] like Figure 2 As shown, step S600 in the method provided in the embodiment of the present application includes:

[0054] S610: According to the , , , make multi-factor judgment and obtain the judgment result;

[0055] S620: Calculate and output the probability value according to the judgment result.

[0056] Wherein, step S610 includes:

[0057] S611: Determine the , , Whether any item in is greater than 0.8, obtain the first-level judgment result;

[0058] S612: If the first-level judgment result is yes, continue to judge whether there is > or > or > , obtain the secondary judgment result;

[0059] S613: Generate the judgment result based on the primary judgment result and the secondary judgment result.

[0060] Before calculating the probability value of fatigue among epidemic prevention personnel, it is necessary to determine whether the conditions for fatigue to occur are met based on the above analysis results.

[0061] Specifically, judge , , Whether any item in is greater than 0.8, obtain the first-level judgment result.

[0062] Furthermore, if the first-level judgment result is yes, then continue to judge whether there is > or > or > , and obtain the secondary judgment result.

[0063] The final judgment result is obtained based on the above-mentioned first-level judgment result and second-level judgment result. The method provided in the embodiment of the present application sets specific judgment conditions to judge the analysis results, judge whether there are ECG data and skin electricity data conditions that may indicate fatigue of the epidemic prevention personnel, and then calculate the probability value based on the judgment result, so as to more accurately detect and analyze the fatigue status of the epidemic prevention personnel.

[0064] Step S620 in the method provided in the embodiment of the present application includes:

[0065] S621: If the first-level judgment result and the second-level judgment result are both yes, then calculate , obtain the probability value and output it;

[0066] S622: If the first-level judgment result or the second-level judgment result is no, the probability value is 0 and output.

[0067] If the first-level judgment result and the second-level judgment result are both yes in the judgment result of step S610, then according to the above , , , calculate the probability value of fatigue of epidemic prevention personnel as follows:

[0068]

[0069] Among them, P is the probability value. P is calculated and output as the probability value of the current epidemic prevention personnel being in a fatigue state.

[0070] If in the judgment result of step S610, the first-level judgment result or the second-level judgment result is no, it can be determined that the epidemic prevention personnel are not in a fatigue state, and the probability value is 0, and it is output as the current epidemic prevention personnel fatigue state detection result.

[0071] Figure 3 A flow chart of a method provided in an embodiment of the present application is shown.

[0072] In summary, the real-time detection method for fatigue of epidemic prevention personnel provided by this application has the following technical effects:

[0073] The embodiment of the present application detects the fatigue status of epidemic prevention personnel by detecting real-time vital sign data including electrocardiogram, skin electricity and other data, which can avoid the noise when the epidemic prevention personnel are working and the influence of epidemic prevention masks and protective clothing, and adapt to the working conditions of the epidemic prevention personnel. The embodiment of the present application also sets up specific data comparison, analysis and processing methods, which can perform more accurate processing and analysis based on the electrocardiogram and skin electricity data of the epidemic prevention personnel to obtain the probability of fatigue status of the epidemic prevention personnel. The present application establishes a detection method suitable for the fatigue status of epidemic prevention personnel, which fills the gap in the prior art that is not suitable for fatigue detection of epidemic prevention personnel, and achieves the technical effect of improving the applicability and accuracy of fatigue status detection of epidemic prevention personnel.

[0074] Embodiment 2

[0075] Based on the same inventive concept as the real-time detection method for fatigue of epidemic prevention personnel in the aforementioned embodiment, the present invention also provides a real-time detection system for fatigue of epidemic prevention personnel, such as Figure 4 As shown, the system comprises:

[0076] The result data collection module 11 is used to collect and obtain multiple types of real-time vital sign data of epidemic prevention personnel, and obtain multiple real-time vital sign data sets as result data;

[0077] The resting state data judging module 12 is used to judge whether there is resting state data in the result data and obtain a judgment result;

[0078] The result data processing module 13 is used for continuing to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel if the judgment result is no, until the time length of the result data is greater than a first preset time length, and deleting the result data;

[0079] A continuous collection module 14 is used to continue to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel if the judgment result is yes, until the time length of the result data is greater than a second preset time length, wherein the second preset time length is less than the first preset time length;

[0080] A data analysis module 15 is used to analyze the resting state data and the accumulated data in the result data to obtain analysis results;

[0081] The fatigue probability calculation module 16 is used to calculate the probability value of fatigue of the epidemic prevention personnel according to the analysis results and output it.

[0082] Furthermore, the result data collection module 11 is also used to implement the following functions:

[0083] Collect and obtain the electrocardiogram data and skin conduction level data of the epidemic prevention personnel;

[0084] until the time length of the electrocardiogram data and the skin conduction level data reaches a third preset time length, performing feature extraction on the electrocardiogram data and the skin conduction level data to obtain HR data, HF data and SCL data;

[0085] The HR data, HF data and SCL data are used as the result data, and the current data cache is cleared.

[0086] Furthermore, the resting state data determination module 12 is also used to implement the following functions:

[0087] Determine whether the plurality of evaluation indicators are rejection indicators, and obtain a determination result;

[0088] According to the judgment result, weight distribution is determined for the plurality of evaluation indicators that are not rejection indicators.

[0089] Furthermore, the data analysis module 15 is also used to implement the following functions:

[0090] Classifying the result data to obtain the resting state data and the cumulative data;

[0091] Using the Kruskal-Wallis method to compare multiple data features of the resting state data and the accumulated data to obtain a comparison result;

[0092] According to the comparison result, the record is obtained , , , as the analysis result.

[0093] Furthermore, the fatigue probability calculation module 16 is also used to implement the following functions:

[0094] According to the , , , make multi-factor judgment and obtain the judgment result;

[0095] According to the judgment result, the probability value is calculated and output.

[0096] Among them, according to the , , , make multi-factor judgments, including:

[0097] Judge the , , Whether any item in is greater than 0.8, obtain the first-level judgment result;

[0098] If the first-level judgment result is yes, continue to judge whether there is > or > or > , obtain the secondary judgment result;

[0099] The judgment result is generated based on the primary judgment result and the secondary judgment result.

[0100] The calculating and outputting the probability value comprises:

[0101] If the first-level judgment result and the second-level judgment result are both yes, then calculate , obtain the probability value and output it;

[0102] If the primary judgment result or the secondary judgment result is no, the probability value is 0 and is output.

[0103] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0104] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A real-time detection method for fatigue of epidemic prevention personnel, characterized in that: The method comprises: Collect and obtain multiple types of real-time vital sign data of epidemic prevention personnel, and obtain multiple real-time vital sign data sets as result data; Determine whether there is resting state data in the result data, and obtain a determination result; If the judgment result is no, continue to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel until the time length of the result data is greater than the first preset time length, and delete the result data; If the judgment result is yes, continue to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel until the time length of the result data is greater than a second preset time length, wherein the second preset time length is less than the first preset time length; Analyzing the resting state data and the accumulated data in the result data to obtain analysis results; According to the analysis results, the probability value of fatigue of the epidemic prevention personnel is calculated and outputted; The analysis of the resting state data and the accumulated data in the result data includes: Classifying the result data to obtain the resting state data and the cumulative data; Using the Kruskal-Wallis method to compare multiple data features of the resting state data and the accumulated data to obtain a comparison result; According to the comparison result, record the obtained P HR , P HF , P SCL , as the analysis result, where P HR is the probability that the resting HR data and cumulative HR data come from the same population, P HF is the probability that the resting HF data and the cumulative HF data come from the same population, P SCL It is the probability that the resting SCL data and the cumulative SCL data come from the same population; The calculating and outputting the probability value of fatigue of the epidemic prevention personnel includes: According to the P HR , P HF , P SCL , make multi-factor judgment and obtain the judgment result; According to the judgment result, the probability value is calculated and outputted; According to the P HR , P HF , P SCL , making multi-factor judgments including: Determine the P HR , P HF , P SCL Whether any item in is greater than 0.8, obtain the first-level judgment result; If the first-level judgment result is yes, continue to judge whether there is HR 静息 >HR 累计 or HF 静息 >HF 累计 or SCL 静息 >SCL 累计 , obtain the secondary judgment result; Based on the primary judgment result and the secondary judgment result, generating the judgment result; The calculating and outputting the probability value comprises: If in the judgment result, the first-level judgment result and the second-level judgment result are both yes, then calculate P HR *P HF *P SCL , obtain the probability value and output it; If the primary judgment result or the secondary judgment result is no, the probability value is 0 and is output.

2. The method according to claim 1, characterized in that The collection and acquisition of multiple types of real-time vital sign data of epidemic prevention personnel include: Collect and obtain the electrocardiogram data and skin conduction level data of the epidemic prevention personnel; until the time length of the electrocardiogram data and the skin conduction level data reaches a third preset time length, performing feature extraction on the electrocardiogram data and the skin conduction level data to obtain HR data, HF data and SCL data; The HR data, HF data and SCL data are used as the result data, and the current data cache is cleared.

3. The method according to claim 1, characterized in that Before comparing the multiple data features of the resting state data and the accumulated data using the Kruskal-Wallis method, the method further includes: The first 10% and the last 10% of the data in the plurality of data features in the resting state data are deleted.

4. A real-time detection system for fatigue of epidemic prevention personnel, characterized in that: A real-time detection method for fatigue of epidemic prevention personnel according to any one of claims 1 to 3, the system comprising: A result data collection module is used to collect and obtain multiple types of real-time vital sign data of epidemic prevention personnel, and obtain multiple real-time vital sign data sets as result data; A resting state data judgment module is used to judge whether there is resting state data in the result data and obtain a judgment result; A result data processing module, for continuing to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel if the judgment result is no, until the time length of the result data is greater than a first preset time length, and deleting the result data; A continuous collection module, for continuing to collect and obtain multiple types of real-time vital sign data of the epidemic prevention personnel if the judgment result is yes, until the time length of the result data is greater than a second preset time length, wherein the second preset time length is less than the first preset time length; A data analysis module, used for analyzing the resting state data and the accumulated data in the result data to obtain analysis results; The fatigue probability calculation module is used to calculate and output the probability value of fatigue of the epidemic prevention personnel according to the analysis results.

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