Multifunctional pre-post health detection system

Abstract

The invention discloses a multifunctional pre-post health detection system, and the system comprises a pre-post health data collection module which is used for collecting pre-post body index data of employees whose identities are successfully verified, and uploading the body index data to a cloud service platform; the attendance checking and card punching module is used for pre-post attendance checking and card punching of the employees and uploading attendance checking information to the cloud service platform; the cloud service platform comprises a cloud database and a cloud computing analysis module; the cloud database is used for storing body indexes and attendance checking information, and the cloud computing analysis module is used for judging whether the body indexes and the attendance checking information are abnormal or not, performing classified statistics on abnormal results, the body indexes and the attendance checking information and then sending the abnormal results, the body indexes and the attendance checking information to a user side with permission; and the communication module is used for communication between the cloud service platform and the pre-post health data acquisition module, the attendance checking and card punching module and the user side. According to the invention, health detection and attendance card punching can be completed in a one-stop manner, and timely feedback and reporting of unqualified employee conditions are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a multi-functional pre-employment health detection system. Background Art

[0002] With the rapid development of modern industry and the ever-changing technology, all walks of life are paying increasing attention to the health management of employees. In transportation, high-risk industries, enterprises and institutions, the health status of employees is directly related to production safety, work efficiency and the sustainable development of enterprises. A multi-functional pre-employment health detection system has emerged as the times require. This system integrates advanced technologies such as face recognition technology, biosensing technology, and big data analysis, aiming to achieve rapid collection, intelligent analysis and instant feedback of employees' health information. By integrating multiple health index screening functions such as body temperature detection, alcohol detection, blood oxygen saturation monitoring, heart rate and blood pressure measurement, this system can comprehensively evaluate the physiological health status of employees and ensure that they can start work in the best physical condition.

[0003] Existing pre-employment health detection systems have achieved a comprehensive upgrade of health detection by leveraging high-tech. These systems skillfully integrate high-precision sensors, artificial intelligence algorithms and cloud data processing technology, not only improving the detection speed, but also ensuring the accuracy and instantaneity of data. With just a few simple operations, the detection of multiple key health indicators can be completed, greatly saving time and labor costs. They can automatically collect and organize employees' health data, and conduct in-depth mining through complex algorithm models, providing personalized health reports and overall health trend analysis for each employee of the enterprise. This not only helps enterprises promptly discover potential health risks, formulate effective preventive measures, but also can customize health management plans for employees, promoting the overall improvement of their physical and mental health.

[0004] However, in the process of implementing the technical solutions of the embodiments of the present invention, it is found that the above technologies have at least the following technical problems: In the prior art, traditional pre-employment health detection systems can cover basic detection items, but there are few reports on the application of pre-employment employees. At the same time, the detection equipment cannot complete health detection and attendance punching in one stop. At the same time, the situation of employees with unqualified detections cannot be promptly feedback, and there is a lack of real-time monitoring function for the accuracy of detection equipment. For problems such as equipment aging, insufficient calibration and human operation errors, resulting in large fluctuations in measurement results, they cannot be promptly discovered. Therefore, there is a problem of low accuracy of data collected by equipment in pre-employment health detection. Summary of the Invention

[0005] In view of the technical problems existing in the above background art, an embodiment of the present invention provides a multi-functional pre-employment health detection system, including: Pre-employment health data collection module, which is used to collect the pre-employment physical index data of the employees whose identity verification is successful and upload the physical index data to the cloud service platform; Attendance check-in module, which is used for employees' pre-employment attendance check-in and uploads the attendance information to the cloud service platform; Cloud service platform, which includes a cloud database and a cloud computing analysis module; The cloud database is used to store the physical indexes and attendance information, and the cloud computing analysis module is used to judge whether there are any abnormalities in the physical indexes and attendance information, and after classifying and statistically analyzing the abnormal results and the physical indexes and attendance information, it sends them to the authorized user terminal; Communication module, which is used for communication between the cloud service platform, the pre-employment health data collection module, the attendance check-in module and the user terminal.

[0006] Further, the collection method of the pre-employment health data collection module is contact or non-contact, The pre-employment health data collection module can customize the physical indexes to be collected; The physical indexes include blood pressure, alcohol, body temperature, heart rate, blood oxygen, and mental stress; The identity verification methods include one of face recognition, fingerprint, iris, voiceprint, ID card, employee card, positioning card, and NFC.

[0007] Further, the process of the cloud computing analysis module judging whether there are any abnormalities in the physical indexes and attendance information specifically includes: Obtain the reference ranges of the normal physical indexes and attendance of the employees stored in the cloud database; Compare the physical indexes of this employee collected by the pre-employment health data collection module and the attendance data obtained by the attendance check-in module with the reference range. If it is not within the reference range, it is determined that there are abnormalities in the physical indexes and the attendance information.

[0008] Further, the user terminal includes a smart phone, a tablet and a computer. The ways for users to obtain information from the cloud service platform include: users install an APP and obtain information through the APP; users open a web page through a browser and obtain information from the web page; users use a small program and interact with the cloud computing service platform based on the development interfaces provided by the small program.

[0009] Further, the permissions of the user terminal are divided according to the user registration identity level, and different levels of users can obtain different types of employees' physical indicators and attendance information; specifically, the cloud service platform automatically pushes the employees' physical indicators, attendance information, and data with anomalies to the employees themselves, their direct supervisors, and senior leaders; and the group company can view and manage the physical indicator and attendance information data of employees in each branch and subsidiary company through the cloud service platform.

[0010] Further, a health and attendance check-in anomaly appeal and approval module is set in the user terminal. Among them, the appeal module is used to file an appeal when there are anomalies in the employees' physical indicators and attendance data and report it to the cloud service platform, and the approval module is used to obtain the appeal request sent by the cloud service platform and push it to the direct supervisor and senior leader of the employee with the permission for approval.

[0011] Further, anti-cheating modules are set on both the pre-employment health data collection module and the attendance check-in module, and the anti-cheating module is used to identify whether the employees have completed the processes of health data collection and attendance check-in normally; The system further includes a wearable positioning module, which is used for real-time positioning of employees.

[0012] Further, the specific process of the face recognition verification is as follows: Collect the facial images of pre-employment employees and obtain the coordinates of face key points from the facial images, and obtain the face feature vector coefficients according to the face feature vectors constructed from the coordinates of face key points. The coordinates of face key points include the abscissa and ordinate of key points; Obtain the face recognition index of pre-employment employees based on the obtained coordinates of face key points and face feature vector coefficients. The face recognition index is used to describe the similarity between the facial images of pre-employment employees and the pre-stored facial images; Compare the obtained face recognition index with the reference face value range. If the obtained face recognition index is within the reference face value range, it indicates that the identity information verification of the pre-employment employee is successful; otherwise, it indicates that the identity information verification of the pre-employment employee fails.

[0013] Further, the system further includes a device detection module, and the device detection module is used to detect the accuracy of blood pressure, alcohol, and blood oxygen data collection in the pre-employment health data collection module.

[0014] Further, the process of detecting the accuracy of blood pressure, alcohol, and blood oxygen data collection in the pre-employment health data collection module specifically includes: The pre-employment health data collection module includes a blood pressure detection device, an alcohol detection device, and a blood oxygen detection device; Device detection is performed based on the blood pressure data collected by a blood pressure detection device to obtain a blood pressure detection device detection index. The blood pressure detection device detection index is compared with a blood pressure detection device range to obtain a blood pressure detection device detection result. The blood pressure detection device detection index is used to describe the abnormal conditions of the blood pressure detection device; Device detection is performed based on the alcohol data collected by an alcohol detection device to obtain an alcohol detection device detection index. The alcohol detection device detection index is compared with an alcohol detection device range to obtain an alcohol detection device detection result. The alcohol detection device detection index is used to describe the abnormal conditions of the alcohol detection device; Device detection is performed based on the blood oxygen data collected by a blood oxygen detection device to obtain a blood oxygen detection device detection index. The blood oxygen detection device detection index is compared with a blood oxygen detection device range to obtain a blood oxygen detection device detection result. The blood oxygen detection device detection index is used to describe the abnormal conditions of the blood oxygen detection device.

[0015] Determine whether the blood pressure detection device detection index, the alcohol detection device detection index, and the blood oxygen detection device detection index are within the corresponding ranges, and issue an abnormal warning and feedback for the detection devices that are not within the corresponding ranges.

[0016] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages: The present invention can complete health detection and attendance check-in in one stop, can perform comprehensive health detection, and at the same time, it can timely feedback and report the situation of employees with unqualified detections to their superiors. In addition, by setting up instant communication between the cloud service platform and the user terminal, the health detection and attendance information of pre-employment employees can be transmitted and managed instantaneously.

[0017] By setting up a real-time device anomaly monitoring function, the present invention can timely detect problems such as large fluctuations in measurement results caused by equipment aging, insufficient calibration, and human operation errors, effectively solving the problem of low accuracy of device-collected data in pre-employment health detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic structural diagram of a multifunctional pre-employment health detection system provided by an embodiment of the present invention; Figure 2 It is a statistical chart of the change of the blood pressure detection device detection index provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0020] As Figure 1As shown in the figure, a multi-functional pre-employment health detection system provided by an embodiment of the present invention includes: A pre-employment health data collection module, which is used to collect the pre-employment physical index data of employees whose identity verification is successful and upload the physical index data to the cloud service platform; an attendance check-in module, which is used for employees to check in before work and upload the attendance information to the cloud service platform; a cloud service platform, which includes a cloud database and a cloud computing analysis module; the cloud database is used to store physical indexes and attendance information, and the cloud computing analysis module is used to judge whether there are any abnormalities in the physical indexes and attendance information, and classify and count the abnormal results, physical indexes and attendance information and then send them to the user terminal with permissions; a communication module, which is used for communication between the cloud service platform, the pre-employment health data collection module, the attendance check-in module and the user terminal. This communication module can be the TCP / TP network protocol and can realize instant communication between each connected module.

[0021] Further, the specific process of the above-mentioned identity verification is: obtaining the identity information of the employee and comparing it with the pre-stored identity information in the identity database to obtain a verification result; judging whether the verification result is verification success. If the verification is successful, it means that the identity verification is over. If the verification fails, it will display verification failure and trigger the processing of the verification failure result.

[0022] In this embodiment, the identity verification method is mainly based on face recognition verification, and also includes fingerprint, iris, voiceprint, ID card, employee card, positioning card, NFC, etc. The specific verification method can be selected by the pre-employment employees themselves; the verification results include verification success and verification failure, and the pre-employment health data collection module corresponds one by one to the identity verification form selected by the employees. An identity database is also set in this system, which is used to pre-store the identity information of employees; the processing of the verification failure result includes notifying to retry and denying access; realizing the accurate verification of the identity information of employees and further improving the accuracy of pre-employment health monitoring.

[0023] Specifically, when the identity verification method is carried out by face recognition verification, the specific process of the above-mentioned verification is: collecting the facial image of the pre-employment employee and obtaining the face key point coordinates from the facial image, and obtaining the face feature vector coefficient according to the face feature vector constructed by the face key point coordinates. The face key point coordinates include the key point abscissa and the key point ordinate, and the face vector feature coefficient is the cosine value of the face feature vector; obtaining the face recognition index of the pre-employment employee according to the obtained face key point coordinates and face feature vector coefficient, and the face recognition index is used to describe the similarity between the facial image of the pre-employment employee and the pre-stored facial image; comparing the obtained face recognition index with the reference face value range. If the obtained face recognition index is within the reference face value range, it means that the identity information of the pre-employment employee is verified successfully, otherwise it means that the identity information of the pre-employment employee is verified failed. The reference face value range is set by the preset personnel.

[0024] In this embodiment, the face recognition index is calculated using the following formula:

[0025] In the formula, is the natural constant, p is the number of the pre-employment employee, , P is the total number of pre-employment employees, f is the number of the face key point coordinates, , F is the total number of face key point coordinates, is the face recognition index of the p-th pre-employment employee, is the face feature vector coefficient of the p-th pre-employment employee, is the key point abscissa of the f-th face key point coordinate of the p-th pre-employment employee, is the pre-stored key point abscissa of the f-th face key point coordinate of the p-th pre-employment employee, is the key point ordinate of the f-th face key point coordinate of the p-th pre-employment employee, is the pre-stored key point ordinate of the f-th face key point coordinate of the p-th pre-employment employee.

[0026] Specifically, the method for constructing the face feature vector based on the face key point coordinates is as follows: all the face key point coordinates are sequentially concatenated to form a long vector, and the length of the long vector is twice the number of key points; the face vector feature coefficient is the cosine value of the face feature vector, and its expression is: , is the face feature vector of the p-th pre-employment employee, is the reference face feature vector of the p-th pre-employment employee; the numericalization of the similarity degree between the facial image of the pre-employment employee and the pre-stored facial image is realized, and the data acquisition accuracy of the pre-employment health data acquisition device is further improved.

[0027] The acquisition method of the pre-employment health data acquisition module in the present invention is contact or non-contact. The non-contact method can be, for example, video, picture AI algorithm, millimeter wave, etc., and the contact method can be, for example, a wearable or other one or more body index data detection and acquisition devices. In addition, the pre-employment health data acquisition module can customize the body indexes to be acquired. For example, the pre-employment health detection items can be set to one or more of blood pressure, alcohol, body temperature, heart rate, blood oxygen, mental stress, etc.

[0028] In this embodiment, the process by which the cloud computing analysis module determines whether there are abnormalities in the physical indicators and attendance information specifically includes: obtaining the reference ranges of the normal physical indicators and attendance of employees stored in the cloud database; comparing the physical indicators of the employee collected by the pre-employment health data collection module and the attendance data obtained by the attendance clock-in module with the reference ranges. If they are not within the reference ranges, it is determined that there are abnormalities in the physical indicators and attendance information. For example, for a certain employee, the reference systolic blood pressure range stored in the cloud database is 90 to 140 mmHg, and the reference diastolic blood pressure range is 60 to 90 mmHg. If the systolic blood pressure and diastolic blood pressure of this employee collected by the pre-employment health data collection module (blood pressure detection device) are not within the above reference ranges, it is determined that there are abnormalities in the physical indicators of this employee. Another example is that for a certain employee, the pre-stored work check-in time in the cloud database is 9:00 am and before. If the work check-in time of this employee obtained by the attendance clock-in module is after 9:00 am, it is determined that the attendance of this employee is abnormal.

[0029] In this embodiment, the user terminals include smart phones, tablets, and computers. The ways for users to obtain information from the cloud service platform through the user terminals include: users install an APP and obtain information through the APP; users open a web page through a browser and obtain information from the web page; users interact with the cloud computing service platform based on the development interfaces provided by the mini-program through the mini-program.

[0030] Furthermore, the permissions of the user terminals are divided according to the user registration identity levels. Different levels of users can obtain different categories of physical indicators and attendance information of employees. Specifically, the cloud service platform automatically pushes the physical indicators and attendance information of employees and the data with abnormalities to the employee, their direct supervisor, and the superior leader. And the group company can view and manage the physical indicator and attendance information data of employees in each branch company and subsidiary company through the cloud service platform. Specifically, the cloud service platform will classify and summarize the pre-employment physical indicators and attendance of employees obtained every day to obtain the Employee Health Attendance Check-in Daily Report (for example, on October 28, 2024, the blood pressure of Employee A was..., the body temperature was..., the heart rate was..., the blood oxygen was..., and the check-in time was..., and the above physical indicators and attendance were all normal), and then push the Employee Health Attendance Check-in Daily Report to the user terminals of the users registered as the direct supervisor and the superior leader of this employee, so that the superior leader can obtain the health and attendance information of this employee in a timely manner. Company employees cannot register as users who can obtain the information of other employees at the same level, and can only obtain the health and attendance information of themselves and their subordinate employees. In addition, the group company can also register as a high-level user with the right to view and manage the physical indicator and attendance information data of employees in each branch company and subsidiary company.

[0031] In addition, the above pre-employment health data collection module also has an audible and visual alarm function. When the cloud computing analysis module determines that there are abnormalities in the physical indicators of the employee, the cloud computing analysis module will feedback the abnormal information to the pre-employment health data collection module again, so that it synchronously conducts audible and visual alarms.

[0032] In this embodiment, a health attendance punch card exception appeal and approval module is set in the user terminal. Among them, the appeal module is used to appeal when there are abnormalities in the physical indicators and attendance data of the employee and report them to the cloud service platform. The approval module is used to obtain the appeal request sent by the cloud service platform and push it to the direct supervisor and superior leader of the employee with permissions for approval. For example, when there are abnormalities in the physical indicators and attendance data of an employee, after logging in to their user terminal, they can file an exception appeal on the appeal module, fill in the reasons for the exception appeal and report it to the cloud service platform. At the same time, the cloud service platform will send the exception appeal log to the approval module of the user terminal with permissions. The direct supervisor or superior leader of the employee who logs in to the user terminal with this permission can view and approve whether to pass the exception appeal through this approval module.

[0033] Furthermore, anti-cheating modules are set on both the pre-employment health data collection module and the attendance punch card module. The anti-cheating module is used to identify whether the employee has completed the processes of health data collection and attendance punch card normally. Specifically, image collection modules can be set on the pre-employment health data collection module and the attendance punch card module. They are used to collect the facial images of the employee before the health data collection and punch card of the employee, and compare them with the preset standard facial images of the employee. If they can be matched, it means that the employee has no cheating behavior. If the match is unsuccessful, it means that the employee has cheating behavior.

[0034] Furthermore, the system also includes a wearable positioning module, which is used for real-time positioning of the employee.

[0035] In this embodiment, the system also includes a device detection module, which is used to detect the accuracy of blood pressure, alcohol and blood oxygen data collection in the pre-employment health data collection module.

[0036] Specifically: The pre-employment health data collection module includes a blood pressure detection device, an alcohol detection device, and a blood oxygen detection device; the device detection is performed on the blood pressure data collected by the blood pressure detection device to obtain the blood pressure detection device detection index, and the blood pressure detection device detection index is compared with the blood pressure detection device range to obtain the blood pressure detection device detection result, and the blood pressure detection device detection index is used to describe the abnormal situation of the blood pressure detection device; the device detection is performed on the alcohol data collected by the alcohol detection device to obtain the alcohol detection device detection index, and the alcohol detection device detection index is compared with the alcohol detection device range to obtain the alcohol detection device detection result, and the alcohol detection device detection index is used to describe the abnormal situation of the alcohol detection device; the device detection is performed on the blood oxygen data collected by the blood oxygen detection device to obtain the blood oxygen detection device detection index, and the blood oxygen detection device detection index is compared with the blood oxygen detection device range to obtain the blood oxygen detection device detection result, and the blood oxygen detection device detection index is used to describe the abnormal situation of the blood oxygen detection device. It is judged whether the blood pressure detection device detection index, the alcohol detection device detection index, and the blood oxygen detection device detection index are within the corresponding ranges, and the detection devices not within the corresponding ranges are given abnormal warnings and feedback.

[0037] Furthermore, the above abnormal warning information includes the abnormal device type of the device detection data, the identity information of the pre-employment employee corresponding to the abnormal device type, and the data detection information of the device detection data of the corresponding pre-employment employee.

[0038] In this embodiment, the abnormal device types include abnormal blood pressure detection device, abnormal alcohol detection device, and abnormal blood oxygen detection device, and the data detection information includes the device detection time, the device detection location, and the historical device abnormal warning situation; the perfection of the information on the abnormal warning of the detection device is realized, and further the improvement of the accuracy of pre-employment health monitoring is realized.

[0039] In this embodiment, the detection results of the blood pressure detection device include that the blood pressure detection device is normal and the blood pressure detection device is abnormal. If the detection index of the blood pressure detection device is within the range of the blood pressure detection device, it indicates that the blood pressure detection device is normal. If it is not within the range of the blood pressure detection device, it indicates that the blood pressure detection device is abnormal. The range of the blood pressure detection device is a reference range of the detection index of the blood pressure detection device set by the preset personnel according to medical standards. Specifically, generally 0.4 is taken for people over 60 years old, and generally 0.2 is taken for people between 20 and 60 years old. Therefore, the range is from 0.2 to 0.4, and it can be adjusted according to the actual situation. The detection result of the blood pressure detection device represents the accuracy of the blood pressure data collected by the blood pressure detection device. The detection results of the alcohol detection device include that the alcohol detection device is normal and the alcohol detection device is abnormal. If the detection index of the alcohol detection device is within the range of the alcohol detection device, it indicates that the alcohol detection device is normal. If it is not within the range of the alcohol detection device, it indicates that the alcohol detection device is abnormal. The range of the alcohol detection device is a reference range of the detection index of the alcohol detection device set by the preset personnel according to medical standards. Specifically, generally 1.600 is taken for people over 60 years old, and generally 1.500 is taken for people between 20 and 60 years old. Therefore, the range is from 1.500 to 1.600, and it can be adjusted according to the actual situation. The detection result of the alcohol detection device represents the accuracy of the alcohol data collected by the alcohol detection device; the detection results of the blood oxygen detection device include that the blood oxygen detection device is normal and the blood oxygen detection device is abnormal. If the detection index of the blood oxygen detection device is within the range of the blood oxygen detection device, it indicates that the blood oxygen detection device is normal. If it is not within the range of the blood oxygen detection device, it indicates that the blood oxygen detection device is abnormal. The range of the blood oxygen detection device is a reference range of the detection index of the blood oxygen detection device set by the preset personnel according to medical standards. Specifically, generally 1.580 is taken for people over 60 years old, and generally 1.500 is taken for people between 20 and 60 years old. Therefore, the range is from 1.500 to 1.580, and it can be adjusted according to the actual situation. The detection result of the blood oxygen detection device represents the accuracy of the blood oxygen data collected by the blood oxygen detection device; the accuracy of obtaining the detection results of the device is improved, and further the accuracy of pre-employment health monitoring is improved.

[0040] Further, the method for obtaining the detection index of the blood pressure detection device is as follows: Obtain the basic blood pressure data of the pre-employment employees from the blood pressure detection device. The basic blood pressure data includes the systolic blood pressure range and the diastolic blood pressure range. The systolic blood pressure range represents the range of the pressure of the blood on the blood vessel wall when the heart of the pre-employment employee contracts, and the diastolic blood pressure range represents the pressure on the blood vessel wall when the heart relaxes; Obtain the corresponding reference blood pressure range according to the basic blood pressure data of the pre-employment employees. The reference blood pressure range includes the reference systolic blood pressure range and the reference diastolic blood pressure range. The reference blood pressure range is set by the preset professional personnel according to the personal information of the pre-employment employees. The personal information includes age, gender, and weight; Obtain the detection index of the blood pressure detection device based on the obtained basic blood pressure data and the reference blood pressure range; The detection index of the blood pressure detection device is calculated using the following formula:

[0041] In the formula, m is the number of the blood pressure detection device, M is the total number of blood pressure detection devices, p is the number of the pre-employment employee, P is the total number of pre-employment employees, is the detection index of the m-th blood pressure detection device, is the systolic blood pressure range of the p-th pre-employment employee of the m-th blood pressure detection device, is the reference systolic blood pressure range of the m-th blood pressure detection device, is the diastolic blood pressure range of the p-th pre-employment employee of the m-th blood pressure detection device, is the reference diastolic blood pressure range of the m-th blood pressure detection device.

[0042] In this embodiment, the reference systolic blood pressure range represents the range of the pressure of the blood on the blood vessel wall when the heart contracts, generally set to 90 to 140 mmHg, and the reference diastolic blood pressure range represents the pressure on the blood vessel wall when the heart relaxes, generally set to 60 to 90 mmHg, both representing the maximum range that a person can reach; Let Let is the systolic blood pressure range coefficient of the p-th pre-employment employee of the m-th blood pressure detection device, is the diastolic blood pressure range coefficient of the p-th pre-employment employee of the m-th blood pressure detection device. Therefore, the detection index of the blood pressure detection device can be simplified to: ; For example Figure 2 ​As shown in the figure, it is a statistical chart of the change of the detection index of the blood pressure detection device provided by the embodiment of the present invention. It can be seen from the figure that both the systolic blood pressure range coefficient and the diastolic blood pressure range coefficient are negatively correlated with the detection index of the blood pressure detection device. When the systolic blood pressure range coefficient increases, the detection index of the blood pressure detection device decreases, and the abnormal conditions of the blood pressure detection device are fewer. When the diastolic blood pressure range coefficient increases, the detection index of the blood pressure detection device also decreases, and the abnormal conditions of the blood pressure detection device are fewer. The numericalization of the abnormal conditions of the blood pressure detection device is realized, and further the improvement of the accuracy of pre-employment health monitoring is realized.

[0043] Furthermore, the method for obtaining the detection index of the alcohol detection device is as follows: Obtain the alcohol detection data of the pre-employment employees from the alcohol detection device, and obtain the detection index of the alcohol detection device according to the alcohol detection data of the pre-employment employees. The alcohol detection data includes blood alcohol content, exhaled alcohol content, and exhalation temperature. Obtain the alcohol detection coefficient according to the alcohol detection data. The alcohol detection coefficient includes blood alcohol content coefficient, exhaled alcohol content coefficient, and exhalation temperature coefficient. The blood alcohol content coefficient is the ratio of the difference between the blood alcohol content and the corresponding reference value to the corresponding reference deviation. The exhaled alcohol content coefficient is the ratio of the difference between the exhaled alcohol content and the corresponding reference value to the corresponding reference deviation. The exhalation temperature coefficient is the ratio of the difference between the exhalation temperature and the corresponding reference value to the corresponding reference deviation.

[0044] In this embodiment, the detection index of the alcohol detection device is calculated using the following formula:

[0045] In the formula, n is the number of the blood pressure detection device, , N is the total number of alcohol detection devices, p is the number of the pre-employment employee, , P is the total number of pre-employment employees, is the alcohol data detection index of the nth alcohol detection device, is the exhaled alcohol content coefficient of the pth pre-employment employee of the nth alcohol detection device, is the blood alcohol content coefficient of the pth pre-employment employee of the nth alcohol detection device, is the exhalation temperature coefficient of the pth pre-employment employee of the nth alcohol detection device.

[0046] Specifically, the expression of the blood alcohol content coefficient is , where is the blood alcohol content of the pth pre-employment employee of the nth alcohol detection device, is the reference blood alcohol content of the pth pre-employment employee of the nth alcohol detection device, is the reference deviation of the blood alcohol content of the nth alcohol detection device; the expression of the exhaled alcohol content coefficient is , where is the exhaled alcohol content of the p-th pre-employment employee of the n-th alcohol detection device, is the reference exhaled alcohol content of the p-th pre-employment employee of the n-th alcohol detection device, is the reference deviation of the exhaled alcohol content of the n-th alcohol detection device; the expression of the exhalation temperature coefficient is , where is the exhalation temperature of the p-th pre-employment employee of the n-th alcohol detection device, is the reference exhalation temperature of the p-th pre-employment employee of the n-th alcohol detection device, is the reference deviation of the exhalation temperature of the n-th alcohol detection device; The algorithm of this embodiment combines the exhaled alcohol content coefficient, blood alcohol content coefficient, and exhalation temperature coefficient of pre-employment employees detected by the alcohol detection device, and comprehensively analyzes to obtain the alcohol data detection index. The exhaled alcohol content coefficient is related to the alcohol concentration in the exhaled gas and reflects the recent alcohol intake of an individual. The blood alcohol content coefficient is theoretically used for blood sample analysis, and the exhalation temperature coefficient reflects the degree of change of the breathing rate with temperature, which affects the accuracy of exhaled alcohol detection. These coefficients act together on the alcohol data detection index to ensure that the device can accurately identify and report the abnormal alcohol intake and physiological status of users, provide a scientific basis for health management, indicate the abnormal situation of the alcohol detection device, and therefore conduct precise analysis by establishing a mathematical form; realizing the numericalization of the abnormal situation of the alcohol detection device and further improving the accuracy of pre-employment health monitoring.

[0047] It should be understood that the reference blood alcohol content is represented by taking the average value after statistically analyzing the blood alcohol content of pre-employment employees, the reference exhaled alcohol content is represented by taking the average value after statistically analyzing the exhaled alcohol content of pre-employment employees, and the reference exhalation temperature is represented by taking the average value after statistically analyzing the exhalation temperature of pre-employment employees; the reference deviation of blood alcohol content is represented by statistically analyzing the blood alcohol content of pre-employment employees through the deviation calculation formula, the reference deviation of exhaled alcohol content is represented by statistically analyzing the exhaled alcohol content of pre-employment employees through the deviation calculation formula, and the reference deviation of exhalation temperature is represented by statistically analyzing the exhalation temperature of pre-employment employees through the deviation calculation formula; the deviation calculation formula is: , where S is the reference deviation value, r is the number of the data, , n is the total number of data, is the data value of the r-th data, is the average value of this data.

[0048] Further, the method for obtaining the detection index of the blood oxygen detection device is as follows: Obtain the blood oxygen data evaluation index and the blood oxygen micro - fraction of the pre - job employees from the blood oxygen detection device, and accordingly obtain the detection index of the blood oxygen detection device of the blood oxygen detection device. The blood oxygen data evaluation index includes blood oxygen saturation, perfusion index, and pulse rate range. The blood oxygen micro - fraction includes saturation micro - fraction and perfusion index micro - fraction. The saturation micro - fraction is the ratio of the blood oxygen saturation reference value to the corresponding reference deviation, and the perfusion index micro - fraction is the ratio of the perfusion index reference value to the corresponding reference deviation; Determine whether the obtained blood oxygen saturation does not belong to the reference blood oxygen saturation range. If the obtained blood oxygen saturation does not belong to the reference blood oxygen saturation range, the value of the detection index of the blood oxygen detection device is 1. Otherwise, the detection index of the blood oxygen detection device is calculated using the following formula:

[0049] In the formula, e is the natural constant, i is the number of the blood oxygen detection device, I is the total number of blood oxygen detection devices, p is the number of the pre - job employee, P is the total number of pre - job employees, is the blood oxygen data detection index of the i - th blood oxygen detection device, is the blood oxygen saturation of the p - th pre - job employee of the i - th blood oxygen detection device, is the saturation micro - fraction of the i - th blood oxygen detection device, is the perfusion index of the p - th pre - job employee of the i - th blood oxygen detection device, is the perfusion index micro - fraction of the i - th blood oxygen detection device, is the pulse rate range of the p - th pre - job employee of the i - th blood oxygen detection device, is the reference pulse rate range of the i - th blood oxygen detection device.

[0050] Among them, the expression of the saturation micro - fraction is , where is the blood oxygen saturation reference value of the i - th blood oxygen detection device, is the blood oxygen saturation reference deviation of the i - th blood oxygen detection device; The expression of the perfusion index micro - fraction is , where is the perfusion index reference value of the i - th blood oxygen detection device, is the reference deviation of the perfusion index for the i-th blood oxygen detection device; the perfusion index is the value of the perfusion index displayed on the pulse oximeter, indicating the blood perfusion status of the body, generally set to 4-5; the reference blood oxygen saturation range represents the percentage of hemoglobin combined with oxygen in the blood accounting for all hemoglobin, generally set to 95%-98%, and the reference pulse rate range is the value of the pulse rate (PulseRate, PR) displayed on the pulse oximeter, representing the frequency of pulse beats, generally set to 60-100 beats per minute, all representing the maximum range that a person can reach.

[0051] In this embodiment, let and let and let , is the blood oxygen saturation coefficient of the p-th pre-job employee of the i-th blood oxygen detection device, is the perfusion index coefficient of the p-th pre-job employee of the i-th blood oxygen detection device, is the pulse rate range coefficient of the p-th pre-job employee of the i-th blood oxygen detection device, then the detection index of the blood oxygen detection device can be simplified as: ; when the blood oxygen saturation is not within the reference blood oxygen saturation range, there must be a fault in the blood oxygen detection device, so the detection index of the blood oxygen detection device is 1. When the blood oxygen saturation is within the reference blood oxygen saturation range, the change statistical table of the detection index of the blood oxygen detection device is shown in Table 1: Table 1 Statistical Table of Changes in the Detection Index of the Blood Oxygen Detection Device

[0052] It can be seen from the formula that the blood oxygen saturation coefficient, perfusion index coefficient, and pulse rate range coefficient are all negatively correlated with the detection index of the blood oxygen detection device in the mathematical relationship. However, from the above table, the detection index of the blood oxygen detection device cannot be directly obtained through a single data and needs to be obtained through comprehensive analysis of the blood oxygen saturation coefficient, perfusion index coefficient, and pulse rate range coefficient. For example, the blood oxygen saturation coefficient of the first group of data is greater than that of the third group of data, but the finally obtained detection index of the blood oxygen detection device is smaller than that of the third group because the perfusion index coefficient and pulse rate range coefficient also have an impact. Therefore, comprehensive analysis is required; the numericalization of the abnormal situation of the blood oxygen detection device is realized, and further the accuracy of pre-job health monitoring is improved.

[0053] It should be understood that the reference value of blood oxygen saturation is represented by taking the average value after statistically analyzing the blood oxygen saturation of pre-job employees, and the reference value of the perfusion index is represented by taking the average value after statistically analyzing the perfusion index of pre-job employees; the reference deviation of blood oxygen saturation is represented by statistically analyzing the blood oxygen saturation of pre-job employees through the deviation calculation formula, and the reference deviation of the perfusion index is represented by statistically analyzing the perfusion index of pre-job employees through the deviation calculation formula; the deviation calculation formula is: , where S is the reference deviation value, r is the number of the data, , n is the total number of the data, is the data value of the r-th data, is the average value of the data.

[0054] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0055] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0056] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0058] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0059] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A multifunctional pre-job health detection system, characterized in that: include: A pre-job health data collection module, which is used to collect the pre-job physical index data of the employee who has successfully authenticated the identity, and upload the physical index data to the cloud service platform; The attendance punch-in module is used for employees to punch in before work and upload the attendance information to the cloud service platform; Cloud service platform, which includes cloud database and cloud computing analysis module; The cloud database is used to store the physical indicators and attendance information, and the cloud computing analysis module is used to determine whether the physical indicators and attendance information are abnormal, and classify and count the abnormal results and the physical indicators and attendance information and send them to the authorized user end; A communication module is used for communication between the cloud service platform and the pre-job health data collection module, the attendance punching module and the user terminal.

2. The multifunctional pre-job health detection system as claimed in claim 1, characterized in that: The collection method of the pre-job health data collection module is contact or non-contact. The pre-job health data collection module can customize the physical indicators that need to be collected; The physical indicators include blood pressure, alcohol, body temperature, heart rate, blood oxygen, and mental stress; The identity authentication method includes one of face recognition, fingerprint, iris, voiceprint, ID card, employee card, positioning card, and NFC.

3. The multifunctional pre-job health detection system according to claim 1, characterized in that: The process of the cloud computing analysis module determining whether the physical indicators and attendance information are abnormal specifically includes: Obtain the reference range of employees’ normal physical indicators and attendance stored in the cloud database; The physical indicators of the employee collected by the pre-job health data collection module and the attendance data obtained by the attendance punching module are compared with the reference range. If they are not within the reference range, it is determined that there are abnormalities in the physical indicators and the attendance information.

4. The multifunctional pre-job health detection system according to claim 1, characterized in that: The user terminal includes a smart phone, a tablet and a computer. The user obtains information from the cloud service platform through the user terminal in the following ways: the user installs an APP and obtains information through the APP; the user opens a web page through a browser and obtains information from the web page; and the user uses a mini program to interact with the cloud computing service platform based on a development interface provided by the mini program.

5. The multifunctional pre-job health detection system according to claim 1, characterized in that: The permissions of the user terminal are divided according to the user's registration identity level, and users of different levels can obtain different categories of employees' physical indicators and attendance information; specifically, the cloud service platform automatically pushes the employee's physical indicators and attendance information and abnormal data to the employee, his or her direct supervisor and superior; and the group company can view and manage the physical indicators and attendance information data of employees of each branch and subsidiary through the cloud service platform.

6. The multifunctional pre-job health detection system as claimed in claim 5, characterized in that: The user terminal is provided with a health attendance punch-in abnormality appeal and approval module, wherein the appeal module is used to appeal when there are abnormalities in the employee's physical indicators and attendance data, and report to the cloud service platform; the approval module is used to obtain the appeal request sent by the cloud service platform, and push it to the authorized direct person in charge and superior leader of the employee for approval.

7. The multifunctional pre-job health detection system according to claim 1, characterized in that: The pre-job health data collection module and the attendance punching module are both provided with an anti-cheating module, and the anti-cheating module is used to identify whether the employee has completed the health data collection and attendance punching process normally; The system also includes a wearable positioning module, which is used for real-time positioning of employees.

8. The multifunctional pre-job health detection system as claimed in claim 2, characterized in that: The specific process of face recognition verification is as follows: Collecting a facial image of a pre-job employee and obtaining the coordinates of facial key points from the facial image, and obtaining facial feature vector coefficients according to a facial feature vector constructed based on the facial key point coordinates, wherein the facial key point coordinates include a key point horizontal coordinate and a key point vertical coordinate; Obtaining a face recognition index of the pre-job employee according to the acquired face key point coordinates and face feature vector coefficients, wherein the face recognition index is used to describe the degree of similarity between the pre-job employee's face image and the pre-stored face image; The obtained face recognition index is compared with the reference face value range. If the obtained face recognition index is within the reference face value range, it indicates that the identity information verification of the pre-job employee is successful, otherwise it indicates that the identity information verification of the pre-job employee has failed.

9. The multifunctional pre-job health detection system according to claim 1, characterized in that: The system also includes an equipment detection module, which is used to detect the accuracy of blood pressure, alcohol and blood oxygen data collection in the pre-job health data collection module.

10. The multifunctional pre-job health detection system according to claim 9, characterized in that: The process of detecting the accuracy of blood pressure, alcohol and blood oxygen data collection in the pre-job health data collection module specifically includes: The pre-job health data collection module includes a blood pressure detection device, an alcohol detection device and a blood oxygen detection device; Performing device detection according to the blood pressure data collected by the blood pressure detection device to obtain a blood pressure detection device detection index, and comparing the blood pressure detection device detection index with the blood pressure detection device interval to obtain a blood pressure detection device detection result, wherein the blood pressure detection device detection index is used to describe an abnormal condition of the blood pressure detection device; Performing device detection according to the alcohol data collected by the alcohol detection device to obtain an alcohol detection device detection index, and comparing the alcohol detection device detection index with the alcohol detection device interval to obtain an alcohol detection device detection result, wherein the alcohol detection device detection index is used to describe an abnormal situation of the alcohol detection device; Performing device detection according to the blood oxygen data collected by the blood oxygen detection device to obtain a blood oxygen detection device detection index, and comparing the blood oxygen detection device detection index with the blood oxygen detection device interval to obtain a blood oxygen detection device detection result, wherein the blood oxygen detection device detection index is used to describe an abnormal situation of the blood oxygen detection device; Determine whether the detection index of the blood pressure detection equipment, the detection index of the alcohol detection equipment and the detection index of the blood oxygen detection equipment are within the corresponding range, and issue abnormal warnings and feedback for the detection equipment that is not within the corresponding range.

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