An immigration and exit port disease monitoring, analysis and early warning system

Abstract

The present invention discloses a disease monitoring, analysis and early warning system for entry-exit ports, belonging to the technical field of disease monitoring and early warning. Through a data collection and monitoring module, a personnel classification detection module, a risk judgment module and an early warning module, accurate disease monitoring and risk assessment of entry-exit personnel are realized; by using a sentinel detector to collect personnel health information and appearance characteristics, combined with risk city marking, total symptom evaluation value and total feature evaluation value to analyze the disease risk value, personnel are divided into high, medium and low risk levels and arranged to corresponding detection channels in the data collection and monitoring module; the risk judgment and early warning module comprehensively considers factors such as the number of personnel, the scope of disease transmission, seasonal factors and fatality rate factors, analyzes and calculates the risk early warning value and divides the early warning level, and takes precise prevention and control measures; effectively improving the scientificity and accuracy of infectious disease prevention and control and ensuring the public health safety of entry-exit ports.

Description

Technical Field

[0001] The present invention belongs to the technical field of disease monitoring and early warning, and particularly relates to a disease monitoring, analysis and early warning system for entry-exit ports. Background Art

[0002] With the acceleration of the globalization process, the number of entry-exit personnel is increasing day by day, and the cross-border movement of people is becoming more and more frequent; as a key hub for the cross-border flow of people and goods, entry-exit ports are facing severe challenges in disease prevention and control; the risk of the spread of various infectious diseases globally has increased significantly. Once there are omissions in the port prevention and control link, infectious diseases are likely to spread rapidly, threatening not only the health of entry-exit personnel but also triggering regional or even global public health crises.

[0003] The following technical challenges mainly exist in the disease monitoring, analysis and early warning at entry-exit ports:

[0004] 1. It is difficult to identify entry-exit personnel who may carry infectious diseases (disease vectors), and there is a lack of an efficient personnel risk assessment mechanism, resulting in loopholes in infectious disease prevention and control.

[0005] 2. The assessment of the disease transmission risk and harm degree is not comprehensive enough.

[0006] 3. There is a lack of real-time monitoring and rapid response capabilities to dynamic changes, resulting in lagging early warning information, making it difficult to adjust prevention and control strategies in a timely manner, and it is also difficult to take precise prevention and control measures according to different risk levels, thus unable to avoid waste of resources while ensuring public health safety. For this reason, we propose a disease monitoring, analysis and early warning system for entry-exit ports. Summary of the Invention

[0007] The purpose of the present invention is to provide a disease monitoring, analysis and early warning system for entry-exit ports to solve the problems raised in the above background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A disease monitoring, analysis and early warning system for entry-exit ports, including: a data collection and monitoring module, a personnel classification monitoring module, and a risk judgment and early warning module;

[0009] The data collection and monitoring module collects detection information and monitors diseases of entry-exit personnel.

[0010] The personnel classification detection module marks risk cities according to the urban infectious disease situation, compares the cities passed by entry-exit personnel, for those passing through risk cities, analyzes the total symptom evaluation value according to personal health information and the total feature evaluation value according to appearance feature information, analyzes the disease risk value according to the total symptom evaluation value and the total feature evaluation value, marks personnel with different levels of risk accordingly, and arranges corresponding levels of risk detection channels for infectious disease screening.

[0011] The risk judgment and early warning module analyzes the number of entry-exit personnel, the distribution of high-risk personnel, the scope of disease transmission, seasonal factors, and disease fatality rates to obtain the total number of inspectors, high-risk transmission values, medium-risk transmission values, high-risk fatality rates, and medium-risk fatality rates. Then, through comprehensive analysis, it obtains the risk early warning value, divides the risk levels according to the risk early warning value, and formulates corresponding prevention and control measures.

[0012] Preferably, the specific process of the data collection and monitoring module for collecting detection information of entry-exit personnel is as follows:

[0013] Collect detection information and conduct disease monitoring on entry-exit personnel at the passenger channels and waiting areas of entry-exit ports; the detection information includes: appearance feature information and personal health information;

[0014] Capture the appearance feature information of entry-exit personnel; the appearance features include: eye features, mouth features, facial muscle features, hand movement features, leg movement features, and body posture features;

[0015] Record the personal health information of entry-exit personnel; the personal health information includes: recent travel history, whether there are disease symptoms, and the disease symptoms include: fever, cough, and diarrhea.

[0016] Preferably, the specific process of the data collection and monitoring module for conducting disease monitoring on entry-exit personnel is as follows:

[0017] Establish three disease monitoring channels, namely the low-risk monitoring channel, the medium-risk monitoring channel, and the high-risk monitoring channel;

[0018] The low-risk monitoring channel is equipped with infrared sensor measurement equipment for measuring the body temperature of entry-exit personnel and synchronously collecting the body temperature information of entry-exit personnel;

[0019] On the basis of the equipment in the low-risk monitoring channel, the medium-risk monitoring channel is also equipped with an antigen detector for antigen reagent testing of entry-exit personnel and collecting reagent test data;

[0020] On the premise of having infrared sensor measurement equipment and an antigen detector, the high-risk monitoring channel is installed with a biochip detector to sample disease samples of entry-exit personnel, and after completing the collection of disease samples, it detects the disease samples. The disease samples include: blood samples, respiratory samples, and saliva samples; and collect disease sample test data.

[0021] Preferably, the specific process of the personnel classification and detection module for analyzing the total symptom evaluation value is as follows:

[0022] Mark the cities with a high incidence of infectious diseases as of the current time as risk cities, forming a list of risk cities. For each entry-exit personnel, retrieve their corresponding appearance feature information and personal health information from the data collection and monitoring module. Obtain the cities passed through during their travel based on the recent travel history of the entry-exit personnel, and compare each city passed through by the entry-exit personnel with the list of risk cities one by one to detect whether there is a situation where a passed-through city is marked as a risk city;

[0023] If there is a risk city among the passed-through cities, obtain all the highly infectious diseases corresponding to the passed-through risk cities and mark them as detected diseases; establish a corresponding disease symptom library and disease feature library for each detected disease; the disease symptom library contains all the symptoms corresponding to this detected disease, and the disease feature library contains all the appearance features corresponding to this detected disease;

[0024] For each detected disease, each disease symptom in the preset disease symptom library corresponds to a disease diagnosis value. Match the disease symptoms existing in the personal health information of the entry-exit personnel with all the disease symptoms in the disease symptom library. If there are the same disease symptoms, output the corresponding disease diagnosis value, and then add up all the disease diagnosis values corresponding to the same disease symptoms to obtain the total symptom evaluation value ZP.

[0025] Preferably, the specific process of the personnel classification detection module analyzing the total feature evaluation value is as follows:

[0026] For eye features, mouth features, facial muscle features, hand movement features, leg movement features, and body posture features, establish the parameter range of various appearance features in the normal state; for each detected disease, preset the deviation interval of various appearance features in the disease state relative to the normal range in the disease feature library, denoted as the disease feature deviation interval standard; calculate the deviation value of various appearance features in the appearance feature information of the entry-exit personnel relative to the normal range, denoted as the actual personnel feature deviation value, and each type of appearance feature in the preset disease feature library corresponds to a feature diagnosis value;

[0027] For each type of appearance feature in the disease feature library, match the corresponding actual personnel feature deviation value with the disease feature deviation interval standard corresponding to this type of appearance feature. If the actual personnel feature deviation value is within the disease feature deviation interval standard range, output the feature diagnosis value corresponding to this type of appearance feature; then summarize the feature diagnosis values of all appearance feature categories to obtain the total feature evaluation value TP.

[0028] Preferably, the specific process of the personnel classification detection module analyzing the disease risk value, marking different levels of risk personnel accordingly, and arranging corresponding levels of risk detection channels for infectious disease screening is as follows:

[0029] For each detected disease, after normalizing the corresponding total symptom evaluation value ZP and the total feature evaluation value TP, the disease risk value JFX is obtained using the formula: JFX = ZP × a1 + TP × a2, where a1 and a2 are preset weight coefficients; the disease risk value of the entry-exit personnel is compared with the disease risk value threshold. If the disease risk value is greater than or equal to the disease risk value threshold, the entry-exit personnel is marked as a suspected patient of this detected disease;

[0030] If, in the calculation of the disease risk values of the entry-exit personnel, the corresponding disease risk values are greater than or equal to the corresponding disease risk value thresholds in all cases, then the entry-exit personnel is marked as a suspected patient of multiple detected diseases;

[0031] The entry-exit personnel marked as a suspected patient of a certain detected disease or a suspected patient of multiple detected diseases are recorded as high-risk personnel;

[0032] The entry-exit personnel who are not marked as suspected patients of detected diseases in all detected diseases are recorded as medium-risk personnel;

[0033] If there are no risk cities among the cities passed through by the entry-exit personnel during the journey, then the entry-exit personnel is recorded as low-risk personnel;

[0034] For each entry-exit personnel, according to the corresponding risk level, they are assigned to the risk detection channels of the corresponding risk level for inspection.

[0035] Preferably, the specific process by which the risk judgment and early warning module analyzes and obtains the total number of inspectors, the high-risk transmission value, and the medium-risk transmission value is as follows:

[0036] For each batch of entry-exit personnel, obtain the total number of entry-exit personnel, recorded as the total number of inspectors ZY. At the same time, obtain the total number of high-risk personnel GR and the total number of medium-risk personnel ZR among the same batch of entry-exit personnel. Obtain the spatial area AS of the passenger channels and the waiting areas at the entry-exit ports. Using the formula: SM = ZY / AS, obtain the actual personnel density SM; at the same time, obtain the area of the regions corresponding to the high-risk monitoring channels and the medium-risk monitoring channels, recorded as the high-channel area GS and the medium-channel area ZS. Using the formula: Kg = (GR / GS) / SM, obtain the high-risk personnel density correction factor Kg. Using the formula: Kz = (ZR / ZS) / SM, obtain the medium-risk personnel density correction factor Kz;

[0037] For the diseases to be detected corresponding to high-risk and medium-risk personnel, obtain the number of cities affected globally by each disease to be detected within one year. For each disease to be detected, a number of ranges of the number of affected cities are preset, and each range of the number of affected cities corresponds to a transmission risk value. For each disease to be detected corresponding to high-risk personnel, match it with the corresponding number of ranges of the number of affected cities, and output the corresponding transmission risk value CFi, where i = 1, 2, ……, n; n is the total number of types of diseases to be detected for high-risk personnel. For each disease to be detected corresponding to high-risk personnel, obtain the corresponding number of high-risk personnel RYi. At the same time, preset the seasonal transmission influence factors respectively, determine the current season, and match this season with all the seasonal transmission influence factors corresponding to the disease to be detected, and output the seasonal transmission influence factor SJi corresponding to the current season; Use the formula: , to obtain the high-risk transmission value CFZ; Record the implementation method of analyzing the high-risk transmission value as the risk transmission analysis method. Similarly, use the risk transmission analysis method to analyze the risk transmission of the diseases to be detected corresponding to medium-risk personnel, and obtain the medium-risk transmission value ZFC.

[0038] Preferably, the specific process of the risk judgment and early warning module analyzing the high-risk fatality rate and the medium-risk fatality rate is as follows:

[0039] Obtain the fatality rate of each disease to be detected corresponding to high-risk and medium-risk personnel. Mark the fatality rate of the diseases to be detected corresponding to high-risk personnel as the high-risk fatality rate GLi; Use the formula: , to obtain the high-risk hazard value GFS. Record the implementation method of analyzing the high-risk hazard value as the hazard degree analysis method. Similarly, use the hazard degree analysis method to analyze the hazard degree of the diseases to be detected corresponding to medium-risk personnel, and obtain the medium-risk hazard value ZFS.

[0040] Preferably, the specific process of the risk judgment and early warning module analyzing the risk early warning value, dividing the risk level according to the risk early warning value, and formulating corresponding prevention and control measures is as follows:

[0041] After normalizing the total number of inspectors ZY, high-risk transmission value CFZ, medium-risk transmission value ZFC, high-risk hazard value GFS, and medium-risk hazard value ZFS corresponding to the current batch and substituting them into the preset formula model: FYZ = ZY×w1 + CFZ×w2 + ZFC×w3 + GFS×w4 + ZFS×w5, obtain the risk early warning value FYZ, where w1, w2, w3, w4, w5 are preset weight coefficients;

[0042] Three warning levels are preset, namely the first-level warning, the second-level warning, and the third-level warning. Each warning level corresponds to a risk warning value range. By matching the risk warning value corresponding to this batch of entry-exit personnel with the risk warning value ranges corresponding to the three warning levels, the corresponding warning level is output, and the prevention and control measures of the corresponding level are implemented.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] (1) For the disease monitoring, analysis and warning system at the entry-exit port, the personnel classification and detection module can accurately identify the entry-exit personnel who may carry infectious diseases. This module marks the risk cities according to the urban infectious disease situation, compares the cities passed by the entry-exit personnel, analyzes the total symptom evaluation value in combination with personal health information and the total feature evaluation value according to the appearance feature information, and then analyzes the disease risk value. Accordingly, high, medium and low risk personnel are marked, and high, medium and low risk detection channels are arranged correspondingly for infectious disease screening; through this comprehensive evaluation method based on multi-dimensional information, the accuracy of personnel risk assessment is greatly improved, effectively filling the loopholes in infectious disease prevention and control, and ensuring the accurate prevention and control of infectious diseases at the entry-exit port.

[0045] (2) For the disease monitoring, analysis and warning system at the entry-exit port, the risk judgment and warning module comprehensively considers various factors, including the number of entry-exit personnel, the distribution of risk personnel, the scope of disease transmission, seasonal factors, disease fatality rate, etc. By analyzing the above data, the total number of inspectors, the high-risk transmission value, the medium-risk transmission value, the high-risk fatality rate and the medium-risk fatality rate are obtained, and a comprehensive analysis is carried out to obtain the risk warning value, and then the risk level is divided; this evaluation method comprehensively and scientifically evaluates the disease transmission risk and harm degree from multiple dimensions, such as the number and distribution of personnel, the scope of disease transmission, seasonal changes and harm degree, providing an accurate basis for subsequent prevention and control measures, and effectively improving the scientificity and effectiveness of disease prevention and control.

[0046] (3) For the disease monitoring, analysis and warning system at the entry-exit port, the sentinel detector continuously collects the health information and appearance features of the entry-exit personnel, and obtains the disease transmission data released by the authoritative agency in real time, dynamically updates the key parameters such as the risk city mark, the scope of disease transmission and the seasonal transmission influence factor, ensuring the timeliness and accuracy of the warning information; calculates the risk warning value according to the real-time monitoring data, divides different warning levels according to the risk warning value, and takes different prevention and control measures, which helps to achieve accurate prevention and control according to the risk degree, avoiding the waste of resources caused by excessive prevention and control, and ensuring the effective prevention and control of disease transmission in different risk situations, and protecting the public health safety of the entry-exit port. Description of the Drawings

[0047] Figure 1 This is the flowchart of the present invention. Specific implementation manners

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Embodiment 1

[0050] Please refer to Figure 1 , the present invention provides a disease monitoring, analysis and early warning system for entry-exit ports, including: a data collection and monitoring module, a personnel classification detection module, and a risk judgment and early warning module;

[0051] The data collection and monitoring module collects detection information and monitors diseases of entry-exit personnel through a sentinel detector; the sentinel detector includes: a high-definition camera, an intelligent voice interaction device, an infrared sensor, an antigen detector, and a biochip detector; the specific process is as follows:

[0052] Deploy sentinel detectors at key positions such as passenger channels and waiting (for planes, vehicles, ships) areas at entry-exit ports to collect detection information and monitor diseases of entry-exit personnel within their working ranges; the detection information includes: appearance feature information and personal health information;

[0053] The appearance feature information of entry-exit personnel is captured in real time through a high-definition camera; the appearance features include: eye features, mouth features, facial muscle features, hand movement features, leg movement features, and body posture features;

[0054] The personal health information of entry-exit personnel is recorded through an intelligent voice interaction device; the personal health information includes: recent travel history, whether there are disease symptoms, and the disease symptoms include: fever, cough, diarrhea, etc.;

[0055] The process of disease monitoring is as follows: establish three disease monitoring channels, namely a low-risk monitoring channel, a medium-risk monitoring channel, and a high-risk monitoring channel;

[0056] The low-risk monitoring channel is equipped with an infrared sensor for measuring the body temperature of entry-exit personnel and synchronously collecting the body temperature information of entry-exit personnel;

[0057] The medium-risk monitoring channel, on the basis of the equipment in the low-risk monitoring channel, is also equipped with an antigen detector for performing antigen reagent detection on entry-exit personnel and collecting reagent detection data;

[0058] On the premise of having infrared sensing measurement equipment and antigen detectors, the high-risk monitoring channel is additionally installed with a biochip detector, which is used to collect disease samples from entry and exit personnel, detect the disease samples after the collection is completed, and the disease samples include: blood samples, respiratory samples, and saliva samples; and collect the disease sample detection data.

[0059] The personnel classification and detection module marks the risk cities according to the urban infectious disease situation, compares the cities passed by the entry and exit personnel, for those passing through the risk cities, analyzes the total symptom evaluation value according to the personal health information and the total feature evaluation value according to the appearance feature information, analyzes the disease risk value according to the total symptom evaluation value and the total feature evaluation value, and accordingly marks high, medium, and low-risk personnel, and arranges high, medium, and low-risk monitoring channels for infectious disease screening respectively. The specific process is as follows:

[0060] Mark the cities with a high incidence of infectious diseases as risk cities up to the current time, sort out all the risk cities to form a risk city list. For each entry and exit personnel, retrieve their corresponding appearance feature information and personal health information from the data collection and monitoring module, obtain the cities passed through during their travel according to the recent travel history of the entry and exit personnel, and compare each city passed through by the entry and exit personnel with the risk city list one by one to detect whether there is a situation where a city is marked as a risk city.

[0061] If there is a risk city among the passed-through cities, obtain all the highly infectious diseases corresponding to the passed-through risk cities and mark them as the detected diseases; establish a corresponding disease symptom library and disease feature library for each detected disease; the established disease symptom library contains all the symptoms corresponding to this detected disease, and the disease feature library contains all the appearance features corresponding to this detected disease.

[0062] For each detected disease, each disease symptom in the preset disease symptom library corresponds to a disease confirmation value. Match the disease symptoms existing in the personal health information of the entry and exit personnel with all the disease symptoms in the disease symptom library. If there are the same disease symptoms, output the corresponding disease confirmation value, and then add up all the disease confirmation values corresponding to the same disease symptoms to obtain the total symptom evaluation value ZP.

[0063] Establish the parameter ranges of various appearance features in the normal state for eye features, mouth features, facial muscle features, hand movement features, leg movement features, and body posture features; for each detected disease, preset the deviation intervals of various appearance features in the disease state relative to the normal range in the disease feature library, denoted as the disease feature deviation interval standard; calculate the deviation values of various appearance features in the appearance feature information of the entry and exit personnel relative to the normal range, denoted as the actual deviation value of personnel features, and each type of appearance feature in the preset disease feature library corresponds to a feature confirmation value.

[0064] For each type of appearance feature in the disease feature library, match the actual deviation value of the corresponding personnel feature with the disease feature deviation interval standard corresponding to this type of appearance feature. If the actual deviation value of the personnel feature is within the range of the disease feature deviation interval standard, output the feature confirmation value corresponding to this type of appearance feature; subsequently, summarize the feature confirmation values of all appearance feature categories to obtain the total feature evaluation value TP.

[0065] For each detected disease, after normalizing the corresponding total symptom evaluation value ZP and the total feature evaluation value TP, use the formula: JFX = ZP × a1 + TP × a2 to obtain the disease risk value JFX, where a1 and a2 are preset weight coefficients; the larger the disease risk value, the greater the probability that the entry-exit personnel has this infectious disease; preset the disease risk value threshold, and compare the disease risk value corresponding to the entry-exit personnel with the disease risk value threshold. If the disease risk value is greater than or equal to the disease risk value threshold, mark this entry-exit personnel as a suspected patient of this detected disease.

[0066] If, in the calculation of multiple disease risk values for the entry-exit personnel, the corresponding disease risk value is greater than or equal to the corresponding disease risk value threshold in all cases, then mark this entry-exit personnel as a suspected patient of multiple detected diseases.

[0067] Mark the entry-exit personnel marked as a suspected patient of a single detected disease or a suspected patient of multiple detected diseases as high-risk personnel.

[0068] Mark the entry-exit personnel who have not been marked as a suspected patient of a detected disease in all detected diseases as medium-risk personnel.

[0069] If there is no risk city among the cities passed through by the entry-exit personnel during the journey, then mark this entry-exit personnel as a low-risk personnel.

[0070] For each entry-exit personnel, according to the corresponding risk level, allocate them to the risk detection channels corresponding to the corresponding risk levels for detection. Among them, the higher the level corresponding to the risk monitoring channel, the more detection items there are.

[0071] It should be noted that by comparing the dynamic markings of risk cities with the transit cities, it is possible to identify the inbound and outbound passengers who may be exposed to the risk of infectious diseases. By analyzing the total symptom evaluation value based on personal health information and the total feature evaluation value based on appearance feature information, and analyzing the disease risk value according to the total symptom evaluation value and the total feature evaluation value, the accuracy of infectious disease risk assessment has been greatly improved; according to the risk level, the personnel are diverted to different detection channels, realizing the efficient utilization of resources; avoiding unnecessary over-detection of all personnel, improving the detection efficiency while ensuring that high-risk personnel receive key attention and accurate detection; by marking high, medium, and low-risk personnel and implementing corresponding detections, the spread of infectious diseases can be effectively blocked.

[0072] The risk judgment and early warning module analyzes the number of inbound and outbound passengers, the distribution of risk personnel, the scope of disease transmission, seasonal factors, and disease fatality rates to obtain the total number of inspectors, high-risk transmission values, medium-risk transmission values, high-risk fatality rates, and medium-risk fatality rates, and conducts comprehensive analysis to obtain the risk early warning value. According to the risk early warning value, the risk level is divided, and corresponding prevention and control measures are formulated. The specific process is as follows:

[0073] For each batch of inbound and outbound passengers, obtain the total number of inbound and outbound passengers, denoted as the total number of inspectors ZY. At the same time, obtain the total number of high-risk personnel GR and the total number of medium-risk personnel ZR among the inbound and outbound passengers in the same batch. Obtain the spatial area AS of the passenger channels at the port of entry / exit and the waiting areas (for planes, trains, ships). Using the formula: SM = ZY / AS, obtain the actual personnel density SM; at the same time, obtain the areas corresponding to the high-risk monitoring channels and medium-risk monitoring channels, denoted as the high-channel area GS and the medium-channel area ZS. Using the formula: Kg = (GR / GS) / SM, obtain the high-risk personnel density correction factor Kg. Using the formula: Kz = (ZR / ZS) / SM, obtain the medium-risk personnel density correction factor Kz;

[0074] For the detection diseases corresponding to high-risk and medium-risk personnel, according to the data released by the World Health Organization, obtain the number of cities affected by each detection disease globally in the recent year. For each detection disease, several ranges of the number of affected cities are preset, and each range of the number of affected cities corresponds to a transmission risk value. For each detection disease corresponding to high-risk personnel, match it with the corresponding several ranges of the number of affected cities, and output the corresponding transmission risk value CFi, i = 1, 2,..., n; n is the total number of detection disease types for high-risk personnel. For each detection disease corresponding to high-risk personnel, obtain the corresponding number of high-risk personnel RYi. At the same time, according to the four seasons of spring, summer, autumn, and winter, preset the corresponding seasonal transmission impact factors respectively. Determine the current season, and match this season with all the seasonal transmission impact factors corresponding to the detection disease, and output the seasonal transmission impact factor SJi corresponding to the current season. Using the formula: , a high - risk transmission value CFZ is obtained; the larger the high - risk transmission value, the wider the global transmission range of the detected diseases carried by the high - risk groups among this batch of entry - exit personnel and the more cities are involved; the implementation method for analyzing the high - risk transmission value is denoted as the risk - transmission analysis method. Similarly, using the risk - transmission analysis method to analyze the risk transmission of the detected diseases corresponding to medium - risk personnel, a medium - risk transmission value ZFC is obtained;

[0075] Obtain the fatality rates of each detected disease corresponding to high - risk and medium - risk personnel from authoritative medical databases, research literature, and public health institution reports. The fatality rate of the detected disease corresponding to high - risk personnel is marked as the high - risk fatality rate GLi. Using the formula: , a high - risk hazard value GFS is obtained. The implementation method for analyzing the high - risk hazard value is denoted as the hazard - degree analysis method. Similarly, using the hazard - degree analysis method to analyze the hazard degree of the detected diseases corresponding to medium - risk personnel, a medium - risk hazard value ZFS is obtained;

[0076] After normalizing the total number of inspectors ZY, high - risk transmission value CFZ, medium - risk transmission value ZFC, high - risk hazard value GFS, and medium - risk hazard value ZFS corresponding to the current batch and substituting them into the preset formula model: FYZ = ZY×w1 + CFZ×w2 + ZFC×w3 + GFS×w4 + ZFS×w5, a risk - warning value FYZ is obtained, where w1, w2, w3, w4, and w5 are preset weight coefficients; the larger the risk - warning value, the greater the public - health safety challenges faced by the entry - exit port in terms of personnel flow, disease - transmission range, and hazard degree, and the higher the required warning level;

[0077] Three warning levels are preset, namely, level - 1 warning, level - 2 warning, and level - 3 warning, and each warning level corresponds to a risk - warning value interval. Among them, level - 1 warning is less than level - 2 warning, and level - 2 warning is less than level - 3 warning. The higher the warning level, the higher the risk degree and the greater the intensity of screening for entry - exit personnel; by matching the risk - warning value corresponding to this batch of entry - exit personnel with the risk - warning value intervals corresponding to the three warning levels, the corresponding warning level is output, and the prevention and control measures at the corresponding level are implemented. The higher the warning level, the stricter the prevention and control measures to be implemented.

[0078] It should be noted that: by comprehensively considering factors such as the total number of inspectors, the number of high- and medium-risk personnel, the area of ports and monitoring channels, etc., calculating the personnel density correction factor can incorporate spatial factors into the risk assessment system, reflect the impact of the relative density of high- and medium-risk personnel in different regions on the disease transmission risk, combine with the data of the World Health Organization, calculate the high-risk transmission value and medium-risk transmission value based on factors such as the number of cities affected globally by the disease and the seasonal transmission impact factor, comprehensively evaluate the disease transmission risk from multiple dimensions such as the global transmission range and seasonal changes, fully consider the dynamics and complexity of disease transmission, and improve the scientificity and accuracy of risk assessment; by analyzing the high-risk hazard value and medium-risk hazard value, it helps to measure the harm degree of infectious diseases to entry-exit personnel. By dividing different warning levels and taking different prevention and control measures, it helps to achieve precise prevention and control according to the risk level, avoiding the waste of resources caused by over-prevention and control, and ensuring the effective prevention and control of disease transmission in different risk situations, and safeguarding the public health safety of entry-exit ports.

[0079] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An entry-exit port disease monitoring, analysis and early warning system, comprising: Data collection and monitoring module, personnel classification monitoring module, risk judgment and early warning module, characterized in that: The data collection and monitoring module collects detection information and conducts disease monitoring on entry-exit personnel; the detection information includes: appearance feature information and personal health information; disease monitoring is carried out through the setting of monitoring channels at different levels. The personnel classification detection module marks risk cities according to the urban infectious disease situation, compares the cities passed by entry-exit personnel, and for those passing through risk cities, determines the corresponding highly prevalent infectious diseases as the diseases to be detected; for each detected disease, constructs a corresponding disease symptom library and a disease feature library; the disease symptom library covers various symptoms corresponding to the disease, and the disease feature library reflects the relevant appearance features when suffering from the disease; at the same time, preset confirmation values for the symptoms in the disease symptom library and the appearance features in the disease feature library respectively. During actual detection, match the symptoms in the personal health information of entry-exit personnel with the disease symptom library; analyze the actual deviation of the personnel's appearance features and compare it with the preset deviation standard in the disease feature library; respectively summarize the confirmation values corresponding to the successfully matched symptoms and the appearance features that meet the deviation standard, and then obtain the total symptom evaluation value and the total feature evaluation value. Analyze the disease risk value according to the total symptom evaluation value and the total feature evaluation value, mark personnel with different levels of risk accordingly, and arrange corresponding levels of risk detection channels for infectious disease screening; different levels of risk personnel include: low-risk personnel, medium-risk personnel, and high-risk personnel. The risk judgment and early warning module analyzes the number of entry-exit personnel, the distribution of risk personnel, the scope of disease transmission, seasonal factors, and disease fatality rate to obtain the total number of inspectors, high-risk transmission value, medium-risk transmission value, high-risk fatality rate, and medium-risk fatality rate, and conducts comprehensive analysis to obtain the risk early warning value, divides the risk levels according to the risk early warning value, and formulates corresponding prevention and control measures. The specific process of analyzing the total number of inspectors, high-risk transmission value, and medium-risk transmission value is as follows: For each batch of entry-exit personnel, obtain the total number of entry-exit personnel ZY, the total number of high-risk personnel GR, the total number of medium-risk personnel ZR, and the spatial area AS of the passenger channels and waiting areas at the entry-exit ports, and use the formula: SM = ZY / AS to obtain the actual personnel density SM. At the same time, obtain the area GS and ZS corresponding to the high-risk monitoring channel and the medium-risk monitoring channel, and use the formula: Kg = (GR / GS) / SM to obtain the high-risk personnel density correction factor Kg, and use the formula: Kz = (ZR / ZS) / SM to obtain the medium-risk personnel density correction factor Kz. For each detected disease corresponding to high- and medium-risk personnel, obtain the number of cities affected globally by the detected disease in one year, preset several ranges of the number of affected cities, and each range corresponds to a transmission risk value. For each detected disease corresponding to high-risk personnel, match it with the corresponding range of the number of affected cities, and output the corresponding transmission risk value CFi, where i = 1, 2, ……, n; n is the total number of detected disease types of high-risk personnel. For each detected disease corresponding to high-risk personnel, obtain the corresponding number of high-risk personnel RYi. At the same time, preset the seasonal transmission impact factor respectively, determine the current season, match this season with all the seasonal transmission impact factors corresponding to the detected disease, and output the seasonal transmission impact factor SJi corresponding to the current season. Use the formula: , to obtain the high-risk transmission value CFZ; similarly, analyze the risk transmission of the detected diseases corresponding to medium-risk personnel to obtain the medium-risk transmission value ZFC.

2. The disease monitoring, analysis and early warning system for entry-exit ports according to claim 1, wherein: The specific process of the data collection and monitoring module collecting detection information on entry-exit personnel is as follows: Collect detection information and conduct disease monitoring on entry-exit personnel at the positions of passenger channels and waiting areas at the entry-exit ports; the detection information includes: appearance feature information and personal health information. Capture the appearance feature information of entry-exit personnel; the appearance features include: eye features, mouth features, facial muscle features, hand movement features, leg movement features, and body posture features; Record the personal health information of entry-exit personnel; the personal health information includes: recent travel history, whether there are disease symptoms, and the disease symptoms include: fever, cough, diarrhea.

3. The disease monitoring, analysis and early warning system for entry-exit ports according to claim 2, characterized in that: The specific process of the data collection and monitoring module for disease monitoring of entry-exit personnel is as follows: Establish three disease monitoring channels, namely the low-risk monitoring channel, the medium-risk monitoring channel, and the high-risk monitoring channel; The low-risk monitoring channel is equipped with infrared sensing measurement equipment for measuring the body temperature of entry-exit personnel and synchronously collecting the body temperature information of entry-exit personnel; On the basis of the equipment in the low-risk monitoring channel, the medium-risk monitoring channel is also equipped with an antigen detector for antigen reagent testing of entry-exit personnel and collecting reagent test data; On the premise of having infrared sensing measurement equipment and an antigen detector, the high-risk monitoring channel is installed with a biochip detector to sample disease samples of entry-exit personnel, and after the disease sample collection is completed, the disease samples are detected. The disease samples include: blood samples, respiratory samples, saliva samples; And collect the disease sample test data.

4. The disease monitoring, analysis and early warning system for entry-exit ports according to claim 3, wherein: The specific process of the personnel classification and detection module for analyzing the total symptom evaluation value is as follows: Mark the cities with a high incidence of infectious diseases as risk cities up to the current time, forming a list of risk cities. For each entry-exit personnel, retrieve their corresponding appearance feature information and personal health information from the data collection and monitoring module, obtain each city passed through during their travel according to the recent travel history of the entry-exit personnel, and compare each city passed through by the entry-exit personnel with the list of risk cities one by one to detect whether there is a situation where a city is marked as a risk city; If there are risk cities among the cities passed through, obtain all the highly infectious diseases corresponding to the risk cities passed through and mark them as detected diseases; establish a corresponding disease symptom library for each detected disease; the disease symptom library contains all the symptoms corresponding to this detected disease, and the disease feature library contains all the appearance features corresponding to this detected disease; For each detected disease, each disease symptom in the preset disease symptom library corresponds to a disease confirmation value. Match the disease symptoms existing in the personal health information of the entry-exit personnel with all the disease symptoms in the disease symptom library. If there are the same disease symptoms, output the corresponding disease confirmation value, and then add up all the disease confirmation values corresponding to the same disease symptoms to obtain the total symptom evaluation value ZP.

5. The disease monitoring, analysis and early warning system for entry-exit ports according to claim 4, wherein: The specific process of the personnel classification and detection module for analyzing the total feature evaluation value is as follows: For eye features, mouth features, facial muscle features, hand movement features, leg movement features, and body posture features, establish the parameter ranges of various appearance features in the normal state; for each detected disease, preset the deviation intervals of various appearance features in the disease feature library relative to the normal range in the diseased state, denoted as the disease feature deviation interval standard; calculate the deviation values of various appearance features in the appearance feature information of the entry-exit personnel relative to the normal range, denoted as the actual deviation values of personnel features, and each type of appearance feature in the preset disease feature library corresponds to a feature confirmation value; For each type of appearance feature in the disease feature library, match the corresponding actual deviation value of personnel features with the disease feature deviation interval standard corresponding to this type of appearance feature. If the actual deviation value of personnel features is within the disease feature deviation interval standard range, output the feature confirmation value corresponding to this type of appearance feature; then summarize the feature confirmation values of all appearance feature categories to obtain the total feature evaluation value TP.

6. The disease monitoring, analysis and early warning system for entry-exit ports according to claim 5, wherein: The specific process of the personnel classification detection module analyzing the disease risk value, marking personnel with different risk levels accordingly, and arranging corresponding risk level detection channels for infectious disease screening is as follows: For each detected disease, after normalizing the corresponding total symptom evaluation value ZP and the total feature evaluation value TP, use the formula: JFX = ZP×a1 + TP×a2 to obtain the disease risk value JFX, where a1 and a2 are preset weight coefficients; compare the disease risk value corresponding to the entry-exit personnel with the disease risk value threshold. If the disease risk value is greater than or equal to the disease risk value threshold, mark the entry-exit personnel as a suspect of this detected disease; If the situation where the corresponding disease risk value is greater than or equal to the corresponding disease risk value threshold occurs in multiple disease risk value calculations for the entry-exit personnel, mark the entry-exit personnel as a suspect of multiple detected diseases; Record the entry-exit personnel marked as a suspect of a certain detected disease or a suspect of multiple detected diseases as high-risk personnel; Record the entry-exit personnel who are not marked as a suspect of a detected disease in all detected diseases as medium-risk personnel; If there is no risk city among the cities passed through by the entry-exit personnel during the journey, record the entry-exit personnel as low-risk personnel; For each entry-exit personnel, according to the corresponding risk level, allocate them to the risk detection channels of the corresponding risk level for detection.

7. The disease monitoring, analysis and early warning system for entry-exit ports according to claim 1, wherein: The specific process of the risk judgment and early warning module analyzing and obtaining the high-risk fatality rate and the stroke fatality rate is as follows: Obtain the fatality rate of each detected disease corresponding to high-risk personnel and medium-risk personnel. Mark the fatality rate of the detected disease corresponding to high-risk personnel as the high-risk fatality rate GLi. Use the formula: , to obtain the high-risk hazard value GFS. Record the implementation method of analyzing the high-risk hazard value as the hazard degree analysis method. Similarly, use the hazard degree analysis method to analyze the hazard degree of the detected disease corresponding to medium-risk personnel to obtain the medium-risk hazard value ZFS.

8. The disease monitoring, analysis and early warning system for entry-exit ports according to claim 7, wherein: The specific process of the risk judgment and early warning module analyzing and obtaining the risk early warning value, dividing the risk level according to the risk early warning value, and formulating corresponding prevention and control measures is as follows: After normalizing the total number of inspectors ZY, high-risk transmission value CFZ, stroke transmission value ZFC, high-risk hazard value GFS, and stroke hazard value ZFS corresponding to the current batch, substitute them into the preset formula model: FYZ = ZY×w1 + CFZ×w2 + ZFC×w3 + GFS×w4 + ZFS×w5 to obtain the risk early warning value FYZ, where w1, w2, w3, w4, w5 are preset weight coefficients; Three warning levels are preset, namely the first-level warning, the second-level warning, and the third-level warning, and each warning level corresponds to a risk warning value range. By matching the risk warning value corresponding to this batch of entry-exit personnel with the risk warning value ranges corresponding to the three warning levels, the corresponding warning level is output, and the prevention and control measures of the corresponding level are implemented.

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