Mask prevention and control system based on the field of respiratory infectious disease viruses

Abstract

The present invention discloses a mask prevention and control system based on the respiratory infectious disease virus field, belonging to the technical field of infectious disease prevention and control. The mask prevention and control system based on the respiratory infectious disease virus field includes the following modules: a data collection module, a transmission module, a data analysis and prediction module, a user interface module, a mask prevention and control optimization module, and a public health data platform. The mask prevention and control system based on the respiratory infectious disease virus field proposed by the present invention, through a high-density sensor network and intelligent chips, real-time monitors virus concentration, environmental conditions, and user health data, based on machine learning algorithms, accurately predicts the virus transmission trend and high-risk areas, timely issues early warning information, dynamically adjusts the protection mode of the mask according to the user's health status and surrounding environment, provides personalized protection, combines user health data and environmental data, generates targeted protection suggestions, and helps users reduce the infection risk.

Description

Technical Field

[0001] The present invention relates to the technical field of prevention and control of infectious diseases, and specifically to a mask prevention and control system based on the respiratory infectious disease virus field. Background Art

[0002] Respiratory infectious diseases (such as influenza, COVID-19, etc.) are characterized by fast transmission speed and wide range. Traditional prevention and control means rely on manual statistics and epidemiological investigations, which have lag and inaccuracy. The Chinese patent with the publication number CN111383771B discloses a prevention and control system based on the epidemic infectious disease virus field. Through information tools, it can quickly identify the people who have had close contact with virus carriers and inform them of the risk of infection. At the same time, it can also provide accurate prevention and control targets for grass-roots disease control centers at all levels. With the development of the Internet of Things and artificial intelligence technologies, it has become possible to achieve precise prevention and control by using intelligent chip and big data analysis technologies.

[0003] However, the existing technologies still have the following problems: ordinary masks cannot adjust the protection performance according to environmental changes; the intelligent chip technology is not fully utilized to collect and analyze individual health data. Therefore, there is an urgent need for a mask prevention and control system that combines intelligent chip technology to improve the efficiency and intelligent level of respiratory infectious disease prevention and control. Summary of the Invention

[0004] The purpose of the present invention is to provide a mask prevention and control system based on the respiratory infectious disease virus field. Through a high-density sensor network and intelligent chips, it can monitor the virus concentration, environmental conditions and user health data in real time. Based on machine learning algorithms, it can accurately predict the virus transmission trend and high-risk areas, and timely issue warning information. According to the user's health status and the surrounding environment, it can dynamically adjust the protection mode of the mask to provide personalized protection, thus solving the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A mask prevention and control system based on the respiratory infectious disease virus field, characterized by including the following modules:

[0007] A data acquisition module, including a virus field monitoring unit and an intelligent chip unit. Among them, the virus field monitoring unit includes a sensor network and a monitoring network deployed in public places and residential areas;

[0008] The intelligent chip unit is an intelligent chip embedded in a mask or a wearable device, which obtains the user's positioning data and real-time monitors the user's health data and the virus concentration in the surrounding environment;

[0009] The data analysis and prediction module, based on machine learning algorithms, models the virus field distribution, analyzes the virus transmission trend and predicts high-risk areas, supplements the spatial distribution map of virus concentration and the user's infection risk index by judging data correlation in combination with the spatial distance threshold, and adjusts the spatial distance threshold according to the virus transmission degree evaluation coefficient to optimize the model accuracy.

[0010] Preferably, the data analysis and prediction module obtains environmental data from the virus field monitoring unit, obtains health data from the smart masks or wearable devices worn by users, and also obtains external data from external systems. The external data includes population density, traffic flow, and weather conditions. It performs data cleaning and processing to form a dataset around the virus field and a dataset around the user. Based on the dataset of the virus field and the dataset of the user, it models to generate a spatial distribution map of virus concentration, analyzes the change trend of virus concentration over time and space, calculates the distances between monitoring points, evaluates the possibility of virus transmission, identifies areas that may become high-risk in the future, analyzes the lagging impact of virus concentration changes on the number of people and protective measures, records the movement trajectories of users in combination with location information, and counts the number of contacts and the duration between people.

[0011] Preferably, the dataset around the virus field includes the following elements: environmental virus concentration C, temperature, humidity and air flow conditions E, the number of people P, the status of personnel protective measures M, and the implementation of disinfection within the area D. The virus field infection risk R1 can be expressed as a function of the above factors: R1 = f(C, E, P, M, D). The simplified infection risk model can be expressed as , where k is a proportionality constant used to adjust the dimension and sensitivity of the model.

[0012] Preferably, the dataset of the user includes but is not limited to the following elements: respiratory system elements, including respiratory rate and blood oxygen level; circulatory system elements, including heart rate and blood pressure; body temperature elements, fatigue and stress-related elements, including sleep quality and stress index; as well as activity level and contact history.

[0013] The user's infection risk R2 is the weighted sum of multiple elements: , x i represents the value of the i-th health element, such as respiratory rate and body temperature, and f i (x i ) represents the influence function of the i-th element on the infection risk, represents the weight of the i-th element.

[0014] Preferably, the data analysis and prediction module first models based on the dataset around the virus field and the dataset around the user respectively, generates a spatial distribution map of the virus concentration and an infection risk index of the user, and combines the correlation between the two sets of data to supplement the spatial distribution map of the virus concentration and the infection risk index of the user. The method for determining the correlation between the spatial distribution map of the virus concentration and the infection risk index data of the user is as follows:

[0015] Using the positioning data in the intelligent chip and the deployment location of the virus field monitoring unit, determine whether the two are in the same area. Define a spatial distance threshold. If the distance between the location of the intelligent chip user and the virus field monitoring unit is less than this threshold, compare the timestamps of the data of the intelligent chip and the virus field monitoring unit, and select the data with a time difference less than a certain threshold for matching. If both conditions are met, it is considered that the two sets of data are relevant.

[0016] Preferably, defining a spatial distance threshold includes:

[0017] Extract the virus diffusion rate, the average wind speed and the pedestrian flow density in the current environmental conditions;

[0018] Use the virus diffusion rate, the average wind speed and the pedestrian flow density in the current environmental conditions to obtain the virus transmission degree evaluation coefficient corresponding to the current environmental conditions;

[0019] Extract the infection risk R1 of the virus field and the infection risk R2 of the user;

[0020] Perform numerical normalization on the infection risk R1 of the virus field and the infection risk R2 of the user to obtain the numerically normalized infection risk R1 of the virus field and the infection risk R2 of the user;

[0021] Use the virus transmission degree evaluation coefficient in combination with the numerically normalized infection risk R1 of the virus field and the infection risk R2 of the user to set the spatial distance threshold.

[0022] Preferably, using the virus transmission degree evaluation coefficient in combination with the numerically normalized infection risk R1 of the virus field and the infection risk R2 of the user to set the spatial distance threshold includes:

[0023] Retrieve the virus transmission degree evaluation coefficient;

[0024] Among them, the virus transmission degree evaluation coefficient is obtained through the following formula:

[0025]

[0026] Among them, U represents the virus transmission degree evaluation coefficient; G represents the virus diffusion rate; v represents the average wind speed of the current environment; ρ represents the pedestrian flow density; ρ maxRepresents the maximum value of the population density within the interference range radius of the virus field monitoring unit;

[0027] Compare the virus transmission degree evaluation coefficient with a preset factor threshold;

[0028] When the virus transmission degree evaluation coefficient is lower than the preset factor threshold, set the spatial distance threshold according to a predetermined initial distance of 50 meters or 100 meters;

[0029] When the virus transmission degree evaluation coefficient is not lower than the preset factor threshold, extract the interference range radius of the virus field monitoring unit;

[0030] Set the spatial distance threshold using the virus transmission degree evaluation coefficient and the virus field infection risk R1 and the user's infection risk R2 after numerical normalization of the combination of the interference range radius

[0031] Among them, the spatial distance threshold is obtained through the following formula:

[0032]

[0033] Among them, L represents the spatial distance threshold; U represents the virus transmission degree evaluation coefficient; R g Represents the interference range radius of the virus field monitoring unit; T represents the attenuation coefficient of the current environmental temperature on the virus transmission speed, and the attenuation coefficient of the current environmental temperature on the virus transmission speed is determined through experiments; W s Represents the attenuation coefficient of the current environmental humidity on the virus transmission speed, and the attenuation coefficient of the current environmental humidity on the virus transmission speed is determined through experiments; w 01 And w 02 Respectively represent the attenuation coefficient of the current environmental temperature on the virus transmission speed and the attenuation coefficient of the current environmental humidity on the virus transmission speed; R1 represents the regional virus field infection risk after numerical normalization; R2 represents the user's infection risk after numerical normalization; A represents the interference radius distance segmentation constant, and its value range can be selected from 5 - 10, or can be set according to the specific range of the interference radius and the actual situation of the two levels.

[0034] Preferably, the method for supplementing the spatial distribution map of the virus concentration and the user's infection risk index is as follows: Numerically normalize the virus field infection risk R1 and the user's infection risk R2 in the area related to the user data in the spatial distribution map of the virus concentration, and then perform numerical comparison. When the virus field infection risk R1 in the area related to the user data is higher than the user's infection risk R2 index, the user's infection risk is redefined as R 2nWhen the user's infection risk R2 is higher than the virus field infection risk R1 related to the user data, the virus field infection risk change ΔR1 caused by the user's activities is added to the virus field infection risk R1 related to the user data ( t )

[0035] ,

[0036] where: C represents the virus concentration, a normalized value, ranging from [0, 1]; T represents the exposure time, a normalized value, ranging from [0, 1]; P represents the effectiveness of protective measures, ranging from [0, 1], 1 indicates complete protection, and 0 indicates no protection; , , , : The weights of each factor are adjusted according to the actual situation.

[0037] , where k is the virus release efficiency coefficient, related to the user's virus load; V is the user's activity intensity, ranging from [0, 1], 1 indicates high-intensity activity, and 0 indicates static; A is the environmental transmission factor, considering the influence of temperature, humidity, air flow velocity, etc.; t is the user's stay time.

[0038] Preferably, the system further includes a user interface module, and the work of the user interface module includes the following: Let the user understand the virus concentration, risk level, and surrounding environmental conditions in the current area, display the virus field distribution in the form of a heat map on the map, provide real-time values of key indicators, mark high-risk areas, and distinguish different risk levels with colors. Provide targeted protective measures according to the user's health status and surrounding environment, generate personalized suggestions by combining the user's health data and environmental data. If the virus concentration is high, it is recommended to wear a mask or leave the area as soon as possible. If the body temperature is abnormal, it is recommended to seek medical examination. If the blood oxygen level drops, it is recommended to reduce activities and monitor the physical condition. Remind the user when approaching a high-risk area or experiencing health abnormalities, use sound, vibration, or notification messages to remind the user to pay attention to protection, provide an escape route or a low-risk alternative route, help the user review historical data, evaluate long-term trends, synchronize the user's health data and environmental data to the cloud, provide a timeline view, and display the user's historical trajectory and risk exposure situation.

[0039] Specifically, it includes the following units:

[0040] Data receiving unit: Obtain real-time virus field information and user health data from the cloud;

[0041] Map display unit: Integrate map services and display the virus field distribution;

[0042] Personalized recommendation unit: Generate prevention and control suggestions based on a machine learning model;

[0043] Notification unit: Implement the early warning function and push real-time reminders.

[0044] Preferably, the system further includes: a transmission module that transmits the data of the smart chip and sensors to the cloud in real time through low-power Bluetooth or 5G technology;

[0045] A public health data platform for building a unified data platform, integrating virus field monitoring data, smart chip operation data, and user health information, supporting government agencies, medical institutions, and individual users to query and analyze, and realizing cross-regional data sharing and collaborative prevention and control

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] The mask prevention and control system based on the respiratory infectious disease virus field proposed by the present invention, through a high-density sensor network and a smart chip, monitors the virus concentration, environmental conditions, and user health data in real time, based on machine learning algorithms, accurately predicts the virus transmission trend and high-risk areas, timely issues early warning information, dynamically adjusts the protection mode of the mask according to the user's health status and the surrounding environment, provides personalized protection, combines the user's health data and environmental data to generate targeted protection suggestions, helps users reduce the infection risk, based on the quantitative analysis of the virus field infection risk and the user's infection risk, formulates scientific prevention and control strategies, through the public health data platform, realizes cross-regional data sharing and collaborative prevention and control, improves the overall prevention and control efficiency, through accurate monitoring and personalized protection, significantly reduces the risk of users being infected with respiratory infectious diseases, and through data sharing and collaborative prevention and control, improves the overall public health safety level. Brief Description of the Drawings

[0048] Figure 1 It is a framework diagram of the mask prevention and control system based on the respiratory infectious disease virus field of the present invention. Detailed Embodiments

[0049] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] In order to solve the problems in the prior art that ordinary masks cannot adjust the protection performance according to environmental changes; the smart chip technology is not fully utilized to collect and analyze individual health data, please refer to Figure 1 , the following technical solutions are provided in this embodiment:

[0051] A mask prevention and control system based on the respiratory infectious disease virus field, including the following modules:

[0052] Data acquisition module, including a virus field monitoring unit and an intelligent chip unit. Among them, the virus field monitoring unit includes a sensor network and a monitoring network deployed in public places and residential areas, equipped with highly sensitive biosensors, environmental sensors, and high-definition cameras to collect data such as the concentration of virus particles, temperature, humidity, air flow velocity, human flow, and video information in real time;

[0053] The sensor network is deployed in a combination of high-density deployment and key-point deployment. Multiple sensors are evenly deployed within the area to ensure the monitoring coverage. At key points within the area, such as entrances, exits, and ventilation openings, key sensors are deployed. Public places can be divided into transportation hubs, commercial facilities, educational institutions, medical institutions, and entertainment venues according to their functions and uses. Transportation hubs include airports, railway stations, subway stations, bus stops, etc.; commercial facilities include shopping malls, supermarkets, markets, etc.; educational institutions include schools, libraries, training institutions, etc.; medical institutions include hospitals, clinics, pharmacies, etc.; entertainment venues include cinemas, theaters, gyms, parks, etc. The sensor network and monitoring network in transportation hubs cover a large area, focusing on crowded areas such as security checkpoints and waiting areas; commercial facilities cover concentrated points of human flow such as main passages, cash registers, and elevators; educational institutions focus on areas such as classrooms, canteens, and playgrounds; medical institutions are mainly arranged in high-risk areas such as outpatient halls and ward corridors.

[0054] The intelligent chip unit is an intelligent chip embedded in a mask or wearable device, which obtains the positioning data of the user and real-time monitors the user's health data and the virus concentration in the surrounding environment. The health data includes but is not limited to respiratory rate, body temperature, heart rate, blood oxygen, and sleep quality;

[0055] Transmission module, which transmits the intelligent chip and sensor data to the cloud in real time through low-power Bluetooth or 5G technology;

[0056] Data analysis and prediction module, which models the virus field distribution based on machine learning algorithms, analyzes the virus transmission trend, and predicts high-risk areas;

[0057] User interface module, which provides real-time virus field information and personalized prevention and control suggestions to users through mobile applications or intelligent devices;

[0058] Public health data platform, which constructs a unified data platform, integrates virus field monitoring data, intelligent chip operation data, and user health information, supports government agencies, medical institutions, and individual users to query and analyze, and realizes cross-regional data sharing and collaborative prevention and control.

[0059] The data analysis and prediction module obtains environmental data from the virus field monitoring unit, health data from the smart masks or wearable devices worn by users, and external data from external systems. The external data includes population density, traffic flow, and weather conditions. It performs data cleaning and processing to form a dataset around the virus field and a dataset around users. Based on the datasets of the virus field and users, it builds a model to generate a spatial distribution map of virus concentration, analyzes the changing trends of virus concentration over time and space, calculates the distances between monitoring points, evaluates the possibility of virus transmission, identifies areas that may become high-risk in the future, analyzes the lagging effects of virus concentration changes on the flow of people and protective measures, records the movement trajectories of users in combination with location information, and counts the number of contacts and the duration between people.

[0060] The dataset around the virus field includes the following elements: environmental virus concentration C, temperature, humidity, and air flow conditions E, the flow of people P, the status of personnel protective measures M, and the implementation of disinfection within the area D. The infectious risk R1 of the virus field can be expressed as a function of the above factors: R1 = f(C, E, P, M, D). The simplified infectious risk model can be expressed as , where k is a proportionality constant used to adjust the dimension and sensitivity of the model.

[0061] Environmental virus concentration C = C max / C actual where C actual represents the actually detected virus particle concentration, unit: number of particles per cubic meter, and C max represents the set maximum allowable virus concentration threshold;

[0062] Flow of people , where P density represents the population density within the area, unit: people per square meter, P threshold represents the set safe population density threshold, and P contact represents the average contact frequency between people within the area, unit: times per hour. α represents the influence coefficient of the contact frequency, usually set to 0.1 - 0.3;

[0063] Status of personnel protective measures , and refer to the weights of the mask wearing rate and the compliance rate of social distancing, satisfying ; M mask represents the mask wearing rate, with a value range of [0, 1]. 0 means no one wears a mask, and 1 means everyone wears a mask; M distance represents the compliance rate of social distancing, with a value range of [0, 1]. 0 means complete non-compliance, and 1 means strict compliance;

[0064] Implementation of disinfection within the area , , represent the weights of disinfection frequency and disinfection efficiency, full ; D frequency represents the disinfection frequency, with a value range of [0, 1]. 0 means never disinfected, and 1 means high-frequency disinfection; D efficiency represents the disinfection efficiency, with a value range of [0, 1]. 0 means ineffective, and 1 means efficient.

[0065] The user's dataset includes, but is not limited to, the following elements: respiratory system elements, including respiratory rate, blood oxygen level, etc.; circulatory system elements, including heart rate, blood pressure, etc.; body temperature elements, fatigue and stress-related elements, including sleep quality, stress index, etc.; and activity level, contact history,

[0066] The user's infection risk R2 is the weighted sum of multiple elements: , x i represents the value of the i-th health element, such as respiratory rate, body temperature, etc., f i (x i ) represents the influence function of the i-th element on the infection risk, represents the weight of the i-th element.

[0067] The data analysis and prediction module first models based on the dataset around the virus field and the dataset around the user respectively, generates a spatial distribution map of the virus concentration and the user's infection risk index, combines the correlation of the two sets of data, and supplements the spatial distribution map of the virus concentration and the user's infection risk index. The method for determining the correlation of the spatial distribution map of the virus concentration and the user's infection risk index data is as follows:

[0068] Using the positioning data in the smart chip and the deployment location of the virus field monitoring unit, determine whether the two are in the same area. Define a spatial distance threshold, such as 50 meters or 100 meters. If the distance between the location of the smart chip user and the virus field monitoring unit is less than the threshold, compare the timestamps of the data of the smart chip and the virus field monitoring unit, and select the data with a time difference less than a certain threshold for matching. If both conditions are met, it is considered that the two sets of data are relevant.

[0069] At the same time, define a spatial distance threshold, which also includes:

[0070] Extract the virus diffusion rate, average wind speed and population density in the current environmental conditions;

[0071] Use the virus diffusion rate, average wind speed and population density in the current environmental conditions to obtain the corresponding virus transmission degree evaluation coefficient under the current environmental conditions;

[0072] Extract the virus field infection risk R1 and the user's infection risk R2;

[0073] Perform numerical normalization on the virus field infection risk R1 and the user's infection risk R2 to obtain the virus field infection risk R1 and the user's infection risk R2 after numerical normalization;

[0074] Use the virus transmission degree evaluation coefficient to set a spatial distance threshold in combination with the virus field infection risk R1 and the user's infection risk R2 after numerical normalization.

[0075] The technical effects of the above technical solution are as follows: By setting the virus transmission degree evaluation coefficient according to the virus transmission conditions under the currently determined environmental conditions, the evaluation accuracy of the virus field infection risk and the user infection risk can be effectively improved. At the same time, according to the dynamic changes of the virus field infection risk and the user infection risk with the environmental conditions, the virus transmission degree evaluation coefficient can be dynamically adjusted, and then the degree of the environmental conditions on the virus infection risk can be dynamically evaluated, effectively improving the accuracy of the degree of the virus infection risk, the followability of the dynamic evaluation, and the timely sensitivity. At the same time, through the defined spatial threshold based on the effective and timely evaluation of the current environmental conditions, virus transmission dynamics, and personal risk exposure, the safety of public places can be effectively improved, and the risk of virus transmission can be reduced.

[0076] Specifically, using the virus transmission degree evaluation coefficient to set a spatial distance threshold in combination with the virus field infection risk R1 and the user's infection risk R2 after numerical normalization includes:

[0077] Retrieve the virus transmission degree evaluation coefficient;

[0078] Among them, the virus transmission degree evaluation coefficient is obtained through the following formula:

[0079]

[0080] Among them, U represents the virus transmission degree evaluation coefficient; G represents the virus diffusion rate; v represents the average wind speed of the current current environment; ρ represents the pedestrian flow density; ρ max represents the maximum value of the pedestrian flow density that appears within the interference range radius of the virus field monitoring unit;

[0081] Compare the virus transmission degree evaluation coefficient with a preset factor threshold;

[0082] When the virus transmission degree evaluation coefficient is lower than the preset factor threshold, set the spatial distance threshold according to a predetermined initial distance of 50 meters or 100 meters;

[0083] When the virus transmission degree evaluation coefficient is not lower than a preset factor threshold, the interference range radius of the virus field monitoring unit is extracted;

[0084] The spatial distance threshold is set using the virus field infection risk R1 and the user's infection risk R2 after numerical normalization of the combination of the virus transmission degree evaluation coefficient and the interference range radius

[0085] Among them, the spatial distance threshold is obtained through the following formula:

[0086]

[0087] Among them, L represents the spatial distance threshold; U represents the virus transmission degree evaluation coefficient; R g represents the interference range radius of the virus field monitoring unit; T represents the attenuation coefficient of the current environmental temperature on the virus transmission speed, and the attenuation coefficient of the current environmental temperature on the virus transmission speed is determined through experiments; W s represents the attenuation coefficient of the current environmental humidity on the virus transmission speed, and the attenuation coefficient of the current environmental humidity on the virus transmission speed is determined through experiments; w 01 and w 02 respectively represent the attenuation coefficient of the current environmental temperature on the virus transmission speed and the attenuation coefficient of the current environmental humidity on the virus transmission speed; R1 represents the regional virus field infection risk after numerical normalization; R2 represents the user's infection risk after numerical normalization; A represents the interference radius distance segmentation constant, and its value range can be selected from 5 - 10, or it can be set according to the specific range of the interference radius and the actual situation of the two levels, such as 20, 50, 100, etc.

[0088] The technical effects of the above technical solution are as follows: By using the virus transmission degree evaluation coefficient and combining with the actual conditions of the current environment to set the spatial distance threshold, the accuracy of setting the spatial distance threshold under the current environmental conditions and the matching degree between the set spatial distance threshold and the current environmental conditions can be effectively improved. At the same time, by comparing the virus transmission degree evaluation coefficient with its corresponding threshold as the condition for triggering the setting of the spatial distance threshold, the triggering accuracy and automation of setting the spatial distance threshold can be effectively improved, thereby preventing the occurrence of unnecessary distance setting scenarios, and then effectively improving the utilization efficiency of computing power and resources and effectively avoiding the utilization rate of computing power and resources. On the other hand, while effectively improving the efficiency of obtaining the spatial distance threshold through the above method, the matching degree between the spatial distance threshold and the current environmental conditions is maximally improved, thereby improving the accuracy of judging the correlation between the subsequent virus field infection risk R1 and the user's infection risk R2. At the same time, it also improves the mobility of the adaptive change and the sensitivity of the adaptive adjustment of the correlation determination between the virus field infection risk R1 and the user's infection risk R2 following the change of environmental conditions, preventing the problem of low accuracy and large error in the correlation evaluation caused by the fixed spatial distance threshold resulting in the correlation determination between the virus field infection risk R1 and the user's infection risk R2 according to the same distance threshold regardless of the environmental conditions.

[0089] The method for supplementing the spatial distribution map of virus concentration and the user's infection risk index is as follows: Numerically normalize the virus field infection risk R1 and the user's infection risk R2 in the area related to user data in the spatial distribution map of virus concentration, and then conduct a numerical comparison. When the virus field infection risk R1 in the area related to user data is higher than the user's infection risk R2 index, the user's infection risk is redefined as R 2n , when the user's infection risk R2 is higher than the virus field infection risk R1 in the area related to user data, the virus field infection risk change ΔR1 caused by the user's activities is added on the basis of the virus field infection risk R1 in the area related to user data ( t ).

[0090] The user's infection risk depends on the following factors: Virus concentration: The higher the virus concentration, the greater the infection risk; Exposure time: The longer the user stays in the area with a high virus concentration, the higher the infection risk; Protective measures: Measures such as wearing masks and maintaining social distance can significantly reduce the infection risk; Individual health status: Users with lower immunity or underlying diseases are more likely to be infected.

[0091] ,

[0092] Where: C represents the virus concentration, a normalized value ranging from [0, 1]; T represents the exposure time, a normalized value ranging from [0, 1]; P represents the effectiveness of protective measures, ranging from [0, 1], where 1 indicates complete protection and 0 indicates no protection; , , , : The weights of each factor are adjusted according to the actual situation.

[0093] , where k is the virus release efficiency coefficient, related to the user's virus load; V is the user's activity intensity, ranging from [0, 1], where 1 indicates high-intensity activity and 0 indicates stationary; A is the environmental transmission factor, considering the effects of temperature, humidity, air flow velocity, etc.; t is the user's staying time.

[0094] The work of the user interface module includes the following: Let the user understand the virus concentration, risk level, and surrounding environmental conditions in the current area, display the virus field distribution in the form of a heat map on the map, provide real-time values of key indicators such as virus concentration, temperature, humidity, and air flow velocity, mark high-risk areas, and distinguish different risk levels with colors, such as green for low risk and red for high risk, provide targeted protective measures according to the user's health status and surrounding environment, generate personalized suggestions by combining the user's health data and environmental data, if the virus concentration is high, recommend wearing a mask or leaving the area as soon as possible, if the body temperature is abnormal, recommend seeking medical examination, if the blood oxygen level drops, recommend reducing activities and monitoring the physical condition, send reminders when the user approaches a high-risk area or has a health abnormality, use sound, vibration, or notification messages to remind the user to pay attention to protection, provide an escape route or a low-risk alternative route, help the user review historical data, evaluate long-term trends, synchronize the user's health data and environmental data to the cloud, provide a timeline view to display the user's historical trajectory and risk exposure situation,

[0095] Specifically, it includes the following units:

[0096] Data receiving unit: Obtain real-time virus field information and user health data from the cloud;

[0097] Map display unit: Integrate map services to display the virus field distribution;

[0098] Personalized recommendation unit: Generate prevention and control suggestions based on machine learning models;

[0099] Notification unit: Implement the early warning function and push real-time reminders.

[0100] For users using smart masks, this system also includes a mask prevention and control optimization module, which dynamically adjusts the protection mode of the mask (such as ventilation volume, filtration efficiency) according to the personal health data and virus field information collected by the smart chip;

[0101] The working process of the mask prevention and control optimization module is as follows:

[0102] Receive personal health data and virus field data, and regulate the mask protection mode with an intelligent chip. The regulated parameters include the following: ventilation volume, control the air flow by adjusting the ventilation valve of the mask; filtration efficiency, adjust the density of the filter layer or enable multi-layer filtration; sealing performance, ensure the sealing performance by adjusting the fit of the mask edge; additional functions, such as enabling activated carbon filtration, ultraviolet sterilization, etc.;

[0103] The adjustment strategies include adjustments based on personal health data and adjustments based on virus field information. Among them, the adjustments based on personal health data include: abnormal breathing frequency, if the breathing frequency is too high, increase the ventilation volume to reduce the breathing resistance, if the breathing frequency is too low, reduce the ventilation volume to improve the filtration efficiency; abnormal body temperature, if the body temperature rises, increase the ventilation volume to help dissipate heat; abnormal blood oxygen saturation, if the blood oxygen saturation decreases, increase the ventilation volume to improve oxygen supply;

[0104] The adjustments based on personal health data include: high virus concentration, improve the filtration efficiency, enable multi-layer filtration or activated carbon filtration, reduce the ventilation volume, and reduce the risk of virus inhalation; high regional risk level, improve the sealing performance, ensure the tight fit of the mask edge, enable additional functions, ultraviolet sterilization; harsh environmental conditions, increase the ventilation volume to improve comfort in high-temperature and high-humidity environments, and reduce the ventilation volume to maintain the internal temperature of the mask in low-temperature environments.

[0105] To achieve adjustable prevention and control of the mask, install on the intelligent chip mask: an adjustable ventilation valve, use a micro motor or shape memory alloy to control the opening and closing degree of the ventilation valve; a multi-layer filtration system, design a switchable filter layer, and adjust the filtration efficiency through a mechanical device; a sealing adjustment device, design an inflatable airbag at the mask edge, and adjust the sealing performance by adjusting the airbag pressure; an additional function module, integrate an ultraviolet LED lamp or an activated carbon filter layer, and enable it as needed. Use machine learning algorithms on the intelligent chip to fuse and analyze personal health data and virus field information, generate the optimal protection mode, and adjust the hardware parameters of the mask in real time through a microcontroller.

[0106] Working process: The virus field monitoring unit deploys a high-density sensor network in public places and residential areas, including highly sensitive biosensors, environmental sensors, and high-definition cameras, to collect in real-time the concentration of virus particles in the air, temperature and humidity, air flow velocity, pedestrian flow, and video information. Key sensors are deployed at key points to ensure the monitoring coverage. The intelligent chip unit is embedded in the intelligent chip of masks or wearable devices to monitor in real-time the positioning data, health data of users, and the virus concentration in the surrounding environment. Through low-power Bluetooth or 5G technology, the data of the intelligent chip and sensors are transmitted to the cloud in real-time. Based on machine learning algorithms, a model of the virus field distribution is built to analyze the virus transmission trend and predict high-risk areas. Combining environmental data, pedestrian flow, protection measures, and disinfection implementation, the infection risk R1 of the virus field is calculated. Based on the health data of users, the infection risk R2 of users is calculated. Combining the virus field infection risk R1 and the user infection risk R2, the infection risk R of users is dynamically adjusted 2n and the virus field infection risk ΔR1(t). The user interface module displays the virus field distribution in the form of a heat map on the map, marks high-risk areas, provides real-time values of key indicators, and provides targeted protection suggestions according to the health status of users and the surrounding environment. The mask prevention and control optimization module dynamically adjusts the mask protection mode. The public health data platform constructs a unified data platform, integrates virus field monitoring data, intelligent chip operation data, and user health information, supports government agencies, medical institutions, and individual users to query and analyze data, and realizes cross-regional data sharing and collaborative prevention and control.

[0107] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0108] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A mask prevention and control system based on the respiratory infectious disease virus field, characterized in that, It includes the following modules: The data acquisition module includes a virus field monitoring unit and an intelligent chip unit. The virus field monitoring unit includes a sensor network and a monitoring network deployed in public places and residential areas; The intelligent chip unit is an intelligent chip embedded in a mask or a wearable device, which obtains the positioning data of the user, and real-time monitors the health data of the user and the virus concentration in the surrounding environment; The data analysis and prediction module models the virus field distribution based on machine learning algorithms, analyzes the virus transmission trend and predicts high-risk areas, combines the spatial distance threshold to judge the data correlation to supplement the spatial distribution map of virus concentration and the infection risk index of the user, and adjusts the spatial distance threshold according to the virus transmission degree evaluation coefficient to optimize the model accuracy; The data analysis and prediction module obtains environmental data from the virus field monitoring unit, obtains health data from the intelligent mask or wearable device worn by the user, and also obtains external data from an external system. The external data includes population density, traffic flow, and weather conditions. It performs data cleaning and processing to form a dataset around the virus field and a dataset around the user, and uses the dataset of the virus field and the dataset of the user as the basis for modeling; The dataset surrounding the virus field includes the following elements: environmental virus concentration C, temperature and humidity and air flow conditions E, pedestrian flow P, status of personnel protection measures M, disinfection implementation in the area D, and the virus field infection risk R1 can be expressed as a function of the above factors: R1 = f(C, E, P, M, D). The simplified infection risk model is expressed as , where k is a proportionality constant used to adjust the dimension and sensitivity of the model.

2. The mask prevention and control system based on the respiratory infectious disease virus field according to claim 1, wherein: The dataset of the user includes, but is not limited to, the following elements: respiratory system elements, circulatory system elements, body temperature elements, fatigue and stress-related elements, as well as activity level and contact history; The infection risk R2 of the user is the weighted sum of multiple factors: , x i represents the value of the i-th health factor, f i (x i ) represents the influence function of the i-th factor on the infection risk, represents the weight of the i-th factor.

3. The mask prevention and control system based on the respiratory infectious disease virus field according to claim 2, characterized in that: The data analysis and prediction module first models according to the dataset around the virus field and the dataset around the user respectively, generates the spatial distribution map of virus concentration and the infection risk index of the user, combines the correlation of the two data, and supplements the spatial distribution map of virus concentration and the infection risk index of the user. The method for determining the correlation of the spatial distribution map of virus concentration and the infection risk index data of the user is as follows: Using the positioning data in the intelligent chip and the deployment location of the virus field monitoring unit, judge whether the two are in the same area, define a spatial distance threshold. If the distance between the location of the intelligent chip user and the virus field monitoring unit is less than this threshold, compare the timestamps of the data of the intelligent chip and the virus field monitoring unit, and select the data with a time difference less than a certain threshold for matching. If both conditions are met, it is considered that the two data are relevant.

4. The mask prevention and control system based on the respiratory infectious disease virus field according to claim 3, wherein: Define a spatial distance threshold, including: Extract the virus diffusion rate, the average wind speed and the population flow density under the current environmental conditions; Use the virus diffusion rate, the average wind speed and the population flow density under the current environmental conditions to obtain the virus transmission degree evaluation coefficient corresponding to the current environmental conditions; Extract the virus field infection risk R1 and the user's infection risk R2; Perform numerical normalization processing on the virus field infection risk R1 and the user's infection risk R2 to obtain the virus field infection risk R1 and the user's infection risk R2 after numerical normalization processing; Use the virus transmission degree evaluation coefficient to combine the virus field infection risk R1 and the user's infection risk R2 after numerical normalization processing to set the spatial distance threshold.

5. The mask prevention and control system based on the respiratory infectious disease virus field according to claim 4, characterized in that: Using the virus transmission degree evaluation coefficient to combine the virus field infection risk R1 and the user's infection risk R2 after numerical normalization processing to set the spatial distance threshold, including: Retrieve the virus transmission degree evaluation coefficient; Compare the virus transmission degree evaluation coefficient with a preset factor threshold; When the virus transmission degree evaluation coefficient is lower than the preset factor threshold, set the spatial distance threshold according to a predetermined initial distance of 50 meters or 100 meters; When the virus transmission degree evaluation coefficient is not lower than the preset factor threshold, extract the interference range radius of the virus field monitoring unit; Set the spatial distance threshold using the virus transmission degree evaluation coefficient and the interference range radius in combination with the numerically normalized virus field infection risk R1 and the user's infection risk R2.

6. The mask prevention and control system based on the respiratory infectious disease virus field according to claim 5, characterized in that: The method for supplementing the spatial distribution map of virus concentration and the user's infection risk index is as follows: Numerically normalize the virus field infection risk R1 in the region related to the spatial distribution map of virus concentration and the user data, and the infection risk R2 of the user, and then conduct a numerical comparison. When the virus field infection risk R1 in the region related to the user data is higher than the infection risk R2 index of the user, the infection risk of the user is redefined as R 2n , when the infection risk R2 of the user is higher than the virus field infection risk R1 in the region related to the user data, increase the change ΔR1 in the virus field infection risk caused by the user's activities on the basis of the virus field infection risk R1 in the region related to the user data ( t ); , where: C represents the virus concentration, a normalized value with a range of [0, 1]; T represents the exposure time, a normalized value with a range of [0, 1]; P represents the effectiveness of protective measures, with a range of [0, 1], where 1 represents complete protection and 0 represents no protection; , , , : The weights of each factor, adjusted according to the actual situation; , where k is the virus release efficiency coefficient, which is related to the user's viral load; V is the user's activity intensity, ranging from [0, 1], where 1 represents high-intensity activity and 0 represents inactivity; A is the environmental transmission factor, and t is the user's residence time.

7. The mask prevention and control system based on the respiratory infectious disease virus field according to claim 1, characterized in that: The system further includes a user interface module. The work of the user interface module includes the following: enabling the user to understand the virus concentration, risk level, and surrounding environmental conditions in the current area, providing targeted protection measures according to the user's health status and the surrounding environment, generating personalized suggestions by combining the user's health data and environmental data, sending reminders when the user approaches a high-risk area or shows health abnormalities, and helping the user review historical data and evaluate long-term trends. Specifically, it includes the following units: Data receiving unit: Obtain real-time virus field information and user health data from the cloud; Map display unit: Integrate map services and display the virus field distribution; Personalized recommendation unit: Generate prevention and control suggestions based on a machine learning model; Notification unit: Implement the early warning function and push real-time reminders.

8. The mask prevention and control system based on the respiratory infectious disease virus field according to claim 1, characterized in that: The system further includes: a transmission module that transmits the smart chip and sensor data to the cloud in real time through low-power Bluetooth or 5G technology; A public health data platform for constructing a unified data platform, integrating virus field monitoring data, smart chip operation data, and user health information, supporting government agencies, medical institutions, and individual users to query and analyze, and realizing cross-regional data sharing and collaborative prevention and control.

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