Mask prevention and control system based on respiratory infectious disease virus field

Abstract

The invention discloses a mask prevention and control system based on a respiratory infectious disease virus field, and belongs to the technical field of infectious disease prevention and control. The mask prevention and control system based on the respiratory infectious disease virus field comprises the following modules: a data acquisition 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. According to the mask prevention and control system based on the respiratory infectious disease virus field, the virus concentration, environmental conditions and user health data are monitored in real time through the high-density sensor network and the intelligent chip, the virus propagation trend and a high-risk area are accurately predicted based on a machine learning algorithm, early warning information is issued in time, and the safety of a user is improved. According to the health state and the surrounding environment of the user, the protection mode of the mask is dynamically adjusted, personalized protection is provided, and targeted protection suggestions are generated in combination with the health data and the environment data of the user to help the user to 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 being infected. 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 combined with intelligent chip technology to improve the efficiency and intelligent level of the prevention and control of respiratory infectious diseases. 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 real-time monitor virus concentration, environmental conditions and user health data. 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 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: A mask prevention and control system based on the respiratory infectious disease virus field, characterized by including the following modules: 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; 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; The data analysis and prediction module, based on machine learning algorithms, models the distribution of the virus field, analyzes the virus transmission trend and predicts high-risk areas, determines data correlation by combining a spatial distance threshold to supplement the spatial distribution map of virus concentration and the infection risk indicators of users, and adjusts the spatial distance threshold according to the virus transmission degree evaluation coefficient to optimize the model accuracy.

[0006] 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 users. Based on the datasets of the virus field and users, 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 flow of people and protective measures, records the movement trajectories of users in combination with positioning information, and counts the number of contacts and the duration between people.

[0007] Preferably, the dataset around the virus field includes the following elements: environmental virus concentration C, temperature, humidity and air flow conditions E, flow of people P, status of personnel protective measures M, implementation of disinfection within the area D, and virus field infection risk R 1 Can be expressed as a function of the above factors: R 1 = 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.

[0008] Preferably, the dataset of users 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. The infection risk R of users 2 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.

[0009] 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: 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.

[0010] Preferably, defining a spatial distance threshold includes: Extract the virus diffusion rate, the average wind speed and the pedestrian flow density of the current environment; Using the virus diffusion rate, the average wind speed and the pedestrian flow density of the current environment, obtain the virus transmission degree evaluation coefficient corresponding to the current environment conditions; Extract the infection risk R of the virus field 1 and the infection risk R of the user 2 ; For the infection risk R of the virus field 1 and the infection risk R of the user 2 Perform numerical normalization processing to obtain the numerically normalized infection risk R of the virus field 1 and the infection risk R of the user 2 ; Using the virus transmission degree evaluation coefficient, combine the numerically normalized infection risk R of the virus field 1 and the infection risk R of the user 2 Set the spatial distance threshold.

[0011] Preferably, using the virus transmission degree evaluation coefficient, combine the numerically normalized infection risk R of the virus field 1 and the infection risk R of the user 2 Set the spatial distance threshold, including: Retrieve the virus transmission degree evaluation coefficient; Among them, the virus transmission degree evaluation coefficient is obtained through the following formula: 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; 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; Use the virus transmission degree evaluation coefficient and the interference range radius to combine the virus field infection risk R after numerical normalization 1 and the user's infection risk R 2 Set the spatial distance threshold Among them, the spatial distance threshold is obtained through the following formula: 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; R 1 represents the virus field infection risk of the area after numerical normalization; R 2 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 at two levels.

[0012] Preferably, the method for supplementing the spatial distribution map of virus concentration and the user's infection risk index is as follows: For the virus field infection risk R of the area related to user data in the spatial distribution map of virus concentration 1 and the user's infection risk R 2 Perform numerical normalization, and then perform numerical comparison. When the virus field infection risk R of the area related to user data 1 is higher than the user's infection risk R 2 index, then the user's infection risk is redefined as R 2n , when the user's infection risk R 2 is higher than the virus field infection risk R of the area related to user data1 When the virus field infection risk R in the area related to user data 1 is increased by the change in virus field infection risk ΔR caused by user activities 1 ( t ), , 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.

[0013] , where k is the virus release efficiency coefficient, related to the user's virus load; V is the user activity intensity, ranging from [0, 1], 1 indicates high-intensity activity, and 0 indicates stationary; A is the environmental transmission factor, considering the influence of temperature, humidity, air flow velocity, etc.; t is the user's stay time.

[0014] Preferably, the system further includes a user interface module, and 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, displaying the virus field distribution in the form of a heat map on the map, providing real-time values of key indicators, marking high-risk areas, and differentiating different risk levels with colors, providing targeted protective measures according to the user's health status and surrounding environment, generating personalized suggestions by combining the user's health data and environmental data, suggesting wearing a mask or leaving the area as soon as possible if the virus concentration is high, suggesting seeking medical attention if the body temperature is abnormal, suggesting reducing activities and monitoring the physical condition if the blood oxygen level drops, sending reminders when the user approaches a high-risk area or shows health abnormalities, using sound, vibration, or notification messages to remind the user to pay attention to protection, providing an escape route or a low-risk alternative route, helping the user review historical data, evaluate long-term trends, synchronizing the user's health data and environmental data to the cloud, providing a timeline view to display the user's historical trajectory and risk exposure situation, 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 to 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.

[0015] 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; 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 Compared with the prior art, the beneficial effects of the present invention are: 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 virus concentration, environmental conditions, and user health data in real time. Based on machine learning algorithms, it 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 to generate targeted protection suggestions, helps users reduce the infection risk, formulates scientific prevention and control strategies based on the quantitative analysis of virus field infection risk and user infection risk, realizes cross-regional data sharing and collaborative prevention and control through the public health data platform, improves the overall prevention and control efficiency, significantly reduces the risk of users being infected with respiratory infectious diseases through accurate monitoring and personalized protection, and improves the overall public health safety level through data sharing and collaborative prevention and control. Description of the Drawings

[0016] 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

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described 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.

[0018] To solve the problems in the prior art that ordinary masks cannot adjust their protection performance according to environmental changes and do not fully utilize smart chip technology to collect and analyze individual health data, please refer to Figure 1 , the following technical solutions are provided in this embodiment: The mask prevention and control system based on the respiratory infectious disease virus field includes the following modules: The data acquisition module includes 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 in the air, temperature, humidity, air flow velocity, pedestrian flow, and video information in real time. The sensor network is deployed by combining 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 and focus on densely populated areas such as security checkpoints and waiting areas; commercial facilities cover concentrated points of pedestrian flow such as main passages, cash desks, 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.

[0019] The intelligent chip unit is an intelligent chip embedded in a mask or wearable device, which obtains the positioning data of the user and monitors the user's health data and the virus concentration in the surrounding environment in real time. The health data includes, but is not limited to, respiratory rate, body temperature, heart rate, blood oxygen, and sleep quality. The transmission module transmits the data of the intelligent chip and sensors to the cloud in real time through low-power Bluetooth or 5G technology. 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. The user interface module provides real-time virus field information and personalized prevention and control suggestions to users through mobile applications or intelligent devices. The public health data platform constructs a unified data platform to integrate virus field monitoring data, intelligent 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.

[0020] 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 likelihood of virus transmission, identifies areas that may become high-risk in the future, analyzes the lag 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.

[0021] The dataset around the virus field includes the following elements: environmental virus concentration C, temperature, humidity, and airflow conditions E, the flow of people P, the status of personnel protective measures M, the implementation of disinfection within the area D, and the infection risk R of the virus field. 1 It can be expressed as a function of the above factors: R 1 = 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.

[0022] Environmental virus concentration C = C max / C actual 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; The flow of people , P density represents the population density within the area, unit: people per square meter, and 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, and α represents the influence coefficient of the contact frequency, usually set to 0.1 - 0.3; The 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], where 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], where 0 means complete non-compliance and 1 means strict compliance; The implementation of disinfection within the area , , represent the weights of the disinfection frequency and efficiency, full ; D frequency represents the disinfection frequency, with a value range of [0, 1], where 0 means never disinfected and 1 means high-frequency disinfection; D efficiency represents the disinfection efficiency, with a value range of [0, 1], where 0 means ineffective and 1 means highly efficient.

[0023] 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, The user's infection risk R 2 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.

[0024] 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, and combines the correlation of the two sets of data to supplement 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: Using the positioning data in the intelligent chip and the deployment location of the virus field monitoring unit, determine whether they 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 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.

[0025] At the same time, define a spatial distance threshold, which also includes: Extract the virus diffusion rate, the average wind speed and the population density in the current environment; Use the virus diffusion rate, the average wind speed and the population density in the current environment to obtain the corresponding virus transmission degree evaluation coefficient in the current environment; Extract the virus field infection risk R 1 and the user's infection risk R 2 ; For the virus field infection risk R1 and the infection risk R of the user 2 Perform numerical normalization to obtain the virus field infection risk R after numerical normalization 1 and the infection risk R of the user 2 ; Utilize the virus transmission degree evaluation coefficient to combine with the virus field infection risk R after numerical normalization 1 and the infection risk R of the user 2 Set the spatial distance threshold

[0026] The technical effects of the above technical solutions 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. It can effectively improve the safety of public places and reduce the risk of virus transmission

[0027] Specifically, utilize the virus transmission degree evaluation coefficient to combine with the virus field infection risk R after numerical normalization 1 and the infection risk R of the user 2 Set the spatial distance threshold, including: Retrieve the virus transmission degree evaluation coefficient Among them, the virus transmission degree evaluation coefficient is obtained through the following formula: 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 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 Use the virus transmission degree evaluation coefficient and the interference range radius to combine with the virus field infection risk R after numerical normalization 1and the user's infection risk R 2 Set spatial distance threshold The spatial distance threshold is obtained by the following formula: 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 ambient temperature on the virus propagation speed, and the attenuation coefficient of the current ambient temperature on the virus propagation speed is determined by 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 They represent the attenuation coefficient of the current ambient temperature on the virus transmission speed and the attenuation coefficient of the current ambient humidity on the virus transmission speed; R 1 represents the regional virus field infection risk after numerical normalization; R 2 It represents the infection risk of the user after the numerical normalization. A represents the interference radius distance division constant, and its value range can be 5-10, or it can be set according to the specific range of the interference radius, such as 20, 50, 100, etc.

[0028] The technical effect of the above technical solution is: by using the virus transmission degree evaluation coefficient in combination with the actual conditions of the current environment to set the spatial distance threshold, the accuracy of the spatial distance threshold setting under the current environmental conditions and the matching of the spatial distance threshold setting with 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 a condition for triggering the setting of the spatial distance threshold, the triggering accuracy and automaticity of the spatial distance threshold setting can be effectively improved, thereby preventing the occurrence of unnecessary distance setting scenarios, thereby 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, the spatial distance threshold obtained by the above method effectively improves the efficiency of obtaining the spatial distance threshold while maximizing the matching degree of the spatial distance threshold with the current environmental conditions, thereby increasing the subsequent virus field transmission risk R 1 and the user's infection risk R 2 At the same time, it also improves the risk of virus field transmission R 1 and the user's infection risk R 2 The correlation between them determines the flexibility and sensitivity of adaptive adjustment to follow the changes in environmental conditions, preventing the fixed spatial distance threshold from causing the virus field infection risk R to be determined according to the same distance threshold regardless of the environmental conditions. 1 and the user's infection risk R2 The problem of low accuracy and large error in the relevance evaluation occurs due to the determination of the relevance between them.

[0029] The method for supplementing the spatial distribution map of virus concentration and the user's infection risk index is as follows: For the virus field infection risk R in the area related to the user data in the spatial distribution map of virus concentration 1 and the user's infection risk R 2 Perform numerical normalization and then numerical comparison. When the virus field infection risk R in the area related to the user data 1 is higher than the user's infection risk R 2 index, then the user's infection risk is redefined as R 2n . When the user's infection risk R 2 is higher than the virus field infection risk R in the area related to the user data 1 , on the basis of the virus field infection risk R in the area related to the user data 1 , add the change ΔR in the virus field infection risk caused by the user's activities 1 ( t ). 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 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.

[0030] , 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 represents complete protection, and 0 represents no protection; , , , : The weights of each factor are adjusted according to the actual situation.

[0031] , 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 represents high-intensity activity, and 0 represents static; A is the environmental transmission factor, considering the influence of temperature, humidity, air flow velocity, etc.; t is the user's stay time.

[0032] 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, displaying the virus field distribution in the form of a heat map on the map, providing real-time values of key indicators such as virus concentration, temperature and humidity, and air flow velocity, marking high-risk areas, and differentiating different risk levels with colors, such as green for low risk and red for high risk, 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, if the virus concentration is high, suggesting wearing a mask or leaving the area as soon as possible, if the body temperature is abnormal, suggesting seeking medical examination, if the blood oxygen level drops, suggesting reducing activities and monitoring the physical condition, sending reminders when the user approaches a high-risk area or has a health abnormality, using sound, vibration, or notification messages to remind the user to pay attention to protection, providing an escape route or a low-risk alternative route, helping the user review historical data and evaluate long-term trends, synchronizing the user's health data and environmental data to the cloud, and providing a timeline view to show the user's historical trajectory and risk exposure situation. 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 to display the virus field distribution; Personalized recommendation unit: Generate prevention and control suggestions based on machine learning models; Notification unit: Implement the early warning function and push real-time reminders.

[0033] For users using smart masks, the 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; The working process of the mask prevention and control optimization module is as follows: Receive personal health data and virus field data, and regulate the protection mode of the mask with a smart 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; airtightness, ensure airtightness by adjusting the fit of the mask edge; additional functions, such as enabling activated carbon filtration, ultraviolet sterilization, etc.; 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 breathing resistance, if the breathing frequency is too low, reduce the ventilation volume to improve 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 drops, increase the ventilation volume to improve oxygen supply; Adjustments based on personal health data include: when the virus concentration is high, increase the filtration efficiency, enable multi-layer filtration or activated carbon filtration, reduce the ventilation volume, and lower the risk of inhaling the virus; when the regional risk level is high, improve the sealing performance, ensure that the mask edge fits tightly, enable additional functions, such as ultraviolet sterilization; when the environmental conditions are harsh, 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.

[0034] To achieve adjustable prevention and control of the mask, install the following on the intelligent chip mask: an adjustable ventilation valve, using a micro motor or shape memory alloy to control the opening and closing degree of the ventilation valve; a multi-layer filtration system, designing a switchable filtration layer, and adjusting the filtration efficiency through a mechanical device; a sealing adjustment device, designing an inflatable airbag at the mask edge, and adjusting the sealing performance by adjusting the airbag pressure; an additional function module, integrating an ultraviolet LED lamp or an activated carbon filtration layer, and enabling 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.

[0035] 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 real-time data on the concentration of virus particles in the air, temperature, humidity, air flow velocity, the number of people, and video information. Deploy key sensors at key points to ensure the monitoring coverage. The intelligent chip unit is embedded in the intelligent chip of the mask or wearable device, which can monitor the user's location data, health data, and the virus concentration in the surrounding environment in real time. Through low-power Bluetooth or 5G technology, the intelligent chip and sensor data are transmitted to the cloud in real time. Based on machine learning algorithms, model the virus field distribution, analyze the virus transmission trend, and predict high-risk areas. Combine environmental data, the number of people, protection measures, and the implementation of disinfection and sterilization to calculate the virus field infection risk R 1 , calculate the user's infection risk R based on the user's health data 2 , combine the virus field infection risk R 1 and the user's infection risk R 2 , dynamically adjust the user's infection risk R 2n and the virus field infection risk ΔR 1 (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 user's health status 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.

[0036] It should be noted that in this text, 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, such 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.

[0037] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art 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 a respiratory infectious disease virus field, characterized in that: Includes the following modules: A data acquisition module, including a virus field monitoring unit and an intelligent chip unit, wherein the virus field monitoring unit includes a sensor network and a monitoring network deployed in public places and residential areas; The smart chip unit is a smart chip embedded in a mask or wearable device to obtain the user's location data and monitor the user's health data and the virus concentration in the surrounding environment in real time; The data analysis and prediction module models the virus field distribution based on machine learning algorithms, analyzes virus transmission trends and predicts high-risk areas, combines spatial distance thresholds to judge data correlation, supplements the spatial distribution map of virus concentration and the user's infection risk index, and adjusts the spatial distance threshold according to the virus transmission degree evaluation coefficient to optimize the model accuracy.

2. The mask prevention and control system based on respiratory infectious disease virus field according to claim 1 is characterized in that: The data analysis and prediction module obtains environmental data from the virus field monitoring unit, obtains health data from the smart mask or wearable device worn by the user, and obtains external data from the external system. The external data includes population density, traffic flow, and weather conditions. It cleans and processes the data to form a data set around the virus field and a data set around the user, and uses the data set of the virus field and the data set of the user as the basis for modeling.

3. The mask prevention and control system based on respiratory infectious disease virus field according to claim 2 is characterized in that: The data set around the virus field includes the following elements: environmental virus concentration C, temperature, humidity and airflow conditions E, human flow P, personnel protection measures status M, and regional disinfection implementation status 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 is expressed as , k is a proportionality constant used to adjust the dimension and sensitivity of the model.

4. The mask prevention and control system based on respiratory infectious disease virus field according to claim 3 is characterized in that: The user's data set includes but is not limited to the following elements: respiratory system elements, circulatory system elements, body temperature elements, fatigue and stress-related elements, activity level, and contact history; The user's infection risk R2 is the weighted sum of multiple factors: , x i represents the value of the ith health factor, f i (x i ) represents the impact function of the ith factor on the infection risk, represents the weight of the ith element.

5. The mask prevention and control system based on respiratory infectious disease virus field according to claim 4 is characterized in that: The data analysis and prediction module first models the data set around the virus field and the data set around the user, respectively, to generate a spatial distribution map of the virus concentration and the infection risk index of the user, and combines the correlation of the two data to supplement the spatial distribution map of the virus concentration and the infection risk index of the user. The correlation determination method of the spatial distribution map of the virus concentration and the infection risk index data of the user is as follows: The positioning data in the smart chip and the deployment location of the virus field monitoring unit are used to determine whether the two are in the same area. A spatial distance threshold is defined. If the distance between the location of the smart chip user and the virus field monitoring unit is less than the threshold, the timestamps of the smart chip and virus field monitoring unit data are compared, and the data with a time difference less than a certain threshold is selected for matching. If both conditions are met, the data of the two are considered to be related.

6. The mask prevention and control system based on respiratory infectious disease virus field according to claim 5 is characterized in that: Define a spatial distance threshold, including: Extract the virus diffusion rate under current environmental conditions, the average wind speed and crowd density of the current environment; The virus diffusion rate under the current environmental conditions, the average wind speed and the crowd density in the current environment are used 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; Performing 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; The spatial distance threshold is set by using the virus transmission degree evaluation coefficient combined with the virus field infection risk R1 and the user's infection risk R2 after numerical normalization.

7. The mask prevention and control system based on respiratory infectious disease virus field according to claim 6 is characterized in that: The spatial distance threshold is set by using the virus transmission degree evaluation coefficient combined with the virus field infection risk R1 and the user's infection risk R2 after numerical normalization, including: Retrieve the virus transmission degree evaluation coefficient; Comparing 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, the spatial distance threshold is set according to the predetermined initial distance of 50 meters or 100 meters; When the virus propagation degree evaluation coefficient is not lower than a preset factor threshold, the interference range radius of the virus field monitoring unit is extracted; The spatial distance threshold is set using the virus transmission degree evaluation coefficient and the interference range radius combined with the virus field infection risk R1 and the user's infection risk R2 after numerical normalization.

8. The mask prevention and control system based on respiratory infectious disease virus field according to claim 5 is characterized in that: The method for supplementing the spatial distribution map of virus concentration and the infection risk index of the user is as follows: The spatial distribution map of virus concentration is normalized with the virus field infection risk R1 of the area related to the user data and the user's infection risk R2, and then the values ​​are compared. When the virus field infection risk R1 of the area related to the user data is higher than the user's infection risk R2, the user's infection risk is redefined as R 2n , when the user's infection risk R2 is higher than the regional virus field infection risk R1 associated with the user data, the virus field infection risk change ΔR1 caused by the user's activity is added to the regional virus field infection risk R1 associated with the user data ( t ); , where: C represents virus concentration, normalized value, ranging from [0, 1]; T represents exposure time, normalized value, ranging from [0, 1]; P represents the effectiveness of protective measures, ranging from [0, 1], 1 represents complete protection, and 0 represents no protection; , , , : The weight of each factor is 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], 1 represents high-intensity activity, and 0 represents stillness; A is the environmental transmission factor, and t is the user's stay time.

9. The mask prevention and control system based on respiratory infectious disease virus field according to claim 8 is characterized in that: The system also includes a user interface module, the work of which includes the following: enabling users to understand the virus concentration, risk level and surrounding environment conditions in the current area, providing targeted protective measures based on the user's health status and surrounding environment, generating personalized suggestions based on the user's health data and environmental data, issuing reminders when the user approaches a high-risk area or has health abnormalities, helping users review historical data and evaluate long-term trends, and specifically including the following units: Data receiving unit: obtains real-time virus field information and user health data from the cloud; Map display unit: integrated map service to display the distribution of virus fields; Personalized recommendation unit: Generates prevention and control recommendations based on machine learning models; Notification unit: realizes early warning function and pushes real-time reminders.

10. The mask prevention and control system based on respiratory infectious disease virus field according to claim 1 is characterized in that: The system also includes: a transmission module, which transmits the smart chip and sensor data to the cloud in real time via low-power Bluetooth or 5G technology; The public health data platform is used to build a unified data platform to integrate virus field monitoring data, smart chip operation data and user health information, support query and analysis by government agencies, medical institutions and individual users, and realize cross-regional data sharing and collaborative prevention and control.

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