Real-time early warning system and method based on indoor microorganism propagation risk

Abstract

The invention discloses a real-time early warning system and method based on an indoor microorganism propagation risk, and particularly relates to the technical field of microorganism propagation risk early warning. The system comprises a monitoring preprocessing module, an air environment data acquisition and analysis module, a personnel activity data acquisition and analysis module, a microorganism data acquisition and analysis module, a propagation risk grade judgment module, a microorganism propagation risk response module, an intelligent display terminal and a database. According to the invention, the monitoring demand level of each time period is divided, and the data acquisition frequency of the time period corresponding to each monitoring demand level is set, so that the working resources of the indoor microbial propagation risk real-time early warning system are saved, and the working efficiency of the indoor microbial propagation risk real-time early warning system is improved; according to the invention, the influence of the indoor air environment and the indoor personnel activity on the microbial propagation risk index is fully considered, and the accuracy and scientificity of the real-time early warning system for the indoor microbial propagation risk are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial transmission risk early warning, and particularly relates to a real-time early warning system and method for indoor microbial transmission risk. Background Art

[0002] Infectious diseases are transmitted through ways such as air and contact. The indoor environment has become a high-risk area for transmission. The air quality of the indoor environment is affected by various factors, including poor air fluidity, poor ventilation, and pollution sources. Microorganisms such as molds and bacteria can rapidly reproduce and spread in these environments, affecting people's health. With the development of the Internet of Things technology, potential microbial transmission risks can be predicted through means such as sensors and data acquisition devices. Therefore, the research on a real-time early warning system for indoor microbial transmission risk is necessary.

[0003] In the prior art, real-time early warning of indoor microbial transmission risk can meet certain requirements, but there are also some potential risks. Specifically: on the one hand, the utilization of monitoring data for each time period within each historical monitoring cycle is insufficient, ignoring the different monitoring requirements for each time period behind the data, using the same data monitoring frequency, resulting in a large amount of redundant monitoring data, increasing the workload of the early warning system and wasting work resources, and reducing the stability of the early warning system. On the other hand, the prior art ignores the analysis of the potential impact of air environment data and personnel activity data on microbial transmission risk, making it difficult to effectively analyze the impact of air environment and personnel activities on the living space of microorganisms, resulting in insufficient early warning of microbial transmission risk and difficulty in meeting the actual monitoring and early warning requirements. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time early warning system and method for indoor microbial transmission risk, which solves the problems existing in the background art.

[0005] To solve the above technical problems, in the first aspect of the present invention, a real-time early warning system for indoor microbial transmission risk is provided. The system includes: a monitoring preprocessing module, which is used to extract the indoor microbial transmission risk index for each time period within the historical monitoring cycle from the database, divide the monitoring requirement levels for each time period, and set the data acquisition frequency for the time periods corresponding to each monitoring requirement level.

[0006] An air environment data acquisition and analysis module, which is used to collect various data of the indoor air environment during the target time period and calculate the indoor air environment influence coefficient during the target time period.

[0007] A personnel activity data acquisition and analysis module, which is used to collect various data of indoor personnel activities during the target time period and calculate the indoor personnel activity influence coefficient during the target time period.

[0008] A microbial data acquisition and analysis module is used to collect various data of indoor microorganisms during a target time period, and calculate the indoor microbial transmission risk index based on the indoor air environment impact coefficient and the indoor human activity impact coefficient during the target time period.

[0009] A transmission risk level judgment module is used to evaluate the indoor microbial transmission risk level during the target time period according to the indoor microbial transmission risk index during the target time period.

[0010] A microbial transmission risk response module is used to implement microbial transmission risk response measures corresponding to each level of indoor microbial transmission risk during the target time period.

[0011] An intelligent display terminal is used to display various data parameters of the indoor air environment and the indoor microbial transmission risk level.

[0012] Preferably, the monitoring requirement levels of each time period are divided, and the data acquisition frequencies corresponding to each monitoring requirement level are set. The specific analysis method is as follows:

[0013] The indoor microbial transmission risk indices in each time period within each historical monitoring cycle are respectively subjected to mean processing to obtain the average indoor microbial transmission risk index αi in each historical time period, where i = 1, 2,..., n, i represents the number of each time period, and n represents the total number of time periods within the monitoring cycle.

[0014] Through the formula: The monitoring requirement level judgment value κi of each time period is obtained, where α′ represents the judgment threshold of the indoor microbial transmission risk index extracted from the database.

[0015] If κi = 1, it is judged that the monitoring requirement level of the i-th time period is a high requirement, and the acquisition frequencies of the air environment data, human activity data, and microbial data in the i-th time period are set to the first acquisition frequency extracted from the database.

[0016] If κi = 0, it is judged that the monitoring requirement level of the i-th time period is a low requirement, and the acquisition frequencies of the air environment data, human activity data, and microbial data in the i-th time period are set to the second acquisition frequency extracted from the database.

[0017] Preferably, the various data of the indoor air environment during the target time period are specifically: various data parameters of the indoor air environment, including the ambient temperature collected by a temperature sensor, the ambient humidity collected by a humidity sensor, and the carbon dioxide concentration collected by a carbon dioxide sensor.

[0018] Preferably, for calculating the indoor air environment impact coefficient within the target time period, the specific analysis method is as follows: Based on the environmental temperature, environmental humidity, and carbon dioxide concentration at each collection point within the target time period, an air environment data set Djk for each collection point within the target time period is formed, where j = 1, 2, …, m, j represents the number of each parameter in the air environment data set, m represents the total number of parameters in the air environment data set, k = 1, 2, …, q, k represents the number of each collection point, and q represents the total number of collection points.

[0019] Through the formula: The abnormal judgment value (QD)jk of each parameter in the air environment data set for each collection point within the target time period is calculated, where represents the standard interval of each parameter in the air environment data set extracted from the database.

[0020] Through the formula: The indoor air environment impact coefficient λ within the target time period is calculated.

[0021] Preferably, for each data of indoor personnel activities within the target time period, specifically: Each data parameter of indoor personnel activities includes: the indoor pedestrian flow, the average stay duration of personnel, and the number of high-density areas of personnel obtained through camera equipment and computer vision technology within the target time period.

[0022] Preferably, for calculating the indoor personnel activity impact coefficient within the target time period, the specific analysis method is as follows: The indoor pedestrian flow, the average stay duration of personnel, and the number of high-density areas of personnel within the target time period are combined to form a personnel activity data set Ra within the target time period, where a = 1, 2, …, x, a represents the number of each parameter in the personnel activity data set, x represents the total number of parameters in the personnel activity data set. Through the formula: The indoor personnel activity impact coefficient ρ within the target time period is calculated, where σa represents the unit impact factor of each parameter in the personnel activity data set extracted from the database.

[0023] Preferably, for calculating the indoor microbial transmission risk index within the target time period, the specific analysis method is as follows: The indoor microbial concentration g within the target time period collected by a bioaerosol detection sensor, based on the indoor air environment impact coefficient λ and the indoor personnel activity impact coefficient ρ within the target time period, through the formula: The indoor microbial transmission risk index η within the target time period is calculated, where represents the impact factor of the unit indoor microbial concentration extracted from the database, and e represents the natural constant.

[0024] Preferably, for the indoor microbial transmission risk level within the evaluated target time period, the specific analysis method is as follows: Based on the indoor microbial transmission risk index η within the target time period, if η is within the first interval of indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level within the target time period is a high risk.

[0025] If η is within the second interval of indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level within the target time period is a medium risk.

[0026] If η is within the third interval of indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level within the target time period is a low risk.

[0027] Preferably, for the microbial transmission risk response measures corresponding to each level of indoor microbial transmission risk within the implementation target time period, the specific analysis method is as follows: If the indoor microbial transmission risk level within the target time period is a high risk, the first microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the first intensity working state and prompting the evacuation of the crowd through a broadcast.

[0028] If the indoor microbial transmission risk level within the target time period is a medium risk, the second microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the second intensity working state and prompting people to maintain a safe distance and reduce personnel gathering through a broadcast.

[0029] If the indoor microbial transmission risk level within the target time period is a low risk, the third microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the third intensity working state and prompting people to wear protective equipment through a broadcast.

[0030] The second aspect of the present invention provides a real-time early warning method based on indoor microbial transmission risk. The method includes: Step 1: Divide the monitoring requirement levels for each time period and set the data collection frequencies corresponding to the time periods of each monitoring requirement level.

[0031] Step 2: Collect each data of the indoor air environment within the target time period and analyze the influence coefficient of the indoor air environment within the target time period.

[0032] Step 3: Collect each data of the personnel activities within the target time period and analyze the influence coefficient of the personnel activities within the target time period.

[0033] Step 4: Calculate the indoor microbial transmission risk index within the target time period.

[0034] Step 5: Evaluate the indoor microbial transmission risk level within the target time period.

[0035] Step 6: Implement the microbial transmission risk response measures corresponding to each level of the indoor microbial transmission risk during the target time period.

[0036] Compared with the prior art, the advantages of the present invention are as follows: First, the present invention divides the monitoring requirement levels for each time period, sets the data collection frequencies corresponding to each monitoring requirement level for the corresponding time periods, effectively avoids the redundancy of a large amount of monitoring data, saves the working resources of the indoor microbial transmission risk real-time warning system, improves the working efficiency of the indoor microbial transmission risk real-time warning system, and ensures the working stability of the indoor microbial transmission risk real-time warning system.

[0037] Second, the present invention analyzes the indoor microbial transmission risk index during the target time period based on the indoor air environment impact coefficient and the indoor personnel activity impact coefficient during the target time period, fully considers the impacts of the indoor air environment and indoor personnel activities on the microbial transmission risk index, and thus corrects the microbial transmission risk index, ensuring the accuracy and scientific nature of the indoor microbial transmission risk real-time warning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is the system structure connection diagram of the present invention.

[0040] Figure 2 It is the method flow schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0042] Embodiment 1:

[0043] Refer to Figure 1As shown in the figure, the first aspect of the present invention provides a real-time warning system for indoor microbial transmission risk. The system includes: a monitoring and preprocessing module, which is used to extract the indoor microbial transmission risk index within each time period during the historical monitoring period from the database, divide the monitoring requirement levels of each time period, and set the data collection frequency corresponding to each monitoring requirement level time period.

[0044] In a specific embodiment of the present invention, for dividing the monitoring requirement levels of each time period and setting the data collection frequency corresponding to each monitoring requirement level time period, the specific analysis method is as follows:

[0045] The indoor microbial transmission risk indexes within each time period during each historical monitoring period are respectively subjected to mean processing to obtain the average indoor microbial transmission risk index αi within each historical time period, where i = 1, 2,..., n, i represents the number of each time period, and n represents the total number of time periods within the monitoring period.

[0046] It should be noted that in a specific embodiment, the calculation method of the average indoor microbial transmission risk index within each historical time period is specifically: based on the indoor microbial transmission risk indexes within each time period during each historical monitoring period where c = 1, 2,..., y, c represents the number of each historical monitoring period, and y represents the total number within the historical monitoring period. Through the formula: The average indoor microbial transmission risk index αi within each historical time period is calculated.

[0047] It should be noted that in a specific embodiment, each monitoring period is, for example, one day, and each time period is composed of different monitoring time intervals within one day.

[0048] Through the formula: The monitoring requirement level judgment value κi of each time period is obtained, where α′ represents the judgment threshold of the indoor microbial transmission risk index extracted from the database.

[0049] If κi = 1, it is determined that the monitoring requirement level of the i-th time period is a high requirement, and the collection frequencies of the air environment data, personnel activity data, and microbial data in the i-th time period are set to the first collection frequency extracted from the database.

[0050] If κi = 0, it is determined that the monitoring requirement level of the i-th time period is a low requirement, and the collection frequencies of the air environment data, personnel activity data, and microbial data in the i-th time period are set to the second collection frequency extracted from the database.

[0051] It should be noted that in specific embodiments, the first data collection frequency and the second data collection frequency are manually set by technicians considering different monitoring requirement levels comprehensively. For example, when the monitoring requirement level in the i-th time period is high, the data collection frequency F in the i-th time period is set to 2H; when the monitoring requirement level in the i-th time period is low, the data collection frequency F in the i-th time period is set to H.

[0052] The present invention divides the monitoring requirement levels of each time period and sets the data collection frequencies corresponding to the time periods of each monitoring requirement level, effectively avoiding the redundancy of a large amount of monitoring data, saving the working resources of the indoor microbial transmission risk real-time warning system, improving the working efficiency of the indoor microbial transmission risk real-time warning system, and ensuring the working stability of the indoor microbial transmission risk real-time warning system.

[0053] The air environment data collection and analysis module is used to collect various data of the indoor air environment in the target time period and calculate the indoor air environment influence coefficient in the target time period.

[0054] In a specific embodiment of the present invention, the various data of the indoor air environment in the target time period are specifically: the various data parameters of the indoor air environment, including the ambient temperature collected by a temperature sensor, the ambient humidity collected by a humidity sensor, and the carbon dioxide concentration collected by a carbon dioxide sensor.

[0055] In a specific embodiment of the present invention, for the calculation of the indoor air environment influence coefficient in the target time period, the specific analysis method is as follows: based on the ambient temperature, ambient humidity, and carbon dioxide concentration at each collection point in the target time period, an air environment data set Djk at each collection point in the target time period is formed, where j = 1, 2,..., m, j represents the number of each parameter of the air environment data set, m represents the total number of parameters of the air environment data set, k = 1, 2,..., q, k represents the number of each collection point, and q represents the total number of collection points.

[0056] Through the formula: the abnormal judgment value (QD)jk of each parameter of the air environment data set at each collection point in the target time period is calculated, where represents the standard interval of each parameter of the air environment data set extracted from the database.

[0057] It should be noted that in a specific embodiment, the calculation method of the abnormal judgment value of each parameter of the air environment data set at each collection point within the target time period is determined by combining whether each parameter of the air environment data set at each collection point within the target time period is within the standard interval of each parameter of the air environment data set. If it is within the standard interval of each parameter of the air environment data set, the abnormal judgment value of each parameter of the air environment data set is set to 0. If it is not within the standard interval of each parameter of the air environment data set, the abnormal judgment value of each parameter of the air environment data set is set to 1.

[0058] Through the formula: The indoor air environment influence coefficient λ within the target time period is calculated.

[0059] The personnel activity data acquisition and analysis module is used to acquire various data of indoor personnel activities within the target time period and calculate the indoor personnel activity influence coefficient within the target time period.

[0060] In a specific embodiment of the present invention, the various data of indoor personnel activities within the target time period are specifically: the various data parameters of indoor personnel activities include: the indoor pedestrian flow, the average stay duration of personnel, and the number of high-density areas of personnel within the target time period obtained through camera devices and computer vision technology.

[0061] It should be noted that in a specific embodiment, the indoor pedestrian flow within the target time period refers to the number of people who have entered and exited the indoor space within the target time period by performing image recognition through a camera device. The average stay duration of personnel within the target time period is obtained by statistically analyzing the stay duration ts of each person who has entered and exited the indoor space through image recognition technology, where s = 1, 2,..., w, s represents the number of each person who has entered and exited the indoor space, and w represents the total number of people who have entered and exited the indoor space. Through the formula: The average stay duration t of personnel within the target time period is calculated.

[0062] The number of high-density areas of personnel within the target time period is calculated through a camera device and image recognition. The U area region is extracted from the database as the judgment region space. If the number of people in the U area region is greater than the number E, it is determined as a high-density area of personnel, where E represents the personnel quantity judgment threshold of the crowded area extracted from the database.

[0063] In a specific embodiment of the present invention, the specific analysis method for calculating the indoor personnel activity influence coefficient within the target time period is as follows: Combine the indoor pedestrian flow, the average personnel stay duration, and the number of high-density personnel areas within the target time period to form a personnel activity data set Ra within the target time period, where a = 1, 2, …, x, a represents the numbering of each parameter of the personnel activity data set, and x represents the total number of parameters of the personnel activity data set. Through the formula: Calculate the indoor personnel activity influence coefficient ρ within the target time period, where σa represents the unit influence factor of each parameter of the personnel activity data set extracted from the database.

[0064] It should be noted that in a specific embodiment, the specific calculation method for the indoor personnel activity influence coefficient within the target time period is obtained by considering the influence factor of the indoor pedestrian flow and the unit indoor pedestrian flow, the influence factor of the average personnel stay duration and the unit average personnel stay duration, and the influence factor of the number of high-density personnel areas and the number of high-density personnel areas for analysis.

[0065] The microbial data acquisition and analysis module is used to collect various indoor microbial data within the target time period, and calculate the indoor microbial transmission risk index within the target time period based on the indoor air environment influence coefficient and the indoor personnel activity influence coefficient within the target time period.

[0066] In a specific embodiment of the present invention, the specific analysis method for calculating the indoor microbial transmission risk index within the target time period is as follows: The indoor microbial concentration g within the target time period collected by the bioaerosol detection sensor, based on the indoor air environment influence coefficient λ and the indoor personnel activity influence coefficient ρ within the target time period, through the formula: Calculate the indoor microbial transmission risk index η within the target time period, where Represents the influence factor of the unit indoor microbial concentration extracted from the database, and e represents the natural constant.

[0067] The transmission risk level judgment module is used to evaluate the indoor microbial transmission risk level within the target time period according to the indoor microbial transmission risk index within the target time period.

[0068] In a specific embodiment of the present invention, the specific analysis method for evaluating the indoor microbial transmission risk level within the target time period is as follows: Based on the indoor microbial transmission risk index η within the target time period, if η is within the first interval of the indoor microbial transmission risk extracted from the database, then it is judged that the indoor microbial transmission risk level within the target time period is a high risk.

[0069] If η is in the second interval of the indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level during the target time period is medium risk.

[0070] If η is in the third interval of the indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level during the target time period is low risk.

[0071] It should be noted that in specific embodiments, the first interval, the second interval, and the third interval of the indoor microbial transmission risk are divided by technicians through comprehensive analysis based on the indoor microbial transmission risk index and subsequent impacts in each time period of historical monitoring cycles and are stored in the database.

[0072] The microbial transmission risk response module is used to implement the microbial transmission risk response measures corresponding to each level of the indoor microbial transmission risk during the target time period.

[0073] In a specific embodiment of the present invention, the specific analysis method for implementing the microbial transmission risk response measures corresponding to each level of the indoor microbial transmission risk during the target time period is as follows: If the indoor microbial transmission risk level during the target time period is high risk, the first microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the first intensity working state and prompting the evacuation of people through broadcasting.

[0074] If the indoor microbial transmission risk level during the target time period is medium risk, the second microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the second intensity working state and prompting people to keep a safe distance and reduce personnel gathering through broadcasting.

[0075] If the indoor microbial transmission risk level during the target time period is low risk, the third microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the third intensity working state and prompting people to wear protective equipment through broadcasting.

[0076] It should be noted that in specific embodiments, the specific method for adjusting the indoor ventilation system is that the indoor microbial transmission risk real-time early warning system is connected to the indoor ventilation system through the network. If it is determined that the indoor microbial transmission risk level during the target time period is medium risk, a request signal is sent to the indoor ventilation system through the network. After receiving the request signal, the indoor ventilation system is triggered, and according to the data characteristic values in the request signal, the working state of the indoor ventilation system is set.

[0077] For example, if the indoor microbial transmission risk level during the target time period is medium risk, the request signal sent contains the data characteristic value QMID.

[0078] An intelligent display terminal is used to display various data parameters of the indoor air environment and the indoor microbial transmission risk level.

[0079] It should be noted that in a specific embodiment, the intelligent display terminal obtains various parameter data of the indoor air environment and the indoor microbial transmission risk level through a network receiving device.

[0080] It should be noted that in a specific embodiment, the monitoring preprocessing module is connected to the air environment data collection and analysis module, the air environment data collection and analysis module is connected to the personnel activity data collection and analysis module, the personnel activity data collection and analysis module is connected to the microbial data collection and analysis module, the microbial data collection and analysis module is connected to the transmission risk level judgment module, the transmission risk level judgment module is connected to the microbial transmission risk response module, the microbial transmission risk response module is connected to the intelligent display terminal, and the database is connected to the detection preprocessing module, the air environment data collection and analysis module, the microbial data collection and analysis module, and the transmission risk level judgment module.

[0081] It should be noted that in a specific embodiment, the database is used to store the indoor microbial transmission risk index within each time period of each historical monitoring cycle, store the judgment threshold of the indoor microbial transmission risk index, store the first data collection frequency and the second data collection frequency, store the standard interval of each parameter of the air environment dataset, store the unit influence factor of each parameter of the personnel activity dataset, store the influence factor of the unit indoor microbial concentration, store the first interval of the indoor microbial transmission risk, the second interval of the indoor microbial transmission risk, and the third interval of the indoor microbial transmission risk.

[0082] The present invention analyzes the indoor microbial transmission risk index according to the indoor air environment influence coefficient and the indoor personnel activity influence coefficient within the target time period, fully considers the influence of the indoor air environment and indoor personnel activities on the microbial transmission risk index, thereby correcting the microbial transmission risk index, ensuring the accuracy and scientificity of the indoor microbial transmission risk real-time warning system.

[0083] Embodiment 2:

[0084] Referring to Figure 2 As shown, the second aspect of the present invention provides a real-time warning method based on indoor microbial transmission risk. The method includes: Step 1, dividing the monitoring requirement levels of each time period and setting the data collection frequencies corresponding to the time periods of each monitoring requirement level.

[0085] Step 2, collecting various data of the indoor air environment within the target time period and analyzing the indoor air environment influence coefficient within the target time period.

[0086] Step 3: Collect various data on personnel activities during the target time period and analyze the personnel activity impact coefficient during the target time period.

[0087] Step 4: Calculate the indoor microbial transmission risk index during the target time period.

[0088] Step 5: Evaluate the indoor microbial transmission risk level during the target time period.

[0089] Step 6: Implement the microbial transmission risk response measures corresponding to each level of indoor microbial transmission risk during the target time period.

[0090] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A real-time warning system and method based on the risk of indoor microbial transmission, characterized in that, The system includes: A monitoring preprocessing module, which is used to extract the indoor microbial transmission risk index in each time period within the historical monitoring period from the database, divide the monitoring requirement levels of each time period, and set the data collection frequency corresponding to each monitoring requirement level; An air environment data collection and analysis module, which is used to collect various data of the indoor air environment in the target time period and calculate the indoor air environment impact coefficient in the target time period; A personnel activity data collection and analysis module, which is used to collect various data of indoor personnel activities in the target time period and calculate the indoor personnel activity impact coefficient in the target time period; A microbial data collection and analysis module, which is used to collect various data of indoor microorganisms in the target time period and calculate the indoor microbial transmission risk index in the target time period based on the indoor air environment impact coefficient and the indoor personnel activity impact coefficient in the target time period; A transmission risk level judgment module, which is used to evaluate the indoor microbial transmission risk level in the target time period according to the indoor microbial transmission risk index in the target time period; A microbial transmission risk response module, which is used to implement the microbial transmission risk response measures corresponding to each level of the indoor microbial transmission risk in the target time period; An intelligent display terminal, which is used to display various data parameters of the indoor air environment and the indoor microbial transmission risk level.

2. The real-time early warning system and method for indoor microbial transmission risk according to claim 1, wherein, The division of the monitoring requirement levels of each time period and the setting of the data collection frequency corresponding to each monitoring requirement level, and its specific analysis method is: The indoor microbial transmission risk index in each time period within each historical monitoring period is respectively subjected to mean processing to obtain the average indoor microbial transmission risk index αi in each historical time period, where i = 1, 2,..., n, i represents the number of each time period, and n represents the total number of time periods within the monitoring period; Through the formula: The monitoring requirement level judgment value κi for each time period is obtained, where α′ represents the judgment threshold of the indoor microbial transmission risk index extracted from the database; If κi = 1, it is determined that the monitoring requirement level of the i-th time period is a high requirement, and the collection frequencies of the air environment data, personnel activity data, and microbial data in the i-th time period are set to the first collection frequency extracted from the database; If κi = 0, it is determined that the monitoring requirement level of the i-th time period is a low requirement, and the collection frequencies of the air environment data, personnel activity data, and microbial data in the i-th time period are set to the second collection frequency extracted from the database.

3. The real-time warning system and method for indoor microbial transmission risk according to claim 2, characterized in that, The various data of the indoor air environment in the target time period are specifically: The various data parameters of the indoor air environment include the ambient temperature collected by the temperature sensor, the ambient humidity collected by the humidity sensor, and the carbon dioxide concentration collected by the carbon dioxide sensor.

4. The real-time early warning system and method for indoor microbial transmission risk according to claim 3, wherein, The calculation of the indoor air environment impact coefficient in the target time period, and its specific analysis method is: Based on the ambient temperature, ambient humidity, and carbon dioxide concentration at each collection point in the target time period, an air environment data set Djk at each collection point in the target time period is formed, where j = 1, 2,..., m, j represents the number of each parameter of the air environment data set, m represents the total number of parameters of the air environment data set, k = 1, 2,..., q, k represents the number of each collection point, and q represents the total number of collection points; Through the formula: The abnormal judgment value (QD)jk of each parameter of the air environment data set at each collection point within the target time period is calculated, where represents the standard interval of each parameter of the air environment data set extracted from the database; Through the formula: The indoor air environment impact coefficient λ within the target time period is calculated.

5. The real-time early warning system and method for indoor microbial transmission risk according to claim 1, characterized in that, The various data of the indoor personnel activities in the target time period are specifically: The data parameters of indoor personnel activities include: the indoor pedestrian flow, the average residence time of personnel, and the number of high-density areas of personnel within the target time period obtained through camera equipment and computer vision technology.

6. The real-time early warning system and method for indoor microbial transmission risk according to claim 5, characterized in that, The specific analysis method for calculating the indoor personnel activity impact coefficient within the target time period is as follows: Combine the indoor pedestrian flow, the average residence time of people, and the number of high-density areas of people within the target time period to form a personnel activity dataset Ra within the target time period, where a = 1, 2, …, x, a represents the serial number of each parameter of the personnel activity dataset, and x represents the total number of parameters of the personnel activity dataset. Through the formula: Calculate the indoor personnel activity influence coefficient ρ within the target time period, where σa represents the unit influence factor of each parameter of the personnel activity dataset extracted from the database.

7. The real-time early warning system and method for indoor microbial transmission risk according to claim 6, characterized in that, The specific analysis method for calculating the indoor microbial transmission risk index within the target time period is as follows: The indoor microbial concentration g within the target time period collected by the bioaerosol detection sensor, based on the indoor air environment impact coefficient λ and the indoor human activity impact coefficient ρ within the target time period, through the formula: Calculate the indoor microbial transmission risk index η within the target time period, where represents the impact factor of the unit indoor microbial concentration extracted from the database, and e represents the natural constant.

8. The real-time warning system and method for indoor microbial transmission risk according to claim 7, characterized in that, The specific analysis method for evaluating the indoor microbial transmission risk level within the target time period is as follows: Based on the indoor microbial transmission risk index η within the target time period, if η is within the first interval of indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level within the target time period is a high risk; If η is within the second interval of indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level within the target time period is a medium risk; If η is within the third interval of indoor microbial transmission risk extracted from the database, it is determined that the indoor microbial transmission risk level within the target time period is a low risk.

9. The real-time early warning system and method for indoor microbial transmission risk according to claim 8, characterized in that, The specific analysis method for implementing the microbial transmission risk response measures corresponding to each level of indoor microbial transmission risk within the target time period is as follows: If the indoor microbial transmission risk level within the target time period is a high risk, the first microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the first intensity working state and prompting the evacuation of the crowd through a broadcast; If the indoor microbial transmission risk level within the target time period is a medium risk, the second microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the second intensity working state and prompting personnel to maintain a safe distance and reduce personnel gathering through a broadcast; If the indoor microbial transmission risk level within the target time period is a low risk, the third microbial transmission risk response measure is executed, including adjusting the indoor ventilation system to the third intensity working state and prompting personnel to wear protective equipment through a broadcast.

10. A real-time early warning method based on the risk of indoor microbial transmission, applied to the real-time early warning system based on the risk of indoor microbial transmission according to any one of claims 1-9, characterized in that, The method includes: Step 1: Divide the monitoring requirement levels for each time period and set the data collection frequencies corresponding to the time periods of each monitoring requirement level; Step 2: Collect the data of the indoor air environment within the target time period and analyze the indoor air environment impact coefficient within the target time period; Step 3: Collect the data of personnel activities within the target time period and analyze the personnel activity impact coefficient within the target time period; Step 4: Calculate the indoor microbial transmission risk index within the target time period; Step 5: Evaluate the indoor microbial transmission risk level within the target time period; Step 6: Implement the microbial transmission risk response measures corresponding to each level of indoor microbial transmission risk within the target time period.

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