Multifunctional air quality monitoring and early warning system

Abstract

The invention relates to the technical field of environment monitoring, in particular to a multifunctional air quality monitoring and early warning system, which comprises a monitoring and adjusting module, a monitoring and early warning module, a monitoring and early warning module, an early warning module and an early warning module, and is characterized in that the monitoring and adjusting module monitors pollutants and temperature and humidity information in air through various sensors and automatically adjusts sensor detection ranges according to air quality conditions; the intelligent early warning module is used for establishing a prediction model, pre-judging the deterioration trend of the indoor air quality in advance through the prediction model, and triggering trend early warning when monitoring that the deterioration risk is high; and the equipment linkage module is used for establishing an equipment linkage rule base and automatically matching an optimal equipment operation strategy according to the air quality data. Various high-precision sensors are integrated, multi-dimensional data of particulate matter, harmful gas, oxygen content and temperature and humidity in air can be comprehensively monitored, the indoor air quality condition is accurately reflected, an air purifier and fresh air system equipment are automatically allocated according to a data analysis result, and intelligent environment adjustment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a multifunctional air quality monitoring and early warning system. Background Art

[0002] With the rapid development of modern society, people have higher and higher requirements for the quality of their living and working environment. Indoor air quality, as a key factor affecting people's health and quality of life, has received widespread attention. Traditional indoor air quality monitoring methods have many limitations and can no longer meet people's needs for comprehensive, real-time, accurate monitoring and early warning of indoor air quality.

[0003] Traditional indoor air quality monitoring mainly relies on manual sampling and laboratory analysis methods. This method has obvious shortcomings. First, the manual sampling process is cumbersome and time-consuming. Professionals need to use specific instruments and equipment to collect air samples at designated locations and then send them to the laboratory for analysis. This not only increases labor costs, but also due to the time interval between sampling and analysis, it cannot reflect the dynamic changes of indoor air quality in real time. Summary of the Invention

[0004] The present invention aims to solve the technical problems existing in the prior art and provides a multifunctional air quality monitoring and early warning system.

[0005] The present invention solves the above technical problems with the following technical solutions: A multifunctional air quality monitoring and early warning system, comprising:

[0006] Monitoring and adjustment module: monitors pollutants and temperature and humidity information in the air through various sensors, and automatically adjusts the sensor detection range according to the air quality conditions;

[0007] Intelligent early warning module: Establishes a prediction model to predict the deterioration trend of indoor air quality in advance. When the risk of deterioration is high, it triggers a trend warning;

[0008] Equipment linkage module: Establishes an equipment linkage rule base to automatically match the optimal equipment operation strategy based on air quality data.

[0009] In a preferred embodiment, the monitoring and adjustment module captures air pollutant and temperature and humidity information by deploying PM2.5 sensors based on laser scattering principles, formaldehyde, carbon monoxide, and carbon dioxide sensors based on electrochemical principles, and TVOC sensors based on semiconductor principles indoors, in combination with high-precision temperature and humidity sensors. Various sensors are integrated on a customized sensor board, and an array layout is adopted to detect the concentration of pollutants in the air. The sensor has a built-in standard gas sample comparison unit and algorithm, and automatically calibrates the sensor regularly to compensate for detection deviations caused by long-term use and environmental changes. The automatic calibration algorithm fuses multiple sensor data through the Kalman filter algorithm, comprehensively analyzes the correlation between pollutants, eliminates single sensor errors, and automatically adjusts the sensor detection range according to the actual air quality conditions, thereby improving detection accuracy when pollution is severe and expanding the detection range when indoor air quality is good. Specifically, the following steps are included:

[0010] S1, there are n different types of sensors, marked as S1, S2, ..., S n , for each sensor S i (i=1,2,...,n) Set the initial detection range, record sensor S i The initial range is [L i0 ,U i0 ], according to the indoor air quality standard, set m different pollution levels, marked as D1, D2, ..., D m , for each sensor S i and each pollution degree D j (j=1,2,...,m) to determine the corresponding threshold range, record sensor S i In pollution degree D j The threshold range under [T ij1 ,T ij2 ];

[0011] S2. When the system is running, it continuously obtains the air quality data processed by Kalman filtering and receives the air quality data from each sensor S in the target area. i The collected pollutant concentration measurement value C it and the pre-stored pollution level judgment threshold range [T ij1 ,T ij2 ] to compare and determine the measured value C it The target threshold range is where T i11 ≤C it ≤T i12 , then determine sensor S i The corresponding pollutants are at the light pollution level D1. When the sensor S i The collected pollutant concentration measurement value C itWhen the pollution level is lower than D1, the detection range of the sensor is expanded and the sampling frequency of the sensor is reduced. i12 <T i21 When the sensor S i The collected pollutant concentration measurement value C it When the pollution level is moderate D2, the detection range of the sensor remains unchanged. it When the upper limit of the maximum threshold is exceeded, that is, C it >T im2 , then it is judged to be in severe pollution level D m , when sensor S i The collected pollutant concentration measurement value C it When the pollution level exceeds the severe level, the detection range of the sensor is reduced and the sampling frequency of the sensor is increased.

[0012] In a preferred embodiment, the intelligent early warning module sets mild, moderate and moderate multi-level warning levels, triggers corresponding level warnings based on the pollution level obtained by the monitoring and adjustment module, and informs the user through the push of the mini program. Based on the prediction model, the air quality deterioration trend is predicted in advance, and a warning is issued when the pollution has not exceeded the standard but has an upward trend. The pollutant concentration data in the last K time periods are extracted from the database of the air quality monitoring system. For each pollutant, its concentration change rate in adjacent time periods and the moving average of each pollutant in n time periods are calculated. At the same time, the corresponding comprehensive pollution index is extracted, and the pollutant concentration data and the comprehensive pollution index are combined to form a training data set. The calculated data change rate and moving average are used as input features of the prediction model. The time series The column prediction algorithm predicts the future indoor air quality and uses the trained ARIMA model to predict the pollutant concentration and comprehensive pollution index in the next T time periods. When the predicted pollutant concentration shows a continuous upward trend in the next T time periods, it is determined that the indoor air quality has a deterioration trend. The trend risk value Rt is obtained by comparing the sum of the differences between the predicted pollutant concentration and the current pollutant concentration in the next T time periods and the ratio of the total current pollutant concentration. A large value of the trend risk value Rt indicates a high risk of air quality deterioration. When the calculated trend risk value Rt exceeds the preset threshold Rth, the system activates the trend warning mechanism. After the trend warning is triggered, the system will push detailed warning information to the user through the mini program. The main content of the warning information includes the current pollutant concentration and the predicted value.

[0013] In a preferred embodiment, the device linkage module establishes a device linkage rule base, sets different threshold intervals for each monitoring indicator, and formulates corresponding device operation strategies for each interval. The set linkage rules are stored in the system database, and the collected sensor data are transmitted to the control center of the system through various air quality sensors. The control center searches the device linkage rule base based on the air quality data obtained in real time, finds the device operation strategy that matches the current data, converts the matched device operation strategy into a control instruction, and sends it to the corresponding device for execution. After receiving the control instruction, the device performs the corresponding operation and feeds back the operating status of the device to the control center. The control center dynamically adjusts the device operation strategy based on the operating status of the device and the real-time air quality data.

[0014] The beneficial effects of the present invention are: the present invention integrates a variety of high-precision sensors, can comprehensively monitor particulate matter, harmful gases, oxygen content, and multi-dimensional data of temperature and humidity in the air, accurately reflect the indoor air quality status, and automatically allocate air purifiers and fresh air system equipment according to data analysis results to achieve intelligent environmental adjustment. It does not require manual operation by the user, is convenient and fast, and can respond to changes in air quality in a timely manner. When pollutants exceed the standard, early warning notifications are pushed on the mini-program of the user interaction layer to inform users of air quality problems in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the present invention;

[0016] Figure 2 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0018] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0019] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0020] Example 1

[0021] like Figure 1-2 This embodiment provides: a multifunctional air quality monitoring and early warning system, comprising:

[0022] Monitoring and adjustment module: monitors pollutants and temperature and humidity information in the air through various sensors, and automatically adjusts the sensor detection range according to the air quality conditions;

[0023] In this embodiment, what needs to be specifically explained is the monitoring and adjustment module. The monitoring and adjustment module captures air pollutants and temperature and humidity information by deploying PM2.5 sensors based on laser scattering principles, formaldehyde, carbon monoxide, and carbon dioxide sensors based on electrochemical principles, and TVOC sensors based on semiconductor principles indoors, and is equipped with high-precision temperature and humidity sensors. Various sensors are integrated on a customized sensor board, and an array layout is adopted to detect the concentration of pollutants in the air. The sensor has a built-in standard gas sample comparison unit and algorithm, and automatically calibrates the sensor regularly to compensate for detection deviations caused by long-term use and environmental changes. The automatic calibration algorithm fuses multiple sensor data through the Kalman filter algorithm, comprehensively analyzes the correlation between pollutants, eliminates single sensor errors, and automatically adjusts the sensor detection range according to the actual air quality conditions, thereby improving detection accuracy when pollution is severe and expanding the detection range when indoor air quality is good. Specifically, the following steps are included:

[0024] S1, there are n different types of sensors, marked as S1, S2, ..., S n , for each sensor S i (i=1,2,...,n) Set the initial detection range, record sensor S i The initial range is [L i0 ,U i0 ], according to the indoor air quality standard, set m different pollution levels, marked as D1, D2, ..., D m, for each sensor S i and each pollution degree D j (j=1,2,...,m) to determine the corresponding threshold range, record sensor S i In pollution degree D j The threshold range under [T ij1 ,T ij2 ];

[0025] For example, the threshold range of PM2.5 sensor S1 under light pollution level D1 is [T i11 ,T i12 ].

[0026] S2. When the system is running, it continuously obtains the air quality data processed by Kalman filtering and receives the air quality data from each sensor S in the target area. i The collected pollutant concentration measurement value C it and the pre-stored pollution level judgment threshold range [T ij1 ,T ij2 ] to compare and determine the measured value C it The target threshold range is where T i11 ≤C it ≤T i12 , then determine sensor S i The corresponding pollutants are at the light pollution level D1. When the sensor S i The collected pollutant concentration measurement value C it When the pollution level is lower than D1, the detection range of the sensor is expanded and the sampling frequency of the sensor is reduced. i12 <T i21 When the sensor S i The collected pollutant concentration measurement value C it When the pollution level is moderate D2, the detection range of the sensor remains unchanged. it When the upper limit of the maximum threshold is exceeded, that is, C it >T im2 , then it is judged to be in severe pollution level D m , when sensor S i The collected pollutant concentration measurement value C it When the pollution level exceeds the severe level, the detection range of the sensor is reduced and the sampling frequency of the sensor is increased.

[0027] Intelligent early warning module: Establishes a prediction model to predict the deterioration trend of indoor air quality in advance. When the risk of deterioration is high, it triggers a trend warning;

[0028] In this embodiment, what needs to be specifically explained is the intelligent early warning module. The intelligent early warning module sets mild, moderate and moderate multi-level warning levels, triggers corresponding level warnings based on the pollution level obtained by the monitoring and adjustment module, and informs the user through the small program push. Based on the prediction model, the air quality deterioration trend is predicted in advance, and a warning is issued when the pollution has not exceeded the standard but has an upward trend. The pollutant concentration data in the last K time periods are extracted from the database of the air quality monitoring system. For each pollutant, its concentration change rate in adjacent time periods and the moving average of each pollutant in n time periods are calculated, that is, the pollutant concentration at the current time t minus the pollutant concentration in the previous time period t-1. This change rate can reflect the dynamic changes in the pollutant concentration. The moving average is obtained by adding the pollutant concentration in n time periods and then dividing it by n. The moving average can smooth the data, reduce the impact of random fluctuations, and enable the model to better identify the long-term trend of the data, while extracting the corresponding comprehensive pollution. Index, pollutant concentration data is combined with the comprehensive pollution index to form a training data set, and the calculated data change rate and moving average are used as input features of the prediction model. The time series prediction algorithm is used to predict the future indoor air quality, and the trained ARIMA model is used to predict the pollutant concentration and comprehensive pollution index in the next T time periods. When the predicted pollutant concentration shows a continuous upward trend in the next T time periods, it is determined that the indoor air quality has a deterioration trend. The trend risk value Rt is obtained by comparing the sum of the differences between the predicted pollutant concentration and the current pollutant concentration in the next T time periods and the ratio of the sum of the current pollutant concentrations. A large value of the trend risk value Rt indicates a high risk of air quality deterioration. When the calculated trend risk value Rt exceeds the preset threshold Rth, the system activates the trend warning mechanism. After the trend warning is triggered, the system will push detailed warning information to the user through the mini program. The main content of the warning information includes the current pollutant concentration and the predicted value.

[0029] Equipment linkage module: Establishes an equipment linkage rule base to automatically match the optimal equipment operation strategy based on air quality data.

[0030] In this embodiment, it is necessary to specifically explain the device linkage module, which establishes a device linkage rule base. For example, the air purifier is associated with PM2.5, PM10, and TVOC indicators, and the fresh air system is associated with carbon dioxide, formaldehyde and other indicators. For each monitoring indicator, different threshold intervals are set, and corresponding device operation strategies are formulated for each interval. For example, for the PM2.5 indicator, when the PM2.5 concentration is between 0-35μg / m 3 When the PM2.5 concentration is between 35-75μg / m 3When the PM2.5 concentration is greater than 75ug / m 3 When the carbon dioxide concentration is between 0-1000ppm, the fresh air system maintains normal ventilation. When the carbon dioxide concentration is between 1000-1500ppm, the fresh air system is linked to increase the ventilation. When the carbon dioxide concentration is greater than 1500ppm, the fresh air system operates at the maximum ventilation. The set linkage rules are stored in the system database. The collected sensor data is transmitted to the control center of the system through various air quality sensors. The control center searches the device linkage rule library based on the real-time air quality data to find the device operation strategy that matches the current data. The matched device operation strategy is converted into a control instruction and sent to the corresponding device for execution. After receiving the control instruction, the device performs the corresponding operation and feeds back the device's operation status to the control center. The control center dynamically adjusts the device's operation strategy based on the device's operation status and real-time air quality data.

[0031] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0032] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0033] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0034] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0035] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0036] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0037] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multifunctional air quality monitoring and early warning system, characterized in that: include: Monitoring and adjustment module: monitors pollutants and temperature and humidity information in the air through various sensors, and automatically adjusts the sensor detection range according to the air quality conditions; Intelligent early warning module: Establishes a prediction model to predict the deterioration trend of indoor air quality in advance. When the risk of deterioration is high, it triggers a trend warning; Equipment linkage module: Establishes an equipment linkage rule base to automatically match the optimal equipment operation strategy based on air quality data.

2. A multifunctional air quality monitoring and early warning system according to claim 1, characterized in that: The monitoring and adjustment module captures information on pollutants and temperature and humidity in the air by deploying PM2.5 sensors based on laser scattering principles, formaldehyde sensors based on electrochemical principles, carbon monoxide sensors, carbon dioxide sensors, TVOC sensors based on semiconductor principles, and high-precision temperature and humidity sensors. The sensors are integrated on a customized sensor board and an array layout is used to detect pollutant concentrations. The sensor has a built-in standard gas sample comparison unit and an automatic calibration algorithm to regularly compensate for detection deviations caused by long-term use or environmental changes. The automatic calibration algorithm fuses multi-sensor data through a Kalman filter algorithm, comprehensively analyzes the correlation between pollutants to eliminate single sensor errors, and automatically adjusts the detection range according to the actual air quality.

3. A multifunctional air quality monitoring and early warning system according to claim 2, characterized in that: The automatic calibration algorithm sets the initial detection range for n different types of sensors, and the sensor S i The initial range is [L i0 ,U i0 ], set m pollution levels according to the indoor air quality standard, marked as D1, D2, ..., D m , for each sensor S i and each pollution degree D j (j=1,2,...,m) to determine the corresponding threshold range, record sensor S i In pollution degree D j The threshold range under [T ij1 ,T ij2 ].

4. A multifunctional air quality monitoring and early warning system according to claim 2, characterized in that: The rule of automatic adjustment of detection range is as follows: i The collected pollutant concentration measurement value C it When the pollution level is lower than the light pollution level D1, the detection range of the sensor is expanded and the sampling frequency of the sensor is reduced. i The collected pollutant concentration measurement value C it When the pollution level is moderate D2, the detection range of the sensor remains unchanged. i The collected pollutant concentration measurement value C it When the pollution level exceeds the severe level, the detection range of the sensor is reduced and the sampling frequency of the sensor is increased.

5. A multifunctional air quality monitoring and early warning system according to claim 4, characterized in that: The pollution level is determined by continuously acquiring air quality data processed by Kalman filtering and receiving the air quality data from each sensor S in the target area. i The collected pollutant concentration measurement value C it and the pre-stored pollution level judgment threshold range [T ij1 ,T ij2 ] to compare and determine the measured value C it The target threshold range is where T i11 ≤C it ≤T i12 , then determine sensor S i The corresponding pollutants are in the light pollution level D1. When T i12 <T i21 When the pollution level is D2, it is judged to be moderate. it When the upper limit of the maximum threshold is exceeded, that is, C it >T im2 , then it is judged to be in severe pollution level D m .

6. A multifunctional air quality monitoring and early warning system according to claim 1, characterized in that: The intelligent early warning module sets mild, moderate and moderate multi-level warning levels, triggers corresponding level warnings based on the pollution level obtained by the monitoring and adjustment module, and informs users through mini-program push. Based on the prediction model, it predicts the deterioration trend of air quality in advance and issues a warning when the pollution has not exceeded the standard but has an upward trend. The pollutant concentration data in the last K time periods are extracted from the database of the air quality monitoring system. For each pollutant, its concentration change rate in adjacent time periods and the moving average of each pollutant in n time periods are calculated. At the same time, the corresponding comprehensive pollution index is extracted, and the pollutant concentration data and the comprehensive pollution index are combined to form a training data set. The calculated data change rate and moving average are used as input features of the prediction model. The time series prediction algorithm is used to predict the future indoor air quality. The trained ARIMA model is used to predict the pollutant concentration and comprehensive pollution index in the next T time periods. When the predicted pollutant concentration shows a continuous upward trend in the next T time periods, it is determined that the indoor air quality has a deterioration trend.

7. A multifunctional air quality monitoring and early warning system according to claim 6, characterized in that: The deterioration trend is triggered by comparing the sum of the differences between the predicted pollutant concentrations and the current pollutant concentrations in the next T time periods, and the ratio of the sum of the current pollutant concentrations to the trend risk value Rt. A large value of the trend risk value Rt indicates a high risk of air quality deterioration. When the calculated trend risk value Rt exceeds the preset threshold Rth, the system activates the trend warning mechanism. After the trend warning is triggered, the system will push detailed warning information to the user through the mini program. The main content of the warning information includes the current pollutant concentration and the predicted value.

8. A multifunctional air quality monitoring and early warning system according to claim 1, characterized in that: The device linkage module establishes a device linkage rule base, sets different threshold intervals for each monitoring indicator, and formulates corresponding device operation strategies for each interval. The set linkage rules are stored in the system database, and the collected sensor data are transmitted to the control center of the system through various air quality sensors. The control center searches the device linkage rule base based on the real-time air quality data, finds the device operation strategy that matches the current data, converts the matched device operation strategy into a control instruction, and sends it to the corresponding device for execution. After receiving the control instruction, the device executes the corresponding operation and feeds back the device's operation status to the control center. The control center dynamically adjusts the device's operation strategy based on the device's operation status and real-time air quality data.

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