An Internet of Things environment monitoring system and monitoring method

Abstract

The invention discloses an Internet of Things environment monitoring system and a monitoring method, which relate to the technical field of environment monitoring, and include an environment data acquisition module, an environment pollution analysis module, a storage module and a web browser terminal. The environment data acquisition module includes a sensor unit and a wireless communication unit. The sensor unit monitors air data in a target area. The environment pollution analysis module includes an air data correction unit, an air comprehensive index unit and a prediction unit. The air data correction unit is used to correct sensor data in combination with environmental information, eliminate deviations caused by temperature and humidity factors, and improve the accuracy of air quality monitoring. The air comprehensive index unit is used to integrate various data to help managers quickly understand the current air conditions. The prediction unit is used to predict future air quality, and a danger threshold Y is set. When the predicted value exceeds Y, the system automatically issues an early warning, takes protective measures in advance, reduces safety hazards, and improves the monitoring accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an Internet of Things environmental monitoring system and a monitoring method. Background Art

[0002] As humans continue to discharge pollutants into the environment, and people do not anticipate the negative impact of highly developed industries and fail to take precautions, three major crises have occurred in the world: resource shortages, environmental pollution, and ecological damage. If the pollutants discharged into the environment exceed the self-purification capacity of the environment, the environmental quality will change adversely, endangering human health and survival, and causing environmental pollution. Under the trend of the development of Internet of Things technology today, with the development of new ways, methods and technologies for environmental monitoring, the construction of environmental monitoring and control systems has become an important means of environmental monitoring in the next step, including air monitoring in environmental monitoring.

[0003] Currently, sensor devices are commonly used to monitor air quality. However, sensor devices are easily affected by the surrounding environment. Temperatures that are too high or too low will cause reading deviations, resulting in inaccurate monitoring data. In addition, only each parameter in the air is detected separately, and there is a lack of integration of the overall air quality, making it difficult to understand the overall air quality. At the same time, there is a lack of air quality prediction function. An alarm will only be issued when the actual air quality is too bad, thereby increasing losses and raising safety risks. Summary of the invention

[0004] The purpose of the present invention is to provide an Internet of Things environment monitoring system and a monitoring method, which solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: an Internet of Things environment monitoring system, comprising an environmental data acquisition module, an environmental pollution analysis module, a storage module and a web browser terminal;

[0006] The environmental data acquisition module includes a sensor unit and a wireless communication unit, wherein the sensor unit is used to monitor air data in the target area, and the wireless communication unit is used to transmit the air data to the environmental pollution analysis module;

[0007] The environmental pollution analysis module includes an air data correction unit, an air comprehensive index unit and a prediction unit;

[0008] The air data correction unit is used to correct the air data to reduce errors. The air comprehensive index unit is used to integrate various parameters in the air data to obtain the air pollution index R. The prediction unit is used to combine the air pollution index R and historical changes to obtain the air pollution index R(t+1) at time t+1, and set a danger threshold Y for the result. When R(t+1) is greater than Y, it means that the air quality in the target area in the future time is too poor, and it is necessary to give an early warning and prepare corresponding measures.

[0009] Optionally, the sensor unit includes a temperature and humidity sensor, a particulate matter sensor, a carbon dioxide sensor, a nitrogen dioxide sensor and a volatile organic compound sensor, and the wireless communication unit includes a wifi module.

[0010] Optionally, the correction process of the air data correction unit is as follows:

[0011] ;

[0012] Among them C cor (i) is the concentration of the ith pollutant after correction;

[0013] C raw (i) is the original concentration of the i-th pollutant;

[0014] T(t) is the temperature at the current moment;

[0015] T ref is the standard ambient temperature;

[0016] α is the temperature influence coefficient, ranging from 0 to 1;

[0017] H(t) is the humidity at the current moment;

[0018] H ref is the standard ambient humidity;

[0019] β is the humidity influence coefficient, ranging from 0 to 1;

[0020] When the environmental data acquisition module is collecting air data, the working performance of the sensor is usually affected by temperature changes. Too high or too low temperature will cause reading deviations, and too low humidity will make it difficult for gas molecules in the air to adsorb on the sensor surface, affecting the sensitivity and accuracy of the sensor. The air data correction unit corrects the original monitoring data according to the influencing factors of environmental temperature and humidity to eliminate deviations caused by environmental factors.

[0021] Optionally, the air comprehensive index unit integration process is as follows:

[0022] ;

[0023] Where R is the air pollution index;

[0024] n is the number of pollutants;

[0025] W i is the impact coefficient of the i-th pollutant, ranging from 0 to 1;

[0026] C cor (i) is the concentration of the ith pollutant after correction;

[0027] C min,i is the minimum standard concentration of the ith pollutant;

[0028] C max,i is the maximum standard concentration of the ith pollutant;

[0029] Indicates the effect of time on air quality;

[0030] Where sin is the sine function;

[0031] t represents the current time, t0 is the base time;

[0032] γ is the time influence coefficient, which is 0.5;

[0033] The larger the air pollution index R, the worse the air quality, and the smaller the air pollution index R, the better the air quality. By integrating various air data and quantifying the results, staff can quickly understand the overall air situation in the current area.

[0034] Optionally, the prediction process of the prediction unit is as follows:

[0035] ;

[0036] Where R(t+1) is the air pollution index at time t+1;

[0037] R(t) is the air pollution index at the current time;

[0038] ΔR avg is the rate of change of the historical average air pollution index;

[0039] Δt is the time interval;

[0040] V(t) is the wind speed at the current moment;

[0041] M is the wind speed influence coefficient, ranging from 0 to 1;

[0042] ΔR avg It is expressed as:

[0043] ;

[0044] Where R(t-j) is the air pollution index at time t-j;

[0045] k is the number of time points;

[0046] The air pollution index R(t+1) at time t+1 represents the air quality in the future. The danger threshold of R(t+1) is set to Y. When R(t+1) is greater than Y, it means that the air quality in the future will be too poor, and management personnel need to make early warnings and protective measures to reduce losses.

[0047] Optionally, when R(t+1) is greater than Y, the change in air quality has a greater impact on public health, and the concentration of pollutants in the air tends to increase rapidly. In a heavily polluted environment, the concentration of pollutants usually changes rapidly and drastically. At this time, the time influence coefficient γ in the comprehensive index unit is increased, and the time influence coefficient γ is adjusted to:

[0048] ;

[0049] Where R max is the maximum value of the air pollution index;

[0050] When R(t+1) is greater than Y, the time influence coefficient γ increases with the increase of the air pollution index, which can effectively capture the change trend and improve the sensitivity of monitoring.

[0051] Optionally, the storage module includes a data integration unit and a cloud service platform, the data integration unit is used to integrate the data uploaded by each module into total monitoring data, and upload the total monitoring data to the cloud service platform via the Internet, and the cloud service platform is used to receive and save the total monitoring data uploaded by the data integration unit.

[0052] Optionally, the web browser terminal is a physical device that can access the Internet through a web browser, and is used to access the cloud service platform through the Internet.

[0053] The present invention also provides an Internet of Things environment monitoring method as follows:

[0054] The sensor unit in the environmental data acquisition module monitors various parameters reflecting air quality, and transmits the air data to the environmental pollution analysis module in real time through the wireless communication unit;

[0055] After the air data is transmitted to the environmental pollution analysis module, the air data is processed by the air data correction unit in the environmental pollution analysis module to reduce errors and improve the quality of the air data;

[0056] After the data processing is completed, the air comprehensive index unit is used to integrate various parameters in the air data and quantify the results to obtain the air pollution index R;

[0057] Finally, the prediction unit combines the air pollution index R, historical changes and other influencing factors to predict the air changes in the future, and sets a danger threshold Y for the result. It determines whether to issue an early warning based on the danger threshold Y, so as to prepare corresponding measures in advance.

[0058] The data of the environmental data acquisition module and the environmental pollution analysis module are uploaded to the storage module, which includes a data integration unit and a cloud service platform. The data integration unit is used to integrate the data uploaded by each module into total monitoring data, and upload the total monitoring data to the cloud service platform via the Internet for storage.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. The present invention corrects the air data collected by the sensor unit in combination with the surrounding environment information through the air data correction unit in the environmental pollution analysis module, eliminates the deviation caused by environmental factors, and can adjust the temperature influence coefficient and the humidity influence coefficient according to the specific detected pollutants, reduce the deviation, and improve the accuracy and stability of the air quality monitoring data. After that, the air comprehensive index unit is used to integrate various data in the air monitoring and quantify the results, so that the management personnel can quickly understand the overall air situation in the current area.

[0061] 2. The present invention predicts the future air quality by combining the current air pollution index R and the average change of the historical air pollution index through the prediction unit, and combining the wind speed influencing factors and the historical average change rate influencing factors, and obtains the air pollution index at time t+1, and the result danger threshold is Y. When R(t+1) is greater than Y, it means that the air quality in the future is too poor, and the management personnel make early warning and protective measures in advance, so that the management personnel can make early warning and protective measures in advance to reduce losses and reduce safety hazards. When R(t+1) is greater than Y, the time influence coefficient γ will increase with the increase of the air pollution index, so that the units can be interconnected and influence each other, thereby improving the sensitivity of monitoring and the accuracy of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a system module diagram of the present invention;

[0063] Figure 2 This is a block diagram of the environmental pollution analysis module of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] For examples, see Figure 1 to Figure 2 ,This implementation provides an Internet of Things environment monitoring system, including an ,environmental data acquisition module, an environmental pollution analysis module, a storage module, and a web browser terminal;

[0066] The environmental data acquisition module includes a sensor unit and a wireless communication unit. The sensor unit is used to monitor the air data in the target area, and the wireless communication unit is used to transmit the air data to the environmental pollution analysis module.

[0067] The environmental pollution analysis module includes an air data correction unit, an air comprehensive index unit and a prediction unit;

[0068] The air data correction unit is used to correct the air data and reduce errors. The air comprehensive index unit is used to integrate various parameters in the air data to obtain the air pollution index R. The prediction unit is used to combine the air pollution index R and historical changes to obtain the air pollution index R(t+1) at time t+1, and set a danger threshold Y for the result. When R(t+1) is greater than Y, it means that the air quality in the target area in the future is too poor, and it is necessary to issue an early warning and prepare corresponding measures.

[0069] In this embodiment, various parameters reflecting air quality are monitored by the environmental data acquisition module, and the air data is transmitted to the environmental pollution analysis module in real time for analysis. First, the air data collected by the sensor unit is corrected by the air data correction unit in the environmental pollution analysis module in combination with the surrounding environmental information, including temperature and humidity, to eliminate the deviation caused by environmental factors and improve the accuracy and stability of the air quality monitoring data. Then, the various data in the air monitoring are integrated through the air comprehensive index unit, and the results are quantified. The larger the air pollution index R, the worse the air quality, and the smaller the air pollution index R, the better the air quality. This allows managers to quickly understand the overall air situation in the current area, so that they can take timely response measures and minimize the harm.

[0070] Finally, the prediction unit combines the current air pollution index R and the average change of the historical air pollution index with other influencing factors to predict the future air quality, and obtains the air pollution index at time t+1, and the result danger threshold is Y. When R(t+1) is greater than Y, it means that the air quality in the future will be too poor, and the management personnel will make early warnings and protective measures in advance, so that the management personnel can respond in time to reduce losses and reduce safety hazards.

[0071] Furthermore, the sensor unit includes a temperature and humidity sensor, a particulate matter sensor, a carbon dioxide sensor, a nitrogen dioxide sensor and a volatile organic compound sensor, and the wireless communication unit includes a wifi module. The wifi module has a high transmission rate and can quickly transmit large amounts of data, and is suitable for monitoring of this system.

[0072] Specifically, by setting up a variety of different sensors, the air is monitored in all directions to ensure the detection data, and including temperature and humidity sensors, the monitoring environment can be analyzed to understand the impact of temperature and humidity on other sensors, so that the temperature influence coefficient or humidity influence coefficient can be adjusted according to the specific situation in the future to improve the accuracy of the detection data.

[0073] Furthermore, the correction process of the air data correction unit is as follows:

[0074] ;

[0075] Among them C cor (i) is the concentration of the ith pollutant after correction;

[0076] C raw (i) is the original concentration of the i-th pollutant;

[0077] T(t) is the temperature at the current moment;

[0078] T ref is the standard ambient temperature;

[0079] α is the temperature influence coefficient, ranging from 0 to 1;

[0080] H(t) is the humidity at the current moment;

[0081] H ref is the standard ambient humidity;

[0082] β is the humidity influence coefficient, ranging from 0 to 1;

[0083] Specifically, since the working performance of the sensor is usually affected by temperature changes during the process of collecting air data by the environmental data acquisition module, too high or too low temperature will cause reading deviations, and too low humidity will make it difficult for gas molecules in the air to adsorb on the sensor surface, affecting the sensitivity and accuracy of the sensor. The air data correction unit corrects the original monitoring data according to the influencing factors of environmental temperature and humidity to eliminate the deviation caused by environmental factors, and can adjust the temperature influence coefficient α and humidity influence coefficient β according to the specific pollutants detected to reduce deviations and further improve the accuracy of the detection data.

[0084] Furthermore, the air comprehensive index unit integration process is as follows:

[0085] ;

[0086] Where R is the air pollution index;

[0087] n is the number of pollutants;

[0088] W i is the impact coefficient of the i-th pollutant, ranging from 0 to 1;

[0089] C cor (i) is the concentration of the ith pollutant after correction;

[0090] C min,i is the minimum standard concentration of the ith pollutant;

[0091] C max,i is the maximum standard concentration of the ith pollutant;

[0092] ;

[0093] Indicates the effect of time on air quality;

[0094] Where sin is the sine function;

[0095] t represents the current time, t0 is the base time;

[0096] γ is the time influence coefficient, which is 0.5;

[0097] Specifically, the larger the air pollution index R, the worse the air quality, and the smaller the air pollution index R, the better the air quality. By integrating various air data and quantifying the results, and introducing the impact of time factors on air quality, staff can quickly understand the overall air situation in the current area so that they can take timely response measures and minimize the harm.

[0098] Furthermore, the prediction process of the prediction unit is as follows:

[0099] ;

[0100] Where R(t+1) is the air pollution index at time t+1;

[0101] R(t) is the air pollution index at the current time;

[0102] ΔR avg is the rate of change of the historical average air pollution index;

[0103] Δt is the time interval;

[0104] V(t) is the wind speed at the current moment;

[0105] M is the wind speed influence coefficient, ranging from 0 to 1;

[0106] ΔR avg It is expressed as:

[0107] ;

[0108] Where R(t-j) is the air pollution index at time t-j;

[0109] k is the number of time points;

[0110] Specifically, by introducing the wind speed factor into the prediction unit, wind speed is a key factor in air quality prediction. Wind speed accelerates the diffusion of pollutants, which may slow down the deterioration of air quality. On the contrary, low wind speed may lead to the accumulation of pollutants and aggravate air pollution. Therefore, by adjusting the wind speed influence coefficient M, the influence of wind speed on R(t+1) can be adjusted according to actual conditions. The air pollution index R(t+1) at the result time t+1 obtained by the prediction unit represents the air quality in the future. By setting the danger threshold of R(t+1) to Y, when R(t+1) is greater than Y, it means that the air quality in the future is too poor. Management personnel can make early warnings and protective measures in advance to reduce losses and reduce safety hazards.

[0111] Furthermore, when R(t+1) is greater than Y, the change in air quality has a greater impact on public health, and the concentration of pollutants in the air tends to increase rapidly. In a heavily polluted environment, the concentration of pollutants usually changes rapidly and dramatically. At this time, the time influence coefficient γ in the comprehensive index unit is increased, and the time influence coefficient γ is adjusted to:

[0112] ;

[0113] Where R max is the maximum value of the air pollution index;

[0114] Specifically, when R(t+1) is greater than Y, the deterioration of air quality may be more rapid and unstable.

[0115] The time impact coefficient γ increases with the increase of the air pollution index, responds more strongly to small changes, can effectively capture the changing trend, improve the sensitivity of monitoring, and pay more attention to short-term fluctuations and sudden changes, thereby reducing the excessive impact of historical data on the forecast and making the forecast results more real-time and accurate.

[0116] Furthermore, the storage module includes a data integration unit and a cloud service platform. The data integration unit is used to integrate the data uploaded by each module into total monitoring data, and upload the total monitoring data to the cloud service platform via the Internet. The cloud service platform is used to receive and save the total monitoring data uploaded by the data integration unit. The web browser terminal is a physical device that can access the Internet through a web browser, and is used to access the cloud service platform through the Internet.

[0117] Specifically, the total monitoring data includes every parameter of air quality and temperature and humidity data, as well as the calculation process and result data of the air data correction unit, the air comprehensive index unit and the prediction unit. By consulting the total monitoring data, managers can quickly understand the air monitoring situation and the impact of external factors on the air, so as to facilitate subsequent optimization and improvement. At the same time, the data is uploaded to the cloud service platform. The cloud service platform has backup, encryption, and access control functions, which can effectively prevent the loss, leakage or tampering of the total monitoring data. At the same time, it avoids the hardware failure risks that may exist in traditional local storage methods, and improves the system reliability and data recovery capabilities. The data of the cloud service platform can then be viewed at any time through the web browser terminal. The web browser terminal enables managers to easily view and interpret data through visualization tools such as charts and dashboards.

[0118] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An Internet of Things environment monitoring system, characterized in that: It includes an environmental data collection module, an environmental pollution analysis module, a storage module and a web browser terminal; The environmental data acquisition module includes a sensor unit and a wireless communication unit, wherein the sensor unit is used to monitor air data in the target area, and the wireless communication unit is used to transmit the air data to the environmental pollution analysis module; The environmental pollution analysis module includes an air data correction unit, an air comprehensive index unit and a prediction unit; The air data correction unit is used to correct the air data to reduce errors. The air comprehensive index unit is used to integrate various parameters in the air data to obtain the air pollution index R. The prediction unit is used to combine the air pollution index R and historical changes to obtain the air pollution index R(t+1) at time t+1, and set a danger threshold Y for the result. When R(t+1) is greater than Y, it means that the air quality in the target area in the future time is too poor, and it is necessary to give an early warning and prepare corresponding measures; The air comprehensive index unit integration process is as follows: ； Where R is the air pollution index; n is the number of pollutants; W i is the impact coefficient of the i-th pollutant, ranging from 0 to 1; C cor (i) is the concentration of the ith pollutant after correction; C min,i is the minimum standard concentration of the ith pollutant; C max,i is the maximum standard concentration of the ith pollutant; Indicates the effect of time on air quality; Where sin is the sine function; t represents the current time, t0 is the base time; γ is the time influence coefficient, which is 0.5; The larger the air pollution index R, the worse the air quality, and the smaller the air pollution index R, the better the air quality; The prediction process of the prediction unit is as follows: ； Where R(t+1) is the air pollution index at time t+1; R(t) is the air pollution index at the current time; ΔR avg is the rate of change of the historical average air pollution index; Δt is the time interval; V(t) is the wind speed at the current moment; M is the wind speed influence coefficient, ranging from 0 to 1; ΔR avg It is expressed as: ； Where R(t-j) is the air pollution index at time t-j; k is the number of time points.

2. The Internet of Things environment monitoring system according to claim 1, characterized in that: The sensor unit includes a temperature and humidity sensor, a particle sensor, a carbon dioxide sensor, a nitrogen dioxide sensor and a volatile organic compound sensor, and the wireless communication unit includes a wifi module.

3. The Internet of Things environment monitoring system according to claim 2, characterized in that: The correction process of the air data correction unit is as follows: ； Among them C cor (i) is the concentration of the ith pollutant after correction; C raw (i) is the original concentration of the i-th pollutant; T(t) is the temperature at the current moment; T ref is the standard ambient temperature; α is the temperature influence coefficient, ranging from 0 to 1; H(t) is the humidity at the current moment; H ref is the standard ambient humidity; β is the humidity influence coefficient, ranging from 0 to 1.

4. The Internet of Things environment monitoring system according to claim 1, characterized in that: When R(t+1) is greater than Y, the time influence coefficient γ in the comprehensive index unit is increased, and the time influence coefficient γ is adjusted to: ； Where R max It is the maximum value of the air pollution index.

5. The Internet of Things environment monitoring system according to claim 1, characterized in that: The storage module includes a data integration unit and a cloud service platform. The data integration unit is used to integrate the data uploaded by each module into total monitoring data, and upload the total monitoring data to the cloud service platform via the Internet. The cloud service platform is used to receive and save the total monitoring data uploaded by the data integration unit.

6. The Internet of Things environment monitoring system according to claim 5, characterized in that: The web browser terminal is a physical device that can access the Internet through a web browser and is used to access the cloud service platform through the Internet.

7. The monitoring method of the Internet of Things environment monitoring system according to claim 6, characterized in that: The following steps are involved: S1: Data collection, monitoring various parameters reflecting air quality through the sensor unit in the environmental data collection module, and transmitting the air data to the environmental pollution analysis module in real time through the wireless communication unit; S2: Data processing: After the air data is transmitted to the environmental pollution analysis module, the air data is processed by the air data correction unit in the environmental pollution analysis module to reduce errors and improve the quality of the air data; S3: Data integration. After the data processing is completed, the air comprehensive index unit is used to integrate various parameters in the air data and quantify the results to obtain the air pollution index R; S4: Forecast and warning. Finally, the forecast unit combines the air pollution index R, historical changes and wind speed to predict the air changes in the future, and sets a danger threshold Y for the result. It is determined whether to issue a warning based on the danger threshold Y, so as to prepare corresponding measures in advance. S5: Data storage. The data from the environmental data collection module and the environmental pollution analysis module are uploaded to the storage module. The storage module includes a data integration unit and a cloud service platform. The data integration unit is used to integrate the data uploaded by each module into the total monitoring data, and upload the total monitoring data to the cloud service platform via the Internet for storage.

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