Environmental protection data monitoring method and system

Abstract

The invention discloses an environmental protection data monitoring method and system, and relates to the technical field of environmental protection data monitoring, and the method comprises the steps: constructing a monitoring network in which a fixed monitoring station and a mobile monitoring unit cooperatively work, and achieving the comprehensive collection of real-time data of air quality; multi-source data is transmitted to a cloud platform by using a high-speed data transmission network formed by low-power wireless communication LPWAN and a 5G network, and a high-resolution thermodynamic diagram is generated through a data puzzle algorithm, so that the defects of insufficient space coverage and data updating lagging of traditional monitoring are effectively overcome. According to the method, a pollutant component quantitative model is further constructed by adopting a big data analysis and stoichiometric method, real-time data of PM2.5, VOC and sulfur dioxide are mapped to each pollution source, and accurate quantification of contribution rates of pollution sources such as industrial emission, traffic tail gas and building dust is realized through automatic normalization of a soft maximization function.

Description

Technical Field

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

[0002] In the long-term environmental supervision practice, it is found that environmental data monitoring mainly relies on fixed monitoring stations and timed sampling, which can play a certain supporting role in the basic supervision level, but it is not comprehensive enough and lacks accuracy. When the pollutant concentration in a local area suddenly rises or the meteorological conditions become abnormal, there will be a significant lag in data updates, which will not be able to reflect the real situation in time, delaying warning and response.

[0003] In addition, traditional monitoring methods have a relatively single data source, and the statistical analysis is also based on a few fixed indicators, which has certain limitations. This makes it difficult or difficult to carry out analysis and tracing of the causes of pollution. This is mainly due to the fixed setting of the monitoring area; and the cost of adding additional fixed monitoring stations is relatively high, and the cost of equipment maintenance is also high. Some areas have tried to add monitoring points, satellite remote sensing and drone inspections to fill data gaps, but in actual operations, there are still problems with data integration and real-time performance between supplementary means and the main system.

[0004] The above-mentioned series of problems not only lead to deviations in the monitoring data in reflecting the real environmental conditions, but also cause the responsible departments in the region to lack sufficient basis when formulating governance strategies. In high-density urban areas, slight fluctuations in air quality may have a significant impact on public health and social confidence. Therefore, it is urgent to build an environmental data monitoring system with wider coverage, faster response and more detailed data to solve such problems. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides an environmental data monitoring method and system to solve the problems of traditional environmental data monitoring, such as uneven distribution of fixed monitoring sites, incomplete data acquisition, the existence of data islands in the pollution source tracing process, difficulty in accurately identifying pollution contribution sources, lack of cross-regional linkage response, and inability to effectively form joint prevention and control of sudden regional pollution incidents.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides an environmental data monitoring system, which includes: Monitoring network, data transmission module, cloud platform, pollution source tracing module, multi-regional collaborative early warning platform and emergency response module; The monitoring network, which consists of fixed monitoring sites and mobile monitoring units, is used to collect real-time data on air quality, including pollutants in the air and environmental parameters; The cloud platform is used to present a heat map of the city's air quality; The data transmission module uses low-power wireless communication LPWAN and 5G network to form a data transmission network. Based on the real-time data collected by the monitoring network, the data puzzle algorithm is used to generate a city air quality heat map in real time on the cloud platform, and regional division is performed on the heat map. The pollution source tracing module, based on big data and chemometric methods, quantitatively compares the pollutant components in the real-time data of each region and builds a regional pollution source tracing model; The multi-region collaborative early warning platform, which conducts cross-regional data sharing and joint regulation, issues an early warning when pollutant concentrations in the upwind area exceed the standard, and pushes the early warning information to the affected areas and corresponding environmental protection departments; The emergency response module includes an event triggering mechanism and a response decision unit.

[0008] As a preferred solution of the environmental data monitoring system of the present invention, wherein: the fixed monitoring site is set up at a fixed location and is in a continuous monitoring state; The mobile monitoring unit is specifically a drone and a vehicle-mounted real-time monitoring device deployed in the urban crowd activity area, traffic nodes and the edge of the industrial zone. It is in the command monitoring state and starts monitoring after receiving the command. The two work together to cover the air quality in the area and collect minute-level data; The monitoring network is divided into zones.

[0009] As a preferred solution of the environmental data monitoring system described in the present invention, the regional division includes division according to urban blocks, and wind direction division based on the urban block division, including upwind direction and downwind direction.

[0010] As a preferred solution of the environmental data monitoring system described in the present invention, wherein: in the pollution source tracing module, the pollutant components in the real-time data include fine particles PM2.5, volatile organic compounds VOC, and sulfur dioxide pollutants; The regional pollution source tracing model is used to quantify the contribution rate of industrial, transportation and construction dust pollution sources.

[0011] As a preferred solution of the environmental data monitoring system described in the present invention, the event triggering mechanism automatically activates the mobile monitoring unit to start and collect data as a supplement to the real-time data for short-term or local air pollution events (such as temporary road construction or traffic accidents causing a surge in exhaust gas); The response decision unit detects the supplemented real-time data, generates a report after detecting anomalies in the collected data, and pushes it to the corresponding environmental protection department.

[0012] In a second aspect, the present invention provides an environmental data monitoring method, comprising: Step S1, fixed monitoring stations and mobile monitoring units collect pollutants and environmental parameters in the air in real time according to a preset data collection frequency as real-time data; Step S2, using LPWAN and 5G networks to transmit real-time data to the cloud platform, which uses a data puzzle algorithm to integrate the real-time data and generate a real-time urban air quality heat map; Step S3, based on big data analysis and chemometric methods, the real-time data transmitted to the cloud platform is processed to construct a quantitative model of pollutant composition, and the contribution rates of industrial emissions, traffic exhaust, and construction dust pollution sources are automatically distinguished and quantified; Step S4, cross-regional data sharing and joint regulation are adopted. When abnormal exceeding of pollutant concentration is detected in the upwind area, the warning information is automatically pushed to the affected area through the joint regulation mechanism.

[0013] As a preferred solution of the environmental data monitoring method described in the present invention, wherein: in step S1, when the system detects that the air quality deteriorates sharply in a short period of time or locally, it sends a command to the mobile monitoring unit in the corresponding area, triggering the mobile monitoring unit in the corresponding area to start, and collect data as a supplement to the real-time data; In step S1, the response decision unit detects the supplemented real-time data, generates a report after detecting an abnormality in the collected data, and pushes it to the environmental protection department of the corresponding area.

[0014] As a preferred solution of the environmental data monitoring method described in the present invention, in which: in step S1, when a short-term or local air quality deteriorates sharply, an instruction is sent to the mobile monitoring unit in the corresponding area to trigger the mobile monitoring unit in the corresponding area to start, The system calculates the command signal during real-time monitoring, and the formula is: , in, Indicates the command signal, Indicates the current air quality index. Indicates the preset threshold value, represents a step function, when Exceed hour, , the system issues a start command to the mobile monitoring unit in the corresponding area; After receiving the command, the mobile monitoring unit starts to collect data, and the supplementary data is recorded as: , in, Represents the supplementary data collected by the mobile monitoring unit, represents the calibration factor, Represents the original collected data; The response decision unit performs anomaly detection on the supplementary data, and the anomaly index calculation formula is: , in, Indicates abnormal indicators. represents the historical baseline mean, represents the standard deviation of baseline data; Finally, the formula for generating the report signal is: , in, Indicates the report generation signal, Indicates the abnormality judgment threshold.

[0015] As a preferred embodiment of the environmental data monitoring method described in the present invention, in step S3, the step of constructing a quantitative model of pollutant components to automatically distinguish and quantify the contribution rates of industrial emissions, traffic exhaust and construction dust pollution sources is as follows: Define the linear mapping function of each pollution source, expressed as: , in, Indicates the source of pollution The mapping result is , and Respectively represent the real-time measurement values ​​of PM2.5, VOC and sulfur dioxide, , and is the corresponding regression coefficient, is the bias term, It represents three pollution sources: industrial emission, traffic exhaust and construction dust; By maximizing the function to normalize the mapping results, the contribution rate of each pollution source is calculated. The formula is: , in, Indicates the source of pollution The contribution rate, represents the natural exponential function, An indicator that traverses all pollution sources.

[0016] As a preferred solution of the environmental data monitoring method described in the present invention, in which: in step S4, the step of adopting cross-regional data sharing and joint regulation, when abnormal exceeding of pollutant concentration is detected in the upwind area, automatically pushing warning information to the affected area through the joint regulation mechanism is, In multi-regional data sharing and joint regulation, pollution data in the upwind area triggers an early warning signal, and the calculation formula is: , in, Indicates a warning signal. Indicates the real-time pollutant concentration in the upwind area. Indicates the preset concentration threshold in the upwind area. represents a step function, when Exceed hour, The system automatically transmits the warning information to the affected areas and corresponding environmental protection departments through the cross-regional data sharing interface.

[0017] The beneficial effects of the present invention are as follows: the present invention constructs a monitoring network in which fixed monitoring sites and mobile monitoring units work together to achieve comprehensive collection of real-time air quality data; utilizes a high-speed data transmission network formed by low-power wireless communication LPWAN and 5G networks to transmit multi-source data to a cloud platform, and generates a high-resolution thermal map through a data jigsaw algorithm, which effectively makes up for the shortcomings of traditional monitoring in terms of insufficient spatial coverage and delayed data updates.

[0018] The present invention further adopts big data analysis and chemometric methods to construct a quantitative model of pollutant composition, maps the real-time data of PM2.5, VOC and sulfur dioxide to each pollution source, and automatically normalizes it through a soft maximization function, thereby achieving accurate quantification of the contribution rate of pollution sources such as industrial emissions, traffic exhaust and construction dust.

[0019] The present invention designs a cross-regional joint early warning mechanism. By monitoring the real-time data of the upwind area, when it is detected that the pollutant concentration exceeds the standard, a step function is used to trigger the early warning signal, and the early warning information is pushed to the affected area and the environmental protection department through the cross-regional data sharing interface to achieve regional coordinated regulation; at the same time, the emergency response module can automatically activate the mobile monitoring unit to supplement the data collection when the local or short-term pollution deteriorates sharply, and the response decision unit can perform abnormal detection on the supplementary data and quickly generate a report.

[0020] In summary, the present invention effectively improves the performance of the environmental monitoring system in terms of real-time, spatial coverage, pollution source tracing and cross-regional early warning, and solves the problems of low spatiotemporal resolution, data delay and single data existing in traditional environmental data monitoring methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 It is a schematic diagram of the framework of the environmental data monitoring system of the present invention.

[0023] Figure 2 It is a schematic diagram of the flow of the environmental data monitoring method of the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0027] Example 1, reference Figure 1 and Figure 2 , this embodiment provides an environmental data monitoring system, including: Monitoring network, data transmission module, cloud platform, pollution source tracing module, multi-regional collaborative early warning platform and emergency response module; A monitoring network, consisting of fixed monitoring stations and mobile monitoring units, is used to collect real-time data on air quality, including air pollutants and environmental parameters; Fixed monitoring stations are set up at fixed locations and are in a continuous monitoring state; The mobile monitoring unit is specifically a drone and vehicle-mounted real-time monitoring device deployed in urban crowd activity areas, traffic nodes and the edge of industrial areas. It is in command monitoring state and starts monitoring after receiving the command. The two work together to cover the air quality in the area and collect minute-level data. The monitoring network is divided into regions; A cloud platform for presenting a heat map of the city’s air quality; The data transmission module uses low-power wireless communication LPWAN and 5G network to form a data transmission network. Based on the real-time data collected by the monitoring network, the data puzzle algorithm is used to generate a city air quality heat map in real time on the cloud platform, and regional division is performed on the heat map. The regional division includes division according to urban blocks, and division according to wind directions based on the division of urban blocks, including upwind and downwind directions; The pollution source tracing module, based on big data and chemometric methods, quantitatively compares the pollutant components in the real-time data of each region and builds a regional pollution source tracing model; In the pollution source tracing module, the pollutant components in the real-time data include fine particles PM2.5, volatile organic compounds VOC, and sulfur dioxide pollutants; Regional pollution source tracing model, used to quantify the contribution of industrial, transportation and construction dust pollution sources; A multi-region collaborative early warning platform, which conducts cross-regional data sharing and joint regulation. When pollutant concentrations in the upwind area exceed the standard, an early warning is issued and the early warning information is pushed to the affected areas and the corresponding environmental protection departments. Emergency response module, including event triggering mechanism and response decision unit; The event trigger mechanism automatically activates the mobile monitoring unit to collect data for short-term or local air pollution events (such as temporary road construction or traffic accidents that cause a surge in exhaust gas) to supplement real-time data; The response decision unit detects the supplemented real-time data, generates a report after detecting anomalies in the collected data, and pushes it to the corresponding environmental protection department.

[0028] This embodiment also provides a monitoring method of the above environmental data monitoring system, including: Step S1, fixed monitoring stations and mobile monitoring units collect pollutants and environmental parameters in the air in real time according to a preset data collection frequency as real-time data; In step S1, when the system detects that the air quality has deteriorated sharply in a short period of time or locally, it sends a command to the mobile monitoring unit in the corresponding area, triggering the mobile monitoring unit in the corresponding area to start and collect data as a supplement to the real-time data; In step S1, the response decision unit detects the supplemented real-time data, generates a report after detecting the collected data is abnormal, and pushes it to the environmental protection department of the corresponding area; In step S1, when a short-term or local air quality deteriorates sharply, an instruction is sent to the mobile monitoring unit in the corresponding area, and the step of triggering the mobile monitoring unit in the corresponding area to start is: The system calculates the command signal during real-time monitoring, and the formula is: , in, Indicates the command signal, Indicates the current air quality index. Indicates the preset threshold value, represents a step function, when Exceed hour, , the system issues a start command to the mobile monitoring unit in the corresponding area; After receiving the command, the mobile monitoring unit starts to collect data, and the supplementary data is recorded as: , in, Represents the supplementary data collected by the mobile monitoring unit, represents the calibration factor, Represents the original collected data; The response decision unit performs anomaly detection on the supplementary data, and the anomaly index calculation formula is: , in, Indicates abnormal indicators. represents the historical baseline mean, represents the standard deviation of baseline data; Finally, the formula for generating the report signal is: , in, Indicates the report generation signal, represents the abnormality determination threshold; Specifically, the difference between the air quality data and the preset threshold is calculated in real time, and the step function is used to issue instructions. After receiving the instructions, the mobile monitoring unit supplements the data collection to provide the system with more detailed real-time information. Then, by comparing with the baseline data, the abnormal index is calculated, and the step function is used to determine whether to generate a report, so that the system can respond quickly to local pollution changes. Step S2, using LPWAN and 5G networks to transmit real-time data to the cloud platform, which uses a data puzzle algorithm to integrate the real-time data and generate a real-time urban air quality heat map; Step S3, based on big data analysis and chemometric methods, the real-time data transmitted to the cloud platform is processed to construct a quantitative model of pollutant composition, and the contribution rates of industrial emissions, traffic exhaust, and construction dust pollution sources are automatically distinguished and quantified; In step S3, the steps of constructing a pollutant component quantitative model to automatically distinguish and quantify the contribution rates of industrial emissions, traffic exhaust and construction dust pollution sources are as follows: Define the linear mapping function of each pollution source, expressed as: , in, Indicates the source of pollution The mapping result is , and Respectively represent the real-time measurement values ​​of PM2.5, VOC and sulfur dioxide, , and is the corresponding regression coefficient, is the bias term, It represents three pollution sources: industrial emission, traffic exhaust and construction dust; By maximizing the function to normalize the mapping results, the contribution rate of each pollution source is calculated. The formula is: , in, Indicates the source of pollution The contribution rate, represents the natural exponential function, An indicator to traverse all pollution sources; Specifically, a quantitative model of pollutant composition based on chemometric methods is constructed here. By mapping the real-time data of PM2.5, VOC and sulfur dioxide to the contribution of each pollution source, and constructing a basic model using linear functions, and then normalizing it through maximization functions, it can automatically distinguish and quantify the relative contributions of industrial emissions, traffic exhaust and construction dust, thereby improving the accuracy of pollution source tracing. Step S4, cross-regional data sharing and joint regulation are adopted. When abnormal exceeding of the pollutant concentration is detected in the upwind area, the warning information is automatically pushed to the affected area through the joint regulation mechanism; In step S4, cross-regional data sharing and joint regulation are adopted. When abnormal exceeding of the pollutant concentration is detected in the upwind area, the step of automatically pushing the warning information to the affected area through the joint regulation mechanism is as follows: In multi-regional data sharing and joint regulation, pollution data in the upwind area triggers an early warning signal, and the calculation formula is: , in, Indicates a warning signal. Indicates the real-time pollutant concentration in the upwind area. Indicates the preset concentration threshold in the upwind area. represents a step function, when Exceed hour, , the system automatically transmits the warning information to the affected areas and corresponding environmental protection departments through the cross-regional data sharing interface; Specifically, this step realizes cross-regional joint early warning by monitoring the real-time data of the upwind area, and uses the step function to determine whether the pollutant concentration in the upwind area exceeds the set threshold, thereby automatically triggering the early warning signal. This signal is pushed to the affected area in a timely manner through the pre-established data sharing interface, enabling relevant departments to take necessary control measures before the pollution spreads, effectively connecting regional data collection, data sharing and joint control.

[0029] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An environmental data monitoring system, characterized in that: include, Monitoring network, data transmission module, cloud platform, pollution source tracing module, multi-regional collaborative early warning platform and emergency response module; The monitoring network, which consists of fixed monitoring sites and mobile monitoring units, is used to collect real-time data on air quality, including pollutants in the air and environmental parameters; The cloud platform is used to present a heat map of the city's air quality; The data transmission module generates a city air quality heat map in real time on the cloud platform based on the real-time data collected by the monitoring network; The pollution source tracing module, based on big data and chemometric methods, quantitatively compares the pollutant components in the real-time data of each region and builds a regional pollution source tracing model; The multi-region collaborative early warning platform, which conducts cross-regional data sharing and joint regulation, and issues early warnings when pollutant concentrations in the upwind area exceed the standard; The emergency response module includes an event triggering mechanism and a response decision unit; The event trigger mechanism automatically activates the mobile monitoring unit to collect data for short-term or local air pollution events as a supplement to real-time data; The response decision unit detects the supplemented real-time data, generates a report after detecting anomalies in the collected data, and pushes it to the corresponding environmental protection department.

2. The environmental data monitoring system according to claim 1, characterized in that: The fixed monitoring site is set up at a fixed location and is in a continuous monitoring state; The mobile monitoring unit is specifically a drone and vehicle-mounted real-time monitoring equipment deployed in urban crowd activity areas, traffic nodes and the edge of industrial areas. It is in command monitoring state and starts monitoring after receiving the command; The monitoring network is divided into zones.

3. The environmental data monitoring system according to claim 2, characterized in that: The regional division includes division according to urban blocks and division according to wind directions based on the division of urban blocks, including upwind and downwind directions.

4. The environmental data monitoring system according to claim 3, characterized in that: In the pollution source tracing module, the pollutant components in the real-time data include fine particles PM2.5, volatile organic compounds VOC, and sulfur dioxide pollutants; The regional pollution source tracing model is used to quantify the contribution rate of industrial, transportation and construction dust pollution sources.

5. An environmental data monitoring method, based on an environmental data monitoring system according to any one of claims 1 to 4, characterized in that: include: Step S1, fixed monitoring stations and mobile monitoring units collect pollutants and environmental parameters in the air in real time according to a preset data collection frequency as real-time data; Step S2, using LPWAN and 5G networks to transmit real-time data to the cloud platform, which uses a data puzzle algorithm to integrate the real-time data and generate a real-time urban air quality heat map; Step S3, based on big data analysis and chemometric methods, the real-time data transmitted to the cloud platform is processed to construct a quantitative model of pollutant composition, and the contribution rates of industrial emissions, traffic exhaust, and construction dust pollution sources are automatically distinguished and quantified; Step S4, cross-regional data sharing and joint regulation are adopted. When abnormal exceeding of pollutant concentration is detected in the upwind area, the warning information is automatically pushed to the affected area through the joint regulation mechanism.

6. The environmental data monitoring method according to claim 5, characterized in that: In step S1, when the system detects that the air quality deteriorates sharply in a short period of time or locally, it sends a command to the mobile monitoring unit in the corresponding area, triggering the mobile monitoring unit in the corresponding area to start and collect data as a supplement to the real-time data; In step S1, the response decision unit detects the supplemented real-time data, generates a report after detecting an abnormality in the collected data, and pushes it to the environmental protection department of the corresponding area.

7. The environmental data monitoring method according to claim 6, characterized in that: In step S1, when a short-term or local air quality deteriorates sharply, a command is sent to a mobile monitoring unit in a corresponding area to trigger the mobile monitoring unit in the corresponding area to start, The system calculates the command signal during real-time monitoring, and the formula is: ， in, Indicates the command signal, Indicates the current air quality index. Indicates the preset threshold value, represents a step function, when Exceed hour, , the system issues a start command to the mobile monitoring unit in the corresponding area; After receiving the command, the mobile monitoring unit starts to collect data, and the supplementary data is recorded as: ， in, Represents the supplementary data collected by the mobile monitoring unit, represents the calibration factor, Represents the original collected data; The response decision unit performs anomaly detection on the supplementary data, and the anomaly index calculation formula is: ， in, Indicates abnormal indicators. represents the historical baseline mean, represents the standard deviation of baseline data; Finally, the formula for generating the report signal is: , in, Indicates the report generation signal, Indicates the abnormality judgment threshold.

8. The environmental data monitoring method according to claim 7, characterized in that: In step S3, the step of constructing a pollutant component quantitative model to automatically distinguish and quantify the contribution rates of industrial emissions, traffic exhaust and construction dust pollution sources is as follows: Define the linear mapping function of each pollution source, expressed as: ， in, Indicates the source of pollution The mapping result is , and Respectively represent the real-time measurement values ​​of PM2.5, VOC and sulfur dioxide, , and is the corresponding regression coefficient, is the bias term, It represents three pollution sources: industrial emission, traffic exhaust and construction dust; By maximizing the function to normalize the mapping results, the contribution rate of each pollution source is calculated. The formula is: ， in, Indicates the source of pollution The contribution rate of represents the natural exponential function, An indicator that traverses all pollution sources.

9. The environmental data monitoring method according to claim 8, characterized in that: In step S4, the step of adopting cross-regional data sharing and joint regulation, when abnormal exceeding of pollutant concentration is detected in the upwind area, automatically pushing warning information to the affected area through the joint regulation mechanism is: In multi-regional data sharing and joint regulation, pollution data in the upwind area triggers an early warning signal, and the calculation formula is: ， in, Indicates a warning signal. Indicates the real-time pollutant concentration in the upwind area. Indicates the preset concentration threshold in the upwind area. represents a step function, when Exceed hour, The system automatically transmits the warning information to the affected areas and corresponding environmental protection departments through the cross-regional data sharing interface.

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