VOCs observation station deployment optimization and monitoring early warning system

Abstract

The invention discloses a VOCs observation station deployment optimization and monitoring early warning system, and the system comprises a VOCs observation station deployment optimization module which mainly achieves the deployment optimization and dynamic adjustment of a VOCs observation station; the monitoring and early warning module is mainly used for realizing VOCs concentration prediction, automatically triggering early warning and issuing early warning decision information; and the data server is mainly used for realizing the functions of data backup and management, data analysis, model training and the like. The VOCs observation station deployment optimization and monitoring early warning method and system have the advantages of dynamic observation station layout adjustment, no blind area in prediction, more accurate prediction, multi-level early warning mechanism, system integration and efficient management.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a method and system for optimizing the deployment, monitoring and early warning of volatile organic compounds (VOCs) monitoring stations. Background Art

[0002] VOCs monitoring station optimization and monitoring and early warning work is a complex system engineering project, and is also a hot topic and challenge in the current research field of environmental science.

[0003] The optimization research of VOCs monitoring stations mainly focuses on the optimization of detection equipment structure, optimization of related operating parameters, and optimization of sensor layout, so as to improve monitoring accuracy and reduce costs. For example, Wang Junbo et al. (2017, Wang Junbo, Wang Xin, Wei Haoyun, et al. Transmission efficiency optimization design of VOCs online monitoring gas absorption cell [J]. Laser Technology, 2017, 41 (2): 163-168.) used the method of optical tracing combined with finite element analysis to optimize the design of the objective lens clamping adjustment mechanism and the optical overall structure fixing method, which effectively solved the problem of optical device deformation correction under the working temperature requirements of the gas cell in VOCs monitoring. Gao et al. (2017, Guo H, Guo A, Gao Y, et al. Optimization of a VOCSensor with a Bilayered Diaphragm Using FBAR as Strain Sensing Elements [J]. Sensors, 2017, 17 (8): 1764.) optimized a VOC sensor based on a polymer film using fiber Bragg grating (FBAR) as a strain element. The results showed that the optimized sensor had good linearity and sensitivity. Zumoffen et al. (2010, Zumoffen D, Basualdo MA Systematic Approach for the Design of Optimal Monitoring Systems for Large Scale Processes [J]. Industrial & Engineering Chemistry Research, 2010, 49 (4): 1749-1761.) proposed a new optimization idea for the VOCs efficient monitoring system of a large chemical plant based on genetic algorithm (GA), combining the optimal position of the sensor to use the least number of sensors and reduce the monitoring cost.

[0004] In terms of VOCs monitoring and early warning, Rutemiller et al. (2007, Rutemiller B, Badmus M, Tanner H. Implementing early warning of toxic chemical intrusion into a WWTP by using remote toxic chemical sensors communicating over cellular wireless telemetry into a central SCADA system with advanced alarming and voice notification. [J]. Proceedings of the Water Environment Federation, 2007 (12): 6305-6315.) used cellular wireless technology to transmit data from remote VOCs sensors to a central monitoring and data acquisition system, realizing alarms and voice notifications. Zhang Tao et al. (2018, Zhang Tao, Jiang Xiaomeng, Jiao Zheng. Design and implementation of VOCs monitoring and early warning cloud computing platform [J]. Journal of Shanghai University (Natural Science Edition), 2018, 24(05): 121-129.) Based on the analysis of the shortcomings of the existing monitoring and early warning mechanism, a VOCs monitoring and early warning cloud platform based on cloud computing was designed. The overall architecture, components and main modules of the platform were introduced, and the key technologies and advantages of the platform were analyzed, which further proved the application value of the platform. Zhao Meichao et al. (2024, Zhao Meichao, Fan Weiwei, Ren Tian, ​​et al. Discussion on the construction ideas of toxic and harmful gas monitoring and early warning system in chemical parks - taking Shenyang Chemical Park as an example [J]. Environmental Protection Science, 2024, 50(04): 118-123.) Through the actual case analysis of Shenyang Chemical Park and combined with the current situation survey at home and abroad, the construction methods of the park's toxic and harmful gas monitoring and early warning system were discussed from multiple aspects such as construction ideas, monitoring point layout, monitoring and early warning factor identification, monitoring and early warning platform design, early warning level setting and supporting facilities construction.

[0005] The above research shows that the deployment of VOCs stations and monitoring and early warning have important practical significance for the accuracy of monitoring results and the provision of timely warning information. However, the current VOCs station deployment optimization and monitoring and early warning have not been effectively combined for research, which may lead to blind spots in VOCs monitoring or unrepresentative monitoring results in the region, resulting in insufficient accuracy, inability to accurately warn and respond to VOCs emission emergencies in a timely manner, and inability to meet VOCs governance needs. Summary of the invention

[0006] In view of the above-mentioned technical problems existing in the prior art, the present invention proposes a VOCs monitoring station deployment optimization and monitoring and early warning system.

[0007] A VOCs station deployment optimization and monitoring early warning system includes a VOCs station deployment optimization module, a dynamic adjustment submodule and a data server:

[0008] The VOCs station deployment optimization module includes a data acquisition and processing submodule, an optimization algorithm submodule, and a dynamic adjustment submodule;

[0009] The data collection and processing submodule collects historical VOCs monitoring data, meteorological data and environmental information, performs data cleaning and preprocessing, and obtains preprocessed data;

[0010] The optimization algorithm submodule uses a non-dominated sorting genetic algorithm to optimize the location of the measuring stations, the number of measuring stations and the monitoring frequency according to the pre-processed data to obtain an optimized configuration scheme;

[0011] The dynamic adjustment submodule dynamically adjusts the measurement station layout based on the optimization configuration scheme to obtain an adjusted monitoring network and sends VOCs monitoring data;

[0012] The monitoring and early warning module is used to obtain monitoring data from the adjusted monitoring network and issue early warnings;

[0013] The data server is used to receive, store and back up the monitoring data of VOCs, and also receive the monitoring data for data analysis and model training.

[0014] The monitoring and early warning module includes:

[0015] The real-time monitoring submodule is used to monitor in real time whether the data in the adjusted monitoring network meets the preset threshold, and directly issue an alarm if it exceeds the preset threshold;

[0016] The early warning model construction submodule uses the long short-term memory network algorithm to combine meteorological data, historical monitoring data of each station and environmental pollution events to build a VOCs concentration prediction model;

[0017] The early warning mechanism setting submodule is used to set the VOCs concentration early warning thresholds at different times and in different spaces. When the predicted VOCs concentration issued by the VOCs concentration prediction model of the early warning model construction submodule exceeds the set VOCs concentration early warning threshold, an early warning message is issued;

[0018] The decision support submodule is used to process the monitoring alarms of the real-time monitoring submodule and the early warning information issued after the early warning mechanism setting submodule is set.

[0019] The data server comprises:

[0020] The data storage and management submodule is used to store the VOCs monitoring data, meteorological data and historical records transmitted by each measuring station, and to perform data backup and management;

[0021] The data analysis and processing submodule is used to analyze and process the data of the data storage and management submodule.

[0022] In the data analysis and processing submodule, data analysis and processing include data mining, trend analysis and model training.

[0023] The VOCs monitoring station deployment optimization and monitoring early warning system also includes: a computer monitoring system background;

[0024] The computer monitoring system backend includes a system management submodule and an interface display submodule;

[0025] The system management submodule comprehensively manages VOCs monitoring stations, monitoring data and early warning information;

[0026] The interface display submodule displays monitoring data, warning information and optimization results through a visual interface.

[0027] The VOCs monitoring station deployment optimization and monitoring early warning system also includes: an early warning client;

[0028] The warning client includes a warning information receiving submodule, a user feedback submodule and a warning response and processing submodule;

[0029] The warning information receiving submodule receives the warning information transmitted by the data server, wherein the warning information includes an alarm of excessive VOCs concentration and emergency treatment suggestions;

[0030] The user feedback submodule is used for users to provide feedback, report problems or provide suggestions;

[0031] The early warning response and processing submodule provides an early warning response function and allows the user to perform early warning processing operations.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] 1. Dynamic station layout adjustment: The system can adjust the station layout according to real-time environmental data to more effectively cover the target area, and can automatically make adjustment suggestions based on monitoring data. Compared with traditional methods, this invention can ensure the maximization of monitoring coverage, thereby more comprehensively and accurately grasping the distribution of VOCs in the environment, and providing more accurate basic data for VOCs monitoring and management in industrial parks.

[0034] 2. Prediction is more accurate without blind spots: This invention uses the LSTM algorithm to build a VOCs concentration prediction model to predict future changes in VOCs concentration. The prediction data comes from the historical monitoring data of the optimized measuring station, which can ensure the maximization of monitoring coverage and provide a more accurate basis for early warning decisions. The advantage of this method is that the optimized measuring station has no blind spots, the prediction is more accurate, and it can help relevant departments and the public make adjustments and preparations in advance that may be caused by changes in VOCs concentration.

[0035] 3. Multi-level early warning mechanism: Establishing a multi-level early warning mechanism can not only predict the long-term trend of VOCs concentration, but also timely detect short-term concentration anomalies and provide more detailed early warning services. It helps relevant departments to respond quickly and minimize the impact of VOCs pollution on public health.

[0036] 4. System integration and efficient management: By tightly integrating the data server, computer monitoring system and early warning client, the system can achieve efficient management and response to the entire VOCs monitoring process. The advantage is that it improves the processing efficiency of monitoring data, and also enhances the timeliness and accuracy of early warning response, ensuring that measures can be taken quickly when the VOCs concentration is abnormal, minimizing the impact of environmental pollution on public health. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly introduced below. Obviously, the drawings in the following description only relate to some embodiments of the present disclosure, but are not intended to limit the present disclosure.

[0038] Figure 1 This is the framework diagram of the VOCs monitoring station deployment optimization and monitoring and early warning system in this application.

[0039] Figure 2 2 is a spatial distribution map of the industrial park in the embodiment.

[0040] Figure 3 It is a schematic diagram of the spatial division and genetic coding of the area to be monitored in this application.

[0041] Figure 4 This is a flow chart of the genetic algorithm NSGA-II used in this application.

[0042] Figure 5 Schematic diagram of the optimization of the spatial distribution of VOCs monitoring stations (network) in the embodiment.

[0043] Figure 6 Schematic diagram of the VOCs (chloromethane concentration) level prediction distribution diagram of the system in the embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0045] In the present application, unless otherwise specified or different meanings can be derived from the context, each term has the meaning generally understood in the art.

[0046] On the one hand, the present invention provides a novel VOCs monitoring station deployment optimization strategy and monitoring and early warning method, which includes:

[0047] A VOCs station deployment optimization and monitoring and early warning method and system, including a VOCs station deployment optimization module, a monitoring and early warning module, a data server, a computer monitoring system background and an early warning client;

[0048] The VOCs station deployment optimization module includes a data acquisition and processing submodule, an optimization algorithm submodule, and a dynamic adjustment submodule. The data acquisition and processing submodule performs data cleaning and preprocessing by collecting historical VOCs monitoring data, meteorological data, and environmental information. The optimization algorithm submodule uses a non-dominated sorting genetic algorithm (NSGA-II) to optimize the location, number, and monitoring frequency of the stations according to the data analysis results to achieve optimal detection of the coverage area. The dynamic adjustment submodule dynamically adjusts the station layout based on real-time data and environmental changes. The system can set the adjustment cycle and automatically make adjustment suggestions based on the monitoring data.

[0049] The monitoring and early warning module includes a real-time monitoring submodule, an early warning model construction submodule, an early warning mechanism setting submodule, and a decision support submodule. The real-time monitoring submodule is optimized and configured through the VOCs station deployment optimization module. Each station transmits real-time data to the central database through wireless networks or Internet of Things technology to ensure the timeliness and accuracy of the data. The early warning model construction submodule combines meteorological data, historical monitoring data and environmental pollution events to build a VOCs concentration prediction model through the long short-term memory network (LSTM) algorithm. The early warning mechanism setting submodule sets an early warning threshold. When the prediction model predicts that the VOCs concentration exceeds the set threshold, the system can automatically trigger an early warning and push early warning information to relevant departments and park users, with necessary emergency measures. The decision support submodule provides a visual interface to display the VOCs concentration change trend, hot spots and early warning information, and assist decision makers in formulating response measures.

[0050] The data server includes a data storage and management submodule and a data analysis and processing submodule. The data storage and management submodule is responsible for storing the VOCs data, meteorological data and historical records transmitted by each station, and performing data backup and management. The data analysis and processing submodule analyzes and processes the stored data, including data mining, trend analysis and model training, to support the functions of the VOCs station deployment optimization module and the monitoring and early warning module.

[0051] The computer monitoring system backend includes a system management submodule and an interface display submodule. The system management submodule provides comprehensive management functions for VOCs monitoring stations, monitoring data, and early warning information, including equipment status monitoring, data query, system configuration, etc. The interface display submodule displays monitoring data, early warning information, and optimization results through a visual interface to support users in decision-making analysis and operations.

[0052] The early warning client includes an early warning information receiving submodule, a user feedback submodule, and an early warning response and processing submodule. The early warning information receiving submodule receives early warning information transmitted by the data server, including VOCs concentration exceeding the standard alarm and emergency treatment suggestions. In the user feedback submodule, users can provide feedback information, report problems or provide suggestions through the client to promote system optimization and improvement. The early warning response and processing submodule provides an early warning response function, allowing users to perform early warning processing operations, such as mobilizing resources and launching emergency plans.

[0053] VOCs station deployment optimization uses a non-dominated genetic optimization algorithm (NSGA-II) to optimize the deployment of VOCs stations to ensure the scientificity and effectiveness of the station layout; the station layout is automatically adjusted according to real-time monitoring data and environmental changes to improve the adaptability of the system.

[0054] VOCs monitoring and early warning, based on the historical data monitored by each monitoring station, uses the long short-term memory network (LSTM) algorithm to build a VOCs concentration prediction model. Each station establishes a prediction model to predict the VOCs concentration in the next 24 hours, 3 days, and 7 days. The early warning mechanism sets up submodules, and establishes a multi-level monitoring and early warning mechanism based on the different prediction periods of the next 24 hours, 3 days, and 7 days predicted by the model, providing more detailed early warning services, including: (1) monitoring concentration exceeding the standard alarm, (2) predicted concentration exceeding the standard alarm, and (3) predicted concentration risk alarm. Different levels of alarms can be set according to the predicted concentration.

[0055] The system integration of data server, computer monitoring system background and early warning client realizes efficient management and response of the whole process of VOCs monitoring through data transmission. As the core of the system, the data server is responsible for real-time reception and storage of VOCs data transmitted by each measuring station, and conducts in-depth analysis and mining to provide a scientific basis for the deployment optimization module of VOCs measuring stations. The computer monitoring system background realizes comprehensive management of the entire system, including equipment status monitoring, data query and system configuration, to ensure the stable operation of the system and the accuracy of the data. The early warning client receives early warning information from the data server, provides instant alarms and emergency handling suggestions, and allows users to feedback problems and make optimization suggestions. The close integration of the three not only improves the processing efficiency of monitoring data, but also enhances the timeliness and accuracy of early warning response, ensuring that measures can be taken quickly when the VOCs concentration is abnormal, and minimizing the impact of environmental pollution on public health.

[0056] The present application can be more easily understood through the following embodiments.

[0057] Example

[0058] The case application is carried out in Ningbo Zhenhai Chemical Industrial Park (core area), which covers an area of ​​about 10 square kilometers. Figure 1 This is the framework diagram of the VOCs monitoring station deployment optimization and monitoring and early warning system for this application. Figure 2 2 is a spatial distribution map of the industrial park in the embodiment. Figure 3 It is a schematic diagram of the spatial division and genetic coding of the area to be monitored in this application. Figure 4 This is a flow chart of the genetic algorithm NSGA-II used in this application. Figure 5 Schematic diagram of the optimization of the spatial distribution of VOCs monitoring stations (network) in the embodiment. Figure 6 Schematic diagram of the VOCs (chloromethane concentration) level prediction distribution diagram of the system in the embodiment.

[0059] The specific plan is:

[0060] Step 1: Prepare the data required for the VOCs station deployment optimization module. Investigate and collect basic data on the case park to obtain the distribution of industrial pollution point sources, terrain change characteristics, meteorological data, and basic data on land use types.

[0061] Step 2: Determine the spatial scope of the area to be monitored and divide it into grids: According to the survey data, determine the spatial scope of the area to be monitored, divide its space into grid units of 0.25km×0.25km, and number them in Arabic numeral sequence (ID), such as 1, 2, 3…M, where M is the number of grids divided into the spatial scope. Figure 3 The grid data is used as the input of the VOCs station deployment optimization module to optimize the station.

[0062] Step 3: Deploy the optimized VOCs monitoring stations, such as Figure 5 As shown, 3-6 months of monitoring data are collected and stored in the data server.

[0063] Step 4: The monitoring and early warning model uses the historical monitoring data on the data server to establish a long short-term memory network (LSTM) algorithm to build a VOCs concentration prediction model. By setting the early warning threshold, when the prediction model predicts that the VOCs concentration exceeds the set threshold, the system can automatically trigger an early warning and push early warning information to relevant departments and park users.

[0064] Step 5: Manage VOCs monitoring stations, monitoring data and early warning information through the computer monitoring system to support users in decision-making analysis and operations.

[0065] Step 6: Receive warning information transmitted by the data server through the warning client, including VOCs concentration exceeding the standard alarm and emergency treatment suggestions. Users can perform warning treatment operations, such as mobilizing resources and launching emergency plans.

[0066] The VOCs station deployment optimization and monitoring and early warning method and system of the present invention have the advantages of dynamic station layout adjustment, more accurate prediction without blind spots, multi-level early warning mechanism, system integration and efficient management. Therefore, compared with the traditional method, the monitoring station optimization scheme obtained by the present invention has a better effect in monitoring VOCs, and on this basis, provides more accurate monitoring data for VOCs prediction and early warning. At the same time, the system improves the processing efficiency of monitoring data, enhances the timeliness and accuracy of early warning response, ensures that measures can be taken quickly when the VOCs concentration is abnormal, and minimizes the impact of environmental pollution on public health.

[0067] The above description is merely an exemplary embodiment of the present disclosure and is not intended to limit the protection scope of the present disclosure. The protection scope of the present disclosure is determined by the appended claims.

Claims

1. A VOCs station deployment optimization and monitoring early warning system, characterized in that: Including VOCs station deployment optimization module, dynamic adjustment submodule and data server: The VOCs station deployment optimization module includes a data acquisition and processing submodule, an optimization algorithm submodule, and a dynamic adjustment submodule; The data collection and processing submodule collects historical VOCs monitoring data, meteorological data and environmental information, performs data cleaning and preprocessing, and obtains preprocessed data; The optimization algorithm submodule uses a non-dominated sorting genetic algorithm to optimize the location of the measuring stations, the number of measuring stations and the monitoring frequency according to the pre-processed data to obtain an optimized configuration scheme; The dynamic adjustment submodule dynamically adjusts the measurement station layout based on the optimization configuration scheme to obtain an adjusted monitoring network and sends VOCs monitoring data; The monitoring and early warning module is used to obtain monitoring data from the adjusted monitoring network and issue early warnings; The data server is used to receive, store and back up the monitoring data of VOCs, and also receive the monitoring data for data analysis and model training.

2. The VOCs station deployment optimization and monitoring early warning system according to claim 1 is characterized in that: The monitoring and early warning module includes: The real-time monitoring submodule is used to monitor in real time whether the data in the adjusted monitoring network meets the preset threshold, and directly issue an alarm if it exceeds the preset threshold; The early warning model construction submodule uses the long short-term memory network algorithm to combine meteorological data, historical monitoring data of each station and environmental pollution events to build a VOCs concentration prediction model; The early warning mechanism setting submodule is used to set the VOCs concentration early warning thresholds at different times and in different spaces. When the predicted VOCs concentration issued by the VOCs concentration prediction model of the early warning model construction submodule exceeds the set VOCs concentration early warning threshold, an early warning message is issued; The decision support submodule is used to process the monitoring alarms of the real-time monitoring submodule and the early warning information issued after the early warning mechanism setting submodule is set.

3. The VOCs station deployment optimization and monitoring early warning system according to claim 1 is characterized in that: The data server comprises: The data storage and management submodule is used to store the VOCs monitoring data, meteorological data and historical records transmitted by each measuring station, and to perform data backup and management; The data analysis and processing submodule is used to analyze and process the data of the data storage and management submodule.

4. The VOCs station deployment optimization and monitoring early warning system according to claim 3 is characterized in that: In the data analysis and processing submodule, data analysis and processing include data mining, trend analysis and model training.

5. The VOCs station deployment optimization and monitoring early warning system according to claim 1 is characterized in that: Also includes: Computer monitoring system background; The computer monitoring system backend includes a system management submodule and an interface display submodule; The system management submodule comprehensively manages VOCs monitoring stations, monitoring data and early warning information; The interface display submodule displays monitoring data, warning information and optimization results through a visual interface.

6. The VOCs station deployment optimization and monitoring early warning system according to claim 1 is characterized in that: Also includes: Early warning client; The warning client includes a warning information receiving submodule, a user feedback submodule and a warning response and processing submodule; The warning information receiving submodule receives the warning information transmitted by the data server, wherein the warning information includes an alarm of excessive VOCs concentration and emergency treatment suggestions; The user feedback submodule is used for users to provide feedback, report problems or provide suggestions; The early warning response and processing submodule provides an early warning response function and allows the user to perform early warning processing operations.

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