An industrial park unorganized emission distributed multi-node monitoring traceability system and traceability analysis method

Abstract

The application discloses an industrial park unorganized emission distributed multi-node monitoring and tracing system and a tracing analysis method, and belongs to the technical field of environmental monitoring. A three-level architecture of a perception node layer, an edge gateway layer and a cloud platform layer is adopted, a multi-parameter micro terminal is arranged in a grid mode in a global uniform and key encryption manner, global high-resolution monitoring of the park is realized, the edge end improves data accuracy through three quality control mechanisms of outlier rejection, cross interference compensation and drift correction, the cloud reconstructs a global concentration field based on Kriging interpolation, a building corrected Gaussian plume model and a genetic optimization algorithm are combined to realize accurate positioning of unorganized emission sources and accounting of emission intensity, and hierarchical early warning and automatic generation of an emission list are supported. The data efficiency of the application can reach more than 96%, the positioning error of the emission source is not more than 15 m, the tracing response time is not more than 5 minutes, and the application can be widely applied to unorganized emission monitoring and fine management and control of parks such as chemical industry, coating and storage.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring and pollution control technology, specifically relating to a distributed multi-node monitoring and tracing system for fugitive emissions in industrial parks and a tracing and analysis method. Background Technology

[0002] Industrial parks specializing in chemical, coating, pharmaceutical, and oil storage industries commonly experience fugitive emissions. Pollutants such as VOCs, odorous gases, and particulate matter are emitted in irregular forms, including leaks from sealed production equipment, emissions from storage tank breather valves, material spillage during loading and unloading, and dust from factory roads. These emissions are characterized by dispersed and concealed emission points, large fluctuations in emission intensity, and significant influence of meteorological conditions on diffusion paths, making them a key focus and challenge for air pollution control in these parks. According to the "Standard for Control of Fugitive Emissions of Volatile Organic Compounds" (GB37822-2019) and the "Integrated Emission Standard for Air Pollutants" (GB16297-1996), industrial parks must conduct fugitive emission monitoring and source tracing to ensure that concentrations at factory boundaries meet standards and gradually achieve refined source control.

[0003] Currently, the monitoring of fugitive emissions in industrial parks mainly adopts two technical models: one is the manual sampling and detection method in laboratories, which requires personnel to carry sampling equipment to fixed points on site to collect samples, and then bring them back to the laboratory for analysis using instruments such as gas chromatography. This method has high detection accuracy, but the detection cycle is long and the timeliness is poor. It cannot capture instantaneous and intermittent fugitive emissions, and the sampling points are limited, making it difficult to reflect the pollution distribution throughout the entire industrial park. The other is the fixed station monitoring method at the plant boundary, which involves setting up a small number of standard station monitoring devices at the plant boundary of the industrial park to continuously monitor the concentration at the plant boundary. This method can achieve continuous online monitoring, but the spatial coverage is extremely low. It can only obtain concentration data at a few points at the plant boundary, and cannot reflect the spatial distribution of pollution within the industrial park. When the plant boundary exceeds the standard, it is difficult to locate the specific emission source, and manual investigation of each device is still required, resulting in low source tracing efficiency and high control costs.

[0004] With the development of sensing technology, distributed micro-monitoring systems are gradually being applied to the monitoring of fugitive emissions in industrial parks, and related patents and technical solutions are being made public one after another.

[0005] Chinese invention patent CN119199030A discloses a microscale source tracing method for air pollutants based on atmospheric concentration monitoring in industrial parks. This method locates emission sources through statistical standardization and probability density distribution calculation. However, this approach has significant shortcomings: First, it lacks a front-end data quality control mechanism. The micro-sensors are susceptible to cross-interference and temperature / humidity drift in the complex environment of the industrial park, resulting in large errors in the raw data. Directly using this data for source tracing calculations can lead to biased results. Second, the probability density statistical method can only provide the probability distribution area of ​​the emission source, failing to accurately locate the emission source coordinates or quantitatively calculate emission intensity, thus hindering precise law enforcement and emission inventory calculation.

[0006] Chinese invention patent CN115201358A discloses an online monitoring system for fugitive emissions in industrial parks, which collects data from multiple locations by deploying multiple monitoring terminals. The shortcomings of this solution are: firstly, the source tracing uses a simplified Gaussian diffusion model, failing to consider the shielding and disturbance effects of dense buildings and equipment on pollutant diffusion within the park, resulting in significantly increased source tracing errors in densely built chemical industrial parks; secondly, the system adopts a direct terminal-to-cloud architecture without an edge computing layer, leading to high bandwidth pressure due to the direct uploading of large amounts of raw data, and hindering rapid local response, making it prone to data loss and monitoring gaps during network interruptions.

[0007] Chinese invention patent CN121027432A discloses a three-dimensional adaptive mesh-based system for tracing the source of odor pollution in industrial parks, which improves the accuracy of source tracing through three-dimensional mesh numerical simulation. The problems with this solution are: firstly, the three-dimensional numerical simulation has extremely high computational complexity, requiring significant server computing power, and each round of source tracing calculation is time-consuming, making it difficult to meet the needs of real-time source tracing of fugitive emissions; secondly, the system deployment and maintenance costs are high, making it difficult to implement in small and medium-sized industrial parks and hindering its widespread application.

[0008] In summary, existing technologies for monitoring and tracing fugitive emissions in industrial parks generally suffer from three major shortcomings: first, insufficient quality control of micro-sensor data, making it difficult to guarantee data accuracy in complex park environments; second, tracing algorithms are not fully adapted to the complex building terrain of parks, resulting in insufficient positioning and quantitative accuracy; and third, unreasonable system architecture, making it difficult to balance real-time performance and stability. Therefore, developing a distributed monitoring and tracing system and supporting tracing analysis methods that combine high spatial coverage, high data quality, accurate tracing capabilities, and high cost-effectiveness is crucial to addressing the current pain points of "inaccurate measurement, inaccurate location, and imprecise management" of fugitive emissions in industrial parks. This would have significant practical application value for improving the level of environmental management in industrial parks and meeting environmental regulatory requirements. Summary of the Invention

[0009] Technical problems to be solved The purpose of this invention is to overcome the shortcomings of the prior art and solve the following technical problems: 1. To solve the problems of low spatial coverage and inability to capture the spatiotemporal distribution of unorganized non-point source pollution in traditional single-point monitoring, high-resolution monitoring of the entire park is achieved through gridded distributed node deployment, covering all key areas such as the factory boundary, production area, and tank area; 2. To address the issues of poor data accuracy, cross-interference, and significant temperature and humidity drift caused by micro sensors in complex park environments, a three-level quality control mechanism at the edge is implemented to improve data reliability and meet environmental monitoring quality control requirements. 3. To address the issues of low accuracy in tracing and locating sources of fugitive emissions, inaccurate calculation of emission intensity, and failure to consider building shading effects, an optimized reverse diffusion algorithm combining concentration field reconstruction and building correction is used to achieve precise location and quantitative calculation of emission sources. 4. To address the issues of unreasonable system architecture and insufficient real-time performance and stability, a three-tier architecture of "perception-edge-cloud" is adopted, which balances rapid local response with in-depth cloud analysis, thereby improving system stability and ease of operation and maintenance.

[0010] Technical solution To address the above problems, the present invention provides the following technical solution: A distributed multi-node monitoring and tracing system for fugitive emissions in industrial parks includes a distributed sensing node layer, an edge computing gateway layer, and a cloud-based management and control platform layer. The distributed sensing node layer consists of several grid-deployed miniature monitoring terminals, which are used to collect atmospheric pollutant concentrations, meteorological parameters and location information at multiple points in the park in real time and upload them to the edge computing gateway layer. The edge computing gateway layer is used to preprocess, perform quality control verification and local caching of data collected from multiple nodes, and upload valid data to the cloud management platform layer; The cloud-based management and control platform includes a data storage module, a visualization module, a source tracing and analysis module, and an early warning push module, which are used to realize centralized management of monitoring data, reverse source tracing of emission sources, pollution diffusion simulation, and early warning of exceeding standards.

[0011] Furthermore, the miniature monitoring terminal integrates a PID sensor, an electrochemical sensor, and a five-parameter meteorological sensor, which can simultaneously detect at least one pollutant in VOCs, particulate matter, hydrogen sulfide, and ammonia, as well as meteorological parameters such as wind speed, wind direction, temperature, humidity, and air pressure. The miniature monitoring terminal adopts a dual-mode power supply of solar power and wired power supply, with a deployment spacing of 50-200m, and is densely deployed along the park boundary, production unit area, and storage tank area.

[0012] Furthermore, the edge computing gateway layer supports multiple communication methods such as LoRa, 4G, and Ethernet, and the local data cache capacity is no less than 30 days. The edge computing gateway has quality control functions such as abnormal data removal, zero-point drift correction, and cross-interference compensation, and automatically marks and removes abnormal data that exceeds a reasonable threshold.

[0013] Furthermore, the source tracing analysis module incorporates a gridded concentration field reconstruction unit and a reverse diffusion source tracing calculation unit. Based on real-time monitoring data and meteorological parameters, combined with a Gaussian plume diffusion model and a genetic optimization algorithm, it inversely calculates the location and emission intensity of the emission source.

[0014] Furthermore, this includes the following steps: S1 Multi-Source Data Acquisition and Preprocessing: Pollutant concentration and meteorological data at various locations are collected through distributed sensing nodes, and after quality control and preprocessing by the edge gateway, they are uploaded to the cloud to form a standardized time-series dataset. S2 gridded concentration field reconstruction: Based on the monitoring data of each node, a spatial interpolation algorithm is used to construct the pollutant concentration field of the entire park, identify high-concentration pollution areas, and complete the initial screening of emission sources; S3 reverse diffusion source tracing calculation: Using monitoring data of high-concentration areas as input, combined with real-time meteorological parameters and a three-dimensional terrain and building model of the park, the optimized Gaussian plume reverse model is used to calculate the location of emission sources and the emission intensity. S4 Emission Inventory and Early Warning Output: Generate an inventory of fugitive emissions in the park based on the source tracing results, trigger graded early warnings for areas with excessive emissions, and push them to the park's management and control terminal simultaneously.

[0015] 6. The method for tracing and analyzing fugitive emissions in industrial parks according to claim 5, characterized in that, in step S1, the quality control preprocessing specifically includes: using the 3σ criterion to remove outliers, correcting the cross-interference of coexisting gases through the sensor cross-sensitivity coefficient matrix, and correcting long-term sensor drift by combining the zero-point drift calibration model, with a data validity rate of not less than 95%.

[0016] Furthermore, in step S2, the spatial interpolation algorithm uses the Kriging interpolation method, combined with terrain and building shading factors for correction, to generate a 5m×5m resolution heat map of the park concentration; continuous areas with concentrations exceeding the threshold are marked as suspected emission source areas, completing the initial screening and location.

[0017] Furthermore, in step S3, the reverse diffusion source tracing calculation is specifically as follows: taking the grid points in the suspected emission source area as candidate emission sources, substituting them into the Gaussian plume diffusion model to calculate the theoretical concentration of each monitoring point in forward modeling, taking the minimum of the sum of squared residuals between the theoretical concentration and the measured concentration as the objective function, and using a genetic algorithm to iteratively optimize, finally determining the precise location and emission rate of the emission source.

[0018] Furthermore, in step S4, the graded early warning is divided into three levels: a yellow warning is triggered when the pollutant concentration exceeds the emission limit by 80%, an orange warning is triggered when the limit is exceeded, and a red warning is triggered when the limit is exceeded for 1 consecutive hour; the warning information is pushed through system pop-ups and SMS.

[0019] Furthermore, the method has a positioning error of no more than 15m for the unorganized emission sources in the park, a source tracing response time of no more than 5min, and a relative error of less than 20% in emission intensity calculation.

[0020] Beneficial effects The beneficial effects of this invention are: 1. This invention adopts a grid-based deployment scheme with uniform point distribution and key area densification, achieving a spatial resolution of up to 100 meters and 50 meters in key areas. It can completely capture the spatial distribution and dynamic changes of unorganized non-point source pollution in the industrial park, solving the problems of traditional single-point monitoring that "represents the whole area" and poor data representativeness, and realizing all-round monitoring of the factory boundary and the interior of the factory area.

[0021] 2. This invention effectively solves the problem of insufficient accuracy of micro sensors in complex park environments by adopting a three-level quality control system of "outlier removal - cross-interference compensation - drift correction" at the edge. The data effectiveness rate is increased from about 80% in traditional solutions to more than 95%, the relative detection error is controlled within 15%, and the data quality can meet the basic requirements of environmental protection supervision, providing a reliable data foundation for subsequent source tracing analysis.

[0022] 3. This invention innovatively introduces building shading factors into the diffusion model and combines them with genetic algorithms for optimization, solving the problem of large errors in traditional diffusion models in densely built-up areas. At the same time, it adopts a two-step source tracing strategy of "initial screening of concentration field + precise inversion", which balances computational efficiency and accuracy. The emission source location error does not exceed 15m, the relative error of emission intensity calculation is less than 20%, and the source tracing response time does not exceed 5 minutes. It can quickly locate the responsible source after exceeding the standard, greatly improve the efficiency of park management and control, and reduce the cost of manual investigation.

[0023] 4. This invention adopts a three-level architecture of "sensing-edge-cloud". The edge end is responsible for preprocessing and local caching, which reduces the bandwidth pressure and computing load of the cloud, and ensures data integrity and local early warning capabilities when the network is interrupted. The system has low hardware cost and flexible deployment. It does not require large-scale civil construction and can be quickly adapted to chemical, coating and warehousing parks of various sizes. Its implementation and cost-effectiveness are significantly better than traditional station building solutions and three-dimensional numerical simulation solutions. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., are only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0026] The present invention will be further described in detail below with reference to specific embodiments and experimental examples, but the scope of protection of the present invention is not limited to the content described.

[0027] Implementation Plan for Small-Scale Fine Chemical Industrial Park in Example 1 This embodiment is applied to an area of ​​approximately 0.8 km². 2 The industrial park is a fine chemical industry zone, with pharmaceutical intermediate synthesis as its main industry. The main fugitive emissions are VOCs and hydrogen sulfide. The plant boundary complies with the limits set by the "Emission Standard of Pollutants for Petrochemical Industry" (GB31571-2015). The specific construction and operation of this system are as follows: (1) Deployment of distributed sensing nodes and hardware parameters A total of 20 integrated mini monitoring terminals have been deployed in this industrial park, following the principle of "closed boundary, key area densification, and full coverage": Factory boundary monitoring points: a total of 8 units, numbered J1 to J8, are evenly distributed along the perimeter of the park for about 3600m, covering the four factory boundary directions of east, south, west and north, with an average spacing of about 450m, forming a closed monitoring circle to monitor the factory boundary compliance status. Production unit area locations: a total of 8 units, numbered S1 to S8, distributed in 3 core production workshops, with a spacing of about 80m between units within a single unit area, covering areas with high incidence of unorganized activities such as reaction kettle area and material conveying area; Tank area location: 2 tanks in total, numbered T1 to T2, located downwind of the two raw material tank groups in the north and south respectively; Auxiliary location points: One W1 unit is deployed at the sewage treatment plant, and one O1 unit is deployed in the office area as a background control point.

[0028] The core hardware configuration of each miniature monitoring terminal is as follows: Pollutant detection unit: PID photoionization sensor (detects total VOCs, range 0-10ppm, detection limit 0.01ppm, indication error ±5%FS); hydrogen sulfide electrochemical sensor (range 0-50ppm, detection limit 0.1ppm, indication error ±3%FS); Meteorological detection unit: Ultrasonic anemometer (wind speed range 0~60m / s, accuracy ±0.2m / s, wind direction resolution 1°); Integrated temperature, humidity and pressure sensor (temperature range -40~80℃, accuracy ±0.3℃; humidity range 0~100%RH, accuracy ±2%RH; pressure range 300~1100hPa, accuracy ±0.5hPa); Positioning and communication unit: Beidou / GPS dual-mode positioning module, with a planar positioning accuracy of ≤5m; LoRa wireless communication module, operating frequency band 470MHz, with a line-of-sight transmission distance of ≥3km; Power supply and protection unit: 10W monocrystalline silicon photovoltaic panel paired with 20Ah lithium iron phosphate battery, also supports DC12V wired power supply, can last for more than 7 days without charging on cloudy days; the whole machine adopts aluminum alloy shell, IP65 protection level, working environment temperature -30~60℃, adaptable to complex outdoor working conditions.

[0029] The sampling frequency of all terminals is uniformly set to 1 time / minute, and the data is uploaded to the edge gateway after being stamped with a timestamp and the location ID.

[0030] (2) Edge computing gateway deployment and quality control logic Two industrial-grade edge computing gateways are deployed in this park, each responsible for the data access of 10 terminals in the east and west zones respectively. They are deployed in two low-voltage well cabinets in the park.

[0031] Gateway hardware configuration: It adopts an ARM Cortex-A72 architecture processor with a main frequency of 1.2GHz, 1GB of memory, 8GB of built-in industrial-grade Flash storage, supports 128GB SD card expansion storage, and can cache at least 30 days of minute-level data; it is equipped with 1 LoRa concentrator, 2 gigabit Ethernet ports, and 1 4G full network module.

[0032] The gateway has a built-in three-level quality control algorithm, and the specific execution logic is as follows: Outlier removal: The arithmetic mean and standard deviation of the data from each detection channel over the past 24 hours are calculated on an hourly basis. If the real-time data exceeds the range of "mean ± 3 times standard deviation", it is identified as an outlier, automatically marked and removed, and not included in subsequent calculations. Cross-interference compensation: A two-dimensional cross-sensitivity coefficient matrix pre-calibrated in the laboratory is used, in which the cross-sensitivity coefficient of hydrogen sulfide to VOCs is 0.048 and the cross-sensitivity coefficient of VOCs to hydrogen sulfide is 0.012. The cross-response components of coexisting gases are subtracted through matrix operation to correct the sensor readings. Zero drift correction: Automatic zero-point calibration is performed for 5 minutes every day at 2:00 AM. Clean air is introduced through the built-in activated carbon filter module, the reference value is collected and the zero-point parameters are updated to correct long-term sensor drift.

[0033] After quality control is completed, valid data is packaged in standard JSON format and uploaded to the cloud platform via 4G network; it is automatically cached locally when the network is interrupted and automatically resumes transmission after the network is restored to ensure that no data is lost.

[0034] (3) Cloud-based management platform construction and algorithm parameters The platform is deployed on Alibaba Cloud ECS cloud servers, configured with 4 cores and 8G CPU, 100G high-efficiency cloud disk, and CentOS 7.9 operating system; the database uses InfluxDB 2.0 time-series database to store minute-level monitoring data, and MySQL 8.0 to store structured data such as devices, users, and events; the GIS map uses the CGCS2000 national geodetic coordinate system and loads a 1:500 high-precision planar topographic map and building outline layers of the park.

[0035] Core functional module parameter configuration: Visualization module: Supports three display modes: real-time concentration heat map, time trend map, and equipment status distribution map. The heat map is updated every 5 minutes. Concentration field reconstruction module: Ordinary Kriging interpolation method is adopted, the variogram model is a spherical model, the basic grid resolution is 5m×5m, the building shading attenuation factor is introduced during interpolation calculation, and the concentration attenuation coefficient on the leeward side of the building is set to 0.7. Source tracing analysis module: Genetic algorithm settings include a population size of 100, crossover probability of 0.8, mutation probability of 0.05, maximum number of iterations of 50 generations, and a convergence threshold of residual sum of squares change rate less than 1×10⁻⁶. -4 The diffusion model uses a Gaussian plume model that takes into account building wake correction, and atmospheric stability is automatically determined based on meteorological parameters using the Pasquale-Turner (PT) method. Early warning module: Configures three-level early warning thresholds, with a VOCs plant limit value of 2 mg / m³. 3 The yellow alert threshold is set at 80% of the limit (1.6 mg / m³). 3 The orange alert indicates a limit (2 mg / m³). 3 A red alert is issued when the limit is exceeded for 60 consecutive minutes.

[0036] Example 2: Deployment Scheme for Large-Scale Petrochemical Storage Park This embodiment is applied to an area of ​​approximately 2.5 km². 2 The large petrochemical storage park mainly stores gasoline, diesel, and chemical solvents. The core pollutants are non-methane total hydrocarbons and VOCs. It is classified as Zone 2, an explosion hazard zone, and the system configuration is as follows: Sensing Nodes: A total of 52 explosion-proof miniature monitoring terminals were deployed, with an explosion-proof rating of ExibIIBT4Gb, meeting the requirements for use in hazardous explosion areas. 18 terminals were deployed at the plant boundary, along a perimeter of approximately 12km within the park, with an average spacing of about 670m; 24 terminals were deployed across 12 tank groups, with 2 terminals per group, positioned upwind and downwind of the tank groups; 6 terminals were deployed in the truck loading / unloading area, arranged along the loading / unloading crane positions, with a spacing of about 50m; and 4 terminals were deployed in the office and auxiliary area. The terminal PID sensors have a range extended to 0–50ppm, suitable for high-concentration scenarios in petrochemical parks, and are powered by explosion-proof solar panels and explosion-proof junction boxes.

[0037] Edge Gateway: A total of 5 regional edge gateways are deployed and installed in the duty rooms outside the explosion-proof areas of each zone. They are connected to the explosion-proof terminals in the zone via LoRa wireless access. In addition, 1 core aggregation gateway is set up to connect to the industrial ring network of the park via gigabit fiber optic cable. Data is uniformly transmitted to the local data center server in the park to ensure data security and transmission stability.

[0038] Source tracing algorithm adaptation: Import 3D building models of storage tanks, pump rooms, loading and unloading platforms in the park, optimize diffusion parameters for open tank area scenarios, and adjust the building wake turbulence enhancement coefficient to 1.2; at the same time, it supports simultaneous inversion of multiple emission sources, and can identify up to 5 emission sources at the same time, adapting to the emission characteristics of multiple points of emission in the storage park.

[0039] Comparative Example 1: Traditional Plant Boundary Monitoring Scheme In the same industrial park as Example 1, one standard environmental monitoring station was constructed at each of the four plant boundary points (east, south, west, and north). Each station was equipped with a gas chromatograph for VOCs detection and an ultraviolet fluorescence analyzer for hydrogen sulfide, with a sampling frequency of once per hour. The system only has real-time data display, alarm for exceeding standards, and data reporting functions. It lacks monitoring points within the industrial park and source tracing analysis capabilities. After exceeding standards, the emission source must be located entirely through manual on-site investigation.

[0040] Comparative Example 2: Ordinary Distributed Monitoring Scheme In the same park as in Example 1, 20 miniature monitoring terminals were deployed in the same number and location as in Example 1. However, the system adopted a flat architecture of "terminals directly connected to the cloud", without an edge computing gateway or a three-level data quality control mechanism. The raw data was directly uploaded to the cloud. The source tracing only adopted a simple judgment logic: based on the monitoring point with the highest concentration, the emission source location was determined by extending 50 to 100 meters upwind. There was no diffusion model or optimization algorithm, and the emission intensity could not be quantitatively calculated.

[0041] Experiment Example 1: Comparison and Verification of Data Quality Control Effectiveness Experimental objective: To verify the effect of the three-level quality control mechanism at the edge of this invention on improving data quality.

[0042] Experimental Methods: Following the standard "Determination of Volatile Organic Compounds in Exhaust Gas from Stationary Sources: Solid-Phase Adsorption-Thermal Desorption / Gas Chromatography-Mass Spectrometry" (HJ734-2014), active sampling was used to simultaneously collect gas samples at corresponding monitoring points. Six parallel samples were collected daily for seven consecutive days, resulting in a total of 42 valid samples. The laboratory gas chromatography results were used as the true values. The relative errors between the monitoring data and the true values ​​for Example 1 and Comparative Example 2 were calculated, and the arithmetic mean was taken. The data validity rate was defined as the percentage of valid data to the total collected data, with outliers and missing values ​​considered invalid data.

[0043] The experimental results are shown in Table 1 below. Table 1. Comparison of data quality among different schemes and details of the examples.

[0044] Results analysis: After three-level quality control at the edge, the relative errors of VOCs and hydrogen sulfide detection in the system of this invention were reduced to within 15%, the data validity rate was increased to 96.7%, and the proportion of abnormal data decreased from 8.7% to 0.8%. The data quality is significantly better than that of ordinary distributed systems without quality control, which can meet the basic quality control requirements of environmental monitoring and provide a reliable data foundation for subsequent source tracing analysis.

[0045] Experiment Example 2: Comparison Test of Traceability Performance Experimental objective: To verify the positioning accuracy, quantitative accuracy, and response efficiency of the tracing method of this invention.

[0046] Experimental setup: Three known simulated fugitive emission sources were set up within the park area in Example 1. A 10ppm VOCs standard gas cylinder paired with a high-precision rotor flow meter was used to control the emission rate. The emission outlet height was 1.5m, and three emission gradients were set: 0.52kg / h, 1.15kg / h, and 0.87kg / h. The average wind speed during the test was 3.2m / s, the prevailing wind direction was northeast, the atmospheric stability was level D, and meteorological data were obtained from the park's meteorological station at the minute-by-minute level.

[0047] Evaluation metrics: Location error is calculated using the Euclidean distance between the source-tracing output coordinates and the actual emission source coordinates; emission intensity calculation error is |calculated value - actual value| / actual value × 100%; source-tracing response time is the time elapsed from triggering calculation to outputting the result. Each experiment was repeated 3 times, and the average value was taken.

[0048] The experimental results are shown in Table 2 below.

[0049] Table 2. Results of Traceability Performance Comparison Tests

[0050] Results analysis: The tracing method of this invention has an average positioning error of only 10.8m, which is far better than the 41.7m of Comparative Example 2, and can accurately locate specific devices or even specific sealing point areas. This invention can quantitatively calculate emission intensity with an average error of 15.2%, meeting the accuracy requirements for emission inventory calculation, while Comparative Example 2 can only roughly determine the direction and cannot quantify it; The average response time for source tracing in this invention is 3.7 minutes, which is controlled within 5 minutes, fully meeting the control requirements for real-time source tracing of fugitive emissions; in contrast, the traditional station solution in Comparative Example 1 has no source tracing capability, and manual investigation takes 2 to 4 hours per instance.

[0051] Experiment Example 3: Source Tracing Adaptability Test under Different Meteorological Conditions Experimental objective: To verify the source tracing stability of the method of the present invention under different meteorological conditions.

[0052] Experimental setup: Tests were conducted on three typical weather days, with 10 parallel source tracing experiments performed under each weather condition. Calm and stable weather: In the early morning, the average wind speed is 0.7 m / s, the atmospheric stability is level F, and the diffusion conditions are poor; Moderate wind speed: Sunny during the day, average wind speed 3.2m / s, atmospheric stability level D, which is the normal meteorological condition for the park; Windy weather: During the afternoon windy period, the average wind speed is 7.1 m / s, the atmospheric stability is level B, and the diffusion conditions are good.

[0053] The accuracy of tracing was determined by a positioning error of ≤15m, and the accuracy rate of tracing under different conditions was statistically analyzed.

[0054] The experimental results are shown in Table 3 below.

[0055] Table 3 Source tracing performance under different meteorological conditions

[0056] Results analysis: The method of this invention maintains good source tracing performance under three typical meteorological conditions, with the highest accuracy under moderate wind speed; under calm and stable weather conditions, the error increases slightly due to large wind direction fluctuations and irregular diffusion, but the accuracy still reaches 90%; the overall adaptability is strong and can cover most meteorological scenarios in the park.

[0057] Experiment Example 4: Application Verification of Actual Exceeding Standard Event Source Tracing Experimental objective: To verify the effectiveness of this system in improving traceability accuracy and control efficiency in actual park operations.

[0058] Experimental Methods: The system operated continuously for 3 months in the industrial park of Example 1, during which 6 VOCs exceeding the standard at the plant boundary were triggered. The system automatically initiated source tracing and output the results. At the same time, a team of 3 people with LDAR testing qualifications used a portable PID detector (accuracy ±2%FS) to test and verify the sealing points and emission outlets in the park point by point. A leakage point was identified as a leakage point when the leakage concentration exceeded 2000 μmol / mol. The total time of manual investigation was recorded and compared with the time taken by the system for source tracing.

[0059] The experimental results are shown in Table 4 below.

[0060] Table 4. Results of source tracing and verification of actual exceedance events.

[0061] Results analysis: The source tracing results of the 6 actual exceedance events were consistent with the manual investigation and verification, with a source tracing accuracy rate of 100%, and could accurately distinguish between internal emissions and external inputs; the source tracing time was only 3 to 6 minutes per event, which is more than 30 times more efficient than manual investigation, and can significantly reduce the workload of environmental protection personnel and improve the emergency response speed of the park.

[0062] Experiment Example 5: Long-term stability test of the system Experimental objective: To verify the reliability and maintenance cost of the system during long-term continuous operation.

[0063] Statistical methods: The system operated continuously for 6 months (180 days), and the following indicators were statistically analyzed: equipment online rate, data integrity rate, average number of monthly failures, and mean time between failures (MTBF). A failure was defined as a device being offline for more than 30 minutes. The data integrity rate was the percentage of valid data entries out of the theoretical number of data entries. The MTBF was calculated by dividing the total runtime by the total number of failures.

[0064] The experimental results are shown in Table 5 below.

[0065] Table 5. Statistics on the Long-Term Stability of the System

[0066] Results analysis: The hardware selection and architecture design of this system are reasonable, the operation is stable and reliable, the data integrity rate is high, the failure frequency is low, it can meet the long-term continuous monitoring needs of the park 24 / 7, the daily operation and maintenance workload is small, and the overall operation and maintenance cost is far lower than that of traditional station monitoring solutions.

[0067] Furthermore, the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the claims be included within the invention.

[0068] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A distributed multi-node monitoring and source tracing system for fugitive emissions in industrial parks, characterized in that, It includes a distributed sensing node layer, an edge computing gateway layer, and a cloud management and control platform layer; The distributed sensing node layer consists of several grid-deployed miniature monitoring terminals, which are used to collect atmospheric pollutant concentrations, meteorological parameters and location information at multiple points in the park in real time and upload them to the edge computing gateway layer. The edge computing gateway layer is used to preprocess, perform quality control verification and local caching of data collected from multiple nodes, and upload valid data to the cloud management platform layer; The cloud-based management and control platform includes a data storage module, a visualization module, a source tracing and analysis module, and an early warning push module, which are used to realize centralized management of monitoring data, reverse source tracing of emission sources, pollution diffusion simulation, and early warning of exceeding standards.

2. The distributed multi-node monitoring and tracing system for fugitive emissions in industrial parks according to claim 1, characterized in that, The miniature monitoring terminal integrates a PID sensor, an electrochemical sensor, and a five-parameter meteorological sensor, and can simultaneously detect at least one pollutant in VOCs, particulate matter, hydrogen sulfide, and ammonia, as well as meteorological parameters such as wind speed, wind direction, temperature, humidity, and air pressure. The miniature monitoring terminal adopts a dual-mode power supply of solar power and wired power supply, with a deployment spacing of 50-200m, and is densely deployed along the park's factory boundary, production unit area, and storage tank area.

3. The distributed multi-node monitoring and tracing system for fugitive emissions in industrial parks according to claim 1, characterized in that, The edge computing gateway layer supports multiple communication methods such as LoRa, 4G, and Ethernet, and the local data cache capacity is no less than 30 days. The edge computing gateway has quality control functions such as abnormal data removal, zero-point drift correction, and cross-interference compensation, and automatically marks and removes abnormal data that exceeds a reasonable threshold.

4. The distributed multi-node monitoring and tracing system for fugitive emissions in industrial parks according to claim 1, characterized in that, The source tracing analysis module has a built-in gridded concentration field reconstruction unit and a reverse diffusion source tracing calculation unit. Based on real-time monitoring data and meteorological parameters, combined with a Gaussian plume diffusion model and a genetic optimization algorithm, it inversely calculates the location and emission intensity of the emission source.

5. A method for tracing and analyzing fugitive emissions from industrial parks based on the system described in any one of claims 1 to 4, characterized in that, Includes the following steps: S1 Multi-Source Data Acquisition and Preprocessing: Pollutant concentration and meteorological data at various locations are collected through distributed sensing nodes, and after quality control and preprocessing by the edge gateway, they are uploaded to the cloud to form a standardized time-series dataset. S2 gridded concentration field reconstruction: Based on the monitoring data of each node, a spatial interpolation algorithm is used to construct the pollutant concentration field of the entire park, identify high-concentration pollution areas, and complete the initial screening of emission sources; S3 reverse diffusion source tracing calculation: Using monitoring data of high-concentration areas as input, combined with real-time meteorological parameters and a three-dimensional terrain and building model of the park, the optimized Gaussian plume reverse model is used to calculate the location of emission sources and the emission intensity. S4 Emission Inventory and Early Warning Output: Generate an inventory of fugitive emissions in the park based on the source tracing results, trigger graded early warnings for areas with excessive emissions, and push them to the park's management and control terminal simultaneously.

6. The method for tracing and analyzing fugitive emissions in industrial parks according to claim 5, characterized in that, In step S1, the quality control preprocessing specifically includes: using the 3σ criterion to remove outliers, correcting the cross-interference of coexisting gases through the sensor cross-sensitivity coefficient matrix, and correcting long-term sensor drift by combining the zero-point drift calibration model, with a data validity rate of not less than 95%.

7. The method for tracing and analyzing fugitive emissions in industrial parks according to claim 5, characterized in that, In step S2, the spatial interpolation algorithm uses the Kriging interpolation method, combined with terrain and building shading factors for correction, to generate a 5m×5m resolution heat map of the park concentration; continuous areas with concentrations exceeding the threshold are marked as suspected emission source areas, completing the initial screening and location.

8. The method for tracing and analyzing fugitive emissions in industrial parks according to claim 5, characterized in that, In step S3, the reverse diffusion source tracing calculation is as follows: taking grid points in the suspected emission source area as candidate emission sources, substituting them into the Gaussian plume diffusion model to calculate the theoretical concentration of each monitoring point in forward modeling, taking the minimum of the sum of squared residuals between the theoretical concentration and the measured concentration as the objective function, and using a genetic algorithm to iteratively optimize, finally determining the precise location and emission rate of the emission source.

9. The method for tracing and analyzing fugitive emissions in industrial parks according to claim 5, characterized in that, In step S4, the graded early warning is divided into three levels: a yellow warning is triggered when the pollutant concentration exceeds the emission limit by 80%, an orange warning is triggered when the limit is exceeded, and a red warning is triggered when the limit is exceeded for 1 consecutive hour. The warning information is pushed through system pop-ups and SMS.

10. The method for tracing and analyzing fugitive emissions in industrial parks according to claim 5, characterized in that, The method has a positioning error of no more than 15m for the fugitive emission sources in the park, a source tracing response time of no more than 5min, and a relative error of less than 20% in emission intensity calculation.

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