An industrial exhaust gas detection and evaluation system and method

Abstract

This invention belongs to the field of environmental monitoring technology, specifically relating to an industrial exhaust gas detection and evaluation system and method. The system includes a multi-point environmental sensing device, a meteorological parameter acquisition device, a digital twin scene modeling device, a spatiotemporal neural network processing device, a reverse source tracing analysis device, and a comprehensive evaluation feedback device. The system reconstructs a three-dimensional flow field through digital twin modeling, utilizes a spatiotemporal neural network to perform spatiotemporal correlation analysis on multi-point concentration data to achieve global concentration field reconstruction, and combines reverse-time evolution logic to perform pollution source location and intensity inversion under flow field constraints. Finally, an evaluation report is generated through the comprehensive evaluation feedback device. This invention, through the combination of digital twin flow field constraints and neural network algorithms, can accurately reconstruct complex flow fields and achieve pollution source tracing with meter-level accuracy, effectively identifying fugitive emissions and significantly improving the intelligence level and timeliness of environmental supervision and source tracing.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring technology, specifically relating to an industrial exhaust gas detection and evaluation system and method. Background Technology

[0002] Industrial exhaust gas detection is a crucial link in environmental protection and industrial safety monitoring, and is of great significance for achieving precise governance and pollutant emission supervision in industrial parks. With the deepening of the concept of smart environmental protection, the use of distributed monitoring networks to achieve real-time sensing of exhaust gas concentration has become an important technical guarantee for maintaining ecological environment quality and preventing industrial accidents.

[0003] Industrial exhaust gas assessment systems integrate sensor networks and meteorological monitoring equipment to achieve real-time monitoring and risk warning of pollutant diffusion dynamics within a region. An ideal assessment system needs to combine geospatial information, real-time wind field parameters, and the chemical characteristics of pollutants to construct a monitoring model that accurately reflects the atmospheric environmental status of the industrial park, thereby providing scientific decision support for management departments.

[0004] However, traditional exhaust gas detection systems mostly rely on fixed-point sensors for discrete monitoring, making it difficult to construct a continuous distribution pattern of pollutants in complex three-dimensional space. When facing multi-source emissions or fugitive emissions scenarios, existing technologies lack in-depth analysis of flow field changes under complex meteorological conditions and building structure interference, resulting in the inability to accurately locate pollution sources and calculate emission intensity. Furthermore, because the monitoring logic often lags behind the gas diffusion process, the system struggles to capture dynamically evolving nonlinear paths, failing to pinpoint the source in the early stages of pollution, leading to poor real-time warning performance and low source tracing accuracy. Summary of the Invention

[0005] The purpose of this invention is to provide an industrial exhaust gas detection and evaluation system that can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An industrial exhaust gas detection and evaluation system includes an environmental multi-point sensing device, a meteorological parameter acquisition device, a digital twin scene modeling device, a spatiotemporal neural network processing device, a reverse source tracing analysis device, and a comprehensive evaluation feedback device, wherein: The environmental multi-point sensing device is configured to capture the real-time concentration of pollutants at preset monitoring points in the industrial park and convert the concentration data into corresponding digital signals for output. The environmental multi-point sensing device forms a sensing network covering a specific area through multiple detection nodes distributed inside and around the industrial park, and records the instantaneous changes in pollutant concentration and the fluctuation characteristics over time, providing basic data for subsequent spatial reconstruction.

[0007] The meteorological parameter acquisition device is configured to synchronously acquire real-time meteorological environmental parameters within the monitoring area; the meteorological parameters include, but are not limited to, wind speed, wind direction, atmospheric pressure, air humidity, and turbulence intensity distribution within the area; the meteorological parameter acquisition device transmits the captured physical environment state data to the digital twin scene modeling device in real time as dynamic boundary conditions for flow field simulation.

[0008] The digital twin scene modeling device is configured to construct a three-dimensional digital mirror space of an industrial park. The digital twin scene modeling device integrates a building structure information database and a pre-trained computational fluid dynamics proxy model. It can restore the real-time distribution of the atmospheric flow field in the virtual space based on the real-time parameters provided by the meteorological parameter acquisition device, and simulate the flow path, velocity vector distribution, and vortex region of gas among complex building groups.

[0009] The spatiotemporal graph neural network processing device is configured to perform deep feature extraction and spatiotemporal correlation analysis on concentration data from multiple locations. The spatiotemporal graph neural network processing device regards each sensing node as a vertex of a graph structure, constructs dynamic edge weights based on the physical distance between nodes and the connectivity of the real-time flow field, and uses deep learning logic to capture the lag correlation of pollutant concentration in the time series and the diffusion trend in the spatial dimension.

[0010] The reverse source tracing analysis device is configured to perform pollution source location and intensity inversion under the constraints of digital twin flow field. The reverse source tracing analysis device introduces reverse time evolution logic, regards the concentration fluctuation recorded by the environmental multi-point sensing device as the starting signal of reverse propagation, and uses the dynamic constraints provided by the digital twin flow field to perform reverse deduction in the time dimension. By finding the focal center of concentration energy in space, the specific physical coordinates of the emission source are determined.

[0011] The comprehensive evaluation feedback device is configured to generate an assessment report based on the source tracing results and monitoring data. The comprehensive evaluation feedback device determines the attributes of pollution emissions by comparing the source tracing location coordinates with preset emission outlet spatial information, and combines the reconstructed pollution field morphology to conduct a graded evaluation and early warning of the air quality status in the region.

[0012] Preferably, the environmental multi-point sensing device includes multiple sensing units with self-calibration function. Each sensing unit is equipped with an electrochemical sensing element or a photoionization detection element specific to different industrial exhaust gas components. The sensing unit performs high-frequency sampling according to a preset sampling frequency and eliminates environmental noise interference through a median filtering algorithm to ensure that the output concentration sequence can accurately reflect the small fluctuations of pollutants driven by airflow.

[0013] Furthermore, the meteorological parameter acquisition device includes an ultrasonic anemometer and wind vane installed at a high point in the park and miniature weather stations distributed on the ground. The meteorological parameter acquisition device uses multi-point observation data to construct a regional meteorological gradient model, and uses interpolation algorithms to compensate for meteorological parameters in monitoring blind spots, providing high-resolution input drive for the computational fluid dynamics proxy model, and ensuring that the flow field distribution in the digital twin scenario is highly consistent with the physical real environment.

[0014] Furthermore, the computational fluid dynamics proxy model in the digital twin scene modeling device adopts operator mapping technology based on deep neural networks to transform the solution process of complex nonlinear partial differential equations into a fast inference process under preset parameters. During the construction process, the proxy model fully considers the obstruction and diversion effects of production workshops, tank areas and office buildings in the park on airflow, and can output the velocity vector and turbulence diffusion coefficient of each grid node in three-dimensional space in real time.

[0015] Preferably, the spatiotemporal graph neural network processing device adopts a multi-layer spatiotemporal convolutional architecture, which is internally configured with a temporal feature extraction layer and a spatial graph convolutional layer; the temporal feature extraction layer is used to identify the periodic and sudden characteristics of the concentration of a single node changing over time; the spatial graph convolutional layer describes the spatial topological relationship between nodes through the Laplacian matrix and dynamically adjusts the feature transfer efficiency between adjacent nodes in combination with real-time wind field vectors, thereby realizing the reconstruction of the global concentration field under sparse monitoring point conditions.

[0016] Furthermore, the reverse-time evolution logic executed by the reverse source tracing analysis device specifically involves reversing the sign of the time term in the atmospheric diffusion equation and using the concentration residual captured by the sensor as the source term for reverse injection. During the deduction process, the reverse source tracing analysis device uses the velocity field provided by the digital twin flow field to perform reverse flow and advection calculations, and uses turbulence parameters to perform reverse diffusion corrections until the maximum value of the concentration gradient converges at a specific spatial location. This convergence location is then identified as the pollution source.

[0017] Furthermore, the reverse source tracing analysis device is also equipped with a source strength assessment algorithm. The source strength assessment algorithm is based on the reconstructed concentration field distribution and flow field dynamic parameters, and uses the principle of mass conservation to calculate the total amount of pollutants released by the emission source per unit time. By analyzing the duration and fluctuation pattern of the concentration characteristic peak, the reverse source tracing analysis device can identify continuous and stable organized emission characteristics or instantaneous and sudden unorganized leakage characteristics.

[0018] Preferably, the comprehensive evaluation feedback device includes a visualization rendering unit and an intelligent early warning unit; the visualization rendering unit fuses and renders the three-dimensional concentration distribution, flow field vector lines and source tracing location results in the digital twin space to form a dynamic pollution diffusion cloud map; the intelligent early warning unit automatically triggers an alarm signal and marks the equipment area suspected of illegal discharge or fault leakage when it determines that the emission intensity corresponding to the source tracing result exceeds a preset threshold.

[0019] Furthermore, the industrial exhaust gas detection and evaluation system also includes a historical data storage and evolution analysis device for storing long-term monitoring data and source tracing results. The historical data storage and evolution analysis device performs statistical analysis on pollution events over a long period of time to uncover the evolution patterns of high-incidence pollution areas under specific meteorological conditions, providing data support for industrial park industrial layout optimization and environmental governance strategies.

[0020] Furthermore, when initializing the model, the digital twin scene modeling device uses lidar scanning data to construct a high-precision building outline model, and converts the micro-topographic features such as vegetation and road undulations in the park into corresponding roughness parameters, which are then input into the computational fluid dynamics proxy model to correct the simulation accuracy of the ground laminar flow field.

[0021] Furthermore, the spatiotemporal graph neural network processing device is equipped with an attention mechanism module. This attention mechanism module can automatically enhance the weight of the downwind sensing node in feature fusion based on the current wind direction parameters, while weakening the interference of nodes in irrelevant areas, thereby improving the robustness of the system under complex wind field fluctuations.

[0022] Preferably, when dealing with multi-source emission scenarios, the reverse source tracing analysis device uses a multi-peak detection algorithm to scan the energy field of reverse propagation; when multiple concentration energy accumulation centers appear simultaneously in the digital twin space and the energy intensity of each center reaches a preset ratio, the system determines that multiple pollution sources are emitting pollutants together, and calculates the contribution ratio for each source.

[0023] Furthermore, the comprehensive evaluation feedback device is also equipped with a mobile terminal interface, which can send the real-time generated evaluation report, pollution source coordinates, and diffusion prediction path to the mobile terminal of the regulatory personnel; the report includes a list of sensitive areas that pollutants may cover in the future within a predetermined time based on the current flow field, guiding relevant personnel to conduct accurate investigations.

[0024] The industrial exhaust gas detection and evaluation method utilizes the aforementioned industrial exhaust gas detection and evaluation system to detect industrial exhaust gases.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention, by constructing a digital twin scene modeling device and integrating a computational fluid dynamics proxy model, changes the limitation of traditional detection systems that rely solely on physical sensor values. It achieves accurate reconstruction of the complex three-dimensional flow field in industrial parks, enabling exhaust gas detection to extend beyond specific locations to the entire space, effectively solving the monitoring blind spot problem caused by multi-source emissions and fugitive emissions.

[0026] 2. This invention introduces a spatiotemporal graph neural network processing device, which uses graph structure depth to capture the geospatial correlation and time series logic between sensing nodes. It can achieve high-precision dynamic pollution field reconstruction under sparsely distributed sensor network, which greatly reduces hardware deployment costs and improves the system's ability to analyze nonlinear gas diffusion paths.

[0027] 3. This invention creatively combines a reverse source tracing analysis device with digital twin flow field constraints, and realizes rapid locking of pollution sources through reverse time evolution logic. This method is similar to the reverse time migration principle of seismic wave detection, and can accurately calculate the physical coordinates and emission intensity of the emission source through the backtracking of concentration fluctuations, improving the source tracing accuracy to the meter level, which is significantly better than the traditional passive waiting alarm mode.

[0028] 4. This invention has strong real-time and predictive capabilities, can distinguish between organized emissions and unorganized leaks, and can predict the diffusion trend of pollutants based on the dynamic flow field, providing a scientific basis for source control. While improving the intelligence of environmental supervision in industrial parks, it greatly shortens the time cycle from pollution detection to source location, and has good social benefits and ecological protection value. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of pollution source tracing analysis based on digital twin flow field constraints and spatiotemporal graph neural networks in this invention; Figure 3 This is a flowchart illustrating the logical process of digital twin scene modeling and dynamic flow field reconstruction based on computational fluid dynamics proxy model in this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the multi-point environmental perception network and the spatiotemporal feature extraction architecture in this invention. Detailed Implementation

[0030] Example 1: To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0031] Please refer to Figures 1 to 4An industrial exhaust gas detection and evaluation system includes an environmental multi-point sensing device, a meteorological parameter acquisition device, a digital twin scene modeling device, a spatiotemporal neural network processing device, a reverse source tracing analysis device, and a comprehensive evaluation feedback device. The environmental multi-point sensing device is used to capture the real-time concentration of pollutants at preset monitoring points in the industrial park and convert the concentration data into corresponding digital signals for output. The meteorological parameter acquisition device is used to synchronously acquire real-time meteorological environmental parameters within the monitoring area and transmit the real-time meteorological environmental parameters to the digital twin scene modeling device. The digital twin scene modeling device is used to construct a three-dimensional digital mirror space of the industrial park and to restore the real-time distribution of the atmospheric flow field within the three-dimensional digital mirror space. The spatiotemporal graph neural network processing device is used to perform deep feature extraction and spatiotemporal correlation analysis on the concentration data output by the multi-point environmental sensing device, so as to realize the dynamic reconstruction of the global concentration field. The reverse source tracing analysis device is used to perform the location and intensity inversion of pollution sources under the flow field constraints generated by the digital twin scene modeling device; The comprehensive evaluation feedback device is used to generate an evaluation report based on the source tracing results and monitoring data, and to perform early warning feedback.

[0032] The environmental multi-point sensing device consists of multiple detection nodes deployed in different functional areas within the industrial park. Each detection node integrates a sensing unit, signal conditioning circuitry, analog-to-digital conversion module, and data communication gateway. The sensing unit is further subdivided into electrochemical sensing elements and photoionization detection elements. The electrochemical sensing elements are configured for high-sensitivity detection of specific inorganic pollutants, such as sulfur dioxide, nitrogen oxides, and carbon monoxide; the photoionization detection elements are configured for wide-range sensing of volatile organic compounds. The detection nodes have a self-calibration function, compensating for zero-point drift and range offset of the sensing elements using internally stored reference voltage curves. During data acquisition, the detection nodes perform continuous sampling at a preset high sampling frequency. To eliminate high-frequency noise introduced by the complex electromagnetic environment and instantaneous airflow disturbances in the industrial site, the signal processing module inside the detection nodes runs a median filtering algorithm. The median filtering algorithm sorts the collected raw concentration values ​​within a sliding time window and takes the median value as the effective observation value at that moment, thereby ensuring that the output concentration sequence can accurately reflect the microscopic fluctuation characteristics of pollutants driven by airflow and avoid abnormal isolated points from interfering with the subsequent spatial reconstruction logic.

[0033] The meteorological parameter acquisition device includes an ultrasonic anemometer and wind vane installed in an open area at a high elevation within the industrial park, as well as miniature weather stations distributed on the ground and building rooftops. The ultrasonic anemometer and wind vane utilize the time difference principle of ultrasonic waves propagating in the air to achieve inertial-free measurement of wind speed and direction, capturing instantaneous turbulent fluctuations in the atmosphere. The miniature weather stations are responsible for acquiring auxiliary parameters such as atmospheric pressure, air humidity, ambient temperature, and solar radiation intensity. Through simultaneous observation at multiple points, the meteorological parameter acquisition device constructs a regional meteorological gradient model within its internal processor. This regional meteorological gradient model uses Kriging interpolation or inverse distance weighted interpolation algorithms to estimate air pressure and temperature in monitoring blind spots, thus forming a meteorological parameter driving source with high spatial resolution covering the entire park. This high-resolution real-time data is transmitted in real-time to the digital twin scene modeling device as dynamic boundary conditions for flow field simulation, ensuring that the gas flow simulation in the virtual space can track changes in the real physical environment in real time.

[0034] The digital twin scene modeling device serves as the virtual foundation of the entire system. Its core comprises a building structure information database and a pre-trained computational fluid dynamics (CFD) proxy model. During system initialization, the digital twin scene modeling device utilizes LiDAR scanning data to perform high-precision 3D modeling of structures within the industrial park, such as production workshops, storage tanks, chimneys, office buildings, and walls, generating geometric outline models. Simultaneously, the device converts micro-topographic features within the park, such as vegetation and road undulations, into corresponding ground roughness parameters. The CFD proxy model no longer employs the traditional, time-consuming numerical solution method for the Navier-Stokes equations but instead uses operator mapping technology based on deep neural networks. Specifically, the proxy model undergoes offline simulation calculations on a high-performance computing cluster for tens of thousands of possible weather combinations within the park. The calculation results are used as training samples to train an inference engine through a deep operator network capable of representing the nonlinear mapping relationship between boundary conditions and the 3D flow field. Upon receiving real-time wind field parameters from the meteorological parameter acquisition device, the surrogate model can output the velocity vector, pressure gradient, and turbulent diffusion coefficient of each grid node in three-dimensional space within milliseconds. This neural network-based reasoning process transforms the solution of complex partial differential equations into tensor operations, greatly improving the real-time performance of flow field reconstruction.

[0035] The spatiotemporal graph neural network processing device is responsible for transforming discrete sensor observations into a continuous spatial concentration field distribution. This device abstracts each detection node within the industrial park as a vertex in a graph structure, with vertex features composed of the concentration value vector of that node at the current moment and within its historical time window. The edge weights of the graph structure are not fixed but dynamically determined by the real-time flow field vectors provided by the digital twin scene modeling device. Specifically, if two nodes are geographically located on the same streamline, and pollutants from the upwind node easily diffuse to the downwind node, the edge weight between them is set to a higher value; conversely, if the flow field shows a physical barrier between two areas or that they are in opposite flow directions, the edge weight is reduced or set to zero. The spatiotemporal graph neural network processing device employs a multi-layer spatiotemporal convolutional architecture, internally containing a temporal feature extraction layer and a spatial graph convolutional layer. The temporal feature extraction layer uses a one-dimensional convolutional kernel to capture the lag characteristics of the concentration changes of a single node over time; the spatial graph convolutional layer describes the spatial topological relationships between nodes using a Laplacian matrix. To further improve accuracy, the device also integrates an attention mechanism module. The attention mechanism module automatically enhances the weight of downwind sensing nodes in the feature fusion process based on the current wind direction parameters, enabling the model to focus on the core path of pollutant diffusion, thereby achieving high-fidelity reconstruction of the concentration field of the entire industrial park under the condition of relatively sparse detection nodes.

[0036] The reverse source tracing analysis device is the core component for achieving location and quantitative analysis. This device incorporates a reverse-time evolution logic borrowed from the concept of seismic wave reverse-time migration. After detecting an abnormal increase in pollutant concentration, the device uses the concentration residuals captured by each sensing node as the reverse source term, reversing the sign of the time term in the atmospheric diffusion equation. During the extrapolation process, the reverse source tracing analysis device uses the velocity field provided by the digital twin flow field to perform reverse flow and advection calculations. This is equivalent to making the smoke plume drift back in the direction of the wind on the time axis. Simultaneously, the device combines turbulence parameters to perform reverse diffusion correction, eliminating the influence of atmospheric dispersion effects. As the reverse extrapolation time increases, the concentration energy originally dispersed in space will focus at a specific physical location. When the concentration gradient at a certain point in the digital twin space reaches its maximum convergence, the reverse source tracing analysis device determines that this convergence location is the physical coordinate of the pollution emission source. To handle complex multi-source emission scenarios, the device also runs a multi-peak detection algorithm, which can identify multiple simultaneously existing energy focusing centers and calculate the contribution ratio for each source. Furthermore, the device incorporates a source strength assessment algorithm. Based on the principle of mass conservation, this algorithm combines the reconstructed concentration field distribution gradient with flow field dynamic parameters to inversely calculate the total amount of pollutants released by the emission source per unit time. By analyzing the duration and fluctuation pattern of concentration characteristic peaks, the device can accurately distinguish between normal but excessive organized emissions from the chimney and sudden, instantaneous unorganized leaks caused by flange or valve damage.

[0037] The comprehensive evaluation and feedback device includes a visualization rendering unit, an intelligent early warning unit, and a mobile terminal interface. The visualization rendering unit fuses a high-precision 3D model within the digital twin space with a real-time generated pollution diffusion cloud map. On the display interface, supervisors can intuitively see how airflow bypasses buildings and how pollutants linger and diffuse in eddy zones. The intelligent early warning unit has built-in hierarchical assessment logic, which determines the responsible party of the discharging unit by comparing the source location coordinates with preset park discharge outlet base map information. When the calculated emission intensity exceeds a preset safety threshold, the system automatically pushes alarm information to relevant responsible persons and environmental supervisors through the mobile terminal interface. The alarm information includes not only the specific latitude, longitude, and altitude coordinates of the pollution source but also a list of sensitive areas that pollutants may cover within the next 30 minutes based on the current flow field prediction, guiding rescue or investigation personnel to take precise action.

[0038] Furthermore, the industrial exhaust gas detection and evaluation system also includes a historical data storage and evolution analysis device. This device employs a distributed database architecture to store all monitoring data, reconstructed flow field data, and source tracing results over a long period. The historical data storage and evolution analysis device internally runs data mining algorithms, capable of performing association rule analysis on pollution events over long periods, thereby identifying the evolution patterns of high-pollution areas within the industrial park under specific meteorological conditions (such as the presence of temperature inversion layers or specific wind direction and speed combinations). This insight based on long-term data can provide quantitative data support for industrial park planning adjustments, optimized layout of greenbelts, and the formulation of environmental governance strategies.

[0039] Example 2: An industrial exhaust gas detection and evaluation system, whose basic architecture is the same as that of Example 1, but adopts a distributed edge computing enhancement scheme in hardware implementation and data interaction logic.

[0040] In this embodiment, each detection node of the multi-point environmental sensing device is endowed with stronger local computing capabilities. Each detection node integrates a microprocessor cluster, enabling the direct execution of lightweight median filtering algorithms and preliminary feature extraction logic at the edge. This design significantly reduces the amount of data that needs to be transmitted to the cloud server, lowering the pressure on the bandwidth of the campus wireless communication network. The detection node is configured to automatically adjust its reporting cycle based on the fluctuation frequency of the observed values: when the concentration value is at a stable noise level, it reports at a low frequency; when a sudden change in the concentration gradient is detected, it immediately switches to a high-frequency trigger mode and simultaneously retrieves sensing data from adjacent nodes for local collaborative verification.

[0041] In this embodiment, the meteorological parameter acquisition device incorporates a mobile sensing unit. In addition to a fixed ultrasonic anemometer and wind vane, the system also includes a drone inspection module equipped with a miniature meteorological payload. This drone inspection module can periodically or upon receiving tracing commands fly to specific altitudes to acquire vertical wind speed and temperature gradient data. This vertical profile data is transmitted to the digital twin scene modeling device to correct atmospheric boundary layer parameters, enabling the computational fluid dynamics surrogate model to more accurately simulate the sinking and diffusion processes following emissions from high-altitude chimneys, especially exhibiting higher simulation accuracy when dealing with diffusion under complex atmospheric stability conditions such as temperature inversions.

[0042] In this embodiment, the digital twin scene modeling device employs multi-level mesh subdivision technology. For the core production area, characterized by densely packed production facilities and crisscrossing pipelines, the device divides the space into extremely fine computational meshes, with mesh sizes reaching the centimeter level. For the open areas surrounding the park, a coarser mesh is used to conserve computational resources. The computational fluid dynamics proxy model utilizes a transfer learning-based architecture, dynamically loading corresponding parameter weight sets based on the characteristics of different seasons and production cycles, thereby more accurately reproducing the local microflow field characteristics under different production conditions.

[0043] The spatiotemporal graph neural network processing device incorporates a multimodal fusion mechanism when processing data. This device not only receives concentration signals but also simultaneously receives flow field topological feature vectors from the digital twin scene modeling device. When constructing dynamic edge weights, the device utilizes a cosine similarity algorithm to calculate the correlation between velocity vectors between nodes. The spatial graph convolutional layer employs a multi-head attention mechanism, where each attention head is configured to learn different diffusion features; for example, one head focuses on capturing convective diffusion along the prevailing wind direction, while the other focuses on capturing turbulent dissipation across the flow direction. This multi-dimensional feature extraction method further improves the reconstruction accuracy of highly nonlinear physical processes such as unorganized leakage.

[0044] The reverse source tracing analysis device in this embodiment executes reverse-time evolution logic, further incorporating a Bayesian inference framework. In determining the energy focus center, the device relies not only on the reverse derivation of physical equations but also on the prior probability distribution of historical pollution discharge behavior. For example, if a convergence point is close to a tank farm with a history of frequent malfunctions, the system automatically increases the confidence level of that location as a potential source. During source strength assessment, the source strength assessment algorithm employs Monte Carlo simulation to analyze the uncertainty of the inversion results, providing a confidence interval for the total emissions, thus offering a more legally valid reference for law enforcement evidence collection.

[0045] In this embodiment, the comprehensive evaluation and feedback device adds an on-site guidance function based on augmented reality technology. When supervisory personnel enter the park for inspection using a mobile terminal, the mobile terminal interface overlays a real-time 3D pollution cloud map with the actual scene captured by the camera. Supervisory personnel can see a virtual image of invisible exhaust gas clouds drifting between factory buildings on the screen and directly point to the identified leak point. In addition, the intelligent early warning unit is linked with the park's automatic sprinkler system or exhaust control system. When the determined pollution intensity reaches the emergency shutdown threshold, the system can automatically send control commands to the corresponding area's production execution system to achieve closed-loop control of pollution control.

[0046] Example 3: An industrial exhaust gas detection and evaluation system, optimized for robustness under extreme industrial environments.

[0047] In this embodiment, the sensing unit of the environmental multi-point sensing device adopts a special packaging design that is corrosion-resistant and high-temperature resistant, enabling it to operate stably for a long time in harsh environments containing acidic gases or located in high-temperature flues. The signal conditioning circuit has a built-in automatic gain control module, which can automatically switch the range according to the strength of the input signal, ensuring a high signal-to-noise ratio between environmental monitoring at extremely low concentrations and leak monitoring at extremely high concentrations. Each detection node is also equipped with a self-powered unit, utilizing a micro wind turbine or solar panel to achieve energy self-sufficiency, ensuring that the system can still maintain monitoring and tracing functions in the event of a large-scale power outage in the park.

[0048] In this embodiment, the digital twin scene modeling device integrates a rapid geometric reconstruction module. When the construction or renovation of production facilities within the park causes changes in the building layout, the rapid geometric reconstruction module can automatically update the geometric model in the three-dimensional digital mirror space by analyzing the latest satellite remote sensing images or UAV aerial photographs. The corresponding computational fluid dynamics proxy model employs a meshless particle method operator, which can directly perform approximate flow field calculations on point cloud data, eliminating the computational bottleneck caused by complex mesh partitioning and greatly improving the system's adaptability to dynamic changes in the park environment.

[0049] In this embodiment, the spatiotemporal graph neural network processing device employs a recurrent graph neural network architecture. This architecture can process concentration data with variable time-series characteristics and has unique advantages in capturing the cyclical flow of pollutants within complex pipe gallery areas. The time feature extraction layer introduces long short-term memory network units, which can effectively distinguish between the slow drift of atmospheric background values ​​and the pulse-like abrupt changes caused by pollution emissions. To enhance the robustness of the system, the attention mechanism module is also configured to identify and automatically remove outlier data generated by sensor malfunctions, and to recover the values ​​of damaged nodes through spatiotemporal interpolation by utilizing the correlation information of surrounding healthy nodes.

[0050] In this embodiment, the reverse source tracing analysis device enhances the ability to identify surface and line sources. For example, for leaks in long-distance pipelines (line sources) or emissions from an entire wastewater treatment plant (area sources), the reverse-time evolution logic can identify multiple continuously distributed energy focusing centers. The multi-peak detection algorithm uses cluster analysis to correlate these centers and fit the geometry of the leak source. Regarding intensity assessment, the source strength assessment algorithm introduces dynamic mass conservation constraints, enabling real-time calculation of the total pollutant throughput of the entire industrial park, thus achieving dynamic monitoring of the regional environmental capacity.

[0051] In this embodiment, the comprehensive evaluation and feedback device possesses multi-level command and coordination capabilities. Besides sending early warnings internally to enterprises, the device also directly connects to the local city's smart environmental protection dispatch center. The assessment report automatically includes recommendations for the chemical composition analysis of pollutants, evacuation suggestions for affected populations in the surrounding area, and suggested emergency plugging plans. The visualization rendering unit supports multi-user remote collaborative interaction, allowing personnel from different functional departments to collaboratively analyze the pollution situation and formulate response decisions within the same virtual digital twin space.

[0052] Furthermore, the system in this embodiment also includes an environmental risk simulation module. This module can utilize a digital twin scenario modeling device to simulate how pollutants will spread if a hazardous chemical leak of a specific level occurs under predicted extreme weather conditions (such as strong typhoons, thunderstorms, or extremely stable weather). This simulation function can help park managers develop more scientific emergency plans and improve the overall intrinsic safety level of the park.

[0053] Example 4: An industrial exhaust gas detection and evaluation system, focusing on the synergistic monitoring of multi-component pollutants and the evolution analysis of complex chemical reactions.

[0054] In this embodiment, each detection node of the environmental multi-point sensing device integrates a miniaturized mass spectrometry analysis unit or a multi-channel spectral analysis unit. This enables the system to not only monitor the total concentration of pollutants but also analyze the specific chemical components of the pollutants in real time. The detection nodes are configured to extract the characteristic spectra of each chemical component and convert them into digital fingerprint signals for output.

[0055] In this embodiment, the digital twin scene modeling device not only recreates the physical flow field but also integrates a dynamic reaction module. This module can simulate secondary chemical reactions that occur when pollutants are affected by ultraviolet radiation and humidity during diffusion, such as the process of nitrogen oxides and volatile organic compounds generating ozone under light. The computational fluid dynamics proxy model outputs the velocity field while simultaneously outputting the temperature field and light intensity field in three-dimensional space, providing precise environmental parameter support for chemical reaction simulation.

[0056] In this embodiment, the spatiotemporal graph neural network processing device employs a multi-graph fusion architecture. This device constructs independent feature maps for each detected chemical component, and simultaneously describes the transformation relationships and synergistic diffusion effects between different components through a cross-graph association layer. The attention mechanism module then assigns weights not only in the spatial dimension but also in the component dimension, automatically identifying the core pollutants that contribute the most to the current air quality.

[0057] The reverse tracing analysis device in this embodiment executes a tracing logic referred to as chemical fingerprint tracing. While using reverse-time evolution logic to find the physical focal point, the device also compares the component ratios at the focal point with the process characteristic fingerprints of various production enterprises within the industrial park. Through this dual verification, the system can significantly reduce the false positive rate of tracing. For example, when a specific proportion of benzene compounds is detected, the system can directly point to a rubber processing plant with a specific production process, rather than a nearby paint shop.

[0058] In this embodiment, the comprehensive evaluation and feedback device includes an ecological footprint assessment unit. This unit calculates the potential impact of each emission event on the surrounding soil, water bodies, and biodiversity based on the total emissions determined through source tracing and the reconstructed contaminated area. The assessment report is presented in an intuitive ecological cost-benefit statement, serving as an important basis for evaluating the company's environmental credit performance.

[0059] Example 5: An industrial exhaust gas detection and evaluation system with an optimized architecture to meet the hierarchical management and control needs of ultra-large-scale industrial clusters.

[0060] In this embodiment, the system adopts a three-level nested digital twin architecture of industrial park-enterprise-equipment. The digital twin scene modeling device consists of a global low-resolution model and multiple local high-resolution models. The global model covers the entire industrial cluster and is responsible for large-scale atmospheric transport simulation; the local models finely depict the internal equipment layout of key enterprises.

[0061] The meteorological parameter acquisition device, by incorporating a phased array wind radar, enables real-time scanning of the three-dimensional wind field and wind vectors over a range of several kilometers above the park. This radar data is injected in real-time into a global digital twin model to correct the incoming flow boundary conditions upstream of the park, ensuring the accuracy of local flow field reconstruction within the context of large-scale meteorological system evolution.

[0062] The spatiotemporal graph neural network processing device employs a hierarchical federated learning architecture. Data from each enterprise's detection nodes is first edge-processed locally, with only the anonymized feature vectors uploaded to the park-level central processing unit. This design protects enterprise production privacy while enabling collaborative monitoring across the entire park. The spatial graph convolutional layer, through a hierarchical indexing mechanism, can quickly locate the enterprise level to which anomaly nodes belong, improving response speed.

[0063] The reverse source tracing analysis device incorporates a spatiotemporal scale switching logic. When the global monitoring network detects an abnormal concentration, the device first performs an initial search on a global scale to identify suspected polluting enterprises. Subsequently, the system automatically activates the corresponding enterprise's refined digital twin model and encrypted monitoring nodes, performing micro-scale reverse time offset positioning with meter-level accuracy, thereby achieving precise tracking from a surface to a specific point.

[0064] The comprehensive evaluation and feedback device is equipped with a digital twin-based auxiliary decision-making system. During periods of heavy pollution, this system can simulate different production restriction schemes and predict their impact on the overall air quality of the industrial park. In this way, the system can provide regulatory authorities with optimized and precise emission reduction guidance, avoiding unnecessary economic losses caused by blanket production shutdowns.

[0065] Example 6: An industrial exhaust gas detection and evaluation system, characterized by the integration of a machine vision-based pollution auxiliary observation device.

[0066] In this embodiment, the multi-point environmental sensing device includes not only an electrochemical sensor but also a long-focal-length infrared thermal imaging camera deployed high up in the park. The camera is configured to identify the infrared absorption characteristics of gases in specific wavelengths, thereby visually capturing the plume morphology emitted from the chimney. The machine vision module fuses the identified plume boundaries with real-time concentration data.

[0067] In this embodiment, the digital twin scene modeling device incorporates a visual constraint operator. When reasoning about the flow field, the computational fluid dynamics proxy model uses the real-time trajectory of the plume observed from infrared images as a hard constraint condition to forcibly correct the diffusion direction of the simulated flow field, making the simulated pollution field morphology highly consistent with the actual observed visual images.

[0068] In this embodiment, the spatiotemporal graph neural network processing device adds an image feature channel. This device uses motion vectors extracted from the infrared video stream as additional vertex feature inputs. The temporal feature extraction layer, through analysis of the video sequence, can more sensitively capture minute fluctuations in wastewater discharge caused by process variations.

[0069] The reverse source tracing analysis device employs visual-assisted positioning technology when executing the reverse-time evolution logic. This device verifies the overlap between the spatial coordinates of the energy focusing center and the emission outlet coordinates identified in the infrared image. If the spatial distance between the two is less than a preset deviation, the emission probability of that outlet is automatically marked as the highest level.

[0070] The comprehensive evaluation and feedback device has video playback and evidence chain integration functions. The generated assessment report automatically extracts infrared video, source cloud map, sensor numerical curves, and meteorological parameters at the moment of pollution, forming a complete, visualized closed-loop evidence package, which is directly uploaded to the environmental law enforcement platform through an encrypted channel.

[0071] Example 7: An industrial exhaust gas detection and evaluation system, the core improvement of which is the introduction of a sampling strategy optimization device based on deep reinforcement learning.

[0072] In this embodiment, the environmental multi-point sensing device includes a certain number of mobile monitoring vehicles. The sampling strategy optimization device, as an extension module of the comprehensive evaluation feedback device, calculates the optimal mobile monitoring path in real time using a reinforcement learning algorithm based on the currently reconstructed uncertainty distribution of the pollution field.

[0073] The digital twin scene modeling device provides real-time road network information and accessibility constraints for the park. When the system detects a low confidence level in the source tracing results, the sampling strategy optimization device guides the monitoring vehicle to a critical singularity location or a region with drastic concentration gradient changes in the flow field for supplementary testing.

[0074] The spatiotemporal graph neural network processing device has dynamic node access capability. When the mobile monitoring vehicle arrives at the designated location and begins uploading data, the device automatically adds vertices to the graph structure and recalculates the edge weights of surrounding nodes, realizing dynamic and seamless expansion of the monitoring network topology.

[0075] The reverse source tracing analysis device can quickly converge the energy focusing center by fusing high-precision data from mobile monitoring points. This method, which combines dynamic sampling with physical inversion, can reduce the source location time by more than half compared to fixed-point systems when dealing with sudden, unorganized leaks at unknown locations.

[0076] In summary, this invention achieves intelligent management of industrial exhaust gas from perception to source tracing and evaluation by constructing a complete system architecture that includes multi-point environmental sensing, meteorological data acquisition, digital twin modeling, spatiotemporal neural network processing, reverse source tracing analysis, and comprehensive evaluation feedback. The system deeply integrates computational fluid dynamics and deep learning technologies, utilizing digital twins to reconstruct complex flow fields and combining reverse-time evolution logic to achieve meter-level source tracing, effectively solving the core technical challenges of traditional systems such as numerous monitoring blind spots, low source tracing accuracy, and delayed early warning in complex industrial environments. Each embodiment, through different focuses (such as distributed computing, mobile supplementary testing, chemical fingerprinting, and multi-level control), demonstrates the system's strong adaptability and scalability in various complex application scenarios.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention. For those skilled in the art, any non-substantial improvements made to the hardware selection, software algorithm parameters, or data interaction processes in the above embodiments based on the basic principles of the present invention are within the protection scope of the present invention.

Claims

1. An industrial off-gas detection and evaluation system, characterized by, It includes a multi-point environmental sensing device, a meteorological parameter acquisition device, a digital twin scene modeling device, a spatiotemporal neural network processing device, a reverse source tracing analysis device, and a comprehensive evaluation feedback device. The environmental multi-point sensing device is configured to capture the real-time concentration of pollutants at preset monitoring points in the industrial park and convert the concentration data into corresponding digital signals for output. The meteorological parameter acquisition device is configured to synchronously acquire real-time meteorological environmental parameters within the monitoring area and transmit them to the digital twin scene modeling device. The digital twin scene modeling device is configured to construct a three-dimensional digital mirror space of the industrial park and restore the real-time distribution of the atmospheric flow field in the virtual space. The spatiotemporal graph neural network processing device is configured to perform deep feature extraction and spatiotemporal correlation analysis on concentration data from multiple locations, thereby realizing dynamic reconstruction of the global concentration field. The reverse source tracing analysis device is configured to perform pollution source location and intensity inversion under the constraints of a digital twin flow field; The comprehensive evaluation feedback device is configured to generate an evaluation report and execute feedback early warning based on the source tracing results and monitoring data.

2. The system of claim 1, wherein, The environmental multi-point sensing device consists of multiple detection nodes distributed inside and around the industrial park. Each detection node integrates a sensing unit, a signal conditioning circuit, an analog-to-digital conversion module, and a data communication gateway. The sensing unit includes an electrochemical sensing element and a photoionization detection element. The electrochemical sensing element is used to detect specific inorganic pollutant components, and the photoionization detection element is used to sense volatile organic compounds. The detection node is equipped with a storage unit for storing a reference voltage curve, which is used to perform self-calibration compensation for zero-point drift and range offset generated by the sensing unit based on the reference voltage curve. The signal processing module inside the detection node runs a median filtering algorithm, which is configured as follows: Within a preset sliding time window, the collected raw concentration values ​​are sorted by numerical value, and the value in the middle of the sorted sequence is extracted as the valid observation value at that moment. By removing isolated outliers in the raw sequence, the interference of environmental electromagnetic noise and instantaneous airflow disturbances on the concentration signal is eliminated.

3. The system of claim 1, wherein, The meteorological parameter acquisition device includes an ultrasonic anemometer and wind vane installed in an open area at a high altitude in the industrial park, as well as miniature weather stations distributed on the ground. The ultrasonic anemometer utilizes the time difference principle of ultrasonic wave propagation in the air to obtain real-time wind speed, wind direction angle, and instantaneous turbulence pulse data within the monitoring area. The micro weather station is configured to acquire physical environmental parameters including atmospheric pressure, air humidity, ambient temperature, and solar radiation intensity. The meteorological parameter acquisition device integrates a meteorological gradient construction module. The meteorological gradient construction module uses synchronous observation data from multiple points to construct a regional meteorological gradient model and uses the Kriging interpolation algorithm to numerically estimate the air pressure and temperature parameters in the monitoring blind area, thereby forming a meteorological parameter driving source with a preset spatial resolution covering the entire industrial park. The meteorological parameter driving source is then transmitted in real time to the digital twin scene modeling device as a dynamic boundary condition for flow field simulation.

4. The system of claim 1, wherein, The digital twin scene modeling device integrates a building structure information database and a computational fluid dynamics proxy model. The digital twin scene modeling device is configured to use LiDAR scanning data to perform high-precision three-dimensional geometric modeling of production workshops, storage tanks, chimneys and office structures in the industrial park, generate building outline models, and convert vegetation distribution, road undulations and micro-topography features into corresponding ground roughness parameters. The computational fluid dynamics proxy model adopts an operator mapping structure based on deep neural networks. It constructs a nonlinear mapping relationship between boundary conditions and three-dimensional flow field by pre-learning simulation calculation samples under different meteorological conditions. The proxy model is configured to output the velocity vector, pressure gradient, and turbulence diffusion coefficient of each grid node in three-dimensional space in real time by performing a fast inference process of tensor operations after receiving real-time meteorological parameters. In areas with dense production facilities, the digital twin scene modeling device adopts a multi-level grid subdivision strategy to refine the size of the computational grid to the centimeter level, while a coarser grid scale is used in the open areas around the park.

5. The system for detecting and evaluating industrial exhaust gas according to claim 1, wherein The spatiotemporal graph neural network processing device adopts a multi-layer spatiotemporal convolutional architecture. The spatiotemporal graph neural network processing device abstracts each detection node in the industrial park as a vertex in a graph structure, and the features of the vertex are composed of the concentration value vector of the node at the current time and within its historical time window. The edge weights of the graph structure are dynamically determined by the real-time flow field vectors provided by the digital twin scene modeling device, specifically: The feature transfer efficiency between adjacent nodes is dynamically adjusted based on the degree of overlap between their geographical locations and streamline directions. The multi-layer spatiotemporal convolutional architecture is configured with a temporal feature extraction layer and a spatial graph convolutional layer. The temporal feature extraction layer uses a one-dimensional convolutional kernel to capture the lag and burst features of the concentration of a single node changing over time. The spatial graph convolutional layer describes the spatial topological relationships between nodes using a Laplacian matrix and integrates an attention mechanism module. The attention mechanism module automatically increases the weight allocation ratio of downwind sensing nodes in the feature fusion process based on the current wind direction parameters, while reducing the weight of nodes in irrelevant areas. This enables the reconstruction of a concentration field distribution with spatiotemporal continuity covering the entire industrial park under the condition of sparse distribution of detection nodes.

6. The system of claim 1, wherein, The reverse tracing analysis device is equipped with a reverse deduction module that executes reverse time evolution logic; After detecting an abnormal increase in pollutant concentration, the reverse inference module uses the concentration residual captured by the sensing node as the source term for reverse injection and reverses the sign of the time term in the atmospheric diffusion equation. The reverse source tracing analysis device uses the velocity field provided by the digital twin flow field to perform reverse flow and advection calculations, so that the pollutant simulated cloud can be traced back in the opposite direction of the wind on the time axis, and the reverse diffusion correction is performed in combination with the turbulence parameters in the flow field to eliminate the atmospheric dispersion effect. As the reverse simulation time increases, the reverse source tracing analysis device monitors the distribution of concentration energy in the digital twin space. When the concentration gradient at a certain point in the space reaches its maximum value and converges, the physical coordinates of the convergence location are determined to be the emission location of the pollution source.

7. The system of claim 6, wherein, The reverse source tracing analysis device also integrates a multi-peak detection module and a source strength evaluation module. The multi-peak detection module is configured to perform a full-domain scan of the energy field of backpropagation in a multi-source emission scenario. When multiple concentration energy accumulation centers are detected simultaneously in the digital twin space and the energy intensity of each center reaches a preset proportional threshold, it is determined that multiple pollution sources are emitting together, and the contribution ratio of each pollution source to the concentration at the monitoring point is calculated. The source strength assessment module is based on the principle of mass conservation and combines the reconstructed concentration field distribution gradient and flow field dynamic parameters to calculate the total amount of pollutants released by the emission source per unit time. The reverse source tracing analysis device identifies and distinguishes between controlled organized emissions from chimneys and sudden unorganized leaks from equipment components by analyzing the duration and fluctuation pattern of concentration characteristic peaks. During the identification process, the reverse source tracing analysis device also introduces a Bayesian inference framework, which combines the prior probability distribution of historical sewage discharge behavior to weight the source tracing results with confidence.

8. The system for detecting and evaluating industrial exhaust gas according to claim 1, wherein The comprehensive evaluation feedback device includes a visualization rendering unit, an intelligent early warning unit, and a mobile terminal interface. The visualization rendering unit is configured to fuse and render the three-dimensional high-precision model, real-time flow field vector lines, and generated pollution diffusion cloud map within the digital twin space to form a dynamic pollution evolution view. The intelligent early warning unit has a built-in hierarchical evaluation logic, which determines the responsible party for pollution discharge by comparing the coordinates of the source location with the preset geographical information of the park's discharge outlets; When the emission intensity value obtained from the reverse calculation exceeds the preset safety alarm threshold, the intelligent early warning unit pushes alarm information to the mobile terminal of the supervisor through the mobile terminal interface. The alarm information includes the latitude and longitude coordinates and altitude information of the pollution source, as well as a list of sensitive areas that the pollutants may cover within a predetermined time in the future based on the current flow field prediction. The mobile terminal interface is also equipped with an augmented reality-based guidance function, which overlays the real-time 3D pollution cloud map with the real-world image captured by the mobile terminal's camera.

9. The system for detecting and evaluating industrial exhaust gas according to claim 1, wherein The industrial exhaust gas detection and evaluation system also includes a historical data storage and evolution analysis device. The historical data storage and evolution analysis device adopts a distributed database architecture and is configured to store the monitoring concentration data, digital twin flow field data and source tracing and positioning results of the entire park for a long time. The historical data storage and evolution analysis device is equipped with a data mining module. The data mining module is configured to perform association rule analysis on pollution events over a long period of time to identify the evolution pattern of high-pollution areas in the industrial park under specific meteorological conditions. The system is also equipped with an environmental risk simulation module. The environmental risk simulation module uses a digital twin scenario modeling device to simulate the diffusion trajectory of a leak event of a preset level under different predicted meteorological scenarios, to conduct stress tests on the environmental safety capacity of the park, and to generate corresponding emergency response plans.

10. A method for detecting and evaluating industrial exhaust gases, characterized in that, The industrial exhaust gas detection and evaluation system according to any one of claims 1 to 9 enables the detection of industrial exhaust gas.

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