Dynamic detection and evaluation early warning system for VOCs emission in industrial park

Abstract

The invention belongs to the technical field of environmental monitoring and pollution prevention and control, and particularly discloses a dynamic detection and evaluation early warning system for VOCs emission in an industrial park. The system comprises a multi-source data acquisition device, an enterprise behavior portrait construction device, an evolutionary game analysis device, a violation tendency index calculation device and a grading early warning decision device. By fusing multi-source data such as volatile organic compound concentration, production conditions and law enforcement records, an enterprise behavior portrait is constructed, an evolutionary game model is introduced to quantify a violation motivation, a violation tendency index is further calculated, and dynamic grading and accurate early warning of enterprise risks are realized. According to the technical scheme, hidden cheating behaviors such as dilution emission and monitoring counterfeiting can be identified, an intervention mechanism is triggered before large-scale emission of pollutants, and the supervision efficiency and the source prevention and control capability are improved.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring and pollution control technology, specifically involving a dynamic detection, assessment and early warning system for VOCs emissions in industrial parks. Background Technology

[0002] With the deepening of industrialization, the coordinated treatment of volatile organic compounds (VOCs) in industrial parks has become a key task in the field of air pollution prevention and control. Traditional environmental monitoring systems mainly focus on deploying sensing terminals at key nodes in the park, and constructing a monitoring network based on physical characterization by collecting real-time data on the concentration of characteristic pollutants, aiming to provide basic data support for improving regional environmental quality.

[0003] Dynamic monitoring and early warning of VOCs emissions in industrial parks is an important technical means for refined environmental management. Its core objective is to achieve accurate identification of emission sources and immediate response to pollution risks. These systems often rely on fluid diffusion models and geographic information technology to transform real-time concentration data into a dynamic depiction of the spatiotemporal distribution of pollution, thereby ensuring compliance with emission limits in the industrial park.

[0004] However, existing monitoring models rely excessively on real-time feedback of end-of-pipe physical concentrations, making it difficult to effectively identify covert pollution discharge behaviors by enterprises through methods such as dilution or tampering with monitoring equipment parameters. This leads to logical paradoxes in how perceived data reflects the true pollution load. Simultaneously, traditional early warning mechanisms lack in-depth integrated analysis of enterprise production data, pollution permit compliance, and the cost-benefit dynamics of regulation. This fails to characterize the enterprises' intent to violate regulations at the motivational level, resulting in significant delays in early warning responses. Furthermore, the allocation of regulatory resources primarily follows a passive model driven by exceeding standards, lacking profiling and risk classification of pollution entities' behavioral patterns. This makes it difficult to accurately deploy and proactively intercept enforcement resources in the highly complex environment of industrial parks.

[0005] Therefore, a dynamic monitoring, assessment, and early warning system for VOCs emissions in industrial parks is desired. Summary of the Invention

[0006] The purpose of this invention is to provide a dynamic detection, assessment and early warning system for VOCs emissions in industrial parks, which can solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] The VOCs emission dynamic monitoring, assessment, and early warning system for industrial parks includes a multi-source data acquisition device, an enterprise behavior profiling device, an evolutionary game analysis device, a violation tendency index calculation device, and a tiered early warning decision-making device, among which:

[0009] The multi-source data acquisition device is configured to simultaneously acquire volatile organic compound (VOC) concentration data, production status data, pollution discharge permit compliance information, and historical enforcement records of each polluting enterprise within the park. The VOC concentration data includes real-time concentration values ​​collected by sensing terminals deployed at enterprise discharge outlets and park boundaries. The production status data includes enterprise electricity consumption, raw material consumption, equipment operating status, and production load information.

[0010] The enterprise behavior profile building device is configured to perform pattern recognition and feature extraction on the daily operation behavior of enterprises based on the data acquired by the multi-source data acquisition device, identify abnormal behavior patterns that contradict normal production logic, including unplanned nighttime startup, mismatch between production load and pollutant emissions, imbalance between raw material consumption and output ratio, and other hidden cheating characteristics, and build an enterprise behavior profile that includes behavioral stability and compliance tendency based on this.

[0011] The evolutionary game analysis device is configured to introduce an evolutionary game theory model to simulate the strategy selection process of enterprises under different regulatory intensities, costs of violations, production benefits, and penalty risks. The device regards regulatory agencies as one party and enterprises as the other party. By setting parameters such as regulatory input, fine amount, and inspection frequency, it dynamically deduces the evolutionary path of enterprises tending to comply with the law or engage in illegal discharge in the long-term game, and outputs a quantitative representation of the potential violation motives of enterprises.

[0012] The violation tendency index calculation device is configured to integrate the behavioral characteristics output by the enterprise behavior profile construction device and the motivational representation output by the evolutionary game analysis device, and calculate the violation tendency index of each enterprise using a weighted fusion algorithm. The index reflects the subjective probability of an enterprise engaging in illegal discharge under the current working conditions and regulatory environment. The higher the value, the greater the possibility that the enterprise has a hidden intention to discharge pollutants.

[0013] The hierarchical early warning decision-making device is configured to classify all enterprises in the park into multiple risk levels based on the level of the violation tendency index and generate corresponding early warning instructions. For high-risk enterprises, the system automatically triggers suggestions for key monitoring, encrypted sampling, or on-site verification, so as to achieve precise allocation of regulatory resources and proactive intervention.

[0014] Preferably, the multi-source data acquisition device is further configured to perform logical consistency verification on the acquired volatile organic compound concentration data, and identify data anomalies where the physical concentration "meets the standard" but does not match the production conditions due to dilution emissions or tampering with monitoring equipment parameters.

[0015] Furthermore, the enterprise behavior profiling device uses a behavioral psychology model, combined with the enterprise's historical behavior sequence, to analyze the degree of behavioral deviation during holidays, nighttime, and before and after regulatory inspections, and uses this degree of deviation as one of the key dimensions of the profile.

[0016] Furthermore, the game parameters set in the evolutionary game analysis device include preset regulatory cost thresholds, expected corporate illegal gains, and social reputation loss weights. The system dynamically adjusts these parameters based on the actual law enforcement history of the park to ensure that the game simulation results are consistent with the real-world situation.

[0017] Furthermore, in the weighted fusion algorithm used by the violation tendency index calculation device, the weights of behavioral characteristics and motivation representations are adaptively adjusted according to the industry category, scale level, and historical credit record of the enterprise, to ensure the fairness and relevance of the index calculation.

[0018] Furthermore, the tiered early warning decision-making device classifies enterprises into three levels: low risk, medium risk, and high risk. The high-risk level corresponds to a situation where the violation tendency index exceeds a preset threshold. The system automatically pushes an early warning report to the environmental regulatory department, which includes the enterprise name, a summary of the risk causes, and suggested remedial measures.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. The VOCs emission dynamic detection, assessment and early warning system for industrial parks provided by this invention breaks through the limitations of traditional environmental monitoring that relies solely on physical concentration data. For the first time, it introduces evolutionary game theory and behavioral psychology into the field of environmental supervision. By constructing corporate behavior profiles and integrating game motivation analysis, the system can identify the logical paradox behind "data compliance" and deal with hidden pollution discharge methods such as dilution emissions and monitoring fraud.

[0021] 2. At the same time, the violation tendency index generated by the system provides a scientific basis for enterprise risk classification, enabling limited regulatory resources to be prioritized for entities with the strongest intention to violate regulations, thereby improving law enforcement efficiency and deterrence.

[0022] 3. The system supports early warning of abnormal behavior, triggering intervention mechanisms before pollutants are emitted on a large scale, which helps to achieve precise prevention and control of pollution sources and intelligent upgrading of park environmental governance. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0024] Figure 2 This is a schematic diagram of the core principle framework for calculating the violation tendency index based on the fusion of corporate behavior profiling and evolutionary game motivation in this invention.

[0025] Figure 3 This is a logical flowchart of the process for identifying abnormal behavior patterns and constructing profiles based on multi-source data of enterprises in this invention.

[0026] Figure 4 This is a logical framework diagram of the evolutionary game analysis of the strategy choices of regulatory agencies and enterprises in this invention;

[0027] Figure 5 This is a schematic diagram of the multi-level interaction relationship and data flow from multi-source data perception to hierarchical early warning decision generation in this invention. Detailed Implementation

[0028] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 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.

[0029] The VOCs emission dynamic monitoring, assessment and early warning system for industrial parks includes a multi-source data acquisition device, an enterprise behavior profiling device, an evolutionary game analysis device, a violation tendency index calculation device and a graded early warning decision-making device;

[0030] The multi-source data acquisition device is used to simultaneously acquire volatile organic compound (VOC) concentration data, production status data, pollution discharge permit compliance information, and historical enforcement records from various polluting enterprises within the industrial park. The device is deployed within the physical space of the industrial park, and a wired and wireless hybrid communication network covering the entire park is constructed to achieve real-time aggregation of data from the underlying sensing layer. VOC concentration data acquisition is achieved through sensing terminals deployed at enterprise discharge outlets and the park's factory boundaries. These terminals integrate photoionization detection sensors, flame ionization detection sensors, and gas chromatography monitoring units, and are configured to continuously monitor characteristic factors such as total hydrocarbons, non-methane hydrocarbons, and benzene compounds in ambient air at preset sampling cycles.

[0031] The production status data acquisition module in the multi-source data acquisition device establishes data interfaces with the enterprise's internal production execution system, distributed control system, and energy management system to retrieve real-time data on the enterprise's current consumption, raw material consumption rate, operating frequency of major production equipment, and production load percentage. For traditional enterprises that have not yet implemented digitalization, the multi-source data acquisition device, through non-intrusive current transformers installed on the power supply lines of the enterprise's main power distribution cabinet and key wastewater treatment facilities, in conjunction with an edge computing gateway, monitors the equipment's start-up and shutdown status and real-time load. The multi-source data acquisition device automatically synchronizes wastewater discharge permit compliance information from the environmental supervision platform via the government extranet interface, including the enterprise's approved emission limits, wastewater discharge rights trading records, and self-monitoring implementation status. It also links this information to administrative penalty details, number of rectification orders, and historical credit scores in the historical enforcement record module, constructing a multi-dimensional holographic data matrix.

[0032] Furthermore, the multi-source data acquisition device includes a logical consistency verification unit, configured to perform deep cleaning and rationality review on the acquired physical data. This unit identifies anomalies caused by diluted emissions or illegal tampering with monitoring equipment parameters by establishing a physical mapping model between pollutant emission concentrations and production load. When the production status data shows the enterprise is operating at high capacity, but the corresponding volatile organic compound (VOC) concentration data remains at extremely low levels with fluctuations less than 10% of the normal baseline value, the logical consistency verification unit will trigger a logical paradox flag and identify this data point as a potential point of falsification.

[0033] The enterprise behavior profiling device, connected to the multi-source data acquisition device, is configured to perform in-depth pattern recognition and feature extraction on the daily operational behavior of enterprises based on multi-source heterogeneous data streams. The device integrates a feature engineering engine and a behavioral psychological analysis module, designed to identify behavioral deviations inconsistent with normal production logic. By analyzing long-term historical data, the enterprise behavior profiling device establishes a baseline behavior model for each enterprise. This baseline behavior model includes the enterprise's emission characteristic curves, electricity load fluctuation patterns, and production cycle characteristics under normal operating conditions.

[0034] The enterprise behavior profiling device is configured to identify hidden cheating characteristics, including but not limited to: first, unplanned nighttime operation, i.e., a surge in production current detected during non-production planning periods, while the operating parameters of the waste gas treatment facility do not synchronize; second, a logical imbalance between production load and pollutant emissions, i.e., raw material consumption remains high, but the pollutant concentration detected at the terminal is significantly lower than the theoretical material balance calculation value; third, behavioral deviation characteristics in specific time periods, using a behavioral psychology model combined with the enterprise's historical behavior sequence to analyze its behavioral changes during statutory holidays, periods of weak nighttime inspections, environmental protection inspections, and after the issuance of regulatory inspection notices. The behavioral psychology model quantifies the enterprise's sensitivity to regulatory pressure by constructing a compliance pressure response function. For example, if an enterprise experiences a sharp drop in emissions after each regulatory notice, but then rebounds during unattended periods, the behavioral psychology model will assign the enterprise a high "behavioral instability" label and use it as one of the key dimensions of the profile, thus drawing an enterprise behavior profile that includes behavioral stability and compliance tendencies.

[0035] The evolutionary game analysis device receives external regulatory parameters and basic business parameters from the multi-source data acquisition device at its input end. It is configured to simulate the dynamic strategy selection process between regulatory agencies and enterprises by introducing an evolutionary game theory model. The device treats regulatory agencies within the park as rational game participants and polluting enterprises as profit-maximizing participants. In the game simulation calculation logic, the evolutionary game analysis device has a preset parameter matrix containing multiple dimensions, including but not limited to: preset regulatory cost thresholds, frequency of regulatory agency inspections, production cost savings resulting from illegal pollution discharge by enterprises, fines faced upon investigation, potential order loss weights due to credit downgrades, and social reputation loss weights.

[0036] The evolutionary game analysis device is configured to describe the strategy evolution process through a system of dynamic differential equations, simulating the probabilistic path of enterprises tending towards "compliance with regulations" or "illegal discharge" in the long-term evolution process. The device can dynamically adjust the game parameters according to the actual enforcement intensity in the industrial park. For example, when regional environmental policies tighten, leading to an increase in the fine coefficient, the system automatically reconstructs the evolutionary trajectory and calculates the magnitude of the enterprise's subjective motivation to still choose to violate regulations under such circumstances. The output of the evolutionary game analysis device is a quantitative representation of the enterprise's potential motivation to violate regulations. This quantitative representation not only reflects the comparison between the enterprise's current financial pressure and the temptation of returns but also incorporates the enterprise's strategic inertia in the game's history.

[0037] The violation tendency index calculation device, connected to both the enterprise behavior profiling device and the evolutionary game analysis device, is configured to employ a weighted fusion algorithm to deeply integrate micro-level behavioral characteristics with macro-level motivational representations, calculating the violation tendency index for each enterprise. This index, a value between 0 and 100, accurately represents the subjective probability of an enterprise engaging in covert pollution discharge under current operating conditions and regulatory environments.

[0038] During the calculation process, the violation tendency index calculation device employs an adaptive weighting mechanism. Specifically, the weight allocation in the weighted fusion algorithm is not fixed but is automatically and adaptively adjusted based on the enterprise's industry category (e.g., the difference in pollution discharge characteristics between the fine chemical industry and the packaging and printing industry), enterprise size level (e.g., the difference in violation motivation between large state-owned enterprises and small and micro enterprises), and historical credit records. For enterprises with low historical credit ratings, the algorithm increases the proportion of "motivational representation" in the index calculation; for enterprises with extremely frequent fluctuations in production conditions, the algorithm focuses on the offset analysis of "behavioral characteristics." The calculation logic ensures that the violation tendency index can penetrate the illusion of "data compliance" and identify entities that bypass physical monitoring through technical means but have a strong subjective intention to violate regulations, thus achieving early prediction of concealed pollution intentions.

[0039] The tiered early warning decision-making device, connected to the violation tendency index calculation device, is configured to automatically classify the risk levels of controlled enterprises throughout the park based on the calculated violation tendency index. The tiered early warning decision-making device has a pre-set hierarchical risk management strategy. Specifically, the device divides enterprises into three main risk levels: the first is a low-risk level, corresponding to a violation tendency index below a preset first threshold; the system maintains regular monitoring frequency for these enterprises and records good compliance credit in their profiles. The second is a medium-risk level, corresponding to a violation tendency index between the first and second thresholds; the system automatically generates an anomaly alert and pushes compliance risk notifications to enterprise management personnel, reminding them to investigate abnormal operating conditions. The third is a high-risk level, corresponding to a violation tendency index exceeding a second preset threshold.

[0040] When a high-risk level warning is triggered, the tiered warning decision-making device is configured to immediately generate a priority warning instruction, which is automatically pushed to the environmental enforcement department via a mobile application or a comprehensive management screen. The warning report includes, but is not limited to: basic enterprise information, the current violation tendency index value, a summary of the specific risk causes triggering the warning (e.g., identifying suspected dilution emissions or obvious loopholes in nighttime production logic), and recommended handling measures. Recommended handling measures include, but are not limited to, suggesting that enforcement personnel conduct surprise on-site inspections, deploy mobile monitoring vehicles to conduct targeted sampling around the enterprise, or retrieve detailed electricity consumption records of the enterprise's waste gas treatment facilities for in-depth comparison. In this way, limited enforcement resources are prioritized for entities with the strongest violation intent and the highest risk, achieving precise allocation of regulatory resources.

[0041] In a preferred embodiment, the multi-source data acquisition device is further equipped with a data security solidification unit. Considering that the monitoring data may involve corporate trade secrets and the rigor required for law enforcement, this unit employs blockchain notarization technology to hash-desensitize and encrypt the original collected VOCs concentrations, production conditions, and historical law enforcement records before storing them on the blockchain. The generation timestamp and geolocation tag of each piece of original data are locked in real time, ensuring that all basic data used in subsequent violation tendency index analysis and graded early warning decision-making processes possess immutability and evidentiary validity, preventing companies from circumventing system compliance checks by tampering with local databases.

[0042] Furthermore, the enterprise behavior profiling device employs a residual analysis technique based on deep neural networks when performing pattern recognition. The device first constructs a compliant emission model based on ideal conditions, using temperature, air pressure, raw material purity, and equipment aging as input variables. The device compares the real-time collected actual emissions data with the output of the ideal emission model, and the resulting difference sequence is the residual. By performing spectral analysis on the residual sequence, it identifies whether there are periodic fluctuations with characteristics of human control. For example, if the residual sequence shows a regular negative shift between 2:00 AM and 4:00 AM every day (i.e., actual emissions are far lower than theoretical values), the system will automatically associate this feature with the behavior profile label of "regular interference monitoring equipment."

[0043] Furthermore, the evolutionary game analysis device incorporates a spatial correlation game model. This model considers not only the cost-benefit of individual enterprises but also the strategy imitation effect between neighboring enterprises. In industrial parks, enterprises often exhibit clustering characteristics. If the illegal discharge behavior of one enterprise goes unpunished, it may trigger imitation by surrounding enterprises, forming a negative evolutionary stable strategy. The evolutionary game analysis device assesses the contagion risk of violations by analyzing the correlation of pollution discharge data in adjacent areas. If the violation tendency index of an enterprise increases, and its surrounding enterprises show a strong herd mentality in historical data, the system will automatically increase the overall game pressure parameter of the area, enhancing the sensitivity of the early warning.

[0044] Furthermore, the violation tendency index calculation device incorporates a feedback compensation closed-loop logic. This logic evaluates the accuracy of previous index calculations by retrieving actual enforcement feedback results after the tiered early warning decision. If a company marked as high-risk does indeed commit violations during enforcement verification, the system will automatically strengthen its corresponding feature weight factors; if it is a false alarm, the system enters a self-learning mode, automatically correcting the pattern recognition rules in the profile building device by analyzing the reasons for the false alarm (such as the company undergoing equipment maintenance rather than illegal discharge), thereby improving the accuracy of subsequent index calculations.

[0045] The tiered early warning decision-making device also features a dynamic threshold adjustment function. This function allows regulators to flexibly adjust the first and second thresholds based on the air quality compliance pressure in different seasons. During heavy pollution weather warnings, the device automatically tightens the threshold standards, allowing more enterprises on the verge of medium risk to be included in the high-risk monitoring list, thus achieving dynamic management of environmental capacity pressure. Furthermore, all early warning instructions generated by the device are linked to a closed-loop task flow, requiring recipients to provide verification results within a preset timeframe, forming a complete closed-loop process of "perception-prediction-decision-execution-feedback".

[0046] Example 2: Based on the VOCs emission dynamic detection, assessment and early warning system for industrial parks described in Example 1, this example provides a hardware implementation scheme based on edge computing and distributed storage architecture to meet the needs of massive data collection and real-time processing in ultra-large-scale industrial parks.

[0047] In this implementation architecture, the multi-source data acquisition device is deployed in a distributed manner as multiple edge acquisition stations. Each edge acquisition station is responsible for managing several enterprises within its jurisdiction, and its hardware consists of a multi-protocol acquisition gateway, a set of sensing probes deployed at key nodes of the enterprises, and an edge computing unit. The edge computing unit is configured to perform preliminary preprocessing of high-frequency data locally, including data resampling, removing abnormal spikes caused by sensor drift, and performing analog-to-digital conversion on analog signals. This distributed design reduces the data bandwidth pressure on the central server.

[0048] Each edge acquisition station in the multi-source data acquisition device is equipped with a breakpoint resume function. When network fluctuations or communication interruptions occur within the park, the collected VOCs concentration and production status data will be temporarily stored in non-volatile memory on the edge side and automatically synchronized to the central system's database according to the time sequence after the connection is restored. This design ensures the integrity and continuity of the data required to build enterprise behavior profiles and avoids profile distortion due to data loss.

[0049] In implementing the enterprise behavior profiling device, this embodiment adopts a cloud-edge collaborative processing architecture. Simple behavioral feature recognition with high real-time requirements (such as instantaneous power surge monitoring) is performed in the edge computing unit, and the recognition results are reported in real time in the form of lightweight tags; while complex, long-cycle behavioral pattern mining (such as psychological offset analysis based on annual production sequences) is completed in the distributed computing cluster of the central cloud platform. The central cloud platform uses massive historical sample data to continuously iterate and update the profiling model based on behavioral psychology, and distributes the updated model parameters to various edge collection stations through containerization technology, achieving a comprehensive improvement in profiling accuracy across the entire enterprise.

[0050] In this embodiment, the evolutionary game analysis device is configured to support parallel simulation across multiple scenarios. The system includes preset virtual game scenarios such as a "strict supervision mode," a "normal supervision mode," and a "flexible guidance mode." In the "strict supervision mode," the system sets the weights for fines and credit losses to the highest values; in the "normal supervision mode," parameters are set based on average enforcement data from the past year. Through parallel simulation, the evolutionary game analysis device can output a set of motivational characterization values ​​reflecting different policy sensitivities. The violation tendency index calculation device enhances the robustness of violation intention prediction and reduces system bias caused by setting a single game scenario by taking the weighted expected value of this set of motivational characterization values.

[0051] In this embodiment, the violation tendency index calculation device employs a real-time processing engine based on streaming computing. This engine is configured to process behavioral tag data from each edge station with millisecond-level latency and, combined with dynamically loaded game motivation parameters, update the violation tendency score for each enterprise in real time. When the score changes across levels (e.g., from low risk to high risk), the tiered early warning decision-making device immediately distributes early warning messages through a message middleware, ensuring the issuance of early warning instructions has extremely high real-time performance.

[0052] In this embodiment, the tiered early warning decision-making device further integrates an augmented reality-assisted law enforcement system. When law enforcement officers receive a high-risk early warning instruction and arrive at the enterprise site, the device can use a mobile terminal to visualize and overlay the enterprise's violation tendencies, historical discharge trajectories, and suspected concealed discharge locations. For example, on the mobile terminal screen, the system will highlight the branch electricity meter with abnormal power consumption and mark the time points when its concentration data contradicts the operating logic, assisting law enforcement officers in quickly securing evidence and improving on-site law enforcement efficiency.

[0053] Furthermore, to ensure the system's reliability under high concurrency, the hierarchical early warning decision-making device also features a dynamic resource scheduling mechanism. When a large number of suspected violations are detected simultaneously within the park, the system automatically prioritizes the warning events based on the absolute value of the violation tendency index and the toxicity weight of the pollutants emitted by the enterprise. The highest priority events will consume more computing resources for deep trajectory backtracking and will be automatically assigned to the most experienced enforcement team, maximizing the reduction of environmental risks under resource constraints.

[0054] The distributed architecture in this embodiment also allows for data interconnection and model sharing between different industrial parks. By constructing a cross-park enterprise compliance credit profile network, the system can identify the consistency of behavior exhibited by enterprises operating across regions in different parks. If an enterprise has a history of illegal or unauthorized emissions in other parks, the evolutionary game analysis device will automatically increase the initial weight of its violation tendency, achieving cross-regional joint supervision and credit deterrence. This system solution, based on the deep integration of edge computing and cloud-based big data analysis, provides a high-performance and highly reliable technological foundation for the refined governance of VOCs in complex industrial environments.

[0055] Example 3: Based on the above examples, this example further discloses the refined configuration of the VOCs emission dynamic detection, assessment, and early warning system for industrial parks in a specific chemical industrial park application scenario. In this scenario, the park involves a large number of fine chemical, pharmaceutical, and pesticide synthesis enterprises, whose VOCs emissions are extremely complex, and whose production processes are characterized by multiple steps and intermittent operation, making it highly difficult to identify violations.

[0056] For this specific scenario, the multi-source data acquisition device in this embodiment incorporates a hyperspectral remote sensing monitoring unit and an infrared thermal imaging monitoring module. The hyperspectral remote sensing monitoring unit is installed at the highest point in the industrial park, and through scanning coverage, it can acquire the infrared absorption spectra above the main sewage outlets and production workshops. Since different types of VOCs components have characteristic absorption bands, the hyperspectral remote sensing monitoring unit is configured to perform component inversion on the acquired spectral data to obtain the proportion distribution of VOCs components, and input this proportion distribution information as supplementary concentration data into the multi-source data acquisition device.

[0057] The infrared thermal imaging monitoring module is configured to monitor the surface temperature of the enterprise's waste gas treatment facilities (such as RTO regenerative combustion furnaces and activated carbon adsorption devices) and the temperature field distribution of their associated pipelines. The multi-source data acquisition device simultaneously acquires this thermal data because there is a significant correlation between the operating status of the waste gas treatment facilities and their surface heat distribution. If the production status data indicates that the equipment is operating, but the infrared thermal imaging shows that the temperature in the core area of ​​the combustion furnace is below the ignition point, the logic consistency verification unit will use this evidence to identify whether the enterprise is directly discharging or illegally bypassing waste gas.

[0058] In this complex intermittent production mode, the enterprise behavior profiling device is configured to employ a knowledge graph-based behavior modeling technology. The system analyzes the production processes of various chemical enterprises, constructing nodes and relationships in a knowledge graph representing raw materials, intermediates, finished products, and corresponding characteristic pollutant emission patterns. The profiling device performs logical comparisons on the knowledge graph based on real-time material flow information captured by multi-source data acquisition devices. If the system detects that an enterprise's material input records show it is in the process of synthesizing a highly polluting product, but the corresponding characteristic components are not detected at the end-of-pipe monitoring point, the profiling module will mark this behavior as "abnormal component camouflage," using this as a core indicator for assessing compliance tendencies.

[0059] To address the unpredictability of intermittent production, the evolutionary game analysis device in this embodiment incorporates a stochastic evolutionary game model. This model no longer uses fixed parameters but introduces noise terms with random fluctuations to simulate the impact of uncertainties such as market raw material price fluctuations, order pressures, and sudden equipment failures on corporate strategy choices. The evolutionary game analysis device is configured to perform thousands of Monte Carlo simulations to calculate the robust probability distribution of firms choosing to violate regulations under various extreme uncertainty scenarios. This in-depth characterization of motivation allows the system to identify "environmentally vulnerable" firms that are prone to violating regulations during market downturns and periods of soaring costs.

[0060] In this embodiment, the violation tendency index calculation device integrates an industry benchmarking algorithm. This algorithm uses the average emission intensity of similar-type and scale enterprises at the same production stage as a benchmark, and compares the target enterprise's real-time data with this benchmark for horizontal deviation. The calculation of the violation tendency index not only relies on the enterprise's own historical longitudinal comparison but also incorporates the impact of industry horizontal deviation. If an enterprise's emission intensity is consistently significantly lower than the industry average (and there is no evidence that it has adopted more advanced processes), the violation tendency index calculation device will automatically increase the enterprise's violation tendency value. This algorithm identifies hidden violations that appear "technologically advanced" but may actually be due to backend fraud.

[0061] In this embodiment, the tiered early warning decision-making device is also configured to work in conjunction with the park's automatic sampling pump station. When the violation tendency index reaches a preset high-risk level, the system can directly trigger the automatic sampling unit downstream of the enterprise's discharge outlet to collect samples remotely without manual intervention. This proactive sampling mechanism can capture fleeting evidence of illegal discharge, overcoming the pain point of delayed on-site arrival of manual enforcement. Simultaneously, the early warning command includes preliminary rapid analysis data from automatically collected water and air samples, providing strong technical support for subsequent administrative penalties.

[0062] Furthermore, the system also features a controlled open interface accessible to the public. After generating a risk assessment, the tiered early warning decision-making device can publish the enterprise compliance index (excluding trade secrets) as a "green credit score" on the park's public bulletin board or official platform. This social oversight mechanism further increases the reputational damage weight of enterprises engaging in violations. Changes in this weight, in turn, serve as feedback input to the evolutionary game analysis device, forming a positive governance ecosystem that guides enterprises towards voluntary compliance through technological means. This deep application in complex chemical engineering scenarios fully demonstrates the unique advantages of the system architecture of this invention in handling high-dimensional, nonlinear environmental regulatory problems.

[0063] Example 4: This example focuses on describing the hardware and software integration details of the industrial park VOCs emission dynamic detection, assessment and early warning system in a large-scale smart park IoT cloud platform, as well as the optimization mechanism of the data processing flow.

[0064] At the hardware level, the system relies on a data center with high-performance computing capabilities. The backend of the multi-source data acquisition device is connected to a highly scalable message bus system (such as a Kafka cluster) to support concurrent data access from tens of thousands of sensing nodes within the campus. Each VOCs sensing terminal and operating condition monitoring sensor is authenticated via a built-in hardware encryption chip, ensuring the authenticity and reliability of the data source. The data center employs a hierarchical storage architecture. Real-time, high-frequency data is stored in a distributed time-series database based on NVMe flash memory, while historical behavior records and profile features are stored in a columnar database supporting large-scale parallel queries, ensuring the read performance of the violation tendency index calculation device during historical backtracking analysis.

[0065] The multi-source data acquisition device integrates a high-precision meteorological sensing unit during data acquisition. This unit is deployed across multiple geographical quadrants within the park, collecting micro-meteorological parameters such as wind direction, wind speed, air pressure, temperature, and humidity. The multi-source data acquisition device aligns these meteorological parameters with VOCs concentration data spatiotemporally. The logical consistency verification unit utilizes an atmospheric diffusion inversion algorithm to simulate source tracing paths based on the spatial distribution of environmental concentrations, comparing the pollution intensity indicated by the source tracing results with the reported operating intensity from the relevant enterprises. If a significant spatial logical discrepancy exists (e.g., extremely high concentrations downwind, but monitoring data from upwind enterprises showing negligible emissions), the verification unit will generate a spatial dimension flag indicating suspected falsification.

[0066] In this embodiment, a transfer learning mechanism is introduced into the enterprise behavior profiling device. For newly relocated enterprises within the park, due to the lack of long-term historical behavior sequences, the system is configured to search for benchmark enterprise profiling models with similar industries, processes, and scales from the profiling knowledge base and transfer their initial parameters to the new enterprise. As the new enterprise's operational data accumulates, the profiling device continuously corrects its specific behavioral characteristic labels through incremental learning. This mechanism solves the problem of low accuracy in predicting regulatory intent during the "cold start" phase of new enterprises. Simultaneously, the profiling device also integrates voice and image recognition modules. By accessing inspection videos from the enterprise's compliance disclosure board or self-inspection video streams uploaded periodically by the enterprise, it identifies whether the operators of its pollution control facilities exhibit behavioral and psychological characteristics of violations (such as hastily shutting down equipment or deliberately obstructing monitoring cameras), and transforms this unstructured information into one of the dimensions of the profiling.

[0067] In this embodiment, the evolutionary game theory analysis device automatically embeds macro-environmental policies. The system connects to national and local environmental laws and regulations databases and uses natural language processing technology to extract the latest policy change characteristics. When the starting price for penalties related to VOC emissions is increased in laws and regulations, or positive incentive policies such as pollution rights mortgage loans are introduced, the evolutionary game theory analysis device can automatically sense these changes in external variables and update parameters in real time during game simulation. This design transforms the system from an isolated detection device into an intelligent analytical entity that dynamically coexists with the external policy environment.

[0068] In this embodiment, the violation tendency index calculation device employs a fusion algorithm based on Deep Forest. Compared to traditional linear weighting, this fusion algorithm can automatically discover high-order nonlinear correlations between behavioral characteristics and game-playing motives. For example, the algorithm might find that in certain specific process types, if a minor anomaly in raw material consumption occurs simultaneously with a specific psychological shift characteristic before regulatory inspection, its confidence in predicting a violation will increase exponentially. The violation tendency index calculation device is configured to periodically derive these high-order correlation rules for manual review by an expert system and for expanding the strategy library.

[0069] In this embodiment, the tiered early warning decision-making device integrates a location-based grid scheduling algorithm. When a high-risk warning is generated, the system not only sends instructions to law enforcement agencies but also automatically retrieves and locates the nearest mobile monitoring vehicle and on-site law enforcement personnel to the suspected violation location. Based on their current real-time location, load status, and professional expertise (such as familiarity with chemical industry enforcement), the system calculates the optimal response path and personnel combination. This automated command and dispatch mechanism reduces the time from warning triggering to law enforcement personnel arrival by more than 40%.

[0070] To further enhance the system's fault tolerance and robustness, the tiered early warning decision-making device also features a shadow early warning mechanism. This mechanism processes the same batch of data by running multiple simulated early warning engines with different configurations in the background. Only when multiple engines unanimously agree that the enterprise has a high-risk tendency will the system officially issue the highest-level official warning. This consensus-based early warning strategy reduces the false alarm rate caused by single algorithm defects or single-point sensor failures. The system also provides enterprises with a closed-loop appeal feedback system. After receiving a low-to-medium risk warning, enterprises can upload supporting materials demonstrating abnormal operating conditions (such as emergency maintenance records) to the cloud. After verification, the system will automatically whitelist the behavioral deviation and adjust its subsequent profile scoring logic. This user-friendly interactive design enables the system to maintain strong supervision while also possessing good compliance guidance capabilities, ultimately building a smart, transparent, and efficient park environmental governance system.

[0071] In summary, the industrial park VOCs emission dynamic monitoring, assessment, and early warning system provided by this invention breaks through the limitations of traditional environmental monitoring that only focuses on physical concentration data through the deep synergy of multi-source data acquisition devices, enterprise behavior profiling devices, evolutionary game analysis devices, violation tendency index calculation devices, and hierarchical early warning decision-making devices. By deeply integrating and analyzing operating condition data, pollution discharge data, policy benefits, and game motivations, this system can accurately penetrate the illusion of "data compliance," identify various hidden pollution discharge methods, provide environmental management departments with a scientific basis for risk classification, and achieve the ultimate precision in the allocation of regulatory resources.

[0072] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention. Without departing from the core logic of the present invention, those skilled in the art can flexibly adjust the internal hardware selection, algorithm module combination, data communication protocol, and specific parameter thresholds of each device in the system according to the needs of actual application scenarios. These variations based on the concept of the present invention are all within the scope of the claims of the present invention.

Claims

1. A dynamic monitoring, assessment, and early warning system for VOCs emissions in industrial parks, characterized in that: include: Multi-source data acquisition device, configured to simultaneously acquire volatile organic compound concentration data, production status data, pollution discharge permit compliance information and historical law enforcement records of various polluting enterprises in the park; The volatile organic compound concentration data comes from sensing terminals deployed at the enterprise's discharge outlets and the boundary of the industrial park. The production status data includes the enterprise's electricity consumption, raw material consumption, equipment operating status, and production load information. The enterprise behavior profile building device is connected to the multi-source data acquisition device and is configured to perform pattern recognition and feature extraction on the daily operation behavior of the enterprise based on the acquired data, identify abnormal behavior patterns that contradict normal production logic, and build an enterprise behavior profile that includes behavioral stability and compliance tendency. The evolutionary game analysis device is configured to simulate the strategy selection process of regulatory agencies and enterprises under different regulatory parameters through an evolutionary game theory model. By setting regulatory input, fine amount and inspection frequency, it dynamically deduces the evolutionary path of enterprises tending to comply with the law or illegally discharge in the long-term game, and outputs a quantitative representation of the potential illegal motivation of enterprises. The violation tendency index calculation device is connected to the enterprise behavior profile construction device and the evolutionary game analysis device, respectively. It is configured to integrate behavioral characteristics and motivational representations, and uses a weighted fusion algorithm to calculate the violation tendency index of each enterprise, which is used to represent the subjective probability of the enterprise carrying out concealed pollution discharge behavior. A graded early warning decision-making device is connected to the violation tendency index calculation device and is configured to classify enterprises into multiple risk levels according to the violation tendency index and generate early warning instructions containing targeted intervention measures.

2. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 1, characterized in that: The multi-source data acquisition device is deployed in a distributed manner within the physical space of the industrial park in terms of hardware architecture. Data aggregation is achieved by constructing a wired and wireless hybrid communication network covering the entire park. The sensing terminal integrates a photoionization detection sensor, a flame ionization detection sensor, and a gas chromatography monitoring unit. It is configured to continuously monitor the total hydrocarbons, non-methane total hydrocarbons, and benzene series characteristic factors in ambient air at a preset sampling period. The multi-source data acquisition device establishes data interfaces with the enterprise's internal production execution system, distributed control system, and energy management system to retrieve the enterprise's electricity current value, raw material consumption rate, operating frequency of major production equipment, and production load percentage in real time. For traditional enterprises that have not yet achieved digitalization, the multi-source data acquisition device monitors the equipment start-up and shutdown status and real-time load by using non-intrusive current transformers installed on the power supply lines of the enterprise's main power distribution cabinet and key sewage treatment facilities, in conjunction with an edge computing gateway.

3. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 1, characterized in that: The multi-source data acquisition device is equipped with a logical consistency verification unit, which is configured to perform deep cleaning and rationality review on the acquired physical data. The logical consistency verification unit identifies abnormal situations caused by dilution emissions or illegal tampering with monitoring equipment parameters by establishing a physical mapping model between pollutant emission concentration and production load. When the production status data shows that the enterprise is in a high-load production state, but the corresponding volatile organic compound concentration data is consistently at a low level and its fluctuation rate is less than 10% of the normal benchmark value, the logical consistency verification unit is configured to trigger a logical paradox flag and mark the data point as a potential point of suspected fraud. The multi-source data acquisition device also integrates a high-precision meteorological sensing unit, which is used to collect micro-meteorological parameters such as wind direction, wind speed, air pressure, temperature and humidity, and align the meteorological parameters with the concentration data in time and space. It uses an atmospheric diffusion inversion algorithm to simulate the source tracing path based on the spatial distribution of environmental concentration, and compares whether the pollution intensity pointed to by the source tracing results matches the operating intensity reported by the enterprise.

4. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 1, characterized in that: The enterprise behavior profiling device integrates a feature engineering engine and a behavioral psychology analysis module, which aims to identify behavioral deviations that are inconsistent with normal production logic. The enterprise behavior profiling device analyzes long-term historical data to establish a baseline behavior model for each enterprise. The baseline behavior model includes the enterprise's emission characteristic curve, electricity load fluctuation pattern, and production cycle characteristics under normal operating conditions. The behavioral psychology analysis module analyzes changes in corporate behavior during statutory holidays, periods of reduced visibility during nighttime inspections, periods of on-site environmental protection inspections, and after regulatory inspection notices are issued by constructing a behavioral psychology model and combining it with the company's historical behavioral sequences. The behavioral psychology model quantifies a company’s sensitivity to regulatory pressure by constructing a compliance pressure response function. If a company experiences a sharp drop in emissions after receiving a regulatory notice, but then the emissions rebound during periods of no human intervention, the behavioral psychology model will be configured to assign the company a high behavioral instability label and use it as a key dimension in the profile.

5. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 4, characterized in that: The enterprise behavior profiling device is configured to identify multi-dimensional hidden cheating features, including unplanned nighttime startup, logical imbalance between production load and pollutant emissions, and behavioral deviation features within a time period. During pattern recognition, the enterprise behavior profile building device uses residual analysis technology based on deep neural networks to build a compliant emission model based on ideal conditions. Temperature, air pressure, raw material purity and equipment aging are used as input variables. The actual pollution discharge data collected in real time is compared with the output of the ideal emission model to generate a difference sequence. By performing spectral analysis on the difference sequence, it can be identified whether there are periodic fluctuations with human control characteristics. If the residual sequence shows a regular negative shift at a fixed time every day, the enterprise behavior profile building device will automatically associate this feature with the behavior profile label of the regular interference monitoring device. The enterprise behavior profiling device also integrates voice and image recognition modules. By accessing the enterprise inspection video stream, it identifies whether operators have violated regulations by shutting down equipment or deliberately obstructing monitoring cameras, and transforms unstructured information into profile dimensions.

6. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 1, characterized in that: The evolutionary game analysis device has a preset parameter matrix containing multiple dimensions in the simulation calculation logic. The parameter matrix includes preset regulatory cost thresholds, frequency of inspections by regulatory agencies, production cost savings caused by illegal discharge of pollutants by enterprises, amount of fines after investigation, weight of potential order loss due to credit downgrade, and weight of social reputation loss. The evolutionary game analysis device is configured to describe the strategy evolution process through a set of dynamic differential equations, simulate the probability path of enterprises tending to comply with regulations or illegally discharge pollutants in the long-term evolution process, and dynamically adjust the game parameters according to the actual law enforcement intensity in the park. The evolutionary game analysis device also incorporates a spatial correlation game model. This model considers not only the cost and benefit of a single enterprise but also the strategy imitation effect between neighboring enterprises. By analyzing the correlation of pollution discharge data in adjacent areas, it assesses the risk of contagion of violations. If the violation tendency index of a certain enterprise increases and its surrounding enterprises show a strong tendency to follow the crowd, the evolutionary game analysis device will automatically increase the overall game pressure parameter of the region to improve the early warning sensitivity.

7. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 6, characterized in that: The evolutionary game analysis device is configured to support parallel simulation of multiple scenarios, and the system has preset strict supervision mode, normal supervision mode and flexible guidance mode. Under strict regulatory mode, the system sets the amount of fines and the weight of credit loss to the highest value, while under normalized regulatory mode, the parameters are set based on the average enforcement data of the past year. Through parallel simulation, the evolutionary game analysis device outputs a set of motivational characterization values ​​reflecting different policy sensitivities, and the violation tendency index calculation device enhances the robustness of violation intention prediction by taking the weighted expected value of this set of motivational characterization values. The evolutionary game analysis device connects to an environmental laws and regulations database and uses natural language processing technology to extract the latest policy change features. When the penalties for volatile organic compound emissions are increased in laws and regulations, the evolutionary game analysis device automatically senses, identifies, and updates the game parameters in real time, enabling the system to dynamically coexist with the external policy environment.

8. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 1, characterized in that: The violation tendency index calculation device adopts an adaptive weighting mechanism, and the violation tendency index it calculates is a value between 0 and 100. The weight allocation in the weighted fusion algorithm is automatically and adaptively adjusted based on the industry category, enterprise size level, and historical credit record of the enterprise. For enterprises whose historical credit rating is below a preset level, the weighted fusion algorithm is configured to increase the proportion of motivational representation in the index calculation; for enterprises whose production condition fluctuation frequency is higher than a preset threshold, the focus is on the deviation analysis of behavioral characteristics. The violation tendency index calculation device is equipped with a feedback compensation closed-loop logic. By retrieving the actual law enforcement feedback results after the graded early warning decision, the accuracy of the previous index calculation is evaluated. If a company marked as high risk does have violations during law enforcement verification, the system will automatically strengthen its corresponding feature weight factor. If it is a false alarm, the system will enter the self-learning mode and automatically correct the pattern recognition rules in the profile building device. In terms of algorithm architecture, the violation tendency index calculation device adopts a deep forest-based fusion algorithm to automatically discover high-order nonlinear correlations between behavioral characteristics and game motivations.

9. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 1, characterized in that: The tiered early warning decision-making device classifies enterprises into low-risk, medium-risk, and high-risk levels. The low-risk level corresponds to a violation tendency index below a preset first threshold, and the system maintains regular monitoring frequency for such enterprises; the medium-risk level corresponds to a violation tendency index between the first and second thresholds, and the system automatically pushes compliance risk notifications to enterprise managers. The high-risk level corresponds to a violation tendency index exceeding a second preset threshold. In this case, the graded early warning decision-making device immediately generates a priority early warning instruction and pushes it to the environmental law enforcement department. The warning instruction includes basic information about the enterprise, a violation tendency index value, a summary of the specific risk causes that triggered the warning, and suggested measures such as on-site verification, encrypted sampling, or retrieval of electricity consumption details. The tiered early warning decision-making device also has a dynamic threshold adjustment function, which allows regulators to adjust the size of the first and second thresholds according to the air quality compliance pressure in different seasons. During heavy pollution weather warnings, the threshold standards are automatically tightened, so that more enterprises on the verge of risk are included in the monitoring list.

10. The industrial park VOCs emission dynamic detection, assessment and early warning system according to claim 1, characterized in that: The system also includes a data security solidification unit, which uses blockchain notarization technology to hash and desensitize the original collected concentration data, production conditions and historical law enforcement records and store them on the blockchain. The generation timestamp and geolocation tag of each piece of original data are locked in real time to ensure that the basic data is tamper-proof. The graded early warning decision-making device further integrates an augmented reality-assisted law enforcement module. When law enforcement officers receive a high-risk early warning instruction and arrive at the scene, the law enforcement module uses a mobile terminal to visualize and overlay the causes of the enterprise's violation tendency, historical sewage discharge trajectory, and the physical location of suspected concealed sewage discharge. Meanwhile, the graded early warning decision-making device is configured to be linked with the park's automatic sampling pump station. When the violation tendency index reaches a high-risk level, the system remotely triggers the automatic sampling unit downstream of the enterprise's discharge outlet to retain samples. The system also automatically prioritizes early warning events based on the absolute value of the violation tendency index and the pollutant toxicity weight, thereby maximizing the reduction of environmental risks under limited computing resources.

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