A method for enterprise classification management based on pollutant discharge permit data evaluation indicators

Abstract

The present invention discloses a method for enterprise classification management based on pollutant discharge permit data evaluation indicators. The present invention relates to the field of environmental supervision technology and includes the following steps: S1: obtaining the enterprise's real-time monitoring data stream and pollutant discharge permit execution report, wherein the real-time monitoring data stream collects pollutant concentration data at an hourly frequency through an Internet of Things sensor. This method for enterprise classification management based on pollutant discharge permit data evaluation indicators breaks through the limitations of traditional static data evaluation systems by constructing a coupled dual-channel decision engine and a spatiotemporal adaptive evaluation model. It performs fractal time series compression and process logic alignment on monitoring data and declared data, achieving lossless fusion of multi-source heterogeneous data. It highlights key management enterprises through classification levels, concentrates regulatory resources to strengthen management and control, and effectively improves the efficiency of intervention in high-pollution emission behaviors.

Description

Technical Field

[0001] The present invention relates to the field of environmental supervision technology, and in particular to an enterprise classification management method based on pollutant discharge permit data evaluation indicators. Background Art

[0002] In the field of environmental supervision, the enterprise classification management method based on pollutant discharge permit data has become an important tool for implementing differentiated supervision; pollutant emissions are standardized and evaluated through preset fixed thresholds, and then the enterprise management level is divided; however, this static and periodic data processing model has significant defects: the pollutant discharge permit execution report data is essentially a phased summary of the enterprise's production activities, which can to a certain extent reflect the enterprise's actual emission behavior at a certain stage; in the actual production process of the enterprise, the concentration and total amount of pollutant emissions may fluctuate due to factors such as equipment operating status, process parameter fluctuations, and changes in raw material quality.

[0003] However, existing technologies are unable to effectively integrate high-frequency monitoring data collected in real time by IoT sensors with low-frequency reporting data, resulting in classification and evaluation models being stuck in a state of "judging the new by the old." Regulatory authorities can only make management decisions based on lagged historical data, failing to accurately identify key management enterprises. Furthermore, fixed-threshold evaluation systems ignore real-time changes in external variables such as regional environmental capacity, meteorological and hydrological conditions. The varying impacts of the same emission intensity on the ecological environment under different spatial and temporal contexts are not quantified, further weakening the scientific validity and applicability of the classification results. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an enterprise classification management method based on pollution discharge permit data evaluation indicators, which solves the problem of failure in the integration of real-time data and periodic management decisions in the existing technology.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for enterprise classification management based on pollutant discharge permit data evaluation indicators, comprising the following steps:

[0008] S1: Obtain the company's real-time monitoring data stream and pollutant discharge permit execution report. The real-time monitoring data stream collects pollutant concentration data at an hourly frequency through IoT sensors. The pollutant discharge permit execution report contains production process parameters and the company's environmental management ledger.

[0009] S2: The real-time monitoring data stream and the pollutant discharge permit execution report are input into a dual-channel decision engine. The high-frequency monitoring data stream is subjected to fractal time series compression through a spatiotemporal encoder-decoder module to extract the equipment start-stop inflection points and emission trend curvature features. At the same time, the implicit emission nodes in the process chain are reversely deduced based on the production log to generate enhanced declaration data. The process feature similarity matrix is ​​used to achieve data fusion across time scales to generate an emission feature vector.

[0010] The construction of the coupled dual-channel decision engine DCDDE architecture involves hardware deployment and software module interaction. Hardware deployment includes edge computing nodes and cloud servers. The edge computing nodes include industrial gateways deployed on the enterprise side, integrated with Modbus and OPC UA protocol parsing modules, and real-time sensor data collection with a sampling frequency of 1Hz. The cloud server uses a Kubernetes cluster architecture and is configured with dedicated containers to process high-frequency data streams and low-frequency reporting data. The dedicated containers process high-frequency data streams using Apache Kafka message queues, while the low-frequency reporting data uses a PostgreSQL database.

[0011] The software module interactions include a data preprocessing module and a process logic verification module. The data preprocessing module is used to implement sliding window standardization on the raw monitoring data, with a window length of 60 seconds and a step length of 5 seconds, to eliminate sensor drift errors. The process logic verification module is used to synchronize the work order execution status of the enterprise MES system every 10 minutes to verify the consistency of the timestamps of the production log and the declared data.

[0012] S3: Input the emission characteristic vector into the environmental capacity responsive evaluation model, access the meteorological grid forecast data and the hydrodynamic model in real time to calculate the regional environmental capacity correction coefficient, and combine the nonlinear relationship matrix between process parameters and pollutant generation based on the knowledge graph, and the pollutant spatial impact weight function generated according to the fluid mechanics equation to calculate the pollution contribution weight of the enterprise;

[0013] The correction of the fluid dynamics equations includes the corrected three-dimensional diffusion equation:

[0014] ;

[0015] Where, C: pollutant concentration, t: time, : partial differential symbol, x, y, z: spatial coordinates, V: pollutant migration rate vector, K x ,K y ,K z Updated every 6 hours based on satellite data. Update rules:

[0016] , where K x´ : Corrected horizontal turbulent diffusion coefficient, Kx : original horizontal turbulence diffusion coefficient, θ: real-time angle between satellite-retrieved diffusion direction and dominant wind direction, : Satellite inversion of pollution diffusion vector field, : Maximum regional pollution diffusion rate;

[0017] S4: The preliminary classification results are sequentially input into a reverse process simulator and a pollution assimilating capacity calculation model for double verification. The reverse process simulator uses an LSTM neural network to reversely deduce the theoretical values ​​of the production process parameters and performs deviation analysis with the declared parameters. The pollution assimilating capacity calculation model uses a Monte Carlo method to simulate the environmental capacity threshold, which is the ecological background.

[0018] S5: Output the double-verified classification results, and update the spatial impact weight function parameters according to the preset update cycle based on the atmospheric pollution diffusion trajectory inverted by satellite remote sensing.

[0019] Preferably, the fractal time series compression includes:

[0020] Perform multifractal dimension analysis on high-frequency monitoring data streams to extract key time windows with process relevance. The multifractal dimension analysis process includes:

[0021] The monitoring data stream is segmented into time series with a window length of 5 minutes, covering a typical production cycle;

[0022] Calculate the generalized Hurst exponent H(q) of each window, select the scale range of q=-5 to +5, and generate the multifractal spectrum f(α);

[0023] The time window with f(α) spectrum width greater than 0.25 is screened as the key process node, such as equipment start-up and shutdown, and raw material switching.

[0024] The persistence of emission trends is identified through wavelet transform and Hurst exponent calculation. During the wavelet transform and Hurst exponent calculation, the data stream is decomposed into five layers using the Daubechies 4 wavelet basis to extract detail coefficients d3-d5 corresponding to 2-8 minute periodic characteristics. The Hurst exponent is calculated for the approximate coefficient a5. When H is greater than 0.75, it is determined to be a persistent emission trend, and an early warning flag is automatically triggered.

[0025] Generate a compressed feature vector containing the emission fluctuation amplitude, frequency and equipment status associated weights.

[0026] Preferably, the construction of the process feature similarity matrix includes:

[0027] Establish a multi-dimensional feature mapping relationship between real-time process parameters and declared data;

[0028] Interpolation weights are calculated based on the correlation between equipment operating status and raw material addition rates in the production log. This includes parsing equipment start and stop records and raw material addition amounts in the production log to construct a time series diagram of the process chain. For time periods with missing reported data, interpolation weights are calculated using the following formula:

[0029] ; Among them, w t : interpolation weight at time t, n: total number of devices, i: device index number, R i : Pollutant correlation degree of equipment i, S t，i : The operating status coefficient of device i at time t, t: time, R i is the pollutant association degree of equipment i, defined by the knowledge graph.

[0030] The process logic consistency of real-time data and historical declaration data is matched through the cosine similarity algorithm.

[0031] Preferably, in the environmental capacity responsiveness evaluation model:

[0032] The meteorological gridded forecast data includes temperature, wind speed and atmospheric diffusion coefficient, and the hydrodynamic model includes basin runoff and water body self-purification rate; that is, meteorological-hydrological data fusion, including:

[0033] Meteorological grid forecast data is accessed at a resolution of 1km×1km to extract the real-time wind speed and temperature vertical gradient of the grid where the target enterprise is located;

[0034] In the hydrodynamic model, the basin runoff is calculated using the Saint-Venant equation:

[0035] ; in, : partial differential symbol, t: time, Q: river flow, x: spatial coordinate, here refers to the longitudinal coordinate of the river, Q 2 / A: momentum flux per unit water area, gA: product of gravitational acceleration and water area, h: water depth, So: river bottom slope, Sf: friction slope, Q is flow rate, A is water area, and h is water depth.

[0036] The nonlinear relationship matrix is ​​optimized using the stochastic gradient descent algorithm to optimize the transfer function coefficients of the reactor temperature and pollutant generation, and then the process-emission correlation model is performed:

[0037] First, transfer function optimization is performed to construct a nonlinear relationship between reactor temperature T and VOCs generation Q:

[0038] Where, G: Volatile organic compound generation, a, b, c, d: All are empirical coefficients, T: Reactor temperature, ln(T): Natural logarithm of temperature, e -d / T : Temperature-dependent exponential decay term; initial coefficients a=0.0023, b=1.78, c=5.6, d=300;

[0039] Then, the coefficients are updated every 24 hours using stochastic gradient descent, with a learning rate of η = 0.001, a batch size of 64, and a loss function of:

[0040] ; Among them, L: total loss value of the model, N: total number of training samples, G i pred : The amount of pollutant generated in the i-th prediction, G i meas : the amount of pollutant generated in the ith measurement, λ: regularization coefficient, ‖θ‖2: L2 norm of model parameters; λ=0.01 to prevent overfitting.

[0041] Preferably, the verification process of the reverse process simulator includes:

[0042] Real-time emission data is input into a pre-trained LSTM neural network to generate theoretical values ​​for production process parameters. The LSTM neural network architecture consists of an input layer, a hidden layer, and an output layer. The input layer contains a 60-minute sliding window of real-time emission data, including COD, NH3-N, and flow rate. The hidden layer contains three layers of LSTM units, each with 128 neurons and a dropout rate of 0.2. The output layer contains production process parameters, namely reactor temperature, stirring rate, and raw material addition rate. The training dataset for the LSTM network training parameters contains 100,000 sets of matching samples of historical process parameters and emission data.

[0043] Calculate the deviation between the theoretical parameters and the enterprise's declared parameters. When the deviation exceeds the set threshold, link the equipment operation log to trace the fault, that is, deviation analysis and tracing, including: calculating the root mean square error (RMSE) between the theoretical value and the declared value, with the threshold set to 15% of the declared value range; when the RMSE exceeds the standard, link the equipment log to extract the operating parameters 2 hours before the fault, such as current fluctuation greater than 10%, temperature sudden change greater than 5°C / min, etc., and generate a verification report for recommended verification measures.

[0044] Preferably, the capacity threshold simulation of the pollution holding capacity calculation model includes:

[0045] Generate the probability density function of pollutant concentration distribution based on the current environmental capacity correction coefficient; among them, the pollutant concentration distribution is fitted and the probability density function is generated using kernel density estimation KDE:

[0046] ;

[0047] in, : probability density function of kernel density estimation, n: number of samples, o: bandwidth parameter, K(·): Gaussian kernel function, p: pollutant concentration value, O i : The concentration value of the i-th sample.

[0048] Random sampling using the Monte Carlo method simulates classification boundaries under different meteorological conditions. Monte Carlo simulation parameters include input variables, sampling times, and classification boundary determination. The input variables are wind speed, which follows a normal distribution with μ = the measured value and σ = 1.5 m / s, and rainfall, which follows a Poisson distribution with λ = 0.2 mm / h. The sampling times are 10,000, and each simulation lasts for 6 hours. Classification boundary determination includes triggering a limit adjustment when the pollutant concentration within the 95% confidence interval exceeds the GB 3838-2002 Class III water standard.

[0049] When the classification result exceeds the classification limit, an enterprise management category adjustment suggestion is triggered.

[0050] Preferably, the emission characteristic vector includes:

[0051] Real-time pollution load intensity, calculated in real time based on pollutant concentration and emission flow;

[0052] The environmental capacity occupancy rate is generated based on the current meteorological conditions and the basin hydrological model. The environmental capacity occupancy rate is calculated as follows:

[0053] ; Among them, U: environmental capacity occupancy rate, n: total number of pollutant types, C i is the real-time concentration of pollutant i, W i is its spatial weight, C cap is the environmental benchmark capacity, f meteo is the meteorological correction factor, ranging from 0.5 to 1.5;

[0054] Process compliance index, calculated by weighting the deviation of process parameters from permitted values;

[0055] The correlation degree of historical violations is quantitatively assessed in combination with the company’s environmental penalty records in the past three years.

[0056] It also includes enterprise management level determination rules, which include grading threshold adjustment and adjustment mechanism. During the grading threshold adjustment period, real-time pollution load intensity PLI grading is carried out, including PLI interval less than 0.3, setting the management level to general management, which can be not regarded as the focus of supervision, and regular spot checks are sufficient; and PLI interval between 0.3-0.7, setting the management level to simplified management, supervision measures should be further strengthened, and the frequency of verification should be more frequent; and PLI interval greater than 0.7, setting the management level to key management, supervision measures are the most stringent, and automatic monitoring equipment and other measures can be used for supervision.

[0057] The adjustment mechanism is: when the regional environmental capacity occupancy rate U is greater than 85%, the PLI threshold of all enterprises will be reduced by 20%.

[0058] Real-time pollution load intensity PLI classification.

[0059] Preferably, step S4 further includes: when the difference between the double verification results exceeds a preset tolerance, triggering the on-site monitoring equipment to start the water quality spectrometer and the exhaust gas online monitor to perform data review, that is, starting the water quality spectrometer with a wavelength range of 200-800nm ​​and a resolution of 1nm to perform instantaneous sampling; the exhaust gas online monitor synchronously collects SO2 and NOx data, performs a Kolmogorov-Smirnov test with the system predicted value, and determines an abnormality when p is less than 0.01; based on the review results, optimize and adjust the enterprise classification level, and generate a differentiated supervision strategy including key monitoring points and verification frequency;

[0060] The control protocol of the water quality spectrometer includes trigger instructions and sampling parameters. The trigger instruction is: when the double verification difference value is greater than 0.25, the sampling instruction is issued through the MQTT protocol;

[0061] Sampling parameters include spectral range, integration time, and data return format. The spectral range is 200-800nm, i.e., UV-visible light; the integration time is automatically adjusted, 100ms-10s; and the data return format is JSON containing wavelength-absorbance key-value pairs.

[0062] Among them, the key monitoring point instruction generation rules include:

[0063] When the process compliance index is less than 0.7 and the historical violation correlation is greater than 0.6, the most stringent regulatory level is triggered;

[0064] Generally managed enterprises, that is, those with a compliance index greater than 0.9, generally adopt remote video verification, with the lowest on-site frequency.

[0065] Preferably, the construction of the knowledge graph includes:

[0066] Extract process flow diagrams and unstructured text descriptions from pollution discharge permit reports;

[0067] Establish the association edges between equipment nodes such as reactors, pipelines, and purification devices and pollutant emissions;

[0068] By training graph weights with historical accident data, high-level management process nodes and their hidden emission paths are identified.

[0069] Preferably, the updating of the spatial influence weight function includes:

[0070] Analyze the atmospheric pollution diffusion trajectory and heat map distribution in satellite remote sensing data. The satellite remote sensing data analysis process includes:

[0071] Input: Aerosol Optical Depth (AOD) data from the Himawari-8 satellite, with a resolution of 1 km;

[0072] Processing: Use dark pixel method to invert PM2.5 spatial distribution and generate pollution diffusion vector field ;

[0073] Calibrate pollutant migration rate parameters based on ground monitoring station data;

[0074] The turbulent diffusion coefficient and sedimentation rate in the fluid mechanics equations are adjusted according to the calibration results.

[0075] Among them, the update of the spatial impact weight function includes: analyzing the PM2.5 / COD concentration gradient distribution in the satellite remote sensing thermal map to identify the main direction of pollution diffusion; adjusting the turbulent diffusion coefficient (K) in the fluid mechanics equation according to the diffusion direction; x , K y ), update formula:

[0076] ; Among them, θ is the angle between the diffusion direction and the dominant wind direction, and θ0 is the terrain correction angle.

[0077] (3) Beneficial effects

[0078] The present invention provides a method for enterprise classification management based on pollutant discharge permit data evaluation indicators. It has the following beneficial effects:

[0079] (1) This enterprise classification management method based on the evaluation indicators of pollutant discharge permit data breaks through the limitations of the traditional static data evaluation system by constructing a coupled dual-channel decision engine and a spatiotemporal adaptive evaluation model. It performs fractal time series compression on monitoring data and declared data and aligns them with process logic to achieve lossless fusion of multi-source heterogeneous data. It highlights key management enterprises through classification levels, concentrates regulatory resources to strengthen management and control, and effectively improves the efficiency of intervention in high-pollution emission behaviors. It introduces an environmental capacity correction mechanism and satellite remote sensing-driven spatial weight updates to quantify the real-time impact of meteorological and hydrological conditions on pollution contribution, thereby reducing the assessment error between dry and wet seasons. The dual verification mechanism ensures that the classification results have both production process rationality and ecological and environmental safety through reverse process simulation and cross-verification of Monte Carlo capacity boundaries, thereby improving the accuracy and timeliness of environmental supervision.

[0080] (2) This enterprise classification management method, based on pollutant emission permit data evaluation indicators, achieves enterprise management assessment and graded response through the intelligent generation of emission characteristic vectors and differentiated regulatory strategies. The integration of satellite remote sensing parameter updates and blockchain evidence storage technology establishes a collaborative supervision network between space, land, and enterprises, improving the supervision efficiency of key managed enterprises and reducing environmental management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0082] Figure 2 Schematic diagram of the system architecture of the enterprise classification management method of the present invention. DETAILED DESCRIPTION

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0084] See also Figure 1 and Figure 2 The present invention provides a technical solution: a method for enterprise classification management based on pollutant discharge permit data evaluation indicators, comprising the following steps:

[0085] S1: Obtain the company's real-time monitoring data stream and pollutant discharge permit implementation report. The real-time monitoring data stream collects pollutant concentration data at an hourly frequency through IoT sensors. The pollutant discharge permit implementation report contains production process parameters and the company's environmental management ledger.

[0086] S2: The real-time monitoring data stream and the pollutant discharge permit execution report are input into a dual-channel decision engine. The spatiotemporal encoder-decoder module performs fractal time series compression on the high-frequency monitoring data stream, extracts the equipment start-stop inflection points and emission trend curvature features, and simultaneously reversely infers the implicit emission nodes in the process chain based on the production log to generate enhanced declaration data. The process feature similarity matrix is ​​used to achieve cross-time scale data fusion and generate emission feature vectors.

[0087] The algorithm flow of fractal time series compression includes time series segmentation, input monitoring data stream {x t}, t=1, 2, ..., T; Output key time window set W={w k |k=1,...,K}; comprising the following steps:

[0088] a: Calculate the Hölder exponent α(t) of the sliding window (length L = 300 seconds):

[0089] ;W: sliding window length, S: standard deviation, R: range; R / S: rescaled range, α(t): Hölder index;

[0090] b: The windows with α(t) greater than 0.75 are marked as candidate windows for device start / stop events.

[0091] Curvature feature extraction includes:

[0092] Perform cubic spline interpolation on the candidate window and calculate the curvature integral:

[0093] ;C(w k ): window w k The curvature integral value, te, ts: the end and start time of the window respectively, : The second derivative of pollutant concentration, : first-order derivative of pollutant concentration;

[0094] Screening C(w k ) greater than 0.15 is regarded as the key process node.

[0095] S3: Input the emission characteristic vector into the environmental capacity responsiveness assessment model, connect to the real-time meteorological grid forecast data and the hydrodynamic model to calculate the regional environmental capacity correction coefficient. Combined with the nonlinear relationship matrix between process parameters and pollutant generation based on the knowledge graph, and the pollutant spatial impact weight function generated by the fluid mechanics equation, the pollution contribution weight of the enterprise is calculated.

[0096] S4: The preliminary classification results are sequentially input into the reverse process simulator and the pollution assimilating capacity calculation model for double verification. The reverse process simulator uses the LSTM neural network to reversely deduce the theoretical values ​​of the production process parameters and conducts deviation analysis with the declared parameters. The pollution assimilating capacity calculation model uses the Monte Carlo method to simulate the environmental capacity threshold.

[0097] S5: Output the double-verified classification results and update the spatial impact weight function parameters every 6 hours based on the atmospheric pollution diffusion trajectory inverted by satellite remote sensing.

[0098] Fractal time series compression includes: performing multi-fractal dimension analysis on high-frequency monitoring data streams to extract key time windows with process relevance; identifying the persistence characteristics of emission trends through wavelet transform and Hurst exponent calculation; and generating a compressed feature vector containing emission fluctuation amplitude, frequency and equipment status correlation weights.

[0099] It should be further explained that, in the specific implementation process, when fractal time series compression is applied to high-frequency monitoring data streams, the data stream is first divided into 5-minute time windows. The multifractal spectrum is generated by calculating the generalized Hurst exponent of each window, and windows with a spectral width greater than 0.25 are selected as key process nodes. Subsequently, a 5-layer decomposition is performed using the Daubechies 4 wavelet basis to extract detail coefficients within the 2- to 8-minute period, and the Hurst exponent is calculated for the approximate coefficients. When the exponent value exceeds 0.75, it is determined to be a persistent emission trend. Then, cubic spline interpolation is performed on the candidate windows, and the curvature characteristics are calculated by integration along the time axis. Windows with a curvature integral value greater than 0.15 are retained. Finally, a compressed feature vector containing the emission fluctuation amplitude, frequency, and equipment status association weight is generated, where the fluctuation amplitude is quantified by the ratio of the range to the mean within the window, the frequency is calculated by zero-crossing rate statistics, and the equipment association weight is assigned based on the matching degree between the process parameters within the window and the equipment operation log.

[0100] The construction of the process feature similarity matrix includes: establishing a multi-dimensional feature mapping relationship between real-time process parameters and declared data; calculating the interpolation weight based on the correlation between the equipment operating status and the raw material injection rate in the production log; matching the process logic consistency of real-time data and historical declared data through the cosine similarity algorithm. The cosine similarity algorithm matching process includes: converting the real-time process parameters, namely temperature, pressure, flow and historical declared data into 128-dimensional feature vectors; calculating the cosine similarity of the real-time vector and the historical vector, with the threshold set to 0.85. If it is lower than the threshold, interpolation enhancement is initiated.

[0101] It should be further explained that, during the specific implementation, when constructing the process feature similarity matrix, the equipment start and stop timestamps and raw material injection rate records in the production log are first parsed to establish a correlation map based on the process chain time series. For periods where reported data is missing, interpolation weights are calculated based on the weighted combination of the equipment operating status coefficient and the pollutant correlation. The equipment operating status coefficient is adjusted according to the deviation between the real-time current fluctuation amplitude and the rated value, and the pollutant correlation is determined by the connection strength between the equipment node and the emission factor in the knowledge graph. Subsequently, the real-time collected temperature, pressure, and flow parameters and historical reported data are converted into a standardized format containing 128-dimensional process feature vectors. The feature dimensions include process parameter statistics, time series change rate, and covariance relationship with equipment status. The matching degree of the real-time vector and the historical vector is calculated using the cosine similarity algorithm. When the similarity falls below the threshold of 0.85, interpolation enhancement based on the process chain time series relationship is initiated, and the reported data of adjacent nodes are weightedly fused according to the proportion of equipment operating time. Finally, enhanced reported data with temporal continuity and process logic consistency are generated, which is aligned with the real-time monitoring data across scales.

[0102] In the environmental capacity response assessment model: meteorological grid forecast data includes temperature, wind speed and atmospheric diffusion coefficient; the hydrodynamic model includes basin runoff and water body self-purification rate; the nonlinear relationship matrix optimizes the transfer function coefficient of reactor temperature and pollutant generation through the stochastic gradient descent algorithm. It should be further explained that, in the specific implementation process, when implementing the environmental capacity responsiveness evaluation model, the 1km×1km grid forecast data released by the Meteorological Bureau is accessed in real time to extract the vertical temperature gradient, 10m height wind speed and atmospheric diffusion coefficient of the grid where the target enterprise is located. At the same time, the Saint-Venant equation in the basin hydrodynamic model is called to calculate the current runoff and water self-purification rate. A nonlinear relationship matrix between process parameters and pollutant generation is constructed based on the knowledge graph, and the transfer function coefficients are optimized by the stochastic gradient descent algorithm. Among them, the relationship function between reactor temperature and VOCs generation adopts a combination of cubic polynomial and exponential terms. The initial coefficient is determined by fitting historical accident data and is adjusted every 24 hours with a batch size of 64 and a learning rate of 0.001. The spatial impact weight function is based on the main direction of pollution diffusion inverted by real-time satellite remote sensing, combined with terrain elevation data to correct the turbulent diffusion coefficient in the fluid mechanics equation. Specifically, the pollution vector field is generated by analyzing the AOD data of the Himawari-8 satellite, and the horizontal diffusion coefficient K in the diffusion equation is updated every 6 hours. x With K y , the modified formula is K x ´=K x·exp(-(θ-θ0) / 30°), where θ is the angle between the remote sensing inversion diffusion direction and the dominant wind direction, and θ0 is the terrain ruggedness correction angle. Finally, the corrected environmental capacity coefficient, process emission transfer matrix, and spatial weight function are coupled through multi-physics fields to output the enterprise pollution contribution weight value. The weight value is accurate to three decimal places and its confidence interval is verified through Monte Carlo simulation.

[0103] The verification process of the reverse process simulator includes: inputting real-time emission data into a pre-trained LSTM neural network to generate theoretical values ​​of production process parameters; calculating the deviation between the theoretical parameters and the parameters declared by the enterprise, and when the deviation exceeds the set threshold, associating the equipment operation log to trace the fault; and generating a verification report for recommended verification measures. It should be further explained that in the specific implementation process, a deviation tracing rule base is set up, including current fluctuation association and raw material addition anomalies. For current fluctuation association, if the theoretical reactor temperature deviation is greater than 8%, the motor current data of the previous 2 hours in the equipment log is retrieved, and the triggering rule is: IFΔI is greater than 15% THEN fault type = motor overload, ΔI>15%: the core judgment threshold for the reverse process simulator to trace the equipment fault, ΔI: the real-time fluctuation amplitude of the motor current, the calculation formula of ΔI: , I real : Real-time monitoring of the motor current value, I rated : Rated working current of the motor; for abnormal raw material feeding, if the deviation of the theoretical feeding acceleration rate is greater than 20%, the material weighing record is associated, and the triggering rule is: IF the standard deviation of the feeding amount is greater than 30% THEN the fault type is equal to the feeding system abnormality.

[0104] It should be further explained that during the specific implementation process, when implementing the reverse process simulator verification, the real-time pollutant concentration and flow rate data were normalized using a 60-minute sliding window and then input into a pre-trained three-layer LSTM neural network. The hidden layer of the network contained 128 neurons with a dropout rate of 0.2. The output layer generated theoretical values ​​for the reactor temperature, stirring rate, and raw material feed rate. The root mean square error between the theoretical values ​​and the declared parameters was calculated. When the temperature deviation exceeded ±8°C or the feed rate deviation exceeded 20%, the current and vibration sensor data from the previous two hours of the equipment operation log were automatically linked to extract abnormal time periods with current fluctuations exceeding 15% or temperature changes exceeding 5°C / min. The knowledge graph was used to match process nodes with the fault type library. If the deviation source was associated with the reactor cooling system, a motor overload warning was triggered. If it was associated with the feeding section, a material weighing record verification instruction was generated. The final output included an abnormality probability value, fault location path, and recommended remediation measures. The abnormality probability was generated by mapping the deviation value through a Sigmoid function. The remediation measures included a graded response plan such as on-site instrument calibration and equipment shutdown and maintenance.

[0105] The capacity threshold simulation of the pollution absorption capacity calculation model includes: generating a probability density function of pollutant concentration distribution based on the current environmental capacity correction coefficient; simulating the classification boundaries under different meteorological conditions by randomly sampling 10,000 times using the Monte Carlo method; and triggering a recommendation for adjustment of the enterprise management category when the classification result exceeds the classification limit.

[0106] It should be further explained that, in the specific implementation process, when implementing the pollution assimilating capacity verification, the probability density function of the pollutant concentration distribution is generated based on the environmental capacity correction coefficient, and the kernel density estimation method is used to perform real-time monitoring data with a bandwidth of 0.5σ·n -1 / 5 A non-parametric fit of σ is used, where σ is the sample standard deviation and n is the number of data points. When simulating using the Monte Carlo method, the wind speed is assumed to follow a normal distribution with the measured mean and a standard deviation of 1.5 m / s, and the rainfall follows a Poisson distribution with λ equal to 0.2 mm / h. Random sampling is performed 10,000 times to generate a pollutant diffusion scenario with a duration of 6 hours. The three-dimensional advection-diffusion equation is used to calculate the pollutant migration trajectory in each simulation. When the concentration at the downstream section within the 95% confidence interval exceeds 1.2 times the limit of the Class III water quality standard of GB 3838-2002, the classification boundary is determined to have been exceeded. After triggering the management category adjustment suggestion, the system automatically generates a control plan including the maximum allowable emission, emission reduction ratio and implementation time window, and synchronizes the new limit to the enterprise's pollution monitoring terminal through the blockchain smart contract. At the same time, a collaborative production restriction assessment model for related enterprises in the basin is activated to ensure that the total regional emission does not exceed the pollution absorption capacity threshold.

[0107] The emission characteristic vector includes real-time pollution load intensity, environmental capacity occupancy rate, process compliance index and historical violation correlation degree; among them, real-time pollution load intensity is calculated in real time based on pollutant concentration and emission flow, environmental capacity occupancy rate is generated based on current meteorological conditions and river basin hydrological model, process compliance index is calculated by weighted deviation between process parameters and permitted values, and historical violation correlation degree is quantitatively evaluated based on the company's environmental penalty records in the past three years.

[0108] It should be further explained that, in the specific implementation process, when generating the emission characteristic vector, the real-time pollution load intensity is calculated by multiplying the instantaneous value of the pollutant concentration by the emission flow rate, where the flow data is calibrated by the ultrasonic flow meter and the invalid value during the equipment downtime is deducted; the environmental capacity occupancy rate is based on the atmospheric stability level in the meteorological grid data and the self-purification rate output by the basin hydrodynamic model, and the current emission volume is converted into a percentage of the regional capacity according to the pollutant diffusion half-life; the process compliance index uses the improved Euclidean distance algorithm to calculate the multi-dimensional deviation between the real-time process parameters and the emission permit value, and the weight of each dimension is determined by the known The enterprise management level of the process node in the recognition map is determined, and the weight of the high-level management node is increased by 30%; the historical violation correlation is analyzed by natural language processing technology to analyze the enterprise's penalty records in the past three years, extract characteristics such as violation type, frequency, and rectification effect, and use a logistic regression model to output a 0-1 standard value, among which the weight coefficient of violation records involving heavy metal emissions is doubled; finally, the four types of indicators are Min-Max standardized and fused at a weight ratio of 0.4:0.3:0.2:0.1 to generate an emission feature vector with a timestamp, which is updated every 5 minutes and uses hash verification to ensure data integrity.

[0109] Step S4 also includes: when the difference between the double verification results exceeds the preset tolerance, triggering the on-site monitoring equipment to start the water quality spectrometer and the exhaust gas online monitor to conduct data review; adjusting the enterprise classification level according to the review results, and generating a differentiated supervision strategy including key monitoring points and verification frequency.

[0110] It should be further explained that, in the specific implementation process, when the double verification difference tolerance mechanism is triggered, when the comprehensive difference value between the theoretical parameter deviation output by the reverse process simulator and the pollution holding capacity verification result exceeds the 0.25 threshold, a spectrum scanning instruction is sent to the water quality spectrometer at the target enterprise end through the MQTT protocol, and the wavelength range is set to 200-800nm, the integration time automatically adapts to the light intensity of 100ms-10s, and the sampling mode of the exhaust gas online monitor is started synchronously to capture SO2 and NO in real time. xThe concentration pulse peak value is measured; a Kolmogorov-Smirnov distribution consistency test is performed on the on-site review data and the system prediction value. When the test statistic D is greater than 0.35 and the p-value is less than 0.01, it is determined to be a substantial data anomaly, and the blockchain evidence storage module is immediately called to record the original spectral data fingerprint; the enterprise classification level is adjusted according to the anomaly confidence level. If the confidence level is greater than 90%, the management level is immediately upgraded to senior management and an on-site inspection plan is generated including three key monitoring points, namely 50m upstream of the sewage outlet, the reactor exhaust port, and the hazardous waste temporary storage area. The inspection frequency is set to 2 full-index tests per day; at the same time, the basin collaborative supervision model is activated, and preliminary production restriction instructions are issued to related enterprises in the same basin whose process similarity is greater than 0.8 until the prediction error of the review data after retraining with the LSTM-Attention hybrid model returns to the threshold range.

[0111] The construction of the knowledge graph includes: extracting process flow charts and unstructured text descriptions from pollution discharge permits and execution reports; establishing the association edges between equipment nodes such as reactors, pipelines, and purification devices and pollutant emissions; and training graph weights through historical accident data to identify high-level management process nodes and their implicit emission paths. It should be further explained that in the specific implementation process, when constructing the knowledge graph, the process flow chart in the enterprise's pollution discharge permit report is first analyzed through OCR technology to identify the topological connection relationship between equipment nodes such as reactors, pipelines, and purification devices. At the same time, the BERT model is used to extract process parameter constraints and hidden danger descriptions in unstructured text; when establishing the association edges between equipment nodes and pollutant emissions, the pollution contribution factors of each equipment are calculated based on historical monitoring data. Among them, the initial weights of reactors and VOCs emissions are set to 0.65, and the weights of pipeline leakage and COD emissions are set to 0.23. The contribution factors are then analyzed through graph neural networks. line correction; when using the environmental accident database of the past five years to train the graph, a weight coefficient of 0.3 is added to the nodes involving high temperature and high pressure processes, and the GraphSAGE algorithm is used to iteratively optimize the edge weights. 500 sets of accident scene data are batch sampled each time for training, and the loss function contains the dual constraints of node classification error and edge weight prediction error; the final generated visual knowledge graph contains a three-dimensional association network of equipment status-emission intensity-environment, and the implicit emission path is detected by the random walk algorithm. When the cumulative value of the process path exceeds the threshold, it is automatically marked as a red warning node, and combined with the LIME interpretability model to output a contribution analysis report of the key factors.

[0112] The updating of the spatial impact weight function includes: analyzing the atmospheric pollution diffusion trajectory and heat map distribution in satellite remote sensing data, calibrating the pollutant migration rate parameters in combination with ground monitoring station data, and adjusting the turbulent diffusion coefficient and sedimentation rate in the fluid mechanics equation according to the calibration results. It should be further explained that in the specific implementation process, when updating the spatial impact weight function, the aerosol optical depth data of the Himawari-8 satellite is analyzed, and the PM2.5 concentration gradient distribution is inverted using the dark pixel method to generate a 5km×5km pollution diffusion heat map centered on the enterprise; combined with the hourly average data of the ground monitoring station, the pollutant migration rate parameters are calibrated through linear regression, and the calibration formula is V cal =0.87V sat +0.13V ground , where V sat is the satellite retrieval rate, V ground is the ground monitoring value; based on the calibrated diffusion direction and terrain elevation data, the turbulent diffusion coefficient K in the fluid mechanics equation is corrected x and sedimentation rate V s , where the horizontal diffusion coefficient is: K x ´=K x ·exp(-0.015·|θ-θ0|), where θ is the real-time angle between the satellite-retrieved diffusion direction and the prevailing wind direction, and θ0 is the terrain roughness correction angle calculated based on the digital elevation model (DEM). The deposition rate is calculated based on the PM2.5 particle size distribution data using the Stokes-Cunningham formula: , where V s : particle settling rate, g: acceleration of gravity, μ: air dynamic viscosity, d p is the median diameter of the particles, ρ p is the density, C c is the slipstream correction coefficient; the updated parameters are synchronized to the three-dimensional diffusion model every 6 hours to generate a spatial impact weight function surface, and the goodness of fit between the weight distribution and the measured data is verified through CUDA parallel computing, with R² greater than 0.92. The final output is the adjusted pollution contribution spatial weight value, accurate to four decimal places.

[0113] By constructing a coupled dual-channel decision engine and a spatiotemporal adaptive evaluation model, we break through the limitations of the traditional static data evaluation system, perform fractal time series compression on monitoring data and declared data, and align them with process logic to achieve lossless fusion of multi-source heterogeneous data. Through classification results, we can clarify the list of key regulated enterprises, implement high-frequency inspections and dynamic limit control on high-polluting enterprises, and significantly improve the targeting of regulatory measures. We introduce an environmental capacity correction mechanism and satellite remote sensing-driven spatial weight updates to quantify the real-time impact of meteorological and hydrological conditions on pollution contributions, thereby reducing the assessment errors during the dry and wet seasons. The dual verification mechanism ensures that the classification results have both production process rationality and ecological environmental safety through reverse process simulation and cross-verification of Monte Carlo capacity boundaries, thereby improving the accuracy and timeliness of environmental supervision.

[0114] Through the intelligent generation of emission signature vectors and differentiated regulatory strategies, enterprise management assessment and tiered response are achieved. The integration of satellite remote sensing parameter updates and blockchain evidence storage technology has established a collaborative regulatory network between space, land, and enterprises, improving the efficiency of key regulatory enterprises and reducing environmental management costs.

[0115] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0116] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for enterprise classification management based on pollutant discharge permit data evaluation indicators, characterized in that: The steps include: S1: Obtain the company's real-time monitoring data stream and pollutant discharge permit execution report. The real-time monitoring data stream collects pollutant concentration data at an hourly frequency through IoT sensors. The pollutant discharge permit execution report contains production process parameters and the company's environmental management ledger. S2: The real-time monitoring data stream and the pollutant discharge permit execution report are input into a dual-channel decision engine. The high-frequency monitoring data stream is subjected to fractal time series compression through a spatiotemporal encoder-decoder module to extract the equipment start-stop inflection points and emission trend curvature features. At the same time, the implicit emission nodes in the process chain are reversely deduced based on the production log to generate enhanced declaration data. The process feature similarity matrix is ​​used to achieve data fusion across time scales to generate an emission feature vector. The emission feature vector includes: Real-time pollution load intensity, calculated in real time based on pollutant concentration and emission flow; Environmental capacity occupancy rate, generated based on current meteorological conditions and the basin hydrological model; Process compliance index, calculated by weighting the deviation of process parameters from permitted values; The correlation degree of historical violations is quantitatively assessed based on the company's environmental penalty records over the past three years; S3: Input the emission characteristic vector into the environmental capacity responsive evaluation model, access the meteorological grid forecast data and the hydrodynamic model in real time to calculate the regional environmental capacity correction coefficient, and combine the nonlinear relationship matrix between process parameters and pollutant generation based on the knowledge graph, and the pollutant spatial impact weight function generated according to the fluid mechanics equation to calculate the pollution contribution weight of the enterprise; S4: The preliminary classification results are sequentially input into a reverse process simulator and a pollution holding capacity calculation model for double verification. The reverse process simulator uses an LSTM neural network to reversely deduce the theoretical values ​​of the production process parameters and performs deviation analysis with the declared parameters. The pollution holding capacity calculation model uses a Monte Carlo method to simulate the environmental capacity threshold; S5: Output the double-verified classification results, and update the spatial impact weight function parameters according to the preset update cycle based on the atmospheric pollution diffusion trajectory inverted by satellite remote sensing.

2. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: The fractal time series compression includes: Perform multi-fractal dimension analysis on high-frequency monitoring data streams to extract key time windows with process relevance; Identify the persistence characteristics of emission trends through wavelet transform and Hurst exponent calculation; Generate a compressed feature vector containing the emission fluctuation amplitude, frequency and equipment status associated weights.

3. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: The construction of the process feature similarity matrix includes: Establish a multi-dimensional feature mapping relationship between real-time process parameters and declared data; Calculate interpolation weights based on the correlation between equipment operating status and raw material injection rate in the production log; The process logic consistency of real-time data and historical declaration data is matched through the cosine similarity algorithm.

4. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: In the environmental capacity responsiveness evaluation model: The meteorological grid forecast data includes temperature, wind speed and atmospheric diffusion coefficient, and the hydrodynamic model includes basin runoff and water body self-purification rate; The nonlinear relationship matrix optimizes the transfer function coefficients of the reactor temperature and the pollutant generation amount through a stochastic gradient descent algorithm.

5. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: The verification process of the reverse process simulator includes: inputting real-time emission data into a pre-trained LSTM neural network to generate theoretical values ​​of production process parameters; calculating the deviation between the theoretical parameters and the parameters declared by the enterprise, and when the deviation exceeds the set threshold, associating the equipment operation log to trace the fault and generate a verification report with recommended verification measures.

6. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: The capacity threshold simulation of the pollution absorption capacity calculation model includes: generating a pollutant concentration distribution probability density function based on the current environmental capacity correction coefficient, and simulating the classification boundaries under different meteorological conditions through random sampling using the Monte Carlo method, where the classification boundaries are management thresholds; when the classification results exceed the classification boundaries, a suggestion for adjusting the enterprise management category is triggered.

7. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: Step S4 also includes: when the difference between the double verification results exceeds the preset tolerance, triggering the on-site monitoring equipment to start the water quality spectrometer and the exhaust gas online monitor to conduct data review; adjusting the enterprise classification level according to the review results, and generating a differentiated supervision strategy including key monitoring points and verification frequency.

8. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: The construction of the knowledge graph includes: Extract process flow diagrams and unstructured text descriptions from discharge permits and implementation reports; Establish the association edges between the nodes of reactors, pipelines, and purification devices and the pollutant emissions; By training graph weights with historical accident data, high-level management process nodes and their hidden emission paths are identified.

9. The enterprise classification management method based on pollutant discharge permit data evaluation indicators according to claim 1 is characterized by: The updating of the spatial influence weight function includes: Analyze the atmospheric pollution diffusion trajectory and heat map distribution in satellite remote sensing data; Calibrate pollutant migration rate parameters based on ground monitoring station data; The turbulent diffusion coefficient and sedimentation rate in the fluid mechanics equations are adjusted according to the calibration results.

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