Cooperative management system and method for chemical treatment analysis and environmental protection data

Abstract

The invention relates to the technical field of chemical environmental protection management, and discloses a collaborative management system and method for chemical treatment analysis and environmental protection data. The system comprises a pollution source monitoring module for acquiring a historical environmental protection monitoring record set; the volatility analysis module is used for extracting dynamic fluctuation characteristics and generating various fluctuation factors; the monitoring point optimization module is used for determining a key monitoring point position and a sampling frequency based on the fluctuation factor; the regulation and control strategy generation module is used for outputting a real-time regulation and control instruction and a long-term treatment scheme in combination with key monitoring point information and a knowledge base; the dynamic execution module drives a sensor to monitor and issue an instruction, and generates reduction effect feedback data; and the collaborative optimization module fuses the feedback data and the strategy weight of the long-term scheme update knowledge base and generates an optimization instruction. According to the system, collaborative management of environmental protection data and dynamic optimization of treatment strategies are realized, and the collaboration and effectiveness of chemical environmental protection management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical environmental management, and in particular to a collaborative management system and method for chemical governance analysis and environmental data. Background Art

[0002] The chemical industry generates large amounts of waste gas, wastewater, and solid waste during production. If these pollutants are not effectively controlled, they will have a serious impact on the ecological environment. Currently, chemical companies' environmental management often relies on decentralized monitoring methods. Monitoring data for different pollutants is often collected and processed by independent systems, lacking effective coordination and integration. This decentralized management model makes data sharing difficult, making it difficult to comprehensively analyze pollutant emission patterns and interrelationships. For example, changes in exhaust gas emission concentrations may be linked to fluctuations in wastewater composition, but due to data separation, this potential relationship cannot be discovered in a timely manner, thus affecting the accuracy of environmental protection management strategies. Furthermore, the establishment of monitoring points is often based on empirical judgment and lacks a scientific dynamic optimization mechanism. When pollutant emissions change, monitoring locations and sampling frequencies cannot be adjusted in a timely manner, which may cause lags or redundancies in monitoring data and reduce the efficiency of environmental protection management. Existing environmental governance strategies often rely on static knowledge bases, making it difficult to dynamically update and optimize them based on real-time monitoring data and feedback on governance effectiveness. When new pollutant emission patterns emerge or governance measures prove ineffective, strategies cannot be quickly adjusted, resulting in insufficiently targeted and effective environmental governance, making it difficult to meet increasingly stringent environmental regulations and the demands of corporate sustainability. Summary of the Invention

[0003] The purpose of the present invention is to provide a collaborative management system and method for chemical remediation analysis and environmental protection data to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides a collaborative management system for chemical engineering management analysis and environmental protection data, the system comprising: The pollution source monitoring module obtains a set of historical environmental monitoring records including a time series dataset of waste gas emission concentration, a dataset of wastewater composition spectrum, and a dataset of solid waste generation distribution; The volatility analysis module traverses the historical environmental monitoring record set to extract dynamic fluctuation characteristics, calculates the discrete degree index of each data set through a moving time window, and generates the exhaust gas concentration fluctuation factor, the wastewater composition fluctuation factor, and the solid waste generation fluctuation factor; A monitoring point optimization module, which performs an adaptive optimization operation in a preset pollution feature space based on the exhaust gas concentration fluctuation factor, the wastewater composition fluctuation factor, and the solid waste generation fluctuation factor to determine the key pollutant monitoring point location set and corresponding sampling frequency parameters; The control strategy generation module takes the key pollutant monitoring point location set as input, loads the environmental protection management strategy knowledge base to perform multi-level strategy matching, and outputs a set of real-time control instructions and a set of long-term management solutions; A dynamic execution module drives the multi-source environmental protection sensor network to perform monitoring tasks according to the sampling frequency parameters, and simultaneously sends the real-time control instruction set to the chemical production equipment control system to generate a pollutant reduction effect feedback data set; The collaborative optimization module integrates the pollutant reduction effect feedback data set with the long-term governance plan set to perform strategy iteration analysis, update the strategy weight distribution in the environmental governance strategy knowledge base, and generate strategy optimization instructions.

[0005] Preferably, when the volatility analysis module extracts dynamic volatility features: Setting a moving time window of variable length and continuously sliding the window on the exhaust gas emission concentration time series data set; Calculate the standard deviation of the data points in each moving time window, and perform weighted average processing on the standard deviation values ​​of three consecutive windows to obtain the local fluctuation intensity index; Extracting peak sequences of all local fluctuation intensity indicators, performing normal distribution fitting processing on the peak sequences, and determining a fluctuation intensity probability density function; The expected value and variance product of the fluctuation intensity probability density function are used as the exhaust gas concentration fluctuation factor; The same operation was used to process the wastewater composition spectrum dataset and the solid waste generation distribution dataset to obtain the wastewater composition fluctuation factor and the solid waste generation fluctuation factor, respectively.

[0006] Preferably, when the monitoring point optimization module performs the adaptive optimization operation: Pre-construct a three-dimensional pollution feature space, the coordinate axes of which respectively map the exhaust gas diffusion characteristic surface, the wastewater migration characteristic curve and the solid waste accumulation characteristic field; Inputting the exhaust gas concentration fluctuation factor into the exhaust gas diffusion characteristic surface to obtain a corresponding diffusion sensitive area coordinate set; Projecting the wastewater component fluctuation factor onto the wastewater migration characteristic curve to identify the endpoint coordinates of the high migration risk section; Based on the solid waste generation fluctuation factor, density cluster analysis is performed on the solid waste accumulation characteristic field to generate the coordinates of the solid waste accumulation core points; Perform spatial superposition operations on the coordinate sets of diffusion-sensitive areas, the endpoint coordinates of high migration risk sections, and the coordinates of solid waste accumulation core points to determine the coordinate position of the center of gravity of the overlapping areas; A polygonal monitoring area is generated with the center coordinate position as the center point according to a preset radiation radius, and the vertex coordinates of the polygonal monitoring area are used as the key pollutant monitoring point position set.

[0007] Preferably, when the control strategy generation module performs multi-level strategy matching: Loading a tree-structured environmental governance strategy knowledge base, which contains a branch-level governance technology type index and a leaf-level strategy parameter combination; Map the key pollutant monitoring point location set to the branch level, and activate all leaf nodes under the corresponding control technology type index; Extract the historical execution effect records of the strategy parameter combination at the blade level, and calculate the matching value of each strategy parameter combination with the current exhaust gas concentration fluctuation factor and wastewater composition fluctuation factor; Select the strategy parameter combination with the highest matching value as the core strategy unit; Traverse the policy parameter combinations in the remaining leaf nodes that are parameter-associated with the core policy unit to form a policy association cluster; The core policy unit and the policy association cluster are integrated with each other to generate a set of real-time control instructions and a set of long-term governance solutions.

[0008] Preferably, when the dynamic execution module performs the monitoring task: Generate a sensor trigger pulse sequence according to the sampling frequency parameter to drive the multi-source environmental protection sensor network to perform synchronous sampling at the key pollutant monitoring point location set; Perform outlier filtering on the real-time pollutant concentration data obtained through sampling to form a standardized monitoring data stream; Input the standardized monitoring data stream into the pollutant diffusion model to simulate the instantaneous impact range and output the diffusion impact boundary coordinates; Compare the diffusion impact boundary coordinates with the preset environmentally sensitive area coordinates, and generate highly responsive control instructions when coordinate overlap is detected; High-responsiveness control instructions are preferentially written into the real-time control instruction set and sent to the chemical production equipment actuators through the industrial control bus.

[0009] Preferably, when the collaborative optimization module performs strategy iteration analysis: Establish a strategy effect evaluation matrix, where the row dimension of the matrix corresponds to the strategy number in the long-term governance plan set, and the column dimension includes the pollutant reduction rate, implementation cost coefficient and environmental improvement index; The pollutant reduction effect feedback dataset is divided into multiple data blocks according to the time window, and each data block is filled into the corresponding column of the strategy effect evaluation matrix; Calculate the Euclidean distance value of each row vector in the strategy effect evaluation matrix, and select the three row vectors with the smallest Euclidean distance value as high-quality strategy seeds; Extract the strategy parameter combinations corresponding to high-quality strategy seeds and perform crossover and mutation operations to generate a new strategy parameter population; Write the new policy parameter population into the temporary storage layer of the environmental governance policy knowledge base and assign initial weight values.

[0010] Preferably, the system further comprises: An assessment and early warning module constructs a dynamic assessment framework including an environmental capacity threshold matrix and a pollutant diffusion model, calculates a regional environmental carrying index based on the historical environmental monitoring record set and the pollutant reduction effect feedback data set, and triggers a graded early warning signal when it is identified that the regional environmental carrying index exceeds the preset risk boundary; When the evaluation and warning module constructs a dynamic evaluation framework: Divide the environmental capacity threshold matrix into multiple geographic grid cells, each grid cell contains the atmospheric capacity threshold, water capacity threshold and soil capacity threshold; Perform spatial interpolation calculations on the real-time monitoring data of key pollutant monitoring point locations according to pollutant type to generate a gridded pollutant distribution map; Calculate the pollutant load percentage of each grid cell in the gridded pollutant distribution map; Superimpose the pollutant load ratio value and the environmental capacity threshold of the corresponding grid unit to obtain the local overload coefficient; Spatial autocorrelation analysis was performed on all local overload coefficients to identify the coordinates of highly clustered overload areas.

[0011] Preferably, when the evaluation and warning module triggers a graded warning signal: Establish a tiered warning threshold system including blue warning threshold, yellow warning threshold and red warning threshold; When the local overload coefficient reaches the blue warning threshold for the first time, the device-level control instruction review mechanism is activated; When the area of ​​the high-aggregate overload zone reaches the yellow alert threshold, the plant-level production load adjustment protocol will be initiated; When a red alert threshold violation event is identified for three consecutive time windows, a regional-level production suspension and emission reduction order is triggered.

[0012] Preferably, the system further includes a governance effect visualization module, which specifically performs: Construct a dynamic three-dimensional geographic information model that includes pollutant migration paths; Overlaying a real-time data stream of key pollutant monitoring point locations on a dynamic three-dimensional geographic information model; Use thermal maps to present the distribution of regional environmental carrying index calculated by the assessment and early warning module; Real-time control of the execution path of instruction sets through dynamic streamline annotation; When a graded warning signal is received, a pulse warning halo is generated at the corresponding geographic coordinate location.

[0013] Preferably, the present invention further includes a method for collaborative management of chemical remediation analysis and environmental protection data, which is applied to the collaborative management system of chemical remediation analysis and environmental protection data as described above, and the method comprises: A multi-source environmental sensor network is used to collect historical environmental monitoring records from chemical production areas, including a time series dataset of waste gas emission concentrations, a dataset of wastewater composition spectra, and a dataset of solid waste generation distribution. The moving time window algorithm is used to process the historical environmental monitoring record set to generate the fluctuating factors of exhaust gas concentration, wastewater composition and solid waste generation. Perform spatial superposition operations in the three-dimensional pollution feature space to determine the location set of key pollutant monitoring points based on the fluctuating factors of exhaust gas concentration, wastewater composition, and solid waste generation. Matching the real-time control instruction set and the long-term control plan set associated with the key pollutant monitoring point location set from the environmental protection control strategy knowledge base; Execute real-time data collection of key pollutant monitoring point locations according to preset sampling frequency parameters, and synchronously drive chemical production equipment to execute real-time control instruction sets; Integrate pollutant reduction effect feedback data with a collection of long-term governance plans to update the weight distribution of the environmental governance strategy knowledge base; Calculate the regional environmental carrying index based on the environmental capacity threshold matrix and pollutant diffusion model; When the regional environmental carrying index exceeds the preset risk boundary, a graded warning signal of the corresponding level is triggered according to the stepped warning threshold system.

[0014] Compared with the prior art, the present invention has the following beneficial effects: Through the organic coordination of multiple modules, integrated and coordinated environmental monitoring and governance are achieved. The pollution source monitoring module comprehensively collects historical monitoring data for various pollutants, providing complete basic information for subsequent analysis and avoiding the limitations caused by data dispersion. The volatility analysis module uses a dynamic fluctuation feature extraction method to calculate the degree of dispersion index through a moving time window. It can accurately capture the dynamic changes in exhaust gas, wastewater, and solid waste emissions, reveal the fluctuation characteristics of pollutant emissions, and provide managers with a clearer understanding of pollutant trends. The monitoring point optimization module uses fluctuation factors to adaptively optimize within the pollution feature space, ensuring that the locations and sampling frequencies of key pollutant monitoring points are more consistent with actual pollution conditions. This ensures that monitoring data accurately reflects the true state of pollutant emissions, reduces data bias caused by inappropriate monitoring point settings, and improves the scientific nature and effectiveness of monitoring. The control strategy generation module combines key monitoring point information with the environmental governance strategy knowledge base for multi-level matching. The resulting real-time control instructions and long-term governance solutions are more targeted, enabling the implementation of appropriate governance measures based on different pollution conditions, achieving a precise connection between governance strategies and actual pollution levels. The dynamic execution module drives the sensor network to monitor and issue real-time control instructions based on the sampling frequency, while also collecting feedback data on pollutant reduction effectiveness. This creates a closed-loop process from monitoring to treatment and feedback, ensuring the timely implementation of treatment measures and real-time tracking of their effectiveness. The collaborative optimization module integrates feedback data with long-term plans to conduct iterative strategy analysis and update the strategy weight distribution within the knowledge base. This allows the environmental governance strategy knowledge base to continuously adapt to new pollution characteristics and governance needs, allowing governance strategies to be continuously refined in practice. This enhances the system's ability to cope with complex pollution situations and promotes the overall improvement of chemical environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a working principle diagram of the collaborative management system for chemical engineering treatment analysis and environmental protection data according to the present invention; Figure 2 Flowchart of dynamic fluctuation feature extraction for volatility analysis module; Figure 3 Flowchart of multi-level strategy matching for control strategy generation module; Figure 4 Flowchart of the iterative analysis of collaborative optimization module strategies. DETAILED DESCRIPTION

[0016] 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.

[0017] See also Figure 1 The present invention provides a collaborative management system for chemical engineering management analysis and environmental protection data, the system comprising: A multi-source environmental sensor network collects historical environmental monitoring records from the chemical production area, including a time-series dataset of waste gas emission concentrations, a dataset of wastewater composition profiles, and a dataset of solid waste generation distribution. After the pollution source monitoring module acquires this data, the volatility analysis module traverses the historical environmental monitoring records and uses a moving time window algorithm to calculate the dispersion index of each dataset, generating fluctuation factors for waste gas concentration, wastewater composition, and solid waste generation. Based on these fluctuation factors, the monitoring point optimization module performs an adaptive optimization operation within a pre-set three-dimensional pollution feature space to determine the locations of key pollutant monitoring points and their corresponding sampling frequency parameters. The control strategy generation module uses the key pollutant monitoring point locations as input, loads the environmental governance policy knowledge base, performs multi-level policy matching, and outputs a set of real-time control instructions and a set of long-term governance solutions. The dynamic execution module drives the multi-source environmental sensor network to execute monitoring tasks based on the sampling frequency parameters. It also sends the real-time control instructions to the chemical production equipment control system, generating a dataset of pollutant reduction feedback. The collaborative optimization module integrates the pollutant reduction feedback dataset with the set of long-term governance solutions to perform policy iteration analysis, update the policy weight distribution within the environmental governance policy knowledge base, and generate policy optimization instructions.

[0018] Example 1: See Figure 2 The volatility analysis module uses a variable-length moving time window algorithm to extract dynamic fluctuation features from a set of historical environmental monitoring records. The window slides continuously on the exhaust gas emission concentration time series data set, and each slide covers data points within a certain time range. In each window, the standard deviation of the data points is calculated to reflect the degree of dispersion of the exhaust gas concentration within that time period. The standard deviation values ​​of three consecutive windows are weighted averaged, and the weights are dynamically adjusted according to the number of data points in the window and the time span to generate a local fluctuation intensity index. This indicator can capture the short-term fluctuation characteristics of exhaust gas emission concentrations and avoid the accidental deviations that may be caused by single-window calculations.

[0019] After extracting the peak sequences of all local fluctuation intensity indicators, the normal distribution fitting method is used to analyze the peak distribution pattern. The parameters of the fluctuation intensity probability density function, including the expected value and variance, are determined by maximum likelihood estimation. The exhaust gas concentration fluctuation factor is calculated by multiplying the expected value and variance of the probability density function. This factor comprehensively reflects the overall fluctuation amplitude and frequency characteristics of the exhaust gas emission concentration. The wastewater composition spectrum dataset uses the same processing flow to calculate the wastewater composition fluctuation factor. For the solid waste generation distribution dataset, due to different data characteristics, a time weighting coefficient is additionally introduced in the moving window calculation to reflect the changing trend of solid waste generation in different time periods.

[0020] Based on these fluctuation factors, the monitoring point optimization module performs an adaptive optimization operation within a three-dimensional pollution feature space. The three coordinate axes of this space correspond to the exhaust gas diffusion characteristic surface, the wastewater migration characteristic curve, and the solid waste accumulation characteristic field. The exhaust gas diffusion characteristic surface is modeled using historical meteorological data and topographic features to reflect the diffusion patterns of exhaust gases under different environmental conditions. After the exhaust gas concentration fluctuation factor is input into this surface, the system identifies diffusion-sensitive areas—geographic locations with large fluctuations in exhaust gas concentration and a wide diffusion range—and generates a coordinate set for these diffusion-sensitive areas.

[0021] The wastewater migration characteristic curve is constructed based on hydrogeological data and simulates the migration paths of wastewater components in soil and groundwater. After the wastewater composition fluctuation factor is projected onto this curve, the system analyzes high-migration risk sections, that is, areas where wastewater composition fluctuates significantly and may affect the downstream environment, and determines their endpoint coordinates. The solid waste accumulation characteristic field is generated using a spatial interpolation algorithm, reflecting the distribution of solid waste accumulation density within the plant area. After the solid waste generation fluctuation factor is input into this characteristic field, a density clustering algorithm is used to identify the core points of solid waste accumulation, that is, the coordinates of areas with large fluctuations in waste generation and high accumulation density.

[0022] The system performs spatial overlay operations on the coordinate sets of diffusion-sensitive areas, the endpoints of high-migration-risk sections, and the core points of solid waste accumulation. By calculating the overlapping areas of each coordinate set, its center of gravity is determined. With this center of gravity as the center, a polygonal monitoring area is generated with a preset radius. This radius is dynamically adjusted based on the diffusion capacity of the pollutants and environmental sensitivity to ensure that the monitoring range covers major pollution risk points. The vertex coordinates of the polygonal monitoring area constitute the location set of key pollutant monitoring points. The system also assigns sampling frequency parameters based on the fluctuation factor of the area where each monitoring point is located.

[0023] In the process of modeling the exhaust gas diffusion characteristic surface, the system integrates historical meteorological data, including wind speed, wind direction, temperature gradient, and other information, to construct a multi-dimensional diffusion model. The identification of diffusion-sensitive areas not only considers concentration fluctuation factors, but also combines terrain elevation data and surface roughness to correct the predicted results of the diffusion path. The construction of the wastewater migration characteristic curve introduces hydrogeological parameters such as soil permeability and groundwater flow direction to improve the accuracy of the migration path simulation. The density clustering analysis of the solid waste accumulation characteristic field uses an adaptive bandwidth algorithm to dynamically adjust the cluster radius according to the data distribution characteristics to ensure the reliability of core point identification.

[0024] Example 2: See Figure 3, the control strategy generation module loads a tree-structured environmental protection strategy knowledge base. The branch level of the knowledge base is indexed according to the type of treatment technology, including major categories such as physical treatment, chemical treatment, and biological treatment. Each major category is further subdivided into specific technical branches. The blade level stores strategy parameter combinations. Each combination contains adjustable variables such as equipment operating parameters, agent addition ratio, reaction time, and is accompanied by historical execution effect records. After the location set of key pollutant monitoring points is input into the system, it is associated with the branch level of the knowledge base through a spatial mapping algorithm to activate all leaf nodes under the corresponding treatment technology type. The system extracts the strategy parameter combination of each leaf node and calculates its matching value with the current exhaust gas concentration fluctuation factor and wastewater composition fluctuation factor. The matching degree is calculated using the following formula:

[0025] in, Indicates the The matching value of the strategy parameter combination, is the total number of volatility factor types, For the The weight coefficient of the volatility factor, For the The first recorded in the strategy parameter combination Historical reference values ​​of volatility factors, The first Actual value of the volatility factor. Weight coefficient It is determined dynamically through the entropy method, and weights are assigned according to the information entropy of each fluctuation factor in historical data. The lower the entropy value, the higher the weight of the factor.

[0026] The policy parameter combination with the highest matching value is determined as the core policy unit. The system traverses the remaining blade nodes and screens combinations that have parameter associations with the core policy unit, including strategies that share the same equipment control parameters or use similar drug formulations. These combinations constitute a policy association cluster, and its parameters are fused with the core policy unit. The fusion process uses a weighted superposition method. The parameters of each strategy in the association cluster are weighted according to their matching values, and finally a real-time control instruction set and a long-term governance solution set are generated. The real-time control instruction set contains specific equipment control commands, such as fan speed adjustment, dosing pump start and stop timing, etc.; the long-term governance solution set involves macro strategies such as process route optimization and equipment upgrade recommendations.

[0027] The dynamic execution module generates a sensor trigger pulse sequence based on the sampling frequency parameters. The time interval of the pulse sequence is dynamically adjusted by the pollution index of each point in the monitoring point location set. The higher the index, the shorter the pulse interval. After receiving the pulse signal, the multi-source environmental protection sensor network performs synchronous sampling at the key pollutant monitoring point location set. The sampled data is aggregated to the central processing unit through a wireless transmission protocol and is processed by outlier filtering. The outlier filtering uses the sliding interquartile range method to calculate the upper quartile of the data stream in real time. and the lower quartile , remove the Data points outside the range, where The filtered data forms a standardized monitoring data stream, with its timestamp and spatial coordinate information preserved intact.

[0028] Standardized monitoring data streams are fed into the pollutant diffusion model to simulate the instantaneous impact range. This model couples computational fluid dynamics with material transport equations, using monitoring data as boundary conditions to solve for the distribution of pollutant concentrations in three-dimensional space. The simulation outputs the coordinates of the diffusion impact boundary, which is the outline of the spatial range where the concentration exceeds the threshold. The system analyzes the spatial relationship between the boundary coordinates and the coordinates of preset environmentally sensitive areas, using the ray method to determine coordinate overlap. When an overlapping area is identified, a highly responsive control instruction is generated. Instruction priority is determined based on the proportion of overlapping area and the toxicity level of the pollutant, with the highest-priority instruction being written to the head of the real-time control instruction set.

[0029] The industrial control bus uses a time-triggered protocol to transmit real-time control command sets. Commands are prioritized and encapsulated into data frames. Each frame header contains the execution timestamp and target device address. Upon receiving the data frames, the actuators of the chemical production equipment parse the specific control parameters and immediately implement them. Equipment status feedback signals are transmitted back to the dynamic execution module via the same bus, forming a closed-loop control loop. A pollutant reduction effect feedback data set is generated by combining the feedback signals with subsequent monitoring data. It records indicators such as the rate of change in pollutant concentration and the increase in equipment energy consumption before and after the execution of each control command.

[0030] The system incorporates a community discovery algorithm from graph theory to generate policy association clusters. Policy parameter combinations are abstracted into nodes, with parameter similarity used as edge weights. The Louvain method is used to identify densely connected subgraph structures. Each subgraph corresponds to a policy association cluster, where the parameter variance of its internal nodes is below a preset threshold. The parameters of the core policy unit and the association clusters are fused using a non-dominated sorting method to avoid over-dominance of a single policy. The generation of real-time control command sets also considers device response latency. For devices with high inertia, the command implementation time is calculated in advance and timing compensation is implemented.

[0031] The sensor triggering logic of the dynamic execution module supports multiple synchronization modes. In basic mode, all monitoring points are sampled at a uniform frequency; in event-triggered mode, when the data at a monitoring point is abnormal, the sampling frequency of the associated area is automatically increased. The sliding window size of the outlier filtering algorithm is dynamically adjusted according to the half-life of the pollutant, with shorter windows used for volatile organic compounds and longer windows for persistent pollutants. The storage of standardized monitoring data streams uses a columnar database to optimize the compression and query efficiency of time series data. The parameter calibration module of the pollutant diffusion model runs regularly, using the latest monitoring data to update key parameters such as turbulence coefficient and degradation rate.

[0032] The control strategy generation module and the dynamic execution module work together to form a closed-loop optimization system. Robustness assessment is incorporated into the selection process for core policy units, prioritizing policies with low historical execution variance. The fusion results of policy association clusters are verified through Monte Carlo simulations to test their adaptability under different environmental conditions. A priority preemptive mechanism is used to issue real-time control instruction sets, allowing high-responsiveness instructions to interrupt the execution of lower-priority instructions. Semantic numbering is used to manage the versioning of long-term governance solutions, documenting the changes and scope of each iteration.

[0033] Example 3: See Figure 4 When the collaborative optimization module establishes the strategy effect evaluation matrix, it uses a three-dimensional structure to organize data. The row dimension of the matrix corresponds to the strategy number in the long-term governance plan set, and the column dimension is divided into three types of indicators: pollutant reduction rate, execution cost coefficient, and environmental improvement index. The pollutant reduction rate is calculated by comparing the monitoring data before and after the execution of the control instruction, reflecting the percentage of decrease in pollutant concentration; the execution cost coefficient integrates economic indicators such as equipment energy consumption, chemical consumption, and the frequency of manual intervention; and the environmental improvement index is calculated based on the weighted ecological sensitivity of the affected area. The filling process of the strategy effect evaluation matrix adopts a sliding time window mechanism. The pollutant reduction effect feedback data set is divided into continuous time blocks, and the data of each time block is normalized and filled into the corresponding column of the matrix.

[0034] The Euclidean distance value is calculated for each row vector of the strategy effect evaluation matrix to quantify the difference in comprehensive effects between different strategies. A dynamic weight adjustment mechanism is introduced into the calculation process to allocate the weight ratios of the three types of indicators according to the focus of the current environmental management objectives. When the system is in the stage of strict control of pollutant emissions, the weight of the pollutant reduction rate is increased; in the stage of priority for cost control, the weight ratio of the execution cost coefficient is increased. The screening of high-quality strategy seeds not only considers the Euclidean distance value, but also combines the stability score of the strategy to avoid selecting abnormal strategies with outstanding accidental effects. The stability score is calculated by analyzing the fluctuation range of the execution effect of the strategy in different historical periods. The smaller the fluctuation range, the higher the score.

[0035] Crossover mutation is performed between high-quality strategy seeds, using a combination of multi-point crossover and Gaussian mutation. The location of the crossover point is dynamically selected based on the type of strategy parameter: arithmetic crossover is used for continuous parameters, and single-point crossover is used for discrete parameters. The standard deviation of the mutation operation is proportional to the parameter's value range, ensuring that the variation is neither too large to cause strategy failure nor too small to lose optimization significance. During the generation of the new strategy parameter population, a copy of the original high-quality strategy seed is retained as a baseline reference to prevent loss of existing good properties during optimization.

[0036] The temporary storage layer of the environmental governance strategy knowledge base adopts a hierarchical storage structure. The new strategy parameter population is assigned to the experimental area and kept isolated from the strategies in the formal area. The initial weight value is set based on the matching degree between the strategy parameter population and the current environmental conditions. The matching degree is calculated by the parameter space projection algorithm. This algorithm maps the current monitoring data to a high-dimensional parameter space and calculates the distribution density of the new strategy in this space. The higher the density, the greater the initial weight. After the strategies in the temporary storage layer have undergone a preset number of verification cycles, those that meet the effectiveness evaluation standards will be transferred to the formal area, and those that do not meet the standards will be eliminated. The evaluation indicators of the verification cycle include the convergence speed, robustness and scalability of the strategy to avoid the premature elimination of potentially excellent strategies.

[0037] The row vectors of the strategy effect evaluation matrix are normalized using the range method to eliminate the impact of dimensional differences between different indicators. For the pollutant reduction rate indicator, an additional logarithmic transformation is introduced to alleviate the numerical dominance effect of the reduction rate of high-concentration pollutants. The formula for calculating the Euclidean distance value is as follows:

[0038] in, Indicates the The weighted Euclidean distance value of each strategy, For the The dynamic weight of each indicator, For the The strategy in The standardized scores on the indicators, For the Ideal reference value of each indicator. According to the dynamic setting of environmental management objectives, the pollutant reduction rate takes the historical optimal value, the execution cost coefficient takes the lower limit of the budget constraint, and the environmental improvement index takes the ecological safety threshold.

[0039] In the specific implementation of the crossover mutation operation, the location of the multi-point crossover is selected using an adaptive mechanism. When the difference between two parent strategies in a certain parameter exceeds a threshold, the parameter position automatically becomes a candidate crossover point. The difference is calculated using the relative error method to avoid the bias caused by the absolute value difference for strategies with different parameter value ranges. The standard deviation of the Gaussian mutation is The allowable adjustment range of the parameters satisfy The relationship between the two is to ensure that 99.7% of the mutation results fall within a reasonable range. After the mutation operation, the system automatically checks the physical feasibility of the parameter combination, such as whether the dosage of the reagent does not exceed the upper limit of dissolution and the equipment load does not exceed the rated capacity, and eliminates unreasonable mutation individuals.

[0040] The weight update mechanism for the temporary storage layer utilizes reinforcement learning. Each time a new strategy parameter population completes a task during the validation cycle, its weight is incrementally adjusted based on the degree of task completion. The completion evaluation function considers the ratio of the actual pollutant reduction rate to the target, the cost overrun ratio, and the environmental improvement rate. The weight adjustment range utilizes an S-curve function to prevent the initial small number of task results from overly influencing the weights. A cool-down period is implemented for policy migration between the experimental and formal areas. New strategies must complete the entire validation cycle and pass stability testing before they can be migrated to prevent frequent migrations from causing knowledge base fluctuations.

[0041] The parameter space projection algorithm uses kernel principal component analysis for high-dimensional mapping. The feature matrix composed of policy parameters and monitoring data is mapped to the reproducing kernel Hilbert space using a Gaussian kernel function. The Mahalanobis distance between the new policy population and the current optimal policy cluster is calculated in this space. The distance values ​​are converted to initial weights after an exponential transformation, ensuring that the weight distribution is between 0 and 1. The knowledge base uses a snapshot mechanism for version control. A complete backup of the official area is performed before each policy migration, supporting rollback to a historical stable version when necessary. Snapshot metadata records key information such as environmental conditions and the distribution of policy execution effects, facilitating the tracing and analysis of policy evolution patterns.

[0042] During the crossover of high-quality strategy seeds, the system constructs a parameter dependency graph. Nodes in the graph represent strategy parameters, and edges represent physical or logical relationships between parameters. The selection of crossover points prioritizes maintaining the integrity of dependency relationships, avoiding splitting strongly correlated parameters into different child strategies. For example, reactor temperature and stirring speed parameters are often strongly correlated, and are treated as a single unit during crossover. The dependency graph is constructed based on correlation analysis of historical strategy execution data, using conditional mutual information to quantify the strength of relationships between parameters. Correlations exceeding a threshold are included in the graph.

[0043] The verification cycle for new strategy parameter populations is implemented using a combination of simulation and field testing. The simulation environment integrates a chemical process simulator to quickly test the performance of the strategy under various working conditions; the field test selects non-critical production units for small-scale verification. Deviation analysis between simulation results and field data is used to calibrate simulator parameters and improve the credibility of subsequent simulation tests. The collection of verification data covers typical scenarios such as normal production, equipment start-up and shutdown, and load fluctuations to ensure the comprehensiveness of strategy evaluation. The capacity limit of the temporary storage layer adopts the least recently used elimination strategy. When there is insufficient space, strategies that have not been weighted for a long time are removed first to maintain the dynamic adaptability of the optimization process.

[0044] Example 4: When constructing a dynamic assessment framework, the assessment and early warning module divides the chemical park into 200m×200m geographic grid units, each of which contains capacity thresholds for three types of environmental media: atmosphere, water, and soil. The atmospheric capacity threshold is set based on the hourly concentration limit of pollutants in the "Ambient Air Quality Standards", the water capacity threshold refers to the Class III water body standard of the "Surface Water Environmental Quality Standards", and the soil capacity threshold adopts the screening value of the "Soil Environmental Quality Construction Land Risk Management Standards". The real-time monitoring data of the key pollutant monitoring point location set is used to generate a gridded pollutant distribution map using the Kriging spatial interpolation algorithm. The pollutant load ratio of each grid unit is calculated by the ratio of the actual monitoring concentration to the capacity threshold.

[0045] A partial example of the environmental capacity threshold matrix for a chemical plant area is shown in the following table, which displays the threshold data for grid cells G-07 to G-09.

[0046] Table 1: Partial example of the environmental capacity threshold matrix for a chemical plant area, showing the threshold data for grid cells G-07 to G-09.

[0047]

[0048] The calculation process of the local overload coefficient takes into account the synergistic effects of pollutants. When multiple pollutants coexist in a grid unit, an additive model is used to calculate the comprehensive load ratio. For example, the G-07 grid detected a BTEX concentration of 0.3mg / m³ and a COD concentration of 0.04mg / L. Its atmospheric load ratio is 0.3 / 0.5=60%, and its water load ratio is 0.04 / 0.05=80%. The comprehensive overload coefficient takes the maximum value of the two, 80%. Spatial autocorrelation analysis uses the Moran's I index, and a spatial lag distance of 500m is set during the calculation to identify the coordinates of highly concentrated overload areas. These coordinates are marked on the digital map of the factory area using geo-fencing technology to form a dynamic early warning hot zone.

[0049] The triggering logic for graded warning signals utilizes a multi-level progressive mechanism. For example, in the exhaust gas treatment system, when the sulfur dioxide concentration in grid G-08 reaches the blue alert threshold (80% of the capacity threshold) for the first time, the system activates a device-level control instruction review mechanism. This mechanism automatically checks the operating parameters of the exhaust gas purification equipment associated with that grid, including the caustic soda circulation pump frequency and scrubber pH, and compares them with best practice parameters in a knowledge base. If a parameter deviation exceeds 5%, a calibration instruction is immediately generated and pushed to the equipment control system.

[0050] When the overload factors of three adjacent grids (e.g., G-07 to G-09) all reach the yellow alert threshold (90% of the capacity threshold) and the Moran's I index shows significant clustering (p<0.05), the system initiates a plant-level production load adjustment protocol. This protocol prioritizes scheduling and can interrupt production processes, such as delaying batch feeds or lowering reactor temperatures. During one actual run, the system detected three grid cells on an acrylic acid production line triggering yellow alerts simultaneously. The system automatically reduced the oxidation reactor feed rate from 1200 L / h to 900 L / h in a stepwise manner, bringing the overload factor back to a safe range within 30 minutes.

[0051] A time persistence condition has been introduced to determine when the red alert threshold (100% of the capacity threshold) has been breached. When ammonia concentrations exceeded the red threshold of 55 mg / m³ in an ammonium nitrate storage area for three consecutive monitoring cycles (15 minutes per cycle), the system triggered a regional production halt and emission reduction order. The execution sequence included immediately closing the feed valves of the relevant process units, starting the emergency absorption tower, and activating the surrounding sprinkler system. Simultaneously, a 3D model of the affected area automatically popped up on the control center's large screen, displaying the simulated pollutant diffusion trajectory and the status of emergency equipment. Historical data shows that this mechanism effectively prevented an ammonia leak caused by a cooling system failure from escalating.

[0052] The lifting of warning signals utilizes a dual-condition verification mechanism. For a blue warning, monitoring values ​​must fall below the threshold for two consecutive cycles and equipment parameters must be calibrated. A yellow warning requires the overload area to be reduced by at least 50% and remain stable for two hours. The lifting of a red warning requires manual confirmation, verification of on-site disposal effectiveness, and completion of a safety hazard investigation. During the lifting of a red warning for a chlorine treatment system, the system required operators to upload emergency response records, equipment maintenance reports, and environmental retest data, creating a complete closed-loop management archive.

[0053] The dynamic assessment framework's adaptive threshold adjustment function is implemented through machine learning. The system analyzes the relationship between historical warning events and subsequent environmental recovery data to dynamically optimize the capacity threshold of each grid cell. For example, the volatile organic compound threshold for a certain area was initially set at 0.3 mg / m³. However, long-term monitoring showed that the area could still maintain environmental safety through natural diffusion at a concentration of 0.4 mg / m³. After three months of data accumulation, the system automatically increased the threshold by 10%. This adjustment process is subject to safety constraints, with a single adjustment not exceeding 15% of the original value and subject to rationality verification by the expert system.

[0054] The parameters of the spatial interpolation algorithm are configured to tailor the characteristics of different pollutants. The range parameter for gaseous pollutants is set to 800 meters to reflect their diffusion capacity; liquid pollutants use a 500-meter range to reflect groundwater flow velocity; and solid particulate matter uses a 300-meter range to match its sedimentation characteristics. During a heavy metal pollution monitoring exercise, the system detected a peak in cadmium pollution in the G-07 grid. By setting a specific range parameter of 200 meters, the pollution source was accurately identified as the electroplating workshop drain outlet in the northwest corner of the grid.

[0055] The visual presentation of warning information utilizes a multi-dimensional encoding method. On the electronic map, blue warnings appear as light blue, translucent ripples, yellow warnings transform into pulsating orange halos, and red warnings appear as flashing red radial icons. Furthermore, the 3D model uses bar graphs of varying heights to indicate the degree of overload of various pollutants, enabling operators to quickly locate the primary pollutant. In one complex pollution incident, the system simultaneously displayed a red bar graph for BTEX and a yellow bar graph for ammonia, providing a visual reminder that the source of the BTEX leak should be addressed first.

[0056] Intelligent scheduling of emergency response resources is based on matching warning levels with pollution types. A blue warning typically triggers automated adjustments, a yellow warning dispatches workshop-level emergency response teams, and a red warning initiates a plant-wide emergency response. The resource allocation algorithm considers the diffusion rate of pollutants. Warnings for gaseous pollutants prioritize the allocation of mobile purification equipment, while liquid pollution incidents prioritize the allocation of leak-proofing materials. Historical records show that this mechanism reduces emergency response time by an average of 40% and improves resource utilization by 25%.

[0057] Root cause analysis of early warning events utilizes time series pattern mining technology. The system establishes a timeline of early warning events and correlates and analyzes equipment failure records, production logs, and environmental monitoring data. During analysis of a persistent yellow alert, the system discovered abnormal fluctuations in the reactor temperature control system two hours prior to the alert. Verification confirmed this was a chain reaction caused by a temperature sensor calibration failure. These analysis results are automatically stored in a case library and used to optimize equipment preventive maintenance plans.

[0058] The seasonal adjustment module for environmental capacity thresholds integrates historical meteorological data. During winter, when temperature inversions are frequent, the system automatically lowers capacity thresholds in areas with poor atmospheric diffusion conditions. During the rainy season, water thresholds are adjusted to prevent non-point source pollution. For one chemical park, the system temporarily lowered the water capacity threshold for riverside grid cells by 20% based on weather forecasts before the arrival of typhoon season, preemptively preventing the risk of pollutants entering the river due to rainwater washout. The adjustment results are submitted to the environmental management department for recordation through an approval workflow to ensure regulatory compliance.

[0059] The early warning system is tested and verified using a fault injection method. During non-production hours, the system automatically simulates various pollution scenarios, including instantaneous leaks, sustained emissions, and combined pollution, to verify the effectiveness of the warning triggering logic and response process. In one simulation, the system injected a BTEX signal into the virtual G-07 grid, successfully triggering the escalation chain from blue to red alerts. The system also fully records the response time at each stage, providing a quantitative basis for system optimization. The test cases cover all major environmental risk sources within the plant, forming a positive cycle of continuous improvement.

[0060] Example 5: When constructing a dynamic three-dimensional geographic information model, the treatment effect visualization module uses high-precision digital elevation data as the basic terrain framework, and superimposes the BIM model details of the factory building facilities. The model coordinate system adopts the National 2000 Geodetic Coordinate System, and the elevation datum uses the 1985 National Elevation Datum to ensure that the spatial positioning accuracy reaches the centimeter level. The simulation results of the pollutant migration path are visualized through a particle system, and different pollutant types correspond to specific color particle flows: exhaust gas is displayed as a gray-white translucent particle group, wastewater shows a blue ribbon flow trajectory, and solid waste is represented by a brown dot matrix to represent the accumulation and diffusion process. The particle movement speed is linked to the real-time monitoring data. When the pollutant concentration increases, the density and movement speed of the corresponding particle flow increase accordingly.

[0061] The real-time data stream of the key pollutant monitoring point locations is displayed in a dynamic labeling overlay. The data of each monitoring point is presented as a circular identifier. The diameter of the identifier dynamically scales with the pollutant concentration value, and the color gradually changes from green to red to indicate the transition from safe to dangerous. Click the identifier to expand the detailed data panel, which displays the multi-pollutant concentration curve, historical trend comparison and associated equipment status of the point. The data refresh frequency is synchronized with the sampling frequency of the monitoring module and is automatically increased to second-level refresh in the event of an early warning event. In an ammonia leak incident, the identifier of monitoring point G-07 changed from green to dark red within 10 seconds, and its diameter expanded three times. At the same time, the operating parameters of the surrounding associated equipment were displayed in real time on the floating panel.

[0062] The thermal map layer renders the regional environmental load index distribution using an improved bilinear interpolation algorithm to process data from discrete monitoring points. The rendering palette defines six levels: dark green indicates a safe state (index <0.3), light green indicates a warning state (0.3-0.5), yellow indicates mild overload (0.5-0.7), orange indicates moderate overload (0.7-0.9), red indicates severe overload (0.9-1.0), and purple indicates extreme overload (>1.0). The transparency of the heat map automatically adjusts based on the zoom level. The global view is set to 50% transparency to facilitate viewing of the underlying geographic information, while zooming in to 90% to highlight details. Thermal rendering utilizes WebGL acceleration technology, enabling smooth display of large datasets containing hundreds of thousands of grid cells in mainstream browsers.

[0063] The execution path of the real-time control instruction set is presented through dynamic streamline annotation. The streamline starts from the virtual coordinates of the strategy generation module and ends at the geographical location of the target device. The path color is coded according to the instruction type: blue represents parameter fine-tuning instructions, yellow represents equipment start and stop instructions, and red represents emergency handling instructions. The streamline width is proportional to the instruction priority. High-responsiveness control instructions correspond to thick solid lines, and regular instructions are displayed as thin dotted lines. The streamline animation uses particle tracking effects. The speed of the light spot moving along the path reflects the instruction transmission delay. When the industrial bus communication delay exceeds 200ms, the light spot movement speed automatically slows down and triggers a yellow flashing reminder. During a plant-wide load adjustment process, the visual interface simultaneously displayed 56 control instruction streamlines radiating from the control center to each production unit. Operators can quickly identify instruction-intensive areas based on the density of the streamlines.

[0064] When a graded warning signal is triggered, a pulsating warning halo generated at the corresponding geographic coordinate location consists of three layers of visual elements. The inner layer is a high-frequency flashing solid ring, the color corresponding to the warning level (blue / yellow / red); the middle layer displays concentric ripples that spread outward, with the intervals between ripples synchronized with the diffusion rate of pollutants; and the outer layer presents a translucent radial light band, the length of which is proportional to the degree of overload of the environmental carrying index. The halo's display priority is set to the highest, and when it overlaps with other visual elements, the viewing angle automatically adjusts to ensure visibility. The warning halo is discontinued using a gradual fading animation, continuing to display for five seconds after the warning condition disappears, to avoid sudden interruptions in visual information. During a composite warning event, red, yellow, and blue halos appeared simultaneously in grids G-07 to G-09, and the system automatically switched to a three-dimensional bird's-eye view to clearly display the spatial relationship of the multiple warning layers.

[0065] The interactive function of the three-dimensional model supports multi-dimensional data exploration. When the mouse hovers over any grid cell, a detailed composition analysis of the environmental carrying index of the cell pops up, including the sub-indices of the atmosphere, water bodies, and soil, and the contribution of major pollutants. Drag the timeline control to replay the historical pollution diffusion process, and the speed can be adjusted from 1 to 60 times the real-time speed. In the comparative analysis mode, the user can select the model status of two time points to display side by side, and the difference is highlighted with a pulse. The spatial measurement tool supports real-time calculation of the actual distance, elevation difference and pollutant concentration gradient between any two points. These functions are used in emergency drills to simulate the comparison of the effects of different disposal plans, helping decision makers to quickly evaluate the pros and cons of the plans.

[0066] The data-driven architecture of the visualization system adopts a publish-subscribe model. Monitoring data update events are broadcast through the message queue, and each visualization component subscribes to the relevant data topic on demand. When the exhaust gas concentration data is updated, only the particle system, monitoring point identifier, and corresponding components in the thermal layer will trigger redrawing, and unrelated components remain static. This mechanism effectively reduces system resource consumption and can maintain a rendering efficiency of 30 frames per second on ordinary workstations. User operation events are transmitted through the event bus, supporting multi-view linkage response. For example, when selecting an area in the plane view, the three-dimensional view automatically rotates to the optimal viewing angle, and the trend chart window synchronously loads the historical data curve of the selected area.

[0067] The parameterized configuration of visual elements supports adaptive scene adjustment. At the turn of day and night, the system automatically switches between light and dark themes to ensure visibility: the day mode uses a high-contrast color scheme, and the night mode reduces brightness and increases the luminous effect. For users with color vision impairments, alternative color palette options are provided, using shape differences to assist in color distinction. The display density is dynamically adjusted according to hardware performance, and when the GPU load is detected to be too high, the number of particles and the accuracy of the thermal grid are automatically simplified. These optimization measures enable the system to maintain stable operation of core functions on terminal devices of different specifications, and a consistent interactive experience can be obtained from the large screen array in the control center to the tablets used by on-site personnel.

[0068] The historical data backtracking function uses layered loading technology. Early data is automatically downsampled and stored, and near-real-time data retains its original accuracy. When a user views a pollution event from three months ago, the system loads the daily average aggregated data; when analyzing events from the last hour, the original minute-level records are displayed. The backtracking process supports the addition of virtual monitoring points, setting observation points at any location in the historical scene and generating simulated data curves for comparative analysis of the rationality of the actual monitoring point layout. During an equipment modification assessment, engineers added virtual points to the historical model to verify the necessity of the new monitoring points. By comparing the virtual data with the actual modified data, they confirmed that the new monitoring points will shorten the anomaly detection response time by 40%.

[0069] Multi-user collaboration is achieved through operation logs and status synchronization. The view operations and annotation actions of each logged-in user are recorded in real time in shared memory, supporting simultaneous collaboration of up to 16 terminals. The leadership decision-making view automatically focuses on key indicators and overall trends, while the technician view highlights equipment parameters and local details. Annotation information during the collaboration process is saved in a geographically anchored manner, and comment bubbles are permanently associated with specific coordinates, maintaining the same position when viewed again. In cross-departmental emergency consultations, users with different roles can add classification annotations to the same model. The safety department marks evacuation routes, and the production department identifies the status of key equipment, forming a comprehensive handling view.

[0070] The visual mapping rule library for environmental parameters contains more than 200 preset schemes. Exclusive visual presentations are preset for different types of pollutants: corrosive substances display corrosion-effect particles, persistent organic pollutants add bioaccumulation animations, and odorous substances are associated with odor diffusion simulations. Users can customize mapping rules, such as converting noise monitoring data into sound wave visualizations and displaying vibration data as ground fluctuation effects. These schemes can be quickly applied by dragging and dropping, and targeted observation perspectives can be quickly established in special pollution incidents. In a sudden hydrogen sulfide leak incident, the system called a preset toxic gas visualization scheme and immediately switched to a display mode focusing on respiratory protection to assist emergency personnel in determining dangerous areas.

[0071] The model update mechanism enables incremental data fusion. When new monitoring points are added or geographic information is adjusted, the system only recalculates the visualization elements of the affected areas, and unchanged areas maintain their existing status. This mechanism enables local updates of large-scale scenes to be completed in milliseconds, avoiding freezes caused by full scene refreshes. The cache of spatial analysis results adopts an intelligent expiration strategy. The analysis results of static areas are retained for a long time, and the calculation results of high-frequency changing areas are set with a short validity period. The system runs a data quality monitoring thread in the background. When abnormal spatial coordinates or outlier values ​​are detected, the problem data is automatically isolated and the data verification process is triggered to ensure that the accuracy of the visualization is always under control.

[0072] The decision-making support function integrates a spatial analysis toolkit. The buffer analysis tool can generate 500m and 1000m equidistant impact zones around pollution sources with a single click, overlaying basic geographic data such as population density and sensitive facilities. The viewshed analysis module simulates the actual observation range of monitoring points and identifies areas with potential monitoring blind spots. The network analysis tool calculates the optimal emergency response path, taking into account real-time road conditions and the direction of pollution spread. The results of these tools are directly fed back into the three-dimensional scene, and the analysis process supports real-time parameter adjustment and instant comparison of results. During a recent emergency drill, command personnel discovered a monitoring blind spot on the west side of the wall through viewshed analysis and promptly adjusted the patrol route of the mobile monitoring vehicle.

[0073] 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 apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0074] 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 collaborative management system for chemical engineering management analysis and environmental protection data, characterized by: The system comprises: The pollution source monitoring module obtains a set of historical environmental monitoring records including a time series dataset of waste gas emission concentration, a dataset of wastewater composition spectrum, and a dataset of solid waste generation distribution; The volatility analysis module traverses the historical environmental monitoring record set to extract dynamic fluctuation characteristics, calculates the discrete degree index of each data set through a moving time window, and generates the exhaust gas concentration fluctuation factor, the wastewater composition fluctuation factor, and the solid waste generation fluctuation factor; A monitoring point optimization module, which performs an adaptive optimization operation in a preset pollution feature space based on the exhaust gas concentration fluctuation factor, the wastewater composition fluctuation factor, and the solid waste generation fluctuation factor to determine the key pollutant monitoring point location set and corresponding sampling frequency parameters; The control strategy generation module takes the key pollutant monitoring point location set as input, loads the environmental protection management strategy knowledge base to perform multi-level strategy matching, and outputs a set of real-time control instructions and a set of long-term management solutions; A dynamic execution module drives the multi-source environmental protection sensor network to perform monitoring tasks according to the sampling frequency parameters, and simultaneously sends the real-time control instruction set to the chemical production equipment control system to generate a pollutant reduction effect feedback data set; The collaborative optimization module integrates the pollutant reduction effect feedback data set with the long-term governance plan set to perform strategy iteration analysis, update the strategy weight distribution in the environmental governance strategy knowledge base, and generate strategy optimization instructions.

2. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 1 is characterized in that: When the volatility analysis module extracts dynamic volatility features: Setting a moving time window of variable length and continuously sliding the window on the exhaust gas emission concentration time series data set; Calculate the standard deviation of the data points in each moving time window, and perform weighted average processing on the standard deviation values ​​of three consecutive windows to obtain the local fluctuation intensity index; Extracting peak sequences of all local fluctuation intensity indicators, performing normal distribution fitting processing on the peak sequences, and determining a fluctuation intensity probability density function; The expected value and variance product of the fluctuation intensity probability density function are used as the exhaust gas concentration fluctuation factor; The same operation was used to process the wastewater composition spectrum dataset and the solid waste generation distribution dataset to obtain the wastewater composition fluctuation factor and the solid waste generation fluctuation factor, respectively.

3. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 1 is characterized in that: When the monitoring point optimization module performs the adaptive optimization operation: Pre-construct a three-dimensional pollution feature space, the coordinate axes of which respectively map the exhaust gas diffusion characteristic surface, the wastewater migration characteristic curve and the solid waste accumulation characteristic field; Inputting the exhaust gas concentration fluctuation factor into the exhaust gas diffusion characteristic surface to obtain a corresponding diffusion sensitive area coordinate set; Projecting the wastewater component fluctuation factor onto the wastewater migration characteristic curve to identify the endpoint coordinates of the high migration risk section; Based on the solid waste generation fluctuation factor, density cluster analysis is performed on the solid waste accumulation characteristic field to generate the coordinates of the solid waste accumulation core points; Perform spatial superposition operations on the coordinate sets of diffusion-sensitive areas, the endpoint coordinates of high migration risk sections, and the coordinates of solid waste accumulation core points to determine the coordinate position of the center of gravity of the overlapping areas; A polygonal monitoring area is generated with the center coordinate position as the center point according to a preset radiation radius, and the vertex coordinates of the polygonal monitoring area are used as the key pollutant monitoring point position set.

4. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 3 is characterized in that: When the control strategy generation module performs multi-level strategy matching: Loading a tree-structured environmental governance strategy knowledge base, which contains a branch-level governance technology type index and a leaf-level strategy parameter combination; Map the key pollutant monitoring point location set to the branch level, and activate all leaf nodes under the corresponding control technology type index; Extract the historical execution effect records of the strategy parameter combination at the blade level, and calculate the matching value of each strategy parameter combination with the current exhaust gas concentration fluctuation factor and wastewater composition fluctuation factor; Select the strategy parameter combination with the highest matching value as the core strategy unit; Traverse the policy parameter combinations in the remaining leaf nodes that are parameter-associated with the core policy unit to form a policy association cluster; The core policy unit and the policy association cluster are integrated with each other to generate a set of real-time control instructions and a set of long-term governance solutions.

5. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 1 is characterized in that: When the dynamic execution module performs the monitoring task: Generate a sensor trigger pulse sequence according to the sampling frequency parameter to drive the multi-source environmental protection sensor network to perform synchronous sampling at the key pollutant monitoring point location set; Perform outlier filtering on the real-time pollutant concentration data obtained through sampling to form a standardized monitoring data stream; Input the standardized monitoring data stream into the pollutant diffusion model to simulate the instantaneous impact range and output the diffusion impact boundary coordinates; Compare the diffusion impact boundary coordinates with the preset environmentally sensitive area coordinates, and generate highly responsive control instructions when coordinate overlap is detected; High-responsiveness control instructions are preferentially written into the real-time control instruction set and sent to the chemical production equipment actuators through the industrial control bus.

6. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 5 is characterized in that: When the collaborative optimization module performs strategy iteration analysis: Establish a strategy effect evaluation matrix, where the row dimension of the matrix corresponds to the strategy number in the long-term governance plan set, and the column dimension includes the pollutant reduction rate, implementation cost coefficient and environmental improvement index; The pollutant reduction effect feedback dataset is divided into multiple data blocks according to the time window, and each data block is filled into the corresponding column of the strategy effect evaluation matrix; Calculate the Euclidean distance value of each row vector in the strategy effect evaluation matrix, and select the three row vectors with the smallest Euclidean distance value as high-quality strategy seeds; Extract the strategy parameter combinations corresponding to high-quality strategy seeds and perform crossover and mutation operations to generate a new strategy parameter population; Write the new policy parameter population into the temporary storage layer of the environmental governance policy knowledge base and assign initial weight values.

7. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 6 is characterized in that: Also includes: An assessment and early warning module constructs a dynamic assessment framework including an environmental capacity threshold matrix and a pollutant diffusion model, calculates a regional environmental carrying index based on the historical environmental monitoring record set and the pollutant reduction effect feedback data set, and triggers a graded early warning signal when it is identified that the regional environmental carrying index exceeds the preset risk boundary; When the evaluation and warning module constructs a dynamic evaluation framework: Divide the environmental capacity threshold matrix into multiple geographic grid cells, each grid cell contains the atmospheric capacity threshold, water capacity threshold and soil capacity threshold; Perform spatial interpolation calculations on the real-time monitoring data of key pollutant monitoring point locations according to pollutant type to generate a gridded pollutant distribution map; Calculate the pollutant load percentage of each grid cell in the gridded pollutant distribution map; Superimpose the pollutant load ratio value and the environmental capacity threshold of the corresponding grid unit to obtain the local overload coefficient; Spatial autocorrelation analysis was performed on all local overload coefficients to identify the coordinates of highly clustered overload areas.

8. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 7 is characterized in that: When the evaluation and warning module triggers a graded warning signal: Establish a tiered warning threshold system including blue warning threshold, yellow warning threshold and red warning threshold; When the local overload coefficient reaches the blue warning threshold for the first time, the device-level control instruction review mechanism is activated; When the area of ​​the high-aggregate overload zone reaches the yellow alert threshold, the plant-level production load adjustment protocol will be initiated; When a red alert threshold violation event is identified for three consecutive time windows, a regional-level production suspension and emission reduction order is triggered.

9. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 1 is characterized in that: The system also includes a governance effect visualization module, which specifically performs: Construct a dynamic three-dimensional geographic information model that includes pollutant migration paths; Overlaying a real-time data stream of key pollutant monitoring point locations on a dynamic three-dimensional geographic information model; Use thermal maps to present the distribution of regional environmental carrying index calculated by the assessment and early warning module; Real-time control of the execution path of instruction sets through dynamic streamline annotation; When a graded warning signal is received, a pulse warning halo is generated at the corresponding geographic coordinate location.

10. A method for collaborative management of chemical remediation analysis and environmental protection data, applied to the collaborative management system of chemical remediation analysis and environmental protection data according to any one of claims 1 to 9, characterized in that: include: A multi-source environmental sensor network is used to collect historical environmental monitoring records from chemical production areas, including a time series dataset of waste gas emission concentrations, a dataset of wastewater composition spectra, and a dataset of solid waste generation distribution. The moving time window algorithm is used to process the historical environmental monitoring record set to generate the fluctuating factors of exhaust gas concentration, wastewater composition and solid waste generation. Perform spatial superposition operations in the three-dimensional pollution feature space to determine the location set of key pollutant monitoring points based on the fluctuating factors of exhaust gas concentration, wastewater composition, and solid waste generation. Matching the real-time control instruction set and the long-term control plan set associated with the key pollutant monitoring point location set from the environmental protection control strategy knowledge base; Execute real-time data collection of key pollutant monitoring point locations according to preset sampling frequency parameters, and synchronously drive chemical production equipment to execute real-time control instruction sets; Integrate pollutant reduction effect feedback data with a collection of long-term governance plans to update the weight distribution of the environmental governance strategy knowledge base; Calculate the regional environmental carrying index based on the environmental capacity threshold matrix and pollutant diffusion model; When the regional environmental carrying index exceeds the preset risk boundary, a graded warning signal of the corresponding level is triggered according to the stepped warning threshold system.

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