A system and method for collaborative management of chemical management analysis and environmental protection data

Abstract

The application relates to the technical field of chemical environmental protection management, and discloses a kind of chemical treatment analysis and environmental protection data's collaborative management system and method.The system includes pollution source monitoring module, obtains historical environmental protection monitoring record set; Fluctuation analysis module extracts dynamic fluctuation characteristics and generates various fluctuation factors; Monitoring point optimization module determines key monitoring point position and sampling frequency based on fluctuation factor; Control strategy generation module combines key monitoring point information and knowledge base to output real-time control instruction and long-term treatment scheme; Dynamic execution module drives sensor monitoring and issues instructions to generate reduction effect feedback data; Collaborative optimization module integrates feedback data and long-term scheme to update knowledge base strategy weight and generate optimization instructions.The system realizes the collaborative management of environmental protection data and the dynamic optimization of treatment strategy, improving the collaboration and effectiveness of chemical environmental protection management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chemical environmental protection management, in particular to a collaborative management system and method for chemical treatment analysis and environmental protection data. BACKGROUND

[0002] In the production process of the chemical industry, a large amount of waste gas, waste water and solid waste will be generated. If the emission of these pollutants cannot be effectively controlled, it will have a serious impact on the ecological environment. At present, the environmental protection management of chemical enterprises mostly adopts a decentralized monitoring mode. The monitoring data of different pollutants is often collected and processed by independent systems, and there is a lack of effective collaboration and integration.

[0003] This decentralized management mode leads to difficulties in data sharing and makes it difficult to analyze the emission rules and mutual relationships of pollutants as a whole. For example, the change in waste gas emission concentration may be related to the fluctuation of waste water composition, but due to the separation of data, this potential relationship cannot be discovered in time, thereby affecting the accuracy of the formulation of environmental protection treatment strategies. At the same time, the setting of monitoring points is mostly based on experience and lacks a scientific dynamic optimization mechanism. When the emission of pollutants changes, the monitoring position and sampling frequency cannot be adjusted in time, which may cause the lag or redundancy of monitoring data and reduce the efficiency of environmental protection management.

[0004] Existing environmental protection treatment strategies often rely on static knowledge bases and are difficult to update and optimize dynamically according to real-time monitoring data and treatment effect feedback. When new characteristics of pollutant emissions appear or the effect of treatment measures is not good, the strategies cannot be quickly adjusted, resulting in insufficient pertinence and effectiveness of environmental protection treatment, and making it difficult to meet the increasingly stringent environmental protection regulations and the needs of sustainable development of enterprises. SUMMARY

[0005] The purpose of the present application is to provide a collaborative management system and method for chemical treatment analysis and environmental protection data to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides a collaborative management system for chemical treatment analysis and environmental protection data, which comprises:

[0007] A pollution source monitoring module acquires a set of historical environmental protection monitoring records containing a waste gas emission concentration time series data set, a waste water composition spectrum data set and a solid waste production amount distribution data set;

[0008] A fluctuation analysis module traverses the set of historical environmental protection monitoring records to extract dynamic fluctuation characteristics, calculates the dispersion degree index of each data set through a moving time window, and generates waste gas concentration fluctuation factors, waste water composition fluctuation factors and solid waste production amount fluctuation factors;

[0009] a monitoring point optimization module, based on the waste gas concentration fluctuation factor, the waste water composition fluctuation factor and the solid waste generation fluctuation factor, performing adaptive optimization operation in a preset pollution characteristic space to determine a set of key pollution monitoring point positions and corresponding sampling frequency parameters;

[0010] a regulation strategy generation module, taking the set of key pollution monitoring point positions as input, loading an environmental protection management strategy knowledge base to perform multi-level strategy matching, and outputting a set of real-time regulation instructions and a set of long-term management schemes;

[0011] a dynamic execution module, driven by the sampling frequency parameters to drive a multi-source environmental protection sensor network to perform a monitoring task, and simultaneously issuing the set of real-time regulation instructions to a chemical production equipment control system to generate a set of pollution reduction effect feedback data;

[0012] a collaborative optimization module, fusing the set of pollution reduction effect feedback data and the set of long-term management schemes to perform strategy iteration analysis, updating the strategy weight distribution in the environmental protection management strategy knowledge base, and generating a strategy optimization instruction.

[0013] Preferably, when the fluctuation analysis module performs dynamic fluctuation feature extraction:

[0014] a variable-length moving time window is set, and the window is continuously slid on the waste gas emission concentration time series data set;

[0015] the standard deviation value of each moving time window is calculated, and the standard deviation values of three consecutive windows are weighted and averaged to obtain a local fluctuation intensity index;

[0016] a peak sequence of all local fluctuation intensity indexes is extracted, and the peak sequence is subjected to normal distribution fitting processing to determine a fluctuation intensity probability density function;

[0017] the product of the expected value and the variance of the fluctuation intensity probability density function is taken as the waste gas concentration fluctuation factor;

[0018] The same operation is performed on the waste water composition spectrum data set and the solid waste generation distribution data set to obtain the waste water composition fluctuation factor and the solid waste generation fluctuation factor, respectively.

[0019] Preferably, when the monitoring point optimization module performs adaptive optimization operation:

[0020] a three-dimensional pollution characteristic space is pre-constructed, and the coordinate axes of the space respectively map the waste gas diffusion characteristic surface, the waste water migration characteristic curve and the solid waste accumulation characteristic field;

[0021] the waste gas concentration fluctuation factor is input into the waste gas diffusion characteristic surface to obtain a set of corresponding diffusion sensitive region coordinates;

[0022] projecting the waste water composition fluctuation factor to the waste water migration characteristic curve to identify high migration risk section end point coordinates;

[0023] performing density clustering analysis on the solid waste accumulation characteristic field based on the solid waste generation amount fluctuation factor to generate solid waste accumulation core point coordinates;

[0024] performing spatial superposition operation on the diffusion sensitive area coordinate set, the high migration risk section end point coordinates, and the solid waste accumulation core point coordinates to determine the barycentric coordinates of the overlapping area;

[0025] taking the barycentric coordinates as the center point, generating a polygon monitoring area according to a preset radiation radius, and taking the vertex coordinates of the polygon monitoring area as a key pollutant monitoring point position set.

[0026] Preferably, when the regulation strategy generation module performs multi-level strategy matching:

[0027] loading an environmental protection governance strategy knowledge base in a tree structure, which includes branch level governance technology type indexes and leaf level strategy parameter combinations;

[0028] mapping the key pollutant monitoring point position set to the branch level to activate all leaf nodes under the corresponding governance technology type index;

[0029] extracting historical execution effect records in the leaf level strategy parameter combinations, and calculating the matching degree values of each strategy parameter combination with the current waste gas concentration fluctuation factor and the waste water composition fluctuation factor;

[0030] selecting the strategy parameter combination with the highest matching degree value as the core strategy unit;

[0031] traversing the strategy parameter combinations in the remaining leaf nodes that have parameter associations with the core strategy unit to form a strategy association cluster;

[0032] performing parameter fusion on the core strategy unit and the strategy association cluster to generate a real-time regulation instruction set and a long-term governance scheme set.

[0033] Preferably, when the dynamic execution module performs a monitoring task:

[0034] generating a sensor trigger pulse sequence according to a sampling frequency parameter to drive the multi-source environmental protection sensor network to perform synchronous sampling at the key pollutant monitoring point position set;

[0035] performing outlier filtering processing on the real-time pollutant concentration data obtained by sampling to form a standardized monitoring data stream;

[0036] inputting the standardized monitoring data stream into a pollutant diffusion model to simulate the instantaneous influence range and output diffusion influence boundary coordinates;

[0037] The high-response control instruction is generated when the coordinate overlap is identified by comparing the diffusion influence boundary coordinate with the preset environment sensitive area coordinate;

[0038] The high-response control instruction is written into the real-time control instruction set in priority, and is issued to the chemical production equipment actuator through the industrial control bus.

[0039] Preferably, when the strategy iteration analysis is performed by the collaborative optimization module:

[0040] A strategy effect evaluation matrix is established, the row dimension of the matrix corresponds to the strategy number in the long-term governance scheme set, and the column dimension includes the pollutant reduction rate, the execution cost coefficient and the environmental improvement index;

[0041] The pollutant reduction effect feedback data set is divided into multiple data blocks according to a time window, and each data block is filled into the corresponding column of the strategy effect evaluation matrix;

[0042] The Euclidean distance values of each row vector in the strategy effect evaluation matrix are calculated, and the three row vectors with the smallest Euclidean distance values are selected as high-quality strategy seeds;

[0043] The strategy parameter combination corresponding to the high-quality strategy seed is extracted to perform cross mutation operation, and a new strategy parameter population is generated;

[0044] The new strategy parameter population is written into the temporary storage layer of the environmental protection governance strategy knowledge base, and is assigned an initial weight value.

[0045] Preferably, the system further comprises:

[0046] An evaluation and early warning module, a dynamic evaluation framework including an environmental capacity threshold matrix and a pollutant diffusion model is constructed, a regional environmental carrying index is calculated according to the historical environmental protection monitoring record set and the pollutant reduction effect feedback data set, and when it is identified that the regional environmental carrying index exceeds a preset risk boundary, a hierarchical early warning signal is triggered;

[0047] When the evaluation and early warning module constructs the dynamic evaluation framework:

[0048] The environmental capacity threshold matrix is divided into multiple geographic grid units, each grid unit includes an atmospheric capacity threshold, a water capacity threshold and a soil capacity threshold;

[0049] The real-time monitoring data of the key pollutant monitoring point position set is spatially interpolated according to the pollutant type to generate a grid pollutant distribution map;

[0050] The pollutant load proportion value of each grid unit in the grid pollutant distribution map is calculated;

[0051] The local overload coefficient is obtained by superimposing the pollution load proportion value on the environmental capacity threshold of the corresponding grid unit;

[0052] The high aggregation overload area coordinates are identified by performing spatial autocorrelation analysis on all local overload coefficients.

[0053] Preferably, when the evaluation and early warning module triggers a hierarchical early warning signal:

[0054] A step-by-step early warning threshold system including a blue alert threshold, a yellow alert threshold and a red alert threshold is set;

[0055] When the local overload coefficient first reaches the blue alert threshold, the device-level regulation instruction review mechanism is activated;

[0056] When the area of the high aggregation overload area reaches the yellow alert threshold, the plant-level production load adjustment protocol is started;

[0057] When three consecutive time windows of red alert threshold breakthrough events are identified, the regional-level production stop and emission reduction instruction is triggered.

[0058] Preferably, the system further comprises a governance effect visualization module, which specifically performs:

[0059] A dynamic three-dimensional geographic information model including the migration path of the pollutant is constructed;

[0060] The real-time data stream of the set of key pollutant monitoring point locations is superimposed and displayed on the dynamic three-dimensional geographic information model;

[0061] The regional environmental carrying index distribution calculated by the evaluation and early warning module is presented using a heat map layer;

[0062] The execution path of the set of real-time regulation instructions is marked by a dynamic flow line;

[0063] When receiving a hierarchical early warning signal, an impulse warning halo is generated at the corresponding geographic coordinate position.

[0064] Preferably, the present application further comprises a collaborative management method for chemical industry governance analysis and environmental protection data, applied to the collaborative management system for chemical industry governance analysis and environmental protection data as described above, the method comprising:

[0065] A set of historical environmental protection monitoring records of the chemical production area is collected through a multi-source environmental protection sensor network, including a set of waste gas emission concentration time series data, a set of waste water composition spectrum data and a set of solid waste production distribution data;

[0066] The set of historical environmental protection monitoring records is processed using a moving time window algorithm to generate waste gas concentration fluctuation factors, waste water composition fluctuation factors and solid waste production fluctuation factors;

[0067] Performing a spatial superposition operation in a three-dimensional pollution characteristic space, determining a key pollution monitoring point position set based on waste gas concentration fluctuation factors, waste water composition fluctuation factors and solid waste generation quantity fluctuation factors;

[0068] Matching a real-time regulation instruction set and a long-term treatment scheme set associated with the key pollution monitoring point position set from an environmental protection treatment strategy knowledge base;

[0069] Performing real-time data collection of the key pollution monitoring point position set according to a preset sampling frequency parameter, and synchronously driving the chemical production equipment to execute the real-time regulation instruction set;

[0070] Fusing pollution reduction effect feedback data and updating the weight distribution of the environmental protection treatment strategy knowledge base;

[0071] Calculating a regional environmental carrying index based on an environmental capacity threshold matrix and a pollutant diffusion model;

[0072] When the regional environmental carrying index breaks through a preset risk boundary, triggering a corresponding level of graded early warning signals according to a ladder type early warning threshold system.

[0073] Compared with the prior art, the beneficial effects of the present application are:

[0074] Through the organic cooperation of multiple modules, the integration and collaborative operation of environmental protection monitoring and treatment are realized. The pollution source monitoring module comprehensively collects historical monitoring data of various pollutants, providing complete basic information for subsequent analysis and avoiding the limitations brought by scattered data. The fluctuation analysis module adopts a dynamic fluctuation feature extraction method, calculates the dispersion degree index through a moving time window, can accurately capture the dynamic change law of waste gas, waste water and solid waste emissions, and reveals the fluctuation characteristics of pollutant emissions, so that the management personnel has a clearer understanding of the change trend of the pollutants.

[0075] The monitoring point optimization module performs adaptive optimization in the pollution characteristic space based on the fluctuation factor, so that the position and sampling frequency of the key pollution monitoring point are more in line with the actual pollution situation, ensuring that the monitoring data can accurately reflect the real emission state of the pollutants, reducing the data deviation caused by unreasonable monitoring point setting, and improving the scientificity and effectiveness of the monitoring work. The regulation strategy generation module combines key monitoring point information and an environmental protection treatment strategy knowledge base for multi-level matching, the output real-time regulation instruction and long-term treatment scheme are more targeted, can take corresponding treatment measures according to different pollution conditions, and realizes the precise docking of treatment strategies and actual pollution.

[0076] The dynamic execution module drives the sensor network monitoring according to the sampling frequency and issues real-time control instructions, while collecting pollutant reduction effect feedback data, forming a closed-loop process from monitoring to treatment to effect feedback, ensuring timely implementation of treatment measures and real-time tracking of effects. The collaborative optimization module integrates feedback data and long-term schemes for strategy iteration analysis, updating the strategy weight distribution of the knowledge base, so that the environmental protection treatment strategy knowledge base can continuously adapt to new pollution characteristics and treatment needs, allowing the treatment strategy to continuously improve in practice, enhancing the system's ability to respond to complex pollution conditions and promoting the overall improvement of chemical environmental management. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 A working principle diagram of the chemical treatment analysis and environmental protection data collaborative management system described in the present application is provided.

[0078] Figure 2 A flowchart for dynamic fluctuation characteristic extraction by the fluctuation analysis module is provided.

[0079] Figure 3 A flowchart for multi-level strategy matching by the control strategy generation module is provided.

[0080] Figure 4 A flowchart for strategy iteration analysis by the collaborative optimization module is provided. DETAILED DESCRIPTION

[0081] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0082] Please refer to Figure 1 The present application provides a chemical treatment analysis and environmental protection data collaborative management system, which comprises:

[0083] The historical environmental protection monitoring record set of the chemical production area is collected by a multi-source environmental protection sensor network, including a waste gas emission concentration time series data set, a waste water composition spectrum data set, and a solid waste production distribution data set. After the pollution source monitoring module obtains the above data, the fluctuation analysis module traverses the historical environmental protection monitoring record set, calculates the dispersion degree index of each data set using a moving time window algorithm, and generates waste gas concentration fluctuation factors, waste water composition fluctuation factors, and solid waste production fluctuation factors. The monitoring point optimization module determines the key pollutant monitoring point position set and the corresponding sampling frequency parameters based on these fluctuation factors in the preset three-dimensional pollution characteristic space by performing adaptive optimization operations. The control strategy generation module inputs the key pollutant monitoring point position set, loads the environmental protection governance strategy knowledge base for multi-level strategy matching, and outputs a real-time control instruction set and a long-term governance scheme set. The dynamic execution module drives the multi-source environmental protection sensor network to perform monitoring tasks according to the sampling frequency parameters, and transmits 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 iteratively analyzes the strategy by fusing the pollutant reduction effect feedback data set and the long-term governance scheme set, updates the strategy weight distribution in the environmental protection governance strategy knowledge base, and generates a strategy optimization instruction.

[0084] Embodiment 1: refer to Figure 2 When the fluctuation analysis module extracts dynamic fluctuation characteristics from the historical environmental protection monitoring record set, a variable-length moving time window algorithm is used. The window slides continuously on the waste gas emission concentration time series data set, covering data points within a certain time range each time. In each window, the standard deviation value of the data points is calculated to reflect the dispersion degree of the waste gas concentration in that time period. The standard deviation values of the three consecutive windows are processed by weighted average, and the weights are dynamically adjusted according to the number of data points and the time span in the window to generate a local fluctuation intensity index. This index can capture the short-term fluctuation characteristics of waste gas emission concentration, avoiding accidental bias caused by single window calculation.

[0085] After extracting the peak value sequence of all local fluctuation intensity indexes, a normal distribution fitting method is used to analyze the peak value distribution law. The parameters of the probability density function of fluctuation intensity are determined by maximum likelihood estimation, including the expected value and the variance. The waste gas concentration fluctuation factor is calculated by the product of the expected value and the variance of the probability density function, which comprehensively reflects the overall fluctuation amplitude and frequency characteristics of the waste gas emission concentration. The same processing flow is used for the waste water composition spectrum data set to calculate the waste water composition fluctuation factor. For the solid waste production distribution data set, due to the different data characteristics, a time weighting coefficient is additionally introduced during the moving window calculation to reflect the change trend of solid waste production in different time periods.

[0086] The monitoring point optimization module performs adaptive optimization operations in a three-dimensional pollution characteristic space based on the above fluctuation factors. The three coordinate axes of this space correspond to the waste gas diffusion characteristic surface, the waste water migration characteristic curve, and the solid waste accumulation characteristic field, respectively. The waste gas diffusion characteristic surface is modeled by historical meteorological data and terrain characteristics, reflecting the diffusion law of waste gas under different environmental conditions. After the waste gas concentration fluctuation factor is input into the surface, the system identifies the diffusion sensitive area, i.e., the geographical location where the waste gas concentration fluctuates greatly and the diffusion range is wide, and generates a set of coordinates of the diffusion sensitive area.

[0087] The waste water migration characteristic curve is constructed based on hydrogeological data, simulating the migration path of waste water components in soil and groundwater. After the waste water component fluctuation factor is projected onto the curve, the system analyzes the high migration risk section, i.e., the area where the waste water component fluctuates significantly and may affect the downstream environment, and determines its endpoint coordinates. The solid waste accumulation characteristic field is generated by a spatial interpolation algorithm, reflecting the accumulation density distribution of solid waste in the plant area. After the solid waste generation fluctuation factor is input into the characteristic field, a density clustering algorithm is used to identify the solid waste accumulation core point, i.e., the area coordinates where the waste generation fluctuates greatly and the accumulation density is high.

[0088] The system performs spatial superposition operations on the diffusion sensitive area coordinate set, the high migration risk section endpoint coordinate, and the solid waste accumulation core point coordinate. By calculating the overlapping area of each coordinate set, the center of gravity position is determined. Taking the center of gravity position as the center, a polygon monitoring area is generated according to the preset radiation radius. The radiation radius is dynamically adjusted according to the diffusion ability of the pollutant and the environmental sensitivity, ensuring that the monitoring range covers the main pollution risk points. The vertex coordinates of the polygon monitoring area constitute the key pollutant monitoring point position set, and the system assigns the corresponding sampling frequency parameters according to the fluctuation factor size of the area where each monitoring point is located.

[0089] In the modeling process of the waste gas diffusion characteristic surface, the system integrates historical meteorological data, including wind speed, wind direction, temperature gradient, etc., to construct a multi-dimensional diffusion model. The identification of the diffusion sensitive area not only considers the concentration fluctuation factor, but also combines terrain elevation data and surface roughness to correct the prediction results of the diffusion path. The construction of the waste water migration characteristic curve introduces hydrogeological parameters such as soil permeability coefficient and groundwater flow direction, improving 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 clustering radius according to the data distribution characteristics, ensuring the reliability of the core point identification.

[0090] Example 2: see Figure 3, the regulation policy generation module loads the tree structure of environmental governance policy knowledge base, the branch level of the knowledge base is indexed according to the type of governance technology, including physical treatment, chemical treatment, biological treatment and other categories, each category is further subdivided into specific technology branches. The leaf level stores the strategy parameter combination, each combination contains device operating parameters, reagent addition ratio, reaction time and other adjustable variables, and is attached with historical execution effect records. After the key pollution monitoring point position set is input into the system, through the spatial mapping algorithm and the branch level of the knowledge base, all leaf nodes under the corresponding governance technology type are activated. The system extracts the strategy parameter combination of each leaf node, calculates the matching degree value of its current waste gas concentration fluctuation factor and waste water composition fluctuation factor. The matching degree calculation uses the following formula:

[0091]

[0092] wherein, represents the matching degree value of the th strategy parameter combination, is the total number of fluctuation factor types, is the weight coefficient of the th fluctuation factor, is the historical reference value of the th fluctuation factor recorded in the th strategy parameter combination, is the actual value of the th fluctuation factor obtained by current monitoring. The weight coefficient is dynamically determined by entropy method, and the weight of each fluctuation factor is allocated according to its information entropy in historical data. The lower the entropy value, the higher the weight of the factor.

[0093] The strategy parameter combination with the highest matching degree value is determined as the core strategy unit. The system traverses the remaining leaf nodes, filters the combinations associated with the core strategy unit, including strategies that share the same device control parameters or use similar reagent formulations. These combinations constitute a strategy association cluster, and their parameters are fused with the core strategy unit. The fusion process uses a weighted superposition method, and the parameters of each strategy in the association cluster are allocated weights according to their matching degree values, finally generating a real-time control instruction set and a long-term governance scheme set. The real-time control instruction set contains specific device control commands, such as fan speed adjustment, dosing pump start-stop timing, etc.; the long-term governance scheme set involves process route optimization, equipment upgrade suggestions and other macro strategies.

[0094] The dynamic execution module generates a sensor trigger pulse sequence according to the sampling frequency parameter. The time interval of the pulse sequence is dynamically adjusted by the pollution index of each point in the monitoring point position set, and 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 pollution monitoring point position set. The sampling data is aggregated to the central processing unit through the wireless transmission protocol, and is subjected to outlier filtering processing. The outlier filtering adopts the sliding quartile range method, and the upper quartile of the data stream is calculated in real time and the lower quartile , and the data points falling outside the range are removed, where . The filtered data form a standardized monitoring data stream, and the timestamp and spatial coordinate information are kept intact.

[0095] The standardized monitoring data stream is input into the pollutant diffusion model for instantaneous influence range simulation. The model couples the computational fluid dynamics and the material transport equation, takes the monitoring data as the boundary condition, and solves the pollutant concentration distribution in three-dimensional space. The simulation outputs the boundary coordinates of the diffusion influence, i.e. the spatial range profile where the concentration exceeds the threshold. The system analyzes the spatial relationship between the boundary coordinates and the preset environmental sensitive region coordinates, and uses the ray method to determine the coordinate overlap. When an overlap region is identified, a high-response control instruction is generated. The priority of the instruction is divided according to the overlap area ratio and the toxicity level of the pollutant, and the instruction with the highest priority is written to the head position of the real-time control instruction set.

[0096] The industrial control bus transmits the real-time control instruction set using the time-triggered protocol. The instructions are sorted by priority and encapsulated into data frames, each frame header containing an execution timestamp and a target device address. The device execution mechanism receives the data frame and immediately implements the specific control parameters. The device state feedback signal is fed back to the dynamic execution module through the same bus, forming a closed-loop control loop. The pollutant reduction effect feedback data set is generated from the feedback signal and the subsequent monitoring data, recording the pollutant concentration change rate, device energy consumption increment, etc. before and after the execution of each control instruction.

[0097] During the generation of the strategy association cluster, the system introduces the community discovery algorithm in graph theory. The strategy parameter combination is abstracted as a node, and the parameter similarity is used as the edge weight. The Louvain method is used to identify the tightly connected subgraph structure. Each subgraph corresponds to a strategy association cluster, and the parameter difference between the internal nodes is lower than the preset threshold. The parameter fusion of the core strategy unit and the association cluster uses the non-dominated sorting method to avoid the over-dominance of a single strategy. The generation of the real-time control instruction set also considers the device response delay characteristics. For devices with large inertia, the instruction execution time is calculated in advance and time compensation is added.

[0098] The sensor trigger logic of the dynamic execution module supports multiple synchronization modes. In the basic mode, all monitoring points are sampled at a uniform frequency; in the event-triggered mode, the sampling frequency of the associated area is automatically increased when the data of a certain monitoring point is abnormal. The sliding window size of the outlier value filtering algorithm is dynamically adjusted according to the half-life of the pollutants, with shorter windows 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 dispersion model runs regularly to update key parameters such as turbulence coefficient and degradation rate using the latest monitoring data.

[0099] The coordination between the control strategy generation module and the dynamic execution module forms a closed-loop optimization system. The selection process of the core strategy unit introduces robustness evaluation, giving priority to strategies with smaller historical execution variance. The fusion results of the strategy association cluster are verified through Monte Carlo simulation to test their adaptability under different environmental conditions. The delivery of the real-time control instruction set uses a priority preemption mechanism, with high-response instructions interrupting the execution of low-priority instructions. The version management of the long-term governance scheme set uses semantic numbering to record the changes and applicable scope of each iteration.

[0100] Embodiment 3: refer to Figure 4 When the collaborative optimization module establishes the strategy effect evaluation matrix, it uses a three-dimensional structure to organize the data. The row dimension of the matrix corresponds to the strategy number in the long-term governance scheme 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 decrease in pollutant concentration; the execution cost coefficient integrates economic indicators such as equipment energy consumption, reagent consumption, and manual intervention frequency; 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 uses a sliding time window mechanism, and the pollutant reduction effect feedback data set is divided into consecutive time blocks. The data in each time block is normalized and filled into the corresponding column of the matrix.

[0101] The calculation of the Euclidean distance value is performed for each row vector of the strategy effect evaluation matrix, which is used to quantify the differences in the overall effects of different strategies. The calculation process introduces a dynamic weight adjustment mechanism, which allocates the weight proportion of the three types of indicators according to the focus of the current environmental management objectives. When the system is in the strict pollution emission control stage, the weight of the pollutant reduction rate is increased; in the cost control priority stage, the weight proportion of the execution cost coefficient is increased. The selection 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 accidental outstanding effects. The stability score is calculated by analyzing the execution effect fluctuation amplitude of the strategy in different historical periods, with smaller fluctuation amplitude and higher score.

[0102] Crossover and mutation operations are performed among high-quality policy seeds, employing a combination of multi-point crossover and Gaussian mutation. The crossover points are dynamically selected based on the type of policy parameters: arithmetic crossover is used for continuous parameters, while 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 mutation amplitude is neither too large, leading to policy failure, nor too small, rendering the optimization meaningless. During the generation of the new policy parameter population, a copy of the original high-quality policy seed is retained as a benchmark to prevent the loss of existing superior characteristics during optimization.

[0103] The temporary storage layer of the environmental governance strategy knowledge base adopts a hierarchical storage structure. New strategy parameter populations are assigned to the experimental zone, isolated from strategies in the formal zone. Initial weight values ​​are set based on the matching degree between the strategy parameter population and the current environmental conditions, calculated using a parameter space projection algorithm. This algorithm maps current monitoring data to a high-dimensional parameter space and calculates the distribution density of new strategies in this space; higher density results in larger initial weights. After a preset number of validation cycles, strategies in the temporary storage layer that meet the effectiveness evaluation criteria are transferred to the formal zone, while those that do not are eliminated. Evaluation metrics for the validation cycles include the strategy's convergence speed, robustness, and scalability, avoiding premature elimination of potentially excellent strategies.

[0104] The row vector standardization of the strategy effectiveness evaluation matrix uses the range method to eliminate the influence of differences in the dimensions of different indicators. For the pollutant reduction rate indicator, an additional logarithmic transformation is introduced to mitigate the numerical dominance effect of the reduction rate for high-concentration pollutants. The formula for calculating the Euclidean distance is as follows:

[0105]

[0106] in, Indicates the first The weighted Euclidean distance values ​​for each strategy For the first The dynamic weights of each indicator For the first The strategy in the first Standardized scores on each indicator For the first Ideal reference values ​​for each indicator. The environmental management objectives are dynamically set, the pollutant reduction rate is taken as the historical best value, the execution cost coefficient is taken as the lower limit of the budget constraint, and the environmental improvement index is taken as the ecological security threshold.

[0107] In the specific implementation of the crossover and mutation operation, an adaptive mechanism is used for selecting the crossover points. When the difference between two parent strategies on a certain parameter exceeds a threshold, that parameter position automatically becomes a candidate crossover point. The difference is calculated using the relative error method to avoid bias caused by absolute value differences for strategies with different parameter value ranges. The standard deviation of Gaussian mutation is used. With respect to the allowable adjustment range of parameters satisfy The system ensures 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 reagent dosage does not exceed the dissolution limit or the equipment load does not exceed the rated capacity, and eliminates unreasonable mutant individuals.

[0108] The weight update mechanism of the temporary storage layer adopts a reinforcement learning approach. Each time the new strategy parameter population completes a task during the validation period, its weights are incrementally adjusted based on the task completion rate. The completion rate evaluation function considers the ratio of actual pollutant reduction rate to expected targets, cost overrun ratio, and environmental improvement achievement rate. The weight adjustment magnitude uses an S-shaped curve function to avoid excessive influence of a small number of initial task results on the weights. A cooling-off period is set for strategy migration between the experimental and formal areas; new strategies must complete the entire validation period and pass stability testing before migration, preventing frequent migrations from causing knowledge base oscillations.

[0109] The high-dimensional mapping of the parameter space projection algorithm employs kernel principal component analysis. The feature matrix, composed of policy parameters and monitoring data, is mapped to the regenerating kernel Hilbert space using a Gaussian kernel function. In this space, the Mahalanobis distance between the new policy population and the current optimal policy cluster is calculated. The distance values ​​are then transformed exponentially into initial weights, ensuring that the weights are distributed between 0 and 1. Version control of the knowledge base uses a snapshot mechanism. A complete backup of the official region is performed before each policy migration, supporting rollback to a historical stable version when necessary. Snapshot metadata records key information such as environmental condition characteristics and the distribution of policy execution effects, facilitating the traceability and analysis of policy evolution patterns.

[0110] During the cross-operation of high-quality strategy seeds, the system constructs a parameter dependency graph. Nodes in the graph represent strategy parameters, and edges represent the physical or logical relationships between parameters. The selection of cross-points prioritizes maintaining the integrity of dependencies, avoiding splitting strongly correlated parameters into different offspring strategies. For example, reactor temperature and stirring speed are typically strongly correlated; during cross-operation, they are treated as a single unit. 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.

[0111] The verification period of the new strategy parameter population is implemented in a combination of simulation and field test. The simulation environment integrates a chemical process simulator, which quickly tests the performance of the strategy under various operating conditions. The field test selects a non-critical production unit for small-scale verification. Deviation analysis of simulation results and field data is used to calibrate the simulator parameters, improving the credibility of subsequent simulation tests. The collection of verification data covers typical scenarios such as normal production, equipment start-up and shut-down, and load fluctuations, ensuring the comprehensiveness of the strategy evaluation. The capacity limit of the temporary storage layer uses the least recently used eviction policy, which removes strategies with long-term weight increases when space is insufficient, maintaining the dynamic adaptability of the optimization process.

[0112] In the construction of the dynamic evaluation framework by the early warning module, the chemical industrial park is divided into 200m x 200m geographical grid units, each containing capacity thresholds for three types of environmental media: air, water, and soil. The air capacity threshold is set based on the hourly concentration limit of pollutants in the Ambient Air Quality Standard, the water capacity threshold refers to the Class III water standard in the Environmental Quality Standard for Surface Water, and the soil capacity threshold uses the screening value in the Risk Control Standard for Construction Land Soil Environmental Quality. Real-time monitoring data from the set of key pollution monitoring point locations is used to generate a grid-based pollution distribution map using the Kriging spatial interpolation algorithm. The pollution load proportion value for each grid unit is calculated by dividing the actual monitoring concentration by the capacity threshold.

[0113] A partial example of the environmental capacity threshold matrix for a certain chemical plant area is shown in the following table, which shows the threshold data for grid units G-07 to G-09.

[0114] Table 1: Partial example of the environmental capacity threshold matrix for a certain chemical plant area, showing threshold data for grid units G-07 to G-09.

[0115]

[0116] The calculation process of the local overload coefficient takes into account the synergistic effect of pollutants. When multiple pollutants exist in a certain grid unit, the additive model is used to calculate the comprehensive load proportion. For example, in grid G-07, benzene series concentration is detected at 0.3 mg / m³ and COD concentration is 0.04 mg / L, the atmospheric load proportion is 0.3 / 0.5 = 60%, and the water load proportion is 0.04 / 0.05 = 80%, and the comprehensive overload coefficient is taken as the maximum value of 80%. Spatial autocorrelation analysis uses the Moran's I index, with a spatial lag distance of 500m set, to identify the coordinates of high aggregation overload areas. These coordinates are marked on the digital map of the plant area using geofencing technology, forming dynamic early warning hot zones.

[0117] The trigger logic of the hierarchical early warning signals adopts a multi-level progressive mechanism. Taking the exhaust gas treatment system as an example, when the sulfur dioxide concentration of the G-08 grid first reaches the blue warning threshold (80% of the capacity threshold), the system activates the device-level regulation instruction review mechanism. This mechanism automatically checks the operating parameters of the exhaust gas purification equipment associated with the grid, including the alkali solution circulating pump frequency, the scrubber pH value, etc., and compares them with the best practice parameters in the knowledge base. If the parameter deviation exceeds 5%, calibration instructions are immediately generated and pushed to the device control system.

[0118] When the overload coefficients of three adjacent grids (such as G-07 to G-09) all reach the yellow warning threshold (90% of the capacity threshold), and the Moran's I index shows significant clustering (p < 0.05), the system starts the plant-level production load adjustment protocol. The protocol prioritizes interruptible production processes, such as delaying batch feeding, reducing reaction kettle temperature, etc. In a certain actual operation, the system detected that the three grid units of the acrylic acid production line simultaneously triggered a yellow early warning, automatically stepped down the feed rate of the oxidation reactor from 1200 L / h to 900 L / h, and within 30 minutes, the overload coefficient fell back to the safe range.

[0119] The determination of the red warning threshold (100% of the capacity threshold) breakthrough event introduces a time persistence condition. In a certain ammonium nitrate storage area, the ammonia gas concentration was detected to exceed the red threshold of 55 mg / m³ for three consecutive monitoring periods (15 minutes per period), and the system triggered the regional-level shutdown and emission reduction instruction. The instruction execution sequence includes: immediately closing the feed valve of the related process unit, starting the emergency absorption tower, activating the surrounding sprinkler system. At the same time, the 3D model of the affected area is automatically popped up on the large screen of the control center, showing the pollutant diffusion simulation trajectory and the status of emergency equipment. Historical data shows that this mechanism has effectively prevented the expansion of an ammonia gas leakage accident caused by a cooling system failure.

[0120] The removal of the early warning signal adopts a double-condition verification mechanism. For blue early warning, the monitoring value needs to fall below the threshold for two consecutive periods and the device parameters need to be calibrated; yellow early warning requires the overload area to be reduced by more than 50% and remain stable for two hours; red early warning must be manually confirmed, checking the on-site disposal effect and completing the safety hazard investigation. During the red early warning removal process of a certain chlorine gas treatment system, the system required the operator to upload emergency treatment records, equipment repair reports, and environmental re-measurement data, forming a complete closed-loop management file.

[0121] The threshold adaptive adjustment function of the dynamic assessment framework is achieved through machine learning. The system analyzes the relationship between historical early warning events and subsequent environmental recovery data to dynamically optimize the capacity thresholds of each grid cell. For example, the threshold of volatile organic compounds in a certain area is initially set at 0.3 mg / m³, but long-term monitoring shows that the area can still maintain environmental safety at a concentration of 0.4 mg / m³ through natural diffusion. After three months of data accumulation, the system automatically adjusts the threshold by 10%. The adjustment process sets safety constraints, with a single adjustment amplitude not exceeding 15% of the original value, and requires expert system rationality verification.

[0122] The parameter configuration of the spatial interpolation algorithm is differentiated for different pollutant characteristics. The range parameter of gaseous pollutants is set to 800 m, reflecting their diffusion ability; the range of liquid pollutants is set to 500 m, corresponding to the characteristics of groundwater flow speed; and the range of solid particles is set to 300 m, matching their settling characteristics. In a heavy metal pollution monitoring, the system detects a peak of cadmium pollution in G-07 grid, and by setting a specific range parameter of 200 m, it accurately identifies the pollution source as the drainage outlet of the electroplating workshop in the northwest corner of the grid.

[0123] The visualization of early warning information uses multi-dimensional coding. On the electronic map, blue early warning is displayed as a light blue semi-transparent wave, yellow early warning is converted to an orange pulse light ring, and red early warning is presented as a flashing red radial icon. At the same time, in the three-dimensional model, different heights of column charts represent the overload degree of various pollutants, supporting operators to quickly locate the main pollution factors. In a complex pollution event, the system displays red column charts of benzene series and yellow column charts of ammonia gas, directly indicating that benzene series leakage source should be prioritized.

[0124] The intelligent scheduling of emergency response resources is based on the matching of early warning levels and pollution types. Blue early warning usually triggers automated adjustment instructions, yellow early warning dispatches workshop-level emergency teams, and red early warning starts full-plant emergency response. The resource allocation algorithm considers the diffusion speed of pollutants, with mobile purification equipment being preferentially allocated for gaseous pollution warnings, and liquid pollution events focusing on leak-blocking materials. Historical records show that this mechanism has reduced the average emergency response time by 40% and improved resource utilization by 25%.

[0125] The root cause analysis of early warning events uses time series pattern mining technology. The system establishes a timeline of early warning events and correlates device failure records, production logs, and environmental monitoring data. In the analysis of a persistent yellow early warning, the system found that the temperature control system of the reactor had abnormal fluctuations 2 hours before the early warning, which was confirmed to be a chain reaction caused by calibration failure of the temperature sensor. Such analysis results are automatically stored in the case library for optimizing device preventive maintenance plans.

[0126] The seasonal adjustment module of the environmental capacity threshold integrates meteorological historical data. In winter, when inversion frequently occurs, the system automatically lowers the capacity threshold of areas with poor atmospheric diffusion conditions; in the rainy season, the water body threshold is adjusted to prevent non-point source pollution. Before the arrival of typhoon season, the system temporarily lowers the water body capacity threshold of the riverfront grid unit by 20% according to weather forecasts to prevent the risk of pollutants entering the river caused by rainwater erosion. The adjustment results are reported to the environmental management department through the approval workflow for record-keeping to ensure compliance.

[0127] The test and verification of the early warning system use the fault injection method. During non-production periods, the system automatically simulates various pollution scenarios, including instantaneous leakage, continuous emission, and compound pollution, to test the effectiveness of the early warning trigger logic and response process. In one simulation test, the system injected an over-standard signal of benzene series into the virtual G-07 grid, successfully triggered the escalation chain from blue to red warning, and recorded the response time of each link, providing quantitative basis for system optimization. The test cases cover all major environmental risk sources in the plant, forming a positive cycle mechanism for continuous improvement.

[0128] In the implementation of the governance effect visualization module, high-precision digital elevation data is used as the base terrain framework, and the BIM model details of the plant's building facilities are superimposed. The model coordinate system uses the national 2000 geodetic coordinate system, and the height reference uses the 1985 national height reference, ensuring that the spatial positioning accuracy reaches centimeters. The simulation results of the pollutant migration path are visualized through the particle system, with different pollutant types corresponding to specific colored particle flows: waste gas is displayed as a gray-white translucent particle group, wastewater is displayed as a blue band-shaped flow trajectory, and solid waste is displayed as a brown dot array representing the accumulation and diffusion process. The particle motion speed is linked with real-time monitoring data, and when the pollutant concentration increases, the density and motion speed of the corresponding particle flow increase accordingly.

[0129] The real-time data stream of the key pollutant monitoring point position set is displayed through dynamic label superposition. The data of each monitoring point is presented in a circular identifier, and the identifier diameter scales dynamically with the pollutant concentration value, with the color gradually changing from green to red to represent the transition from safe to dangerous. Clicking on the identifier can expand the detailed data panel, which displays the multi-pollutant concentration curve, historical trend comparison, and associated equipment status of that point. The data refresh frequency is synchronized with the sampling frequency of the monitoring module, and automatically increases to seconds when a warning event occurs. In an ammonia leakage event, the identifier of monitoring point G-07 changed from green to deep red within 10 seconds, with the diameter expanding three times, and the operating parameters of the surrounding associated equipment were displayed in real time on the floating panel.

[0130] When rendering the environment carrying index distribution of the thermal map layer, the improved bilinear interpolation algorithm is used to process the discrete monitoring point data. The rendering color palette defines six levels: dark green represents the safe state (index <0.3), light green is the warning state (0.3-0.5), yellow is the mild overload (0.5-0.7), orange is the moderate overload (0.7-0.9), red is the severe overload (0.9-1.0), and purple is the extreme overload (>1.0). The transparency of the heat map is automatically adjusted according to the view zoom level. When the global view is set to 50% transparency to observe the underlying geographic information, the local zoom is set to 90% to highlight the details. The heat rendering uses WebGL acceleration technology to support smooth display of large-scale data sets containing hundreds of thousands of grid units in mainstream browsers.

[0131] The execution path of the real-time control instruction set is presented through dynamic flow line annotation. The flow line 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 for parameter fine-tuning instructions, yellow for device start-stop instructions, and red for emergency handling instructions. The flow line width is proportional to the instruction priority, with high-response control instructions corresponding to thick solid lines and regular instructions displayed as thin dashed lines. The flow line animation uses particle tracking effects, with light points moving along the path at a speed reflecting the instruction transmission delay. When the industrial bus communication delay exceeds 200ms, the light point movement speed automatically slows down and triggers a yellow flashing reminder. During a full-plant load adjustment process, the visualization interface simultaneously displays 56 control instruction flow lines radiating from the control center to various production devices. Operators can quickly identify instruction-intensive areas by the density distribution of flow lines.

[0132] When the hierarchical early warning signal is triggered, a pulse-type warning halo is generated at the corresponding geographical coordinate position, containing three layers of visual elements. The inner layer is a high-frequency flashing solid ring, with the color corresponding to the warning level (blue / yellow / red); the middle layer displays outwardly expanding concentric ripples, with the ripple interval time synchronized with the pollutant diffusion speed; the outer layer presents a semi-transparent radial light strip, with the light strip length proportional to the environmental carrying index overload amplitude. The display priority of the light halo is set to the highest, and when overlapping with other visualization elements, the viewing angle is automatically adjusted to ensure visibility. The warning halo is removed using a gradual fading animation, which continues to display for 5 seconds after the warning condition disappears, avoiding sudden interruption of visual information. In a complex early warning event, the G-07 to G-09 grid simultaneously appears in red, yellow, and blue light halos, and the system automatically switches to a three-dimensional overhead view to clearly display the spatial relationship of multiple layers of early warning.

[0133] The interactive function of the three-dimensional model supports multi-dimensional data exploration. When the mouse hovers over any grid cell, a pop-up window displays detailed composition analysis of the environmental carrying index of that cell, including atmospheric, water body, and soil sub-indexes and the contribution of major pollutants. The time axis control can be dragged to replay the historical pollution diffusion process, with adjustable speed ranging from 1 to 60 times real-time speed. In the comparative analysis mode, the user can select the model states of two time points to display side by side, with the differences highlighted in pulses. 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 effects of different disposal schemes and assist decision-makers in quickly evaluating the pros and cons of the schemes.

[0134] The data-driven architecture of the visualization system adopts a publish-subscribe mode. Monitoring data updates are broadcast through a message queue, and each visualization component subscribes to related data topics as needed. When the exhaust concentration data is updated, only the corresponding components in the particle system, monitor point identifier, and heat map layer will trigger a redraw, and irrelevant components remain static. This mechanism effectively reduces system resource consumption and maintains a rendering efficiency of 30 frames per second on ordinary workstations. User operation events are transmitted through an event bus, supporting multi-view linked responses. For example, when a region is selected in the plan view, the three-dimensional view automatically rotates to the best viewing angle, and the trend chart window synchronously loads the historical data curves of the selected region.

[0135] Parameterized configuration of visual elements supports scene adaptive adjustment. During the day-night transition, the system automatically switches between light / dark themes to ensure visibility: the daytime mode uses high-contrast color schemes, and the nighttime mode reduces brightness and increases lighting effects. For users with color vision impairment, an alternative color palette option is provided to assist in distinguishing colors by shape differences. The display density is dynamically adjusted according to hardware performance, and when high GPU load is detected, the number of particles and the accuracy of the heat grid are automatically simplified. These optimization measures ensure the stable operation of core functions on different specifications of terminal devices, from large screen arrays in control centers to tablets used by field personnel, providing a consistent interactive experience.

[0136] The historical data backtracking function uses a hierarchical loading technique. Early data is automatically down-sampled for storage, and near-real-time data retains original accuracy. When the user views a pollution event three months ago, the system loads daily aggregated data; when analyzing an event within the last hour, it displays minute-level original records. The backtracking process supports the addition of virtual monitoring points, allowing the user to set observation points at any location in the historical scene and generate simulated data curves for comparative analysis of the rationality of actual monitoring point layout. In an evaluation of a device modification, engineers added virtual points to the historical model to verify the necessity of the new monitoring points, and by comparing virtual data with post-modification data, they confirmed that the new monitoring points shortened the abnormal detection response time by 40%.

[0137] The multi-user collaboration function is implemented through operation logs and state synchronization. The view operations and annotation actions of each logged-in user are recorded in shared memory in real time, supporting up to 16 terminals to synchronize and collaborate. The leader's decision view automatically focuses on key indicators and overall trends, while the technician's view highlights device parameters and local details. The comment information during the collaboration process is saved in a geographically anchored manner, with the comment bubble permanently associated with a specific coordinate, and the position remains unchanged when viewed again. In cross-department emergency consultations, users with different roles can add classification annotations on the same model, with the security department marking evacuation routes and the production department identifying the status of key equipment, forming a comprehensive disposal view.

[0138] The visual mapping rule library of environmental parameters contains more than 200 preset schemes. Special visual representations are preset for different types of pollutants: corrosive substances display corrosion special effects particles, persistent organic pollutants increase biological enrichment animation, and malodorous substances associate with odor diffusion simulation. Users can customize mapping rules, such as converting noise monitoring data into sound wave ripple visualization and presenting vibration data as ground wave effect. These schemes can be quickly applied through drag-and-drop, and a targeted observation perspective can be quickly established in special pollution incidents. In a sudden hydrogen sulfide leakage incident, the system calls the preset toxic gas visualization scheme and immediately switches to a display mode focused on respiratory protection, assisting emergency personnel in determining dangerous areas.

[0139] The model update mechanism realizes incremental data fusion. When new monitoring points or geographic information is adjusted, the system only recalculates the visualization elements of the affected area, and the unchanged area remains in the existing state. This mechanism enables local updates in large-scale scenarios to be completed in milliseconds, avoiding the lag caused by full-scene refresh. The cache of spatial analysis results uses an intelligent expiration strategy, with analysis results in static areas being retained for a long time and calculation results in high-frequency change areas being set to a short validity period. The system runs a data quality monitoring thread in the background, which automatically isolates problem data and triggers data verification processes when detecting abnormal spatial coordinates or outliers, ensuring that the accuracy of the visualization is always under control.

[0140] The decision support function integrates a spatial analysis toolkit. The buffer analysis tool can generate a 500m, 1000m, etc. buffer zone around the pollution source with one click, and superimpose basic geographic data such as population density and sensitive facilities. The viewshed analysis module simulates the actual observation range of the monitoring point, identifying areas that may have monitoring dead zones. The network analysis tool calculates the optimal emergency disposal path, taking into account real-time traffic conditions and pollution diffusion direction. The operation results of these tools are directly fed back to the three-dimensional scene, and the analysis process supports real-time parameter adjustment and immediate result comparison. In the recent emergency drill, the commander found through viewshed analysis that there was a monitoring blind spot on the west side of the fence, and timely adjusted the patrol route of the mobile monitoring vehicle.

[0141] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it should be taken in its broadest possible sense. This is especially so where "comprising", "including", "containing", "characterized by" or the like are used as subordinating conjunctions. In this context, "comprising" is intended to mean that the embodiments can include a combination of one or more elements and / or features, including those not expressly described, without necessarily relying on any of the other features or elements not expressly described. In addition, it is to be understood that the described embodiments include any alterative or equivalent implementations or substitutions for one or more of the elements or features described. Any such alternatives or equivalents are intended to be encompassed by the description of the described embodiments.

[0142] While the embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since the scope of the expenditure of the application is defined with respect to the appended claims.

Claims

1. A collaborative management system for chemical treatment analysis and environmental protection data, characterized in that, The system includes: The pollution source monitoring module acquires a set of historical environmental monitoring records, including a time-series dataset of exhaust gas emission concentrations, a dataset of wastewater composition profiles, and a dataset of solid waste generation distribution. The volatility analysis module iterates through the historical environmental monitoring record set to extract dynamic volatility features, calculates the dispersion index of each dataset through a moving time window, and generates the waste gas concentration volatility factor, wastewater composition volatility factor, and solid waste generation volatility factor. The monitoring point optimization module performs an adaptive optimization operation within a preset pollution characteristic space based on the exhaust gas concentration fluctuation factor, wastewater composition fluctuation factor and solid waste generation fluctuation factor to determine the set of key pollutant monitoring point locations and corresponding sampling frequency parameters. The regulation strategy generation module takes the set of key pollutant monitoring point locations as input, loads the environmental governance strategy knowledge base to perform multi-level strategy matching, and outputs a set of real-time regulation instructions and a set of long-term governance schemes. The dynamic execution module drives the multi-source environmental protection sensor network to perform monitoring tasks according to the sampling frequency parameters, and at the same time sends the real-time control command set to the chemical production equipment control system to generate a pollutant reduction effect feedback dataset. The collaborative optimization module integrates the pollutant reduction effect feedback dataset with the long-term governance scheme set to perform strategy iterative analysis, updates the strategy weight distribution in the environmental governance strategy knowledge base, and generates strategy optimization instructions. When the volatility analysis module extracts dynamic volatility features: Set a variable-length shift time window and continuously slide the window over the time-series dataset of exhaust gas emission concentrations; Calculate the standard deviation of data points within each moving time window, and perform a weighted average of the standard deviations of three consecutive windows to obtain the local fluctuation intensity index. Extract the peak sequence of all local fluctuation intensity indices, perform normal distribution fitting on the peak sequence, and determine the fluctuation intensity probability density function; The product of the expected value and variance of the fluctuation intensity probability density function is 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. When the monitoring point optimization module performs adaptive optimization: A three-dimensional pollution feature space is pre-constructed, in which the coordinate axes are mapped to the waste gas diffusion feature surface, the wastewater migration feature curve, and the solid waste accumulation feature field, respectively. Input the exhaust gas concentration fluctuation factor into the exhaust gas diffusion characteristic surface to obtain the corresponding set of coordinates of the diffusion-sensitive area; The wastewater component fluctuation factor is projected onto the wastewater migration characteristic curve to identify the endpoint coordinates of high migration risk sections. Based on the solid waste generation fluctuation factor, density clustering analysis is performed on the solid waste accumulation characteristic field to generate the coordinates of the core points of solid waste aggregation. Spatial overlay calculations were performed 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 centroid coordinates of the overlapping areas. Using the centroid coordinates as the center point, a polygonal monitoring area is generated according to a preset radiation radius, and the vertex coordinates of the polygonal monitoring area are used as the set of key pollutant monitoring point locations.

2. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 1, characterized in that, When the regulation strategy generation module performs multi-level strategy matching: Load a tree-structured environmental governance strategy knowledge base, which includes a branch-level index of governance technology types and a leaf-level combination of strategy parameters; Map the set of key pollutant monitoring point locations to the branch level and activate all leaf nodes under the corresponding treatment technology type index; Extract historical execution effect records from the strategy parameter combinations at the blade level, and calculate the matching degree values ​​between each strategy parameter combination and the current exhaust gas concentration fluctuation factor and wastewater composition fluctuation factor. Select the strategy parameter combination with the highest matching degree as the core strategy unit; Traverse the remaining leaf nodes for strategy parameter combinations that are associated with the core strategy unit to form a strategy association cluster; By fusing parameters of core strategy units and strategy-related clusters, a set of real-time control instructions and a set of long-term governance solutions are generated.

3. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 1, characterized in that, When the dynamic execution module performs the monitoring task: A sensor trigger pulse sequence is generated based on the sampling frequency parameter to drive the multi-source environmental protection sensor network to perform synchronous sampling at the set of key pollutant monitoring points. Outlier filtering is performed on the real-time pollutant concentration data obtained from sampling to form a standardized monitoring data stream; The standardized monitoring data stream is input into the pollutant diffusion model to simulate the instantaneous impact range, and the boundary coordinates of the diffusion impact are output. Compare the boundary coordinates of the diffusion impact with the coordinates of the preset environmentally sensitive area, and generate a high-response control command when coordinate overlap is detected; High-response control commands are preferentially written into the real-time control command set and then sent to the actuators of chemical production equipment via the industrial control bus.

4. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 3, characterized in that, When the collaborative optimization module performs strategy iterative analysis: Establish a strategy effectiveness evaluation matrix. The row dimension of this matrix corresponds to the strategy number in the set of long-term governance solutions, and the column dimension includes 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 of each row vector in the strategy performance evaluation matrix, and select the three row vectors with the smallest Euclidean distance 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; The new strategy parameter population is written into the temporary storage layer of the environmental governance strategy knowledge base and assigned initial weight values.

5. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 4, characterized in that, Also includes: The assessment and early warning module constructs a dynamic assessment framework that includes an environmental capacity threshold matrix and a pollutant diffusion model. It calculates the regional environmental carrying capacity index based on the historical environmental monitoring record set and the pollutant reduction effect feedback dataset. When the regional environmental carrying capacity index is found to exceed the preset risk boundary, a graded early warning signal is triggered. When the assessment and early warning module constructs the dynamic assessment framework: The environmental capacity threshold matrix is ​​divided into multiple geographic grid cells, each grid cell containing atmospheric capacity threshold, water capacity threshold, and soil capacity threshold. The real-time monitoring data of the key pollutant monitoring point set is spatially interpolated according to the pollutant type to generate a gridded pollutant distribution map; Calculate the percentage of pollutant load in each grid cell of the gridded pollutant distribution map; By superimposing the pollutant load percentage value with the corresponding environmental capacity threshold of the grid cell, the local overload coefficient is obtained; Spatial autocorrelation analysis was performed on all local overload coefficients to identify the coordinates of highly clustered overload regions.

6. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 5, characterized in that, When the assessment and early warning module triggers a graded early warning signal: Establish a tiered early warning threshold system that includes blue, yellow, and red warning thresholds; When the local overload coefficient first reaches the blue warning threshold, the equipment-level control command review mechanism is activated; When the area of ​​a highly concentrated overloaded zone reaches the yellow warning threshold, the plant-level production load adjustment protocol is activated. When the red alert threshold is exceeded for three consecutive time windows, a regional-level shutdown and emission reduction order is triggered.

7. The collaborative management system for chemical treatment analysis and environmental protection data according to claim 1, characterized in that, The system also includes a governance effect visualization module, which specifically performs the following: Construct a dynamic three-dimensional geographic information model that includes the migration paths of pollutants; Real-time data streams of key pollutant monitoring point locations are overlaid and displayed on a dynamic three-dimensional geographic information model; A heat map is used to present the distribution of the regional environmental carrying capacity index calculated by the assessment and early warning module. The execution path of the instruction set is controlled in real time by using dynamic streamline annotation; When a graded early warning signal is received, a pulsed warning halo is generated at the corresponding geographical coordinates.

8. A collaborative management method for chemical pollution control analysis and environmental protection data, applied to the collaborative management system for chemical pollution control analysis and environmental protection data as described in any one of claims 1-7, characterized in that, include: Historical environmental monitoring records of chemical production areas were collected through a multi-source environmental sensor network, including time-series datasets of exhaust gas emission concentrations, wastewater composition spectral datasets, and solid waste generation distribution datasets. The historical environmental monitoring record set was processed using a moving time window algorithm to generate fluctuating factors for waste gas concentration, wastewater composition, and solid waste generation. In the three-dimensional pollution feature space, spatial superposition operation is performed to determine the set of key pollutant monitoring point locations based on the fluctuating factors of exhaust gas concentration, wastewater composition, and solid waste generation. Match the set of real-time control instructions and the set of long-term governance plans associated with the location set of key pollutant monitoring points from the environmental governance strategy knowledge base; Real-time data acquisition of the key pollutant monitoring point location set is performed according to the preset sampling frequency parameters, and the chemical production equipment is simultaneously driven to execute the real-time control command set. The weight distribution of the environmental governance strategy knowledge base is updated by integrating feedback data on pollutant reduction effects with a set of long-term governance solutions. Calculate the regional environmental carrying capacity index based on the environmental capacity threshold matrix and pollutant diffusion model; When the regional environmental carrying capacity index exceeds the preset risk boundary, a graded early warning signal of the corresponding level is triggered according to the tiered early warning threshold system.

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