Regional influence assessment method for chemical sewage discharge

Abstract

The invention relates to the technical field of chemical environment assessment, and discloses a regional influence assessment method for chemical sewage discharge. The method comprises the following steps: acquiring historical discharge data and regional environment monitoring data of a chemical sewage discharge source, generating a historical influence data set, extracting characteristic parameters, and establishing a trend change characteristic matrix; a multi-dimensional influence space is constructed, and a feature early warning index set is determined by calculating the state aggregation degree of historical pollution events; a regional environment digital twinborn model is constructed, real-time emission and environment monitoring data collected by a sensor are synchronized, an environment state is simulated in the model, and potential influences are predicted. Extracting real-time characteristic parameters according to the real-time emission data, establishing a state vector, calculating spatial position correlation with the characteristic early warning index set, and determining a real-time risk correlation factor; and combining the historical characteristic parameter set and the trend change characteristic matrix to generate an influence probability prediction model, predicting a regional influence probability, and generating a graded early warning signal when the regional influence probability exceeds a threshold value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chemical environmental assessment, in particular to a regional influence evaluation method for chemical wastewater discharge. BACKGROUND

[0002] The wastewater discharge generated in the production process of the chemical industry poses a potential threat to the regional ecological environment and human health, so it is crucial to scientifically evaluate its regional influence. Currently, there are many limitations in the influence evaluation method of chemical wastewater discharge. Traditional evaluation methods mostly rely on single monitoring data or local sampling analysis, which is difficult to fully reflect the dynamic correlation between wastewater discharge and regional environmental changes. Some methods only focus on statistical analysis of historical data, ignoring the immediate response relationship between real-time discharge data and environmental state, resulting in evaluation results lagging behind actual environmental changes and failing to identify potential risks in a timely manner. In terms of data processing, the existing technology lacks integration of historical discharge data and environmental monitoring data, often failing to effectively extract key feature parameters from the data, making it difficult to establish a systematic influence trend model. Most evaluation methods lack a multi-dimensional analysis perspective, only analyzing from a single pollution indicator or a local area, which cannot build a complete regional influence space, leading to deviations in judging the pollution diffusion path and influence range. At the same time, traditional evaluation models are relatively static and difficult to adapt to dynamic changes in regional environmental conditions. When the emission source, meteorological conditions, topography, and other factors change, the prediction accuracy of the model significantly decreases. The application of digital twinning technology in environmental assessment is still in its early stages, and existing methods fail to fully utilize the real-time simulation and dynamic feedback capabilities of digital twinning, making it impossible to achieve real-time synchronization of discharge data, environmental monitoring data, and virtual models, resulting in significant differences between simulation results and actual environmental conditions. In addition, the setting of early warning mechanisms is not precise enough, and most methods rely on fixed threshold values to determine risks without considering the trend changes of historical feature parameters and the dynamic correlation of real-time data, which can easily lead to false positives or false negatives, making it difficult to meet the actual needs of regional risk prevention and control. These problems make existing evaluation methods lack accuracy, real-time performance, and dynamic adaptability, which cannot provide reliable support for regional environmental management of chemical wastewater discharge. SUMMARY

[0003] The present application aims to provide a regional influence evaluation method for chemical wastewater discharge to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides a regional influence evaluation method for chemical wastewater discharge, which comprises: obtaining historical discharge data of a chemical wastewater discharge source and regional environmental monitoring data, and generating a historical influence data set according to the historical discharge data and regional environmental monitoring data; performing feature parameter extraction processing on the historical influence data set to obtain a historical feature parameter set, and establishing a trend change feature matrix according to a trend of change over time of the historical feature parameters; constructing a multi-dimensional influence space according to the trend change feature matrix, calculating a state aggregation degree of a historical pollution event in the multi-dimensional influence space, and determining a feature early warning index set according to the state aggregation degree; constructing a regional environment digital twin model, collecting real-time emission data and real-time environmental monitoring data through sensors deployed in the monitoring region, and synchronizing the real-time emission data and real-time environmental monitoring data to the regional environment digital twin model; simulating an actual environmental state in the regional environment digital twin model and predicting potential regional influence; extracting real-time feature parameters according to the real-time emission data, establishing a real-time feature state vector, and performing spatial position correlation calculation with the feature early warning index set in the multi-dimensional influence space to determine a real-time risk correlation factor; generating an influence probability prediction model according to the real-time risk correlation factor, the historical feature parameter set, and the trend change feature matrix, and predicting a regional influence probability of chemical wastewater discharge; generating a graded early warning signal when the predicted regional influence probability exceeds a preset threshold; continuously adjusting the regional environment digital twin model based on recorded feedback data.

[0005] Preferably, historical emission data and regional environmental monitoring data of a chemical wastewater discharge source are obtained, and a historical influence data set is generated according to the historical emission data and regional environmental monitoring data, specifically: collecting the historical emission data and regional environmental monitoring data within a preset time period; performing data validity screening according to the change range, trend, and fluctuation amplitude of each parameter in the historical emission data and regional environmental monitoring data, and removing parameter data that exceeds a preset reasonable range; classifying and integrating the historical emission data and regional environmental monitoring data that have passed the data validity screening according to parameter categories to generate a historical influence data set containing emission concentration parameters, water flow parameters, and soil permeability parameters.

[0006] Preferably, feature parameter extraction processing is performed on the historical influence data set to obtain a historical feature parameter set, and a trend change feature matrix is established according to a trend of change over time of the historical feature parameters, specifically: Performing historical feature parameter extraction processing on the historical impact data set; establishing a historical feature parameter set according to different dimensions of the historical feature parameters; and calculating change trend values ​​within a specified time period for different historical feature parameters in the historical feature parameter set; Arrange the change trend values ​​of the historical characteristic parameters in chronological order to form a trend change characteristic matrix; wherein the rows of the trend change characteristic matrix represent different historical characteristic parameters, and the columns represent corresponding time series data.

[0007] Preferably, a multidimensional influence space is constructed based on the trend change feature matrix, the state aggregation degree of historical pollution events in the multidimensional influence space is calculated, and a characteristic warning indicator set is determined based on the state aggregation degree, specifically: The dimension of the multidimensional influence space is determined according to the number of historical characteristic parameters in the trend change characteristic matrix; the coordinate axes of the multidimensional influence space are the different historical characteristic parameters of the trend change characteristic matrix, and the characteristic position coordinates in the multidimensional influence space are the change trend values ​​of the historical characteristic parameters; Calculating the aggregation degree of state points when historical pollution events occurred based on the coordinates of each characteristic position in the multidimensional influence space, and obtaining the state aggregation degree value of the historical pollution events in the multidimensional influence space; A warning threshold is set according to the state concentration value, and historical feature parameters corresponding to feature position coordinates whose state concentration values ​​exceed the warning threshold are selected as a feature warning indicator set.

[0008] Preferably, a regional environmental digital twin model is constructed, and real-time emission data and real-time environmental monitoring data are collected by sensors deployed in the monitoring area, and the real-time emission data and real-time environmental monitoring data are synchronized to the regional environmental digital twin model, specifically: Initialize the regional environmental digital twin model according to regional geographic information and environmental baseline parameters; collect real-time emission data and real-time environmental monitoring data of the monitoring area in real time; input the real-time emission data and real-time environmental monitoring data into the regional environmental digital twin model in real time for data synchronization.

[0009] Preferably, the actual environmental state is simulated in the regional environmental digital twin model to predict potential regional impacts, specifically: The entire monitoring area is divided into multiple sub-areas, each sub-area corresponds to a monitoring unit; Based on the regional environmental digital twin model, simulate the environmental state changes of each sub-region; Combining historical pollution distribution patterns and real-time data trends, the potential pollution diffusion path and impact range of each sub-region are predicted.

[0010] Preferably, real-time feature parameters are extracted according to the real-time emission data, a real-time feature state vector is established, and spatial position correlation calculation is performed with the feature early warning index set in the multi-dimensional influence space to determine a real-time risk correlation factor, specifically: Real-time feature parameters are extracted based on the real-time emission data; a real-time feature state vector is formed according to the parameter order of the historical feature parameter set; the real-time feature state vector is mapped to the multi-dimensional influence space, and the spatial distance between the real-time feature state vector and the feature position coordinates in the feature early warning index set is calculated; the spatial position correlation is calculated according to the spatial distance, and the real-time risk correlation factor is determined according to the spatial position correlation.

[0011] Preferably, an influence probability prediction model is generated according to the real-time risk correlation factor, the historical feature parameter set and the trend change feature matrix, and the regional influence probability of chemical wastewater discharge is predicted, specifically: The real-time risk correlation factor and the historical feature parameter set are used as input data of the influence probability prediction model; The influence probability prediction model is used to calculate the change trend value of the historical feature parameter in the trend change feature matrix, and the current regional influence probability value is predicted.

[0012] Preferably, when the predicted regional influence probability exceeds a preset threshold, a hierarchical early warning signal is generated, specifically: A multi-dimensional early warning parameter set including pollution diffusion rate, ecological sensitivity and population exposure is constructed; When a single parameter exceeds a first-level threshold, a primary early warning is triggered, and when the combined effect of multiple parameters exceeds a second-level threshold, a high-level early warning instruction is triggered.

[0013] Preferably, the regional environmental digital twin model is continuously adjusted based on recorded feedback data, specifically: A plurality of environmental adjustment task sets are constructed using historical feedback data, and each task represents a specific monitoring sub-scene; The regional environmental digital twin model is gradually fine-tuned in each task; Through iterative optimization of multiple tasks, the parameters of the regional environmental digital twin model are updated; In actual operation, new tasks are generated in real time according to newly collected feedback data and input into the fine-tuning framework for model parameter adjustment.

[0014] Compared with the prior art, the present application has the following advantages: The regional influence evaluation method for chemical wastewater discharge realizes comprehensive, dynamic and accurate evaluation of regional environmental influence through multi-link cooperation. On the data basis level, historical emission data and regional environmental monitoring data are integrated to generate a historical influence data set, providing rich basic information for analysis, so that the evaluation is no longer limited to a single time node or local data, but can mine potential laws based on long-term accumulated information. Feature parameter extraction is performed on the historical influence data set, and a trend change feature matrix is established, which can clearly capture the evolution trajectory of pollution influence over time and reveal the correlation pattern between different characteristic parameters, providing a structured analysis framework for understanding the long-term influence mechanism of pollution. A multi-dimensional influence space is constructed according to the trend change feature matrix, breaking through the limitations of traditional single-dimensional evaluation, and integrating multiple influence factors into a unified analysis system. The state aggregation degree of historical pollution events is calculated to determine a set of characteristic early warning indicators, making the setting of early warning indicators more targeted and scientific, and enabling accurate identification of key nodes that may trigger significant environmental impact. The construction of a regional environmental digital twin model realizes the virtual mapping of the actual environmental state, and real-time data collected by sensors are synchronized to the model to ensure high consistency between the virtual model and the real environment, providing a reliable digital carrier for simulation and prediction. Simulating the actual environmental state in the digital twin model and predicting potential regional influence can predict pollution diffusion trends and possible impact ranges in advance without interfering with the actual environment, providing time for risk prevention and control. Real-time feature parameters are extracted from real-time emission data to establish a state vector, and correlation calculation is performed with the set of characteristic early warning indicators in the multi-dimensional influence space, realizing the dynamic combination of real-time data and historical rules. The real-time risk correlation factor determined can objectively reflect the correlation degree between the current pollution state and potential risks. The influence probability prediction model generated based on the real-time risk correlation factor, the set of historical characteristic parameters and the trend change feature matrix integrates historical experience and real-time dynamic information, making the prediction of regional influence probability more in line with the actual situation. When the prediction probability exceeds the preset threshold, a graded early warning signal is generated, which can take appropriate prevention and control measures according to the risk level to improve the targeting of risk response. The regional environmental digital twin model is continuously adjusted based on feedback data to ensure that the model can adapt to changes in environmental conditions and the deepening of knowledge brought by data accumulation, so that the evaluation method always maintains good adaptability and accuracy, effectively supporting environmental management and risk prevention and control of regional chemical wastewater discharge. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A working principle diagram of the regional influence evaluation method for chemical wastewater discharge described in the present application; Figure 2 A flowchart for establishing a trend change feature matrix; Figure 3 Flow chart for determining the set of characteristic early warning indicators; Figure 4 Flow chart for determining the real-time risk correlation factor. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only 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.

[0017] Please refer to Figure 1 The present application provides a regional impact assessment method for chemical wastewater discharge, which comprises: By combining historical data analysis, real-time monitoring and digital twin simulation technology, dynamic prediction and early warning of the impact of chemical wastewater discharge are realized. The historical emission data of the chemical wastewater discharge source and the regional environmental monitoring data are obtained to generate a historical impact data set. The characteristic parameters of the historical impact data set are extracted, a trend change feature matrix is established, and a multi-dimensional impact space is constructed based on the matrix. The state aggregation degree of the historical pollution event is calculated, and the set of characteristic early warning indicators is determined. At the same time, a regional environmental digital twin model is constructed to monitor the emission data and environmental monitoring data of the region in real time, simulate the actual environmental state and predict the potential impact. By extracting real-time characteristic parameters, a real-time characteristic state vector is established, and the spatial position correlation of the real-time risk correlation factor with the set of characteristic early warning indicators in the multi-dimensional impact space is calculated to determine the real-time risk correlation factor. Based on the real-time risk correlation factor, the set of historical characteristic parameters and the trend change feature matrix, an impact probability prediction model is generated to predict the regional impact probability. When the predicted probability exceeds the preset threshold, a graded early warning signal is generated, and the digital twin model is continuously optimized based on the feedback data.

[0018] Example 1: refer to Figure 2, a complete environmental impact assessment framework is constructed. The system forms a systematic workflow from data collection, processing to model building. In the data collection stage, the system needs to obtain the historical emission data of the emission source and the regional environmental monitoring data. Emission data covers information in three dimensions of concentration, flow and time, including instantaneous and average concentration, instantaneous flow and cumulative emission, start and end time of emission event and periodic characteristics. Environmental monitoring data is divided into two categories: water and soil. Water monitoring involves pH, dissolved oxygen, turbidity, ammonia nitrogen and heavy metals. Soil monitoring includes pollution penetration depth, pH, organic matter content and heavy metal adsorption capacity. These data are collected according to the preset time period to ensure the integrity of the time series.

[0019] Data quality control uses a multi-level screening mechanism. First, based on industry standards and natural environmental characteristics, set reasonable range threshold for each parameter, identify and process abnormal data. Abnormal value processing uses methods such as adjacent data interpolation or historical trend estimation to ensure data continuity. The filtered data are classified and integrated according to parameter categories to form a structured historical impact data set. The data set is stored in time series format, each record contains a timestamp and corresponding emission and environmental monitoring parameters. Structured database or time series database is selected for storage scheme to meet different analysis needs.

[0020] Feature extraction identifies key impact factors from historical data. Emission features focus on peak frequency, duration and concentration fluctuation, while environmental features focus on pollutant accumulation, migration trend and abnormal fluctuation. Extraction methods include statistical analysis, sliding window calculation and trend decomposition. These feature parameters cover three dimensions of time, space and environmental medium, reflecting short-term fluctuations and long-term trends, local pollution and regional diffusion, and multi-medium interaction.

[0021] The construction of trend change feature matrix is the core analysis step. The matrix arranges the trend values of each feature parameter in chronological order, with rows representing different feature parameters and columns corresponding to time series data. Trend values are calculated by linear regression, moving average and seasonal decomposition. Matrix analysis needs to consider rainfall, temperature changes and other environmental background factors to distinguish natural fluctuations from human emissions. Matrix maintenance mechanism ensures the continuous inclusion of new data and timely updates, supporting the identification of periodic patterns and abnormal emission triggers of pollution events.

[0022] Data quality assurance is guaranteed throughout the whole workflow. Regular calibration and maintenance of monitoring equipment ensure data accuracy, redundancy and version control guarantee data integrity, anomaly detection and correction ensure data processing rationality, cross-validation and uncertainty evaluation improve data analysis reliability. Historical data set is dynamically updated by incorporating new monitoring data and re-generating, meanwhile, data sharing supports multi-departmental collaborative analysis.

[0023] For system optimization, feature extraction automation is achieved by rule identification and machine learning selection, and trend calculation efficiency is improved by parallel processing. The application scenarios of feature parameter set are expanding, covering pollution early warning, source analysis and treatment evaluation, etc. The trend matrix is enhanced in comprehensiveness by incorporating meteorological, ecological and socio-economic data, and the dynamic updating mechanism balances the calculation efficiency and data timeliness.

[0024] The characteristics of this system are: multi-dimensional data collection covering key indicators of emissions and environment; strict quality control to ensure data reliability; multi-level feature extraction to reveal the internal laws of environmental impact; dynamic matrix analysis to realize real-time tracking of trends; and continuous optimization mechanism to maintain the advanced nature of the system. This method not only improves the accuracy of regional impact assessment, but also provides strong support for environmental management decision-making.

[0025] In practical application, the system needs to focus on several key points: reasonable layout of monitoring network to ensure data representativeness; scientific setting of parameter threshold value to affect screening effect; dimension selection of feature extraction related to analysis depth; frequency of matrix updating needs to match actual demand; quality control standards should be improved with technological progress. These factors together determine the operation effect and application value of the system.

[0026] In the future development direction, Internet of Things technology improves the real-time and coverage of data collection; artificial intelligence optimizes feature recognition and trend prediction; cloud computing resources support large-scale data analysis; visualization technology improves the presentation effect of results. Through technological innovation and method improvement, the system will play a greater role in environmental risk prevention and control.

[0027] Example 2: refer to Figure 3 The construction of multi-dimensional impact space is based on the trend change feature matrix, and each historical feature parameter in the matrix corresponds to a dimension in the space. The number of space dimensions is consistent with the number of feature parameters, for example, if n key feature parameters are selected for analysis, an n-dimensional space is constructed. The coordinate axis scale range of each dimension is determined according to the historical change range of the corresponding feature parameter, and the data in each dimension is normalized to a comparable range. In the multi-dimensional impact space, each historical pollution event can be represented as a coordinate point composed of the trend values of each feature parameter, denoted as where denotes the trend value of the i-th event in the j-th feature parameter dimension.

[0028] The calculation of state aggregation needs to consider the distribution density of points in the multi-dimensional space. For any point P in the space, its local aggregation can be characterized by calculating the density of historical pollution event points in its neighborhood. Define the state aggregation of point P as

[0029] denotes the standardized Euclidean distance between point P and the k-th historical pollution event point, and m is the total number of historical pollution events. When calculating the distance, each dimension is standardized by standard deviation to eliminate the dimensional difference. The greater the value, the higher the probability of pollution events occurring in the region in history.

[0030] The determination of the early warning threshold is based on the difference in aggregation distribution between historical normal state points and pollution event points. By analyzing a large amount of historical data, the aggregation background value of normal environmental state points is calculated, and the upper limit of its statistical distribution is determined. The value exceeding a certain percentage of the upper limit is set as the early warning threshold When the state aggregation of a certain region is considered to be at risk of pollution. The threshold setting needs to consider seasonal changes, and dynamic adjustment of the threshold scheme can be used for different time periods.

[0031] The selection of the feature early warning indicator set is based on the contribution analysis of each dimension to the state aggregation. For regions that exceed the early warning threshold, identify the feature parameter dimension that contributes most to its aggregation. These parameters show significant abnormal changes when pollution events occur in history, and have high early warning value. The maintenance of the indicator set adopts a rolling update mechanism, regularly evaluates the early warning performance of each parameter, eliminates parameters with decreased sensitivity, and supplements newly discovered potential early warning indicators.

[0032] The structure optimization of multi-dimensional influence space is an iterative process. With the continuous accumulation of new monitoring data, the point set in the space will be dynamically updated. After the new historical pollution event points are included in the space, the state aggregation distribution of each region needs to be recalculated. The adjustment of space dimensions includes the addition and deletion of feature parameters and the optimization of weights, and the parameters that significantly affect the aggregation calculation are identified through sensitivity analysis. The update frequency of the space structure needs to match the data acquisition period to maintain the timeliness of the model.

[0033] ​​​The visualization of state aggregation employs dimension reduction projection techniques. For high-dimensional spaces, methods such as principal component analysis are used to project them onto a two- or three-dimensional plane, preserving the main variation information. The projected point set can visually display the aggregation area of pollution event points and their separation degree from the normal state. The visualization tool supports interactive exploration, allowing users to select specific time periods or parameter combinations for focused analysis.

[0034] The environmental significance analysis of early warning indicators is an important step. Each selected indicator set needs to clearly define the environmental process it reflects. For example, a parameter may represent the intensity of pollutant input, while another parameter may reflect the environmental self-purification ability. The coordinated change pattern of indicator combination often corresponds to the pollution type or transmission path. By analyzing the evolution trajectory of each indicator in historical pollution events, recognition rules for different pollution scenarios can be established.

[0035] The analysis of multi-dimensional impact space needs to adapt to the needs of different spatial scales. For local key monitoring areas, a more detailed feature parameter set can be used to construct a high-dimensional space to capture subtle changes; for large-scale regional monitoring, more representative macro indicators need to be selected to control the spatial dimension within the analyzable range. The choice of spatial scale needs to balance the accuracy of analysis and the efficiency of calculation.

[0036] The quality control of historical data directly affects the reliability of spatial analysis. The labeling of each historical pollution event needs to accurately record its occurrence time, spatial location and impact degree. The completeness check of monitoring data includes checking sensor failure periods, removing obvious outliers and handling data missing situations. For key pollution events, multiple source data need to be collected for mutual verification to ensure the accuracy of event labeling.

[0037] The docking of real-time data and multi-dimensional space requires efficient computing support. Newly collected monitoring data needs to be quickly converted into feature parameters and mapped into multi-dimensional space to calculate its real-time state aggregation. The data processing pipeline of this process needs to be optimized to ensure that the delay from data collection to early warning output is within an acceptable range. For large-scale monitoring networks, distributed computing architecture can improve real-time analysis capability.

[0038] The environmental interpretation of spatial analysis results needs professional knowledge support. Simply relying on the calculation results of the data model may have limitations and needs to be reasonably interpreted in combination with environmental science knowledge. For example, abnormal changes in some parameters may be caused by natural factors rather than pollution emissions. Establishing an expert knowledge base to assist model interpretation can improve the credibility and usability of early warning information.

[0039] The presentation of early warning information needs to consider the user's cognitive characteristics. Multidimensional spatial analysis results should be converted into intuitive risk assessments, accompanied by necessary environmental context. The information display interface can be designed in a layered manner, providing both concise and clear risk warnings and in-depth technical details. Visualization tools such as heat maps and trend curves can help quickly grasp changes in environmental conditions.

[0040] The application of multidimensional impact space technology requires a supporting management mechanism. This includes regular evaluation of model performance, recording of the analysis and decision-making process, and updating of historical event databases. Standardized operating procedures and quality control standards should be established to ensure the consistency and traceability of analytical results. During the application of this technology, communication and feedback with frontline monitoring personnel should be maintained to continuously optimize and improve analytical methods.

[0041] The continued development of this method requires close integration with real-world environmental challenges. New pollution types or changing environmental conditions require timely adjustments to the characteristic parameter system and spatial structure. Collaboration with research institutions can introduce the latest analytical techniques and methods, enhancing the model's adaptability. Lessons learned from practical applications provide valuable insights for methodological refinement.

[0042] Cross-regional exchange of experience will help refine multidimensional spatial analysis methods. The unique solutions and solutions developed in different regions during their application can be systematically collated and leveraged. Establishing unified benchmark test cases to evaluate the performance of different parameter combinations and algorithms in various scenarios will promote the standardization and regularization of analytical techniques.

[0043] Technological innovation requires a balance between advancement and practicality. When introducing new mathematical tools and computational techniques, their applicability and operability in the field of environmental early warning must be considered. Overly complex models can reduce the interpretability of results, while oversimplified methods can lose important information. Finding the right balance is a key consideration in technological development.

[0044] Multidimensional impact space analysis holds broad promise for environmental applications. Beyond pollution early warning, this method can be expanded to other areas, such as environmental quality trend forecasting, evaluating the effectiveness of remediation measures, and early identification of ecological risks. With the increasing availability of monitoring data and advancements in computing technology, multidimensional spatial analysis methods will play an even more important role in environmental management decision-making.

[0045] Example 3: The construction of a regional environmental digital twin model is a systematic engineering that requires the integration of multi-source data and technical means. Model construction begins with the collection of basic environmental information, including geographic data such as terrain, water systems, and soil types, as well as background parameters such as climate and hydrogeology. These data are standardized to form the spatial framework of the model, with initial values of environmental parameters based on long-term monitoring data or environmental standards. The spatial resolution of the model is dynamically adjusted according to monitoring needs, with high resolution in key areas and appropriate reduction in general areas to balance computational load.

[0046] The real-time monitoring network is the data foundation of the model, composed of water quality sensors, soil probes, and meteorological equipment. Water quality monitoring points are placed at key locations such as downstream of sewage outlets and river sections, soil monitoring covers potential pollution areas, and weather stations provide key parameters affecting pollutant diffusion. All monitoring equipment is regularly calibrated and maintained, and data collection frequency is differentiated according to parameter variation characteristics. Data transmission uses encryption and verification technology to ensure safety and integrity, and reduces delay through optimized transmission paths and compression algorithms. Data is strictly quality controlled before synchronization, with outliers removed and missing values filled.

[0047] The model uses a partitioned architecture, dividing the monitoring area into sub-units based on environmental characteristics. The division is based on natural boundaries such as watershed boundaries and soil types, and each sub-unit is equipped with corresponding monitoring equipment. The partition granularity is dynamically adjusted according to risk level and computing resources, with more detailed division in key pollution areas. This architecture ensures the capture of local details while maintaining overall computational efficiency.

[0048] Environmental simulation integrates multi-disciplinary methods, with water body simulation covering migration processes such as convection and diffusion, soil simulation focusing on behaviors such as infiltration and adsorption, and meteorological factors as external drivers incorporated into the system. The simulation process considers interactions between media, such as water-soil exchange and gas-water volatilization. Algorithm design balances precision and efficiency, with complex processes simplified reasonably. Pollution diffusion prediction combines historical patterns and real-time trends to give different confidence levels of impact range, with time scales set flexibly from hours to days.

[0049] The spatial representation of prediction results uses a hierarchical partitioning system to identify core impact areas, potential impact areas, and safe areas. More stringent standards are set for ecologically sensitive areas and socially sensitive points. Prediction updates are synchronized with data synchronization to ensure timeliness. Model verification uses independent historical data to improve accuracy through parameter calibration. Model performance is evaluated periodically, with a focus on verification when the monitoring network expands or environmental changes occur.

[0050] The system integrates the model functions, realizes spatial visualization combined with GIS, provides early warning support by connecting with decision-making systems, and optimizes response schemes by linking with emergency systems. The integration process focuses on data format standardization and interface compatibility. Model maintenance establishes a continuous updating mechanism, incorporates new monitoring data, optimizes algorithm structure, and preserves version traceability. The updating frequency balances stability and adaptability needs.

[0051] Model application requires multidisciplinary team collaboration, with environmental engineers responsible for parameter setting, data analysts handling feature information, and decision-makers formulating response measures. Supporting training enhances usage effectiveness. The model clearly labels sources of uncertainty, including data errors and parameter deviations, and guides improvement directions through sensitivity analysis. Monitoring network density directly affects model effectiveness, and optimization of point distribution is based on sensitivity analysis, with mobile devices supplementing fixed network deficiencies.

[0052] Technological development promotes model intelligence, with automated calibration reducing human intervention, machine learning enhancing nonlinear relationship identification, real-time data assimilation improving tracking capabilities, and visualization improving result presentation. These advancements continuously enhance the practical value of models in environmental management. Model construction focuses on practical application needs, seeking a balance between precision and efficiency, stability and adaptability, and specialization and ease of use.

[0053] Example 4: Refer to Figure 4 The extraction process of real-time feature parameters needs to maintain dimensional consistency with the historical feature parameter set. From real-time emission data, select the same types of feature parameters as historical analysis, including emission intensity indicators, pollutant composition characteristics, and emission fluctuation patterns. The calculation method of each real-time feature parameter continues the historical data processing method to ensure data comparability. The time window for parameter extraction is determined based on parameter variation characteristics, with shorter time windows for rapidly changing parameters and longer windows for slowly changing parameters. The construction of the real-time feature state vector follows the order of the historical feature parameter set, forming a standardized data format for comparison and analysis in the multi-dimensional influence space.

[0054] The process of mapping the real-time feature state vector to the multi-dimensional influence space involves coordinate transformation. Standardize each feature parameter value extracted in real-time according to the corresponding dimension normalization method to eliminate dimensional differences. The standardized parameter values serve as coordinate components to determine the position of the real-time state point in the multi-dimensional space. This mapping process needs to consider the correlation between dimensions to avoid deviations in spatial position calculation caused by highly correlated dimensions. The spatial position calculation uses weighted distance measurement, considering the importance differences of different feature parameters in early warning.

[0055] The spatial position correlation calculation uses an improved distance similarity measurement method. Define the real-time feature state vector and the kth early warning state point in the feature early warning index set correlation degree between the real-time feature state vector and the kth early-warning state point is:

[0056] wherein: represents the parameter value of the jth dimension of the real-time feature state vector, represents the parameter value of the jth dimension of the kth early-warning state point, is the historical fluctuation standard deviation of the jth feature parameter. This measurement method takes into account the different fluctuation characteristics of each dimension parameter, and the dimension with larger fluctuation allows greater difference. The correlation degree value is between 0 and 1, and the larger the value, the higher the similarity between the real-time state and the historical early-warning state.

[0057] The determination of the real-time risk correlation factor is based on the spatial position correlation analysis result. The correlation degree of the early-warning state point in all feature early-warning indicator sets with the real-time state is calculated, and the highest correlation degree value is selected as the basic risk indicator. After being corrected by the environmental background factor, the final real-time risk correlation factor is obtained. The background correction factor includes the seasonal adjustment coefficient, the hydrological condition coefficient, etc., reflecting the particularity of pollution impact under the current environmental conditions. The dynamic updating frequency of the risk correlation factor is synchronized with the real-time data acquisition frequency, ensuring the timeliness of the risk evaluation.

[0058] The construction of the impact probability prediction model adopts a historical data-driven method. The model input includes the real-time risk correlation factor, the historical feature parameter set and the trend change feature matrix, and the output is the regional impact probability value. The model structure considers the fusion of time series characteristics and spatial correlation characteristics, capturing the spatio-temporal propagation law of pollution impact. The training process uses the historical pollution events and the environmental state data before the events to learn the mapping relationship from feature change to pollution occurrence. The model verification uses the time cross-validation method to ensure the stability of the prediction ability in different time periods.

[0059] The calculation of the regional impact probability integrates multi-dimensional information. The basic probability value comes directly from the output of the impact probability prediction model, and is adjusted in combination with the particularity of the current environmental state. The adjustment factors include meteorological warning information, hydrological extreme conditions, special activity arrangements and other factors that may change the degree of pollution impact. The probability value is expressed in a hierarchical interval form, corresponding to different warning levels and response preparation levels. The probability update trigger conditions include significant changes in real-time data, model input updates or changes in external environmental conditions, etc.

[0060] The time scale of real-time risk assessment is flexible according to application requirements. Short-term risk assessment focuses on rapid changes within a few hours, suitable for sudden pollution event warning. Medium-term risk assessment covers the trend of changes over several days, supporting pollution prevention planning. Long-term risk assessment focuses on seasonal change patterns, assisting in the development of environmental management strategies. Different time scales of evaluation use corresponding feature parameter extraction methods and model adjustment strategies to ensure the applicability of the evaluation results.

[0061] The spatial expression of risk assessment results considers regional differences. Based on the division scheme of monitoring sub-regions, the independent real-time risk correlation factor and impact probability of each sub-region are calculated. Spatial interpolation methods are used to fill in the risk estimates of unmonitored areas, forming a continuous risk distribution map. The identification of risk hotspots combines spatial clustering analysis to find pollution risk aggregation areas that need to be focused on. Spatial expression methods include risk level zoning maps, risk change trend maps, and other forms to meet different use requirements.

[0062] Adaptive updating of impact probability prediction models maintains prediction accuracy. The model periodically uses new monitoring data for incremental learning, adjusting internal parameters to adapt to environmental changes. The update frequency balances the stability and adaptability needs of the model, avoiding frequent updates that cause prediction fluctuations. The model performance monitoring system tracks the trend of prediction error changes, triggering necessary model structure adjustments. Special model evaluation is started when significant environmental conditions change, ensuring the continuous effectiveness of the prediction method.

[0063] The connection between real-time risk assessment and early warning response requires clear conversion rules. Risk assessment results are divided into different warning levels according to preset thresholds, each level corresponding to response processes and measures. The conversion rule considers the certainty of the evaluation results, with high certainty predictions using more aggressive response strategies. Early warning response effect feedback is incorporated into the evaluation system, forming a closed-loop management from risk assessment to response implementation and then to effect evaluation.

[0064] Uncertainty management in the evaluation process uses a probabilistic expression method. Risk assessment results are accompanied by confidence interval estimates, reflecting the uncertainty range of the prediction. Uncertainty sources include data collection errors, model simplification assumptions, parameter estimation biases, and other aspects. Sensitivity analysis identifies the factors that most affect the evaluation results, guiding the focus of data quality improvement and model optimization. Uncertainty information is clearly labeled when the results are presented, assisting decision-makers in the reasonable use of evaluation conclusions.

[0065] The operation of the real-time risk assessment system requires efficient computing support. The optimization of algorithms in data preprocessing, feature extraction, and model calculation ensures real-time performance. Distributed computing architecture processes massive data generated by large-scale monitoring networks, ensuring system response speed. Dynamic allocation of computing resources adjusts to data processing load, improving resource utilization efficiency. Real-time monitoring of system operation status helps identify and address performance bottlenecks or abnormal situations promptly.

[0066] Interpretive analysis of evaluation results enhances decision support effectiveness. Combining risk assessment results with environmental background information provides a more comprehensive situation explanation. Key factor analysis identifies the main parameters that dominate risk changes, suggesting areas of focus. Historical case retrieval provides references from past similar risk situations. Interpretive analysis requires the combination of environmental expertise and data analysis techniques to form easily understood decision support information.

[0067] Multi-source data fusion improves the comprehensiveness of risk assessment. In addition to conventional monitoring data, supplementary data sources such as remote sensing observations, mobile monitoring, and public reports are included to compensate for the shortcomings of fixed monitoring networks. Data fusion methods consider the accuracy differences and spatial and temporal resolution characteristics of different data sources, and reasonably integrate multi-source information. Quality control of unconventional data is particularly important to avoid introducing evaluation bias from low-quality data. The scalability design of the data fusion system supports the easy access of new data sources.

[0068] Interactive functions of the risk assessment system improve user experience. The visualization interface supports multi-angle display and dynamic query of evaluation results. Parameter adjustment function allows users to modify key assumptions and observe the impact on evaluation results. Scenario simulation function tests the risk trend under different assumption conditions. Interactive design considers the technical backgrounds of different types of users, providing differentiated operation complexity options. User operation log records support the tracing and analysis of the evaluation process.

[0069] Standardization of evaluation methods promotes cross-regional application. Unified core algorithms and parameter definitions make evaluation results from different regions comparable. Standardized evaluation report formats facilitate result exchange and upper-level department summary analysis. Method documentation details technical details and usage restrictions, supporting new users to quickly master the system. Regional adaptability adjustments retain necessary flexibility within the standardized framework, meeting local special needs.

[0070] The future direction of real-time risk assessment technology includes more refined feature representation, more intelligent model architecture, and more intuitive result expression. The development of feature extraction methods can capture more subtle environmental change signals and detect potential risks in advance. The application of machine learning technology improves the modeling ability of complex nonlinear relationships and improves prediction accuracy. The innovation of visualization technology makes multi-dimensional risk information more intuitive, reducing the use threshold. These technological advancements will continuously improve the accuracy and practicality of risk assessment.

[0071] The generation mechanism of the hierarchical early warning signal is based on the comprehensive evaluation of a multi-dimensional early warning parameter set. The early warning parameter set includes three core dimensions: pollution diffusion rate, ecological sensitivity, and population exposure. Each dimension has several specific evaluation indicators. The pollution diffusion rate dimension considers factors such as the migration speed, diffusion range, and concentration gradient of pollutants in the environment. The ecological sensitivity dimension evaluates characteristics such as the ecological value, species diversity, and ecological system vulnerability of the affected area. The population exposure dimension analyzes factors such as the number of potentially affected populations, the sensitivity of the population, and the distribution of important infrastructure. The evaluation indicator data for each dimension comes from real-time monitoring networks, environmental database, and population statistics, etc. After standardization, it forms comparable evaluation parameters.

[0072] The setting of early warning thresholds adopts a hierarchical classification principle. The first-level threshold is aimed at the abnormal change of a single early warning parameter. When any core parameter exceeds its historical normal fluctuation range, the primary early warning is triggered. The second-level threshold focuses on the combined effect of multiple parameters. When two or more core parameters simultaneously exceed the set limit, or the synergistic effect between parameters reaches a dangerous level, the advanced early warning is triggered. The threshold determination process refers to the parameter combination characteristics during historical pollution events, and sets reasonable boundary values based on expert experience. The threshold system remains dynamically adjustable, adapting to environmental background factors such as seasonal characteristics and regional features. The early warning level determination logic is realized by a rule engine, supporting flexible configuration and rapid judgment of complex conditions.

[0073] The generation of primary early warning signals focuses on the early identification of potential risks. When monitoring data shows an abnormality in a single parameter, the system automatically generates an early warning notification containing basic information such as parameter name, abnormality degree, and possible impact. The primary early warning is mainly aimed at technical management personnel, prompting to strengthen the monitoring frequency and data analysis of relevant parameters. The early warning information is pushed through a professional platform, accompanied by historical similar case references and preliminary troubleshooting suggestions. The primary early warning response process includes data review, cause analysis, and monitoring network adjustment, aiming to confirm the authenticity and environmental significance of abnormal data.

[0074] The generation of advanced early warning signals marks the possibility of significant environmental risks. When multiple parameters combine to meet the early warning conditions, the system generates an early warning instruction containing detailed information such as risk level, impact range, and urgency. The advanced early warning is aimed at decision-making and emergency management departments, triggering a more comprehensive response mechanism. The early warning information is published through multiple channels simultaneously, including professional systems, mobile terminals, and emergency communication networks. The advanced early warning is accompanied by decision support information such as impact prediction maps, sensitive point distribution maps, and emergency resource scheduling suggestions. The response process starts the emergency plan, organizing comprehensive measures such as on-site verification, pollution control, and public protection.

[0075] The hierarchical management of early warning signals requires clear escalation and de-escalation rules. The early warning level is dynamically adjusted according to the parameter trend, continuously monitoring the direction and speed of parameter change to judge the development of the risk. When the parameters continue to deteriorate or new abnormal parameters are added, the early warning level is correspondingly raised. When the parameters improve and stabilize within the safe range, the early warning level is gradually lowered until it is lifted. The early warning state transition sets a reasonable buffer interval to avoid frequent jumps in the early warning level due to short-term fluctuations in parameters. The complete record of the early warning life cycle includes key node information such as trigger time, escalation process, response measures, and lifting basis.

[0076] The release format and content of early warning information are designed differently according to different levels. The primary early warning uses a simple technical report format, highlighting data facts and analysis conclusions. The advanced early warning uses a multi-level comprehensive report structure, including technical analysis, impact assessment, and response suggestions. The information expression takes into account both professional accuracy and popular understanding, providing appropriate depth of content for different audiences. Visualization methods are widely used in advanced early warning, visually displaying risk conditions through map overlays, trend curves, and heat maps. The early warning document template is standardized to ensure the normativity and completeness of information organization.

[0077] The feedback mechanism of early warning response effect realizes closed-loop management. The implementation and effect evaluation of response actions form feedback data, which is recorded in the early warning system database. Feedback data includes on-site monitoring results, disposal measure implementation, and environmental impact changes. Response effect evaluation analyzes the accuracy of early warning and the appropriateness of response, identifying deficiencies and improvement space in the early warning process. The experience summary of typical cases refines effective early warning judgment standards and response patterns, optimizing future early warning decisions. Systematic analysis of feedback data provides empirical evidence for adjustment of early warning thresholds and rules.

[0078] The construction of environmental adjustment task set is based on the clustering analysis of historical feedback data. Early warning cases with similar characteristics are classified to form adjustment tasks representing different types of environmental scenarios. Each task contains parameter combination patterns, environmental background conditions, and processing experience. The task set covers common pollution types and typical environmental states, forming a systematic adjustment knowledge base. The task attribute description uses a standardized structure for easy retrieval and matching. The maintenance mechanism of the task set regularly incorporates new early warning cases to maintain the timeliness and coverage of the knowledge base.

[0079] The progressive fine-tuning of the digital twin model adopts a task-driven learning approach. For each adjustment task, the differences between model predictions and actual responses are analyzed to identify parameters and structures that need optimization. The fine-tuning process is carried out in steps, first adjusting key parameters that have the most impact on the current task, and then gradually optimizing secondary parameters. The adjustment range of model parameters adopts a small-step progressive strategy to avoid instability caused by large-scale modifications. After each fine-tuning, historical data is used for verification to confirm the improvement of model performance. The fine-tuning process records detailed modification logs, including adjustment reasons, parameter changes, and verification results, etc.

[0080] Model iterative optimization is achieved through continuous processing of multiple tasks. According to the task priority, the model optimization requirements in each adjustment task are processed in turn. Parameter adjustment between tasks is coordinated to avoid solving one problem while causing other problems. During the iterative process, the overall performance of the model on the entire task set is evaluated regularly to balance the prediction ability in different scenarios. Major iterative versions undergo comprehensive regression testing to ensure the stability of the original functions. The iterative optimization records form the development history of the model, reflecting the evolution process of the model's ability.

[0081] The immediate processing mechanism of new feedback data maintains the dynamic adaptability of the model. When a new early warning case occurs, key features are quickly extracted to construct an interim adjustment task. The task generation module analyzes the particularity and representativeness of the new case to decide whether to handle it immediately or accumulate more similar cases for batch processing. Urgent and important cases can interrupt the current optimization process and be handled first. The priority assessment of new tasks considers factors such as the severity and frequency of environmental risks. The immediate processing mechanism ensures that the model can adapt to new pollution types and environmental changes in a timely manner.

[0082] The degree of automation of model parameter adjustment is gradually improved. Optimization of routine parameters is automatically completed by algorithms, reducing human intervention. Complex structure adjustment retains expert review, ensuring the reasonableness of the modification. The automatic adjustment process sets reasonable constraints to prevent parameters from exceeding the physical meaning range. The adjustment results of key parameters require confirmation of environmental expertise, balancing data-driven and mechanism-driven optimization directions. The automatic log records the basis and process of adjustment decisions, supporting traceability and analysis.

[0083] The collaborative optimization of the early warning system and the digital twin model forms a virtuous cycle. Feedback data generated from early warning practice drives model improvement, and improved model capabilities support more accurate early warning judgments. The interface between the two systems is designed with standardized specifications to ensure smooth and efficient data exchange. The collaborative optimization process sets a reasonable pace to balance the needs of immediate response and system stability. The evaluation of optimization effects uses comprehensive indicators such as early warning accuracy, response timeliness, etc. to comprehensively measure the actual value of system improvement.

[0084] The long-term development of the early warning system requires continuous evolution of the technical architecture. The improvement of computing power supports more complex early warning models and larger-scale data processing. The expansion of the monitoring network provides more comprehensive environmental state information, enriching the basis for early warning judgment. The introduction of new algorithms improves the ability of feature extraction and pattern recognition, enhancing the accuracy and forward-looking nature of early warning. The modular design of the system architecture facilitates functional expansion and technical upgrade, maintaining the long-term vitality of the system. The technology roadmap planning guides the orderly development of the system, coordinating short-term improvements and long-term goals.

[0085] The application and promotion of the early warning system need to consider the capability differences of different institutions. For grassroots environmental protection departments, a simplified operation interface and a highly automated early warning process are provided. For professional technical institutions, more advanced analysis tools and model adjustment functions are opened. The training system is designed in stages to meet the learning needs of users at different levels. The technical support mechanism establishes an expert resource library to provide professional guidance for difficult problems in system application. The compilation and sharing of application cases promote the dissemination of best practices and improve the overall application level.

[0086] The standardization construction of the early warning system promotes cross-regional cooperation. The unified core early warning index system makes the risk assessment of different regions comparable. The standardized data interface supports information sharing and joint early warning between systems. The unified operation specification reduces the communication cost of cross-regional cooperation. The standardization work maintains appropriate flexibility, allowing for the addition and expansion of local characteristics. The standard system maintenance mechanism is periodically evaluated and updated to adapt to technological development and changes in demand.

[0087] The social value of the early warning system lies in risk communication and public participation. Proper disclosure of early warning information promotes social understanding and attention to environmental risks. Public feedback channels collect civilian observation information to supplement the shortcomings of professional monitoring networks. Community-level early warning response drills improve the society's ability to respond to environmental risks. Early warning education and popularization work cultivate the public's risk awareness and basic discrimination ability. The multi-party participation of the early warning system builds a more comprehensive environmental risk prevention and control network.

[0088] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or action from another, and do not necessarily require or imply any actual such relationship or order between the entities or actions. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0089] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A regional impact assessment method for chemical wastewater discharge, characterized in that: The steps include: Obtaining historical emission data and regional environmental monitoring data from chemical wastewater discharge sources, and generating a historical impact data set based on the historical emission data and regional environmental monitoring data; Performing feature parameter extraction processing on the historical impact data set to obtain a historical feature parameter set, and establishing a trend change feature matrix based on the temporal change trend of the historical feature parameters; Constructing a multidimensional impact space based on the trend change feature matrix, calculating the state aggregation degree of historical pollution events in the multidimensional impact space, and determining a characteristic early warning indicator set based on the state aggregation degree; Constructing a regional environmental digital twin model, collecting real-time emission data and real-time environmental monitoring data through sensors deployed in the monitoring area, and synchronizing the real-time emission data and real-time environmental monitoring data into the regional environmental digital twin model; Simulating actual environmental conditions in the regional environmental digital twin model to predict potential regional impacts; Extracting real-time characteristic parameters based on the real-time emission data, establishing a real-time characteristic state vector, and performing spatial position correlation calculation with the characteristic warning indicator set in the multi-dimensional impact space to determine a real-time risk association factor; Generate an impact probability prediction model based on the real-time risk association factor, the historical characteristic parameter set, and the trend change characteristic matrix to predict the regional impact probability of chemical wastewater discharge; When the predicted probability of impact on the area exceeds a preset threshold, a graded warning signal is generated; The digital twin model of the regional environment is continuously adjusted based on the recorded feedback data.

2. The regional impact assessment method for chemical wastewater discharge according to claim 1 is characterized by: Obtain historical emission data and regional environmental monitoring data from chemical wastewater discharge sources, and generate a historical impact dataset based on the historical emission data and regional environmental monitoring data, specifically: Collecting the historical emission data and regional environmental monitoring data within a preset time period; Perform data validity screening based on the change range, change trend and fluctuation amplitude of each parameter in the historical emission data and regional environmental monitoring data, and remove parameter data that exceeds the preset reasonable range; The historical emission data and regional environmental monitoring data that have been screened for data validity are classified and integrated according to parameter categories to generate a historical impact data set containing emission concentration parameters, water flow parameters and soil infiltration parameters.

3. The regional impact assessment method for chemical wastewater discharge according to claim 2 is characterized in that: The historical impact data set is subjected to feature parameter extraction processing to obtain a historical feature parameter set, and a trend change feature matrix is ​​established according to the temporal change trend of the historical feature parameters, specifically: Performing historical feature parameter extraction processing on the historical impact data set; establishing a historical feature parameter set according to different dimensions of the historical feature parameters; and calculating the change trend values ​​of different historical feature parameters in the historical feature parameter set within a specified time period; Arrange the change trend values ​​of the historical characteristic parameters in chronological order to form a trend change characteristic matrix; wherein the rows of the trend change characteristic matrix represent different historical characteristic parameters, and the columns represent corresponding time series data.

4. The regional impact assessment method for chemical wastewater discharge according to claim 3 is characterized by: A multidimensional impact space is constructed based on the trend change feature matrix, the state aggregation degree of historical pollution events in the multidimensional impact space is calculated, and a characteristic warning indicator set is determined based on the state aggregation degree, specifically: The dimension of the multidimensional influence space is determined according to the number of historical characteristic parameters in the trend change characteristic matrix; the coordinate axes of the multidimensional influence space are the different historical characteristic parameters of the trend change characteristic matrix, and the characteristic position coordinates in the multidimensional influence space are the change trend values ​​of the historical characteristic parameters; Calculating the aggregation degree of state points when historical pollution events occurred based on the coordinates of each characteristic position in the multidimensional influence space, and obtaining the state aggregation degree value of the historical pollution events in the multidimensional influence space; A warning threshold is set according to the state concentration value, and historical feature parameters corresponding to feature position coordinates whose state concentration values ​​exceed the warning threshold are selected as a feature warning indicator set.

5. The regional impact assessment method for chemical wastewater discharge according to claim 4 is characterized in that: Construct a regional environmental digital twin model, collect real-time emission data and real-time environmental monitoring data through sensors deployed in the monitoring area, and synchronize the real-time emission data and real-time environmental monitoring data into the regional environmental digital twin model, specifically: Initialize the regional environmental digital twin model according to regional geographic information and environmental baseline parameters; collect real-time emission data and real-time environmental monitoring data of the monitoring area in real time; input the real-time emission data and real-time environmental monitoring data into the regional environmental digital twin model in real time for data synchronization.

6. The regional impact assessment method for chemical wastewater discharge according to claim 5 is characterized by: The actual environmental status is simulated in the regional environmental digital twin model to predict potential regional impacts, specifically: The entire monitoring area is divided into multiple sub-areas, each sub-area corresponds to a monitoring unit; Based on the regional environmental digital twin model, simulate the environmental state changes of each sub-region; Combining historical pollution distribution patterns and real-time data trends, the potential pollution diffusion path and impact range of each sub-region are predicted.

7. The regional impact assessment method for chemical wastewater discharge according to claim 6 is characterized in that: Real-time characteristic parameters are extracted based on the real-time emission data to establish a real-time characteristic state vector. Spatial position correlation calculation is performed with the characteristic warning indicator set in the multi-dimensional impact space to determine a real-time risk association factor, specifically: Real-time characteristic parameters are extracted based on the real-time emission data; a real-time characteristic state vector is formed according to the parameter order of the historical characteristic parameter set; the real-time characteristic state vector is mapped to the multi-dimensional impact space, and the spatial distance between the real-time characteristic state vector and the characteristic position coordinates in the characteristic warning indicator set is calculated; the spatial position correlation is calculated according to the spatial distance, and the real-time risk association factor is determined based on the spatial position correlation.

8. The regional impact assessment method for chemical wastewater discharge according to claim 7 is characterized by: Based on the real-time risk association factor, the historical characteristic parameter set, and the trend change characteristic matrix, an impact probability prediction model is generated to predict the regional impact probability of chemical wastewater discharge, specifically: Using the real-time risk association factor and the historical characteristic parameter set as input data for an impact probability prediction model; The impact probability prediction model is used to calculate the change trend values ​​of the historical characteristic parameters in the trend change characteristic matrix to predict the current regional impact probability value.

9. The regional impact assessment method for chemical wastewater discharge according to claim 8 is characterized by: When the predicted probability of impact on the area exceeds a preset threshold, a graded warning signal is generated, specifically: Construct a multidimensional early warning parameter set including pollution diffusion rate, ecological sensitivity and population exposure; When a single parameter exceeds the first-level threshold, a primary warning is triggered. When the combined effect of multiple parameters exceeds the second-level threshold, an advanced warning instruction is triggered.

10. The regional impact assessment method for chemical wastewater discharge according to claim 9, characterized in that: Continuously adjust the regional environment digital twin model based on the recorded feedback data, specifically: Utilize historical feedback data to construct multiple sets of environment adjustment tasks, each task representing a specific monitoring sub-scenario; Gradually fine-tune the digital twin model of the regional environment in each task; Updating the parameters of the regional environment digital twin model through iterative optimization of multiple tasks; In actual operation, new tasks are generated instantly based on the newly collected feedback data and input into the fine-tuning framework to adjust the model parameters.

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