Emission reduction path optimization method and system based on system dynamics model analysis

Abstract

The present invention relates to the field of environmental management technology, specifically to an emission reduction path optimization method and system based on system dynamics model analysis, comprising the following steps: capturing pollution source emission data, screening key variables, including emission volume and time, inputting them into a dynamics model to simulate pollutant propagation, and obtaining preliminary dynamics behavior simulation results. In the present invention, by capturing and collating pollution source emission data in real time, dynamically simulating key variables to accurately predict pollution propagation behavior, updating the dynamics model in combination with real-time environmental data, optimizing its reaction speed and environmental matching ability, improving the adaptability and efficiency of environmental management strategies, and after a detailed analysis of pollution diffusion, designing precise emission reduction measures for high-incidence areas of pollution, refining spatial layout and time scheduling to enhance the application value of measures, and continuously optimizing environmental management through dynamic monitoring and parameter adjustment, improving the real-time response and long-term sustainability of pollution control.

Description

Technical Field

[0001] The present invention relates to the field of environmental management technology, and in particular to an emission reduction path optimization method and system based on system dynamics model analysis. Background Art

[0002] The field of environmental management technology involves the application of various tools and strategies to maintain and improve the quality of the natural environment. The field focuses on effectively monitoring and controlling environmental pollution, achieving sustainable use of natural resources, and promoting the restoration and protection of ecosystems. Environmental management technologies include pollution control technology, resource recycling technology, ecological restoration technology, and environmental monitoring technology. Computational models such as system dynamics play a key role in the field and are used to simulate and predict the effects of environmental policies and interventions, and to assist decision makers in formulating scientific environmental management strategies.

[0003] Among them, the emission reduction path optimization method mainly focuses on how to effectively reduce greenhouse gas and pollutant emissions through scientific and policy interventions. This method involves evaluating existing emission levels, setting emission reduction targets, and designing strategies to achieve the targets. Emission reduction path optimization includes not only technical solutions, such as improving energy efficiency and adopting clean energy technologies, but also adjustments at the behavioral and policy levels, such as setting stricter emission standards and encouraging environmentally friendly behaviors. Its purpose is to mitigate the impact of climate change, protect environmental health, and support sustainable economic development.

[0004] Existing technologies mainly rely on regular monitoring and subsequent treatment. This method appears to be slow to respond to rapidly changing environmental conditions and cannot adjust management strategies in real time to respond to sudden environmental events. For example, after a pollution incident occurs, traditional technologies require a long time to collect complete data and analyze the data. During this period, pollutants have spread widely, resulting in a significant increase in control and remediation costs. Existing technologies lack the ability to simulate environmental dynamics in real time and it is difficult to predict the diffusion path and impact range of pollutants, making environmental strategies passive responses only when the problem is already serious. This model is not only inefficient, but also misses the best governance opportunities due to untimely responses, increasing the difficulty and economic burden of environmental restoration. Traditional methods lack pertinence and adaptability in the design of emission reduction paths, and fail to refer to the specific needs of differentiated regions and differentiated pollution source characteristics, thus affecting the maximization of emission reduction effects. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose an emission reduction path optimization method and system based on system dynamics model analysis.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution, which is an emission reduction path optimization method based on system dynamics model analysis, comprising the following steps:

[0007] S1: Capture emission data from pollution sources, screen key variables, including emission volume and time, input them into the kinetic model to simulate pollutant propagation, and obtain preliminary kinetic behavior simulation results;

[0008] S2: analyzing the data in the preliminary kinetic behavior simulation results, extracting diffusion nodes and time series, optimizing the model response speed, regularly updating the model with real-time environmental data, and generating an adjusted kinetic model;

[0009] S3: using the adjusted kinetic model, determining the impact range of the pollution source, analyzing the impact of emission reduction measures on diffusion, and constructing impact prediction analysis results;

[0010] S4: Based on the impact prediction and analysis results, refine the spatial layout and time scheduling of emission reduction measures, adjust the configuration of emission reduction facilities according to environmental and meteorological conditions, and build an optimized emission reduction path design plan;

[0011] S5: Implement the optimized emission reduction path design plan, capture implementation effect data through environmental monitoring, adjust operating parameters according to the data, match environmental changes, monitor the implementation of emission reduction measures in real time, and obtain dynamic adjustment strategy implementation records;

[0012] S6: By analyzing the data in the implementation records of the dynamic adjustment strategy, the continuous optimization trend of environmental quality is identified, and the adjustment direction of environmental management in the future time period is predicted to obtain the long-term emission reduction effect evaluation results.

[0013] As a further scheme of the present invention, the preliminary kinetic behavior simulation results include pollution concentration diagrams, propagation rate indicators, and variable response diagrams; the adjusted kinetic model includes parameter tuning details, adaptability assessment, and update algorithm descriptions; the impact prediction analysis results include diffusion prediction diagrams, impact assessment results, and key node data; the optimized emission reduction path design scheme includes a spatial optimization diagram, a time management table, and a facility layout scheme; the dynamic adjustment strategy implementation records include adjustment measure details, effect tracking records, and environmental feedback analysis; the long-term emission reduction effect evaluation results include trend analysis diagrams, strategy optimization schemes, and environmental response assessments.

[0014] As a further solution of the present invention, the steps of capturing the emission data of pollution sources, screening key variables, including emission amount and time, inputting them into the kinetic model to simulate the propagation of pollutants, and obtaining preliminary kinetic behavior simulation results are as follows:

[0015] S101: Capture pollution source emission data, process the collected pollution source emission data, filter the items in the data, including pollutant type identification and corresponding emission amount, verify the data, unify the data format, and generate an emission data list;

[0016] S102: extracting key variables based on the emission data list, selecting emission amount and time variables in the data, using data segmentation technology to determine the emission peak interval, selecting the time point corresponding to the peak interval, and generating a key variable data set;

[0017] S103: Through the key variable data set, a time series analysis is performed on the data set to calculate the pollutant diffusion rate at different time points, estimate the propagation distance and range of the pollutants based on the relationship between the rate and time, perform pollutant propagation simulation, and generate preliminary kinetic behavior simulation results.

[0018] As a further solution of the present invention, the steps of analyzing the data in the preliminary kinetic behavior simulation results, extracting diffusion nodes and time series, optimizing the model response speed, and regularly updating the model using real-time environmental data to generate an adjusted kinetic model are specifically as follows:

[0019] S201: based on the preliminary kinetic behavior simulation results, extract key diffusion nodes, analyze environmental conditions and time series data, organize node information, and generate a key diffusion node database;

[0020] S202: Based on the key diffusion node database, adjust the model parameters, optimize the parameters by evaluating the impact of the original data, analyze the impact of the differentiated parameters on the model reaction speed, and generate the model parameter adjustment results;

[0021] S203: Based on the model parameter adjustment result, applying the Mann filter algorithm, combining with real-time environmental data, regularly updating the model, performing performance evaluation, and generating an adjusted dynamic model.

[0022] As a further solution of the present invention, the formula of the particle filtering algorithm is as follows:

[0023] ,

[0024] Calculate the optimal state estimate and obtain the adjusted model parameter values, where represents the estimated value of the state at time k, represents the state prediction value at time k based on the previous time k-1, represents the real-time measurement value at time k, Represents the linear relationship matrix of the observation model, stands for Kalman gain.

[0025] As a further solution of the present invention, the adjusted kinetic model is used to determine the impact range of the pollution source, analyze the impact of emission reduction measures on diffusion, and construct the impact prediction analysis results in the following steps:

[0026] S301: updating the input parameters of the adjusted dynamic model, including pollutant concentration and meteorological conditions, adjusting the pollutant propagation rate and diffusion parameters through time series analysis of data, and generating an impact range analysis result;

[0027] S302: using the impact range analysis results, evaluating the impact of differentiated emission reduction measures, including emission time adjustment and filtration equipment upgrade on pollution distribution, determining emission reduction configuration by simulating the change of pollutant diffusion caused by the measures, and generating emission reduction measure simulation results;

[0028] S303: Based on the simulation results of the emission reduction measures, combined with the pollution source location data in the geographic information, the spatial analysis method is used to predict the geographical effect differences of the differentiated emission reduction measures, and the impact of terrain and geographical barriers on pollution diffusion are referred to to generate impact prediction analysis results.

[0029] As a further solution of the present invention, according to the impact prediction and analysis results, the spatial layout and time scheduling of emission reduction measures are refined, the configuration of emission reduction facilities is adjusted according to environmental and meteorological conditions, and the steps of constructing an optimized emission reduction path design scheme are specifically as follows:

[0030] S401: Based on the impact prediction and analysis results, capture regional environmental data and meteorological conditions, organize and classify the data, and generate a spatial layout plan for emission reduction measures;

[0031] S402: Based on the spatial layout plan of emission reduction measures, evaluate the matching of multi-region emission reduction facilities, match the number and type of facilities, integrate resources for optimal configuration, and generate an emission reduction facility configuration plan;

[0032] S403: Based on the emission reduction facility configuration plan, analyze the time scheduling of multiple emission reduction paths, integrate resources and operation plans, and generate an optimized emission reduction path design plan.

[0033] As a further solution of the present invention, the optimized emission reduction path design scheme is implemented, the implementation effect data is captured through environmental monitoring, the operation parameters are adjusted according to the data, the implementation of emission reduction measures is monitored in real time to match environmental changes, and the steps of obtaining the dynamic adjustment strategy implementation record are specifically as follows:

[0034] S501: Based on the optimized emission reduction path design scheme, preliminary effect data collection is performed, monitoring frequency is set, multiple parameters such as temperature, humidity, and pollutant concentration are recorded, and data is classified and sorted to generate an environmental monitoring data set;

[0035] S502: Based on the environmental monitoring data set, perform data analysis, extract the relationship between key parameters and emission reduction measures, record the impact of each parameter change, and perform corresponding performance evaluation to generate emission reduction effect analysis results;

[0036] S503: Based on the emission reduction effect analysis results, dynamically adjust the operating parameters, set a real-time monitoring strategy, update the emission reduction measures according to each parameter change, record the adjustment content and execution status, and generate a dynamic adjustment strategy implementation record.

[0037] As a further solution of the present invention, by analyzing the data in the implementation record of the dynamic adjustment strategy, identifying the continuous optimization trend of environmental quality, and predicting the adjustment direction of environmental management in the future time period, the steps of obtaining the long-term emission reduction effect evaluation result are specifically as follows:

[0038] S601: Based on the dynamic adjustment strategy implementation record, extract data, analyze changes in environmental parameters, record multi-source data within a time period, calculate the variation range of each parameter, and compare them to obtain environmental parameter change records;

[0039] S602: Based on the environmental parameter change records, analyze the environmental management adjustment direction in the future time period, integrate the original data, and generate an adjustment direction plan in combination with the current change trend;

[0040] S603: Based on the adjustment direction plan, the emission reduction effect is evaluated, the original adjustment records and the plan are integrated, the implementation of the measures is analyzed, and the long-term emission reduction effect evaluation results are obtained.

[0041] An emission reduction path optimization system based on system dynamics model analysis, the emission reduction path optimization system based on system dynamics model analysis is used to execute the above-mentioned emission reduction path optimization method based on system dynamics model analysis, the system comprises:

[0042] The pollution identification and preliminary analysis module captures pollution source emission data, uses time series analysis to identify pollution trends, summarizes pollution data, and compiles the information to generate pollution trend profiles;

[0043] The simulation and parameter adjustment module initializes the dynamic model through the pollution trend archive, uses environmental feedback to perform model sensitivity tests, adjusts seasonal and geographical target parameters according to the test results, and constructs a dynamic model parameter set;

[0044] The impact scope and emission reduction plan module uses the dynamic model parameter set to simulate and analyze the impact of differentiated emission reduction strategies on pollution control, selects the optimal strategy through effect comparison, and obtains a strategy effect comparison table;

[0045] The emission reduction measures implementation and optimization module adjusts and optimizes the implementation details of the emission reduction measures according to the strategy effect comparison table, simultaneously monitors and records environmental responses, updates emission reduction measures and maintains optimal performance, and forms an adjusted emission reduction implementation plan;

[0046] The environmental quality continuous optimization and prediction module analyzes the implementation effect of the adjusted emission reduction execution plan, predicts the long-term environmental changes of the current trend, updates and optimizes the emission reduction strategy to cope with the challenges in the future time period, and obtains the long-term emission reduction effect evaluation results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by capturing and collating the emission data of pollution sources in real time, dynamic simulation is performed on key variables to achieve accurate prediction of pollution propagation behavior. By continuously updating the dynamic model and combining it with real-time environmental data, the reaction speed of the model and the ability to match the environment are optimized, thereby improving the adaptability and efficiency of the environmental management strategy. Through detailed analysis of pollution diffusion, emission reduction measures can be designed more accurately for high-incidence areas and key time points. The refined spatial layout and time scheduling further improve the refinement level of emission reduction path design, enhance the practical application value of the measures, and effectively achieve continuous optimization of environmental management measures by dynamically monitoring the implementation effect and adjusting the operating parameters, promoting continuous improvement of environmental quality, not only improving the real-time response capability of pollution control, but also optimizing the long-term sustainability of environmental management strategies through prediction and timely adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for optimizing emission reduction paths based on system dynamics model analysis, comprising the following steps:

[0060] S1: Capture the emission data of pollution sources, including types and amounts of pollution, organize the data, screen key variables, including emission amounts and emission time, input the variables into the kinetic model, simulate the initial propagation behavior of pollutants, and obtain preliminary kinetic behavior simulation results;

[0061] S2: Analyze the data in the preliminary kinetic behavior simulation results, extract key diffusion nodes and time series, adjust the model parameters, optimize the model's reaction speed and ability to match the environment, use real-time environmental data to regularly update the model, and generate an adjusted kinetic model;

[0062] S3: Use the adjusted kinetic model to determine the impact range of the pollution source. Based on the model results, analyze the impact of differentiated emission reduction measures on pollution diffusion, and construct the impact prediction analysis results with reference to the location and efficiency of the pollution source;

[0063] S4: Based on the results of impact prediction and analysis, refine the spatial layout and time scheduling of emission reduction measures, adjust the configuration of emission reduction facilities according to environmental and meteorological conditions, and build an optimized emission reduction path design plan;

[0064] S5: Implement optimized emission reduction path design plans, capture implementation effect data through environmental monitoring, adjust operating parameters based on data to match environmental changes, monitor the implementation of emission reduction measures in real time, and obtain dynamic adjustment strategy implementation records;

[0065] S6: By analyzing the data in the dynamic adjustment strategy implementation records, we can identify the continuous optimization trend of environmental quality, predict the adjustment direction of environmental management in the future time period, and obtain the long-term emission reduction effect evaluation results.

[0066] The preliminary kinetic behavior simulation results include pollution concentration maps, transmission rate indicators, and variable response maps. The adjusted kinetic model includes parameter tuning details, adaptability assessment, and update algorithm description. The impact prediction analysis results include diffusion prediction maps, impact assessment results, and key node data. The optimized emission reduction path design plan includes a spatial optimization map, a time management table, and a facility layout plan. The dynamic adjustment strategy implementation records include adjustment measure details, effect tracking records, and environmental feedback analysis. The long-term emission reduction effect evaluation results include trend analysis diagrams, strategy optimization plans, and environmental response assessments.

[0067] Specifically, if Figure 2 As shown in the figure, the steps of capturing the emission data of pollution sources, screening key variables, including emission amount and time, inputting them into the kinetic model to simulate the propagation of pollutants, and obtaining preliminary kinetic behavior simulation results are as follows:

[0068] S101: Capture pollution source emission data, process the collected pollution source emission data, filter the items in the data, including pollutant type identification and corresponding emission amount, and perform data verification to unify the data format. The execution process of generating an emission data list is as follows;

[0069] Capture emission data from pollution sources, identify and record the types of pollutants and corresponding emissions in the data based on the collected data, verify the integrity and accuracy of the data to ensure that the collected data meets the needs of subsequent processing, screen and classify the items in the data, including the identification of pollutant types and corresponding emissions, and ensure the uniformity and availability of data through formatting, laying the foundation for generating an emission data inventory. The generated emission data inventory reflects the emission type and amount of each pollution source, provides an intuitive understanding of the pollution sources, and facilitates subsequent data processing and analysis.

[0070] S102: Based on the emission data list, extract key variables, select the emission amount and time variables in the data, use data segmentation technology to determine the emission peak interval, select the time point corresponding to the peak interval, and generate the key variable data set. The execution process is as follows;

[0071] Based on the emission data inventory, key variables that affect pollutant analysis, including emission volume and time, are extracted from the data. Through data segmentation technology, the fluctuation trend in the data set is analyzed, the peak interval of emissions is identified, and then the specific time point corresponding to the peak interval is determined. The key time point is the basis for further time series analysis. The generated key variable data set provides the necessary input for the next step of simulation and analysis.

[0072] S103: Through the key variable data set, the data set is analyzed in time series, the pollutant diffusion rate at the differentiated time points is calculated, the propagation distance and range of the pollutants are estimated according to the relationship between the rate and time, and the pollutant propagation simulation is performed to generate the preliminary kinetic behavior simulation results. The execution process is as follows;

[0073] Perform time series analysis on the data set and calculate the pollutant diffusion rate at different time points according to the formula: Calculate the propagation distance and range of pollutants, where represents the diffusion rate of pollutants (unit: m / s), represents the propagation distance of pollutants (unit: meter), Represents the elapsed time (in seconds);

[0074] Set up a specific pollution event, , Set in Time point( Second, Pollutants are discharged from the source point. Time point( Seconds) The pollutants were measured The concentration at meters reaches the observation threshold;

[0075] Using the above formula, the diffusion rate is calculated as m / s;

[0076] This result shows that pollutants diffuse outward 150 meters within 5 minutes, reflecting the transmission ability of pollutants and the influence of environmental media. This data can be used for further environmental impact assessment and early warning system setting.

[0077] Specifically, if Figure 3 As shown in the figure, the steps of analyzing the data in the preliminary kinetic behavior simulation results, extracting diffusion nodes and time series, optimizing the model response speed, and regularly updating the model with real-time environmental data to generate the adjusted kinetic model are as follows:

[0078] S201: Based on the preliminary dynamic behavior simulation results, extract key diffusion nodes, analyze environmental conditions and time series data, organize node information, and generate the key diffusion node database. The execution process is as follows;

[0079] Based on the preliminary kinetic behavior simulation results, the key diffusion nodes are extracted. First, the nodes that play a decisive role in the kinetic behavior are identified according to the simulation data. The nodes have a significant impact on the overall system dynamics due to their position or connectivity in the network. The criticality of the nodes is further determined by analyzing the response of each node under differentiated environmental conditions and the change pattern of time series data. The steps involve detailed recording of the performance of each node under differentiated experimental conditions, including the influence of environmental factors such as temperature, humidity, and pressure. After the data is sorted, each key node and its associated attributes are entered into the database system to create a key diffusion node database. This database will contain the location of the node, the quality of connectivity, and the performance data under the influence of differentiated environmental factors, providing the necessary input for subsequent steps. Through such processing, it can be ensured that the data of the key nodes are accurately recorded and classified, which is convenient for subsequent in-depth analysis and utilization. The key diffusion node database is generated, which will include multi-dimensional information such as node identifiers, connection data, and environmental response data, providing a data basis for further analysis and optimization of the model.

[0080] S202: Based on the key diffusion node database, adjust the model parameters, optimize the parameters by evaluating the impact of the original data, analyze the impact of the differentiated parameters on the model reaction speed, and generate the model parameter adjustment results. The execution process is as follows;

[0081] Based on the key diffusion node database, adjust the model parameters. This step begins with extracting data from the key diffusion node database and performing an initial evaluation of the parameters in the model. The evaluation is mainly based on how the performance of the node responds to differentiated environmental variables and historical data. The stages include comparing the performance of the node under various environmental settings, identifying the parameters that have the greatest impact on the model output, and optimizing the parameters to improve the accuracy and response speed of the model. The specific adjustment process includes modifying the parameter values, testing the output of the model under the new parameter settings, and iterating repeatedly until the optimal parameter configuration is reached. It is also necessary to analyze differentiated parameters, that is, those parameters that change significantly under differentiated conditions. This requires quantitative analysis of the response time of the model under differentiated parameter configurations to identify which parameters are the key determinants of model performance. All adjustment results are integrated to generate model parameter adjustment results, which will detail the changes before and after each parameter adjustment, the test methods adopted, and the final optimization effect, to ensure that the model can provide higher efficiency and better prediction performance in practical applications.

[0082] S203: Based on the model parameter adjustment results, the Mann filter algorithm is applied, combined with real-time environmental data, the model is updated regularly, and performance evaluation is performed to generate the adjusted dynamic model. The execution process is as follows;

[0083] The formula of the particle filter algorithm is as follows: ,

[0084] Calculate the optimal state estimate and obtain the adjusted model parameter values, where represents the estimated value of the state at time k, represents the state prediction value at time k based on the previous time k-1, represents the real-time measurement value at time k, Represents the linear relationship matrix of the observation model, stands for Kalman gain.

[0085] Detailed explanation of the formula and the process of formula calculation and derivation:

[0086] : The predicted value of the state at time k based on time k-1 is set to 10.2 (calculated by the previous state update);

[0087] : Kalman gain, used to update the state estimate, with a value of 0.3 (calculated based on the ratio of the prediction error to the actual measurement error, this value adjusts the dynamic response of the coefficient);

[0088] : The actual measured value at time k is set to 12.5 (obtained through real-time sensor monitoring);

[0089] : The linear relationship matrix of the observation model, connecting the state space and the measurement space, where a simple scaling factor of 1.0 is taken (obtained through experimental data analysis, indicating direct influence);

[0090] Setting parameters: , , , ;

[0091] calculate :

[0092] ;

[0093] Will Multiplying this with the above result:

[0094] ;

[0095] calculate :

[0096] ;

[0097] The results show that the state estimate at time k is 10.89. This value updates the prediction of the previous moment to more accurately reflect the impact of actual measurement data and environmental changes. It represents the optimal estimated state of the adjusted dynamic model at the current time point. This value becomes the starting prediction value for the next calculation cycle to ensure the continuity and accuracy of the model.

[0098] Specifically, if Figure 4 As shown in the figure, the adjusted kinetic model is used to determine the impact range of pollution sources, analyze the impact of emission reduction measures on diffusion, and construct the impact prediction analysis results in the following steps:

[0099] S301: updating the input parameters of the adjusted dynamic model, including pollutant concentration and meteorological conditions, adjusting the pollutant propagation rate and diffusion parameters through time series analysis of data, and generating the impact range analysis results. The execution process is as follows;

[0100] The pollutant propagation rate and diffusion parameters are adjusted through time series analysis of the data, according to the formula: Calculate the impact range analysis results, where: represents the area of ​​influence (unit: square kilometers), represents the pollutant concentration (unit: mg / m3), represents wind speed (unit: m / s), The average altitude of the representative area (in meters);

[0101] As a specific example, assume that the pollutant concentration in a certain area is mg / m^3, wind speed V=5m / s, average altitude A=50m, calculated by formula : square kilometers;

[0102] The calculation results reveal the geographical scope of pollutant impact under given meteorological conditions and geographical factors, providing environmental protection departments with numerical references for actual environmental management and emergency response.

[0103] S302: Using the results of the impact range analysis, evaluate the impact of differentiated emission reduction measures, including the adjustment of emission time and the upgrade of filtration equipment on pollution distribution, and determine the emission reduction configuration by simulating the changes in pollutant diffusion caused by the measures. The execution process of generating the simulation results of emission reduction measures is as follows;

[0104] Using the generated impact range analysis results, we further analyze the impact of differentiated emission reduction measures, including emission time adjustment and filtration equipment upgrades. The measures are aimed at optimizing the management and control of pollutants. By simulating the implementation of measures, we evaluate their changes in pollutant diffusion patterns. In the specific implementation process, we first set an adjustment plan for the emission time of pollution sources. By changing the emission time, we observe the temporal dynamic changes in pollution distribution. At the same time, we upgrade the filtration equipment to enhance the capture efficiency of pollutants, simulate the specific impact of changes on pollution distribution, and use statistical models to calculate the diffusion of pollutants under differentiated control measures. We evaluate the effectiveness of the measures, determine the optimal emission reduction configuration through analysis and simulation, and generate simulation results for emission reduction measures. The results are of great significance for guiding actual pollution control strategies.

[0105] S303: Based on the simulation results of emission reduction measures, combined with the pollution source location data in the geographic information, the spatial analysis method is used to predict the geographical effect differences of differentiated emission reduction measures, and the impact of terrain and geographical barriers on pollution diffusion are referred to. The execution process of generating the impact prediction analysis results is as follows;

[0106] Based on the simulation results of emission reduction measures, combined with the geographic information system (GIS), the geographical effect differences of differentiated emission reduction measures are predicted. During the implementation process, the pollution source location data, including the specific location information of industrial and residential areas, are first collected and integrated. Then, the spatial analysis method is used to analyze the physical restrictions of terrain and geographical barriers such as mountains and rivers on the spread of pollution. The diffusion path and range of pollutants under differentiated terrain conditions are simulated through GIS tools. This analysis helps identify areas with higher pollution risks and potential safe areas. The final impact prediction analysis results provide a scientific basis for local governments to formulate region-specific environmental protection measures, optimize resource allocation, and improve environmental protection efficiency.

[0107] Specifically, if Figure 5 As shown in the figure, according to the results of impact prediction and analysis, the spatial layout and time scheduling of emission reduction measures are refined, the configuration of emission reduction facilities is adjusted according to environmental and meteorological conditions, and the steps to construct an optimized emission reduction path design plan are as follows:

[0108] S401: Based on the impact prediction analysis results, the regional environmental data and meteorological conditions are captured, and the data are collated and classified to generate the spatial layout plan of emission reduction measures. The execution process is as follows;

[0109] Based on the results of impact prediction analysis, the environmental data and meteorological conditions in the region are captured for detailed classification and collation. The process involves collecting data from multiple monitoring sites, including meteorological variables such as temperature, humidity, wind speed, wind direction, and pollutant concentration data. Data sources include ground observation stations, satellite data, and online sensor networks. During the data collation process, the collected data are time synchronized and spatially aligned to ensure data consistency and comparability, outliers are processed and screened, and data points with large noise are deleted. Based on the pre-processed data, a specific spatial layout plan for emission reduction measures is formulated. The plan specifies in detail the specific emission reduction measures to be implemented in differentiated areas, such as adding filtration facilities in areas with more serious pollution, or adjusting the operating hours of existing facilities to adapt to real-time environmental changes and meteorological conditions. The generated spatial layout plan will provide a basis for subsequent implementation and adjustment, and optimize environmental management and resource allocation in the entire region.

[0110] S402: Based on the spatial layout plan of emission reduction measures, the matching of multi-regional emission reduction facilities is evaluated, the number and type of facilities are matched, resources are integrated for optimal configuration, and the execution process of generating the emission reduction facility configuration plan is as follows;

[0111] Based on the generated spatial layout plan of emission reduction measures, the matching of emission reduction facilities and resource allocation efficiency in differentiated regions are further evaluated, which involves analyzing the relationship between the needs of each region and the number and type of existing facilities, and evaluating the performance and cost-effectiveness of differentiated types of emission reduction facilities through data models. For example, the emission reduction effect and maintenance cost of filters and catalysts are compared to ensure that the selected facilities can provide the maximum emission reduction effect within the budget. In the process of integrating resources, reference is made to the life cycle and update frequency of the facilities, as well as the compatibility with existing infrastructure, such as the feasibility of power supply and maintenance support. The optimized configuration aims to maximize resource utilization efficiency and emission reduction effects. Through analysis and configuration, the generated emission reduction facility configuration plan specifies in detail what type and number of facilities to install in each key area, providing clear guidance and implementation standards for actual operations.

[0112] S403: Based on the emission reduction facility configuration plan, the time scheduling of multiple emission reduction paths is analyzed, resources and operation plans are integrated, and the execution process of generating an optimized emission reduction path design plan is as follows;

[0113] Analyze the time scheduling of multiple emission reduction paths, integrate resources and operation plans, according to the formula: Calculate the optimized emission reduction pathway design, where: The time required for the representative (unit: hours), Represents the total emission reduction task distance (unit: km), The average service speed of the representative facility (unit: km / h), represents the efficiency factor;

[0114] The total emission reduction task is set to involve a distance of km, average service rate of facilities km / h, efficiency factor (reflecting the delay or efficiency loss in actual operation), the time required is calculated as Hour;

[0115] The results show that, with reference to the efficiency loss, it is estimated that it will take 12.5 hours to complete the emission reduction task. This time assessment helps dispatchers to reasonably arrange the emission reduction path and schedule to ensure that the task is carried out efficiently as planned.

[0116] Specifically, if Figure 6 As shown in the figure, the steps to implement the optimized emission reduction path design plan, capture the implementation effect data through environmental monitoring, adjust the operating parameters according to the data, match the environmental changes, monitor the implementation of emission reduction measures in real time, and obtain the implementation records of the dynamic adjustment strategy are as follows:

[0117] S501: Based on the optimized emission reduction path design, preliminary effect data collection is carried out, monitoring frequency is set, multiple parameters such as temperature, humidity, and pollutant concentration are recorded, and data is classified and sorted. The execution process of generating environmental monitoring data sets is as follows;

[0118] Based on the optimized emission reduction path design plan, preliminary effect data collection is carried out, including setting the monitoring frequency and collecting multiple parameters of environmental and meteorological conditions such as temperature, humidity and pollutant concentration. During the collection process, the environmental monitoring instruments arranged in various regions automatically record the data. Each device updates the data at a preset frequency to ensure the timeliness and accuracy of the data. Data sorting and classification work involves archiving the collected data according to type and source. For example, meteorological data and pollution data are stored separately, and outliers in the data are identified and processed to clean the data and ensure the quality of subsequent analysis. The generated environmental monitoring data set will be used for further analysis and decision support, providing real-time data for environmental protection departments to assist in monitoring the effectiveness of emission reduction measures and adjusting strategies in a timely manner.

[0119] S502: Based on the environmental monitoring data set, perform data analysis, extract the relationship between key parameters and emission reduction measures, record the impact of each parameter change, and perform corresponding performance evaluation to generate emission reduction effect analysis results. The execution process is as follows;

[0120] Conduct data analysis to extract the relationship between key parameters and emission reduction measures, according to the formula: Calculate the emission reduction effect analysis results, where: Represents the comprehensive score of emission reduction effect, Representative The measured value of a parameter, Represents the weight factor of the parameter, Represents the total number of parameters;

[0121] Referring to a specific case, there are three key parameters, namely temperature, humidity and pollutant concentration, with weights of 0.3, 0.3 and 0.4 respectively. If in a certain evaluation, the temperature score is 85, the humidity score is 90, and the pollutant concentration score is 75, then the comprehensive score of the emission reduction effect is calculated as ;

[0122] The results show that after comprehensively referring to the impact of differentiated environmental parameters and their weights, the emission reduction effect score is 83.33, which can assist environmental protection agencies in evaluating the overall effectiveness of current emission reduction measures and adjusting strategies accordingly.

[0123] S503: Based on the results of the emission reduction effect analysis, the operating parameters are dynamically adjusted, the real-time monitoring strategy is set, the emission reduction measures are updated according to the changes in each parameter, the adjustment content and implementation status are recorded, and the execution process of generating the dynamic adjustment strategy implementation record is as follows;

[0124] Based on the results of the emission reduction effect analysis, the operating parameters are dynamically adjusted and the real-time monitoring strategy is set. First, according to the changes in each parameter, such as the increase or decrease in pollutant concentration, the working intensity or operating time of the emission reduction equipment is adjusted. For example, if the pollutant concentration in a specific area is monitored to continue to rise, the operating time of the emission reduction equipment in that area will be increased or its processing capacity will be improved. During the adjustment process, all operations and changes will be recorded in the dynamic adjustment strategy implementation record, including the content of the adjustment, the time of implementation, the expected effect and the actual effect evaluation. The record not only provides transparency of the operation, but also provides data support for future strategy adjustments. The generated records assist decision makers in understanding the implementation and effect of the real-time adjustment strategy, ensuring that emission reduction measures can flexibly respond to environmental changes.

[0125] Specifically, if Figure 7 As shown in the figure, by analyzing the data in the dynamic adjustment strategy implementation records, identifying the continuous optimization trend of environmental quality, and predicting the adjustment direction of environmental management in the future time period, the steps for obtaining the long-term emission reduction effect evaluation results are as follows:

[0126] S601: Based on the dynamic adjustment strategy implementation record, data extraction is performed, changes in environmental parameters are analyzed, multi-source data within a time period is recorded, the variation range of each parameter is calculated, and the comparison is performed to obtain the execution process of the environmental parameter change record as follows;

[0127] Based on the dynamic adjustment strategy implementation records, the data extraction process is executed to analyze the changes in environmental parameters, including temperature, humidity, pollutant concentration, etc. The data is automatically collected from the real-time monitoring system. The collected data includes a specific time period to ensure that various environmental changes are covered. The collected data is classified and organized, and archived according to time series and parameter types. For example, temperature data and humidity data are stored separately. At the same time, the variation range of each parameter is calculated, including its maximum, minimum and average values, as well as the rate of change. Statistical analysis assists in identifying key change trends. The generated environmental parameter change records will be used for further environmental impact assessment and strategy adjustment.

[0128] S602: Based on the environmental parameter change records, analyze the environmental management adjustment direction in the future time period, integrate the original data, and combine the current change trend to generate the execution process of the adjustment direction plan as follows;

[0129] Based on the records of environmental parameter changes, an analysis of the direction of environmental management adjustments in future time periods is conducted. The analysis process involves integrating and analyzing the original data and the current change trends. The original data is imported through dedicated data analysis software, and statistical tools are used to determine the long-term change trends of the parameters, such as linear regression analysis, which helps to predict future environmental conditions. According to the analysis results, a specific adjustment direction plan is designed. The plan includes adding or reducing certain monitoring points, or adjusting the monitoring frequency and parameter thresholds to more accurately control the environmental quality. The generated adjustment direction plan will provide a scientific basis for the implementation of more effective environmental management measures.

[0130] S603: Based on the adjustment direction plan, the emission reduction effect is evaluated, the original adjustment records and the plan are integrated, the implementation of the measures is analyzed, and the execution process of obtaining the long-term emission reduction effect evaluation result is as follows;

[0131] Evaluate the emission reduction effect, integrate the original adjustment records and plans, according to the formula: Calculate the long-term emission reduction effect assessment results, where: Represents the score of emission reduction effect, Representative The coverage area of ​​the measures, Represents the efficiency of the response measures;

[0132] There are three emission reduction measures, covering areas of 100, 150 and 200 square kilometers respectively, with corresponding emission reduction efficiencies of 0.9, 0.8 and 0.75. ;

[0133] The calculated emission reduction effect score is 0.807. This result shows that after considering the regional coverage and efficiency of various measures, the overall emission reduction effect is good, providing a quantitative evaluation basis for further environmental protection and policy adjustments.

[0134] like Figure 8 As shown, the emission reduction path optimization system based on system dynamics model analysis includes:

[0135] The pollution identification and preliminary analysis module captures pollution source emission data, uses time series analysis to identify pollution trends, summarizes pollution data, and compiles the information to generate pollution trend profiles;

[0136] The simulation and parameter adjustment module initializes the dynamic model through pollution trend archives, uses environmental feedback to test the model sensitivity, adjusts seasonal and geographical target parameters according to the test results, and constructs a dynamic model parameter set;

[0137] The impact scope and emission reduction plan module uses a dynamic model parameter set to simulate and analyze the impact of differentiated emission reduction strategies on pollution control, select the optimal strategy through effect comparison, and obtain a strategy effect comparison table;

[0138] The emission reduction measures implementation and optimization module adjusts and optimizes the implementation details of emission reduction measures according to the strategy effect comparison table, simultaneously monitors and records environmental responses, updates emission reduction measures and maintains optimal performance, and forms an adjusted emission reduction implementation plan;

[0139] The environmental quality continuous optimization and prediction module analyzes the implementation effect of the adjusted emission reduction execution plan, predicts the long-term environmental changes of the current trend, updates and optimizes the emission reduction strategy to cope with the challenges in the future time period, and obtains the long-term emission reduction effect evaluation results.

[0140] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The emission reduction path optimization method based on system dynamics model analysis is characterized by: The following steps are involved: Capture emission data from pollution sources, screen key variables, including emission volume and time, input them into the kinetic model to simulate pollutant propagation, and obtain preliminary kinetic behavior simulation results; Analyzing data from the preliminary kinetic behavior simulation results, extracting diffusion nodes and time series, optimizing the model response speed, regularly updating the model using real-time environmental data, and generating an adjusted kinetic model; Using the adjusted kinetic model, determine the impact range of pollution sources, analyze the impact of emission reduction measures on diffusion, and construct impact prediction analysis results; Based on the impact prediction and analysis results, refine the spatial layout and time scheduling of emission reduction measures, adjust the configuration of emission reduction facilities according to environmental and meteorological conditions, and build an optimized emission reduction path design plan; Implement the optimized emission reduction path design plan, capture implementation effect data through environmental monitoring, adjust operating parameters according to the data, match environmental changes, monitor the implementation of emission reduction measures in real time, and obtain dynamic adjustment strategy implementation records; By analyzing the data in the implementation records of the dynamic adjustment strategy, the continuous optimization trend of environmental quality is identified, and the adjustment direction of environmental management in the future time period is predicted to obtain the long-term emission reduction effect evaluation results; The steps to capture the emission data of pollution sources, screen key variables, including emission amount and time, input them into the kinetic model to simulate the propagation of pollutants, and obtain preliminary kinetic behavior simulation results are as follows: Capture pollution source emission data, process the collected pollution source emission data, screen the items in the data, including pollutant type identification and corresponding emission amount, verify the data, unify the data format, and generate an emission data list; Based on the emission data list, extract key variables, select emission and time variables in the data, use data segmentation technology to determine the emission peak interval, select the time point corresponding to the peak interval, and generate a key variable data set; Through the key variable data set, the data set is analyzed in time series, the pollutant diffusion rate at different time points is calculated, the propagation distance and range of the pollutants are estimated based on the relationship between the rate and time, the pollutant propagation simulation is performed, and preliminary dynamic behavior simulation results are generated; The steps of analyzing the data in the preliminary kinetic behavior simulation results, extracting diffusion nodes and time series, optimizing the model response speed, and regularly updating the model with real-time environmental data to generate an adjusted kinetic model are as follows: Based on the preliminary kinetic behavior simulation results, key diffusion nodes are extracted, environmental conditions and time series data are analyzed, node information is sorted, and a key diffusion node database is generated; Based on the key diffusion node database, adjust the model parameters, optimize the parameters by evaluating the impact of the original data, analyze the impact of the differentiated parameters on the model reaction speed, and generate the model parameter adjustment results; Based on the model parameter adjustment results, a particle filter algorithm is applied, combined with real-time environmental data, the model is regularly updated, performance evaluation is performed, and an adjusted dynamic model is generated.

2. The emission reduction path optimization method based on system dynamics model analysis according to claim 1 is characterized in that: The preliminary kinetic behavior simulation results include pollution concentration diagrams, transmission rate indicators, and variable response diagrams; the adjusted kinetic model includes parameter tuning details, adaptability assessment, and update algorithm descriptions; the impact prediction analysis results include diffusion prediction diagrams, impact assessment results, and key node data; the optimized emission reduction path design plan includes a spatial optimization diagram, a time management table, and a facility layout plan; the dynamic adjustment strategy implementation records include adjustment measure details, effect tracking records, and environmental feedback analysis; the long-term emission reduction effect evaluation results include trend analysis diagrams, strategy optimization plans, and environmental response assessments.

3. The emission reduction path optimization method based on system dynamics model analysis according to claim 1 is characterized in that: The formula of the particle filter algorithm is as follows: ， Calculate the optimal state estimate and obtain the adjusted model parameter values, where represents the estimated value of the state at time k, represents the state prediction value at time k based on the previous time k-1, represents the real-time measurement value at time k, Represents the linear relationship matrix of the observation model, stands for Kalman gain.

4. The emission reduction path optimization method based on system dynamics model analysis according to claim 1 is characterized in that: The adjusted kinetic model is used to determine the impact range of the pollution source, analyze the impact of emission reduction measures on diffusion, and construct the impact prediction analysis results in the following steps: Updating input parameters of the adjusted kinetic model, including pollutant concentrations and meteorological conditions, adjusting pollutant propagation rates and diffusion parameters through time series analysis of data, and generating impact range analysis results; Using the results of the impact range analysis, evaluate the impact of differentiated emission reduction measures, including emission time adjustment and filtration equipment upgrade on pollution distribution, determine emission reduction configuration by simulating the changes in pollutant diffusion caused by the measures, and generate emission reduction measures simulation results; Based on the simulation results of the emission reduction measures, combined with the pollution source location data in the geographic information, the spatial analysis method is used to predict the geographical effect differences of differentiated emission reduction measures, and the impact prediction analysis results are generated by referring to the impact of terrain and geographical barriers on pollution diffusion.

5. The emission reduction path optimization method based on system dynamics model analysis according to claim 1 is characterized in that: According to the impact prediction and analysis results, the spatial layout and time scheduling of emission reduction measures are refined, the configuration of emission reduction facilities is adjusted according to environmental and meteorological conditions, and the steps to construct an optimized emission reduction path design plan are as follows: Based on the impact prediction and analysis results, capture regional environmental data and meteorological conditions, organize and classify the data, and generate a spatial layout plan for emission reduction measures; Based on the spatial layout plan of emission reduction measures, evaluate the compatibility of multi-regional emission reduction facilities, match the number and type of facilities, integrate resources for optimal configuration, and generate an emission reduction facility configuration plan; Based on the emission reduction facility configuration plan, the time scheduling of multiple emission reduction paths is analyzed, resources and operation plans are integrated, and an optimized emission reduction path design plan is generated.

6. The emission reduction path optimization method based on system dynamics model analysis according to claim 1 is characterized in that: The specific steps of implementing the optimized emission reduction path design plan, capturing implementation effect data through environmental monitoring, adjusting operating parameters according to the data, matching environmental changes, monitoring the implementation of emission reduction measures in real time, and obtaining dynamic adjustment strategy implementation records are as follows: Based on the optimized emission reduction path design, preliminary effect data collection is carried out, monitoring frequency is set, multiple parameters such as temperature, humidity, and pollutant concentration are recorded, and data is classified and sorted to generate an environmental monitoring data set; Based on the environmental monitoring data set, perform data analysis, extract the relationship between key parameters and emission reduction measures, record the impact of each parameter change, and perform corresponding performance evaluation to generate emission reduction effect analysis results; Based on the emission reduction effect analysis results, the operating parameters are dynamically adjusted, a real-time monitoring strategy is set, emission reduction measures are updated according to each parameter change, the adjustment content and execution status are recorded, and a dynamic adjustment strategy implementation record is generated.

7. The emission reduction path optimization method based on system dynamics model analysis according to claim 1 is characterized in that: By analyzing the data in the implementation records of the dynamic adjustment strategy, identifying the continuous optimization trend of environmental quality, and predicting the adjustment direction of environmental management in the future time period, the steps for obtaining the long-term emission reduction effect evaluation results are as follows: Based on the dynamic adjustment strategy implementation record, data extraction is performed, changes in environmental parameters are analyzed, multi-source data within a time period is recorded, the variation range of each parameter is calculated, and comparison is performed to obtain a record of environmental parameter changes; Based on the environmental parameter change records, analyze the environmental management adjustment direction in the future time period, integrate the original data, combine the current change trend, and generate an adjustment direction plan; Based on the adjustment direction plan, an emission reduction effect evaluation is conducted, the original adjustment records and plan are integrated, the implementation of the measures is analyzed, and the long-term emission reduction effect evaluation results are obtained.

8. The emission reduction path optimization system based on system dynamics model analysis is characterized by: According to the method for optimizing emission reduction paths based on system dynamics model analysis according to any one of claims 1 to 7, the system comprises: The pollution identification and preliminary analysis module captures pollution source emission data, uses time series analysis to identify pollution trends, summarizes pollution data, and compiles the information to generate pollution trend profiles; The simulation and parameter adjustment module initializes the dynamic model through the pollution trend archive, uses environmental feedback to perform model sensitivity tests, adjusts seasonal and geographical target parameters according to the test results, and constructs a dynamic model parameter set; The impact scope and emission reduction plan module uses the dynamic model parameter set to simulate and analyze the impact of differentiated emission reduction strategies on pollution control, selects the optimal strategy through effect comparison, and obtains a strategy effect comparison table; The emission reduction measures implementation and optimization module adjusts and optimizes the implementation details of the emission reduction measures according to the strategy effect comparison table, simultaneously monitors and records environmental responses, updates emission reduction measures and maintains optimal performance, and forms an adjusted emission reduction implementation plan; The environmental quality continuous optimization and prediction module analyzes the implementation effect of the adjusted emission reduction execution plan, predicts the long-term environmental changes of the current trend, updates and optimizes the emission reduction strategy to cope with the challenges in the future time period, and obtains the long-term emission reduction effect evaluation results.

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