Urban pollution source dynamic distribution regulation and control system and method

Abstract

The invention relates to the technical field of pollution control, and particularly discloses an urban pollution source dynamic distribution regulation and control system and method, and the core method is as follows: firstly, collecting the air quality and meteorological data of each region of a city in real time through a grid-deployed sensor network; then, the data processing center cleans and calibrates the data, calculates the sub-index of each pollutant by using a piecewise linear interpolation method, and obtains the regional comprehensive air quality index by weighting and fusing the evaluation indexes of the industrial area and the residential area; then introducing a meteorological correction factor based on real-time wind speed, temperature and humidity deviation to dynamically correct the index; and finally, the system automatically judges the pollution level according to the corrected index value, identifies the primary pollutant and triggers a corresponding hierarchical regulation and control instruction, so that the refinement and intelligence level of environmental governance is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of pollution control technology, specifically to a dynamic distribution control system and method for urban pollution sources. Background Technology

[0002] With the rapid development of urbanization, the urban population has grown dramatically, various economic activities have become increasingly frequent, and urban pollution problems have become more and more serious. In urban environmental governance, air pollution occupies a central position due to its direct threat to public health and ecological security.

[0003] Existing technologies suffer from the following problems that need to be addressed: 1. Existing technologies struggle to effectively integrate dispersed and heterogeneous multi-source data (industrial / residential pollution concentrations, real-time meteorological parameters); 2. Traditional decision-making models relying on human experience are slow to respond and cannot quickly identify key pollutants and key areas causing pollution; 3. "One-size-fits-all" control measures have high social costs and low efficiency. Therefore, this invention proposes a dynamic distribution control system and method for urban pollution sources. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for dynamic distribution control of urban pollution sources, thereby solving the above-mentioned technical problems: The objective of this invention can be achieved through the following technical solutions: A dynamic distribution control system for urban pollution sources, the system comprising a data acquisition layer, a transmission network, a data processing and analysis center, and a control execution system; The data acquisition layer deploys various sensors in different areas of the city to monitor air quality parameters; The transmission network is used to connect sensors and monitoring devices scattered throughout the city into an organic whole through wireless communication modules, so that the collected data can be transmitted to the data analysis and processing center in a very short time. The data processing and analysis center preprocesses the collected data, analyzes the preprocessed data, extracts pollution risk assessment indicators, and quantifies the pollution risk of different regions. The control and execution system automatically executes corresponding control measures based on the evaluation results obtained from the data analysis and processing center.

[0005] As a further description of the technical solution of the present invention, the working process of the data acquisition layer includes: The city is divided into grid areas, each of which includes industrial areas and residential areas. Air pollution sensors are installed at the exhaust outlets of industrial areas and the densely populated areas of residential areas. Before data acquisition, all types of monitoring equipment are rigorously calibrated and debugged. Standard gases are used to calibrate the air pollution sensors to ensure the accuracy of their measurement data.

[0006] As a further description of the technical solution of the present invention, the data processing and analysis center's data preprocessing process includes: data cleaning to remove outliers and erroneous data; data calibration to calibrate the measurement errors of sensors and improve the accuracy of the data; and data normalization to unify the data from different sensors to the same scale range, facilitating subsequent fusion processing.

[0007] As a further description of the technical solution of the present invention, the working process of the data processing and analysis center also includes: Get the number of industrial zones in the xth region. and number of residential areas The air quality parameters of each industrial zone in the x-th region and the air pollutant concentrations and meteorological parameters of each residential area in the x-th region are obtained respectively. Based on the air quality parameters of the i-th industrial zone in the x-th region, where i belongs to Calculate the sub-index of each pollutant in the i-th industrial zone of the x-th region, and then sum the weighted sub-indexes of each pollutant in the i-th industrial zone of the x-th region to obtain the air quality assessment index for the i-th industrial zone of the x-th region. ; Based on the air quality parameters of the j-th residential area in the x-th region, where j belongs to... Calculate the sub-index of each pollutant for each residential area in the x-th region, and then sum the weighted sub-indexes of each pollutant for each residential area in the x-th region to obtain the air quality assessment index for the j-th residential area in the x-th region. ; First, determine the air quality assessment indicators for the x-th region and the i-th industrial zone. Air quality assessment indicators for the xth region and the jth residential area Each value is compared with its corresponding system preset reference value to obtain a ratio, reflecting the degree of exceeding or meeting the standard; then, these ratios for industrial areas and residential areas are weighted and summed separately, and finally, two macro-weighting coefficients are used. and To balance the differentiated contributions of industrial and residential areas to the overall air quality of the region, the final output is a single, quantitative regional overall air quality assessment index. .

[0008] As a further description of the technical solution of the present invention, the calculation method for the sub-index of each pollutant in the i-th industrial zone of the x-th region and the sub-index of each pollutant in the j-th residential zone of the x-th region is as follows: Get the Real-time concentration of various pollutants The formula for calculating its sub-index is: ; in, The first set for the system Limit ranges for various pollutants The first set for the system Limit ranges for various pollutant sub-indices.

[0009] As a further description of the technical solution of the present invention, the working process of the data processing and analysis center also includes: Obtain real-time meteorological parameters for the x-th region, including wind speed, temperature, and humidity; The deviation value is obtained by subtracting the real-time wind speed from the system-set wind speed reference value. The absolute value of the deviation value is then divided by the system-set wind speed deviation reference value to obtain the wind speed correction factor for the x-th regional air quality assessment index. ; The real-time temperature is subtracted from the system-set temperature baseline value to obtain the corresponding deviation value. The temperature deviation value is then divided by the system-set temperature deviation reference value to obtain the temperature correction factor for the x-th regional air quality assessment index. ; The real-time humidity is subtracted from the system-set humidity baseline value to obtain the corresponding deviation value. The humidity deviation value is then divided by the system-set humidity deviation reference value to obtain the humidity correction factor for the x-th regional air quality assessment index. ; The revised value of the air quality assessment index for the xth region is .

[0010] As a further description of the technical solution of the present invention, the working process of the data processing and analysis center also includes: Compare the corrected value of the air quality assessment index for region x with the various judgment intervals of the air quality assessment index for region x set by the system. If the corrected value of the air quality assessment index for region x belongs to... This indicates that there is slight air pollution in region x; if the corrected value of the air quality assessment index for region x belongs to... This indicates that moderate air pollution exists in region x; if the corrected value of the air quality assessment index for region x belongs to... This indicates that the xth region is severely polluted. When air pollution exists in the x-th region, obtain the sub-indices of all pollutants and sort them in descending order, with the pollutant with the largest sub-indice being the current primary pollutant.

[0011] As a further description of the technical solution of the present invention, the working process of the control execution system includes: When there is mild air pollution in the xth region, key polluting enterprises will be reminded or required to conduct self-inspections on production restrictions. They will not be forced to stop work or production, but supervision and inspection will be strengthened, and the public will be encouraged to use green travel. When moderate air pollution occurs in region x, production will be restricted for some high-emission enterprises and high-emission vehicles will be restricted from driving. When high air pollution occurs in region x, some high-emission enterprises will be shut down and vehicle restrictions based on odd and even license plate numbers will be implemented.

[0012] A method for dynamic distribution control of urban pollution sources, the method comprising the following steps: Step S1: Divide the city into multiple grid areas, each covering industrial and residential areas. Deploy air pollution sensors at the exhaust outlets of industrial areas and densely populated residential areas to form a comprehensive data collection network. Step S2: Activate the rigorously calibrated sensors to continuously monitor air quality parameters in each area, and transmit the collected data to the data processing and analysis center in real time and quickly via a wireless transmission network. Step S3: The data processing center cleans, calibrates, and normalizes the received raw data to provide a high-quality data foundation for subsequent analysis. Step S4: Calculate the overall air quality assessment index for the monitoring area; Step S5: Obtain real-time meteorological data (wind speed, temperature, humidity) for the monitoring area. Calculate the deviation between the real-time values ​​and the baseline values ​​to obtain the correction coefficients for each meteorological factor. Multiply the total regional index obtained in Step S4 by all meteorological correction coefficients to obtain the corrected evaluation index. Step S6: Compare the revised assessment index with the preset judgment interval to determine the pollution level of the area. At the same time, find the one with the highest sub-index of all pollutants in the area and determine it as the current primary pollutant. Step S6: Based on the determined pollution level, the control execution system automatically triggers the corresponding control measures.

[0013] The beneficial effects of this invention are: This invention achieves a leap from passive response to proactive and precise regulation in urban air pollution control by constructing a closed-loop intelligent system encompassing data acquisition, analysis, decision-making, and execution. Relying on grid-based monitoring and multi-source data fusion technology, the system can dynamically perceive and accurately quantify the pollution contribution of different functional zones. Combined with a nonlinear meteorological correction model, it significantly improves the accuracy and timeliness of air quality assessment. By automatically determining pollution levels and intelligently identifying primary pollutants, it achieves rapid and accurate source tracing of pollution. Finally, based on a tiered early warning mechanism, it triggers differentiated control strategies, effectively overcoming the drawbacks of the traditional "one-size-fits-all" control model. While ensuring pollution control effectiveness, it significantly reduces socio-economic costs and comprehensively improves the refinement, intelligence, and scientific level of urban environmental governance. Attached Figure Description

[0014] The invention will now be further described with reference to the accompanying drawings.

[0015] Figure 1 This is a partial structural diagram of the urban pollution source dynamic distribution control system of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, the present invention provides a dynamic distribution control system for urban pollution sources, the system comprising a data acquisition layer, a transmission network, a data processing and analysis center, and a control execution system; The data acquisition layer deploys various sensors in different areas of the city to monitor air quality parameters; The transmission network is used to connect sensors and monitoring devices scattered throughout the city into an organic whole through wireless communication modules, so that the collected data can be transmitted to the data analysis and processing center in a very short time. The data processing and analysis center preprocesses the collected data, analyzes the preprocessed data, extracts pollution risk assessment indicators, and quantifies the pollution risk of different regions. The control and execution system automatically executes corresponding control measures based on the evaluation results obtained from the data analysis and processing center.

[0018] As a further description of the technical solution of the present invention, the working process of the data acquisition layer includes: The city is divided into grid areas, each of which includes industrial areas and residential areas. Air pollution sensors are installed at the exhaust outlets of industrial areas and the densely populated areas of residential areas. Before data acquisition, all types of monitoring equipment are rigorously calibrated and debugged. Standard gases are used to calibrate the air pollution sensors to ensure the accuracy of their measurement data.

[0019] Through the above technical solutions, this invention constructs a closed-loop decision-making system from pollution assessment to intelligent regulation, and achieves refined and intelligent management of urban air pollution through multi-source data fusion, dynamic risk assessment and hierarchical precise response.

[0020] The system first comprehensively collects urban air quality data through a sensor network. In practice, the city is divided into multiple grid areas, and rigorously calibrated air pollution sensors are deployed at industrial exhaust outlets and densely populated residential areas in each area, forming a dual monitoring network covering key pollution sources and sensitive areas. These sensors rapidly aggregate real-time monitoring data to the data processing and analysis center via a wireless transmission network, ensuring the timeliness and completeness of the data.

[0021] In the data processing and analysis phase, the system first performs preprocessing such as cleaning, calibration, and normalization on the raw data to eliminate outliers and measurement errors, providing a reliable data foundation for subsequent analysis. Next, the system converts the real-time concentration of each pollutant into a standardized sub-index. After obtaining the sub-index for each pollutant, the system calculates the air quality assessment index for each industrial zone and residential zone separately using a weighted summation method. Then, it integrates the assessment results of all sub-zones within the region and constructs an overall regional air quality assessment index through weighted configuration, where the weighting coefficients are differentiated based on the pollution contribution characteristics of industrial zones and residential zones.

[0022] To more accurately reflect the impact of meteorological conditions on pollution diffusion and accumulation, the system innovatively introduces a meteorological correction mechanism. By acquiring key meteorological parameters such as wind speed, temperature, and humidity in real time, the deviation from the baseline values ​​is calculated, corresponding correction factors are generated, and these factors are applied to the initial assessment indicators in a product form, thereby obtaining a corrected air quality assessment value that more closely reflects actual perception. This correction method can effectively capture the amplifying effect of unfavorable meteorological conditions such as calm winds, temperature inversions, and high humidity on the degree of pollution.

[0023] After completing the comprehensive assessment, the system achieves precise regulation through an intelligent decision-making mechanism. It compares the revised assessment value with preset pollution level thresholds to automatically determine whether the area's pollution level is light, moderate, or heavy. Simultaneously, the system accurately identifies the primary pollutant in the current area by recognizing the maximum value among all pollutant sub-indices. Based on this determination, the regulation execution system initiates corresponding tiered response measures: for light pollution, mild measures such as reminding businesses to limit production and strengthening patrols are implemented; for moderate pollution, stronger controls such as limiting production at enterprises and restricting high-emission vehicles are implemented; and for heavy pollution, strict emergency measures such as suspending production at enterprises and implementing odd-even license plate restrictions are initiated.

[0024] Through continuous data collection and dynamic evaluation, the entire system continuously optimizes control strategies, achieving precise spatial positioning and timely temporal response to urban pollution sources. This ensures the effectiveness of pollution control while avoiding resource waste caused by "one-size-fits-all" management, significantly improving the scientific nature and precision of urban environmental governance.

[0025] As a further description of the technical solution of the present invention, the data processing and analysis center's data preprocessing process includes: data cleaning to remove outliers and erroneous data; data calibration to calibrate the measurement errors of sensors and improve the accuracy of the data; and data normalization to unify the data from different sensors to the same scale range, facilitating subsequent fusion processing.

[0026] As a further description of the technical solution of the present invention, the working process of the data processing and analysis center also includes: Get the number of industrial zones in the xth region. and number of residential areas The air quality parameters of each industrial zone in the x-th region and the air pollutant concentrations and meteorological parameters of each residential area in the x-th region are obtained respectively. Based on the air quality parameters of the i-th industrial zone in the x-th region, where i belongs to Calculate the sub-index of each pollutant in the i-th industrial zone of the x-th region, and then sum the weighted sub-indexes of each pollutant in the i-th industrial zone of the x-th region to obtain the air quality assessment index for the i-th industrial zone of the x-th region. ; Based on the air quality parameters of the j-th residential area in the x-th region, where j belongs to... Calculate the sub-index of each pollutant for each residential area in the x-th region, and then sum the weighted sub-indexes of each pollutant for each residential area in the x-th region to obtain the air quality assessment index for the j-th residential area in the x-th region. ; Construct a mathematical model for the air quality assessment index of the x-th region, with the following expression: ; In the formula, The reference value for the air quality assessment index of the i-th industrial zone set by the system. The reference value for the air quality assessment index of the j-th residential area set by the system. Let be the weighting coefficient corresponding to the air quality assessment index of the i-th industrial zone. Let be the weighting coefficient of the air quality assessment index for the j-th residential area. and These are the weighting coefficients for industrial zones and residential zones, respectively. Let x be the air quality assessment index for the xth region.

[0027] Through the above technical solution, this embodiment integrates scattered air quality data of different types within a region into a representative comprehensive evaluation index through a structured mathematical modeling process. The system first obtains all industrial zones (number of zones) within the specified area (the xth zone). ) and residential areas (number of Air quality parameters; Subsequently, the core two-stage calculation process begins. The first stage is sub-regional assessment, where the system processes each independent industrial zone and residential area separately: For each monitoring point (the i-th industrial zone or the j-th residential area), the system calculates a sub-index for each pollutant (e.g., PM2.5, SO2), which unifies the concentrations of pollutants with different dimensions and standards into dimensionless standardized values; then, by weighted summation of the sub-indices of all pollutants within that monitoring point, assessment indicators representing the pollution status of that industrial zone are obtained. and assessment indicators representing the air quality of the residential area The second stage is regional integration, where the system performs high-level fusion of the evaluation indicators of all the above sub-regions: first, it combines the evaluation indicators of each sub-region with their corresponding system preset reference values ​​( or By comparing these ratios, a ratio is obtained to reflect the degree of exceeding or meeting the standard; then, these ratios for industrial areas and residential areas are weighted and summed separately (with weights of 1 / 2). and Finally, use two macro-weighting coefficients. and To balance the differentiated contributions of industrial and residential areas to the overall air quality of the region, the final output is a single, quantitative regional overall air quality assessment index. The principle of this model ensures that the assessment results reflect both the absolute level of pollution and the relative impact of different functional zones on the pollution structure.

[0028] As a further description of the technical solution of the present invention, the calculation method for the sub-index of each pollutant in the i-th industrial zone of the x-th region and the sub-index of each pollutant in the j-th residential zone of the x-th region is as follows: Get the Real-time concentration of various pollutants The formula for calculating its sub-index is: ; in, The first set for the system Limit ranges for various pollutants The first set for the system Limit ranges for various pollutant sub-indices.

[0029] Through the above technical solution, this embodiment pre-defines two key ranges for each pollutant (type P): one is a concentration limit range based on environmental standards or health effects. The other is the corresponding sub-index range. During calculation, the system will measure the real-time concentration. The concentration range to which it belongs is located, and then, based on its relative position within that range, it is precisely mapped to the corresponding sub-index range using a linear interpolation formula, thereby calculating the final sub-index. This allows for the quantitative comparison and synthesis of pollutant concentrations with different properties and units under a unified index system, laying a solid foundation for the comprehensive calculation of subsequent assessment indicators.

[0030] As a further description of the technical solution of the present invention, the working process of the data processing and analysis center also includes: Obtain real-time meteorological parameters for the x-th region, including wind speed, temperature, and humidity; The deviation value is obtained by subtracting the real-time wind speed from the system-set wind speed reference value. The absolute value of the deviation value is then divided by the system-set wind speed deviation reference value to obtain the wind speed correction factor for the x-th regional air quality assessment index. ; The real-time temperature is subtracted from the system-set temperature baseline value to obtain the corresponding deviation value. The temperature deviation value is then divided by the system-set temperature deviation reference value to obtain the temperature correction factor for the x-th regional air quality assessment index. ; The real-time humidity is subtracted from the system-set humidity baseline value to obtain the corresponding deviation value. The humidity deviation value is then divided by the system-set humidity deviation reference value to obtain the humidity correction factor for the x-th regional air quality assessment index. ; The revised value of the air quality assessment index for the xth region is .

[0031] Through the above technical solution, this embodiment first acquires real-time wind speed, temperature, and humidity data for a designated area (the xth area), and compares the measured value of each meteorological parameter with the corresponding preset benchmark value of the system to calculate the specific deviation value. Subsequently, the system divides these absolute deviation values ​​by their respective preset "deviation reference values," thereby standardizing the absolute deviations into relative ratios to obtain wind speed correction factors. Temperature correction factor Humidity correction factor Ultimately, the original air quality assessment indicators will be... The corrected comprehensive assessment value is obtained by continuously multiplying the three meteorological correction factors. This means that when unfavorable diffusion conditions such as measured wind speed being lower than the baseline (negative deviation) or humidity being higher than the baseline (positive deviation) occur, the correction factor will be greater than 1, thereby amplifying the final assessment value and more accurately reflecting the actual amplification effect of meteorological conditions on pollution accumulation.

[0032] As a further description of the technical solution of the present invention, the working process of the data processing and analysis center also includes: Compare the corrected value of the air quality assessment index for region x with the various judgment intervals of the air quality assessment index for region x set by the system. If the corrected value of the air quality assessment index for region x belongs to... This indicates that there is slight air pollution in region x; if the corrected value of the air quality assessment index for region x belongs to... This indicates that moderate air pollution exists in region x; if the corrected value of the air quality assessment index for region x belongs to... This indicates that the xth region is severely polluted. When air pollution exists in the x-th region, obtain the sub-indices of all pollutants and sort them in descending order, with the pollutant with the largest sub-indice being the current primary pollutant.

[0033] Through the above technical solution, this embodiment achieves automatic pollution level determination and intelligent identification of primary pollutants by comparing the meteorologically corrected comprehensive air quality assessment index with preset pollution level threshold ranges. The system pre-sets multiple numerical ranges for each region, corresponding to light, moderate, and heavy pollution levels respectively. After calculating the corrected assessment index, the system automatically matches it with these threshold ranges to objectively determine the specific pollution level of the region. Simultaneously, the system back-analyzes the real-time sub-index data of all pollutants within the region, sorts and filters out the single pollutant with the highest value, and identifies it as the current primary pollutant. This mechanism not only achieves quantitative grading of pollution severity but also more accurately identifies key pollution sources causing air quality deterioration, providing a clear decision-making basis and target for subsequent implementation of differentiated and precise control measures.

[0034] As a further description of the technical solution of the present invention, the working process of the control execution system includes: When there is mild air pollution in the xth region, key polluting enterprises will be reminded or required to conduct self-inspections on production restrictions. They will not be forced to stop work or production, but supervision and inspection will be strengthened, and the public will be encouraged to use green travel. When moderate air pollution occurs in region x, production will be restricted for some high-emission enterprises and high-emission vehicles will be restricted from driving. When high air pollution occurs in region x, some high-emission enterprises will be shut down and vehicle restrictions based on odd and even license plate numbers will be implemented.

[0035] Through the above technical solution, this embodiment establishes a tiered emergency response mechanism that is precisely linked to pollution levels, automatically converting assessment results into specific and executable control instructions. When the system determines that an area is lightly polluted, it primarily adopts a flexible management strategy based on reminders, self-inspections, and advocacy, aiming to enhance regulatory deterrence and public environmental awareness without significantly impacting socio-economic activities. When pollution escalates to moderate, mandatory intervention is initiated, directly reducing pollutant emissions from both industrial and mobile sources by imposing production restrictions on some high-emission enterprises and traffic restrictions on high-emission vehicles. When pollution reaches a severe level, the system will implement the strictest emergency measures, namely, production shutdowns for enterprises and odd-even license plate restrictions for motor vehicles, to curb the pollution situation with maximum force and in the shortest time. This entire set of control logic, from weak to strong and escalating at each level, ensures the scientific, proportional, and operable nature of the control measures, achieving efficient pollution prevention and control with minimal social costs.

[0036] A method for dynamic distribution control of urban pollution sources, the method comprising the following steps: Step S1: Divide the city into multiple grid areas, each covering industrial and residential areas. Deploy air pollution sensors at the exhaust outlets of industrial areas and densely populated residential areas to form a comprehensive data collection network. Step S2: Activate the rigorously calibrated sensors to continuously monitor air quality parameters in each area, and transmit the collected data to the data processing and analysis center in real time and quickly via a wireless transmission network. Step S3: The data processing center cleans, calibrates, and normalizes the received raw data to provide a high-quality data foundation for subsequent analysis. Step S4: Calculate the overall air quality assessment index for the monitoring area; Step S5: Obtain real-time meteorological data (wind speed, temperature, humidity) for the monitoring area. Calculate the deviation between the real-time values ​​and the baseline values ​​to obtain the correction coefficients for each meteorological factor. Multiply the total regional index obtained in Step S4 by all meteorological correction coefficients to obtain the corrected evaluation index. Step S6: Compare the revised assessment index with the preset judgment interval to determine the pollution level of the area. At the same time, find the one with the highest sub-index of all pollutants in the area and determine it as the current primary pollutant. Step S6: Based on the determined pollution level, the control execution system automatically triggers the corresponding control measures.

[0037] Calculation Example We are focusing on region X, which contains one industrial zone and one residential zone.

[0038] The pollutants monitored are and .

[0039] All calculations used 24-hour average concentrations.

[0040] Parameter setting 1 The measured concentrations in the industrial zone were as follows: , ; The measured concentrations in residential areas were as follows: , ; and All areas have the same weight of 0.5, with industrial areas having a weight of 0.6 and residential areas having a weight of 0.4. Calculated ; Parameter setting two Calculate respectively The revised index is 28.2, and the pollution level is determined to be light pollution. Among them, (0,50]: mild, (50,100]: moderate, (100,150]: severe pollution; Although the overall pollution level is light, the system still tracks sub-indices. Among industrial and residential areas, the highest sub-indice is found in the industrial area. Sub-index. Therefore, if pollution worsens, the primary pollutant will be identified as... .

[0041] It should be noted that the formulas in this application are all dimensionless and calculated numerically. The thresholds, intervals and coefficients involved in this application are all empirical values, and the selection should be made by those skilled in the art according to the actual situation.

[0042] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A dynamic distribution control system for urban pollution sources, characterized in that, The system includes a data acquisition layer, a transmission network, a data processing and analysis center, and a control and execution system; The data acquisition layer deploys various sensors in different areas of the city to monitor air quality parameters; The transmission network is used to connect sensors and monitoring devices scattered throughout the city into an organic whole through wireless communication modules, so that the collected data can be transmitted to the data analysis and processing center in a very short time. The data processing and analysis center preprocesses the collected data, analyzes the preprocessed data, extracts pollution risk assessment indicators, and quantifies the pollution risk of different regions. The control and execution system automatically executes corresponding control measures based on the evaluation results obtained from the data analysis and processing center.

2. The urban pollution source dynamic distribution control system according to claim 1, characterized in that, The working process of the data acquisition layer includes: The city is divided into grid areas, each of which includes industrial areas and residential areas. Air pollution sensors are installed at the exhaust outlets of industrial areas and the densely populated areas of residential areas. Before data acquisition, all types of monitoring equipment are rigorously calibrated and debugged. Standard gases are used to calibrate the air pollution sensors to ensure the accuracy of their measurement data.

3. The urban pollution source dynamic distribution control system according to claim 1, characterized in that, The data processing and analysis center's data preprocessing process includes: data cleaning to remove outliers and erroneous data; data calibration to calibrate sensor measurement errors and improve data accuracy; and data normalization to unify data from different sensors to the same scale range for easier subsequent fusion processing.

4. The urban pollution source dynamic distribution control system according to claim 3, characterized in that, The working process of the data processing and analysis center also includes: Get the number of industrial zones in the xth region. and number of residential areas The air quality parameters of each industrial zone in the x-th region and the air pollutant concentrations and meteorological parameters of each residential area in the x-th region are obtained respectively. Based on the air quality parameters of the i-th industrial zone in the x-th region, where i belongs to Calculate the sub-index of each pollutant in the i-th industrial zone of the x-th region, and then sum the weighted sub-indexes of each pollutant in the i-th industrial zone of the x-th region to obtain the air quality assessment index for the i-th industrial zone of the x-th region. ; Based on the air quality parameters of the j-th residential area in the x-th region, where j belongs to... Calculate the sub-index of each pollutant for each residential area in the x-th region, and then sum the weighted sub-indexes of each pollutant for each residential area in the x-th region to obtain the air quality assessment index for the j-th residential area in the x-th region. ; First, determine the air quality assessment indicators for the x-th region and the i-th industrial zone. Air quality assessment indicators for the xth region and the jth residential area Each value is compared with its corresponding system preset reference value to obtain a ratio, reflecting the degree of exceeding or meeting the standard; then, these ratios for industrial areas and residential areas are weighted and summed separately, and finally, two macro-weighting coefficients are used. and To balance the differentiated contributions of industrial and residential areas to the overall air quality of the region, the final output is a single, quantitative regional overall air quality assessment index. .

5. The urban pollution source dynamic distribution control system according to claim 4, characterized in that, The calculation methods for the sub-index of each pollutant in the i-th industrial zone of the x-th region and the sub-index of each pollutant in the j-th residential zone of the x-th region are as follows: Get the Real-time concentration of various pollutants The formula for calculating its sub-index is: ; in, The first set for the system Limit ranges for various pollutants The first set for the system Limit ranges for various pollutant sub-indices.

6. The urban pollution source dynamic distribution control system according to claim 4, characterized in that, The working process of the data processing and analysis center also includes: Obtain real-time meteorological parameters for the x-th region, including wind speed, temperature, and humidity; The deviation value is obtained by subtracting the real-time wind speed from the system-set wind speed reference value. The absolute value of the deviation value is then divided by the system-set wind speed deviation reference value to obtain the wind speed correction factor for the x-th regional air quality assessment index. ; The real-time temperature is subtracted from the system-set temperature benchmark value to obtain the corresponding deviation value. The temperature deviation value is then divided by the system-set temperature deviation reference value to obtain the temperature correction factor for the x-th regional air quality assessment index. ; The real-time humidity is subtracted from the system-set humidity baseline value to obtain the corresponding deviation value. The humidity deviation value is then divided by the system-set humidity deviation reference value to obtain the humidity correction factor for the x-th regional air quality assessment index. ; The revised value of the air quality assessment index for the xth region is .

7. The urban pollution source dynamic distribution control system according to claim 6, characterized in that, The working process of the data processing and analysis center also includes: Compare the corrected value of the air quality assessment index for region x with the various judgment intervals of the air quality assessment index for region x set by the system. If the corrected value of the air quality assessment index for region x belongs to... This indicates that there is slight air pollution in region x; if the corrected value of the air quality assessment index for region x belongs to... This indicates that moderate air pollution exists in region x; if the corrected value of the air quality assessment index for region x belongs to... This indicates that the xth region is severely polluted. When air pollution exists in the x-th region, obtain the sub-indices of all pollutants and sort them in descending order, with the pollutant with the largest sub-indice being the current primary pollutant.

8. The urban pollution source dynamic distribution control system according to claim 1, characterized in that, The working process of the control and execution system includes: When there is mild air pollution in the xth region, key polluting enterprises will be reminded or required to conduct self-inspections on production restrictions. They will not be forced to stop work or production, but supervision and inspection will be strengthened, and the public will be encouraged to use green travel. When moderate air pollution occurs in region x, production will be restricted for some high-emission enterprises and high-emission vehicles will be restricted from driving. When high air pollution occurs in region x, some high-emission enterprises will be shut down and vehicle restrictions based on odd and even license plate numbers will be implemented.

9. A method for dynamic distribution control of urban pollution sources, said method being implemented based on the urban pollution source dynamic distribution control system according to any one of claims 1-8, characterized in that, The method includes the following steps: Step S1: Divide the city into multiple grid areas, each covering industrial and residential areas. Deploy air pollution sensors at the exhaust outlets of industrial areas and densely populated residential areas to form a comprehensive data collection network. Step S2: Activate the rigorously calibrated sensors to continuously monitor air quality parameters in each area, and transmit the collected data to the data processing and analysis center in real time and quickly via a wireless transmission network. Step S3: The data processing center cleans, calibrates, and normalizes the received raw data to provide a high-quality data foundation for subsequent analysis. Step S4: Calculate the overall air quality assessment index for the monitoring area; Step S5: Obtain real-time meteorological data (wind speed, temperature, humidity) for the monitoring area. Calculate the deviation between the real-time values ​​and the baseline values ​​to obtain the correction coefficients for each meteorological factor. Multiply the total regional index obtained in Step S4 by all meteorological correction coefficients to obtain the corrected evaluation index. Step S6: Compare the revised assessment index with the preset judgment interval to determine the pollution level of the area. At the same time, find the one with the highest sub-index of all pollutants in the area and determine it as the current primary pollutant. Step S6: Based on the determined pollution level, the control execution system automatically triggers the corresponding control measures.

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