A Traceable Monitoring Method and System for Road Surface Runoff Pollutants

Abstract

The present invention discloses a traceable monitoring method and system for road surface runoff pollutants, which relates to the technical field of water pollution monitoring. The method includes the following steps: According to the basic road surface monitoring data, capture environmental factors such as precipitation, temperature, and traffic flow, extract pollutant concentration data at the sampling points, analyze the correlation of environmental factors, identify and classify pollutant concentrations, and obtain the characteristics of pollutant concentration change trends. In the present invention, by capturing environmental factors and pollutant concentration data, accurately analyze the relationship between environmental factors and pollutants, improve the understanding of the change trends of pollutant concentrations, effectively improve the prediction accuracy of future changes. In real-time data comparison, error analysis helps to identify deviations and adjust the model weights to improve prediction accuracy. Through monitoring resource demand prediction, optimize the layout of monitoring points and resource allocation, dynamically adjust the monitoring strategy, make the pollutant concentration monitoring more flexible and accurate, and improve the monitoring efficiency and response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of water pollution monitoring, and particularly to a traceable monitoring method and system for road runoff pollutants. Background Art

[0002] The technical field of water pollution monitoring includes the detection, monitoring, and assessment of pollutants in water bodies, mainly used to ensure the safety of water resources and control of environmental quality. The core content of this field includes sampling, analysis, and monitoring methods for water body pollutants, with the focus on improving the accuracy and real-time nature of monitoring. Technical means cover various methods such as water quality sensors, remote sensing technology, and data analysis technology to achieve comprehensive monitoring and assessment of water body pollution conditions. Systematic research focuses on the identification of pollution sources, quantitative analysis of pollutants, and accurate feedback of monitoring data to formulate more effective pollution control and prevention measures.

[0003] Among them, the road runoff pollutant monitoring method refers to a method for collecting, analyzing, and monitoring runoff pollutants generated on road traffic surfaces. This patent theme covers collecting pollutant information in the flowing water on the road surface by means of deploying monitoring equipment, sampling points, etc., and accurately detecting the type, concentration, etc. of pollutants based on specific technical means. This monitoring method involves tracking pollution sources and processing data to achieve effective monitoring of pollutants in road runoff and help researchers understand the sources and diffusion patterns of pollutants.

[0004] Existing technologies generally have problems of insufficient real-time response to changes in pollutant concentration in water pollution monitoring methods. Existing technologies mostly rely on fixed monitoring points and equipment and cannot be dynamically adjusted according to changes in environmental conditions, resulting in insufficient response to sudden pollution events and even missing important monitoring opportunities. The monitoring frequency and monitoring resource allocation of traditional methods lack flexibility and cannot be effectively optimized in real time according to the spatio-temporal distribution of changes in pollutant concentration. This static monitoring method not only fails to provide sufficient monitoring data during high pollution periods but also leads to the emergence of monitoring blind spots due to unreasonable resource allocation, reducing monitoring accuracy and response capabilities. Existing technologies still have limitations in pollution source tracking and quantitative analysis of pollutants and are difficult to accurately determine the temporal changes in pollutant concentration and the environmental factors behind them, resulting in the feedback of monitoring data not accurately reflecting the true situation of water body pollution and affecting the effectiveness of pollution control measures. Summary of the Invention

[0005] To solve the technical problems existing in the prior art, embodiments of the present invention provide a traceable monitoring method and system for road runoff pollutants. The technical solutions are as follows:

[0006] On the one hand, a traceable monitoring method for road runoff pollutants is provided, and the method includes:

[0007] S1: According to the road surface basic monitoring data, capture environmental factors such as precipitation, temperature, and traffic flow, extract pollutant concentration data at sampling points, analyze the correlation of environmental factors, identify and classify pollutant concentrations, and obtain the characteristics of pollutant concentration change trends;

[0008] S2: Utilize the characteristics of pollutant concentration change trends, combine with the time period characteristics in the original data, construct a time series analysis model, extract the key environmental factors affecting pollutant concentrations, optimize the model parameters according to the spatio-temporal change rules, and generate long-term pollutant concentration prediction results;

[0009] S3: Compare the long-term pollutant concentration prediction results with the real-time monitoring data, identify the error values and analyze the reasons for the errors, judge whether the model needs to be updated through error analysis, adjust the weight parameters of the model, and generate pollutant concentration error correction results;

[0010] S4: Based on the pollutant concentration error correction results, analyze the relationship between pollutant concentrations and monitoring resource requirements, analyze the spatio-temporal distribution of pollutant concentration changes, predict the monitoring demand in high-pollution time periods and high-pollution areas, and generate monitoring resource demand prediction results;

[0011] S5: According to the monitoring resource demand prediction results, adjust the monitoring frequency for time periods with large changes in pollutant concentrations, optimize the number of monitoring points and resource allocation, and dynamically adjust the monitoring configuration according to sudden pollution situations and change trends to generate a dynamic monitoring plan for road surface pollutants.

[0012] As a further solution of the present invention, the characteristics of pollutant concentration change trends include precipitation, temperature, traffic flow, pollutant concentration classification, and pollutant concentration change rules; the long-term pollutant concentration prediction results include key environmental factors, spatio-temporal change rules, optimized model parameters, and long-term prediction values; the pollutant concentration error correction results include error values, error analysis, model update judgment, and model weight adjustment; the monitoring resource demand prediction results include the relationship between pollutant concentrations and monitoring demand, high-pollution time periods, and high-pollution areas; and the dynamic monitoring plan for road surface pollutants includes monitoring frequency adjustment, optimization of the number of monitoring points, optimization of resource allocation, and dynamic adjustment of the monitoring configuration.

[0013] As a further solution of the present invention, the steps of capturing environmental factors such as precipitation, temperature, and traffic flow according to the road surface basic monitoring data, extracting pollutant concentration data at sampling points, analyzing the correlation of environmental factors, identifying and classifying pollutant concentrations, and obtaining the characteristics of pollutant concentration change trends are specifically as follows:

[0014] S101: According to the road surface foundation monitoring data, record environmental factors such as precipitation, temperature, and traffic flow, extract pollutant concentration data in the order of time and position, integrate the time-stamp spatial nodes and pollutant concentrations, and obtain the initial data table of environmental factors and pollutant concentrations at the sampling points;

[0015] S102: Based on the initial data table of environmental factors and pollutant concentrations at the sampling points, extract environmental factor parameters and pollutant concentration data, classify the correlation degree of each parameter, and classify the classification results into concentration categories to obtain the classification correlation data table of pollutant concentrations and environmental factors;

[0016] S103: Based on the classification correlation data table of pollutant concentrations and environmental factors, extract the time-series data of concentration categories, analyze the change range of concentration in the time dimension, summarize the change direction of concentration values at time points, integrate the concentration change trend data, and obtain the pollutant concentration change trend characteristics.

[0017] As a further solution of the present invention, the steps of constructing a time-series analysis model by using the pollutant concentration change trend characteristics, combining with the time period characteristics in the original data, extracting the key environmental factors affecting the pollutant concentration, and optimizing the model parameters according to the spatio-temporal change law to generate the long-term pollutant concentration prediction result are specifically as follows:

[0018] S201: Based on the pollutant concentration change trend characteristics, divide the original data by time intervals, count the concentration extreme values in each interval, identify the mean fluctuation range, analyze the change trend of concentration over time, extract the interval trend index, and obtain the concentration distribution result of the time period;

[0019] S202: Based on the concentration distribution result of the time period, screen the environmental factor records, identify the difference ratio between the environmental factor values and the change values within the time interval of the pollutant concentration, count and arrange the correlation coefficients, and extract the factor set within the correlation threshold range to obtain the key environmental factor correlation data;

[0020] S203: Based on the key environmental factor correlation data, determine the distribution parameters of the interval weight factors, adjust the factor correlation coefficients and interval contribution values, optimize the weight distribution of the factor set, and generate the long-term pollutant concentration prediction result.

[0021] As a further solution of the present invention, the acquisition formula of the long-term pollutant concentration prediction result is:

[0022] ;

[0023] Wherein, represents the long-term pollutant concentration prediction value, represents the weighted coefficient associated with the key environmental factor and represents the The real-time observed values of key environmental factors represent the adjustment coefficient associated with the factor represent the weight normalization parameter associated with the factor represent the interaction strength factor of the associated factor group within the interval represent the time-scale impact factor of the nth interval

[0024] As a further solution of the present invention, the steps of comparing the long-term pollutant concentration prediction result with the real-time monitoring data, identifying the error value and analyzing the cause of the error, judging whether the model needs to be updated through error analysis, adjusting the weight parameters of the model, and generating the pollutant concentration error correction result are specifically as follows:

[0025] S301: Based on the long-term pollutant concentration prediction result, extract the time interval values of the predicted value and the real-time monitoring data, identify the difference between the prediction and the real-time value, statistically calculate the average error and the fluctuation range, analyze the error distribution characteristics of each time interval, and obtain the pollutant concentration error data;

[0026] S302: Based on the pollutant concentration error data, analyze the relationship between the error distribution and the environmental factor records, adjust the weight parameters and the distribution interval within the factor set, re-allocate the numerical weights of the parameters within the model, and obtain the adjusted model parameters;

[0027] S303: Based on the adjusted model parameters, calculate the pollutant concentration predicted value according to the weight parameter distribution data, and generate the pollutant concentration error correction result.

[0028] As a further solution of the present invention, the steps of analyzing the relationship between the pollutant concentration and the monitoring resource demand based on the pollutant concentration error correction result, analyzing the spatio-temporal distribution of the pollutant concentration change, predicting the monitoring demand for high-pollution periods and high-pollution areas, and generating the monitoring resource demand prediction result are specifically as follows:

[0029] S401: Based on the pollutant concentration error correction result, extract the concentration change data of the region and the time interval, statistically calculate the pollutant peak value of the region and the concentration fluctuation range of the time interval, analyze the proportional relationship between the concentration change trend and the monitoring resource demand, and obtain the monitoring resource demand distribution data;

[0030] S402: Based on the monitoring resource demand distribution data, group and process the pollutant concentration change data of the time interval, analyze the change amplitude of the high-pollution period, combine the regional data to extract the total resource demand of the high-pollution area, identify the resource demand peak value of the high-pollution period in the region, and obtain the high-pollution spatio-temporal monitoring demand data;

[0031] ​​S403: Based on the high-pollution time-space monitoring requirement data, summarize the total resource requirement data for high-pollution time periods and regions, analyze the overall distribution of monitoring resource requirements, and generate a monitoring resource requirement prediction result.

[0032] As a further solution of the present invention, the acquisition formula for the monitoring resource requirement prediction result is:

[0033] ;

[0034] Wherein, represents the monitoring resource requirement prediction value, represents the resource input ratio of the monitoring resource during high-pollution time periods, represents the resource input ratio of the monitoring resource in high-pollution regions, represents the monitoring time span, represents the difference value of pollution levels in the high-pollution time-space monitoring requirement data, is the resource weight adjustment coefficient for high-pollution time periods, is the resource weight adjustment coefficient for high-pollution regions, is the adjustment coefficient for the impact of pollution level differences on monitoring resource prediction.

[0035] As a further solution of the present invention, based on the monitoring resource requirement prediction result, the steps of adjusting the monitoring frequency for time periods with large changes in pollutant concentration, optimizing the number of monitoring points and resource allocation, and dynamically adjusting the monitoring configuration according to sudden pollution situations and change trends to generate a dynamic monitoring plan for road surface pollutants are specifically as follows:

[0036] S501: Based on the monitoring resource requirement prediction result, extract the time interval data of pollutant concentration changes, analyze the matching situation between time interval resource allocation and monitoring frequency, calculate the monitoring frequency adjustment ratio, optimize the distribution of adjustment parameters, and obtain the optimized data for time period monitoring frequency;

[0037] S502: Based on the optimized data for time period monitoring frequency, analyze the relationship between the distribution of monitoring points and the resource requirements in high-pollution regions, identify the adjustment ratio of the number of monitoring points in high-pollution regions, reallocate the spatial layout of monitoring points and resource quantities, and obtain the monitoring point resource allocation data;

[0038] S503: Based on the monitoring point resource allocation data, combined with the records of sudden pollution changes, analyze the dynamic adjustment range of monitoring points, optimize the allocation of the spatial positions and resource quantities of monitoring points, update the regional monitoring configuration, and generate a dynamic monitoring plan for road surface pollutants.

[0039] On the other hand, a traceable road surface runoff pollutant monitoring system is provided. The system is used to execute the above-mentioned traceable road surface runoff pollutant monitoring method, and the system includes:

[0040] Based on the road surface foundation monitoring data, the environmental factor recognition module extracts precipitation, temperature, and traffic flow data, corrects and filters the environmental factors, compares the monitoring point data, conducts spatio-temporal feature analysis, filters the compliant data intervals, analyzes the pollutant concentration trend, and obtains the pollutant concentration change trend characteristics;

[0041] Based on the pollutant concentration change trend characteristics, the pollutant classification module classifies the pollutant concentrations at the sampling points, groups the data using statistical analysis, filters the correlation characteristics between the pollutant concentrations and environmental factors, classifies the pollutants according to the grouping rules, and generates the pollutant risk level classification results;

[0042] Based on the pollutant risk level classification results, the spatio-temporal analysis module conducts spatio-temporal distribution analysis, identifies high-pollution periods and regions using the data of differential sampling points within the region, extracts the pollutant concentration distribution trend, compares the changing data, determines the potential pollution sources, and obtains the spatio-temporal distribution characteristic data of the pollutants;

[0043] Based on the spatio-temporal distribution characteristic data of the pollutants, the pollutant concentration adjustment module calculates the difference value, analyzes the error sources, determines whether the error is caused by the model parameters, adjusts the model parameters according to the error value change rules, adjusts the model weights with reference to the pollution source speculation results, and generates the pollutant concentration error correction results;

[0044] Based on the pollutant concentration error correction results, the monitoring demand prediction module predicts the spatio-temporal change trend of the pollutant concentration, analyzes the high-pollution periods and regions, calculates the monitoring demand, adjusts the monitoring frequency, optimizes the monitoring points and resource allocation, and generates a dynamic monitoring plan for road surface pollutants.

[0045] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0046] By capturing environmental factors (such as precipitation, temperature, traffic flow) and extracting pollutant concentration data, not only can the understanding of the pollutant concentration change trend be enhanced, but also the correlation between different environmental factors and pollutants can be analyzed more accurately. This prediction model optimized based on time series analysis and spatio-temporal change rules effectively improves the prediction accuracy of future changes in pollutant concentration. During the comparison with real-time monitoring data, error analysis helps to timely identify the deviation of the prediction results and correct them by adjusting the weight parameters of the model, further improving the accuracy of the prediction results. Through the prediction of monitoring resource requirements, the layout of monitoring points and resource allocation can be efficiently optimized, and the monitoring strategy can be dynamically adjusted according to sudden pollution situations and change trends, making the pollutant concentration monitoring more flexible and accurate, capable of resource scheduling within different time and space ranges, and effectively improving the monitoring efficiency and response speed. Description of the Drawings

[0047] Figure 1 It is a schematic diagram of the working process of the present invention;

[0048] Figure 2 It is a refined flowchart of S1 of the present invention;

[0049] Figure 3 It is a refined flowchart of S2 of the present invention;

[0050] Figure 4 It is a refined flowchart of S3 of the present invention;

[0051] Figure 5 It is a refined flowchart of S4 of the present invention;

[0052] Figure 6 It is a refined flowchart of S5 of the present invention;

[0053] Figure 7 It is the system flowchart of the present invention. Specific embodiments

[0054] The following will describe the technical solutions in the present invention with reference to the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0056] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0057] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0058] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0059] Please refer to Figure 1, embodiments of the present invention provide a traceable monitoring method for road surface runoff pollutants. The processing flow of this method may include the following steps:

[0060] S1: According to the road surface basic monitoring data, capture environmental factors such as precipitation, temperature, and traffic flow, extract the pollutant concentration data of the sampling points, analyze the correlation of environmental factors, identify and classify the pollutant concentrations, and obtain the characteristics of the pollutant concentration change trend;

[0061] S2: Utilize the characteristics of the pollutant concentration change trend, combine with the time period characteristics in the original data, construct a time series analysis model, extract the key environmental factors affecting the pollutant concentration, optimize the model parameters according to the spatio-temporal change law, and generate the long-term pollutant concentration prediction result;

[0062] S3: Compare the long-term pollutant concentration prediction result with the real-time monitoring data, identify the error value and analyze the cause of the error, judge whether the model needs to be updated through error analysis, adjust the weight parameters of the model, and generate the pollutant concentration error correction result;

[0063] S4: Based on the pollutant concentration error correction result, analyze the relationship between the pollutant concentration and the monitoring resource demand, analyze the spatio-temporal distribution of the pollutant concentration change, predict the monitoring demand in high-pollution time periods and high-pollution areas, and generate the monitoring resource demand prediction result;

[0064] S5: According to the monitoring resource demand prediction result, adjust the monitoring frequency for the time periods with large changes in pollutant concentration, optimize the number of monitoring points and resource allocation, and dynamically adjust the monitoring configuration according to the sudden pollution situation and change trend, and generate a dynamic monitoring plan for road surface pollutants.

[0065] The characteristics of the pollutant concentration change trend include precipitation, temperature, traffic flow, pollutant concentration classification, and pollutant concentration change law. The long-term pollutant concentration prediction result includes key environmental factors, spatio-temporal change law, optimized model parameters, and long-term prediction values. The pollutant concentration error correction result includes error value, error analysis, model update judgment, and model weight adjustment. The monitoring resource demand prediction result includes the relationship between pollutant concentration and monitoring demand, high-pollution time periods, and high-pollution areas. The dynamic monitoring plan for road surface pollutants includes monitoring frequency adjustment, optimization of the number of monitoring points, optimization of resource allocation, and dynamic adjustment of the monitoring configuration.

[0066] Specifically, as Figure 2 shown, according to the road surface basic monitoring data, the steps of capturing environmental factors such as precipitation, temperature, and traffic flow, extracting the pollutant concentration data of the sampling points, analyzing the correlation of environmental factors, identifying and classifying the pollutant concentrations, and obtaining the characteristics of the pollutant concentration change trend are specifically as follows:

[0067] S101: Based on the basic road surface monitoring data, environmental factors such as precipitation, temperature, and traffic volume are recorded, pollutant concentration data are extracted in time and location order, and the time stamp spatial nodes and pollutant concentrations are integrated to obtain the initial data table of environmental factors and pollutant concentrations at the sampling points;

[0068] According to the sampling time series and spatial node coordinates, the integrity and accuracy of pollutant concentration data are obtained and verified one by one, outliers and missing values ​​are eliminated, and the data are rearranged to form time series data for continuity analysis. In the process of pollutant concentration extraction, the precipitation, temperature and traffic flow data of each sampling point are normalized to ensure the unit consistency of various environmental factors. Combined with the timestamp and spatial node, linear interpolation is used to supplement the existing spatiotemporal data breakpoints to avoid deviations in subsequent calculations due to uneven data intervals. When integrating the environmental factors and pollutant concentrations of the sampling points, the environmental factors are decomposed into two forms: daily average values ​​and hourly values, so as to analyze the impact of environmental factors on pollutant concentrations from multiple dimensions, and obtain the initial data table of environmental factors and pollutant concentrations of the sampling points, which contains the time nodes, spatial nodes, environmental factor values ​​and corresponding pollutant concentration values ​​of each sampling point.

[0069] S102: Based on the initial data table of environmental factors and pollutant concentrations at the sampling points, extract environmental factor parameters and pollutant concentration data, classify the correlation degree of each parameter, classify the classification results into concentration categories, and obtain a data table of pollutant concentration and environmental factor classification correlation;

[0070] Firstly, the numerical pairs of each environmental factor parameter and pollutant concentration are normalized to ensure the rationality of the comparison between different dimensions. By setting different concentration intervals, such as low concentration interval, medium concentration interval and high concentration interval, the distribution characteristics of each environmental factor in different concentration intervals are statistically analyzed, and the degree of influence is graded and classified using the proportional distribution method. Each classification result is re-merged according to the pollutant concentration category to generate a data table of pollutant concentration and environmental factor classification association. This data table records in detail the correlation between the distribution characteristics of environmental factors and pollutant concentrations, and provides the distribution weight of each environmental factor for a specific concentration category, so as to further explore the influence mechanism between them.

[0071] S103: Based on the pollutant concentration and environmental factor classification association data table, extract the time series data of the concentration category, analyze the concentration change amplitude in the time dimension, summarize the change direction of the concentration value at the time point, integrate the concentration change trend data, and obtain the pollutant concentration change trend characteristics;

[0072] Calculate the difference of concentration values at different time nodes to quantify the amplitude of concentration change in the time dimension. By comparing the differences in concentration values before and after each time point, analyze the change direction and integrate it into a trend data table. When summarizing the change direction of concentration values, utilize the concentration change conditions of consecutive time nodes to extract the change rules of each concentration category. By combining daily average concentration data with hourly concentration data, conduct multi-scale analysis of the concentration change in the time dimension to identify the characteristics of short-term fluctuations and long-term trends. Obtain the characteristics of the pollutant concentration change trend, and specifically mark the change amplitude, fluctuation period, and mutation point time nodes of each concentration category at different time periods, providing key data support for subsequent pollutant source tracing analysis and pollution control decision-making.

[0073] Specifically, as Figure 3 shown, the steps of constructing a time series analysis model by using the characteristics of the pollutant concentration change trend and combining the time period characteristics in the original data, extracting the key environmental factors affecting the pollutant concentration, optimizing the model parameters according to the spatio-temporal change rules, and generating the long-term pollutant concentration prediction results are as follows:

[0074] S201: Based on the characteristics of the pollutant concentration change trend, divide the original data by time interval, count the concentration extreme values in each interval, identify the mean fluctuation range, analyze the trend of concentration change over time, extract the interval trend indicators, and obtain the concentration distribution results for the time period;

[0075] Divide the original data into equal-length sub-intervals according to the time interval, count the concentration extreme values in each interval one by one, including the maximum and minimum values, and calculate the average value and standard deviation of each interval. By comparing the extreme values with the mean, clarify the upper and lower limits of the pollutant concentration fluctuation within the interval, and then analyze the time change trend of the concentration. During the statistical process, based on the hourly data of each day as the basic unit, integrate the distribution range of the hourly concentration values and construct a graph of the concentration change over time. When extracting the interval trend indicators, use the extreme value difference, mean change amplitude, and fluctuation frequency of each interval as key evaluation indicators, compare consecutive intervals through a dynamic sliding window, identify the high-frequency fluctuation areas in the short term and the concentration stable areas in the long term, and generate the concentration distribution results for the time period according to the analysis, clarifying the concentration characteristics and trend rules of each interval, laying a foundation for subsequent environmental factor screening.

[0076] S202: Based on the concentration distribution results for the time period, screen the environmental factor records, identify the difference ratio between the environmental factor values and the change values of the pollutant concentration within the time interval, count and arrange the correlation coefficients, extract the factor set within the correlation threshold range, and obtain the key environmental factor correlation data;

[0077] Screen environmental factor records from the corresponding time interval, extract the numerical characteristics of environmental factors and the change values of pollutant concentrations item by item, calculate the difference ratio between the environmental factor values and the concentration change values. During the process of statistical ratio, take each hour as the time unit, compare the change values of environmental factors and concentrations, mark the abnormal points whose change ranges exceed a specific range, and record the proportional relationship between each environmental factor and the concentration change at the same time. By calculating the correlation coefficients one by one, arrange the correlation coefficients from high to low, screen the factor set whose correlation exceeds the threshold, form the key environmental factor correlation data, and stratify and classify the impacts of different environmental factors on the change of pollutant concentration in the data, clarify the action intensity of key factors, and provide a basis for subsequent adjustment of weight distribution.

[0078] S203: Based on the key environmental factor correlation data, determine the distribution parameters of the interval weight factors, adjust the factor correlation coefficients and interval contribution values, optimize the weight distribution of the factor set, and generate the long-term pollutant concentration prediction results;

[0079] The acquisition formula for the long-term pollutant concentration prediction results is:

[0080] ;

[0081] Among them, represents the long-term pollutant concentration prediction value, represents the weighted coefficient associated with the key environmental factor , represents the th real-time observation value of the key environmental factor, represents the adjustment coefficient associated with the factor , represents the weight normalization parameter associated with the factor , represents the interaction intensity factor of the associated factor group within the interval, represents the th time scale impact factor of the interval;

[0082] Explanation of the formula and the derivation process of formula calculation:

[0083] This formula is used to calculate the long-term pollutant concentration prediction results , and the obtained results are used to evaluate the environmental quality and guide the formulation of pollution control strategies;

[0084] : represents the weighted coefficient associated with the key environmental factor , which is used to adjust the contribution intensity of each environmental factor to the prediction results. The set value is 0.2, reflecting the relative importance of this factor in multi-objective optimization;

[0085] : Represents the real-time observed value of the th key environmental factor, sourced from long-term environmental monitoring data. The set value is 50, reflecting the current measurement level of this factor;

[0086] : Represents the adjustment coefficient associated with factor , reflecting the internal dynamic change characteristics of this factor. The set value is 10, calculated based on the volatility of historical data;

[0087] : Represents the weight normalization parameter associated with factor , used to maintain the consistency of calculation results under different dimensions. The set value is 5, standardized according to the dimensions and units of each factor;

[0088] : Represents the interaction intensity factor of the associated factor group within the interval, reflecting the synergy among multiple factors. The set value is 0.8, obtained based on statistical analysis;

[0089] : Represents the time-scale impact factor of the th interval, reflecting the variation law of factor weights in different time periods. The set value is 2, dynamically adjusted according to the time-window distribution;

[0090] Substitute the parameters into the formula for calculation:

[0091] Calculate the internal term: ;

[0092] Calculate the absolute value difference: ;

[0093] Calculate the weighted product: ;

[0094] Calculate the numerator part: ;

[0095] Calculate the internal term of the denominator: ;

[0096] Calculate the denominator part: ;

[0097] Calculate the final result: ;

[0098] The result 7.683 indicates that under the current parameter settings, the predicted long-term pollutant concentration is 7.683 units. This result can be used to evaluate environmental quality and provide a basis for formulating pollution control strategies.

[0099] Specifically, such as Figure 4As shown, the steps of comparing the long-term pollutant concentration prediction results with the real-time monitoring data, identifying the error values, analyzing the causes of the errors, judging whether the model needs to be updated through error analysis, adjusting the weight parameters of the model, and generating the pollutant concentration error correction results are as follows:

[0100] S301: Based on the long-term pollutant concentration prediction results, extract the time interval values of the predicted values and the real-time monitoring data, identify the differences between the predictions and the real-time values, calculate the mean error and the fluctuation range, analyze the error distribution characteristics of each time interval, and obtain the pollutant concentration error data;

[0101] Match the predicted values with the real-time monitoring data according to the time intervals, extract the predicted concentration values and the monitored concentration values within each time interval, and calculate the difference between the two. During the process of identifying the differences between the predicted values and the real-time values, classify the difference values statistically, record the change amplitude and its mean of each time interval, and at the same time extract the extreme points of the maximum error and the minimum error, calculate the standard deviation of the overall error and the interval fluctuation range. When calculating the mean error, accumulate the difference data within each interval in time series and take the mean as the error characteristic value, and generate the error distribution characteristics of each interval in combination with the time dimension. When analyzing the error distribution of each time interval, use the distribution frequency of the difference values to clarify the positions of the high-error intervals and the low-error intervals, and integrate the pollutant concentration error data, providing data support for subsequent model adjustment.

[0102] S302: Based on the pollutant concentration error data, analyze the relationship between the error distribution and the environmental factor records, adjust the weight parameters and the distribution intervals within the factor set, reallocate the numerical weights of the parameters within the model, and obtain the adjusted model parameters;

[0103] Conduct item-by-item correlation analysis between the error characteristics of each time interval and the environmental factor records to clarify the variation law between the error distribution and the environmental factors. When analyzing the error distribution, conduct hierarchical statistics on the environmental factor values according to the time intervals, identify the correlation relationship between the environmental factor values and the error data, and combine the analysis results to adjust the weight parameters within the factor set. Reallocate the weight distribution values of the environmental factors within the model according to their influence degrees. When adjusting the distribution intervals, refine the distribution intervals of the highly correlated factors to improve the sensitivity of the model to the subtle changes of the factors. At the same time, appropriately expand the distribution range of the low-correlated factors to reduce their influence on the error correction. When reallocating the parameter weights within the model, optimize the balance between the weight parameters and the distribution intervals in combination with the factor contribution values within the interval and the overall error change trend, and generate the adjusted model parameters, laying a foundation for subsequent prediction correction.

[0104] S303: Based on the adjusted model parameters, calculate the pollutant concentration predicted values according to the weight parameter distribution data, and generate the pollutant concentration error correction results;

[0105] The pollutant concentration for each time interval is corrected, and the predicted value is recalculated. During the calculation process, with the adjusted weight parameter as the core, the contribution values of environmental factors and error data within each interval are superimposed to generate a corrected concentration prediction value. The difference between the corrected predicted value and the real-time monitoring value is extracted, the error mean and fluctuation range within each interval are statistically calculated again, and the error changes before and after correction are compared to verify the correction effect. The correction results are detailedly marked with the concentration value change characteristics of each time interval and the corrected error distribution characteristics, providing a quantifiable analysis result for optimizing the long-term pollutant concentration prediction and further improving the accuracy and operability of the monitoring system, and generating the pollutant concentration error correction result.

[0106] Specifically, as Figure 5 shown, based on the pollutant concentration error correction result, analyzing the relationship between the pollutant concentration and the monitoring resource demand, analyzing the spatio-temporal distribution of the pollutant concentration change, and predicting the monitoring demand in high-pollution time periods and high-pollution regions, the steps for generating the monitoring resource demand prediction result are specifically as follows:

[0107] S401: Based on the pollutant concentration error correction result, extract the concentration change data of regions and time intervals, statistically calculate the pollutant peak value in the region and the concentration fluctuation range in the time interval, analyze the proportional relationship between the concentration change trend and the monitoring resource demand, and obtain the monitoring resource demand distribution data;

[0108] Extract the concentration change data of each region and time interval from the monitoring data, statistically calculate the pollutant concentration peak value in each region one by one, and at the same time calculate the concentration fluctuation range in the time interval. Sort the regional data according to the pollutant concentration extreme value, mark the high-pollution regions, and combine the fluctuation range data of the time interval to analyze the change trend of the pollutant concentration over time. Taking hours and days as the time basis, record the change slope and fluctuation amplitude of the concentration value, classify the high-fluctuation intervals and low-fluctuation intervals, calculate the demand ratio of the monitoring resources through the concentration change characteristics of the high-pollution regions and time intervals, clarify the time priority and spatial distribution characteristics of the monitoring resource demand, obtain the monitoring resource demand distribution data, and comprehensively present the specific impact of the pollutant concentration change trend on the resource demand.

[0109] S402: Based on the monitoring resource demand distribution data, group and process the pollutant concentration change data of the time interval, analyze the change amplitude in the high-pollution time period, combine the regional data to extract the total resource demand in the high-pollution region, identify the resource demand peak value in the high-pollution time period of the region, and obtain the high-pollution spatio-temporal monitoring demand data;

[0110] Group the pollutant concentration change data within a time interval. Taking hours and regions as the classification criteria, calculate the concentration change amplitude during high-pollution periods respectively. When analyzing the change amplitude during high-pollution periods, compare the change amount of the hourly concentration value with the mean value, mark the time periods with a change amplitude exceeding the set threshold, and identify the corresponding regional data for the time periods. Combining the regional data, calculate the total resource demand within high-pollution regions one by one. Through the matching of concentration peaks and time persistence, further identify the peak resource demand during high-pollution periods in the regions. Integrate the high-pollution demand data in the regional and time dimensions to obtain the high-pollution spatio-temporal monitoring demand data, providing a basis for accurately allocating monitoring resources.

[0111] S403: Based on the high-pollution spatio-temporal monitoring demand data, summarize the total resource demand data during high-pollution periods and regions, analyze the overall distribution of monitoring resource demand, and generate the monitoring resource demand prediction results;

[0112] Formula for obtaining the monitoring resource demand prediction results:

[0113] ;

[0114] Among them, represents the monitoring resource demand prediction value, represents the resource input ratio of monitoring resources during high-pollution periods, represents the resource input ratio of monitoring resources in high-pollution regions, represents the monitoring time span, represents the difference value of pollution levels in the high-pollution spatio-temporal monitoring demand data, is the resource weight adjustment coefficient during high-pollution periods, is the resource weight adjustment coefficient in high-pollution regions, is the adjustment coefficient of the impact of pollution level differences on monitoring resource prediction;

[0115] Detailed explanation of the formula and calculation derivation process:

[0116] This formula is used to calculate the monitoring resource demand prediction value , and the result is used to guide the resource allocation during high-pollution periods and regions;

[0117] : The resource input ratio of monitoring resources during high-pollution periods. By statistically analyzing the pollution levels in each period of historical data, determine the high-pollution periods, and calculate the resource input ratio during these periods. For example, if the total amount of runoff pollutants SS (suspended solids) introduced on a certain highway section significantly increases during the rainy season (July - August), then this period is regarded as a high-pollution period. Assuming that the resource input during this period accounts for 60% of the total annual input, then ;

[0118] : The input ratio of monitoring resources in highly polluted areas. By analyzing the pollution data of different areas, identify the pollution hotspots and calculate the input ratio of monitoring resources in the area. For example, if the road or bridge surface near a certain water body is more sensitive to runoff pollutants due to high water quality requirements, then this area is regarded as a highly polluted area. If the input of monitoring resources in this area accounts for 50% of the total input of the whole city, then ;

[0119] : The monitoring time span, which represents the length of the monitoring period. According to the monitoring plan, determine the time range of monitoring. For example, if the monitoring period is one year, then months;

[0120] : The pollution level difference value, which represents the difference in pollution levels between different areas or time periods. By calculating the average pollutant concentration in each area or time period, obtain the difference value. For example, if the average pollutant density of suspended solids in road surface runoff is 150 mg / m 3 , and in another area it is 80 mg / m 3 , then mg / m 3 ;

[0121] : The resource weight adjustment coefficient for highly polluted time periods is set according to the impact degree of highly polluted time periods on the overall pollution. If the highly polluted time periods contribute more to the annual pollution, then take a higher value and set ;

[0122] : The resource weight adjustment coefficient for highly polluted areas is set according to the impact degree of highly polluted areas on the overall pollution. If the highly polluted areas contribute more to the city's pollution, then take a higher value and set ;

[0123] : The adjustment coefficient for the impact of pollution level differences on monitoring resource prediction is set according to the impact degree of pollution level differences on the monitoring resource demand. If the pollution level differences have a significant impact on the monitoring resource demand, then take a higher value and set ;

[0124] Substitute the parameters into the formula for calculation:

[0125] Calculate : ;

[0126] Calculate :

[0127] Calculate :

[0128] Calculate : ;

[0129] Calculate : ;

[0130] Calculate : ;

[0131] Calculate : ;

[0132] Take the absolute value and square root: ;

[0133] Calculation result indicates that the predicted value of the monitoring resource demand is 62.63, and this result is used to guide the reasonable allocation of monitoring resources in high-pollution periods and regions, ensuring the effectiveness and pertinence of the monitoring work.

[0134] Specifically, as Figure 6 shown, based on the predicted results of the monitoring resource demand, adjust the monitoring frequency for the periods with large changes in pollutant concentration, optimize the number of monitoring points and resource allocation, and dynamically adjust the monitoring configuration according to the sudden pollution situation and change trend. The specific steps for generating the dynamic monitoring plan for road surface pollutants are as follows:

[0135] S501: Based on the predicted results of the monitoring resource demand, extract the time interval data of the change in pollutant concentration, analyze the matching situation between the resource allocation in the time interval and the monitoring frequency, calculate the adjustment ratio of the monitoring frequency, optimize the distribution of adjustment parameters, and obtain the optimized data of the monitoring frequency for each period;

[0136] Extract the records of the monitoring frequency and resource allocation from the time interval data of the change in pollutant concentration, perform a matching analysis on the monitoring frequency in different time periods and the actual resource usage situation, compare the monitoring frequency with the change trend of the pollutant concentration, identify the periods where the monitoring frequency does not match the concentration change, and record the frequency deviation situation of the periods. Combining the analysis results, calculate the adjustment ratio of the monitoring frequency in each time interval. By setting segment weights, increase the monitoring frequency for the periods with frequent high-concentration fluctuations and appropriately reduce the monitoring frequency for the periods with stable low-concentration fluctuations. When optimizing the distribution of adjustment parameters, combine the adjustment ratio of each time period with the proportion of resource allocation to ensure that the matching degree between resources and the monitoring frequency reaches the optimal balance. After optimization, obtain the optimized data of the monitoring frequency for each period, which records the optimized frequency and adjustment parameters for each time interval, providing an accurate basis for the subsequent resource allocation of monitoring points.

[0137] S502: Optimize data based on the monitoring frequency of time periods, analyze the relationship between the distribution of monitoring points and the resource requirements in high-pollution areas, identify the adjustment ratio of the number of monitoring points in high-pollution areas, redistribute the spatial layout of monitoring points and the quantity of resources, and obtain the monitoring point resource allocation data;

[0138] Deeply analyze the relationship between the distribution of monitoring points and the resource requirements in high-pollution areas, extract the number of monitoring points and the monitoring frequency of time periods in high-pollution areas, identify whether the existing number of monitoring points meets the resource requirement ratio, calculate the coverage range of each monitoring point and its contribution value to resource requirements by statistically analyzing the concentration change records and monitoring frequencies of each monitoring point in high-pollution areas. When identifying the adjustment ratio of the number of monitoring points in high-pollution areas, expand the number of monitoring points with insufficient coverage, adjust the density of monitoring points in high-frequency change areas, and at the same time reduce the resource allocation of monitoring points in low-demand areas. According to the redistributed spatial layout of monitoring points and the quantity of resources, redistribute the monitoring resources in high-pollution areas and other areas according to the demand priority level, obtain the monitoring point resource allocation data, and provide a reliable basis for optimizing the spatial layout of monitoring points.

[0139] S503: Based on the monitoring point resource allocation data, combined with the records of sudden pollution changes, analyze the dynamic adjustment range of monitoring points, optimize the spatial location of monitoring points and the allocation of resource quantity, update the regional monitoring configuration, and generate a dynamic monitoring plan for road surface pollutants;

[0140] Statistically analyze the concentration change trend and resource requirements of high-pollution points in emergencies period by period. When optimizing the spatial location of monitoring points, mark the areas with frequent sudden pollution changes as dynamic adjustment areas, calculate the boundary of the adjustment range and the resource allocation quantity in combination with the monitoring point coverage range and resource distribution records. In the resource quantity allocation, give priority to ensuring the resource supply of monitoring points in high-pollution areas, and at the same time leave a flexible allocation ratio for dynamic adjustment areas to cope with the uncertainty of pollution changes. When updating the regional monitoring configuration, integrate the re-optimized monitoring point positions and resource quantities to generate a dynamic monitoring plan for road surface pollutants. This plan comprehensively covers the resource requirements of high-pollution areas and sudden pollution events, and provides a practical implementation plan for building a real-time and efficient monitoring system.

[0141] As Figure 7 shown, a traceable road surface runoff pollutant monitoring system, the system includes:

[0142] The environmental factor identification module extracts precipitation, temperature, and traffic flow data according to the road surface basic monitoring data, corrects and screens the environmental factors, compares the monitoring point data, conducts spatio-temporal feature analysis, screens the compliant data intervals, and analyzes the pollutant concentration trend to obtain the pollutant concentration change trend characteristics;

[0143] The pollutant classification module classifies the pollutant concentrations at sampling points based on the characteristics of the changing trends of pollutant concentrations, groups the data using statistical analysis, screens the correlation characteristics between pollutant concentrations and environmental factors, classifies the pollutants according to the grouping rules, and generates the results of the pollutant risk level division;

[0144] The spatio-temporal analysis module conducts spatio-temporal distribution analysis based on the results of the pollutant risk level division, uses the data of differentiated sampling points within the region to identify high-pollution time periods and regions, extracts the distribution trends of pollutant concentrations, compares the changing data, determines potential pollution sources, and obtains the spatio-temporal distribution characteristic data of pollutants;

[0145] The pollutant concentration adjustment module calculates the difference value based on the spatio-temporal distribution characteristic data of pollutants, analyzes the sources of errors, determines whether the errors are caused by model parameters, adjusts the model parameters according to the error value change rules, adjusts the model weights with reference to the speculation results of pollution sources, and generates the pollutant concentration error correction results;

[0146] The monitoring demand prediction module predicts the spatio-temporal changing trends of pollutant concentrations based on the pollutant concentration error correction results, analyzes high-pollution time periods and regions, estimates the monitoring demand, adjusts the monitoring frequency, optimizes the monitoring points and resource allocation, and generates a dynamic monitoring plan for road surface pollutants.

[0147] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A traceable road runoff pollutant monitoring method, characterized in that: The following steps are involved: S1: Based on the basic road monitoring data, capture the environmental factors of precipitation, temperature, and traffic volume, extract the pollutant concentration data of the sampling points, analyze the correlation of environmental factors, identify and classify pollutant concentrations, and obtain the trend characteristics of pollutant concentration changes; S2: Using the pollutant concentration change trend characteristics and combining them with the time period characteristics in the original data, a time series analysis model is constructed to extract key environmental factors that affect pollutant concentrations, optimize model parameters according to the temporal and spatial variation laws, and generate long-term pollutant concentration prediction results; S3: Compare the long-term pollutant concentration prediction results with the real-time monitoring data, identify the error value and analyze the cause of the error, determine whether the model needs to be updated through error analysis, adjust the weight parameters of the model, and generate the pollutant concentration error correction result; S4: Based on the pollutant concentration error correction result, analyze the relationship between pollutant concentration and monitoring resource demand, analyze the temporal and spatial distribution of pollutant concentration changes, predict the monitoring demand during high pollution periods and high pollution areas, and generate monitoring resource demand prediction results; S5: Based on the monitoring resource demand prediction results, the monitoring frequency is adjusted during the period when the pollutant concentration changes greatly, the number of monitoring points and resource allocation are optimized, and the monitoring configuration is dynamically adjusted according to sudden pollution situations and changing trends to generate a dynamic monitoring plan for road pollutants.

2. The traceable road runoff pollutant monitoring method according to claim 1 is characterized in that: The pollutant concentration change trend characteristics include precipitation, temperature, traffic volume, pollutant concentration classification, and pollutant concentration change law. The long-term pollutant concentration prediction results include key environmental factors, temporal and spatial change laws, optimized model parameters, and long-term prediction values. The pollutant concentration error correction results include error values, error analysis, model update judgment, and model weight adjustment. The monitoring resource demand prediction results include the relationship between pollutant concentration and monitoring demand, high pollution periods, and high pollution areas. The road pollutant dynamic monitoring plan includes monitoring frequency adjustment, optimization of the number of monitoring points, resource allocation optimization, and dynamic adjustment of monitoring configuration.

3. The traceable road runoff pollutant monitoring method according to claim 1 is characterized in that: Based on the basic road monitoring data, the environmental factors of precipitation, temperature, and traffic volume are captured, the pollutant concentration data of the sampling points are extracted, the correlation of environmental factors is analyzed, the pollutant concentration is identified and classified, and the steps to obtain the trend characteristics of the pollutant concentration are as follows: S101: Based on the basic road surface monitoring data, environmental factors such as precipitation, temperature, and traffic volume are recorded, pollutant concentration data are extracted in time and location order, and the time stamp spatial nodes and pollutant concentrations are integrated to obtain the initial data table of environmental factors and pollutant concentrations at the sampling points; S102: Based on the initial data table of environmental factors and pollutant concentrations at the sampling points, extract environmental factor parameters and pollutant concentration data, classify the correlation degree of each parameter, classify the classification results into concentration categories, and obtain a data table of pollutant concentration and environmental factor classification correlation; S103: Based on the pollutant concentration and environmental factor classification association data table, extract the time series data of the concentration category, analyze the concentration change amplitude in the time dimension, summarize the change direction of the concentration value at the time point, integrate the concentration change trend data, and obtain the pollutant concentration change trend characteristics.

4. The traceable road runoff pollutant monitoring method according to claim 1 is characterized in that: The steps of constructing a time series analysis model by using the pollutant concentration change trend characteristics and combining the time period characteristics in the original data, extracting the key environmental factors affecting the pollutant concentration, optimizing the model parameters according to the temporal and spatial variation rules, and generating long-term pollutant concentration prediction results are as follows: S201: Based on the pollutant concentration change trend characteristics, the original data is divided into time intervals, the extreme concentration values ​​of each interval are counted, the mean fluctuation range is identified, the concentration change trend over time is analyzed, the interval trend index is extracted, and the time period concentration distribution result is obtained; S202: Based on the concentration distribution results of the time period, filter the environmental factor records, identify the difference ratio between the environmental factor values ​​and the change values ​​of the pollutant concentration within the time interval, calculate the correlation coefficients and arrange them, extract the factor set within the correlation threshold range, and obtain the key environmental factor correlation data; S203: Based on the key environmental factor association data, determine the interval weight factor distribution parameters, adjust the factor association coefficient and the interval contribution value, optimize the factor set weight distribution, and generate long-term pollutant concentration prediction results.

5. The traceable road runoff pollutant monitoring method according to claim 4 is characterized in that: The formula for obtaining the long-term pollutant concentration prediction result is: ； in, represents the long-term pollutant concentration prediction value, Expression and key environmental factors The associated weighting coefficient, Indicates Real-time observations of key environmental factors, Expression and key environmental factors The associated adjustment coefficient, Expression and key environmental factors The associated weight normalization parameter, represents the interaction intensity factor with the associated factor group within the interval, Indicates The time scale influencing factor of the interval.

6. The traceable road runoff pollutant monitoring method according to claim 1 is characterized in that: The steps of comparing the long-term pollutant concentration prediction results with the real-time monitoring data, identifying the error value and analyzing the cause of the error, judging whether the model needs to be updated through error analysis, adjusting the weight parameters of the model, and generating the pollutant concentration error correction results are as follows: S301: Based on the long-term pollutant concentration prediction result, extract the time interval value of the prediction value and the real-time monitoring data, identify the difference between the prediction and the real-time value, calculate the error mean and fluctuation range, analyze the error distribution characteristics of each time interval, and obtain the pollutant concentration error data; S302: Based on the pollutant concentration error data, analyze the relationship between the error distribution and the environmental factor records, adjust the weight parameters and distribution intervals in the factor set, reallocate the numerical weights of the parameters in the model, and obtain the adjusted model parameters; S303: Based on the adjusted model parameters and according to the weight parameter distribution data, the pollutant concentration prediction value is calculated to generate a pollutant concentration error correction result.

7. The traceable road runoff pollutant monitoring method according to claim 1 is characterized in that: Based on the pollutant concentration error correction result, the relationship between pollutant concentration and monitoring resource demand is analyzed, the temporal and spatial distribution of pollutant concentration changes is analyzed, and the monitoring demand in high pollution periods and high pollution areas is predicted. The steps of generating the monitoring resource demand prediction result are specifically as follows: S401: Based on the pollutant concentration error correction result, extract the regional and time interval concentration change data, count the regional pollutant peak value and time interval concentration fluctuation range, analyze the relationship between the concentration change trend and the monitoring resource demand ratio, and obtain the monitoring resource demand distribution data; S402: Based on the monitoring resource demand distribution data, the pollutant concentration change data of the time interval is processed in groups, the change range of the high pollution period is analyzed, the total resource demand of the high pollution area is extracted in combination with the regional data, the resource demand peak value of the high pollution period of the region is identified, and the high pollution spatiotemporal monitoring demand data is obtained; S403: Based on the high-pollution spatiotemporal monitoring demand data, the total resource demand data for high-pollution periods and regions is summarized, the overall distribution of monitoring resource demand is analyzed, and a monitoring resource demand prediction result is generated.

8. The traceable road runoff pollutant monitoring method according to claim 7 is characterized in that: The formula for obtaining the monitoring resource demand prediction result is: ； in, Represents the predicted value of monitoring resource demand, Represents the proportion of monitoring resources invested in high pollution periods, Represents the proportion of monitoring resources invested in high-pollution areas, represents the monitoring time span, Represents the difference value of pollution level in the high pollution spatiotemporal monitoring demand data, is the resource weight adjustment coefficient for high pollution periods, is the resource weight adjustment coefficient of high-pollution areas, It is the adjustment coefficient of the impact of pollution level differences on monitoring resource prediction.

9. The traceable road runoff pollutant monitoring method according to claim 1 is characterized in that: According to the monitoring resource demand prediction results, the monitoring frequency is adjusted during the period when the pollutant concentration changes greatly, the number of monitoring points and resource allocation are optimized, and the monitoring configuration is dynamically adjusted according to the sudden pollution situation and change trend. The specific steps of generating a dynamic monitoring plan for road pollutants are as follows: S501: Based on the monitoring resource demand prediction result, extract the time interval data of pollutant concentration changes, analyze the matching situation between time interval resource configuration and monitoring frequency, calculate the monitoring frequency adjustment ratio, optimize the adjustment parameter distribution, and obtain the time period monitoring frequency optimization data; S502: Based on the monitoring frequency optimization data of the time period, analyze the relationship between the distribution of monitoring points and the resource demand of the high-pollution area, identify the adjustment ratio of the number of monitoring points in the high-pollution area, reallocate the spatial layout and resource quantity of the monitoring points, and obtain the resource configuration data of the monitoring points; S503: Based on the resource configuration data of the monitoring points and in combination with the records of sudden pollution changes, the dynamic adjustment range of the monitoring points is analyzed, the spatial location of the monitoring points and the allocation of resource quantities are optimized, the regional monitoring configuration is updated, and a dynamic monitoring plan for road pollutants is generated.

10. A traceable road runoff pollutant monitoring system, characterized in that: The traceable road runoff pollutant monitoring method according to any one of claims 1 to 9 is implemented, wherein the system comprises: The environmental factor identification module is used to extract precipitation, temperature, and traffic flow data based on the basic road surface monitoring data, calibrate and screen environmental factors, compare monitoring point data, perform spatiotemporal feature analysis, screen compliant data intervals, analyze pollutant concentration trends, and obtain pollutant concentration change trend characteristics; The pollutant classification module is used to classify the pollutant concentrations at the sampling points based on the pollutant concentration change trend characteristics, group the data using statistical analysis, screen the correlation characteristics between pollutant concentrations and environmental factors, classify pollutants according to grouping rules, and generate pollutant risk level classification results; The spatiotemporal analysis module is used to perform spatiotemporal distribution analysis based on the pollutant risk level classification results, identify high pollution periods and areas using differentiated sampling point data within the region, extract pollutant concentration distribution trends, compare change data, determine potential pollution sources, and obtain pollutant spatiotemporal distribution characteristic data; The pollutant concentration adjustment module is used to calculate the difference value based on the temporal and spatial distribution characteristic data of the pollutants, analyze the source of the error, determine whether the error is caused by the model parameters, adjust the model parameters according to the error value change rules, adjust the model weights with reference to the pollution source speculation results, and generate the pollutant concentration error correction results; The monitoring demand prediction module is used to predict the temporal and spatial variation trend of pollutant concentration based on the pollutant concentration error correction results, analyze high pollution periods and areas, calculate monitoring needs, adjust monitoring frequency, optimize monitoring points and resource allocation, and generate a dynamic monitoring plan for road pollutants.

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