Rainfall runoff pollution early warning system and method

Abstract

The invention relates to the technical field of data processing, and discloses a rainfall runoff pollution early warning system and method.The system comprises a sample analysis module, a pollutant model construction module, a water quality prediction module, an early warning signal generation module and a pollution control module; carrying out physicochemical property analysis on the pavement sediment sample and the rainfall runoff sample of the service area to obtain pollutant concentration data and pollutant composition data; constructing a pollutant migration and transformation model based on dynamic changes of pollutants in the pollutant concentration data and the pollutant composition data in the runoff process; hydrology and hydrodynamic force of the service area are predicted based on the pollutant migration and transformation model, and runoff water quality change trends under different rainfall scenes are obtained; when the water quality prediction index in the flow water quality change trend exceeds a pollution early warning threshold value, a pollution early warning signal is generated; the pollution early warning signal is pushed to a management terminal, and a corresponding runoff pollution control measure is triggered; the rainfall runoff pollution early warning method can improve the accuracy of rainfall runoff pollution early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a rainfall runoff pollution early warning system and method. Background Art

[0002] Existing technologies for rainfall runoff pollution early warning do not provide comprehensive analysis of the physical and chemical properties of samples. They only focus on a single type of sample or limited indicators, and fail to fully combine the pollutant concentration and composition data of road sediments and rainfall runoff samples. This leads to an incomplete understanding of the sources and characteristics of pollution, and the basic data provided for subsequent model construction is not accurate enough.

[0003] At the same time, existing pollutant migration and transformation models lack in-depth analysis of pollutant dynamics and fail to effectively integrate empirical association rules and pollution transmission characteristics from historical monitoring data, resulting in inaccurate predictions of water quality changes under different rainfall scenarios. Furthermore, the linkage between warning signal generation and pollution control measures is weak, making it difficult to accurately trigger corresponding measures based on warning levels and polluted areas, affecting the timeliness and effectiveness of pollution prevention and control. Summary of the Invention

[0004] The present invention provides a rainfall runoff pollution early warning system and method to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides a rainfall runoff pollution early warning system, characterized in that the system includes a sample analysis module, a pollutant model construction module, a water quality prediction module, a warning signal generation module and a pollution control module, wherein:

[0006] The sample analysis module is used to analyze the physical and chemical properties of road sediment samples and rainfall runoff samples in the service area to obtain pollutant concentration data and pollutant composition data of the service area;

[0007] The pollutant model building module is used to build a pollutant migration and transformation model for the service area based on the dynamic changes of pollutants in the runoff process in the pollutant concentration data and the pollutant composition data;

[0008] The water quality prediction module is used to predict the hydrological and hydrodynamic characteristics of the service area based on the pollutant migration and transformation model, and obtain the runoff water quality change trend of the service area under different rainfall scenarios;

[0009] The warning signal generating module is configured to generate a pollution warning signal for the service area when the water quality prediction index in the water quality change trend exceeds the pollution warning threshold of the rainfall runoff;

[0010] The pollution control module is used to push the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures.

[0011] In a preferred embodiment, when the sample analysis module performs physical and chemical property analysis on road sediment samples and rainfall runoff samples in the service area to obtain pollutant concentration data and pollutant composition data of the service area, it is specifically used to:

[0012] Collect dry-season pavement sediment samples and runoff samples from rainfall events at representative locations within the service area;

[0013] Grinding and sieving the road sediment sample to obtain a test sample of the service area with a standard particle size;

[0014] Determining pollutant concentration data in the experimental sample;

[0015] Identify the types of organic pollutants in the runoff sample and obtain pollutant composition data of the runoff sample.

[0016] In a preferred embodiment, when the pollutant model construction module constructs the pollutant migration and transformation model of the service area based on the dynamic changes of pollutants in the pollutant concentration data and the pollutant composition data during the runoff process, it is specifically used to:

[0017] Extracting initial concentration values ​​and spatiotemporal distribution characteristics of pollutants from the pollutant concentration data;

[0018] determining the pollution propagation characteristics of the pollutants during runoff according to the pollutant composition data;

[0019] Establish empirical association rules based on the changes of pollutant concentrations with runoff duration and hydraulic conditions in historical rainfall runoff monitoring data;

[0020] Building a modular rule base for the service area based on the empirical association rules and the pollution propagation characteristics;

[0021] The modular rule base and the spatiotemporal distribution characteristics are integrated to generate a pollutant migration and transformation model for the service area.

[0022] In a preferred embodiment, when the pollutant model construction module integrates the modular rule base and the spatiotemporal distribution characteristics to generate the pollutant migration and transformation model of the service area, it is specifically used to:

[0023] Matching the spatial data of initial pollutant concentrations in the spatiotemporal distribution characteristics with corresponding migration and transformation rules in the modular rule base;

[0024] According to the matching result, a behavior mapping relationship set is established with the pollutant type and spatial location in the pollutant concentration data as an index;

[0025] According to the mapping relationship set, a simulation verification of the dynamic process of pollutants at the global scale is performed on the service area;

[0026] Based on the verification results of the simulation verification, a structured model expression of pollutants from the road surface to the receiving water body in the service area under different rainfall scenarios is generated, and the structured model expression is used as the pollutant migration and transformation model of the service area.

[0027] In a preferred embodiment, when the water quality prediction module performs a hydrological and hydrodynamic prediction of the service area based on the pollutant migration and transformation model to obtain the runoff water quality change trend of the service area under different rainfall scenarios, it is specifically used to:

[0028] Obtaining multiple preset rainfall scenario parameters for the service area;

[0029] Inputting the pollutant migration and transformation model to couple the multiple preset rainfall scenario parameters with hydrology and pollution, and obtaining the runoff process line and pollutant load flux of the outlet section of the sub-area within the service area;

[0030] Performing a time series correlation analysis on the runoff process line and the pollutant load flux to obtain a runoff water quality change trend map of the sub-region;

[0031] A dynamic attribute list of key water quality indicators in the runoff water quality change trend map is extracted, and runoff prediction is performed on the service area based on the dynamic attribute list to obtain the runoff water quality change trend of the service area.

[0032] In a preferred embodiment, when extracting the dynamic attribute list of key water quality indicators from the runoff water quality change trend map, the water quality prediction module is specifically used to:

[0033] Identifying characteristic turning points on a pollutant concentration curve in the runoff water quality change trend map;

[0034] Determining the starting phase, peak phase, and duration of changes in key water quality indicators in the service area based on the distribution of the characteristic turning points on the time axis;

[0035] Classifying the change intensity level of the key water quality indicator according to the quantified values ​​of the starting phase, the peak phase, and the duration;

[0036] The change phase of the characteristic turning point is temporally and spatially correlated with the change intensity level to obtain a dynamic attribute list of the key water quality indicators.

[0037] In a preferred embodiment, when performing runoff prediction for the service area based on the dynamic attribute list to obtain the runoff water quality change trend of the service area, the water quality prediction module is specifically configured to:

[0038] Obtaining the change intensity level and key time phase of the target pollutant from the dynamic attribute list;

[0039] Determining a pollution response time sequence of the sub-region according to the key time phase;

[0040] Based on the change intensity level and the pollution response time series, the predicted concentration time series value of the target pollutant at the outlet section of the sub-area is calculated, wherein the calculation formula of the predicted concentration time series value is as follows:

[0041]

[0042] Where, To express The predicted concentration time series value of the service area outlet section at the time, The pollutant source intensity coefficient determined for the said change intensity level, is the natural exponential function, is the comprehensive attenuation coefficient determined based on the underlying surface characteristics in the service area, It is the pollutant transfer time parameter of the sub-region determined based on the pollution response time series.

[0043] In a preferred embodiment, when the water quality prediction index in the water quality change trend of the flow exceeds the pollution warning threshold of the rainfall runoff, the warning signal generation module generates the pollution warning signal of the service area, specifically for:

[0044] Retrieving a warning concentration limit value that matches the target pollutant from a preset pollution warning threshold database;

[0045] Comparing the time series data of the water quality prediction index in the runoff water quality change trend with the warning concentration limit in real time;

[0046] When the water quality prediction index in a specific time period continuously exceeds the warning concentration limit, it is determined that a pollution warning event of a corresponding level occurs in the service area;

[0047] A pollution warning signal for the service area is generated according to the level of the pollution warning event and the specific time period.

[0048] In a preferred embodiment, when the pollution control module pushes the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures, it is specifically configured to:

[0049] According to the warning level and core area in the pollution warning signal, matching the corresponding control measure instruction set in the preset pollution control strategy library;

[0050] Binding the control measure instruction set with the pollution warning signal to obtain a control instruction data packet for the service area;

[0051] Pushing the control instruction data packet to the on-site management terminal corresponding to the core area in real time;

[0052] After receiving and parsing the control instruction data packet, the on-site management terminal starts the pollution control process.

[0053] In order to solve the above problems, the present invention also provides a rainfall runoff pollution early warning method, the method comprising:

[0054] S1. Analyze the physical and chemical properties of road sediment samples and rainfall runoff samples in the service area to obtain pollutant concentration data and pollutant composition data of the service area;

[0055] S2. constructing a pollutant migration and transformation model for the service area based on the pollutant concentration data and the dynamic changes of pollutants in the pollutant composition data during runoff;

[0056] S3. Predicting the hydrological and hydrodynamic characteristics of the service area based on the pollutant migration and transformation model to obtain the runoff water quality change trend of the service area under different rainfall scenarios;

[0057] S4. When the water quality prediction index in the water quality change trend exceeds the pollution warning threshold of rainfall runoff, generating a pollution warning signal for the service area;

[0058] S5. Push the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. This invention conducts a comprehensive physical and chemical property analysis of service area road sediments and rainfall runoff samples to accurately obtain pollutant concentration and composition data. Combining the dynamic changes of pollutants in the runoff process to construct a migration and transformation model, it can fully capture the pollution propagation characteristics and spatiotemporal distribution patterns, providing a reliable basis for water quality prediction, and significantly improving the accuracy of runoff water quality change trend prediction under different rainfall scenarios.

[0061] 2. The present application realizes efficient connection of early warning and prevention and control by comparing the water quality prediction index with the pollution early warning threshold in real time to generate a corresponding grade of early warning signal and push it to the management terminal to trigger targeted control measures, thereby improving the accuracy of rainfall runoff pollution early warning and enhancing the timeliness and effectiveness of pollution control, providing strong support for service area runoff pollution prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A system architecture diagram of a rainfall runoff pollution early warning system provided by an embodiment of the present application is provided.

[0063] Figure 2 A flowchart of a rainfall runoff pollution early warning method provided by an embodiment of the present application is provided.

[0064] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments belong to some of the embodiments of the present application but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0066] The terms used in the embodiments of the present application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. "Multiple" generally includes at least two.

[0067] Depending on the context, the word "if" or "if" as used herein can be interpreted as "when" or "when" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0068] In addition, the step sequence in each of the following method embodiments is only an example and is not strictly limited.

[0069] In fact, the server-side device deployed by a rainfall runoff pollution early warning system may be composed of one or more devices. The above-mentioned rainfall runoff pollution early warning system can be implemented as: a business instance, a virtual machine, and a hardware device. For example, the rainfall runoff pollution early warning system can be implemented as a business instance deployed on one or more devices in a cloud node. In simple terms, the rainfall runoff pollution early warning system can be understood as a software deployed on a cloud node, which is used to provide a rainfall runoff pollution early warning system for each user terminal. Alternatively, the rainfall runoff pollution early warning system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine is installed with application software for managing each user terminal. Alternatively, the rainfall runoff pollution early warning system can also be implemented as a server composed of many hardware devices of the same or different types, and one or more hardware devices are set to provide a rainfall runoff pollution early warning system for each user terminal.

[0070] In terms of implementation, a rainfall runoff pollution early warning system and a user terminal are mutually compatible. Specifically, if the rainfall runoff pollution early warning system is an application installed on a cloud service platform, the user terminal is the client that establishes a communication connection with the application; or if the rainfall runoff pollution early warning system is implemented as a website, the user terminal is implemented as a webpage; or if the rainfall runoff pollution early warning system is implemented as a cloud service platform, the user terminal is implemented as a mini-program within an instant messaging application.

[0071] like Figure 1 FIG. 1 is a system architecture diagram of a rainfall runoff pollution early warning system provided by an embodiment of the present invention.

[0072] The rainfall runoff pollution early warning system 100 described in the present invention can be set up in a cloud server. In terms of implementation, it can be implemented as one or more service devices, or as an application installed on the cloud (such as a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the rainfall runoff pollution early warning system 100 can include a sample analysis module 101, a pollutant model construction module 102, a water quality prediction module 103, a warning signal generation module 104, and a pollution control module 105. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can perform fixed functions, and is stored in the memory of the electronic device.

[0073] In an embodiment of the present invention, in a rainfall runoff pollution early warning system, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. In a rainfall runoff pollution early warning system provided by an embodiment of the present invention, the scope of application of a rainfall runoff pollution early warning system architecture can be adjusted by adding modules and directly calling them without modifying the program code, thereby realizing cluster-type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding a rainfall runoff pollution early warning system. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0074] The following describes the various components and specific workflow of a rainfall runoff pollution early warning system in conjunction with specific embodiments:

[0075] The sample analysis module 101 is used to analyze the physical and chemical properties of road sediment samples and rainfall runoff samples in the service area to obtain pollutant concentration data and pollutant composition data of the service area;

[0076] In an embodiment of the present invention, when performing physical and chemical property analysis on road sediment samples and rainfall runoff samples in a service area to obtain pollutant concentration data and pollutant composition data of the service area, the sample analysis module is specifically used to:

[0077] Collect dry-season pavement sediment samples and runoff samples from rainfall events at representative locations within the service area;

[0078] Grinding and sieving the road sediment sample to obtain a test sample of the service area with a standard particle size;

[0079] Determining pollutant concentration data in the experimental sample;

[0080] Identify the types of organic pollutants in the runoff sample and obtain pollutant composition data of the runoff sample.

[0081] Specifically, when collecting dry period pavement sediment samples and runoff samples during rainfall events at typical locations within the service area, areas where pollutants are prone to accumulate, such as main roads with heavy traffic volume, parking lot entrances and exits, and areas around refueling areas, are selected as typical locations.

[0082] Furthermore, during a dry period with no rainfall for a long period of time, a clean sampling shovel was used to collect road sediments in the shallow surface area of ​​the above-mentioned typical locations, and the samples were placed in clean polyethylene sample bags and marked as dry period road sediment samples.

[0083] Furthermore, during rainfall, when runoff is formed and the flow is stable, runoff samples are collected at the main drainage outlets in the service area using pre-cleaned polyethylene containers, ensuring that the containers are completely immersed in the runoff to avoid collecting surface floating objects. After collection, they are sealed and marked as runoff samples in the rainfall event.

[0084] Furthermore, the pavement sediment samples were ground and sieved to obtain experimental samples of the service area with standard particle size. The pavement sediment samples in the drying period were taken out, spread flat on a clean porcelain plate, and dried in an oven at an appropriate temperature to constant weight.

[0085] Furthermore, the dried sample is taken out, placed in an agate mortar, and repeatedly ground with a pestle until no obvious granular matter is visible in the sample; the ground sample is poured into a pre-cleaned and dried standard sieve, and the sieve is held handheld for horizontal oscillation screening for a certain period of time, and the sample passing through the sieve is collected into a clean sample bottle. This part of the sample is the experimental sample of the service area with a standard particle size.

[0086] Furthermore, when determining the pollutant concentration data in the experimental sample, a certain amount of sample is weighed from the experimental sample, placed in a polytetrafluoroethylene digestion tube, an appropriate amount of mixed digestion solution is added, the tube is covered and placed in a microwave digester for digestion according to a preset program.

[0087] Furthermore, after the digestion is completed, the digestion tube is cooled to room temperature, the digestion solution is filtered into a volumetric flask, the digestion tube and the filter paper are washed with deionized water several times, the washing solution is transferred to the volumetric flask, and finally the volume is fixed with deionized water.

[0088] Furthermore, an atomic absorption spectrophotometer was used to determine the concentrations of heavy metal elements such as lead, zinc, and copper in the solution, and an ultraviolet-visible spectrophotometer was used to determine the concentrations of total nitrogen and total phosphorus. All measurements were carried out in parallel multiple times, and the average value was taken as the pollutant concentration data in the experimental samples.

[0089] Furthermore, to identify the types of organic pollutants in runoff samples and obtain the pollutant composition data of runoff samples, a certain amount of runoff samples from a rainfall event is taken, poured into a separatory funnel, an appropriate amount of n-hexane is added, the separatory funnel is shaken for a certain time, and allowed to stand for a period of time. After the liquid is separated into layers, the lower aqueous phase is released and the upper organic phase is collected into a rotary evaporation flask.

[0090] Furthermore, the above extraction process is repeated multiple times, and the organic phases obtained by multiple extractions are combined, placed in a rotary evaporator, and distilled under reduced pressure in a water bath at an appropriate temperature to concentrate to a certain volume; the concentrated solution is filtered through an organic phase filter membrane with an appropriate pore size and transferred to a sampling bottle.

[0091] Furthermore, the filtrate was analyzed using a gas chromatography-mass spectrometer, with a capillary column of a suitable model selected as the chromatographic column, helium as the carrier gas, and a certain flow rate and temperature program set.

[0092] Furthermore, the mass spectrometer uses an electron bombardment ion source and sets a certain scanning range; the mass spectrum obtained by detection is compared with the standard spectral library, and substances with a higher degree of match are identified as organic pollutants present in the runoff sample. The names of these organic pollutants are recorded to form the pollutant composition data of the runoff sample.

[0093] In general, collecting dry period pavement sediment samples and runoff samples during rainfall events at typical locations within the service area can specifically capture the pollution conditions in areas where pollutants are prone to accumulate, covering the pollution background and migration status during dry and rainy periods, and providing a comprehensive sample basis for subsequent analysis.

[0094] In general, grinding and screening road sediment samples to obtain experimental samples of standard particle size can eliminate the interference of particle size differences on the test results, ensure the uniformity and representativeness of the experimental samples, and improve the accuracy of pollutant concentration determination.

[0095] In general, measuring the pollutant concentration data in experimental samples can accurately grasp the specific content of pollutants in road sediments, provide a quantitative basis for analyzing the degree and source of pollution, and is the basic data support for building pollutant migration and transformation models.

[0096] In general, identifying the types of organic pollutants in runoff samples and obtaining pollutant composition data can clarify the specific components of organic pollutants in rainfall runoff, supplement the deficiencies in inorganic pollutant analysis, comprehensively reflect the chemical composition characteristics of pollution, and provide more detailed pollution information for subsequent targeted prevention and control.

[0097] The pollutant model building module 102 is used to build a pollutant migration and transformation model for the service area based on the dynamic changes of pollutants in the runoff process in the pollutant concentration data and the pollutant composition data;

[0098] In an embodiment of the present invention, when the pollutant model construction module constructs the pollutant migration and transformation model of the service area based on the dynamic changes of pollutants in the pollutant concentration data and the pollutant composition data during the runoff process, it is specifically used to:

[0099] Extracting initial concentration values ​​and spatiotemporal distribution characteristics of pollutants from the pollutant concentration data;

[0100] determining the pollution propagation characteristics of the pollutants during runoff according to the pollutant composition data;

[0101] According to the historical rainfall runoff monitoring data, an empirical correlation rule is established for the change of pollutant concentration with runoff duration and hydraulic condition;

[0102] Based on the empirical correlation rule and the pollution propagation characteristics, a modular rule library of the service area is constructed;

[0103] The modular rule library and the spatio-temporal distribution characteristics are integrated to generate a pollutant migration and transformation model of the service area.

[0104] When the pollutant model construction module integrates the modular rule library and the spatio-temporal distribution characteristics to generate the pollutant migration and transformation model of the service area, it is specifically used for:

[0105] Matching the initial concentration spatial data of the pollutant in the spatio-temporal distribution characteristics with the corresponding migration and transformation rules in the modular rule library;

[0106] According to the matching result, a set of behavior mapping relationships is established with the pollutant type and spatial position in the pollutant concentration data as the index;

[0107] According to the set of mapping relationships, the simulation and verification of the dynamic process of the pollutant in the service area at the global scale are performed;

[0108] Based on the verification result of the simulation and verification, a structured model expression of the pollutant migration and transformation from the road surface to the receiving water body in the service area under different rainfall scenarios is generated, and the structured model expression is taken as the pollutant migration and transformation model of the service area.

[0109] Specifically, when extracting the initial concentration value and spatio-temporal distribution characteristics of the pollutant from the pollutant concentration data, the pollutant concentration data of the road surface sediment sample and the runoff sample at the monitoring start time are selected as the initial concentration value; the data is classified and arranged according to different sampling time points and positions, the concentration values at each time point and position are recorded, the distribution difference is presented through drawing curves and charts, and the spatio-temporal distribution characteristics are obtained.

[0110] Further, when determining the pollution propagation characteristics according to the pollutant composition data, the types of organic and inorganic pollutants are sorted out, and their physical and chemical properties such as solubility, density, adsorbability, etc. are analyzed.

[0111] Further, it is judged that the easily soluble pollutants diffuse in dissolved state, the pollutants with large density or easy to be adsorbed are easy to settle or migrate with particulate matter, and the diffusion speed, migration direction and aggregation rule are comprehensively determined to obtain the pollution propagation characteristics.

[0112] Furthermore, empirical association rules were established based on historical rainfall runoff monitoring data. Monitoring data from multiple past rainfall events were collected to extract hydraulic conditions such as runoff duration, flow velocity, water depth, and corresponding pollutant concentrations. The data were grouped by rainfall events, and the variation patterns of concentrations with runoff duration and hydraulic conditions were analyzed. These were summarized into descriptive rules to form empirical association rules.

[0113] Furthermore, when constructing a modular rule base based on empirical association rules and pollution propagation characteristics, the empirical association rules are classified according to the hydraulic condition type to form a hydraulic condition association module, and the pollution propagation characteristics are classified according to the pollutant type or propagation mode to form a propagation characteristic module; the modules are integrated to establish a structured storage system to obtain a modular rule base.

[0114] Furthermore, when integrating the modular rule base with the spatiotemporal distribution characteristics to generate a pollutant migration and transformation model, the time and space dimensions of the spatiotemporal distribution characteristics are used as the basic framework, the rules in the rule base are matched with the framework, and the rules and framework are combined through a computer program to set input and output items, so that the model can dynamically calculate the concentration changes and morphological transformations of pollutants to generate a pollutant migration and transformation model.

[0115] Specifically, when matching the spatial data of initial pollutant concentrations in the spatiotemporal distribution characteristics with the corresponding migration and transformation rules in the modular rule base, the initial pollutant concentration data at different spatial locations are first extracted from the spatiotemporal distribution characteristics to form the spatial data of initial pollutant concentrations containing location information and corresponding initial concentrations.

[0116] Furthermore, the modular rule base is consulted again, and based on the type of pollutant and the environmental characteristics of the spatial location, the migration and transformation rules for this type of pollutant at that type of location are retrieved from the rule base; the initial concentration data of each spatial location are matched one-to-one with the retrieved corresponding rules to ensure that the initial concentration of a certain type of pollutant at a certain location matches the migration and transformation rules applicable to the pollutant at that location, thus completing the matching process.

[0117] Furthermore, based on the matching results, when establishing a behavior mapping relationship set with the pollutant type and spatial location in the pollutant concentration data as the index, the specific type of each pollutant and its spatial location are extracted from the matching results; the pollutant type and spatial location are combined as the retrieval index, corresponding to the pollutant behavior described by the matched migration and transformation rules.

[0118] Furthermore, the associations between all indexes and corresponding behaviors are organized into a set, where each entry contains the pollutant type, spatial location, and corresponding migration and transformation behavior description, forming a behavior mapping relationship set.

[0119] Furthermore, based on the mapping relationship set, when simulating and verifying the dynamic process of pollutants at the global scale in the service area, the entire service area is used as the simulation range, covering all sampling locations and the runoff paths connecting these locations; based on the behavioral mapping relationship set, the dynamic changes of pollutants during rainfall are simulated in chronological order, including being washed away by rainwater from the initial position, migrating in different areas with runoff, settling or dissolving due to physical and chemical properties, and changes in concentration over time and space.

[0120] Furthermore, at the same time, the pollutant concentration monitoring data of different locations in the service area during historical rainfall events are retrieved, and the simulated dynamic change results of pollutants are compared with the historical monitoring data. If the simulated concentration at a certain location at a certain time deviates greatly from the actual monitored concentration, the mapping relationship set is traced back to check whether the corresponding migration and transformation rules are applicable. After adjusting the rules, the simulation is repeated until the simulation results are consistent with the historical data trend, completing the simulation verification at the global scale.

[0121] Furthermore, based on the verification results of the simulation verification, a structured model expression of pollutants from the road surface to the receiving water body in the service area under different rainfall scenarios is generated. When this structured model expression is used as the pollutant migration and transformation model of the service area, different rainfall scenarios are first set, including light rain, moderate rain, heavy rain and different rainfall durations.

[0122] Furthermore, for each scenario, based on the behavioral mapping relationship set confirmed after simulation verification, the entire process of pollutants being washed away from the road surface by rainwater, entering the runoff system, flowing along the drainage path, passing through the catchment area within the service area, and finally entering the receiving water body is simulated, and the changes in pollutant concentration, migration path and transformation form in each link are recorded.

[0123] Furthermore, these simulation results are organized into a structured framework that includes time dimension, spatial dimension, and pollutant behavior dimension. The framework clearly marks the pollutant status and correlation relationship of each node under different scenarios. This structured model expression is the pollutant migration and transformation model of the service area.

[0124] In general, the initial concentration and spatiotemporal distribution characteristics are extracted from the pollutant concentration data to clarify the initial state and distribution differences of the pollutants, provide benchmarks and rules for the model, and ensure that the initial state is accurately portrayed.

[0125] In general, the propagation characteristics are determined based on the pollutant composition data, and the diffusion and migration laws are clarified in combination with its physical and chemical properties, so as to improve the targetedness of the model in simulating the pollution process.

[0126] In general, empirical association rules are established based on historical data, and the changing patterns of concentration, runoff duration, and hydraulic conditions are incorporated to enhance the model's fit to actual conditions.

[0127] Overall, based on the empirical rule and the propagation characteristics, a modular rule base is constructed, the rules are stored in a structured manner, and the model provides a clear framework.

[0128] Overall, the rule base is integrated with the space-time distribution characteristics to generate a migration and transformation model, which combines dynamic rules and distribution characteristics to simulate the whole process and improve prediction accuracy, providing a reliable tool for water quality prediction.

[0129] Overall, the initial concentration of pollutants in the space-time distribution characteristics is matched with the corresponding migration and transformation rules in the modular rule base, so that the initial concentration of different spatial positions can accurately correspond to the applicable migration rules, forming an effective association and providing precise basic association data for model construction.

[0130] Overall, according to the matching results, a behavior mapping relationship set is established based on the pollutant type and spatial position as the index, so that the pollutant type, position and its migration and transformation behavior are clearly corresponding, and the behavior characteristics of various pollutants in different regions are presented, providing structured behavior basis for subsequent simulation.

[0131] Overall, according to the mapping relationship set, the dynamic process of pollutants in the service area is simulated and verified at the global scale, and the dynamic changes such as migration and transformation of pollutants in the entire service area are simulated, and the rules are adjusted by comparing with historical data to ensure that the simulation results fit the actual situation and improve the reliability of the model.

[0132] Overall, based on the simulation verification results, a structured model expression of pollutants from the road surface to the receiving water body under different rainfall scenarios is generated as a migration and transformation model, which fully reflects the whole process of pollutants from generation to entering the receiving water body under different rainfall conditions, and the model structure is clear and covers the time and space dimensions, which can accurately predict the pollution changes under different scenarios, providing high-quality model support for water quality prediction.

[0133] The water quality prediction module 103 is configured to predict the hydrology and hydrodynamics of the service area based on the pollutant migration and transformation model, and obtain the runoff water quality change trend of the service area under different rainfall scenarios.

[0134] In the embodiment of the present application, when the water quality prediction module performs prediction of the hydrology and hydrodynamics of the service area based on the pollutant migration and transformation model, and obtains the runoff water quality change trend of the service area under different rainfall scenarios, it is specifically used for:

[0135] Obtain a plurality of preset rainfall scenario parameters of the service area;

[0136] Input the hydrology and pollution coupling of the plurality of preset rainfall scenario parameters based on the pollutant migration and transformation model, and obtain the runoff process line and pollutant load flux of the outlet cross section of the sub-region in the service area.

[0137] Performing a time series correlation analysis on the runoff process line and the pollutant load flux to obtain a runoff water quality change trend map of the sub-region;

[0138] A dynamic attribute list of key water quality indicators in the runoff water quality change trend map is extracted, and runoff prediction is performed on the service area based on the dynamic attribute list to obtain the runoff water quality change trend of the service area.

[0139] When extracting the dynamic attribute list of key water quality indicators from the runoff water quality change trend map, the water quality prediction module is specifically used to:

[0140] Identifying characteristic turning points on a pollutant concentration curve in the runoff water quality change trend map;

[0141] Determining the starting phase, peak phase, and duration of changes in key water quality indicators in the service area based on the distribution of the characteristic turning points on the time axis;

[0142] Classifying the change intensity level of the key water quality indicator according to the quantified values ​​of the starting phase, the peak phase, and the duration;

[0143] The change phase of the characteristic turning point is temporally and spatially correlated with the change intensity level to obtain a dynamic attribute list of the key water quality indicators.

[0144] When the water quality prediction module performs runoff prediction on the service area based on the dynamic attribute list to obtain the runoff water quality change trend of the service area, it is specifically used to:

[0145] Obtaining the change intensity level and key time phase of the target pollutant from the dynamic attribute list;

[0146] Determining a pollution response time sequence of the sub-region according to the key time phase;

[0147] Based on the change intensity level and the pollution response time series, the predicted concentration time series value of the target pollutant at the outlet section of the sub-area is calculated, wherein the calculation formula of the predicted concentration time series value is as follows:

[0148]

[0149] Where, To express The predicted concentration time series value of the service area outlet section at the time, The pollutant source intensity coefficient determined for the said change intensity level, is the natural exponential function, is the comprehensive attenuation coefficient determined based on the underlying surface characteristics in the service area, It is the pollutant transfer time parameter of the sub-region determined based on the pollution response time series.

[0150] Specifically, when obtaining various preset rainfall scenario parameters in the service area, historical rainfall data in the area is collected, covering characteristics of different intensities, durations and frequencies of occurrence. Combined with the rainfall grade classification standards of the local meteorological department, multiple scenarios including parameters such as rainfall intensity and duration are set and organized into a structured data set as the basis for subsequent forecast input.

[0151] Furthermore, when the hydrological and pollution coupling of multiple preset rainfall scenario parameters is performed based on the pollutant migration and transformation model, and the runoff process line and pollutant load flux of the outlet section of the sub-area within the service area are obtained, each scenario parameter is input into the model one by one. The model simulates the runoff generation process of each sub-area to form the runoff process line of the outlet section. At the same time, the pollutant load flux is calculated in combination with the initial pollutant concentration and migration and transformation rules to ensure that the two correspond one-to-one with the corresponding scenario parameters.

[0152] Furthermore, a time-series correlation analysis was conducted on the runoff process line and the pollutant load flux to obtain a sub-region runoff water quality change trend map. With time as the horizontal axis, the curves of both were drawn in the same coordinate system, and the synchronization of the changes was analyzed. The trajectories were marked with different colors and the key time nodes were marked to form a map that intuitively shows the change pattern of the sub-region runoff water quality over time.

[0153] Furthermore, a dynamic attribute list of key water quality indicators in the runoff water quality change trend map is extracted, and runoff prediction is performed for the service area based on the dynamic attribute list. When the runoff water quality change trend of the service area is obtained, key water quality indicators are screened from the map, and their dynamic attributes are extracted to form a list. The change trend is integrated in combination with the spatial location and drainage connection method of the sub-area, and the migration and transmission of pollutants are simulated. The comprehensive results are used to obtain the overall change law of runoff water quality in the entire service area over time.

[0154] Specifically, when identifying characteristic turning points on the pollutant concentration curve in the runoff water quality change trend map, observe the curve trend and focus on the positions where the slope changes significantly, such as the starting point where the concentration begins to rise, the inflection point where the rising rate changes, the peak where the maximum value is reached, the inflection point where the falling rate changes, and the end point where it tends to stabilize. Mark these points in the map to clarify the specific location.

[0155] Furthermore, the starting phase, peak phase and duration of the changes in key water quality indicators are determined based on the distribution of characteristic turning points on the time axis. The time scale corresponding to each turning point is checked, the starting time when the concentration begins to rise is set as the starting phase, and the peak time of the highest concentration is set as the peak phase. The time interval from the starting phase to the end point of the stable concentration is calculated as the duration to ensure accurate correspondence with the time axis moment.

[0156] Furthermore, when dividing the change intensity levels according to the quantitative values ​​of the starting phase, peak phase and duration, the time interval from the start to the peak is compared, and combined with the peak concentration value and duration, the change intensity is divided into strong, medium and weak levels. The strong level is characterized by a fast rise, high peak and long duration, the medium level is characterized by a slow rise, medium peak and medium duration, and the weak level is characterized by a slow rise, low peak and short duration.

[0157] Furthermore, when the change phase of the characteristic turning point is temporally and spatially correlated with the change intensity level to obtain a dynamic attribute list, the starting phase and peak phase of each indicator are corresponded to the change intensity level with the key water quality indicators as the unit, and the spatial location of the occurrence is marked. The indicators are classified and organized into a structured list including the indicator name, the time and position of each phase, and the change intensity level.

[0158] Specifically, when obtaining the change intensity levels and key time phases of target pollutants from the dynamic attribute list, the target pollutants that need to be predicted are clearly identified, the corresponding entries are found in the list, the change intensity levels and key time phases are extracted, and the information is ensured to match the target pollutants, providing basic data for subsequent predictions.

[0159] Furthermore, when determining the pollution response time series of the sub-area based on the key time phase, the initial phase is taken as the starting point and the peak phase is taken as the key node. The response end time point is determined in combination with the duration. The time period is divided into continuous time intervals as sequence units in chronological order, and the time range of each unit is clarified to form a time series covering the entire pollution process.

[0160] Furthermore, when calculating the predicted concentration time series value of the target pollutant at the outlet section of the sub-area based on the change intensity level and the pollution response time series, the concentration change rate is determined according to the level. The strong level corresponds to a fast rise, a high peak value, and a slow fall, the medium level corresponds to a medium rate and a peak value, and the weak level corresponds to a slow rise, a low peak value, and a fast fall. The concentration is calculated according to the rate within each time unit, rising from the initial concentration to the peak value and then falling to a stable state. The concentration of each unit is arranged according to time to obtain the predicted concentration time series value.

[0161] Specifically, the pollutant source strength coefficient determined by the changing intensity level comes from the changing intensity level in the dynamic attribute list, where the strong level corresponds to a larger source strength coefficient, the medium level corresponds to a medium source strength coefficient, and the weak level corresponds to a smaller source strength coefficient. This coefficient directly reflects the initial release capacity of the target pollutant at different intensity levels.

[0162] Furthermore, the comprehensive attenuation coefficient determined based on the underlying surface characteristics in the service area comes from a field survey of the underlying surface in the service area. The underlying surface characteristics include road surface material, vegetation coverage, drainage facility type, etc. By analyzing the adsorption, filtration, and degradation effects of these characteristics on pollutants, the attenuation degree of pollutant concentration under different underlying surface conditions is determined, and then the comprehensive attenuation coefficient is obtained.

[0163] Furthermore, the sub-region pollutant transfer time parameters determined based on the pollution response time series come from the pollution response time series, which covers the entire time period from the starting phase to the end of the response. The transfer time parameters are determined based on the time interval for the pollutants to migrate from the sub-region to the outlet section in the sequence, and directly reflect the migration speed of pollutants within the sub-region.

[0164] Furthermore, the calculation process is to obtain the pollutant source strength coefficient determined by the variable intensity level, combined with the calculation based on the comprehensive attenuation coefficient and the sub-region pollutant transport time parameter in the natural exponential function. The predicted concentration time series value of the service area outlet section at a specific moment. This value comprehensively reflects the initial release intensity of pollutants, the degree of attenuation during migration, and the impact of migration time on concentration. It is used to accurately predict the pollutant concentration at the outlet section at different moments.

[0165] Furthermore, when other conditions remain unchanged, the pollutant source intensity coefficient determined by the change intensity level increases. The predicted concentration time series value of the service area outlet section increases; the pollutant source strength coefficient determined by the change intensity level decreases. The predicted concentration time series value of the service area exit section decreases at this moment.

[0166] Furthermore, when other conditions remain unchanged, the comprehensive attenuation coefficient determined based on the underlying surface characteristics in the service area increases, and the result of the natural exponential function decreases. The predicted concentration time series value at the service area exit section decreases; the comprehensive attenuation coefficient determined based on the underlying surface characteristics in the service area decreases, and the result of the natural exponential function increases. The predicted concentration time series value of the service area exit section increases at this moment.

[0167] Furthermore, when other conditions remain unchanged, the sub-region pollutant transfer time parameter determined based on the pollution response time series increases, and the result of the natural exponential function increases. The predicted concentration time series value of the service area outlet section increases; the sub-region pollutant transfer time parameter determined based on the pollution response time series decreases, and the result of the natural exponential function decreases. The predicted concentration time series value of the service area exit section decreases at this moment.

[0168] In general, obtaining a variety of preset rainfall scenario parameters can cover different rainfall characteristics, provide diverse inputs for prediction, and ensure effective prediction of water quality changes under various scenarios.

[0169] In general, by coupling hydrology and pollution with preset rainfall parameters based on the model, the runoff process line and pollutant load flux of the sub-area are obtained. This can combine hydrology and pollution laws, capture the changes in runoff and pollution during rainfall in the sub-area, and provide specific data support.

[0170] In general, a temporal correlation analysis of runoff and pollutant load is conducted to obtain a sub-regional water quality change trend map, which can intuitively display the temporal correlation between the two, present the impact of runoff on pollutant load, and provide a visualization basis.

[0171] In general, extracting a list of dynamic attributes of key water quality indicators and making predictions based on it can focus on core characteristics, integrate sub-regional trends, and achieve comprehensive and accurate predictions of water quality changes under different rainfall scenarios in the service area, providing a reliable basis for subsequent early warning and control.

[0172] In general, identifying characteristic turning points on the pollutant concentration curve can capture key nodes, provide clear time markers for analyzing dynamic changes in water quality, and ensure that key stages are not missed.

[0173] In general, by determining the starting phase, peak phase and duration based on the distribution of characteristic turning points on the time axis, the water quality change stage and duration can be clearly defined, providing accurate time dimension data.

[0174] In general, dividing the intensity of changes according to the quantitative values ​​of the starting phase, peak phase and duration can comprehensively evaluate the strength of changes, achieve scientific classification, and enhance the grasp of the degree of change.

[0175] In general, the dynamic attribute list obtained by temporally and spatially correlating the changing phase with the intensity level can integrate time, intensity and spatial information, provide a comprehensive and accurate dynamic basis for subsequent runoff prediction, and improve the reliability of the prediction.

[0176] In general, obtaining the changing intensity levels and key time phases of target pollutants from the dynamic attribute list can extract the core characteristic parameters, ensure that runoff prediction focuses on the intensity and time nodes of specific pollutants, and avoid parameter generalization bias.

[0177] In general, determining the pollution response time series of the sub-region based on the key time phase can clarify the complete time process of pollution, convert the abstract phase into a continuous time unit, and provide a clear framework for concentration time series simulation, covering the entire cycle of pollution response.

[0178] In general, the predicted concentration time series values ​​calculated based on the change intensity level and pollution response time series can combine the concentration change rate and time unit corresponding to the intensity to dynamically simulate the entire concentration change process, making it more in line with the actual migration law.

[0179] In general, the formula integrates the source strength coefficient, comprehensive attenuation coefficient and transfer time parameters, comprehensively reflects the influence of initial release intensity, attenuation effect and transfer time, covers multiple dimensions, improves the accuracy of concentration prediction, and provides reliable data for grasping the changing trend of runoff water quality.

[0180] The warning signal generating module 104 is configured to generate a pollution warning signal for the service area when the water quality prediction index in the water quality change trend exceeds the pollution warning threshold of the rainfall runoff;

[0181] In an embodiment of the present invention, when the water quality prediction index in the water quality change trend of the flow exceeds the pollution warning threshold of the rainfall runoff, the warning signal generation module is specifically used to:

[0182] Retrieving a warning concentration limit value that matches the target pollutant from a preset pollution warning threshold database;

[0183] Comparing the time series data of the water quality prediction index in the runoff water quality change trend with the warning concentration limit in real time;

[0184] When the water quality prediction index in a specific time period continuously exceeds the warning concentration limit, it is determined that a pollution warning event of a corresponding level occurs in the service area;

[0185] A pollution warning signal for the service area is generated according to the level of the pollution warning event and the specific time period.

[0186] Specifically, when calling the warning concentration limit that matches the target pollutant from the preset pollution warning threshold database, the database stores the warning concentration limit corresponding to each type of pollutant. These limits are set based on relevant standards and the environmental sensitivity of the service area; after the target pollutant is identified, the corresponding limit is retrieved and extracted by name or code to ensure that it matches the target pollutant.

[0187] Furthermore, when comparing the time series data of water quality prediction indicators in the runoff water quality change trend with the warning concentration limit in real time, the water quality prediction indicator values ​​at each moment are extracted from the time series data in chronological order, and compared with the warning concentration limit one by one, and whether the limit is exceeded is recorded to form a comparison result sequence to ensure that the data at each time point are compared.

[0188] Furthermore, when the water quality prediction indicators in a specific time period continue to exceed the warning concentration limit, it is determined that a pollution warning event of the corresponding level has occurred in the service area. The specific time period is the preset duration for judging the duration of the pollution. Check the comparison results. If the values ​​exceed the limit in a certain continuous specific time period, the warning judgment is triggered, and the warning level is determined based on the preset level standards and the exceeding of the limit.

[0189] Furthermore, when a pollution warning signal for a service area is generated based on the level of the pollution warning event and the specific time period, the signal contains information such as the warning level, specific time period, type of target pollutant, and name of the service area. This information is integrated in a preset format to form a pollution warning signal that can trigger subsequent measures.

[0190] In general, matching warning concentration limits are called from the preset database to ensure that the thresholds correspond accurately to the pollutants, avoid misjudgments and missed judgments, and provide scientific judgment standards.

[0191] In general, comparing water quality prediction time series data with warning limits in real time, dynamically tracking changes, and promptly detecting violations lay the foundation for rapid response.

[0192] In general, when the standard is exceeded continuously during a specific period of time, a warning event of the corresponding level is determined to avoid false alarms of instantaneous exceeding of the standard. The level division reflects the degree of pollution and improves the reliability and discrimination of the warning.

[0193] In general, signals are generated based on the warning level and specific time period, including the severity and duration of pollution, to provide a clear basis for subsequent prevention and control and ensure that the measures are targeted.

[0194] The pollution control module 105 is configured to push the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures.

[0195] In an embodiment of the present invention, when the pollution control module pushes the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures, it is specifically configured to:

[0196] According to the warning level and core area in the pollution warning signal, matching the corresponding control measure instruction set in the preset pollution control strategy library;

[0197] Binding the control measure instruction set with the pollution warning signal to obtain a control instruction data packet for the service area;

[0198] Pushing the control instruction data packet to the on-site management terminal corresponding to the core area in real time;

[0199] After receiving and parsing the control instruction data packet, the on-site management terminal starts the pollution control process.

[0200] Specifically, according to the warning level and core area in the pollution warning signal, when matching the corresponding control measure instruction set in the preset pollution control strategy library, the preset strategy library stores the control measure instructions corresponding to different warning levels and core areas, including emergency dredging and other operations; the warning level and core area are extracted from the warning signal, and the corresponding entry is found in the joint search strategy library, and the instruction set containing the operation steps, equipment, and person in charge is extracted to ensure that it matches the warning level and core area.

[0201] Furthermore, the control measure instruction set is bound to the pollution warning signal. When the control instruction data packet of the service area is obtained, the two parts of information are merged into a structured data packet through a data integration tool, with the warning signal as the header and the instruction set as the body. A unique identification number is added to form a control instruction data packet.

[0202] Furthermore, when the control instruction data packet is pushed to the on-site management terminal corresponding to the core area in real time, the pre-assigned on-site management terminal corresponding to the core area queries the terminal identification through the wireless communication network, converts the data packet format and sends it, and receives confirmation feedback to ensure that the push is successful.

[0203] Furthermore, after the on-site management terminal receives and parses the control instruction data packet, when the pollution control process is started, the terminal receives and decodes the data packet, separates the warning signal and instruction set and displays them. The management personnel start the equipment and perform the operation according to the steps, and records the operation status to form a startup record.

[0204] In general, matching the corresponding control measure instruction set according to the warning level and core area can ensure that the control measures accurately correspond to the degree and scope of pollution, avoid insufficient response or waste of resources, and improve the targetedness and effectiveness.

[0205] In general, binding the control measure instruction set and the early warning signal into a control instruction data packet can integrate the early warning information and operation instructions, avoid deviations or omissions caused by information dispersion, and provide a complete basis for rapid response.

[0206] In general, pushing control command data packets to the on-site management terminals corresponding to the core areas in real time can quickly deliver information and instructions, reduce delays, accurately locate terminals, and improve response timeliness and accuracy.

[0207] In general, starting the pollution control process after analysis by the on-site management terminal allows managers to quickly identify the pollution situation and operating steps, ensure standardized implementation of measures, form a closed-loop management, improve execution and traceability, and ensure effective prevention and control.

[0208] Reference Figure 2FIG. 1 is a flow chart of a method for early warning of rainfall runoff pollution according to an embodiment of the present invention. In this embodiment, the method for early warning of rainfall runoff pollution includes:

[0209] S1. Analyze the physical and chemical properties of road sediment samples and rainfall runoff samples in the service area to obtain pollutant concentration data and pollutant composition data of the service area;

[0210] S2. constructing a pollutant migration and transformation model for the service area based on the dynamic changes of pollutants in the pollutant concentration data and the pollutant composition data during the runoff process;

[0211] S3. Predicting the hydrological and hydrodynamic characteristics of the service area based on the pollutant migration and transformation model to obtain the runoff water quality change trend of the service area under different rainfall scenarios;

[0212] S4. When the water quality prediction index in the water quality change trend exceeds the pollution warning threshold of rainfall runoff, generating a pollution warning signal for the service area;

[0213] S5. Push the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures.

[0214] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0215] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A rainfall runoff pollution early warning system, characterized in that: The system includes a sample analysis module, a pollutant model building module, a water quality prediction module, an early warning signal generation module and a pollution control module, wherein: The sample analysis module is used to analyze the physical and chemical properties of road sediment samples and rainfall runoff samples in the service area to obtain pollutant concentration data and pollutant composition data of the service area; The pollutant model building module is used to build a pollutant migration and transformation model for the service area based on the dynamic changes of pollutants in the runoff process in the pollutant concentration data and the pollutant composition data; The water quality prediction module is used to predict the hydrological and hydrodynamic characteristics of the service area based on the pollutant migration and transformation model, and obtain the runoff water quality change trend of the service area under different rainfall scenarios; The warning signal generating module is configured to generate a pollution warning signal for the service area when the water quality prediction index in the water quality change trend exceeds the pollution warning threshold of the rainfall runoff; The pollution control module is used to push the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures.

2. A rainfall runoff pollution early warning system according to claim 1, characterized in that: When the sample analysis module performs physical and chemical property analysis on the road sediment samples and rainfall runoff samples in the service area to obtain the pollutant concentration data and pollutant composition data of the service area, it is specifically used to: Collect dry-season pavement sediment samples and runoff samples from rainfall events at representative locations within the service area; Grinding and sieving the road sediment sample to obtain a test sample of the service area with a standard particle size; Determining pollutant concentration data in the experimental sample; Identify the types of organic pollutants in the runoff sample and obtain pollutant composition data of the runoff sample.

3. A rainfall runoff pollution early warning system according to claim 1, characterized in that: The pollutant model building module, when executing the construction of the pollutant migration and transformation model of the service area based on the dynamic changes of pollutants in the pollutant concentration data and the pollutant composition data during the runoff process, is specifically used to: Extracting initial concentration values ​​and spatiotemporal distribution characteristics of pollutants from the pollutant concentration data; determining the pollution propagation characteristics of the pollutants during runoff according to the pollutant composition data; Establish empirical association rules based on the changes of pollutant concentrations with runoff duration and hydraulic conditions in historical rainfall runoff monitoring data; Building a modular rule base for the service area based on the empirical association rules and the pollution propagation characteristics; The modular rule base and the spatiotemporal distribution characteristics are integrated to generate a pollutant migration and transformation model for the service area.

4. A rainfall runoff pollution early warning system according to claim 3, characterized in that: The pollutant model construction module is specifically used to integrate the modular rule base and the spatiotemporal distribution characteristics to generate the pollutant migration and transformation model of the service area: Matching the spatial data of initial pollutant concentrations in the spatiotemporal distribution characteristics with corresponding migration and transformation rules in the modular rule base; According to the matching result, a behavior mapping relationship set is established with the pollutant type and spatial location in the pollutant concentration data as an index; According to the mapping relationship set, a simulation verification of the dynamic process of pollutants at the global scale is performed on the service area; Based on the verification results of the simulation verification, a structured model expression of pollutants from the road surface to the receiving water body in the service area under different rainfall scenarios is generated, and the structured model expression is used as the pollutant migration and transformation model of the service area.

5. The rainfall runoff pollution early warning system according to claim 1, characterized in that: The water quality prediction module is specifically used to predict the hydrological and hydrodynamic characteristics of the service area based on the pollutant migration and transformation model to obtain the runoff water quality change trend of the service area under different rainfall scenarios: Obtaining multiple preset rainfall scenario parameters for the service area; Inputting the pollutant migration and transformation model to couple the multiple preset rainfall scenario parameters with hydrology and pollution, and obtaining the runoff process line and pollutant load flux of the outlet section of the sub-area within the service area; Performing a time series correlation analysis on the runoff process line and the pollutant load flux to obtain a runoff water quality change trend map of the sub-region; A dynamic attribute list of key water quality indicators in the runoff water quality change trend map is extracted, and runoff prediction is performed on the service area based on the dynamic attribute list to obtain the runoff water quality change trend of the service area.

6. A rainfall runoff pollution early warning system according to claim 5, characterized in that: When extracting the dynamic attribute list of key water quality indicators from the runoff water quality change trend map, the water quality prediction module is specifically used to: Identifying characteristic turning points on a pollutant concentration curve in the runoff water quality change trend map; Determining the starting phase, peak phase, and duration of changes in key water quality indicators in the service area based on the distribution of the characteristic turning points on the time axis; Classifying the change intensity level of the key water quality indicator according to the quantified values ​​of the starting phase, the peak phase, and the duration; The change phase of the characteristic turning point is temporally and spatially correlated with the change intensity level to obtain a dynamic attribute list of the key water quality indicators.

7. A rainfall runoff pollution early warning system according to claim 6, characterized in that: When the water quality prediction module performs runoff prediction on the service area based on the dynamic attribute list to obtain the runoff water quality change trend of the service area, it is specifically used to: Obtaining the change intensity level and key time phase of the target pollutant from the dynamic attribute list; Determining a pollution response time sequence of the sub-region according to the key time phase; Based on the change intensity level and the pollution response time series, the predicted concentration time series value of the target pollutant at the outlet section of the sub-area is calculated, wherein the calculation formula of the predicted concentration time series value is as follows: Where, To express The predicted concentration time series value of the service area outlet section at the time, The pollutant source intensity coefficient determined for the said change intensity level, is the natural exponential function, is the comprehensive attenuation coefficient determined based on the underlying surface characteristics in the service area, It is the pollutant transfer time parameter of the sub-region determined based on the pollution response time series.

8. A rainfall runoff pollution early warning system according to claim 7, characterized in that: The warning signal generation module is specifically configured to generate a pollution warning signal for the service area when the water quality prediction index in the water quality change trend exceeds the pollution warning threshold of the rainfall runoff: Retrieving a warning concentration limit value that matches the target pollutant from a preset pollution warning threshold database; Comparing the time series data of the water quality prediction index in the runoff water quality change trend with the warning concentration limit in real time; When the water quality prediction index in a specific time period continuously exceeds the warning concentration limit, it is determined that a pollution warning event of a corresponding level occurs in the service area; A pollution warning signal for the service area is generated according to the level of the pollution warning event and the specific time period.

9. The rainfall runoff pollution early warning system according to claim 1, characterized in that: When the pollution control module pushes the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures, it is specifically used to: According to the warning level and core area in the pollution warning signal, matching the corresponding control measure instruction set in the preset pollution control strategy library; Binding the control measure instruction set with the pollution warning signal to obtain a control instruction data packet for the service area; Pushing the control instruction data packet to the on-site management terminal corresponding to the core area in real time; After receiving and parsing the control instruction data packet, the on-site management terminal starts the pollution control process.

10. A rainfall runoff pollution early warning method, characterized in that: The method comprises: S1. Analyze the physical and chemical properties of road sediment samples and rainfall runoff samples in the service area to obtain pollutant concentration data and pollutant composition data of the service area; S2. constructing a pollutant migration and transformation model for the service area based on the pollutant concentration data and the dynamic changes of pollutants in the pollutant composition data during runoff; S3. Predicting the hydrological and hydrodynamic characteristics of the service area based on the pollutant migration and transformation model to obtain the runoff water quality change trend of the service area under different rainfall scenarios; S4. When the water quality prediction index in the water quality change trend exceeds the pollution warning threshold of rainfall runoff, generating a pollution warning signal for the service area; S5. Push the pollution warning signal to the management terminal of the service area to trigger corresponding runoff pollution control measures.

Images