Flood season water environment pollution control method

Abstract

The invention discloses a flood season water environment pollution prevention and control method, and relates to the technical field of water pollution prevention and control, and the method comprises the following specific steps: data collection and preprocessing: collecting related data covering hydrology, water quality, land utilization, population distribution and industrial layout from each information department, according to the method, the influence of climate change on flood season water pollution is considered, historical meteorological data of a target area is collected and analyzed, a long-term climate change trend is accurately identified by using a meteorological analysis model, a climate background basis is provided for correlation analysis, and on the basis, a water pollution diffusion prediction model and a hydrological simulation model are combined, so that the flood season water pollution prediction accuracy is improved. The water regimen change characteristics and the water pollution risk degree of the flood season in the future specific prediction period are analyzed, the current flood season water pollution problem can be effectively solved, the adaptability to future climate change challenges can be improved, and a scientific basis is provided for making a long-term water pollution prevention and control strategy.

Description

Technical Field

[0001] The invention relates to the technical field of water pollution prevention and control, and in particular to a method for preventing and controlling water environment pollution during flood season. Background Art

[0002] Water pollution prevention and control has always been an important topic in the field of environmental protection, especially during the flood season. Due to the significant increase in rainfall and changes in water flow conditions, water pollution is particularly prominent. During the flood season, a large amount of rainwater carrying pollutants accumulated on the surface quickly flows into the water body, causing a sharp deterioration in water quality, which not only affects the safety of human drinking water, but also poses a serious threat to aquatic ecosystems. In order to effectively deal with water pollution during the flood season, it is necessary to comprehensively use a variety of technical means for precise prevention and control. With the development of information technology and data science, water pollution prevention and control methods based on big data and intelligent algorithms have gradually become a research hotspot, providing new possibilities for achieving scientific and efficient pollution prevention and control.

[0003] Traditional means of water environment pollution prevention and control have obvious limitations when facing flood season water pollution. On the one hand, these methods are mostly focused on governance during regular periods, and lack response strategies for the special water flow conditions and complex pollution superposition during the flood season. During the flood season, due to the fast water flow speed and high pollutant concentration, traditional methods often find it difficult to quickly and accurately lock in the source of pollution, resulting in the difficulty in effectively controlling the spread of pollution. On the other hand, traditional technologies ignore the long-term impact of climate change on flood season water pollution and lack effective response strategies for complex climate conditions in the future. With global warming, extreme weather events occur frequently, and rainfall and rainfall intensity during the flood season continue to increase, traditional static prevention and control strategies can no longer meet current prevention and control needs.

[0004] In response to the above problems, it is necessary to optimize the existing methods for preventing and controlling water pollution during the flood season. By comprehensively using multi-source data, climate analysis and intelligent prevention and control measures, we can accurately predict water situation changes and water pollution risks during the flood season, build an intelligent source tracing model to quickly locate pollution sources, and formulate targeted prevention and control strategies. Therefore, it is of great significance to develop a method for preventing and controlling water pollution during the flood season that can comprehensively achieve the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a method for preventing and controlling water environment pollution during the flood season. By collecting and preprocessing data from multiple relevant departments, an accurate and reliable data basis is provided for subsequent analysis. Combined with climate change trend analysis, the water situation change characteristics and water pollution risk level in the future flood season are accurately estimated, providing a scientific basis for formulating targeted prevention and control strategies. By constructing an intelligent tracing model, the rapid positioning of pollution sources and accurate simulation of pollutant diffusion paths are achieved, providing technical support for real-time prevention and control. In addition, detailed drainage system optimization, sewage treatment facility adjustment and riverbank buffer zone planning strategies are formulated to cope with the challenges of water pollution during the flood season. Through real-time monitoring and dynamic adjustment mechanisms, the prevention and control system is continuously optimized so that it can adapt to changes in actual conditions in real time and always maintain an effective water pollution prevention and control state.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for preventing and controlling water environment pollution during flood season, the method comprising the following specific steps:

[0007] Data collection and preprocessing: Collect relevant data covering hydrology, water quality, land use, population distribution and industrial layout from various information departments. For special water bodies including the Broken Head River, collect information on the distribution of pollution sources around it, connectivity with surrounding water bodies, and accumulation of pollutants in the dry season. After preprocessing, store it in the data repository;

[0008] Climate change trend and water pollution risk analysis: collect historical meteorological data of the target area, use meteorological analysis models for in-depth analysis, identify the long-term climate change trend of the region, and combine data related to water conditions and water pollution, use hydrological simulation models and water pollution diffusion prediction models for joint analysis, and consider the accumulation of pollutants in the dry season and the pollution impact of the broken river water body, and use formulas to estimate the water flow estimate value and water pollution risk level during the flood season in the future forecast period;

[0009] Intelligent source tracing model construction: Based on the principles of hydrodynamics and the law of water quality change, the intelligent source tracing model is constructed and trained. The comprehensive characteristics of hydrodynamics and water quality at different spatial coordinate positions and at different times are considered to measure the similarity between potential pollution sources and actual pollution events, thereby assisting in locking the pollution source. At the same time, based on the influence of water flow and the probability of pollutant transfer between different locations, the diffusion of pollutants in space over time is simulated by dynamic calculation to determine the diffusion of pollutants;

[0010] Prevention and control strategy formulation: Based on the results of climate change trends and water pollution risk analysis, comprehensive consideration of catchment characteristics, sewage flow distribution and plant community construction factors, targeted optimization strategies are formulated for drainage systems, sewage treatment facilities and riverbank buffer zones;

[0011] Real-time monitoring and dynamic adjustment: Water quality and hydrological monitoring equipment are deployed at rivers, drainage outlets and sewage treatment facilities to collect data and determine whether the water quality is abnormal. If there is an abnormality, prevention and control instructions are issued based on the output of the traceability model. At the same time, data is continuously analyzed, the priority of prevention and control strategy adjustments is evaluated, and the prevention and control strategy and traceability model parameters are dynamically adjusted accordingly.

[0012] Furthermore, in the step of analyzing the climate change trend and water pollution risk, a meteorological analysis model is used to conduct an in-depth analysis to identify the long-term climate change trend in the region. For rainfall, the model formula is: Among them, T rainfall Represents the long-term trend index of rainfall, r i is the actual rainfall observation value in year i, is the average rainfall in the analyzed n-year period, i is the year number, and n is the total number of years selected for analyzing rainfall trends. For the frequency change of extreme weather with heavy rain, the model formula is: Among them, F etreme Represents the comprehensive index of frequency change of extreme rainstorm weather, δ j is the original frequency of extreme rainstorm weather in the jth time period, β j is the adjustment coefficient corresponding to the jth time period, and m is the number of divided time periods.

[0013] Furthermore, in the step of analyzing the climate change trend and water pollution risk, the water flow estimation value and water pollution risk level during the flood season in the future prediction period are estimated by a formula, and the flood season water flow estimation formula is: Q flood =Q base ×(1+α 1 ×T rainfall +α 2 ×F extreme )×γ land , where Q flood is the estimated value of water flow during the flood season in the future prediction period, Q base is the currently known multi-year average flood season water flow benchmark value, α 1 and α 2 are the weight coefficients of the impact of rainfall trend and the frequency change of extreme rainstorm weather on water flow, γ land It is the influencing factor of land use type. For the degree of water pollution risk, its model formula is: Calculate, where R pollution Indicates the comprehensive assessment value of water pollution risk level, C k is the current average concentration of the kth major pollutant, ω k is the weight coefficient of the kth pollutant, λ 1 and λ 2are the correlation coefficients of the impact of rainfall trend and the frequency of extreme rainstorm weather on pollutant concentration, ξ source is the pollution source change influencing factor, ζ s It is a correction factor that takes into account the contribution of pollution to the Broken Head River water body. Its value is determined according to the pollution level of the Broken Head River water body and its connectivity with surrounding water bodies, and its value range is between [0,1].

[0014] Furthermore, in the step of constructing the intelligent source tracing model, the similarity between the potential pollution source and the actual pollution incident is measured, and the similarity formula is: Among them, S source It represents the traceability similarity index between potential pollution sources and target pollution events, H xyt is the comprehensive characteristic value of hydrodynamics and water quality at the spatial coordinate (x, y) and time t, W xyt It is the weight value corresponding to the space-time point (x, y, t), where X and Y are the number of spatial grid points in the horizontal direction of the studied area, corresponding to the geographic coordinate range.

[0015] Furthermore, in the step of constructing the intelligent tracing model, the diffusion path of pollutants in space over time is simulated by dynamic calculation, and the calculation formula is: Among them, P diffusion (x, y, t+1) represents the probability value of the pollutant diffusing to the location at the spatial coordinate (x, y) at time t+1, P diffusion (xi, yj, t) is the probability at the previous moment that the pollutant located at the spatial coordinate (xi, yj) at time t diffuses to the current position, M ij is the transition probability matrix element from position (xi, yj) to position (x, y), θ flow is the water flow influence factor, and I and J represent the offset ranges of adjacent positions considered in the horizontal and vertical directions, respectively.

[0016] Furthermore, in the step of formulating the prevention and control strategy, targeted optimization strategies are formulated for the drainage system, sewage treatment facilities, and riverbank buffer zones. In terms of the formulation of the drainage system optimization strategy, the layout and scale expansion of the rainwater storage tank are planned based on the changes in water flow obtained from the climate change trend and water pollution risk analysis, taking into account the peak rainwater flow in the catchment area, emptying time, importance weight, effective volume of the storage tank, and loss rate factors. For the areas surrounding the Duantou River, the number and scale of rainwater storage tanks are increased to enable them to collect and treat sewage discharged from the Duantou River during the flood season. In terms of the formulation of the sewage treatment facility adjustment strategy, at the sewage treatment plant An intelligent flow allocation device is configured at the water inlet end to dynamically allocate sewage flow based on the pollutant treatment efficiency of each treatment unit, the upper limit of treatment flow, the adaptability of inlet water quality and the total inlet flow. The site and scale of the new sewage treatment plant are determined through spatial analysis, sewage volume prediction and sewage treatment process simulation to adapt to future sewage volume. For the formulation of riverbank buffer zone planning strategies, the flood inundation simulation analysis software based on hydrodynamic principles is used in combination with relevant climate and risk analysis results to obtain flood inundation range data, and the widening width is determined in combination with the existing riverbank buffer zone conditions. The plant community construction is optimized by comprehensively considering plant interception efficiency, planting density, area ratio and plant synergy factors.

[0017] Furthermore, in the step of formulating the prevention and control strategy, in terms of the formulation of the drainage system optimization strategy, the layout and scale of the rainwater storage tank are planned by comprehensively considering the peak rainwater flow rate in the catchment area, the emptying time, the importance weight, the effective volume of the storage tank and the loss rate factors. The planning formula is: Among them, D pool represents the reasonable layout density index of rainwater storage tanks, Q s is the peak flow rate of the sth catchment area under the design storm return period, τ s is the rainwater emptying time standard of the sth catchment area, ρ s is the importance weight coefficient of the sth catchment area, V p It is the designed effective volume of a single rainwater storage tank. is the loss rate in the process of rainwater storage, S is the total number of catchment areas divided in the study area, and P is the total number of planned rainwater storage tanks.

[0018] Furthermore, in the prevention and control strategy formulation step, in terms of the formulation of the sewage treatment facility adjustment strategy, the sewage flow is dynamically allocated based on the pollutant treatment efficiency of each treatment unit, the upper limit of the treatment flow, the adaptability of the influent water quality and the total influent flow. The dynamic allocation formula is: Among them, F allocation (i) represents the sewage flow rate allocated to the i-th sewage treatment unit, C i is the treatment efficiency coefficient of the i-th sewage treatment unit for specific key pollutants, Qi is the upper limit of the design treatment flow of the i-th sewage treatment unit, η i is the adaptability coefficient of the ith sewage treatment unit based on the current influent water quality, J is the total number of sewage treatment units in the sewage treatment plant, Q total It is the total inflow flow monitored in real time by the sewage treatment plant.

[0019] Furthermore, in the step of formulating the prevention and control strategy, the widening width is determined in combination with the existing buffer zone of the river bank, and the plant community construction is optimized by comprehensively considering the plant interception efficiency, planting density, area ratio and plant synergy factors. The optimization formula is: Among them, E buffer Represents the comprehensive index of ecological function of plant communities in riparian buffer zones, α l is the interception efficiency coefficient per unit area of ​​the first plant for the main pollutants, N l is the planting density of the lth plant in the riparian buffer zone, A l is the proportion of the planting area of ​​the lth plant in the riparian buffer zone, β l is the synergy coefficient between the lth plant and other plants, and L is the number of plant species selected in the plant community of the riparian buffer zone.

[0020] Furthermore, in the real-time monitoring and dynamic adjustment step, water quality and hydrological monitoring equipment are deployed at the river, drainage outlet and sewage treatment facility to collect data and determine whether the water quality is abnormal through a formula, and the judgment formula is: Among them, A abnormal Indicates the degree of abnormal water quality. is the measured concentration value of the mth water quality monitoring indicator at the current time t, is the reference standard concentration value corresponding to the mth water quality monitoring indicator, ω m is the weight coefficient of the mth water quality monitoring index in abnormal judgment, θ tolerance is the abnormal tolerance factor, and M is the number of monitoring indicator types involved in the determination of water quality abnormalities.

[0021] Compared with the prior art, this method for preventing and controlling water pollution during flood season has the following beneficial effects:

[0022] 1. The present invention considers the impact of climate change on water pollution during the flood season, collects and analyzes historical meteorological data of the target area, and uses meteorological analysis models to accurately identify long-term climate change trends, providing a climate background basis for correlation analysis. On this basis, combined with the hydrological simulation model and the water pollution diffusion prediction model, the water situation change characteristics and water pollution risk level during the flood season in the future specific prediction period are analyzed, which can not only effectively respond to the current flood season water pollution problem, but also enhance the ability to adapt to future climate change challenges, and provide a scientific basis for the formulation of long-term water pollution prevention and control strategies.

[0023] 2. The present invention can achieve accurate prediction and rapid response to water pollution during the flood season by comprehensively using multi-source data collection and preprocessing, climate change trend and water pollution risk analysis, and intelligent source tracing model construction and training methods. The intelligent source tracing model can quickly trace possible sources of pollution and accurately simulate the diffusion path and impact range of pollutants. At the same time, real-time monitoring and dynamic adjustment steps ensure all-round and real-time monitoring of water conditions and water quality. Once abnormal water quality is found, targeted prevention and control instructions can be immediately generated and issued to achieve timely positioning and effective blocking of pollution sources.

[0024] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 It is a process operation diagram of a method for preventing and controlling water environment pollution during flood season;

[0027] Figure 2 It is a flow chart of a method for preventing and controlling water environment pollution during flood season;

[0028] Figure 3 This is a flow chart of the water environment pollution prevention and control methods for Duantou River during the flood season. DETAILED DESCRIPTION

[0029] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0030] Embodiment 1

[0031] A river in a certain city runs through the urban area, surrounded by residential areas, commercial areas and industrial clusters. During the flood season, the river water level rises and the water quality often deteriorates, causing adverse effects on the surrounding ecological environment and residents' lives.

[0032] By cooperating with the local water conservancy department, we obtained detailed hydrological data such as water level, flow rate, and flow rate during the flood season of the inland river in the past 20 years. We collected water quality data such as concentration changes of various pollutants (such as chemical oxygen demand, ammonia nitrogen, total phosphorus, etc.) and pH from the environmental monitoring department during the same period. We applied to the land and resources department for land use type data in the surrounding areas to clarify which areas are construction land, farmland, green space, etc. We obtained population distribution data from the statistics department to understand the population density around different river sections. We obtained industrial layout data through the industry and information technology department, and knew the distribution of industrial enterprises near the river and production scale and other information. Using data preprocessing tools, we first cleaned all the collected data, such as removing abnormal water level and flow rate data and duplicated data points caused by equipment failure, and then standardized the data according to a unified format and specification, such as unifying the units of pollutant concentration. Finally, we aggregated and stored the processed data in a specially built urban inland river data repository.

[0033] Collect the city's meteorological data for the past 30 years, focusing on rainfall records and the occurrence of extreme rainstorms. i is the actual rainfall observation value in the i-th year (the rainfall data of the corresponding year is obtained from the 30-year meteorological data collected), is the average rainfall during this 30-year period (calculated by dividing the total rainfall for 30 years by 30), i is the year number (ranging from 1 to 30), n = 30 (the total number of years selected for analyzing rainfall trends), and the formula is: Calculate the long-term trend index of rainfall T rainfall , it is calculated that T rainfall >0, indicating that rainfall is on the rise. This trend has an important impact on the subsequent analysis of water flow and water pollution risks. The past 30 years are divided into 5-year time periods, that is, m = 6 (the number of divided time periods). For the jth time period (j ranges from 1 to 6), δ j is the original frequency of rainstorm extreme weather in this period (obtained by statistically analyzing historical meteorological data according to established rainstorm judgment criteria, such as when the rainfall per unit time reaches a specific threshold), β jis the adjustment coefficient corresponding to the jth time period (taking into account factors such as possible errors in early observations, the corresponding correction coefficient is assigned through historical comparative analysis, and the value range is [-1, 1]). Using the formula Calculate the comprehensive index F of frequency change of rainstorm extreme weather extreme The calculation results show that F extreme The value has increased, which means that the frequency of rainstorm extreme weather is increasing. Relevant water conditions and water pollution data are extracted from the data repository. Combined with the above climate change trends, hydrological simulation and water pollution diffusion prediction model are used to estimate that the water flow of inland rivers will increase significantly in the next 10 years during the flood season, and the risk of water pollution will also increase accordingly. Especially after heavy rain, the possibility of exceeding the concentration of pollutants such as chemical oxygen demand and ammonia nitrogen increases, mainly concentrated in the lower reaches of the river and the river sections close to industrial clusters. base is the currently known multi-year average flood season water flow benchmark value (obtained through statistical analysis of historical measured water flow data), α 1 and α 2 are the weight coefficients of the impact of rainfall trend and the change of extreme weather frequency of rainstorm on water flow (determined by regression analysis of different historical rainfall and rainstorm frequency change stages and water flow data of the same period, and the value range is [0, 1]), γ land is the land use type influencing factor (determined based on the differences in surface runoff coefficients of different land use types, such as urban construction land, farmland, and forest land, and the specific value is obtained through field observation, experiments, and related model simulation comparative analysis, and the value is in the range of [0, 1]). Formula Q flood =Q buse ×(1+α 1 ×T rainfall +α 2 ×F extreme )×γ land Estimated water flow Q of inland rivers during flood season in the next 10 years flood , Q is calculated flood Compared with the current situation, there is a significant increase, which provides a key basis for the formulation of prevention and control strategies such as drainage systems. k is the current average concentration value of the kth major pollutant (such as chemical oxygen demand, ammonia nitrogen, etc.) (the concentration data of the corresponding pollutants are obtained from historical water quality monitoring data), ω k is the weight coefficient of the kth pollutant (determined based on factors such as the degree of harm to the water environment and the persistence of the pollutant in the environment. Its relative importance weight is determined through expert evaluation, environmental impact assessment methods, etc. The sum of all pollutant weight coefficients is 1), λ 1 and λ 2are the correlation coefficients of the impact of rainfall trends and changes in the frequency of extreme rainstorm weather on pollutant concentrations (the values ​​are determined by analyzing the changes in pollutant concentrations under different meteorological conditions in history and using statistical analysis and other methods), ξ source is the influencing factor of pollution source changes (taking into account the increase or decrease in the number of industrial, agricultural, and domestic pollution sources in the surrounding area, changes in emission intensity, etc., and quantified by comparing relevant data of pollution sources in different periods), ζ s It is a correction factor that takes into account the contribution of water pollution in the Broken Head River. Its value is determined according to the pollution level of the Broken Head River and the connectivity factors with surrounding water bodies. Its value range is between [0,1]. The comprehensive assessment value R of the water pollution risk level is calculated. pollution The results show that R pollution The increase indicates that the risk of water pollution in the future flood season will increase, and we need to pay special attention to the downstream and industrially concentrated river sections.

[0034] Based on the principle of hydrodynamics and the law of water quality change, an intelligent source tracing model is constructed. The model is trained using rich historical data in the data repository. During the training process, the model parameters are continuously adjusted so that it can accurately learn the propagation characteristics of pollutants under different water flow speeds and different pollution emission intensities, as well as the correlation between pollutants and surrounding environmental factors. For example, when an abnormal increase in pollutant concentration is detected in a certain place in the river, the model can quickly lock possible pollution sources based on real-time water flow, surrounding land use, etc., such as the sewage outlet of a factory upstream or the area near a residential area with serious surface source pollution, and simulate the diffusion path and approximate impact range of pollutants. Suppose H xyt It is the comprehensive characteristic value of hydrodynamics and water quality at the spatial coordinate (x, y) and time t (calculated by weighted combination of multiple factors such as water flow velocity, flow direction, water level and concentration of various pollutants at the location and time according to certain rules), W xyt is the weight value of the corresponding time and space point (x, y, t) (determined through historical data analysis, expert experience, etc., taking into account factors such as data reliability in different regions and at different times, and differences in importance to pollution source tracing, for example, the weight near the mainstream of the river and near the time of pollution occurrence is relatively high), X and Y are the number of spatial grid points divided horizontally in the studied area (urban rivers and a certain range around them) (determined according to the size of the area and the accuracy requirements), T is the length of the time series analyzed (covering the time period before and after the pollution event and possible related time periods), and the formula is used. Calculate the traceability similarity index S between the potential pollution source and the target pollution event source , by comparing S at different locations sourceThe value is used to assist in determining the location of the pollution source. The higher the value, the more likely it is that the pollution source is located. At the same time, based on the influence of water flow and the probability of pollutant transfer between different locations, the diffusion path of pollutants in space over time is simulated through dynamic calculation to determine the diffusion of pollutants. Let P diffusion (x, y, t+1) represents the probability value P of the pollutant diffusing to the location at the spatial coordinate (x, y) at time t+1. diffusion (xi, yj, t) is the probability at the previous moment that the pollutant located at the spatial coordinate (xi, yj) at time t diffuses to the current position, M ij is the element of the transfer probability matrix from position (xi, yj) to position (x, y) (taking into account the influence of water flow direction, flow velocity, topography, obstacles and other factors on pollutant diffusion, the probability of pollutant transfer between different positions is determined by hydrodynamic model simulation, field observation statistics and other methods, and its value is in the interval [0, 1]), θ flow is the water flow influencing factor (determined according to real-time water flow velocity, flow rate and other hydrodynamic parameters. Water flow plays a key role in promoting the diffusion of pollutants. The stronger the water flow, the larger the factor. The value range is determined according to the actual hydrodynamic conditions, generally in the interval [0, 1]). I and J represent the offset range of adjacent positions considered in the horizontal and vertical directions, respectively (for example, I = 3, J = 3 means considering the diffusion of the positions in the nine-square grid around the current position, determined according to the simulation accuracy and regional characteristics). The formula is used. Dynamically calculate the pollutant diffusion probability at each location at different times to simulate the diffusion path of pollutants over time and provide a basis for prevention and control decisions.

[0035] According to the above analysis results, prevention and control strategies are formulated, including drainage system optimization strategy formulation, sewage treatment facility adjustment strategy and riverbank buffer zone planning strategy. For drainage system optimization strategy formulation, according to the change of water flow obtained from climate change trend and water pollution risk analysis, the layout and scale expansion of rainwater storage tanks are planned by comprehensively considering the peak value of rainwater flow in the catchment area, emptying time, importance weight, effective volume of storage tanks and loss rate factors. s is the peak value of rainwater flow in the sth catchment area (the area surrounding the urban inland river system is divided into multiple catchment areas according to the drainage and confluence characteristics) under the designed rainstorm return period (calculated by analyzing the runoff generation and confluence of the catchment area through hydrological methods combined with regional topography, land use and other factors), τ s is the rainwater emptying time standard of the sth catchment area (taking into account factors such as avoiding water accumulation affecting traffic and environment, and according to regional functional requirements, such as different emptying time requirements for commercial areas and residential areas, the maximum time allowed for rainwater to stay in the storage tank for each catchment area is determined), ρ sis the importance weight coefficient of the sth catchment area (determined according to the population density, distribution of important infrastructure, etc. in the catchment area. The weight of the area with dense population or important facilities is higher. It is assigned through comprehensive evaluation and the value range is [0, 1]), V p It is the designed effective volume of a single rainwater storage tank (determined by the type of storage tank, construction standards, etc.). is the loss rate in the process of rainwater storage (taking into account the loss of rainwater in the storage tank due to evaporation and leakage, and determining its proportion of the total storage capacity through experimental tests and similar engineering experience, with a value range of [0, 1]), S is the total number of catchment areas divided in the study area, and P is the total number of planned rainwater storage tanks. Using the formula Calculate the reasonable layout density index D of the rainwater storage tank pool , According to the calculation results, rainwater storage tanks are reasonably arranged in the waterlogged areas around the urban inland river system and near the main drainage channels. For example, a number of large-capacity rainwater storage tanks are planned to be newly built downstream of some major drainage outlets. At the same time, the scale of the existing rainwater storage tanks is appropriately expanded to cope with the increased rainfall. For the formulation of riverbank buffer zone planning strategies, flood inundation simulation analysis software is used, combined with the simulation results of flood inundation range under different rainstorm intensities, to formulate a riverbank buffer zone planning scheme. On the basis of the existing riverbank buffer zone, a certain width is widened to the land side, such as 5-10 meters on both sides of the river. At the same time, plants with good waterlogging resistance and water purification capabilities such as calamus and canna are selected to construct a multi-layer composite plant community, and their planting density and layout are reasonably planned to enhance the interception and purification function of the riverbank buffer zone for pollutants carried by floods, and reduce the amount of pollutants entering the inland river during the flood period. Set α l is the unit area interception efficiency coefficient of the first plant (such as calamus, canna, etc.) for major pollutants (such as nitrogen, phosphorus, etc.) (the interception ratio of the plant for different pollutants per unit area is determined by laboratory simulation experiments, field planting observations, etc., and the value range is [0, 1]), N l is the planting density of the first plant in the riverbank buffer zone (the number of plants planted per square meter is determined based on plant growth characteristics, landscape design requirements, etc.), A l is the planting area ratio of the lth plant in the riverbank buffer zone (the area ratio of each plant in the entire buffer zone is determined through planning and design, the value range is [0, 1], and the sum of the area ratios of all plants is 1), β lis the synergistic coefficient between the lth plant and other plants (considering the mutual influence between plants in the ecosystem and the synergistic purification of pollutants, determined through ecological research, field comparative analysis, etc., and the value range is [0, 1]), L is the number of plant species selected in the plant community of the riparian buffer zone (determined based on the selected plant species suitable for local growth and with good ecological functions), and the formula is used. Calculation of the comprehensive ecological function index E of the plant community in the riparian buffer zone buffer By adjusting the parameters such as plant planting density and area ratio, the plant community construction is optimized to make E buffer The value should be increased as much as possible to strengthen the ecological function of the riverbank buffer zone.

[0036] Water quality monitoring sensors and hydrological monitoring equipment are deployed at key river nodes, drainage outlets, and inlets and outlets of sewage treatment facilities in inland rivers. The collected water quality (such as chemical oxygen demand, ammonia nitrogen, total phosphorus concentration, pH, etc.) and water condition (water level, flow rate, etc.) data are analyzed and judged using the intelligent traceability model, and the formula is used to To make real-time judgment on water quality abnormalities, is the measured concentration value of the mth water quality monitoring indicator (such as chemical oxygen demand, ammonia nitrogen, total phosphorus, etc.) at the current time t (obtained in real time by water quality monitoring sensors deployed at key locations), is the reference standard concentration value corresponding to the mth water quality monitoring indicator (determined according to the relevant national water environment quality standards, regional water quality targets, etc.), ω m is the weight coefficient of the mth water quality monitoring index in abnormal judgment (according to the importance of different indicators on water environment quality, the degree of harm to ecology and human health, etc., the weight of each indicator is comprehensively determined through expert evaluation, environmental impact assessment and other methods, and the sum of all indicator weight coefficients is 1), θ tolerance is the abnormal tolerance factor (taking into account the existence of certain errors in monitoring data and the dynamic changes of the environment itself, its value range is determined to be in the interval [0, 1] through statistical analysis of historical data and environmental background research), M is the number of monitoring indicators involved in water quality anomaly determination (covering various water quality parameters of focus), when A abnormalWhen the calculated result exceeds the set threshold, it is determined that the water quality is abnormal, thereby triggering subsequent corresponding prevention and control actions. When abnormal changes in water quality are found, such as the chemical oxygen demand concentration in a certain river section suddenly exceeds the set threshold, the prevention and control instructions are immediately issued based on the pollution source and diffusion path information output by the model, and arrangements are made for the investigation of suspected pollution source areas. Temporary pollution interception measures are initiated (such as setting up temporary pollution interception nets in the corresponding river sections, etc.), and the operating parameters of the sewage treatment plant are adjusted at the same time (such as increasing the amount of treatment agents), and real-time data is continuously compared with the preset prevention and control targets and early risk assessment data. If differences are found, the prevention and control strategies (such as the operation and scheduling of rainwater storage tanks, the allocation of treatment capacity of sewage treatment plants, etc.) and the relevant parameters of the intelligent traceability model are dynamically adjusted in a timely manner to ensure the effectiveness of water pollution prevention and control throughout the flood season.

[0037] Embodiment 2

[0038] In a certain city, there are many dead-end rivers and streams, which are surrounded by residential areas, small factories and farmlands. During the dry season, the water flow in the dead-end rivers and streams is slow, the self-purification ability of the water body is poor, a large amount of pollutants accumulate, and the water quality deteriorates. During the flood season, as the rainfall increases, the sewage accumulated in the dead-end rivers and streams will flow into the surrounding rivers, seriously affecting the surrounding water environment quality and threatening the safety of residents' drinking water and the stability of the ecosystem.

[0039] like Figure 3 As shown, first, we cooperated with the water conservancy department to collect hydrological data such as the water level, flow rate, and flow of Duantou River, and at the same time obtained relevant hydrological data of surrounding rivers in flood season and non-flood season. We obtained water quality data of Duantou River and surrounding water bodies from the environmental protection department, including the concentration of pollutants such as chemical oxygen demand, ammonia nitrogen, and total phosphorus. We learned about the land use type around Duantou River from the land and resources department, obtained surrounding population distribution data from the statistics department, and communicated with the industry and information technology department to master the industrial layout and sewage discharge information of surrounding factories. In addition, we arranged professional personnel to measure the water area and depth of Duantou River on site, record the specific location and type of surrounding pollution sources, investigate their connectivity with surrounding water bodies and the degree of pollutant accumulation in the dry season, use data processing tools to clean the collected data, remove abnormal data and duplicate data caused by equipment failure, standardize data according to a unified format, and store the processed data in a special data repository.

[0040] The city's meteorological data for the past 30 years were collected, and meteorological analysis models were used to analyze the changing trends of rainfall, rainstorms and extreme weather. The water conditions and water pollution data of Duantou River and surrounding water bodies were extracted from the data repository. Combined with the trend of climate change, the hydrological simulation model and the water pollution diffusion prediction model were used for joint analysis. Taking into account the accumulation of pollutants in Duantou River during the dry season, as well as the amount of pollutants that may be released during the flood season due to water level changes and water flow connectivity, the water condition changes and water pollution risk during the flood season were estimated. The flood season water flow estimation formula is: Q flood =Q base ×(1+α 1 ×T rainfall +α 2 ×F extreme )×γ land , where Q flood is the estimated value of water flow during the flood season in the future prediction period, Q base is the currently known multi-year average flood season water flow benchmark value, α 1 and α 2 are the weight coefficients of the impact of rainfall trend and the frequency change of extreme rainstorm weather on water flow, γ land It is the influencing factor of land use type. For the degree of water pollution risk, its model formula is: Calculate, where R pollution Indicates the comprehensive assessment value of water pollution risk level, C k is the current average concentration of the kth major pollutant, ω k is the weight coefficient of the kth pollutant, λ 1 and λ 2 are the correlation coefficients of the impact of rainfall trend and the frequency of extreme rainstorm weather on pollutant concentration, ξ source is the pollution source change influencing factor, ξ s It is a correction factor that takes into account the contribution of Broken Head River to water pollution. Its value is determined according to the pollution level of Broken Head River and its connectivity with surrounding water bodies. Its value range is between [0,1]. The analysis results show that with the increase of rainfall and the frequency of rainstorms, the pollution risk of Broken Head River to surrounding rivers during the flood season increases significantly, especially the concentrations of pollutants such as chemical oxygen demand and ammonia nitrogen may exceed the standard significantly.

[0041] An intelligent tracing model is constructed based on the principles of hydrodynamics and the laws of water quality changes. During the model construction process, the unique water flow characteristics, pollutant release laws and hydraulic connections with surrounding water bodies of Broken Head River are fully considered. The model is trained using historical data in the data repository to enable the model to learn the propagation characteristics of pollutants under different working conditions. When abnormal water quality in surrounding rivers is monitored, the model can combine real-time water flow, water level, water quality data and relevant information of Broken Head River to quickly identify whether the pollution source comes from Broken Head River and its specific location, and simulate the path and impact range of pollutants spreading from Broken Head River to surrounding rivers through dynamic calculation.

[0042] According to the results of climate change trends and water pollution risk analysis, and taking into account the large amount of sewage discharge that may be generated by Duantou River during the flood season, multiple rainwater storage tanks are planned and built around it. The layout and scale of the storage tanks are determined based on factors such as the peak rainwater flow rate, emptying time, importance weight, and effective volume and loss rate of the storage tanks in the surrounding catchment areas. An intelligent flow allocation device is installed at the water inlet of the nearby sewage treatment plant. The sewage flow is dynamically allocated according to the treatment efficiency, treatment flow upper limit, inlet water quality adaptability, and total inlet flow of each treatment unit. Combined with sewage volume prediction and water pollution risk assessment, and considering the sewage discharge of Duantou River, it is determined through spatial analysis and sewage treatment process simulation to build a small new one near Duantou River. A type of sewage treatment plant is built to specially treat sewage from Duantou River and its surrounding areas, improve sewage treatment capacity, and ensure that sewage is discharged in compliance with standards. At the same time, flood inundation simulation analysis software is used, combined with relevant climate and risk analysis results, to determine the flood inundation range data of the riverbank buffer zone around Duantou River. According to the existing buffer zone situation, the width of the buffer zone is widened to the land side, and plants with strong pollutant interception capabilities such as calamus and reed are selected and planted according to reasonable planting density and layout to optimize the construction of plant communities. An ecological isolation zone is set up at the junction of Duantou River and surrounding rivers, and plants that are water-resistant and have strong purification capabilities are planted to enhance the interception and purification functions of the riverbank buffer zone for pollutants carried by floods, and reduce the amount of pollutants entering the surrounding rivers.

[0043] Water quality monitoring sensors and hydrological monitoring equipment are deployed at key locations such as Duantou River, surrounding rivers, drainage outlets and sewage treatment facilities to collect water quality (such as chemical oxygen demand, ammonia nitrogen, total phosphorus concentration, etc.) and water conditions (water level, flow rate, etc.) data in real time and transmit them to the intelligent management and control system. The intelligent management and control system uses the set rules to determine whether the water quality is abnormal. When the water quality is abnormal, the control order is quickly issued according to the output results of the traceability model. For example, when it is detected that the chemical oxygen demand concentration in the surrounding rivers exceeds the standard and the traceability result points to Duantou River, personnel are immediately arranged to investigate the pollution sources around Duantou River and initiate temporary sewage interception measures, such as setting up a temporary trash rack at the outlet of Duantou River. At the same time, the operating parameters of the sewage treatment plant are adjusted, the amount of treatment agents added is increased, and the sewage treatment capacity is improved. The intelligent management and control system continuously analyzes the monitoring data, evaluates the effectiveness of the prevention and control strategy, and dynamically adjusts the prevention and control strategy and traceability model parameters according to the actual situation. For example, the operation and scheduling of the rainwater storage tank is dynamically adjusted according to the water level changes of Duantou River to ensure the effectiveness of the prevention and control work.

[0044] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for preventing and controlling water environment pollution during flood season, characterized in that: The method comprises the following specific steps: Data collection and preprocessing: Collect relevant data covering hydrology, water quality, land use, population distribution and industrial layout from various information departments. For special water bodies including the Broken Head River, collect information on the distribution of pollution sources around it, connectivity with surrounding water bodies, and accumulation of pollutants in the dry season. After preprocessing, store it in the data repository; Climate change trend and water pollution risk analysis: collect historical meteorological data of the target area, use meteorological analysis models for in-depth analysis, identify the long-term climate change trend of the region, and combine data related to water conditions and water pollution, use hydrological simulation models and water pollution diffusion prediction models for joint analysis, and consider the accumulation of pollutants in the dry season and the pollution impact of the broken river water body, and use formulas to estimate the water flow estimate value and water pollution risk level during the flood season in the future forecast period; Intelligent source tracing model construction: Based on the principles of hydrodynamics and the law of water quality change, the intelligent source tracing model is constructed and trained. The comprehensive characteristics of hydrodynamics and water quality at different spatial coordinate positions and at different times are considered to measure the similarity between potential pollution sources and actual pollution events, thereby assisting in locking the pollution source. At the same time, based on the influence of water flow and the probability of pollutant transfer between different locations, the diffusion of pollutants in space over time is simulated by dynamic calculation to determine the diffusion of pollutants; Prevention and control strategy formulation: Based on the results of climate change trends and water pollution risk analysis, comprehensive consideration of catchment characteristics, sewage flow distribution and plant community construction factors, targeted optimization strategies are formulated for drainage systems, sewage treatment facilities and riverbank buffer zones; Real-time monitoring and dynamic adjustment: Water quality and hydrological monitoring equipment are deployed at rivers, drainage outlets and sewage treatment facilities to collect data and determine whether the water quality is abnormal. If there is an abnormality, prevention and control instructions are issued based on the output of the traceability model. At the same time, data is continuously analyzed, the priority of prevention and control strategy adjustments is evaluated, and the prevention and control strategy and traceability model parameters are dynamically adjusted accordingly.

2. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the step of analyzing the climate change trend and water pollution risk, the meteorological analysis model is used to conduct in-depth analysis to identify the long-term climate change trend in the region. For rainfall, the model formula is: Among them, T rainfall Represents the long-term trend index of rainfall, r i is the actual rainfall observation value in year i, is the average rainfall in the analyzed n-year period, i is the year number, and n is the total number of years selected for analyzing rainfall trends. For the frequency change of extreme weather with heavy rain, the model formula is: Among them, F extreme Represents the comprehensive index of frequency change of extreme rainstorm weather, δ j is the original frequency of extreme rainstorm weather in the jth time period, β j is the adjustment coefficient corresponding to the jth time period, and m is the number of divided time periods.

3. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the step of analyzing climate change trend and water pollution risk, the water flow estimation value and water pollution risk level during the flood season in the future prediction period are estimated by formula. The flood season water flow estimation formula is: Q flood =Q base ×(1+α1×T rainfall +α2×F extreme )×γ land , where Q flood is the estimated value of water flow during the flood season in the future prediction period, Q base is the currently known multi-year average flood season water flow benchmark value, α1 and α2 are the weight coefficients of the impact of rainfall trend and the change of extreme weather frequency of rainstorm on water flow, γ land It is the influencing factor of land use type. For the degree of water pollution risk, its model formula is: Calculate, where R pollution Indicates the comprehensive assessment value of water pollution risk level, C k is the current average concentration of the kth major pollutant, ω k is the weight coefficient of the kth pollutant, λ1 and λ2 are the correlation coefficients of the impact of rainfall trend and the frequency change of extreme rainstorm weather on pollutant concentration, ξ source is the pollution source change influencing factor, ζ s It is a correction factor that takes into account the contribution of the pollution of the Broken Head River water body. Its value is determined according to the pollution level of the Broken Head River water body and the connectivity factors with the surrounding water bodies, and its value range is between [0, 1].

4. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the step of constructing the intelligent source tracing model, the similarity between the potential pollution source and the actual pollution incident is measured, and the similarity formula is: Among them, S source It represents the traceability similarity index between potential pollution sources and target pollution events, H xyt is the comprehensive characteristic value of hydrodynamics and water quality at the spatial coordinate (x, y) and time t, W xyt It is the weight value corresponding to the space-time point (x, y, t), where X and Y are the number of spatial grid points in the horizontal direction of the studied area, corresponding to the geographic coordinate range.

5. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the step of constructing the intelligent tracing model, the diffusion of pollutants is determined by dynamically calculating and simulating the diffusion path of pollutants in space over time. The calculation formula is: Among them, P diffusion (x, y, t+1) represents the probability value of the pollutant diffusing to the location at the spatial coordinate (x, y) at time t+1, P diffusion (xi, yj, t) is the probability at the previous moment that the pollutant located at the spatial coordinate (xi, yj) at time t diffuses to the current position, M ij is the transition probability matrix element from position (xi, yj) to position (x, y), θ flow is the water flow influence factor, and I and J represent the offset ranges of adjacent positions considered in the horizontal and vertical directions, respectively.

6. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the steps of formulating the prevention and control strategies, targeted optimization strategies are formulated for the drainage system, sewage treatment facilities, and riverbank buffer zones. In terms of the formulation of drainage system optimization strategies, the layout and scale expansion of rainwater storage tanks are planned based on the changes in water flow obtained from climate change trends and water pollution risk analysis, taking into account the peak rainwater flow in the catchment area, emptying time, importance weight, effective volume of the storage tank, and loss rate factors. For the areas surrounding the Duantou River, the number and scale of rainwater storage tanks are increased to enable them to collect and treat sewage discharged from the Duantou River during the flood season. In terms of the formulation of sewage treatment facility adjustment strategies, at the water inlet end of the sewage treatment plant, An intelligent flow allocation device is configured to dynamically allocate sewage flow based on the pollutant treatment efficiency of each treatment unit, the upper limit of treatment flow, the adaptability of influent water quality and the total influent flow. The site selection and scale of the new sewage treatment plant are determined through spatial analysis, sewage volume prediction and sewage treatment process simulation to adapt to future sewage volume. For the formulation of riverbank buffer zone planning strategies, the flood inundation simulation analysis software based on hydrodynamic principles is used in combination with relevant climate and risk analysis results to obtain flood inundation range data, and the widening width is determined in combination with the existing riverbank buffer zone conditions. The plant community construction is optimized by comprehensively considering plant interception efficiency, planting density, area ratio and plant synergy factors.

7. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the prevention and control strategy formulation step, for the drainage system optimization strategy formulation, the layout and scale of the rainwater storage tank are planned by comprehensively considering the peak rainwater flow rate in the catchment area, emptying time, importance weight, effective volume of the storage tank and loss rate factors. The planning formula is: Among them, D pool represents the reasonable layout density index of rainwater storage tanks, Q s is the peak flow rate of the sth catchment area under the design storm return period, τ s is the rainwater emptying time standard of the sth catchment area, ρ s is the importance weight coefficient of the sth catchment area, V p It is the designed effective volume of a single rainwater storage tank. is the loss rate in the process of rainwater storage, S is the total number of catchment areas divided in the study area, and P is the total number of planned rainwater storage tanks.

8. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the prevention and control strategy formulation step, for the formulation of the sewage treatment facility adjustment strategy, the sewage flow is dynamically allocated based on the pollutant treatment efficiency of each treatment unit, the upper limit of the treatment flow, the adaptability of the influent water quality and the total influent flow. The dynamic allocation formula is: Among them, F allocation (i) represents the sewage flow rate allocated to the i-th sewage treatment unit, C i is the treatment efficiency coefficient of the i-th sewage treatment unit for specific key pollutants, Q i is the upper limit of the design treatment flow of the i-th sewage treatment unit, η i is the adaptability coefficient of the ith sewage treatment unit based on the current influent water quality, J is the total number of sewage treatment units in the sewage treatment plant, Q total It is the total inflow flow monitored in real time by the sewage treatment plant.

9. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the step of formulating the prevention and control strategy, the widening width is determined in combination with the existing buffer zone of the river bank, and the plant community construction is optimized by comprehensively considering the plant interception efficiency, planting density, area ratio and plant synergy factors. The optimization formula is: Among them, E buffer It represents the comprehensive index of ecological function of plant community in riparian buffer zone, α l is the interception efficiency coefficient per unit area of ​​the first plant for the main pollutants, N l is the planting density of the lth plant in the riparian buffer zone, A l is the proportion of the planting area of ​​the lth plant in the riparian buffer zone, β l is the synergy coefficient between the lth plant and other plants, and L is the number of plant species selected in the plant community of the riparian buffer zone.

10. A method for preventing and controlling water environment pollution during flood season according to claim 1, characterized in that: In the real-time monitoring and dynamic adjustment step, water quality and hydrological monitoring equipment are deployed at the river, drainage outlet and sewage treatment facility to collect data, and the water quality is judged to be abnormal through the formula. The judgment formula is: Among them, A a b normal Indicates the degree of abnormal water quality. is the measured concentration value of the mth water quality monitoring indicator at the current time t, is the reference standard concentration value corresponding to the mth water quality monitoring indicator, ω m is the weight coefficient of the mth water quality monitoring index in abnormal judgment, θ tolerance is the abnormal tolerance factor, and M is the number of monitoring indicator types involved in the determination of water quality abnormalities.

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