Flood season water environment quality evaluation system based on big data analysis
By designing a flood water environment quality evaluation system that integrates multi-source data and adopts advanced algorithms and models, the shortcomings of the existing systems in data collection, processing, and simulation prediction are solved, and accurate evaluation and prediction of the flood water environment quality is achieved, providing strong support for water resource management and environmental protection.
Patent Information
- Application Number
- CN202510278434.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-10
- Publication Date
- 2025-06-27
AI Technical Summary
The existing water environment quality evaluation system based on big data analysis has shortcomings in data collection, processing and modeling, and it is difficult to fully reflect the true status of water environment quality during the flood season. The simulation and prediction capabilities are limited, so it is impossible to accurately simulate water flow movement and pollutant diffusion during the flood season.
A flood water environment quality evaluation system based on big data analysis was designed. By integrating hydrological and water quality sensor data, multi-source heterogeneous data interface, Hadoop distributed file system, data fusion algorithm, water environment quality prediction model and evaluation model, accurate evaluation and prediction of water environment quality during flood water, and simulate water flow movement and pollutant diffusion under different scenarios.
It significantly improves the accuracy and timeliness of water environment quality evaluation during flood season, provides reliable data support for water resource management and environmental protection, and can provide decision makers with rich decision support information and reduce the impact of pollution incidents on the water environment.
Smart Images
Figure CN120218718A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of water environment monitoring and evaluation, and particularly to a flood season water environment quality evaluation system based on big data analysis. Background Art
[0002] In the fields of environmental protection and water resources management, the evaluation of water environment quality has always been an important research direction. With the continuous development of big data and Internet of Things technologies, people have begun to explore using these advanced technologies for more efficient and accurate evaluation of water environment quality. Especially during the flood season, the quality of the water environment is jointly affected by multiple factors, such as rainfall intensity, flood scale, and pollution emissions, making the traditional methods for evaluating water environment quality face huge challenges. Therefore, a water environment quality evaluation system that can comprehensively consider multiple factors and achieve real-time monitoring and prediction is particularly important.
[0003] Although the existing water environment quality evaluation systems based on big data analysis have achieved certain results, there are still some deficiencies. First of all, traditional systems often rely on a single data source for data collection, which leads to insufficient data comprehensiveness and makes it difficult to fully reflect the true situation of water environment quality. Secondly, traditional systems may be too simplified in data processing and modeling, failing to fully consider the complexity and variability of water environment quality during the flood season, resulting in the accuracy of evaluation results needing to be improved. In addition, traditional systems also have limitations in simulation and prediction. They often cannot accurately simulate the processes of water flow movement and pollutant diffusion during the flood season, nor can they provide effective decision-making support. These problems limit the application effect of traditional systems in the evaluation of water environment quality during the flood season.
[0004] Therefore, developing a flood season water environment quality evaluation system based on big data analysis will greatly improve the accuracy and efficiency of flood season water environment quality evaluation and provide strong support for water resources management and environmental protection. Summary of the Invention
[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide a flood season water environment quality evaluation system based on big data analysis. This system has been comprehensively optimized and innovated in data collection, processing, modeling, simulation, and prediction. By integrating hydrological and water quality sensor data and docking multi-source heterogeneous data from environmental protection, water conservancy, and meteorological departments, the system can comprehensively and accurately reflect the true situation of water environment quality during the flood season. At the same time, the system adopts advanced algorithms and models, such as the Hadoop distributed file system, data fusion algorithms, water environment quality prediction models, and evaluation models, to achieve precise evaluation and prediction of water environment quality during the flood season. In addition, the system can also simulate the processes of water flow movement and pollutant diffusion under different scenarios and present the simulation results in a visual way, providing rich decision-making support information for decision-makers.
[0006] To solve the above technical problems, the present invention provides the following technical solution: A flood season water environment quality evaluation system based on big data analysis, which includes a water data transmission and collection module, a data processing and modeling module, a flood season water environment simulation module, and an evaluation and decision-making support module;
[0007] The water data transmission and collection module: Real-time collects water environment-related data through hydrological and water quality sensors, interfaces with environmental protection, water conservancy, and meteorological departments to obtain multi-source heterogeneous data of sewage outlets, hydrology, and meteorology, and transmits the collected data to the data processing and modeling module by means of a Mesh network topology structure;
[0008] The data processing and modeling module: Builds a fusion storage architecture based on the Hadoop Distributed File System (HDFS) and a distributed database, establishes a cold and hot data hierarchical storage mechanism based on time series characteristics, stores high-frequency access data in the distributed database, archives low-frequency historical data to HDFS, preprocesses and standardizes the collected data, fuses the standardized data through a data fusion algorithm, extracts the characteristics of the fused data, and constructs a water environment quality prediction model and an evaluation model according to the extracted characteristics;
[0009] The flood season water environment simulation module: Couples a hydrological and hydrodynamic model with a pollutant migration and diffusion model, constructs different flood season water environment quality scenarios, based on the constructed scenarios, simulates the water flow movement, pollutant diffusion, and water environment quality change processes under different scenarios, and presents the simulation results in a visual way;
[0010] The evaluation and decision-making support module: Establishes a comprehensive evaluation model to conduct real-time evaluation by combining real-time monitoring data and scenario simulation analysis results, establishes a hierarchical early warning mechanism, starts SMS notifications when the evaluation index exceeds the level I early warning threshold, synchronously triggers an emergency dispatching plan when it exceeds the level II early warning threshold, notifies relevant personnel by means of a warning window popping up on the system interface and sending SMS, and provides decision-making suggestions for pollution source control, water resource scheduling, and ecological restoration for decision-makers.
[0011] Further, the hydrological and water quality sensors used in the water data transmission and collection module are turbidity sensors, dissolved oxygen sensors, pH value sensors, chemical oxygen demand (COD) sensors, water level sensors, flow velocity sensors, and flow sensors.
[0012] Furthermore, in the data processing and modeling module, the standardized data is fused through a data fusion algorithm. Let the water level data collected by the sensor be H, the flow velocity data be V, the flow rate data be Q, the precipitation data be R, the wind speed data be W, the wind direction data be represented by the angular value θ, the sewage discharge flow rate of the sewage outlet be Q p , and the pollutant concentration be C p , and the supplementary water level data of the hydrological station be H s, the flow rate data is Q s , the fused data is F, and the calculation formula is: , where ω1 - ω7 are the weight coefficients corresponding to each data respectively, and the value range is between 0 and 1, and satisfies The weights are dynamically adjusted by the analytic hierarchy process according to different monitoring purposes, data importance, and data accuracy factors.
[0013] Furthermore, for the construction of the water environment quality prediction model in the data processing and modeling module, let the water environment quality prediction value be WQ pred , the water environment quality prediction model WQ pred The calculation formula is: where is calculated by analyzing the changes in water environment quality indicators in the past n days, t represents the predicted time span, and k S is the seasonal adjustment coefficient, which is used to adjust the potential impact of different seasons on the water environment quality, and is determined by fitting the historical seasonal fluctuations with Fourier series, and satisfies
[0014] Furthermore, for the construction of the water environment quality evaluation model in the data processing and modeling module, let the standard threshold of the water environment quality be WQ standard , and the environmental carrying capacity coefficient K of different monitoring areas, with the value range between 0.5 - 1.5, is determined according to the regional ecological environment status and water body self-purification ability factors, and a water environment quality evaluation model is constructed, and the formula is: where WQ is the comprehensive evaluation value of the water environment quality. When WQ ≤ 1, it indicates that the water environment quality is within the acceptable range, and the closer the value is to 0, the better the water quality; when WQ > 1, it indicates that the water environment quality exceeds the standard threshold, and there are pollution or other problems.
[0015] Furthermore, in the flood season water environment simulation module, different flood season water environment quality scenarios are set through the flood season water environment quality comprehensive index. Let the comprehensive scenario index be WQSI, the rainfall intensity be RI, the flood scale be FS, the pollution emission source intensity be PES, the topographic and geomorphic impact factor be TMF, and the water environment quality prediction value be WQ pred , the water environment quality evaluation value is WQ, and the calculation formula is:
[0016] Furthermore, in the flood season water environment simulation module, the water flow movement under different scenarios is simulated through the scenario constructed by the flood season water environment quality comprehensive index. Let the water flow velocity be v, and the water flow velocity formula is: v = v base ×(1 + WQSI × RIF × FSF × TMFC), where WQSI is the flood season water environment comprehensive scenario index, RIF is the rainfall intensity impact factor, RI is the current rainfall intensity, RI max is the historical maximum rainfall intensity in this area, FSF is the flood scale impact factor, FS is the current flood scale, FS max is the historical maximum flood scale in this area, TMFC is the terrain and landform impact correction coefficient, TMFC = 1 + 0.5×TMF, TMF is the terrain and landform impact factor, v base is the basic water flow velocity.
[0017] Furthermore, in the flood season water environment simulation module, based on the constructed scenarios, the pollutant diffusion coefficient is used to simulate the pollutant diffusion under different scenarios. Let the pollutant diffusion coefficient be D, and the calculation formula is: D = D base ×(1 + WQSI×PES 0.5 ×RIF×FSF×TMFC), where WQSI is the comprehensive water environment quality index in the flood season, PES is the pollution emission source intensity, RIF is the rainfall intensity impact factor, RI is the current rainfall intensity, RI max is the historical maximum rainfall intensity in this area, FSF is the flood scale impact factor, FS is the current flood scale, FS max is the historical maximum flood scale in this area, TMFC is the terrain and landform impact correction coefficient, TMFC = 1 + 0.5×TMF, TMF is the terrain and landform impact factor, D base is the basic pollutant diffusion coefficient.
[0018] Furthermore, different flood season water environment quality scenarios are constructed in the flood season water environment simulation module to simulate the water environment quality change process under different scenarios. Let the water environment quality change value be ΔWQ, the initial water environment quality value be WQ0, the comprehensive water environment quality index in the flood season be WQSI, the time be t, and the environmental self-purification coefficient be k. The calculation formula is: ΔWQ = WQ0×(1 + WQSI×e -kt ).
[0019] Furthermore, the evaluation and decision support module conducts real-time evaluation by fusing real-time monitoring data and scenario simulation analysis results through a real-time evaluation index. Let the real-time evaluation index be REI, the initial real-time evaluation value be REI0, the water environment quality change value be ΔWQ, the pollutant diffusion coefficient be D, the water flow velocity be v, the time weight be α, the space weight be β, and 0 < α < 1, 0 < β < 1, α + β = 1. The calculation formula is: REI = REI0 + α×ΔWQ + β×(D×v). Let R low and R mid be the pre-set level I warning threshold and level II warning threshold, determined according to the evaluation index. REI < R low means no risk; Rlow ≤REI < R mid is the warning threshold of level I; REI ≥ R mid is the warning threshold of level II.
[0020] Compared with the prior art, the flood season water environment quality evaluation system based on big data analysis has the following beneficial effects:
[0021] First, by integrating multi-source data and constructing complex models, the present invention realizes the accurate evaluation of the flood season water environment quality. The system uses a variety of sensors to collect real-time water environment parameters such as turbidity, dissolved oxygen, pH value, chemical oxygen demand, water level, flow velocity and flow rate. At the same time, it interfaces with environmental protection, water conservancy and meteorological departments to obtain multi-source heterogeneous data of sewage outfalls, hydrology and meteorology. These data are fused and stored and processed by the Hadoop distributed file system and distributed database, and an accurate water environment quality prediction model and evaluation model can be constructed. This big data fusion and processing technology significantly improves the accuracy and timeliness of the flood season water environment quality evaluation, provides reliable data support for relevant departments, and helps them make scientific and reasonable decisions in a timely manner.
[0022] Second, by simulating the water flow movement, pollutant diffusion and water environment quality change processes under different scenarios, the present invention realizes the visual prediction and evaluation of the flood season water environment quality. The system can comprehensively consider various factors affecting the flood season water environment quality, construct different flood season water environment quality scenarios, and under these scenarios, the system can simulate the water flow movement and pollutant diffusion processes and present the simulation results in a visual way. This not only enables relevant personnel to intuitively understand the change trend of the flood season water environment, but also provides them with rich decision-making support information, and provides suggestions for pollution source control and water resource scheduling for decision-makers, so as to effectively reduce the impact of pollution events on the water environment.
[0023] Other advantages, objectives and features of the present invention will be described to some extent in the subsequent description, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.
[0025] Figure 1 is a flowchart of a flood season water environment quality evaluation system based on big data analysis;
[0026] Figure 2 It is a process framework diagram of a flood season water environment quality evaluation system based on big data analysis. Specific implementation manners
[0027] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and their effects of the present invention as follows.
[0028] Example 1:
[0029] Water environment quality evaluation of urban inland rivers during the flood season
[0030] The water data transmission module sets a sensor node every 200 meters along the urban inland river and its tributaries according to factors such as the bending degree of the river course, the area of changing water flow velocity, and the distribution of surrounding pollution sources. Each sensor node integrates hydrological and water quality sensors, and the sensor nodes communicate with each other through a Mesh network topology structure to ensure that data can be stably transmitted to the data processing and modeling module. At the same time, it is docked with the online monitoring system of the sewage outfall of the environmental protection department to obtain the location information, discharge flow, types and concentrations of pollutants of industrial pollution sources and domestic sewage outfalls in real time; it is connected to the hydrological monitoring network of the water conservancy department to supplement the extensive hydrological station data in the region, including the water level and flow change trends of the upstream and downstream of the river, and the influence information of water conservancy project facilities on the water flow; it conducts data interaction with the meteorological data platform of the meteorological department to obtain high-resolution weather forecasts, historical meteorological data backtracking, and meteorological disaster warning information.
[0031] The data processing and modeling module builds a fusion storage architecture based on the Hadoop distributed file system and distributed database to receive data, classifies and stores the received data according to the access frequency and time characteristics of the data, preprocesses and standardizes the collected data synchronously, and fuses the relevant data through a data fusion algorithm. Let the water level sensor data be H, the water flow velocity sensor data be V, the water flow sensor data be Q, the precipitation data in the meteorological sensor be R, the wind speed data be W, the wind direction data be represented by the angle value θ, the discharge flow of the sewage outfall be Q p , and the pollutant concentration be C p , the supplementary water level data of the hydrological station be H s , the flow data be Q s , the fused data be F, and the calculation formula is: , where ω1 - ω7 are the weight coefficients corresponding to each data respectively, and the value range is between 0 and 1, and satisfies The weights are dynamically adjusted through the analytic hierarchy process according to different monitoring purposes, data importance, and data accuracy factors, and the characteristics of the fused data are extracted. Based on the extracted characteristics, a water environment quality prediction model and a water environment quality evaluation model are established. For the establishment of the water environment quality prediction model, let the water environment quality prediction value be WQ pred , the water environment quality prediction model WQ pred The calculation formula is: Where is calculated by analyzing the changes in water environment quality indicators in the past n days, t represents the prediction time span, and k S is the seasonal adjustment coefficient, which is used to adjust the potential impact of different seasons on the water environment quality. It is determined by fitting the historical seasonal fluctuations through Fourier series and satisfies For the establishment of the water environment quality evaluation model, let the standard threshold of the water environment quality be WQ standard , and the environmental carrying capacity coefficient K of different monitoring areas, whose value range is between 0.5 - 1.5, is determined according to the ecological environment status and water body self-purification ability factors of the region. A water environment quality evaluation model is constructed, and the formula is: Where WQ is the comprehensive evaluation value of the water environment quality. When WQ ≤ 1, it indicates that the water environment quality is within the acceptable range, and the closer the value is to 0, the better the water quality; when WQ > 1, it indicates that the water environment quality exceeds the standard threshold, and there are pollution or other problems. For the simulation of the flood season water environment module, the rainfall intensity RI, flood scale FS, pollution emission source intensity PES, topographic and geomorphic impact factor TMF of the urban inland river, as well as the water environment quality prediction value WQ pred and the evaluation value WQ are comprehensively considered. The comprehensive index of the water environment quality in the flood season WQSI is calculated through the formula: Based on this index, the water flow movement under different scenarios is simulated through the comprehensive index of the water environment quality in the flood season. The formula is: v = v base ×(1 + WQSI × RIF × FSF × TMFC), and the pollutant diffusion is simulated. Let the basic pollutant diffusion coefficient be D base , and the pollutant diffusion under different scenarios is simulated through the pollutant diffusion coefficient based on the constructed scenario: D = D base ×(1 + WQSI × PES 0.5 × RIF × FSF × TMFC), and the change process of the water environment quality under different scenarios is simulated through the change value of the water environment quality based on the constructed scenario. The formula is: ΔWQ = WQ0 × (1 + WQSI × e -kt ).
[0032] The evaluation and decision support module evaluates and makes decisions. Set the initial real-time evaluation value REI0. According to the calculated water environment quality change value ΔWQ, pollutant diffusion coefficient D, water flow velocity v, with a time weight of α and a space weight of β, the calculation formula is: REI = REI0 + α×ΔWQ + β×(D×v). After calculation, if REI ≥ R mid is the level II warning threshold, a warning window will pop up on the system interface, displaying "The water environment quality in the river basin during the flood season is abnormal. Please pay attention in time!", and at the same time, send a text message notification to the relevant personnel of the basin management department and issue an audible alarm. Based on the simulation and evaluation results, the system provides suggestions for decision-makers. For example, for areas with a relatively high intensity of pollution emission sources, it is recommended to strengthen the supervision and treatment of the sewage discharge of surrounding factories and limit their sewage discharge during the flood season; for the water flow velocity and pollutant diffusion situation, it is recommended to reasonably dispatch water resources, such as regulating the water flow through water conservancy facilities to prevent excessive diffusion of pollutants and affect the downstream ecological protection area.
[0033] In summary, in the evaluation of the water environment during the flood season of urban inland rivers, the water data transmission module collects and transmits data by reasonably arranging points according to the river conditions. The data processing and modeling module uses a distributed architecture to process data and build models. The flood season water environment simulation module comprehensively simulates the water situation changes with multiple factors. The evaluation and decision support module calculates an index based on the results. When the threshold is exceeded, it alarms and provides suggestions for pollution source control, water resource allocation, and ecological restoration. Each module operates in coordination to create a complete, scientific, and efficient system for the management of the water environment during the flood season of urban inland rivers, effectively maintaining the ecological stability of urban inland rivers and the rational utilization of water resources.
[0034] Example 2:
[0035] Evaluation of the water environment quality during the flood season of mountain reservoirs
[0036] The water data transmission module arranges sensor nodes around the mountain reservoir, near the reservoir dam, in the center of the reservoir, and at the entrances of the main tributaries. Set one node every 250 meters on the incoming rivers, 10 nodes near the dam, 6 nodes in the center of the reservoir, and 4 nodes at each tributary entrance. The sensor nodes are equipped with hydrological and water quality sensors and are also connected to the environmental protection, water conservancy, and meteorological departments to obtain relevant data. The specific connection method is the same as that in the urban inland river example. The collected data is transmitted to the data processing and modeling module through the Mesh network topology structure.
[0037] Data processing and modeling module, which builds a fusion storage architecture based on the Hadoop distributed file system and distributed database, classifies and stores data according to the source of data (such as different monitoring areas, different sensor types) and time. For example, the monitoring data of the incoming rivers is stored in a specific HDFS directory and hierarchically stored according to date and time for subsequent analysis. At the same time, preprocess and standardize the water quality index data, and fuse the relevant data through a data fusion algorithm. Let the water level sensor data be H, the flow velocity sensor data be V, the flow rate sensor data be Q, the precipitation data in the meteorological sensor be R, the wind speed data be W, the wind direction data be represented by the angular value θ, the discharge flow rate of the sewage outlet be Q p , and the pollutant concentration be C p , and the supplementary water level data of the hydrological station be H s , and the flow rate data be Q s , and the fused data be F, and the calculation formula is:
[0038] , and extract the characteristics of the fused data. According to the extracted characteristics, build a water environment quality prediction model and a water environment quality evaluation model. For the construction of the water environment quality prediction model, let the water environment quality prediction value be WQ pred , the water environment quality prediction model WQ pred The calculation formula is: For the construction of the water environment quality evaluation model, let the standard threshold of the water environment quality be WQ standard , and the environmental carrying capacity coefficient K of different monitoring areas, whose value range is between 0.5 - 1.5, is determined according to the ecological environment status and water body self-purification ability factors of the region, and build a water environment quality evaluation model, and the formula is:
[0039] Analysis of the flood season water environment simulation module, considering the rainfall intensity RI in the area where the mountain reservoir is located (obtained through real-time data of the mountain meteorological station), the flood scale FS (calculated according to the reservoir water level change, incoming flow monitoring and hydrological model), the pollution emission source intensity PES (mainly considering the surrounding agricultural non-point source pollution and a small amount of domestic sewage discharge, estimated through the investigation of agricultural activities and resident distribution in the basin), the topographic and geomorphic influence factor TMF (determined by analyzing the reservoir surrounding terrain, slope and catchment area characteristics using geographic information system), and the previously calculated water environment quality prediction value WQ pred and the evaluation value WQ, and calculate the comprehensive index of water environment quality in the flood season WQSI through the formula, and the formula is: Based on WQSI, simulate the water flow movement, pollutant diffusion and water environment quality change process in the reservoir. For example, through the constructed scenarios based on the comprehensive index of water environment quality in the flood season, simulate the water flow movement under different scenarios, and the formula is: v = v base×(1 + WQSI × RIF × FSF × TMFC), simulate the pollutant diffusion, and set the basic pollutant diffusion coefficient D base , based on the constructed scenarios through the pollutant diffusion coefficient, simulate the pollutant diffusion under different scenarios: D = D base ×(1 + WQSI × PES 0.5 ×RIF × FSF × TMFC), based on the constructed scenarios through the change value of water environment quality, simulate the change process of water environment quality under different scenarios. The formula is: ΔWQ = WQ0 × (1 + WQSI × e -kt ).
[0040] Evaluate the response of the decision - making support module, calculate the real - time evaluation index REI, set the initial value REI0, according to the calculated change value of water environment quality ΔWQ, pollutant diffusion coefficient D, water flow velocity v, and the set time weight α and space weight β, calculate the real - time evaluation index REI through the formula. The formula is: REI = REI0+α×ΔWQ + β×(D×v). After calculation, when REI≥R mid is the level - II warning threshold, the system immediately sends alarm information to the reservoir management department, local environmental protection department and downstream water - using units. A warning window pops up on the system interface, showing "An emergency has occurred in the water environment of the mountain reservoir during the flood season. Please take countermeasures in time!" The SMS notification details the water quality changes of the reservoir, possible risks and recommended actions. At the same time, start the sound alarm system to ensure that relevant personnel can receive the notice in time and provide suggestions for decision - makers. For example, for the surrounding agricultural non - point source pollution, it is recommended to strengthen the management of farmland during the flood season, implement ecological interception measures, reduce the use of pesticides and fertilizers, and set up an ecological buffer zone at the inflowing rivers to filter pollutants. For the changes in water flow and water quality in the reservoir, it is recommended to reasonably regulate the flood discharge and water storage of the reservoir, optimize the water flow conditions on the premise of ensuring the safety of the dam, promote the dilution and diffusion of pollutants, and at the same time strengthen the monitoring frequency of the reservoir water quality, timely master the dynamic changes of water quality, and put ecological restoration agents when necessary to improve the water environment quality of the reservoir.
[0041] To sum up, for the evaluation of the water environment of mountain reservoirs during the flood season, the intelligent sensing network collects water quality data at key points around the reservoir and transmits it efficiently. The data - processing and modeling module analyzes the data and establishes a model. The flood - season water - environment simulation module simulates the water situation combined with the characteristics of mountain areas. The evaluation and decision - making support module evaluates and alarms multiple departments in case of anomalies and gives suggestions on agricultural pollution control, reservoir scheduling optimization and ecological restoration. The whole system effectively improves the management efficiency of the water environment of mountain reservoirs during the flood season and ensures the ecological safety of the reservoir and the water use safety of downstream areas.
[0042] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.
Claims
1. A flood season water environment quality evaluation system based on big data analysis, characterized in that: The system includes a water data transmission module, a data processing modeling module, a flood season water environment simulation module and an assessment decision support module; The water data collection and transmission module collects water environment related data in real time through hydrological and water quality sensors, connects with environmental protection, water conservancy and meteorological departments to obtain multi-source heterogeneous data of sewage outlets, hydrology and meteorology, and transmits the collected data to the data processing modeling module with the help of Mesh network topology structure; The data processing modeling module: builds a fusion storage architecture based on the Hadoop distributed file system HDFS and distributed database, establishes a tiered storage mechanism for hot and cold data based on time series characteristics, stores high-frequency access data in the distributed database, archives low-frequency historical data to HDFS, and pre-processes and standardizes the collected data, fuses the standardized data through a data fusion algorithm, extracts fused data features, and builds a water environment quality prediction model and evaluation model based on the extracted features; The flood season water environment simulation module: couples the hydrological and hydrodynamic model with the pollutant migration and diffusion model to construct different flood season water environment quality scenarios, simulates the water flow movement, pollutant diffusion and water environment quality change process under different scenarios based on the constructed scenarios, and presents the simulation results in a visual manner; The assessment decision support module: establishes a comprehensive assessment model that combines real-time monitoring data with scenario simulation analysis results for real-time assessment, establishes a graded early warning mechanism, initiates SMS notification when the assessment index exceeds the Level I warning threshold, and simultaneously triggers the emergency dispatch plan when it exceeds the Level II warning threshold, notifies relevant personnel by popping up an alert window on the system interface and sending SMS messages, and provides decision-making recommendations for pollution source control, water resource scheduling, and ecological restoration to decision makers.
2. According to claim 1, a flood season water environment quality evaluation system based on big data analysis is characterized in that: The hydrological and water quality sensors used in the water data transmission module are turbidity sensors, dissolved oxygen sensors, pH sensors, chemical oxygen demand (COD) sensors, water level sensors, flow rate sensors and flow sensors.
3. A flood season water environment quality evaluation system based on big data analysis according to claim 1, characterized in that: The data processing modeling module fuses the standardized data through the data fusion algorithm. Suppose the water level data collected by the sensor is H, the flow velocity data is V, the flow data is Q, the precipitation data is R, the wind speed data is W, the wind direction data is represented by the angle value θ, and the discharge flow of the sewage outlet is Q p , the pollutant concentration is C p , the water level data supplemented by the hydrological station is H s , the flow data is Q s , the fused data is F, and the calculation formula is: , where ω1-ω7 are weight coefficients corresponding to each data, ranging from 0 to 1, and satisfying The weights are dynamically adjusted through the analytic hierarchy process according to different monitoring purposes, data importance and data accuracy factors.
4. A flood season water environment quality evaluation system based on big data analysis according to claim 1, characterized in that: The water environment quality prediction model in the data processing modeling module is constructed, and the water environment quality prediction value is set to WQ pred , water environment quality prediction model WQ pred The calculation formula is: in It is calculated by analyzing the changes in water environment quality indicators in the past n days. t represents the predicted time span, k S is the seasonal adjustment coefficient, which is used to adjust the potential impact of different seasons on water environment quality. It is determined by fitting the historical seasonal fluctuations through Fourier series and satisfies 5. The flood season water environment quality evaluation system based on big data analysis according to claim 1 is characterized in that: The water environment quality evaluation model in the data processing modeling module is constructed, and the standard threshold value WQ of water environment quality is set. standard , and the environmental carrying capacity coefficient K of different monitoring areas, with a value range of 0.5-1.5, is determined according to the regional ecological environment conditions and water body self-purification capacity factors to construct a water environment quality evaluation model. The formula is: WQ is the comprehensive evaluation value of water environment quality. When WQ≤1, it means that the water environment quality is within an acceptable range. The closer the value is to 0, the better the water quality. When WQ>1, it indicates that the water environment quality exceeds the standard threshold and there is pollution or other problems.
6. The flood season water environment quality evaluation system based on big data analysis according to claim 1 is characterized in that: In the flood season water environment simulation module, different flood season water environment quality scenarios are implemented through the comprehensive index of flood season water environment quality. The comprehensive scenario index is WQSI, the rainfall intensity is RI, the flood scale is FS, the pollution emission source intensity is PES, the topographic influence factor is TMF, and the water environment quality prediction value is WQ pred , the water environment quality evaluation value is WQ, and the calculation formula is:
7. The flood season water environment quality evaluation system based on big data analysis according to claim 1 is characterized in that: The flood season water environment simulation module uses the scenario constructed by the comprehensive index of flood season water environment quality to simulate the water flow movement under different scenarios. Assuming the water flow velocity is v, the water flow velocity formula is: v = v base ×(1+WQSI×RIF×FSF×TMFC), where WQSI is the comprehensive scenario index of water environment during flood season, and RIF is the rainfall intensity influencing factor. RI is the current rainfall intensity, RI max is the maximum rainfall intensity in the history of the region, FSF is the flood scale influencing factor, FS is the current flood scale, TS is max is the largest flood scale in the history of the region, TMFC is the correction coefficient of topographic influence, TMFC=1+0.5×TMF, TMF is the topographic influence factor, v base The basic water velocity.
8. The flood season water environment quality evaluation system based on big data analysis according to claim 1 is characterized in that: In the flood season water environment simulation module, the pollutant diffusion coefficient is used based on the constructed scenario to simulate the diffusion of pollutants under different scenarios. Assuming the pollutant diffusion coefficient is D, the calculation formula is: D = D base ×(1+WQSI×PES 0.5 ×RIF×FSF×TMFC), where WQSI is the comprehensive index of water environment quality during the flood season, PES is the intensity of pollution emission sources, and RIF is the rainfall intensity influencing factor. RI is the current rainfall intensity, RI max is the maximum rainfall intensity in the history of the region, FSF is the flood scale influencing factor, FS is the current flood scale, FS max is the largest flood scale in the history of the region, TMFC is the correction coefficient of topographic influence, TMFC=1+0.5×TMF, TMF is the topographic influence factor, D base is the diffusion coefficient of the basic pollutant.
9. The flood season water environment quality evaluation system based on big data analysis according to claim 1 is characterized in that: In the flood season water environment simulation module, different flood season water environment quality scenarios are constructed to simulate the water environment quality change process under different scenarios. The water environment quality change value is ΔWQ, the initial water environment quality value is WQ0, the flood season water environment quality comprehensive index is WQSI, the time is t, and the environmental self-purification coefficient is k. The calculation formula is: ΔWQ=WQ0×(1+WQSI×e -kt ).
10. The flood season water environment quality evaluation system based on big data analysis according to claim 1 is characterized in that: The evaluation decision support module performs real-time evaluation by fusing real-time monitoring data with scenario simulation analysis results through real-time evaluation index. Assume that the real-time evaluation index is REI, the initial real-time evaluation value is REI0, the water environment quality change value is ΔWQ, the pollutant diffusion coefficient is D, the water flow velocity is v, the time weight is α, the space weight is β, and 0<α<1, 0<β<1, α+β=1. The calculation formula is: REI=REl0+α×ΔWQ+β×(D×v), Assume R low and R mid It is the pre-set level I warning threshold and level II warning threshold, which are determined according to the evaluation index. REI<R low is risk-free; R low ≤REI<R mid is the level Ⅰ warning threshold; REI ≥ R mid It is the Level II warning threshold.