Flood season water environment quality monitoring system based on wireless communication

Abstract

The invention discloses a flood season water environment quality monitoring system based on wireless communication, and relates to the technical field of water environment monitoring, the system comprises the following components: a data acquisition and transmission module, an intelligent early warning module, a traceability analysis module and a decision support module; the flood season water environment quality monitoring system integrates a data acquisition and transmission module, an intelligent early warning module, a traceability analysis module and a decision support module, realizes comprehensive and real-time monitoring of flood season water environment quality, and can rapidly capture water quality changes and timely send early warning information by means of a high-precision (high-resolution) water quality monitoring sensor and an advanced wireless communication technology, thereby realizing the purpose of flood season water environment quality monitoring. And the pollution source is accurately traced, and scientific treatment decisions are provided for related departments, so that the water environment quality monitoring efficiency and accuracy are greatly improved, effective prevention and control measures can be taken in time, water quality deterioration is prevented, and the safety of water resources is protected.

Description

Technical Field

[0001] The present invention relates to the technical field of water environment monitoring, and in particular to a flood season water environment quality monitoring system based on wireless communication. Background Art

[0002] With the acceleration of global climate change and urbanization, water environment quality monitoring during the flood season faces unprecedented challenges. During the flood season, due to the sharp increase in rainfall, rivers and lakes are easily impacted by pollutants brought in by rainwater, causing water quality to deteriorate sharply, posing a serious threat to the aquatic ecosystem and human health.

[0003] In terms of pre-flood risk investigation, there are many problems in urban and rural non-point source pollution prevention and control, investigation and remediation of sewage outlets into rivers, construction and operation of sewage collection and treatment facilities in industrial parks and towns, investigation and remediation of illegal dumping and landfill of solid waste, and cleaning of plastic waste within the management scope of rivers flowing into the sea and surrounding river channels. If these problems are not properly resolved before the flood season, the risk of water environment deterioration will be further aggravated.

[0004] Traditional water environment quality monitoring systems have many shortcomings. First, traditional systems mostly use wired transmission methods, which limits the layout of monitoring sites and makes it difficult to achieve comprehensive coverage of vast water areas. Second, traditional systems have low monitoring frequency and timeliness, and often cannot capture the dynamic process of water quality changes in a timely manner, resulting in delayed early warning and poor governance effects. In addition, traditional systems have obvious shortcomings in source tracing analysis and lack effective data mining and analysis methods, making it difficult to accurately determine the source of pollution, which brings difficulties to governance decision-making.

[0005] In summary, the traditional water environment quality monitoring system has exposed many limitations in flood season monitoring and is difficult to meet the urgent needs of current water resource protection and water environment governance. Therefore, it is particularly important to develop a flood season water environment quality monitoring system based on wireless communication. Summary of the Invention

[0006] The purpose of this invention is to make up for the shortcomings of the existing technology and provide a flood season water environment quality monitoring system based on wireless communication. It can integrate multiple modules such as data acquisition and transmission, intelligent early warning, source tracing analysis and decision support, combine high-precision (resolution) sensors and advanced wireless communication technology, and realize comprehensive and real-time monitoring of flood season water environment quality, accurately trace the source of pollution, and provide timely and scientific governance measures for relevant departments.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a flood season water environment quality monitoring system based on wireless communication, the system includes the following components: a data acquisition and transmission module, an intelligent early warning module, a source tracing analysis module and a decision support module;

[0008] The data acquisition and transmission module is equipped with multiple water quality monitoring sensors distributed in different water areas to collect real-time water quality parameter data, including pH, dissolved oxygen, and chemical oxygen demand parameters in relevant water areas such as urban and rural non-point source pollution, sewage outlets into rivers, areas around industrial parks and urban sewage collection and treatment facilities, areas where solid waste is illegally dumped and landfilled, and rivers flowing into the sea and around river channels, and transmits the data to the data processing center via a wireless communication module;

[0009] The intelligent early warning module is located in the data processing center and uses a pre-built water quality assessment model to perform real-time analysis on the received monitoring data and issue an early warning message when the monitoring data exceeds the normal range;

[0010] The source tracing analysis module is activated at the same time as the warning is issued, and combines the water flow model with the existing pollution source distribution information database to analyze the real-time water quality data to determine the source of pollution;

[0011] The decision support module generates a governance recommendation report based on the traceability results and sends it to relevant departments via wireless communication.

[0012] Furthermore, in the data acquisition and transmission module, the water quality monitoring sensors include sensors for pH, dissolved oxygen, and chemical oxygen demand parameters. Each sensor is manufactured using MEMS technology and has the characteristics of high precision and low power consumption. For the pH sensor, its measurement accuracy can reach ±0.01pH. By optimizing the sensitive membrane material, it can quickly respond to changes in the pH of the water body. The dissolved oxygen sensor uses the fluorescence quenching principle, and the measurement error is less than ±0.1mg / L. The fluorescent substance inside it has been specially screened and can work stably at different temperatures. The chemical oxygen demand sensor uses electrochemical oxidation method to calculate the chemical oxygen demand by measuring the current change under a specific voltage. The measurement range is 0-500mg / L and the accuracy is ±5mg / L. The sensor is calibrated using a multi-point calibration method, and the sensor is calibrated regularly with a standard solution to ensure the accuracy of the measurement data. The wireless communication module automatically switches to the 5G communication mode in areas with good signals. Its data transmission rate can reach more than 1Gbps, meeting the needs of rapid transmission of large amounts of real-time data.

[0013] Furthermore, in the intelligent early warning module, the water quality assessment model is constructed based on the improved fuzzy comprehensive evaluation algorithm, and the water quality parameter set is X = {x1, x2, ..., x n}, the corresponding standard value set of each parameter is Y={y1,y2,…,y n}, the weight set of each parameter is W={w1,w2,…,w n}, weight w i The determination of the analytic hierarchy process is adopted. First, the judgment matrix A is constructed. The matrix element aij Represents parameter x i Relative to parameter x j The importance of the impact on water quality is determined by expert scoring method. ij The value of λ is used to calculate the maximum eigenvalue of the judgment matrix. max and its corresponding eigenvector W. After consistency test, the weight of each parameter is obtained. The fuzzy membership function is determined according to the characteristics of different water quality parameters. For dissolved oxygen, the trapezoidal membership function is used:

[0014]

[0015] in is the upper limit standard value of dissolved oxygen, To obtain the appropriate dissolved oxygen value, the fuzzy transformation B = W·R is used, where R is the fuzzy relation matrix and the element r ij is the parameter x i For the membership of the j-th water quality level, the comprehensive water quality evaluation result B is obtained. When B exceeds the normal water quality level range, the intelligent early warning module starts the early warning.

[0016] Furthermore, in the source tracing analysis module, the water flow model uses the finite element product method based on unstructured grids to solve the water equation. The basic form of the shallow water equation is:

[0017]

[0018] in h is the water depth, u and v are the flow velocities in the x and y directions respectively, F and G are flux vectors, and S is the source term vector. The unstructured grid is adaptively divided according to the terrain and river shape. The grid is encrypted in areas where the water flow changes dramatically to improve the calculation accuracy. Model parameters such as the Manning coefficient are determined by combining field measurements and empirical formulas according to different riverbed materials and roughness. The pollution source distribution information database contains data on urban and rural non-point source pollution, sewage outlets into rivers, industrial parks and the surrounding areas of urban sewage collection and treatment facilities, illegal solid waste dumping and landfill areas, rivers entering the sea and the surrounding areas of rivers, and other related waters. Data updates are based on a combination of regular censuses and real-time monitoring. For industrial enterprise emission data, real-time acquisition is achieved by installing online monitoring equipment. Urban and rural non-point source, domestic sewage discharge and other data are updated through regular field surveys to ensure the accuracy and timeliness of the database.

[0019] Furthermore, the data mining algorithm used by the traceability analysis module is an improved association rule mining algorithm. Suppose the water quality data set D = {d1, d2, ..., d m}, each data record d iContains multiple water quality parameter values. First, the data is discretized and the continuous water quality parameter values ​​are divided into several intervals. Then, a transaction database T is constructed, where each transaction corresponds to a water quality data record of a monitoring point at a certain moment. For the association rule A→B, the support Confidence Where σ(A) represents the number of transactions containing item set A. To improve the efficiency of the algorithm, a pruning strategy is introduced. According to the set minimum support and minimum confidence thresholds, candidate item sets and rules that do not meet the conditions are deleted. When determining the source of pollution, the abnormal water quality data is associated with the pollution source emission characteristic data. It is found that the concentration of heavy metals in the water quality is abnormally elevated. Through association rule mining, it is found that the heavy metals are highly correlated with the factory emission characteristics. At the same time, combined with the water flow model, it is determined that the water flow at the location of the factory can affect the monitoring point, and the factory is determined to be the source of pollution.

[0020] Furthermore, when the decision support module generates a governance recommendation report, it uses a linear programming algorithm to determine the optimal governance plan for industrial pollution sources based on the types and emissions of pollutants at the pollution sources. The governance measure set is M = {m1, m2, ..., m k}, the cost of each governance measure is C = {c1,c2,…,c k}, the amount of pollutant reduction after treatment is R = {r1, r2, …, r k The goal is to minimize the cost of treatment while meeting the requirements of pollutant emission reduction, and establish a linear programming model The constraints are where x i is a decision variable, indicating whether to adopt the i-th governance measure, R target In order to reduce the target pollutants, for agricultural non-point source pollution, zoning management plans are formulated using geographic information system technology based on the crop planting types, fertilization and pesticide application conditions, and topography in the region. Ecological interception zones are set up in areas prone to soil erosion, and green agricultural production technologies are promoted in high-pollution risk areas.

[0021] Furthermore, the system also includes an equipment self-maintenance module, which regularly performs status detection on water quality monitoring sensors. By setting a self-test circuit inside the sensor, the electrical performance and signal transmission stability parameters of the sensor are detected. For the wireless communication module, the communication quality is evaluated by combining signal strength detection with bit error rate analysis. When a sensor failure or a decrease in communication quality is detected, the equipment self-maintenance module sends a fault message to the data processing center via wireless communication and attempts to perform self-repair. For minor sensor failures, the module uses an automatic calibration program to repair them. For signal interference problems of the communication module, the module automatically adjusts the communication frequency band or power. At the same time, the equipment self-maintenance module predicts the life of the sensor based on the sensor's usage time and working environment, and issues a replacement warning to the staff in advance to ensure the long-term stable operation of the system.

[0022] Furthermore, the system has a data sharing interface with other monitoring systems. Through standardized data interface protocols, it can interact with meteorological monitoring systems, water conservancy monitoring systems, etc., obtain rainfall and temperature meteorological data during the flood season from the meteorological monitoring system, and obtain water level and flow hydrological data from the water conservancy monitoring system. These data are comprehensively analyzed with water quality monitoring data, and water quality change trends are predicted in combination with rainfall and water level changes. The temperature information in the meteorological data is used to optimize the temperature correction parameters in the water quality assessment model, further improving the accuracy and comprehensiveness of the system's flood season water environment quality monitoring and early warning tracing, and realizing multi-system collaborative monitoring and analysis.

[0023] Furthermore, the system uses blockchain technology to ensure data security and reliability. In the data collection process, the data collected by the sensor is hashed to generate a unique hash value, which is transmitted to the data processing center together with the data itself through wireless communication. The data processing center organizes the received data into data blocks in chronological order. Each data block contains the hash value of the previous data block, the hash value of the current data block and the data content. Through the distributed ledger technology of the blockchain, the data blocks are stored on multiple nodes to ensure that the data cannot be tampered with and is traceable. When performing early warning and traceability analysis, the data on the blockchain is used as the basis. Any illegal modification of the data will cause the hash value to mismatch, thereby ensuring the authenticity and reliability of the data and providing a solid data foundation for governance decisions.

[0024] Compared with the existing technology, this flood season water environment quality monitoring system based on wireless communication has the following beneficial effects:

[0025] 1. This system achieves comprehensive, real-time monitoring of pre-flood water environment quality by integrating multiple modules including data collection and transmission, intelligent early warning, source tracing analysis, and decision support. With the help of high-precision water quality monitoring sensors and advanced wireless communication technology, the system can quickly capture changes in water quality, issue early warning information in a timely manner, and accurately trace the source of pollution, providing scientific governance recommendations to relevant departments. This greatly improves the efficiency and accuracy of water environment quality monitoring, helps to take effective measures in a timely manner to prevent water quality deterioration and protect water resource security.

[0026] 2. This system uses blockchain technology to ensure data security and credibility. During the entire process of data collection, transmission, storage and analysis, the system ensures the data's non-tamperability and traceability through hash operations and distributed ledger technology. This provides a solid data foundation for early warning and traceability analysis, and enhances the reliability and authority of governance decisions. At the same time, the system also has a data sharing interface with other monitoring systems, which can realize multi-system collaborative monitoring and analysis, further improving the accuracy and comprehensiveness of flood season water environment quality monitoring.

[0027] Other advantages, objects and features of the present invention will be described 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 learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0029] Figure 1 This is a flow chart for the function implementation of a flood season water environment quality monitoring system based on wireless communication;

[0030] Figure 2 This is a flow chart of the overall architecture of a flood season water environment quality monitoring system based on wireless communication. DETAILED DESCRIPTION

[0031] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0032] Example 1

[0033] This embodiment describes an industrial cluster where a river flows through, home to numerous chemical, electroplating, and other enterprises. With the onset of the flood season, the river flow and velocity change significantly, making water quality monitoring in the area crucial. In this context, a wireless communication-based flood season water quality monitoring system is fully operational.

[0034] The data acquisition and transmission module's multiple water quality monitoring sensors are meticulously deployed at key river locations, such as downstream of factory sewage outlets and at river confluences. The pH sensor, through optimization of the sensitive membrane material, can rapidly respond to changes in the water's pH. The dissolved oxygen sensor utilizes the principle of fluorescence quenching, and its internal specially screened fluorescent material ensures stable operation even under the fluctuating water temperatures of the flood season. The chemical oxygen demand sensor employs an electrochemical oxidation method, accurately calculating chemical oxygen demand by precisely measuring current changes at a specific voltage. These sensors are manufactured using MEMS technology, offering the advantages of small size, low power consumption, and high precision. For data transmission, the wireless communication module constantly monitors signal quality. Once in a good signal area, it automatically switches to 5G communication mode to ensure that collected water quality parameter data, such as pH, dissolved oxygen (DO), and chemical oxygen demand (COD), can be quickly and stably transmitted to the data processing center.

[0035] The intelligent early warning module is located in the data processing center. Its core is a water quality assessment model based on the advanced fuzzy comprehensive evaluation algorithm. The water quality parameter set X = {x1, x2, x3}, corresponding to pH, dissolved oxygen, and chemical oxygen demand, the standard value set Y = {y1, y2, y3}, and the weight set of each parameter is W = {w1, w2, w3}. The weight is determined by the hierarchical analysis method. First, the judgment matrix A is constructed. The matrix element a ij Represents parameter x i Relative to parameter x j The importance of the impact on water quality. For example, in this industrial cluster, since the wastewater discharged by chemical companies may have a greater impact on pH and chemical oxygen demand, when constructing the judgment matrix, it will be reflected that these two parameters are more important to water quality than dissolved oxygen. By calculating the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector W, and after strict consistency testing, the accurate weights of each parameter are obtained. Taking dissolved oxygen as an example, its trapezoidal membership function is:

[0036]

[0037] in is the upper limit standard value of dissolved oxygen, To obtain the appropriate dissolved oxygen value, the fuzzy transformation B = W·R is used, where the element r of the fuzzy relation matrix R is ijis the parameter x i For the membership of the jth water quality level, the final comprehensive water quality evaluation result B is obtained. When B exceeds the normal water quality level range, the intelligent early warning module quickly issues an early warning to remind relevant personnel that the water quality is abnormal.

[0038] The source analysis module is activated immediately when the warning is issued. The water flow model uses the finite element product method based on unstructured grids to solve the shallow water equation. in h is the water depth, u and v are the flow velocities in the x and y directions respectively, F and G are flux vectors, and S is the source term vector. The unstructured grid is adaptively divided according to the complex local terrain and the winding river shape. In areas where the water flow changes drastically, such as sharp bends in the river and near factory outlets, the grid will be encrypted to improve the accuracy of the model calculation. The pollution source distribution information database records the location information of industrial enterprises in detail, as well as the types, concentrations and emission patterns of pollutants emitted by each enterprise. For industrial enterprise emission data, it is obtained in real time by installing online monitoring equipment. Using an improved association rule mining algorithm, the water quality data set D = {d1, d2, ..., d m} for processing. Suppose that at a certain moment, the monitoring data shows that the concentration of heavy metal lead in the water quality is abnormally elevated. Through association rule mining, it is found that the heavy metal is highly correlated with the emission characteristics of an electroplating plant. Further combined with the water flow model analysis, it is determined that the water flow at the location of the electroplating plant can affect the monitoring point along the river. After comprehensive judgment, it is determined that the electroplating plant is the source of this water pollution.

[0039] The decision support module works on the pollution source of the electroplating plant, and sets the control measures set M = {m1,m2,…,m k For example, m1 represents the installation of advanced heavy metal wastewater treatment equipment, and m2 represents the improvement of electroplating production process to reduce heavy metal emissions. The cost of each treatment measure C = {c1, c2, ..., c k}, the amount of pollutant reduction after treatment R = {r1, r2, …, r k In order to minimize the cost of treatment while meeting the requirements of pollutant emission reduction, a linear programming model is established. The constraints are where x i is a decision variable, indicating whether to adopt the i-th governance measure, R target In order to reduce the target pollutants, the most economical and effective treatment plan is obtained by solving the linear programming model, such as giving priority to improving production processes and installing sewage treatment equipment when funds permit, providing a scientific and reasonable decision-making basis for treatment work.

[0040] Example 2

[0041] This embodiment describes a large lake that serves as an important source of drinking water and an ecological tourist attraction for surrounding cities. During the flood season, the lake's water quality is easily deteriorated by surrounding agricultural activities and rainfall erosion, threatening drinking water safety and ecological balance. This embodiment aims to utilize a flood season water environment quality monitoring system based on wireless communication to conduct real-time monitoring, early warning, and control of lake water quality, thereby ensuring the stability of the lake's ecological environment.

[0042] Five water quality monitoring sensors are installed at the lake's inlet, focusing on monitoring the impact of pollutants carried by rivers on the lake's water quality. Three sensors are evenly distributed in the lake's center to obtain the overall water quality of the lake. Four sensors are installed around the drinking water source protection area to ensure drinking water safety. These sensors monitor pH, dissolved oxygen, and chemical oxygen demand parameters respectively, and are all manufactured using MEMS technology. For example, the pH sensor can quickly respond to changes in water pH within 5 seconds by optimizing the sensitive membrane material. The dissolved oxygen sensor uses the principle of fluorescence quenching, and the internally screened fluorescent substance can work stably within the water temperature range of 15-35°C. The chemical oxygen demand sensor uses the electrochemical oxidation method to accurately calculate the chemical oxygen demand by measuring the current change under a specific voltage. The sensor collects data every 30 minutes and transmits it using a wireless communication module. In areas with good signals, the wireless communication module automatically switches to 5G communication mode to ensure fast and stable data transmission to the data processing center. At the same time, the sensor calibration adopts a multi-point calibration method, and calibration is performed once a week using a standard solution to ensure data accuracy.

[0043] The data processing center's intelligent early warning module uses a water quality assessment model built based on an improved fuzzy comprehensive evaluation algorithm to conduct real-time analysis of monitoring data. During the summer flood season, after a large amount of fertilizer was applied to the surrounding farmland, heavy rainfall occurred and the rain washed the fertilizer into the lake. Monitoring data showed that the chemical oxygen demand in the lake quickly rose from the normal 20mg / L to 35mg / L, and the dissolved oxygen content dropped from 8mg / L to 6mg / L. The intelligent early warning module calculated the comprehensive water quality evaluation result beyond the normal range through fuzzy transformation based on the pre-set water quality parameter set, standard value set and weight set, and immediately issued a warning message to notify relevant departments and staff.

[0044] The source tracing analysis module was quickly launched after the warning was issued, and an improved association rule mining algorithm was used in combination with the water flow model and the pollution source distribution information database for analysis. The water flow model used the finite element product method based on unstructured grids to solve the shallow water equation. The unstructured grid was adaptively divided according to the lake topography and water flow conditions. The grid was encrypted in the lake entrance and lake center areas where the water flow changed dramatically. The pollution source distribution information database contains location information such as surrounding agricultural non-point sources and domestic sewage outlets, as well as the types, concentrations and emission patterns of pollutants emitted by each pollution source. Through analysis, it was found that after fertilization in the surrounding farmland, the chemical oxygen demand increased abnormally, and the water flow in this area would flow into the lake monitoring point. At the same time, by comparing the data of other possible pollution sources, it was determined that agricultural non-point source pollution was the main cause of the water quality abnormality.

[0045] The decision support module uses geographic information system technology to develop zoning management plans for agricultural non-point source pollution. First, the area around the lake is divided in detail, and different pollution risk areas are determined based on crop planting types, fertilization and drug use, and topographic factors. On the lakeside slopes prone to soil erosion, ecological interception belts are set up, and plants with well-developed root systems, such as alfalfa and bermudagrass, are planted to intercept pollutants washed into the lake with rainwater. In farmland areas with high pollution risks, green agricultural production technologies, such as precision fertilization technology, are promoted. The amount of fertilizer is accurately calculated according to soil fertility and crop needs to reduce fertilizer waste and pollution. The use of environmentally friendly pesticides is promoted to reduce the impact of pesticide residues on lake water quality. The decision support module sends the management recommendation report to relevant agricultural and environmental protection departments to assist in formulating specific management measures and action plans.

[0046] The equipment's self-maintenance module regularly inspects and maintains water quality monitoring sensors and wireless communication modules, performing a daily status check on the sensors. The module uses a self-test circuit inside the sensor to detect parameters such as electrical performance and signal transmission stability. During one test, it was discovered that the signal transmission of a dissolved oxygen sensor was fluctuating. The module immediately repaired the sensor through an automatic calibration procedure and sent a fault message to the data processing center. For the wireless communication module, the communication quality is assessed using a combination of signal strength detection and bit error rate analysis. If signal interference is detected, the module automatically adjusts the communication frequency band or power to ensure normal data transmission. At the same time, the module predicts the life of the sensor based on its usage time and working environment. For example, analysis revealed that a chemical oxygen demand sensor operating in a high-humidity, highly corrosive environment is expected to have a shortened lifespan. A replacement warning is issued to staff one month in advance to ensure the long-term, stable operation of the system.

[0047] The system exchanges data with the meteorological monitoring system and the water conservancy monitoring system through a standardized data interface protocol. It obtains meteorological data such as rainfall and temperature during the flood season from the meteorological monitoring system, and obtains lake water level and flow hydrological data from the water conservancy monitoring system. Combined with rainfall and water level changes, it predicts water quality change trends. For example, after continuous heavy rainfall, the water level rises, and the chemical oxygen demand is predicted to increase further. Preparations are made in advance, and the temperature information in the meteorological data is used to optimize the temperature correction parameters in the water quality assessment model to improve the accuracy of water quality assessment.

[0048] The system uses blockchain technology to ensure data security. In the data collection process, the data collected by the sensor undergoes hash operation to generate a unique hash value, which is transmitted to the data processing center together with the data itself through wireless communication. The data processing center organizes the received data into data blocks in chronological order. Each data block contains the hash value of the previous data block, the hash value of the current data block and the data content. Through the distributed ledger technology of the blockchain, the data blocks are stored on multiple nodes. When performing early warning and traceability analysis, the data on the blockchain is used as the basis. Any illegal modification of the data will cause the hash value to mismatch, ensuring the security and credibility of the data and providing reliable data support for subsequent governance decisions.

[0049] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. 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 flood season water environment quality monitoring system based on wireless communication, characterized in that: The system includes the following components: data acquisition and transmission module, intelligent early warning module, traceability analysis module and decision support module; The data acquisition and transmission module is equipped with multiple water quality monitoring sensors distributed in different water areas to collect real-time water quality parameter data, including pH, dissolved oxygen, and chemical oxygen demand parameters in relevant water areas such as urban and rural non-point source pollution, sewage outlets into rivers, areas around industrial parks and urban sewage collection and treatment facilities, areas where solid waste is illegally dumped and landfilled, and rivers flowing into the sea and around river channels, and transmits the data to the data processing center via a wireless communication module; The intelligent early warning module is located in the data processing center and uses a pre-built water quality assessment model to perform real-time analysis on the received monitoring data and issue an early warning message when the monitoring data exceeds the normal range; The source tracing analysis module is activated at the same time as the warning is issued, and combines the water flow model with the existing pollution source distribution information database to analyze the real-time water quality data to determine the source of pollution; The decision support module generates a governance recommendation report based on the traceability results and sends it to relevant departments via wireless communication.

2. A flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: In the data acquisition and transmission module, the water quality monitoring sensors include sensors for pH, dissolved oxygen, and chemical oxygen demand parameters. Each sensor is manufactured using MEMS technology. For the pH sensor, by optimizing the sensitive membrane material, it can quickly respond to changes in the pH of the water body. The dissolved oxygen sensor uses the fluorescence quenching principle, and the fluorescent substance inside it has been specially screened and can work stably at different temperatures. The chemical oxygen demand sensor uses the electrochemical oxidation method to calculate the chemical oxygen demand by measuring the current change under a specific voltage. The sensor is calibrated using a multi-point calibration method, and the sensor is regularly calibrated using a standard solution. The wireless communication module automatically switches to 5G communication mode in areas with good signals.

3. The flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: In the intelligent early warning module, the water quality assessment model is constructed based on the improved fuzzy comprehensive evaluation algorithm. The water quality parameter set is X = {x1, x2, ..., x n }, the corresponding standard value set of each parameter is Y={y1,y2,…,y n }, the weight set of each parameter is W={w1,w2,…,w n }, weight w i The determination of the analytic hierarchy process is adopted. First, the judgment matrix A is constructed. The matrix element a ij Represents parameter x i Relative to parameter x j The importance of the impact on water quality, calculate the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector W. After consistency test, the weight of each parameter is obtained. The fuzzy membership function is determined according to the characteristics of different water quality parameters. For dissolved oxygen, the trapezoidal membership function is used: in is the upper limit standard value of dissolved oxygen, To obtain the appropriate dissolved oxygen value, the fuzzy transformation B = W·R is used, where R is the fuzzy relation matrix and the element r ij is the parameter x i For the membership of the j-th water quality level, the comprehensive water quality evaluation result B is obtained. When B exceeds the normal water quality level range, the intelligent early warning module starts the early warning.

4. The flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: In the traceability analysis module, the water flow model uses the finite element product method based on unstructured grids to solve the water equation. The basic form of the shallow water equation is: in h is the water depth, u and v are the flow velocities in the x and y directions respectively, F and G are flux vectors, S is the source term vector, and the unstructured grid is adaptively divided according to the terrain and river shape. The grid is encrypted in areas where the water flow changes dramatically. Model parameters such as the Manning coefficient are based on different riverbed materials and roughness. The pollution source distribution information database is updated by combining regular census and real-time monitoring. For industrial enterprise emission data, real-time acquisition is achieved by installing online monitoring equipment, and agricultural non-point source and domestic sewage emission data are updated through regular field surveys.

5. The flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: The data mining algorithm used by the traceability analysis module is an improved association rule mining algorithm. Suppose the water quality data set D = {d1, d2, ..., d m }, each data record d i Contains multiple water quality parameter values. First, the data is discretized and the continuous water quality parameter values ​​are divided into several intervals. Then, a transaction database T is constructed, where each transaction corresponds to a water quality data record of a monitoring point at a certain moment. For the association rule A→B, the support Confidence Where σ(A) represents the number of transactions containing item set A. To improve the efficiency of the algorithm, a pruning strategy is introduced. According to the set minimum support and minimum confidence thresholds, candidate item sets and rules that do not meet the conditions are deleted. When determining the source of pollution, the abnormal water quality data is associated with the pollution source emission characteristic data. It is found that the concentration of heavy metals in the water quality is abnormally elevated. Through association rule mining, it is found that the heavy metals are highly correlated with the factory emission characteristics. At the same time, combined with the water flow model, it is determined that the water flow at the location of the factory can affect the monitoring point, and the factory is determined to be the source of pollution.

6. The flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: When the decision support module generates a governance recommendation report, it uses a linear programming algorithm to determine the optimal governance plan for industrial pollution sources based on the types and emissions of pollutants at the pollution sources. The governance measure set is M = {m1, m2, ..., m k }, the cost of each governance measure is C = {c1,c2,…,c k }, the amount of pollutant reduction after treatment is R = {r1, r2, …, r k The goal is to minimize the cost of treatment while meeting the requirements of pollutant emission reduction, and establish a linear programming model The constraints are where x i is a decision variable, indicating whether to adopt the i-th governance measure, R target In order to reduce the target pollutants, for agricultural non-point source pollution, zoning management plans are formulated using geographic information system technology based on the crop planting types, fertilization and pesticide application conditions, and topography in the region. Ecological interception zones are set up in areas prone to soil erosion, and green agricultural production technologies are promoted in high-pollution risk areas.

7. The flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: The system also includes an equipment self-maintenance module, which regularly performs status detection on water quality monitoring sensors. By setting a self-test circuit inside the sensor, the electrical performance and signal transmission stability parameters of the sensor are detected. For the wireless communication module, the communication quality is evaluated by combining signal strength detection with bit error rate analysis. If a sensor failure or a decrease in communication quality is detected, the equipment self-maintenance module sends a fault message to the data processing center via wireless communication and attempts to perform self-repair. For minor sensor failures, the module uses an automatic calibration program to repair them. For signal interference problems of the communication module, the module automatically adjusts the communication frequency band or power. At the same time, the equipment self-maintenance module predicts the life of the sensor based on the sensor's usage time and working environment, and issues a replacement warning to the staff in advance to ensure the long-term stable operation of the system.

8. The flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: The system has a data sharing interface with other monitoring systems. Through standardized data interface protocols, it can exchange data with meteorological monitoring systems, water conservancy monitoring systems, etc., obtain rainfall and temperature meteorological data during the flood season from the meteorological monitoring system, and obtain water level and flow hydrological data from the water conservancy monitoring system. These data are comprehensively analyzed with water quality monitoring data, and water quality change trends are predicted in combination with rainfall and water level changes. The temperature information in the meteorological data is used to optimize the temperature correction parameters in the water quality assessment model.

9. The flood season water environment quality monitoring system based on wireless communication according to claim 1, characterized in that: The system uses blockchain technology to ensure data security and reliability. In the data collection process, the data collected by the sensor is hashed to generate a unique hash value, which is transmitted to the data processing center together with the data itself via wireless communication. The data processing center organizes the received data into data blocks in chronological order. Each data block contains the hash value of the previous data block, the hash value of the current data block, and the data content. The data blocks are stored on multiple nodes through the distributed ledger technology of the blockchain. When performing early warning and traceability analysis, the data on the blockchain is used as the basis. Any illegal modification of the data will result in a hash value mismatch.

Images