Water quality multi-parameter real-time monitoring system

Abstract

The application relates to the technical field of water quality monitoring, and discloses a water quality multi-parameter real-time monitoring system. The system comprises a monitoring point position configuration module, a water quality parameter acquisition module, a pollution index calculation module, an anomaly detection module, a central control unit and the like. The monitoring point position configuration module sets monitoring point positions and generates acquisition rules; the water quality parameter acquisition module acquires a water quality parameter set according to the rules; the pollution index calculation module calculates a pollution index; the anomaly detection module acquires anomaly detection indexes; and the central control unit comprehensively analyzes and generates water quality evaluation parameters. In addition, the system is also provided with a pollution source tracking module, a storage unit and an alarm module, can track pollution sources, store data and alarm according to the evaluation parameters. The system realizes multi-parameter real-time monitoring, accurate analysis and evaluation and pollution source tracking, provides comprehensive support for water quality management, effectively improves the water environment monitoring level and guarantees water resource safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quality monitoring, in particular to a water quality multi-parameter real-time monitoring system. BACKGROUND

[0002] With the acceleration of industrialization and urbanization, water pollution problems are becoming increasingly serious. Monitoring and controlling water environmental quality have become key links to protect ecological safety and human health. Traditional water quality monitoring methods have many limitations in practical application.

[0003] From the monitoring method, manual sampling detection is one of the important ways of traditional monitoring. Manual sampling requires professional personnel to go to the monitoring point, collect water samples according to specific specifications, and then send them back to the laboratory for analysis and detection. This process consumes a lot of manpower, material resources and time cost, and the monitoring frequency is often low, which is difficult to meet the demand of real-time grasp of water quality dynamic changes. In some sudden water pollution incidents, the lag of manual sampling detection will lead to the inability to discover pollution in time and take effective measures, thereby expanding the scope of pollution and causing greater threat to the ecological environment and human water safety. For example, if a chemical raw material leakage accident occurs in the upper reaches of a river, it may take hours or even days to detect water quality abnormalities by relying on manual sampling detection. At this time, the pollution may have spread to many areas downstream, affecting a large number of residents' water use and aquatic life survival.

[0004] In terms of monitoring parameters, early water quality monitoring systems can usually only monitor a few common parameters such as pH value, dissolved oxygen, etc. However, modern water environment has a wide variety of pollutants and complex composition, and only monitoring these conventional parameters cannot comprehensively and accurately evaluate the water quality. Taking the problem of lake eutrophication as an example, in addition to the conventional parameters, it is also necessary to monitor various nutrient salt indicators such as total nitrogen, total phosphorus, ammonia nitrogen, and biological indicators such as algal density. If the monitoring system cannot cover these key parameters, it is impossible to discover the trend of lake eutrophication in time, and it is difficult to early warn the risk of water bloom outbreak, thereby causing serious damage to the lake ecosystem and affecting the surrounding ecological balance and economic development.

[0005] In the aspect of pollution source tracking, the traditional monitoring technology is also facing great challenges. When water pollution occurs, it is difficult to quickly and accurately determine the location, type and pollution degree of the pollution source. Because of the flow of water, pollutants will diffuse and migrate with the water flow, and their propagation path is complex and variable. Moreover, pollutants discharged by different pollution sources mix and transform in the water body, further increasing the difficulty of pollution source tracking. For example, in the pollution control of urban rivers, there may be multiple industrial enterprises, sewage discharge outlets and agricultural non-point source pollution, etc. The traditional monitoring means cannot accurately locate the main pollution source, making the treatment work lack of pertinence, and the treatment effect is still not ideal after investing a large amount of funds and manpower, and the problems of water resource waste and ecological destruction continue to exist.

[0006] In the aspect of data analysis and evaluation, the data collected by the traditional monitoring system is often scattered, and lacks effective integration and depth analysis capability. It is difficult to comprehensively evaluate based on multi-parameter data, and it is difficult to accurately judge the overall situation and potential risks of water quality. Moreover, there are serious deficiencies in real-time data processing and feedback, and intuitive and easy-to-understand evaluation reports and early warning information cannot be generated in time based on monitoring data to provide scientific basis for water resource management decision-making.

[0007] In view of the above shortcomings of the traditional water quality monitoring technology, it is urgent to develop a water quality multi-parameter real-time monitoring system with the functions of multi-parameter real-time monitoring, accurate pollution index calculation, efficient anomaly detection, accurate pollution source tracking and comprehensive analysis and evaluation. This has important practical significance for improving the level of water environment monitoring and ensuring the safety of water resources. SUMMARY

[0008] The purpose of the present application is to provide a water quality multi-parameter real-time monitoring system to solve the problems raised in the background art.

[0009] To achieve the above purpose, the present application provides the following technical scheme: a water quality multi-parameter real-time monitoring system, the system comprising:

[0010] A monitoring point configuration module is configured to set monitoring points in a target monitoring area and generate corresponding parameter collection rules based on the monitoring points.

[0011] A water quality parameter collection module is configured to acquire a set of water quality parameters of the monitoring points from the water quality database in real time according to the parameter collection rules, and use all parameters in the set of water quality parameters as monitoring dimension parameters of the monitoring points.

[0012] A pollution index calculation module is configured to calculate the pollution index of the monitoring points by a multi-dimensional fusion algorithm based on a pre-set pollution source correlation relationship in the water quality database, and use the pollution index of the monitoring points as monitoring dimension parameters of the monitoring points.

[0013] An anomaly detection module is used to obtain anomaly detection indicators of the monitoring points through a preset dynamic threshold analysis method, and to use the anomaly detection indicators of the monitoring points as monitoring dimension parameters of the monitoring points.

[0014] The central control unit is used to send the monitoring points to the pollution index calculation module and the anomaly detection module, send the parameter acquisition rules to the water quality parameter acquisition module, and also to perform comprehensive analysis on all the monitoring dimension parameters of the monitoring points to generate water quality assessment parameters for the monitoring points.

[0015] The water quality database includes a pre-set pollution source database and / or a real-time monitoring database.

[0016] Preferably, the step of obtaining the anomaly detection indicators of the monitoring points through a preset dynamic threshold analysis method includes:

[0017] The monitoring points and the preset reference points within the watershed to which the monitoring points belong are all used as dynamic analysis units. Based on the watershed to which the monitoring points belong, all historical water quality data are obtained and the parameter fluctuation patterns of the dynamic analysis units in historical periods are determined.

[0018] Based on the parameter fluctuation pattern, and based on the fluctuation amplitude of each historical water quality data in the corresponding dynamic analysis unit, the abnormal initial index of each dynamic analysis unit and the deviation initial index of each historical water quality data are calculated.

[0019] Based on the initial abnormality index of each dynamic analysis unit and the initial deviation index of each historical water quality data, the anomaly detection index of each dynamic analysis unit and the deviation index of each historical water quality data are synchronously optimized through a bidirectional iterative model to obtain the final abnormality index of each dynamic analysis unit. The final abnormality index of the dynamic analysis unit corresponding to the monitoring point is used as the anomaly detection index of the monitoring point.

[0020] The comparison dynamic analysis unit is set as any one of all dynamic analysis units, and the comparison historical water quality data is set as any one of all historical water quality data. Based on the parameter fluctuation mode, the fluctuation of the comparison dynamic analysis unit in the comparison historical water quality data is taken as the individual fluctuation, the total fluctuation of the comparison dynamic analysis unit in all historical water quality data is taken as the individual total fluctuation, the fluctuation of all dynamic analysis units in the comparison historical water quality data is taken as the overall fluctuation, and the total fluctuation of all dynamic analysis units in all historical water quality data is taken as the overall total fluctuation. At this time, it is determined whether the ratio of the individual fluctuation to the individual total fluctuation is greater than the ratio of the overall fluctuation to the overall total fluctuation. If so, it is determined that the comparison dynamic analysis unit has a significant anomaly in the comparison historical water quality data; otherwise, it is determined that the comparison dynamic analysis unit does not have a significant anomaly in the comparison historical water quality data.

[0021] Preferably, the parameter acquisition rules include a basic parameter type group and an extended parameter type group; the basic parameter type group includes multiple basic parameter types and a fixed weight corresponding to each basic parameter type; the extended parameter type group includes multiple extended parameter types and a dynamic weight corresponding to each extended parameter type.

[0022] Preferably, the step of obtaining the water quality parameter set of the monitoring point from the water quality database in real time according to the parameter acquisition rules includes:

[0023] The water quality parameter acquisition module extracts structured monitoring data of the monitoring points from the water quality database, extracts corresponding basic parameters from the structured monitoring data based on each basic parameter type in the basic parameter type group, and extracts corresponding extended parameters from the structured monitoring data based on each extended parameter type in the extended parameter type group, and summarizes all the basic parameters and all the extended parameters into a water quality parameter set for the monitoring points.

[0024] Preferably, when the preset pollution source association relationship is a direct pollution source relationship, it is determined whether the monitoring point is associated with a pollution source node in the water quality database. If so, the pollution index of the monitoring point is the sum of the pollution contribution values ​​of all pollution source nodes associated with the monitoring point; otherwise, the pollution index of the monitoring point is the basic pollution value of the monitoring point itself.

[0025] When the preset pollution source association relationship is an indirect pollution source relationship, the pollution index of the monitoring point is the weighted sum of the pollution contribution values ​​of all pollution source nodes obtained by the monitoring point through the upstream and downstream association paths.

[0026] Preferably, it also includes a pollution source tracking module connected to the central control unit;

[0027] The pollution source tracking module is used to set a target pollution type, obtain the spatiotemporal correlation degree between the monitoring point and the target pollution type through a preset spatiotemporal correlation analysis method, and use the spatiotemporal correlation degree as the monitoring dimension parameter of the monitoring point.

[0028] Preferably, the pollution source tracing module obtains the spatiotemporal correlation between the monitoring point and the target pollution type through a preset spatiotemporal correlation analysis method, including:

[0029] The historical pollution diffusion path set of the monitoring point is obtained from the pollution diffusion model library, and the standard diffusion path set of the target pollution type is obtained from the pollution diffusion model library. The spatiotemporal overlap between the historical pollution diffusion path set and the standard diffusion path set is used as the first correlation between the monitoring point and the target pollution type.

[0030] The pollution concentration change curve of the monitoring point within a preset time period is dynamically matched with the standard concentration change curve of the target pollution type, and the waveform similarity between the two is used as the second correlation between the monitoring point and the target pollution type.

[0031] The first correlation, the second correlation, or the weighted average of the first correlation and the second correlation are used as the spatiotemporal correlation between the monitoring point and the target pollution type.

[0032] Preferably, the central control unit performs linear superposition or nonlinear fusion of all the monitoring dimension parameters of the monitoring points to generate the water quality assessment parameters.

[0033] Preferably, the system further includes a storage unit connected to the central control unit, the storage unit being used to store the water quality parameter set of the monitoring point, the pollution index of the monitoring point, the abnormal detection index of the monitoring point, and the water quality assessment parameters of the monitoring point.

[0034] Preferably, it also includes an alarm module connected to the central control unit. The alarm module is used to generate a graded alarm signal based on the comparison result between the water quality assessment parameters and the preset safety threshold, and to send the graded alarm signal to a designated terminal device through a communication network.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] Regarding the comprehensiveness and real-time performance of the monitoring function, the monitoring point configuration module can flexibly set monitoring points within the target monitoring area and generate corresponding parameter acquisition rules. The water quality parameter acquisition module, based on these rules, retrieves the water quality parameter set for each monitoring point from the water quality database in real time. This set covers both basic and extended parameters, greatly enriching the types of monitoring parameters and comprehensively reflecting the water quality status. Compared to traditional monitoring methods, it eliminates the need for frequent manual sampling and testing, enabling real-time updates of water quality data. This allows managers to promptly grasp dynamic changes in water quality and respond quickly to sudden pollution events.

[0037] For pollution analysis, the pollution index calculation module calculates the pollution index based on preset pollution source relationships using a multi-dimensional fusion algorithm. Whether the relationship is with direct or indirect pollution sources, it can accurately determine the pollution index at monitoring points, providing precise data support for pollution control. For example, in river monitoring in industrial clusters, it can clearly distinguish the degree of influence of different pollution sources on each monitoring point, helping control personnel to develop targeted control plans, improve control efficiency, and reduce control costs.

[0038] The anomaly detection module employs a preset dynamic threshold analysis method to obtain anomaly detection indicators. It comprehensively considers historical water quality data and parameter fluctuation patterns from monitoring points and preset reference points within the watershed, optimizing the indicators through a bidirectional iterative model to accurately determine water quality anomalies. This method is more flexible and accurate than traditional fixed threshold detection, effectively avoiding false alarms and missed alarms, promptly identifying potential water quality risks, and ensuring the stability of the aquatic ecosystem and water safety.

[0039] In terms of pollution source tracing, the system has added a pollution source tracing module, which obtains the spatiotemporal correlation between monitoring points and target pollution types through a preset spatiotemporal correlation analysis method. By analyzing the spatiotemporal overlap of historical pollution diffusion path sets and standard diffusion path sets, as well as the waveform similarity of pollution concentration change curves, the system can accurately trace pollution sources, providing strong evidence for pollution source tracing and responsibility determination, and helping to solve water pollution problems at their source.

[0040] The central control unit comprehensively analyzes all monitored parameters to generate water quality assessment parameters. It can select linear overlay or nonlinear fusion methods to fully extract data value and comprehensively and accurately assess water quality. Simultaneously, the storage unit stores various monitoring data for easy retrieval, analysis, and comparison, providing data support for long-term water environment research and governance. The alarm module generates tiered alarm signals based on a comparison of water quality assessment parameters with preset safety thresholds and transmits them to designated terminal devices via the communication network. This ensures that relevant personnel receive timely information about water quality anomalies and can quickly take countermeasures to reduce losses caused by water pollution.

[0041] The entire system integrates water quality monitoring, analysis, assessment, early warning, and pollution source tracing, improving the intelligence and scientific level of water quality monitoring. It provides efficient and reliable technical means for water resource protection and management, effectively promotes the development of water environment governance, and is of great significance for ensuring ecological balance and promoting sustainable social and economic development. Attached Figure Description

[0042] Figure 1 This is a schematic diagram illustrating the working principle of the real-time water quality multi-parameter monitoring system described in this invention.

[0043] Figure 2 A flowchart for obtaining anomaly detection indicators;

[0044] Figure 3 A flowchart for obtaining a set of water quality parameters;

[0045] Figure 4 A flowchart for generating water quality assessment parameters. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figures 1-4 This invention provides a real-time monitoring system for multiple water quality parameters, and its specific implementation is described in detail below.

[0048] The monitoring point configuration module is responsible for setting the monitoring points within the target monitoring area. Staff determine the locations of each monitoring point based on the actual conditions of the monitoring area, such as river course and water function classification. After the monitoring points are determined, this module generates corresponding parameter acquisition rules. These rules specify which parameters to collect from the water quality database and how to collect them.

[0049] The water quality parameter acquisition module retrieves the water quality parameter set for each monitoring point in real time from the water quality database according to the parameter acquisition rules generated above. The water quality database includes a pre-set pollution source database and / or a real-time monitoring database. This module uses all acquired parameters as monitoring dimension parameters for each monitoring point, providing basic data for subsequent analysis.

[0050] The pollution index calculation module calculates the pollution index of monitoring points based on the preset pollution source correlations in the water quality database and through a multi-dimensional fusion algorithm. This pollution index also serves as a monitoring dimension parameter for the monitoring points, used to measure the degree of pollution at those points.

[0051] The anomaly detection module uses a preset dynamic threshold analysis method to obtain anomaly detection indicators at monitoring points, and uses these indicators as monitoring dimension parameters for the monitoring points to determine whether there are any abnormalities in water quality.

[0052] As the core of the system, the central control unit undertakes several important tasks. It sends monitoring point data to the pollution index calculation module and the anomaly detection module, while simultaneously sending parameter acquisition rules to the water quality parameter acquisition module, enabling data exchange between these modules. Furthermore, the central control unit comprehensively analyzes all monitoring dimensions of the monitoring points to generate water quality assessment parameters for each point, thus providing a comprehensive evaluation of the water quality status at each monitoring point.

[0053] The present invention will be further described below with reference to Examples 1 to 6: Example 1

[0054] This embodiment illustrates the process by which the anomaly detection module obtains anomaly detection indicators at monitoring points using a preset dynamic threshold analysis method. The monitoring point and preset reference points within the watershed to which it belongs are both designated as dynamic analysis units. To accurately grasp the patterns of water quality changes, it is necessary to obtain all historical water quality data based on the watershed to which the monitoring point belongs. For example, this involves collecting and organizing water quality monitoring records from previous years, covering data from different seasons and weather conditions. Then, the parameter fluctuation patterns of these dynamic analysis units over historical periods are determined. For instance, at some monitoring points, the dissolved oxygen content in the water may show an upward trend during the summer high-water season and a downward trend during the winter low-water season; this is a parameter fluctuation pattern.

[0055] Based on the established parameter fluctuation patterns, calculate the initial abnormality index for each dynamic analysis unit and the initial deviation index for each historical water quality data point. For each historical water quality data point, observe its fluctuation amplitude within the corresponding dynamic analysis unit. If the fluctuation amplitude of a certain water quality parameter (such as ammonia nitrogen content) at a certain monitoring point is significantly greater than its normal fluctuation range over a certain period of time, then it can be preliminarily determined that the data may be abnormal, and the corresponding initial abnormality index and initial deviation index can be calculated.

[0056] Based on the initial anomaly indicators of each dynamic analysis unit and the initial deviation indicators of each historical water quality data point, a bidirectional iterative model is used to simultaneously optimize the anomaly detection indicators of each dynamic analysis unit and the deviation indicators of each historical water quality data point. During the iteration process, the calculation method of the indicators is continuously adjusted to more accurately reflect the actual situation. After multiple iterations, the final anomaly indicators of each dynamic analysis unit are obtained, and the final anomaly indicators of the dynamic analysis unit corresponding to the monitoring point are used as the anomaly detection indicators for that monitoring point.

[0057] Anomaly detection is performed. The dynamic analysis unit is set as any one of all dynamic analysis units, and the historical water quality data is set as any one of all historical water quality data. Based on the parameter fluctuation pattern, the fluctuation amount of the dynamic analysis unit in the historical water quality data is calculated and treated as an individual fluctuation amount; the total fluctuation amount of the dynamic analysis unit in all historical water quality data is calculated and treated as the individual total fluctuation amount; the fluctuation amount of all dynamic analysis units in the historical water quality data is calculated and treated as the overall fluctuation amount; the total fluctuation amount of all dynamic analysis units in all historical water quality data is calculated and treated as the overall total fluctuation amount. Whether there is a significant anomaly in the historical water quality data for the dynamic analysis unit is determined by whether the ratio of the individual fluctuation amount to the individual total fluctuation amount is greater than the ratio of the overall fluctuation amount to the overall total fluctuation amount. If it is greater, a significant anomaly is determined; otherwise, no significant anomaly is determined. This allows for accurate detection of water quality anomalies at monitoring points, providing a strong basis for subsequent appropriate measures. Example 2

[0058] The parameter collection rules include a basic parameter type group and an extended parameter type group. The basic parameter type group contains multiple basic parameter types, such as common parameters like water temperature, pH value, and dissolved oxygen. Each basic parameter type has a fixed weight because the importance of different basic parameters varies in comprehensive water quality assessment. Taking drinking water source monitoring as an example, dissolved oxygen is crucial for maintaining the survival of aquatic organisms and the self-purification capacity of water bodies, so its weight may be relatively high when assessing water quality. While water temperature also affects water quality, its impact on comprehensive water quality assessment is relatively small in some cases, and therefore its weight is lower.

[0059] Assuming the basic parameter types are One, respectively , ,..., The corresponding fixed weights are respectively , ,..., The formula for calculating the comprehensive influence value of basic parameters is:

[0060]

[0061] in, This indicates the comprehensive impact value of the basic parameters, and the index of the basic parameter type. Indicates the first Values ​​of basic parameter types, Indicates the first Fixed weights corresponding to each basic parameter type.

[0062] The extended parameter type group also includes multiple extended parameter types, such as specific heavy metal contents (e.g., lead, mercury) and organic pollutant indicators (e.g., chemical oxygen demand, biochemical oxygen demand). Unlike the basic parameter types, each extended parameter type corresponds to a dynamic weight. This is because the importance of these extended parameters changes in different monitoring scenarios and time periods. In areas with frequent industrial activity, the weight of heavy metal content parameters may be adjusted according to fluctuations in industrial production; in agricultural irrigation water monitoring, the weight of organic pollutant indicators related to agricultural non-point source pollution may change with the seasons (e.g., agricultural fertilization season).

[0063] Assuming the extended parameter types are One, respectively , ,..., The corresponding dynamic weights are respectively , ,..., The formula for calculating the comprehensive influence value of the extended parameters is:

[0064]

[0065] in, This represents the combined impact value of the extended parameters. Indicates the ordinal number of the extended parameter type. Indicates the first Values ​​of an extended parameter type, Indicates the first The dynamic weights corresponding to each extended parameter type.

[0066] During the actual data collection process, the water quality parameter acquisition module retrieves data from the water quality database according to these rules. First, the module extracts structured monitoring data from the monitoring points in the database. This data is stored in a specific format for easy extraction later. Then, based on each basic parameter type in the basic parameter type group, the module extracts the corresponding basic parameters from the structured monitoring data. For example, it accurately extracts the values ​​of basic parameters such as water temperature and pH from the data. Next, based on each extended parameter type in the extended parameter type group, the module extracts the corresponding extended parameters from the structured monitoring data. Finally, all extracted basic and extended parameters are aggregated into a water quality parameter set for the monitoring points, ensuring that the collected data is comprehensive and meets monitoring requirements. Example 3

[0067] When the preset pollution source association is a direct pollution source relationship, the pollution index calculation module will determine whether the monitoring point is associated with a pollution source node in the water quality database. Taking a river as an example, if there is a chemical plant near the river and its wastewater flows directly into the river, then the chemical plant is a pollution source node in the water quality database. If the monitoring point is associated with such a pollution source node, for example, a monitoring point set up near the chemical plant's discharge outlet, then the pollution index of that monitoring point is the sum of the pollution contribution values ​​of all its associated pollution source nodes. Each pollution source node has a corresponding pollution contribution value, which is determined based on factors such as the type and amount of pollutants discharged, and the degree of impact of the discharged pollutants on water quality. If the monitoring point is not associated with a pollution source node, for example, a monitoring point set up upstream of a river far from a pollution source, then the pollution index of that monitoring point is its own baseline pollution value. This baseline pollution value is usually determined based on the natural background pollution situation of the area and past monitoring data.

[0068] When the preset pollution source relationship is an indirect pollution source relationship, the calculation of the pollution index for a monitoring point is relatively complex. In this case, the pollution index of a monitoring point is the weighted sum of the pollution contribution values ​​of all pollution source nodes obtained through upstream and downstream correlation paths. Taking a river flowing through multiple cities as an example, although the sewage discharge of upstream cities does not directly affect a specific downstream monitoring point, pollutants will gradually spread and migrate through the river's flow. The pollution index calculation module determines the upstream and downstream correlation paths of the monitoring point and finds all pollution source nodes along the paths. For each pollution source node, a weight is determined based on factors such as its distance from the monitoring point and the attenuation of pollutants in the water body. Then, the weighted sum of the pollution contribution values ​​of these pollution source nodes is calculated to obtain the pollution index for that monitoring point. This can more accurately reflect the degree of influence of indirect pollution sources on the water quality of the monitoring point, providing more targeted data support for water pollution control. Example 4

[0069] This embodiment describes the operation of the pollution source tracking module connected to the central control unit. The pollution source tracking module first sets the target pollution type, such as heavy metal pollution or organic pollution. Then, it obtains the spatiotemporal correlation degree between the monitoring point and the target pollution type through a preset spatiotemporal correlation analysis method. This spatiotemporal correlation degree will be used as the monitoring dimension parameter for the monitoring point.

[0070] In acquiring spatiotemporal correlation, the pollution source tracing module retrieves a set of historical pollution diffusion paths for monitoring sites from a pollution diffusion model library. This library stores various models built based on different geographical environments, meteorological conditions, and pollutant characteristics, which can simulate the diffusion paths of pollutants under different conditions. For example, in rivers, models built based on factors such as water flow velocity and riverbed shape can calculate the diffusion paths of pollutants over different time periods. Simultaneously, it retrieves a set of standard diffusion paths for the target pollution type from the library. These are diffusion paths for that pollution type under ideal conditions, determined based on extensive experimental data and theoretical analysis. The spatiotemporal overlap between the historical pollution diffusion path set and the standard diffusion path set is used as the primary correlation between the monitoring site and the target pollution type. If the historical pollution diffusion paths of a monitoring site and the standard diffusion paths of the target pollution type have significant overlap in time and space, it indicates a high correlation between the monitoring site and the target pollution type.

[0071] The pollution concentration change curves of monitoring points within a preset time period are dynamically matched with the standard concentration change curves of the target pollution type. For example, the heavy metal concentration changes at a certain monitoring point are monitored over a period of time and compared with the standard concentration change curves of heavy metal pollution. The second correlation between the monitoring point and the target pollution type is obtained by calculating the waveform similarity between the two curves. If the two curves are similar in shape, it indicates that the monitoring point is more likely to be affected by the target pollution type.

[0072] The spatiotemporal correlation between monitoring points and target pollution types is determined by the first correlation, the second correlation, or a weighted average of the first and second correlations. The specific method chosen depends on actual monitoring needs and the importance placed on the two correlations. This approach allows for more accurate tracking of pollution sources, providing valuable clues for pollution control. Example 5

[0073] After receiving all monitoring parameters from the monitoring points, the central control unit performs comprehensive analysis to generate water quality assessment parameters. One processing method is linear superposition. Assuming the monitoring parameters include a set of water quality parameters, pollution indices, and anomaly detection indicators, the central control unit assigns a weight to each parameter based on its importance. For example, certain key parameters in the water quality parameter set (such as dissolved oxygen and pH) have higher weights, and the pollution indices and anomaly detection indicators are also assigned corresponding weights based on their importance to the water quality assessment. These parameters are then multiplied by their corresponding weights and summed to obtain a comprehensive value, which is the preliminary water quality assessment parameter. This linear superposition method allows for the rapid acquisition of a value reflecting the overall water quality status.

[0074] Another approach is nonlinear fusion. Because water quality monitoring parameters may have complex interrelationships, simple linear superposition may not accurately reflect the true water quality situation. Therefore, the central control unit employs a nonlinear fusion method, utilizing machine learning algorithms or other complex mathematical models to process these monitoring parameters. For example, a neural network model can be used, taking all monitoring parameters as input, processing them through multiple layers of neurons within the network, and outputting a more accurate water quality assessment parameter. This approach can better capture the complex relationships between parameters and improve the accuracy of water quality assessment.

[0075] The system also includes a storage unit connected to the central control unit. This storage unit stores various data from the monitoring points, including a set of water quality parameters. This data is collected and updated in real time, recording the water quality parameters at different times for easy retrieval and analysis of water quality trends. The storage unit also stores pollution indices for the monitoring points. By recording pollution indices over a long period, changes in the degree of pollution at each monitoring point can be observed, allowing for assessment of the effectiveness of pollution control measures. Anomaly detection indicators are also stored, facilitating the review and summarization of historical anomalies and providing data support for optimizing anomaly detection algorithms. Furthermore, the storage unit stores water quality assessment parameters for the monitoring points. These parameters provide a comprehensive evaluation of water quality conditions and are crucial for water resource management and protection. By storing this data, the system can better manage and analyze data, providing strong support for water quality monitoring. Example 6

[0076] The alarm module is connected to the central control unit. Its main function is to generate graded alarm signals based on the comparison results between water quality assessment parameters and preset safety thresholds, and to send these signals to designated terminal devices through the communication network.

[0077] Preset safety thresholds are set based on relevant water quality standards and actual monitoring needs. For example, for monitoring drinking water sources, safety threshold ranges for various water quality parameters are set according to national drinking water hygiene standards. Minimum limits are specified for dissolved oxygen; maximum allowable concentrations are set for heavy metal content, etc.

[0078] The alarm module acquires water quality assessment parameters generated by the central control unit in real time. When the water quality assessment parameters exceed the preset safety threshold range, the alarm module generates a graded alarm signal according to the degree of exceedance. For example, when the water quality parameters slightly exceed the safety threshold, a level one alarm signal is generated, indicating a possible risk of mild pollution; when the water quality parameters significantly exceed the safety threshold, a higher-level alarm signal is generated, such as a level three alarm signal, indicating a risk of severe pollution.

[0079] After generating an alarm signal, the alarm module transmits it to designated terminal devices via a communication network. The communication network can employ various methods, such as wireless communication technologies (e.g., 4G, 5G networks) or wired communication networks (e.g., fiber optic networks). Designated terminal devices include mobile phones and computers belonging to relevant management personnel. This ensures that relevant personnel receive alarm information promptly and take appropriate measures, such as activating emergency response plans, investigating and treating contaminated areas, thereby effectively ensuring the safety and rational use of water resources.

[0080] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0081] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring system for multiple water quality parameters, characterized in that, include: The monitoring point configuration module is used to set the monitoring points within the target monitoring area and generate corresponding parameter acquisition rules based on the monitoring points. The water quality parameter acquisition module is used to obtain the water quality parameter set of the monitoring point from the water quality database in real time according to the parameter acquisition rules, and use all the parameters in the water quality parameter set as the monitoring dimension parameters of the monitoring point. The pollution index calculation module is used to calculate the pollution index of the monitoring point based on the preset pollution source correlation in the water quality database and through a multi-dimensional fusion algorithm, and to use the pollution index of the monitoring point as the monitoring dimension parameter of the monitoring point. An anomaly detection module is used to obtain anomaly detection indicators of the monitoring points through a preset dynamic threshold analysis method, and to use the anomaly detection indicators of the monitoring points as monitoring dimension parameters of the monitoring points. The central control unit is used to send the monitoring points to the pollution index calculation module and the anomaly detection module, send the parameter acquisition rules to the water quality parameter acquisition module, and also to perform comprehensive analysis on all the monitoring dimension parameters of the monitoring points to generate water quality assessment parameters for the monitoring points. The water quality database includes a pre-set pollution source database and / or a real-time monitoring database; The method of obtaining the abnormal detection indicators of the monitoring points through a preset dynamic threshold analysis method includes: The monitoring points and the preset reference points within the watershed to which the monitoring points belong are all used as dynamic analysis units. Based on the watershed to which the monitoring points belong, all historical water quality data are obtained and the parameter fluctuation patterns of the dynamic analysis units in historical periods are determined. Based on the parameter fluctuation pattern, and based on the fluctuation amplitude of each historical water quality data in the corresponding dynamic analysis unit, the abnormal initial index of each dynamic analysis unit and the deviation initial index of each historical water quality data are calculated. Based on the initial abnormality index of each dynamic analysis unit and the initial deviation index of each historical water quality data, the anomaly detection index of each dynamic analysis unit and the deviation index of each historical water quality data are synchronously optimized through a bidirectional iterative model to obtain the final abnormality index of each dynamic analysis unit. The final abnormality index of the dynamic analysis unit corresponding to the monitoring point is used as the anomaly detection index of the monitoring point. The comparison dynamic analysis unit is set as any one of all dynamic analysis units, and the comparison historical water quality data is set as any one of all historical water quality data. Based on the parameter fluctuation mode, the fluctuation of the comparison dynamic analysis unit in the comparison historical water quality data is taken as the individual fluctuation, the total fluctuation of the comparison dynamic analysis unit in all historical water quality data is taken as the individual total fluctuation, the fluctuation of all dynamic analysis units in the comparison historical water quality data is taken as the overall fluctuation, and the total fluctuation of all dynamic analysis units in all historical water quality data is taken as the overall total fluctuation. At this time, it is determined whether the ratio of the individual fluctuation to the individual total fluctuation is greater than the ratio of the overall fluctuation to the overall total fluctuation. If so, it is determined that the comparison dynamic analysis unit has a significant anomaly in the comparison historical water quality data; otherwise, it is determined that the comparison dynamic analysis unit does not have a significant anomaly in the comparison historical water quality data. When the preset pollution source association relationship is a direct pollution source relationship, it is determined whether the monitoring point is associated with a pollution source node in the water quality database. If so, the pollution index of the monitoring point is the sum of the pollution contribution values ​​of all pollution source nodes associated with the monitoring point; otherwise, the pollution index of the monitoring point is the basic pollution value of the monitoring point itself. When the preset pollution source association relationship is an indirect pollution source relationship, the pollution index of the monitoring point is the weighted sum of the pollution contribution values ​​of all pollution source nodes obtained by the monitoring point through the upstream and downstream association paths; It also includes a pollution source tracking module connected to the central control unit; The pollution source tracking module is used to set a target pollution type, obtain the spatiotemporal correlation degree between the monitoring point and the target pollution type through a preset spatiotemporal correlation analysis method, and use the spatiotemporal correlation degree as the monitoring dimension parameter of the monitoring point. The pollution source tracing module obtains the spatiotemporal correlation between the monitoring point and the target pollution type through a preset spatiotemporal correlation analysis method, including: The historical pollution diffusion path set of the monitoring point is obtained from the pollution diffusion model library, and the standard diffusion path set of the target pollution type is obtained from the pollution diffusion model library. The spatiotemporal overlap between the historical pollution diffusion path set and the standard diffusion path set is used as the first correlation between the monitoring point and the target pollution type. The pollution concentration change curve of the monitoring point within a preset time period is dynamically matched with the standard concentration change curve of the target pollution type, and the waveform similarity between the two is used as the second correlation between the monitoring point and the target pollution type. The first correlation, the second correlation, or the weighted average of the first correlation and the second correlation are used as the spatiotemporal correlation between the monitoring point and the target pollution type.

2. The real-time monitoring system according to claim 1, characterized in that, The parameter acquisition rules include a basic parameter type group and an extended parameter type group; the basic parameter type group contains multiple basic parameter types and a fixed weight corresponding to each basic parameter type; the extended parameter type group contains multiple extended parameter types and a dynamic weight corresponding to each extended parameter type.

3. The real-time monitoring system according to claim 2, characterized in that, The step of obtaining the water quality parameter set of the monitoring point from the water quality database in real time according to the parameter acquisition rules includes: The water quality parameter acquisition module extracts structured monitoring data of the monitoring points from the water quality database, extracts corresponding basic parameters from the structured monitoring data based on each basic parameter type in the basic parameter type group, and extracts corresponding extended parameters from the structured monitoring data based on each extended parameter type in the extended parameter type group, and summarizes all the basic parameters and all the extended parameters into a water quality parameter set for the monitoring points.

4. The real-time monitoring system according to claim 1, characterized in that, The central control unit performs linear superposition or nonlinear fusion of all the monitoring dimension parameters of the monitoring points to generate the water quality assessment parameters.

5. The real-time monitoring system according to claim 1, characterized in that, It also includes a storage unit connected to the central control unit, the storage unit being used to store the water quality parameter set of the monitoring point, the pollution index of the monitoring point, the abnormal detection index of the monitoring point, and the water quality assessment parameters of the monitoring point.

6. The real-time monitoring system according to claim 1, characterized in that, It also includes an alarm module connected to the central control unit. The alarm module is used to generate a graded alarm signal based on the comparison result between the water quality assessment parameters and the preset safety threshold, and to send the graded alarm signal to a designated terminal device through a communication network.

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