Flowing water area water quality monitoring management system based on dynamic data analysis

Abstract

The application belongs to the technical field of flowing water area supervision, and particularly relates to a flowing water area water quality monitoring management system based on dynamic data analysis, which comprises a flowing water area dynamic sensing module, a monitoring data self-adaptive regularization module, a water quality comprehensive analysis module, a water quality anomaly research and judgment output module and a water area supervision end; the flowing water area dynamic sensing module is used to guarantee the comprehensiveness, safety and traceability of water quality monitoring data of the water area from the root, the monitoring data self-adaptive regularization module is used to receive original data and eliminate data defects, the water quality condition is accurately quantified according to the regularized data, the anomaly is accurately identified based on the water quality condition, the node deployment rationality is evaluated through a monitoring distribution rationality evaluation module, the monitoring node migration necessity is analyzed through a node migration necessity decision module, the flowing water area pollution prevention and control and environmental protection are effectively supported, and the difficulty of flowing water area water quality monitoring management is significantly reduced.

Description

Technical Field

[0001] This invention relates to the field of flowing water area monitoring technology, specifically a flowing water area water quality monitoring and management system based on dynamic data analysis. Background Technology

[0002] The water quality of flowing water bodies (such as rivers, canals, and irrigation ditches) is directly related to ecological balance, agricultural irrigation, and the safety of residential water use. The dynamic nature of water flow and the complexity of the outdoor environment lead to high requirements for the real-time nature of water quality monitoring, data reliability, and timely early warning. Traditional water quality monitoring relies on manual fixed-point sampling, which has problems such as low frequency, data lag, and limited coverage, making it difficult to cope with sudden pollution. Therefore, the industry has gradually developed online water quality monitoring technology.

[0003] For example, Chinese invention patent CN113125659A discloses a "water quality monitoring platform." This invention's technical solution involves acquiring water quality testing data and equipment status data through a data acquisition module, analyzing data from various cross-sections grouped by water flow direction through a processing module, and triggering alarms when multiple cross-sections show abnormal data. Combined with playback and display modules, it enables pollution source tracing and data visualization, which helps improve the real-time nature of pollution warnings. However, this solution still has the following significant drawbacks when practically applied to flowing water areas:

[0004] First, alarms are triggered solely based on fixed thresholds or multi-section anomaly correlations, without considering the core dynamic characteristics of flowing water areas. The warning logic lacks dynamic adaptability and is prone to response lag. Furthermore, the aforementioned technical solutions neither construct a rationality assessment mechanism for the distribution of monitoring nodes nor provide decision-making basis for the necessity of monitoring node relocation. They lack optimization decision-making capabilities, cannot achieve full-process monitoring and control of water quality in flowing water areas, and are difficult to effectively support pollution prevention and control and environmental protection in flowing water areas. They are not conducive to significantly reducing the difficulty of water quality monitoring and management in flowing water areas and ensuring monitoring and management performance. Therefore, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a water quality monitoring and management system for flowing water areas based on dynamic data analysis, so as to solve the technical defects mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a dynamic data analysis-based water quality monitoring and management system for flowing water areas, comprising a dynamic sensing module for flowing water areas, an adaptive data normalization module for monitoring data, a comprehensive water quality analysis module, a water quality anomaly judgment and output module, and a water area monitoring terminal;

[0007] The dynamic sensing module for flowing water areas monitors water quality based on several monitoring nodes deployed in the flowing water area and sends the collected monitoring dataset to the adaptive data normalization module. The adaptive data normalization module performs targeted cleaning, standardization and normalization, and missing value completion on the original monitoring data.

[0008] The comprehensive water quality analysis module quantifies the water quality status, obtains the comprehensive water quality evaluation index (QI) through analysis, and sends it to the water quality anomaly judgment output module in real time. The water quality anomaly judgment output module performs water quality anomaly judgment analysis based on the comprehensive water quality evaluation results, and determines whether to generate a water quality early warning signal through analysis. The water quality early warning signal and the corresponding monitoring node number are sent to the water area monitoring terminal. When the water area monitoring terminal receives the water quality early warning signal, it issues a corresponding warning.

[0009] Furthermore, each monitoring node integrates a water quality sensor group, a hydrological sensor group, and an environmental sensor group. When collecting parameters, each monitoring node automatically includes a unique node number, GPS geographical coordinates, and a precise timestamp. The multi-dimensional raw monitoring data is uploaded to the monitoring data adaptive normalization module in real time. Data fragmentation and encryption are used during transmission to ensure the security of raw data transmission.

[0010] Furthermore, the specific analysis process of the comprehensive water quality analysis module is as follows:

[0011] Once the water quality parameters to be monitored are obtained, if the corresponding water quality parameter is positively correlated with the water quality assessment result, the corresponding water quality parameter is marked as a positive indicator; if the corresponding water quality parameter is negatively correlated with the water quality assessment result, the corresponding water quality parameter is marked as a negative indicator.

[0012] If the actual monitoring data of various water quality parameters are obtained, and the corresponding water quality parameter is a positive indicator, the ratio of the actual monitoring data of the corresponding water quality parameter to the corresponding preset lower limit threshold is calculated to obtain the parameter characteristic value; if the corresponding water quality parameter is a negative indicator, the ratio of the preset data safety threshold of the corresponding water quality parameter to the corresponding actual monitoring data is calculated to obtain the parameter characteristic value.

[0013] Each water quality parameter is pre-set to correspond to a set of preset weight values. The parameter characteristic value of the corresponding water quality parameter is multiplied by the corresponding preset weight value to obtain the parameter analysis value. The parameter analysis values ​​of all water quality parameters are summed to calculate the comprehensive water quality evaluation index (QI).

[0014] Furthermore, the specific analysis process of the water quality anomaly assessment output module includes:

[0015] The water quality comprehensive evaluation index QI is obtained. The basic warning threshold Te is determined according to the functional planning of the flowing water area. Based on the basic warning threshold Te, the dynamic warning threshold Th is obtained through threshold correction decision analysis. The water quality comprehensive evaluation index QI and the dynamic warning threshold Th are numerically compared. If QI < Th, a water quality warning signal is generated.

[0016] Furthermore, the specific analysis process of threshold correction decision analysis is as follows:

[0017] The increase ΔQI in the comprehensive water quality evaluation index QI over the past five minutes, as well as the water flow velocity V, are obtained. The dynamic water quality early warning threshold Th is then calculated.

[0018] Furthermore, the water area monitoring terminal is connected to the monitoring distribution rationality assessment module. The monitoring distribution rationality assessment module divides the water area to be monitored into several target areas of equal area, evaluates and analyzes the rationality of the monitoring distribution in each target area, and determines whether an unreasonable monitoring distribution signal is generated for the corresponding target area. The unreasonable monitoring distribution signal and the corresponding target area are sent to the water area monitoring terminal. When the water area monitoring terminal receives the unreasonable monitoring distribution signal, it issues a corresponding warning.

[0019] Furthermore, the specific analysis process for the monitoring distribution rationality assessment module includes:

[0020] The number of monitoring nodes in the corresponding target area is obtained and marked as the node count measurement value. The number of times the corresponding target area was involved in water quality early warning signals in the historical period is marked as the water quality early warning value. The ratio of the water quality early warning value and the node count measurement value is calculated to obtain the distribution assessment value of the corresponding target area. The distribution assessment value is compared with the preset distribution assessment value range. If the distribution assessment value is not within the preset distribution decision value range, an unreasonable monitoring distribution signal for the corresponding target area is generated.

[0021] Furthermore, if the distribution evaluation value is within the preset distribution evaluation value range, the target area is divided into several sub-areas of equal area. The variance of the number of monitoring nodes in each sub-area is calculated to obtain the distribution discrete value. The distribution discrete value is compared with the preset distribution discrete threshold. If the distribution discrete value exceeds the preset distribution discrete threshold, an unreasonable monitoring distribution signal is generated for the corresponding target area.

[0022] Furthermore, the water area monitoring terminal communication connection node migration necessity decision module obtains all monitoring nodes in the water area that needs to be monitored, analyzes the migration necessity of the corresponding monitoring nodes, determines whether to generate a migration early warning signal for the corresponding monitoring node through analysis, and sends the migration early warning signal and the corresponding monitoring node to the water area monitoring terminal. When the water area monitoring terminal receives the migration early warning signal, it issues a corresponding early warning.

[0023] Furthermore, the specific analysis process of the node migration necessity decision module is as follows:

[0024] Within the set statistical period, if the corresponding monitoring node is unable to complete the water quality parameter acquisition operation normally due to hardware failure, software abnormality or environmental interference, the corresponding monitoring node is judged to be in a fault state and the duration of the fault state is recorded. The duration of all fault states of the corresponding monitoring node within the statistical period is summed to obtain the cumulative fault duration. The total monitoring duration is obtained by subtracting the normal downtime maintenance time of the corresponding monitoring node from the total duration of the statistical period. The node fault characteristic value is obtained by calculating the ratio of the cumulative fault duration to the total monitoring duration.

[0025] The system obtains the effective data volume successfully transmitted by the corresponding monitoring node within the statistical period, calculates the ratio of this data volume to the theoretically required data volume to obtain the transmission efficiency characteristic value, compares the node fault characteristic value and the transmission efficiency characteristic value with the preset node fault characteristic threshold and the preset transmission efficiency characteristic threshold respectively, and generates a migration warning signal for the corresponding monitoring node if the node fault characteristic value or the transmission efficiency characteristic value exceeds the corresponding preset threshold.

[0026] If neither the node fault characteristic value nor the transmission efficiency characteristic value exceeds the corresponding preset threshold, the node migration warning value is calculated by weighted summation of the node fault characteristic value and the transmission efficiency characteristic value. The node migration warning value is then compared with the preset node migration warning threshold. If the node migration warning value exceeds the preset node migration warning threshold, a migration warning signal for the corresponding monitoring node is generated.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] 1. In this invention, the dynamic sensing module for flowing water areas ensures the comprehensiveness, security and traceability of water quality monitoring data from the source. The adaptive data normalization module takes over the original data and eliminates data defects. It accurately quantifies the water quality status based on the normalized data, avoiding the one-sidedness of judging by a single parameter. It accurately identifies anomalies based on the water quality status and generates early warning signals with node numbers, thereby improving emergency response efficiency.

[0029] 2. In this invention, the rationality of node deployment is evaluated by the monitoring distribution rationality assessment module, which facilitates timely optimization of the monitoring layout to reduce resource waste and ensure the monitoring capabilities of each area. Furthermore, the necessity of monitoring node migration is analyzed by the node migration necessity decision module, and timely warnings are issued, which helps to avoid monitoring blind spots caused by node failure, ensures the continuous and stable operation of the monitoring system, and reduces the difficulty of water quality monitoring and management in flowing water areas. Attached Figure Description

[0030] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0031] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0032] Figure 2 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0033] 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.

[0034] Example 1: As Figure 1 As shown, the dynamic data analysis-based water quality monitoring and management system for flowing water areas proposed in this invention includes a dynamic sensing module for flowing water areas, an adaptive data normalization module for monitoring data, a comprehensive water quality analysis module, a water quality anomaly judgment and output module, and a water area monitoring terminal.

[0035] The dynamic sensing module for flowing water areas monitors water quality based on several monitoring nodes deployed in the flowing water area, and sends the collected monitoring dataset to the adaptive data normalization module, ensuring the comprehensiveness, real-time nature and security of the raw data, and providing comprehensive and real-time raw data support for the system.

[0036] Specifically, each monitoring node integrates a water quality sensor group (dissolved oxygen, pH value, ammonia nitrogen, total phosphorus, etc.), a hydrological sensor group (flow velocity, water temperature, etc.), and an environmental sensor group (light intensity, wind speed, air pressure, etc.). When collecting parameters, each monitoring node automatically includes a unique node number, GPS geographical coordinates, and a precise timestamp (accurate to milliseconds). The multi-dimensional raw monitoring data is uploaded to the monitoring data adaptive normalization module in real time. Data fragmentation and encryption are used during transmission to ensure the security of raw data transmission.

[0037] The adaptive data normalization module cleans, standardizes, normalizes, and fills in missing values ​​for the raw monitoring data, eliminating data noise, heterogeneity, and integrity defects. It transforms the scattered and disordered raw data into high-quality, highly consistent normalized data, clearing data obstacles for subsequent water quality assessment and anomaly detection, and ensuring the accuracy of the analysis results.

[0038] The comprehensive water quality analysis module quantitatively assesses water quality conditions, generating a comprehensive water quality evaluation index (QI) which is sent in real-time to the water quality anomaly assessment output module. This achieves accurate quantitative assessment of water quality conditions, avoiding the limitations of single-parameter judgments and providing the water quality anomaly assessment output module with intuitive and reliable core analytical basis, ensuring the rationality and accuracy of water quality anomaly judgment results. The specific analysis process of the comprehensive water quality analysis module is as follows:

[0039] Once the water quality parameters to be monitored are obtained, if the corresponding water quality parameter is positively correlated with the water quality assessment result (i.e., the higher the monitored value of the parameter, the better the water quality, such as dissolved oxygen content), then the corresponding water quality parameter is marked as a positive indicator; if the corresponding water quality parameter is negatively correlated with the water quality assessment result (i.e., the higher the monitored value of the parameter, the worse the water quality, such as heavy metal ion concentration), then the corresponding water quality parameter is marked as a negative indicator.

[0040] If the actual monitoring data of various water quality parameters are obtained, and the corresponding water quality parameter is a positive indicator, the ratio of the actual monitoring data of the corresponding water quality parameter to the corresponding preset lower limit threshold is calculated to obtain the parameter characteristic value; if the corresponding water quality parameter is a negative indicator, the ratio of the preset data safety threshold of the corresponding water quality parameter to the corresponding actual monitoring data is calculated to obtain the parameter characteristic value.

[0041] Each water quality parameter is pre-set to correspond to a set of preset weight values, and the preset weight values ​​are all positive numbers. It should be noted that the greater the influence of the corresponding water quality parameter on the water quality assessment result, the larger the preset weight value corresponding to it.

[0042] Furthermore, the parameter characteristic value of the corresponding water quality parameter is multiplied by the corresponding preset weight value to obtain the parameter analysis value, and the parameter analysis values ​​of all water quality parameters are summed to calculate the comprehensive water quality evaluation index (QI). It should be noted that the smaller the value of the comprehensive water quality evaluation index (QI), the worse the overall water quality of the corresponding monitoring node.

[0043] The water quality anomaly analysis and output module analyzes water quality anomalies based on comprehensive water quality evaluation results. This analysis determines whether a water quality early warning signal should be generated, and the early warning signal, along with its corresponding monitoring node number, is sent to the water area monitoring terminal. Upon receiving the early warning signal, the terminal issues a corresponding alert. This system accurately identifies water quality anomalies and generates early warning signals with node numbers, allowing water area monitoring personnel to quickly locate polluted areas, achieve dynamic early warning and precise source tracing, and effectively improve the efficiency of emergency response to water quality anomalies. The specific analysis process of the water quality anomaly analysis and output module is as follows:

[0044] The water quality comprehensive evaluation index (QI) is obtained, and the basic early warning threshold (Te) is determined based on the functional planning of the flowing water area (such as drinking water source, landscape water, industrial water, etc.). For example, Te=0.4 for drinking water source, Te=0.2 for landscape water, and Te=0.1 for industrial water, which are set and stored in advance.

[0045] The increase in the comprehensive water quality evaluation index (QI) ΔQI over the past five minutes is obtained (if the index shows a downward trend, the increase ΔQI is zero), and the water flow velocity V is collected. The dynamic water quality warning threshold Th is then calculated using the following formula:

[0046] ;

[0047] Wherein, α and β are preset weight coefficients, and the values ​​of α and β are both positive numbers;

[0048] Vmax represents the preset flow velocity impact threshold. It should be noted that the larger the value of V / Vmax, the larger the final water quality dynamic early warning threshold Th value, ensuring that the faster the water flow velocity (indicating that the polluted water body spreads faster and the risk level is higher), the higher the early warning threshold, and thus enabling early warning.

[0049] Δt represents the monitoring duration, i.e., Δt = 300s; it should be noted that ΔQI / Δt represents the rate of water quality deterioration, ensuring that the faster the water quality deteriorates, the higher the warning threshold, thus enabling early warning;

[0050] The water quality comprehensive evaluation index QI is compared with the water quality dynamic early warning threshold Th. If QI < Th, it indicates that the water quality pollution risk of the corresponding monitoring node is high, and a water quality early warning signal is generated.

[0051] Example 2: Figure 2 As shown, the difference between this embodiment and Embodiment 1 is that the water area monitoring terminal communication connection monitoring distribution rationality assessment module divides the water area to be monitored into several target areas of equal area, evaluates and analyzes the rationality of the monitoring distribution of each target area, and determines whether an unreasonable monitoring distribution signal is generated for the corresponding target area through analysis.

[0052] Furthermore, signals indicating unreasonable monitoring distribution, along with their corresponding target areas, are sent to the water area monitoring terminal. Upon receiving such signals, the terminal issues a warning, providing decision support for optimizing monitoring layout and reducing resource waste. This reminds water area monitoring personnel to promptly replan or adjust the deployment of monitoring nodes in each area, ensuring optimal allocation of system monitoring resources. This approach helps to meet the water quality monitoring needs of flowing water areas while minimizing resource waste, significantly reducing the difficulty of managing flowing water monitoring and improving the overall effectiveness of the monitoring system. The specific analysis process of the monitoring distribution rationality assessment module is as follows:

[0053] The number of monitoring nodes in the corresponding target area is obtained and marked as the node count measurement value. The number of times the corresponding target area is involved in water quality early warning signals in the historical period (preferably, the historical period means the past three years) is marked as the water quality early warning value. The distribution evaluation value of the corresponding target area is obtained by calculating the ratio of the water quality early warning value and the node count measurement value.

[0054] The distribution assessment value is compared with the preset distribution assessment value range. If the distribution assessment value is not within the preset distribution decision value range, it indicates that the number of monitoring nodes in the corresponding target area is unreasonable. It is necessary to increase or decrease the number of monitoring nodes in the corresponding target area to reduce resource waste while meeting monitoring needs. In this case, an unreasonable monitoring distribution signal for the corresponding target area is generated.

[0055] Furthermore, if the distribution evaluation value is within the preset distribution evaluation value range, the target area is divided into several sub-regions of equal area. The variance of the number of monitoring nodes in each sub-region is calculated to obtain the distribution discrete value. The larger the value of the distribution discrete value, the more uneven the distribution of monitoring nodes in the corresponding target area. The distribution discrete value is compared with the preset distribution discrete threshold. If the distribution discrete value exceeds the preset distribution discrete threshold, it indicates that the distribution of monitoring nodes in the corresponding target area is uneven, and an unreasonable monitoring distribution signal for the corresponding target area is generated.

[0056] Example 3: Figure 2 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the water area monitoring terminal communication connection node migration necessity decision module obtains all monitoring nodes in the water area that needs to be monitored, analyzes the migration necessity of the corresponding monitoring nodes, determines whether to generate a migration warning signal for the corresponding monitoring node through analysis, and sends the migration warning signal and the corresponding monitoring node to the water area monitoring terminal.

[0057] When the water area monitoring terminal receives a migration warning signal, it issues a corresponding warning, which can accurately determine whether the monitoring node is unstable due to improper location. This reminds water area monitoring personnel to relocate the corresponding monitoring node in a timely manner, such as to a nearby area with less water flow impact and better wireless signal, to ensure the continuous and effective operation of the monitoring node, avoid monitoring blind spots caused by node failure, further reduce the difficulty of monitoring and managing flowing water areas, and ensure water quality monitoring performance. The specific analysis process of the node migration necessity decision module is as follows:

[0058] Within the set statistical period, preferably, the statistical period is sixty days; if the corresponding monitoring node is unable to complete the water quality parameter collection operation normally due to hardware failure (sensor damage, power supply unit failure, etc.), software abnormality (data acquisition program crash, protocol parsing failure, etc.) or environmental interference, the corresponding monitoring node is judged to be in a fault state and the duration of the fault state is recorded.

[0059] Furthermore, the cumulative fault duration is calculated by summing the duration of all fault states of the corresponding monitoring nodes within the statistical period, and the total monitoring duration is obtained by subtracting the normal downtime and maintenance time of the corresponding monitoring nodes from the total duration of the statistical period. The node fault characteristic value is obtained by calculating the ratio of the cumulative fault duration to the total monitoring duration. This value reflects the stable operation capability of the monitoring node.

[0060] In addition, the effective data volume successfully transmitted by the corresponding monitoring nodes within the statistical period is obtained, and the ratio of this to the theoretically required data volume is calculated to obtain the transmission efficiency characteristic value, which reflects the reliability and integrity of the data transmission of the monitoring nodes.

[0061] The node fault characteristic value and transmission efficiency characteristic value are compared with the preset node fault characteristic threshold and preset transmission efficiency characteristic threshold respectively. If the node fault characteristic value or transmission efficiency characteristic value exceeds the corresponding preset threshold, it indicates that the location of the corresponding monitoring node is not suitable and the corresponding monitoring node needs to be relocated in time. Then, a relocation warning signal for the corresponding monitoring node is generated.

[0062] If neither the node fault characteristic value nor the transmission efficiency characteristic value exceeds the corresponding preset threshold, then the node migration warning value is obtained by weighted summation of the node fault characteristic value and the transmission efficiency characteristic value. That is, the node fault characteristic value and the transmission efficiency characteristic value are respectively assigned corresponding preset weight coefficients, and the node fault characteristic value and the transmission efficiency characteristic value are respectively multiplied by the corresponding preset weight coefficients. The sum of the two sets of product results is marked as the node migration warning value.

[0063] It should be noted that the larger the node migration warning value, the more unsuitable the location of the corresponding monitoring node is overall, and the more necessary it is to relocate the corresponding monitoring node in a timely manner. The node migration warning value is compared with the preset node migration warning threshold. If the node migration warning value exceeds the preset node migration warning threshold, it indicates that the location of the corresponding monitoring node is unsuitable overall, and the corresponding monitoring node needs to be relocated in a timely manner. In this case, a migration warning signal for the corresponding monitoring node is generated.

[0064] The working principle of this invention is as follows: During use, the dynamic sensing module for flowing water areas ensures the comprehensiveness, security, and traceability of water quality monitoring data from the source. The adaptive data normalization module receives the original data and eliminates data defects. The comprehensive water quality analysis module accurately quantifies the water quality status based on the normalized data, avoiding the bias of single-parameter judgments. The water quality anomaly analysis and output module accurately identifies anomalies based on the water quality status and generates early warning signals with node numbers, improving emergency response efficiency. Furthermore, the monitoring distribution rationality assessment module evaluates the rationality of node deployment, optimizing the monitoring layout to reduce resource waste. The node migration necessity decision module analyzes the necessity of monitoring node migration, avoiding monitoring blind spots caused by node failure, ensuring the continuous and stable operation of the monitoring system. This achieves full-process control of reliable data, accurate analysis, timely early warning, and optimized monitoring management, effectively supporting pollution prevention and control and environmental protection in flowing water areas, significantly improving the efficiency and reliability of water quality monitoring and management in flowing water areas, and helping to reduce the difficulty of water quality monitoring and management in flowing water areas.

[0065] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0066] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A water quality monitoring and management system for flowing water areas based on dynamic data analysis, characterized in that, It includes a dynamic sensing module for flowing water areas, an adaptive data normalization module for monitoring, a comprehensive water quality analysis module, a water quality anomaly assessment and output module, and a water area monitoring terminal; The dynamic sensing module for flowing water areas monitors water quality based on several monitoring nodes deployed in the flowing water area. The adaptive data normalization module cleans, standardizes, normalizes, and fills in missing values ​​for the original monitoring data. The water quality comprehensive analysis module quantifies the water quality status and obtains the water quality comprehensive evaluation index (QI) through analysis. The water quality anomaly judgment output module performs water quality anomaly judgment analysis based on the water quality comprehensive evaluation results. Through analysis, it determines whether a water quality early warning signal should be generated and sends the water quality early warning signal and the corresponding monitoring node number to the water area supervision terminal. When the water area supervision terminal receives the water quality early warning signal, it issues a corresponding warning. The specific analysis process of the comprehensive water quality analysis module is as follows: Once the water quality parameters to be monitored are obtained, if the corresponding water quality parameter is positively correlated with the water quality assessment result, the corresponding water quality parameter is marked as a positive indicator; if the corresponding water quality parameter is negatively correlated with the water quality assessment result, the corresponding water quality parameter is marked as a negative indicator. If the actual monitoring data of various water quality parameters are obtained, and the corresponding water quality parameter is a positive indicator, the parameter characteristic value is obtained by calculating the ratio between the actual monitoring data of the corresponding water quality parameter and the corresponding preset lower limit threshold. If the corresponding water quality parameter is a negative indicator, the parameter characteristic value is obtained by calculating the ratio between the preset data safety threshold of the corresponding water quality parameter and the corresponding actual monitoring data. Each water quality parameter is pre-set to correspond to a set of preset weight values. The parameter characteristic value of the corresponding water quality parameter is multiplied by the corresponding preset weight value to obtain the parameter analysis value. The parameter analysis values ​​of all water quality parameters are summed to calculate the comprehensive water quality evaluation index QI. The specific analysis process of the water quality anomaly assessment output module is as follows: The comprehensive water quality evaluation index (QI) is obtained, and the basic early warning threshold Te is determined based on the functional planning of the flowing water area. The increase in the comprehensive water quality evaluation index (QI) ΔQI over the past five minutes is obtained, along with the collected water flow velocity V. The dynamic early warning threshold Th is calculated using the following formula: ； Wherein, α and β are preset weight coefficients, and the values ​​of α and β are both positive numbers; Vmax represents the preset threshold for the influence of flow rate; Δt represents the monitoring duration, and ΔQI / Δt represents the rate of water quality deterioration. The water quality comprehensive evaluation index QI is compared with the water quality dynamic early warning threshold Th. If QI < Th, it indicates that the water quality pollution risk of the corresponding monitoring node is high, and a water quality early warning signal is generated.

2. The water quality monitoring and management system for flowing water areas based on dynamic data analysis according to claim 1, characterized in that, Each monitoring node integrates a water quality sensor group, a hydrological sensor group, and an environmental sensor group. When collecting parameters, each monitoring node automatically includes a unique node number, GPS geographical coordinates, and a precise timestamp. The multi-dimensional raw monitoring data is uploaded to the monitoring data adaptive normalization module in real time, and data fragmentation and encryption are used during the transmission process.

3. The water quality monitoring and management system for flowing water areas based on dynamic data analysis according to claim 1, characterized in that, The water area monitoring terminal communication connection monitoring distribution rationality assessment module divides the water area to be monitored into several target areas of equal area, evaluates and analyzes the rationality of the monitoring distribution of each target area, and sends the unreasonable monitoring distribution signals and corresponding target areas to the water area monitoring terminal.

4. The water quality monitoring and management system for flowing water areas based on dynamic data analysis according to claim 3, characterized in that, The specific analysis process of the monitoring distribution rationality assessment module includes: The number of monitoring nodes in the corresponding target area is obtained and marked as the node count measurement value. The number of times the corresponding target area was involved in water quality early warning signals in the historical period is marked as the water quality early warning value. The distribution evaluation value of the corresponding target area is obtained by calculating the ratio of the water quality early warning value and the node count measurement value. If the distribution evaluation value is not within the preset distribution decision value range, an unreasonable monitoring distribution signal for the corresponding target area is generated.

5. The water quality monitoring and management system for flowing water areas based on dynamic data analysis according to claim 4, characterized in that, If the distribution evaluation value is within the preset distribution evaluation value range, the variance of the number of monitoring nodes in each sub-region is calculated to obtain the distribution discrete value. If the distribution discrete value exceeds the preset distribution discrete threshold, an unreasonable monitoring distribution signal is generated for the corresponding target region.

6. The water quality monitoring and management system for flowing water areas based on dynamic data analysis according to claim 3, characterized in that, The water area monitoring terminal communication connection node migration necessity decision module obtains all monitoring nodes in the water area that needs to be monitored, analyzes the migration necessity of the corresponding monitoring nodes, and sends the migration early warning signal and the corresponding monitoring node to the water area monitoring terminal.

7. The water quality monitoring and management system for flowing water areas based on dynamic data analysis according to claim 6, characterized in that, The specific analysis process for the node migration necessity decision module is as follows: Within the set statistical period, if the corresponding monitoring node is unable to complete the water quality parameter acquisition operation normally due to hardware failure, software abnormality or environmental interference, the corresponding monitoring node is judged to be in a fault state and the duration of the fault state is recorded. The duration of all fault states of the corresponding monitoring node within the statistical period is summed to obtain the cumulative fault duration. The total monitoring duration is obtained by subtracting the normal downtime maintenance time of the corresponding monitoring node from the total duration of the statistical period. The node fault characteristic value is obtained by calculating the ratio of the cumulative fault duration to the total monitoring duration. The system obtains the effective data volume successfully transmitted by the corresponding monitoring node within the statistical period, calculates the ratio of this data volume to the theoretically required data volume to obtain the transmission efficiency characteristic value, compares the node fault characteristic value and the transmission efficiency characteristic value with the preset node fault characteristic threshold and the preset transmission efficiency characteristic threshold respectively, and generates a migration warning signal for the corresponding monitoring node if the node fault characteristic value or the transmission efficiency characteristic value exceeds the corresponding preset threshold. If neither the node fault characteristic value nor the transmission efficiency characteristic value exceeds the corresponding preset threshold, the node migration warning value is calculated by weighted summation of the node fault characteristic value and the transmission efficiency characteristic value. The node migration warning value is then compared with the preset node migration warning threshold. If the node migration warning value exceeds the preset node migration warning threshold, a migration warning signal for the corresponding monitoring node is generated.

Images