Water quality online analysis method and system

Abstract

The invention relates to the field of water quality detection, in particular to a water quality online analysis method and system. The water quality on-line analysis system comprises a water quality monitoring module, a calibration module and a comprehensive evaluation module. The monitoring frequency is dynamically adjusted according to the water flow rate, and when the water flow rate is high, the system can automatically increase the monitoring frequency to capture key water quality changes; when the water flow velocity is relatively low, the monitoring frequency of the system is reduced, and unnecessary resource waste is avoided, so that more efficient resource utilization is realized; meanwhile, the time for next calibration of each sampling point is determined by comprehensively analyzing the water flow velocity and the water quality change, and the intelligent adjustment mechanism not only improves the timeliness and accuracy of monitoring, but also reduces the energy consumption and the cost, so that the water quality monitoring is more economical and efficient.

Description

Technical Field

[0001] The present invention relates to the field of water quality detection, and in particular to an online water quality analysis method and system. Background Art

[0002] With the rapid development of industrialization and urbanization, water pollution is becoming increasingly serious, and real-time monitoring and evaluation of water quality has become particularly important. Traditional water quality monitoring methods often rely on laboratory analysis, which is not only time-consuming and costly, but also unable to provide continuous and real-time water quality data. In order to effectively respond to water pollution and protect water resources, it is particularly urgent to develop a method that can monitor water quality online and in real time.

[0003] In the existing technology, some water quality monitoring systems can provide continuous water quality data, but these systems often use a fixed monitoring frequency and cannot dynamically adjust the monitoring frequency according to actual water quality changes, resulting in an inability to respond in time when the water quality changes drastically, or causing waste of resources when the water quality is stable; at the same time, the sensor will drift due to environmental factors and its own aging during use, and needs to be calibrated regularly to ensure the accuracy of the monitoring data; however, if there is a lack of effective calibration strategies, the sensor calibration will not be timely enough, affecting the reliability of the data; on the other hand, the existing monitoring systems can often only provide water quality data for a single sampling point, lack a comprehensive evaluation of data from multiple sampling points, and cannot fully reflect the water quality status of the entire water area.

[0004] In order to solve the above problems, the present invention proposes a water quality online analysis method and system. The method can dynamically adjust the monitoring frequency, timely calibrate the sensor, and integrate the data of multiple sampling points to provide a more accurate and comprehensive water quality assessment; through this method, real-time monitoring of water quality can be achieved, abnormal water quality can be discovered and warned in time, and a scientific basis can be provided for water resource management and protection. Summary of the invention

[0005] The present invention dynamically adjusts the monitoring frequency according to the water flow rate. When the water flow rate is high, the system will automatically increase the monitoring frequency to capture key water quality changes. When the water flow rate is relatively low, the system will reduce the monitoring frequency to avoid unnecessary waste of resources, thereby achieving more efficient resource utilization. At the same time, by comprehensively analyzing the water flow rate and water quality changes, the time for the next calibration of each sampling point is determined. This intelligent adjustment mechanism not only improves the timeliness and accuracy of monitoring, but also reduces energy consumption and costs, making water quality monitoring more economical and efficient.

[0006] A water quality online analysis method, comprising: For each sampling point in the water area to be monitored, water quality data is obtained at each monitoring frequency within the current acquisition time window. The water quality data includes several water quality parameters. The monitoring frequency is adjusted by the water quality data obtained by each sampling point in the previous acquisition time window. After the sensor at any sampling point is calibrated, the time interval for the next calibration of the sampling point is calculated based on all water quality data obtained at the sampling point in the previous calibration interval, and the obtained time interval is used for the next sensor calibration; At the end of any acquisition time window, based on the water quality data obtained from all sampling points, the weight coefficient of each sampling point is calculated, and the local water quality data of each sampling point is calculated and obtained; based on the weight coefficient and local water quality data of each sampling point obtained, the comprehensive water quality data of the water area to be tested is obtained, and the comprehensive water quality data includes several comprehensive water quality parameters; The obtained several comprehensive water quality parameters are compared with the preset normal range of each water quality parameter to obtain the comprehensive assessment results of the water area to be monitored.

[0007] Preferably, the monitoring frequency is adjusted by the water quality data obtained by the sampling point in the previous collection time window, and the specific operation is as follows: At the beginning of the current acquisition time window, for any sampling point, based on all the water quality data obtained at the sampling point in the previous acquisition time window, extract the corresponding amount of water flow rate data , j = 1, 2, ..., m; m represents the number of water quality data obtained by the sampling point in the current acquisition time window; using the formula Calculate and obtain the average water flow rate data of the sampling point ; Finally, the average water flow rate data of all sampling points are obtained , i=1, 2,…, n; n represents the number of sampling points; According to the average water flow rate data of each sampling point , using the formula Calculate and obtain the monitoring frequency of each sampling point ,in The basic monitoring frequency, which indicates the monitoring frequency required when the water is completely still. ; k is the average water velocity data for monitoring frequency The adjustment factor.

[0008] Preferably, the time interval for the next calibration of the sampling point is calculated, and the specific operation is as follows: For any sampling point, based on all the water quality data obtained at the sampling point in the last calibration interval, extract the corresponding amount of water flow rate data , q = 1, 2, …, v; v represents the number of water quality data obtained in the last calibration interval; using the formula Calculate and obtain the average water flow rate data of the sampling point in the last calibration interval ; Any water quality parameter in the water quality data obtained at the sampling point , p=1, 2, ..., u; u represents the number of water quality parameters contained in the water quality data, and each water quality parameter is calculated and obtained The average value during the last calibration interval , then use the formula Calculate and obtain each water quality parameter of the current sampling point Standard deviation over the last calibration interval ; Using the formula Calculate the fluctuation value B of the current sampling point in the previous calibration interval; finally obtain the average water flow rate data of all sampling points in their previous calibration interval and fluctuation value ; Based on the average water velocity data obtained from all sampling points and fluctuation value , using the formula Calculate the water flow rate adjustment value for all sampling points ,in and are the preset maximum water flow rate and minimum water flow rate respectively; using the formula Calculate the fluctuation adjustment value of all sampling points ,in and The maximum and minimum fluctuation values ​​are preset respectively; the water flow rate adjustment value based on all sampling points and volatility adjustment , using the formula Calculate the calibration interval adjustment value for all sampling points ; Setting the minimum calibration interval and maximum calibration interval , using the formula Calculate the calibration interval for the next calibration of all sampling points .

[0009] Preferably, the corresponding weight coefficient is calculated for the local water quality data of each sampling point, and the specific operation is as follows: Set the water flow rate standard range to At the end of each acquisition time window, for any sampling point, if the average water flow rate data of the sampling point Within the water flow rate standard range to If the average water velocity data of the sampling point is within Greater than , then use the formula Calculate the representative score of the sampling point , Between 0 and 1; if the average water flow rate data at the sampling point Lower than , then use the formula Calculate the representative score of the sampling point ,in and are the first control constant and the second control constant, respectively, used to control the representative score The range of decline; using the formula Calculate the weight coefficient of the sampling point .

[0010] Preferably, the weights of each sampling point are applied to calculate the comprehensive water quality data, and the specific operation is as follows: For any sampling point, all water quality parameters in the water quality data obtained from the sampling point , calculate all water quality parameters separately The average value in the last acquisition time window , and the average value obtained will be calculated As the local water quality parameters of the sampling point, the local water quality parameters of each sampling point are finally obtained. ; For any water quality parameter , using the formula Calculate and obtain various water quality parameters of the water area to be monitored Comprehensive water quality parameters .

[0011] Preferably, the value of k is selected by a swarm optimization algorithm.

[0012] Preferably, the population optimization algorithm for selecting the value of k is a genetic algorithm, and the specific operation is as follows: Step 1: Set the population size, randomly generate d candidate k values, form the randomly generated d candidate k values ​​into an initial population, and set the maximum number of iterations; Step 2: For any candidate k value, use the formula The fitness value E of the candidate k value is calculated, where k is the size of the candidate k value, and C represents the accuracy of collecting water quality data using the monitoring frequency when the candidate k value is used to adjust the monitoring frequency; this formula indicates that the optimization goal of the candidate k value is to minimize the size of the k value and maximize the accuracy of collecting water quality data; the higher the fitness value E obtained by calculation, the better the performance of the candidate k value; Step 3: Apply the roulette wheel selection method to select the corresponding number of candidate k values ​​with the best performance according to the preset selection ratio, and form the selected candidate k values ​​into a selection individual set; Step 4: Apply the preset crossover probability to perform a crossover operation on the candidate k values ​​in the selected individual set to generate a corresponding number of new candidate k values, and form the obtained new candidate k values ​​into a crossover individual set; randomly select candidate k values ​​from the crossover individual set, and make a small adjustment to the candidate k values ​​according to the preset mutation probability. The adjusted crossover individual set is the mutation individual set; Step 5: Merge the selected individual set and the mutated individual set to obtain the merged individual set, calculate the fitness of all candidate k values ​​in the merged individual set, and take the d candidate k values ​​with the best fitness as the new generation population; Step 6: Repeat steps 3 to 5 to iteratively update the population until the maximum number of iterations is reached. The population with the best fitness performance in the last generation is the optimal k value.

[0013] Preferably, a water quality online analysis system comprises: The water quality monitoring module includes a water quality data acquisition unit and a monitoring frequency adjustment unit; the water quality data acquisition unit is used to enable each sampling point to acquire water quality data at its own monitoring frequency within the acquisition time window, and the water quality data includes several water quality parameters; the monitoring frequency adjustment unit is used to adjust the monitoring frequency through the water quality data acquired by each sampling point within the previous acquisition time window; A calibration module is used to calculate the time interval for the next calibration of any sampling point based on all water quality data obtained at the sampling point in the previous calibration interval after the sensor at any sampling point is calibrated, and use the obtained time interval to perform the next sensor calibration; The comprehensive evaluation module includes a weight calculation unit, a comprehensive water quality data acquisition unit and a comprehensive evaluation unit; the weight calculation unit is used to calculate the weight coefficient of each sampling point based on the water quality data obtained from all sampling points at the end of any collection time window; the comprehensive water quality data acquisition unit is used to calculate and obtain the local water quality data of each sampling point, and based on the weight coefficient and local water quality data of each sampling point obtained, obtain the comprehensive water quality data of the water area to be tested, and the comprehensive water quality data includes several comprehensive water quality parameters; the comprehensive evaluation unit is used to compare the obtained several comprehensive water quality parameters with the preset normal range of each water quality parameter to obtain the comprehensive evaluation result of the water area to be monitored.

[0014] The present invention has the following advantages: 1. The present invention dynamically adjusts the monitoring frequency according to the water flow rate. When the water flow rate is high, the system will automatically increase the monitoring frequency to capture key water quality changes; when the water flow rate is relatively low, the system will reduce the monitoring frequency to avoid unnecessary waste of resources, thereby achieving more efficient resource utilization; at the same time, by comprehensively analyzing the water flow rate and water quality changes, the time for the next calibration of each sampling point is determined. This intelligent adjustment mechanism not only improves the timeliness and accuracy of monitoring, but also reduces energy consumption and costs, making water quality monitoring more economical and efficient.

[0015] 2. The present invention obtains the comprehensive water quality parameters of the entire water area by calculating the weight coefficient and local water quality data of each sampling point, and integrates the data of multiple sampling points to provide a more comprehensive and accurate water quality assessment; this method can more accurately reflect the water quality status of the entire water area, avoiding the deviations and limitations that may be caused by data from a single sampling point; and providing a more reliable scientific basis for water quality management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the structure of the water quality online analysis system used in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0018] Embodiment 1, a method for online analysis of water quality, comprising: The present invention monitors and analyzes water quality through the Huawo Zhichuang ZCX-1000 water quality online analyzer. In the water area to be monitored, each sampling point has its own monitoring frequency, which determines the frequency of obtaining water quality data at the sampling point within the current acquisition time window; for example, if the monitoring frequency of a sampling point is set to once every 5 minutes, then it will automatically collect water quality data once every 5 minutes; by setting a different monitoring frequency for each sampling point, the allocation of monitoring resources can be optimized according to the importance of each sampling point and the sensitivity of water quality changes, ensuring that key areas are monitored more intensively; for Each sampling point in the water area to be monitored obtains water quality data at its own monitoring frequency within the current collection time window. The water quality data includes several water quality parameters, such as pH value, dissolved oxygen, turbidity, temperature, etc. These parameters are key indicators for evaluating water quality conditions. By collecting these water quality parameters, the water quality conditions can be monitored in real time, water quality anomalies can be discovered in time, and data support can be provided for water quality management and protection. The monitoring frequency is adjusted by the water quality data obtained by each sampling point in the previous collection time window. This method enables the monitoring system to adapt to water quality changes and improves the flexibility and accuracy of monitoring. After the sensor at any sampling point is calibrated, the time interval for the next calibration of the sampling point is calculated based on all the water quality data obtained at the sampling point in the previous calibration interval, and the next sensor calibration is performed using the obtained time interval; sensor calibration refers to the adjustment of the water quality monitoring equipment at the sampling point to ensure the accuracy of its measurement results; due to environmental changes, equipment aging and other factors, the sensor may drift, resulting in inaccurate measurement results; therefore, regular calibration is necessary; after the sensor is calibrated, the system will calculate the time interval for the next calibration based on all the water quality data obtained at the sampling point in the previous calibration interval; this means that the calibration time interval is not fixed, but is dynamically adjusted according to the actual changes in water quality data; this method can make calibration more flexible and accurate, ensuring that calibration can be performed in a timely manner when water quality changes are large or sensor drift is serious, and when water quality is relatively stable, the calibration cycle can be appropriately extended, thereby improving efficiency and reducing costs; At the end of any collection time window, based on the water quality data obtained from all sampling points, the weight coefficient of each sampling point is calculated, and the local water quality data of each sampling point is calculated; the weight coefficient reflects the representativeness of the data of each sampling point in the overall water quality assessment; by assigning weights to each sampling point, the impact of water quality in different regions on the overall water quality can be more accurately reflected, making the comprehensive water quality data more comprehensive and accurate; based on the weight coefficients and local water quality data of each sampling point obtained, the comprehensive water quality data of the water area to be tested is obtained, which represents the overall water quality status of the entire water area to be tested within the collection time window. The comprehensive water quality data provides a macro perspective, allowing managers to quickly understand the water quality status of the entire water area and provide a basis for decision-making. The comprehensive water quality data contains several comprehensive water quality parameters; By comparing several comprehensive water quality parameters obtained with the preset normal range of each water quality parameter, the comprehensive assessment results of the monitored water area can be obtained. Through comparison, it is possible to quickly identify whether the water quality exceeds the normal range, so that corresponding measures can be taken in time, such as issuing early warnings, adjusting water quality management strategies, etc. The comprehensive assessment results are crucial for water quality management. It can help managers understand the overall status of water quality, assess water quality risks, and formulate corresponding water quality protection and improvement measures.

[0019] The monitoring frequency is adjusted based on the water quality data obtained by the sampling point in the previous collection time window. The specific operations are as follows: At the beginning of the current acquisition time window, for any sampling point, based on all the water quality data obtained at the sampling point in the previous acquisition time window, extract the corresponding amount of water flow rate data , j = 1, 2, ..., m; m represents the number of water quality data obtained by the sampling point in the current acquisition time window; using the formula Calculate and obtain the average water flow rate data of the sampling point ; Finally, the average water flow rate data of all sampling points are obtained , i=1, 2,…, n; n represents the number of sampling points; According to the average water flow rate data of each sampling point , using the formula Calculate and obtain the monitoring frequency of each sampling point ,in The basic monitoring frequency, which indicates the monitoring frequency required when the water is completely still. ; k is the average water velocity data for monitoring frequency The adjustment factor.

[0020] Calculate the time interval for the next calibration of the sampling point. The specific operations are as follows: For any sampling point, based on all the water quality data obtained at the sampling point in the last calibration interval, extract the corresponding amount of water flow rate data , q = 1, 2, …, v; v represents the number of water quality data obtained in the last calibration interval; using the formula Calculate and obtain the average water flow rate data of the sampling point in the last calibration interval ; Any water quality parameter in the water quality data obtained at the sampling point , p=1, 2, ..., u; u represents the number of water quality parameters contained in the water quality data, and each water quality parameter is calculated and obtained The average value during the last calibration interval , then use the formula Calculate and obtain each water quality parameter of the current sampling point Standard deviation over the last calibration interval ; Using the formula Calculate the fluctuation value B of the current sampling point in the previous calibration interval; finally obtain the average water flow rate data of all sampling points in their previous calibration interval and fluctuation value ; Based on the average water velocity data obtained from all sampling points and fluctuation value , using the formula Calculate the water flow rate adjustment value for all sampling points ,in and are the preset maximum water flow rate and minimum water flow rate respectively; using the formula Calculate the fluctuation adjustment value of all sampling points ,in and The maximum and minimum fluctuation values ​​are preset respectively; the water flow rate adjustment value based on all sampling points and volatility adjustment , using the formula Calculate the calibration interval adjustment value for all sampling points ; Setting the minimum calibration interval and maximum calibration interval , using the formula Calculate the calibration interval for the next calibration of all sampling points .

[0021] Calculate the corresponding weight coefficient for the local water quality data of each sampling point. The specific operation is as follows: Set the water flow rate standard range to At the end of each acquisition time window, for any sampling point, if the average water flow rate data of the sampling point Within the water flow rate standard range to If the average water velocity data of the sampling point is within Greater than , then use the formula Calculate the representative score of the sampling point , Between 0 and 1; if the average water flow rate data at the sampling point Lower than , then use the formula Calculate the representative score of the sampling point ,in and are the first control constant and the second control constant, respectively, used to control the representative score The range of decline; using the formula Calculate the weight coefficient of the sampling point .

[0022] Apply the weights of each sampling point to calculate the comprehensive water quality data. The specific operations are as follows: For any sampling point, all water quality parameters in the water quality data obtained from the sampling point , calculate all water quality parameters separately The average value in the last acquisition time window , and the average value obtained will be calculated As the local water quality parameters of the sampling point, the local water quality parameters of each sampling point are finally obtained. ; For any water quality parameter , using the formula Calculate and obtain various water quality parameters of the water area to be monitored Comprehensive water quality parameters .

[0023] The value of k is selected through a swarm optimization algorithm, which is a type of search algorithm that simulates the behavior of biological groups in nature. They solve optimization problems by simulating competition, cooperation, and genetic mechanisms among individuals in a population.

[0024] The population optimization algorithm for selecting the value of k is a genetic algorithm. The genetic algorithm is a search heuristic algorithm that simulates the biological evolution process. It iteratively evolves in the population of candidate solutions through operations such as selection, crossover and mutation to find the optimal or approximately optimal solution. The reason for using the genetic algorithm in the present invention is that it is good at finding the global optimal solution in a complex search space, and is particularly suitable for the optimization problem of the parameter k value in the water quality monitoring frequency adjustment. The genetic algorithm can handle multiple sampling points, multiple water quality parameters and dynamically changing monitoring frequencies. Through natural selection and genetic mechanisms, it can effectively search for the k value that optimizes the monitoring frequency adjustment, thereby improving the accuracy and efficiency of water quality monitoring. In addition, the parallel processing capability of the genetic algorithm makes it more efficient in processing large-scale data, which is suitable for the complex requirements of the real-time water quality monitoring system. The specific operations are as follows: Step 1: Set the population size, randomly generate d candidate k values, form the randomly generated d candidate k values ​​into an initial population, and set the maximum number of iterations; Step 2: For any candidate k value, use the formula The fitness value E of the candidate k value is calculated, where k is the size of the candidate k value, and C represents the accuracy of collecting water quality data using the monitoring frequency when the candidate k value is used to adjust the monitoring frequency; this formula indicates that the optimization goal of the candidate k value is to minimize the size of the k value and maximize the accuracy of collecting water quality data; the higher the fitness value E obtained by calculation, the better the performance of the candidate k value; Step 3: Apply the roulette wheel selection method to select the corresponding number of candidate k values ​​with the best performance according to the preset selection ratio, and form the selected candidate k values ​​into a selection individual set; Step 4: Apply the preset crossover probability to perform a crossover operation on the candidate k values ​​in the selected individual set to generate a corresponding number of new candidate k values, and form the obtained new candidate k values ​​into a crossover individual set; randomly select candidate k values ​​from the crossover individual set, and make a small adjustment to the candidate k values ​​according to the preset mutation probability. The adjusted crossover individual set is the mutation individual set; Step 5: Merge the selected individual set and the mutated individual set to obtain the merged individual set, calculate the fitness of all candidate k values ​​in the merged individual set, and take the d candidate k values ​​with the best fitness as the new generation population; Step 6: Repeat steps 3 to 5 to iteratively update the population until the maximum number of iterations is reached. The population with the best fitness performance in the last generation is the optimal k value.

[0025] Embodiment 2, a water quality online analysis system, such as Figure 1 As shown, including: The water quality monitoring module includes a water quality data acquisition unit and a monitoring frequency adjustment unit; the water quality data acquisition unit is used to enable each sampling point to acquire water quality data at its own monitoring frequency within the acquisition time window, and the water quality data includes several water quality parameters; the monitoring frequency adjustment unit is used to adjust the monitoring frequency through the water quality data acquired by each sampling point within the previous acquisition time window; A calibration module is used to calculate the time interval for the next calibration of any sampling point based on all water quality data obtained at the sampling point in the previous calibration interval after the sensor at any sampling point is calibrated, and use the obtained time interval to perform the next sensor calibration; The comprehensive evaluation module includes a weight calculation unit, a comprehensive water quality data acquisition unit and a comprehensive evaluation unit; the weight calculation unit is used to calculate the weight coefficient of each sampling point based on the water quality data obtained from all sampling points at the end of any collection time window; the comprehensive water quality data acquisition unit is used to calculate and obtain the local water quality data of each sampling point, and based on the weight coefficient and local water quality data of each sampling point obtained, obtain the comprehensive water quality data of the water area to be tested, and the comprehensive water quality data includes several comprehensive water quality parameters; the comprehensive evaluation unit is used to compare the obtained several comprehensive water quality parameters with the preset normal range of each water quality parameter to obtain the comprehensive evaluation result of the water area to be monitored.

[0026] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A method for online water quality analysis, characterized in that: include: For each sampling point in the water area to be monitored, water quality data is obtained at each monitoring frequency within the current acquisition time window. The water quality data includes several water quality parameters. The monitoring frequency is adjusted by the water quality data obtained by each sampling point in the previous acquisition time window. After the sensor at any sampling point is calibrated, the time interval for the next calibration of the sampling point is calculated based on all water quality data obtained at the sampling point in the previous calibration interval, and the obtained time interval is used for the next sensor calibration; At the end of any acquisition time window, based on the water quality data obtained from all sampling points, the weight coefficient of each sampling point is calculated, and the local water quality data of each sampling point is calculated and obtained; based on the weight coefficient and local water quality data of each sampling point obtained, the comprehensive water quality data of the water area to be tested is obtained, and the comprehensive water quality data includes several comprehensive water quality parameters; The obtained several comprehensive water quality parameters are compared with the preset normal range of each water quality parameter to obtain the comprehensive assessment results of the water area to be monitored.

2. A water quality online analysis method according to claim 1, characterized in that: The monitoring frequency is adjusted based on the water quality data obtained by the sampling point in the previous collection time window. The specific operations are as follows: At the beginning of the current acquisition time window, for any sampling point, based on all the water quality data obtained at the sampling point in the previous acquisition time window, extract the corresponding amount of water flow rate data , j = 1, 2, …, m; m represents the number of water quality data obtained by the sampling point in the current acquisition time window; using the formula Calculate and obtain the average water flow rate data of the sampling point ; Finally, the average water flow rate data of all sampling points are obtained , i=1, 2,…, n; n represents the number of sampling points; According to the average water flow rate data of each sampling point , using the formula Calculate and obtain the monitoring frequency of each sampling point ,in The basic monitoring frequency, which indicates the monitoring frequency required when the water is completely still. ; k is the average water velocity data for monitoring frequency The adjustment factor.

3. A water quality online analysis method according to claim 2, characterized in that: Calculate the time interval for the next calibration of the sampling point. The specific operations are as follows: For any sampling point, based on all the water quality data obtained at the sampling point in the last calibration interval, extract the corresponding amount of water flow rate data , q = 1, 2, …, v; v represents the number of water quality data obtained in the last calibration interval; using the formula Calculate and obtain the average water flow rate data of the sampling point in the last calibration interval ; Any water quality parameter in the water quality data obtained at the sampling point , p=1, 2, ..., u; u represents the number of water quality parameters contained in the water quality data, and each water quality parameter is calculated and obtained The average value during the last calibration interval , then use the formula Calculate and obtain each water quality parameter of the current sampling point Standard deviation over the last calibration interval ; Using the formula Calculate the fluctuation value B of the current sampling point in the previous calibration interval; finally obtain the average water flow rate data of all sampling points in their previous calibration interval and fluctuation value ; Based on the average water velocity data obtained from all sampling points and fluctuation value , using the formula Calculate the water flow rate adjustment value for all sampling points ,in and are the preset maximum water flow rate and minimum water flow rate respectively; using the formula Calculate the fluctuation adjustment value of all sampling points ,in and The maximum and minimum fluctuation values ​​are preset respectively; the water flow rate adjustment value based on all sampling points and volatility adjustment , using the formula Calculate the calibration interval adjustment value for all sampling points ; Setting the minimum calibration interval and maximum calibration interval , using the formula Calculate the calibration interval for the next calibration of all sampling points .

4. A water quality online analysis method according to claim 3, characterized in that: Calculate the corresponding weight coefficient for the local water quality data of each sampling point. The specific operation is as follows: Set the water flow rate standard range to At the end of each acquisition time window, for any sampling point, if the average water flow rate data of the sampling point Within the water flow rate standard range to If the average water velocity data of the sampling point is within Greater than , then use the formula Calculate the representative score of the sampling point , Between 0 and 1; if the average water flow rate data at the sampling point Lower than , then use the formula Calculate the representative score of the sampling point ,in and are the first control constant and the second control constant, respectively, used to control the representative score The range of decline; using the formula Calculate the weight coefficient of the sampling point .

5. A water quality online analysis method according to claim 4, characterized in that: Apply the weights of each sampling point to calculate the comprehensive water quality data. The specific operations are as follows: For any sampling point, all water quality parameters in the water quality data obtained from the sampling point , calculate all water quality parameters separately The average value in the last acquisition time window , and the average value obtained will be calculated As the local water quality parameters of the sampling point, the local water quality parameters of each sampling point are finally obtained. ; For any water quality parameter , using the formula Calculate and obtain various water quality parameters of the water area to be monitored Comprehensive water quality parameters .

6. A water quality online analysis method according to claim 5, characterized in that: The value of k is selected through a swarm optimization algorithm.

7. A water quality online analysis method according to claim 6, characterized in that: The population optimization algorithm for selecting the value of k is a genetic algorithm, and the specific operations are as follows: Step 1: Set the population size, randomly generate d candidate k values, form the randomly generated d candidate k values ​​into an initial population, and set the maximum number of iterations; Step 2: For any candidate k value, use the formula The fitness value E of the candidate k value is calculated, where k is the size of the candidate k value, and C represents the accuracy of collecting water quality data using the monitoring frequency when the candidate k value is used to adjust the monitoring frequency; this formula indicates that the optimization goal of the candidate k value is to minimize the size of the k value and maximize the accuracy of collecting water quality data; the higher the fitness value E obtained by calculation, the better the performance of the candidate k value; Step 3: Apply the roulette wheel selection method to select the corresponding number of candidate k values ​​with the best performance according to the preset selection ratio, and form the selected candidate k values ​​into a selection individual set; Step 4: Apply the preset crossover probability to perform a crossover operation on the candidate k values ​​in the selected individual set to generate a corresponding number of new candidate k values, and form the obtained new candidate k values ​​into a crossover individual set; randomly select candidate k values ​​from the crossover individual set, and make a small adjustment to the candidate k values ​​according to the preset mutation probability. The adjusted crossover individual set is the mutation individual set; Step 5: Merge the selected individual set and the mutated individual set to obtain the merged individual set, calculate the fitness of all candidate k values ​​in the merged individual set, and take the d candidate k values ​​with the best fitness as the new generation population; Step 6: Repeat steps 3 to 5 to iteratively update the population until the maximum number of iterations is reached. The population with the best fitness performance in the last generation is the optimal k value.

8. A water quality online analysis system, characterized in that: The system is applied to a water quality online analysis method according to any one of claims 1 to 7, comprising: The water quality monitoring module includes a water quality data acquisition unit and a monitoring frequency adjustment unit; the water quality data acquisition unit is used to enable each sampling point to acquire water quality data at its own monitoring frequency within the acquisition time window, and the water quality data includes several water quality parameters; the monitoring frequency adjustment unit is used to adjust the monitoring frequency through the water quality data acquired by each sampling point within the previous acquisition time window; A calibration module is used to calculate the time interval for the next calibration of any sampling point based on all water quality data obtained at the sampling point in the previous calibration interval after the sensor at any sampling point is calibrated, and use the obtained time interval to perform the next sensor calibration; The comprehensive evaluation module includes a weight calculation unit, a comprehensive water quality data acquisition unit and a comprehensive evaluation unit; the weight calculation unit is used to calculate the weight coefficient of each sampling point based on the water quality data obtained from all sampling points at the end of any collection time window; the comprehensive water quality data acquisition unit is used to calculate and obtain the local water quality data of each sampling point, and based on the weight coefficient and local water quality data of each sampling point obtained, obtain the comprehensive water quality data of the water area to be tested, and the comprehensive water quality data includes several comprehensive water quality parameters; the comprehensive evaluation unit is used to compare the obtained several comprehensive water quality parameters with the preset normal range of each water quality parameter to obtain the comprehensive evaluation result of the water area to be monitored.

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