Method for dynamic detection of water quality of water supply pipeline realizing linkage control of rural water supply device

Abstract

The application relates to the technical field of water quality dynamic detection, in particular to a water quality dynamic detection method for realizing linkage control of a rural water supply device, which comprises the following steps: arranging a plurality of monitoring points along a main pipeline and a pipeline terminal, and monitoring water quality data and water flow in real time; calculating a water quality fluctuation coefficient according to the change of the water quality data and the water flow in the water supply pipeline; calculating a water quality disturbance coefficient according to the sudden water quality fluctuation; constructing a water quality coefficient of a to-be-detected sample in each monitoring section by using the water quality fluctuation coefficient of the to-be-detected sample at each moment and the water quality disturbance coefficient of each period in each monitoring section, and combining a preset water quality coefficient threshold to judge the detection condition of the water quality. The application aims to dynamically monitor the space-time change characteristics of the water quality in the water supply pipeline and improve the accuracy of the water quality detection.

Description

Technical Field

[0001] This application relates to the field of dynamic water quality monitoring technology, specifically to a method for dynamic water quality monitoring of water supply pipelines that enables coordinated control of rural water supply devices. Background Technology

[0002] Water supply security is a core component of rural revitalization and basic livelihood security. However, constrained by natural geographical conditions and development levels, rural settlements in my country's vast hilly, mountainous, and remote areas are highly scattered, and their water supply systems are generally characterized by small scale, long pipelines, dispersed users, and weak management. This structural feature makes it difficult to apply the monitoring and management models of centralized urban water supply to rural water supply, necessitating the development of intelligent and precise water quality security technologies adapted to their specific conditions.

[0003] Under the current technological system, water quality testing of rural water supply pipelines mainly relies on a limited number of fixed monitoring points. Furthermore, due to the small water consumption, low flow velocity, and long water retention time in rural areas, the water quality within the pipelines is prone to significant dynamic changes over time and space. However, current testing methods are mostly static and discrete "point measurement" models, which cannot reflect the spatiotemporal evolution of water quality during transportation, nor can they capture the actual water quality status at the end-user's point of use. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for dynamic water quality monitoring of water supply pipelines to achieve coordinated control of rural water supply devices, thereby resolving the existing issues.

[0005] The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices in this application adopts the following technical solution:

[0006] One embodiment of this application provides a method for dynamic water quality monitoring of water supply pipelines to achieve coordinated control of rural water supply devices. The method includes the following steps:

[0007] Several monitoring points are set up along the main pipeline and the end of the pipeline to monitor water quality data and water flow in real time.

[0008] A monitoring segment is formed by the interval between any two adjacent monitoring points. Based on the water quality fluctuation characteristics of any monitoring segment at each time and the difference in water quality fluctuation characteristics of any monitoring segment at each time when it flows through all its previous adjacent monitoring segments, the water quality fluctuation coefficient of any monitoring segment at each time is constructed.

[0009] Construct a feature vector for the data collection sample corresponding to each preset period by assembling a sequence of all water quality fluctuation coefficients collected in all monitoring sections; analyze the abnormal scores of the feature vectors of all samples to screen out normal samples; obtain the fitting curves constructed from the water quality fluctuation coefficients for all samples and all normal samples in each monitoring section.

[0010] The water quality disturbance coefficient for each period is constructed by using the abnormal scores of the feature vectors of the corresponding samples in each period, as well as the difference measure between the fitted curves constructed by all samples and normal samples in each monitoring segment and the determination coefficient of the fitted curves.

[0011] By using the water quality fluctuation coefficient and the water quality disturbance coefficient of the sample to be tested at each time and each period in each monitoring section, the water quality coefficient of the sample to be tested in each monitoring section is constructed, and combined with the preset water quality coefficient threshold, the water quality detection status is judged.

[0012] Preferably, the water quality data includes turbidity, residual chlorine, and metal content.

[0013] Preferably, the water quality fluctuation coefficient of any monitoring segment at any time is positively correlated with the water quality fluctuation characteristics of any monitoring segment at any time, and is positively correlated with the average difference in water quality fluctuation characteristics of any monitoring segment at any time when it flows through all its previous adjacent monitoring segments.

[0014] Preferably, the water quality fluctuation characteristics are determined by the standard deviation and average value of various water quality data in any monitoring segment at any time.

[0015] Preferably, based on determining the water quality fluctuation characteristics using standard deviation and mean, the standard deviation and mean of the corresponding water quality data are weighted by using the correlation measure between the trend sequence of water flow in any monitoring segment at any time and the trend sequence of various types of water quality data.

[0016] Preferably, when the number of elements in the trend sequence of water flow rate is different from that in the trend sequences of various types of water quality data, the trend sequence with the most elements is subjected to a moving average so that the two trend sequences have the same number of elements when analyzing the correlation measurement.

[0017] Preferably, in each monitoring segment, the monitoring point at the end of the monitoring segment collects various monitoring data during the time period when the current water quality flows through the monitoring segment, and uses these data as the various monitoring data for each monitoring segment.

[0018] Preferably, the formula for calculating the water quality disturbance coefficient is:

[0019]

[0020] In the formula, It is the first Water quality disturbance coefficient for each cycle It is the first The LOF value of the feature vector of the sample corresponding to each period. It is the maximum LOF value among the feature vectors of all periodic corresponding samples. It is the segmentation threshold of the LOF value of the feature vectors of all corresponding samples in all periods. It is the total number of monitoring sections. , They are the first The coefficient of determination of the fitted curves constructed from all samples and all normal samples in each monitoring segment. It is the first The difference measure between two fitted curves constructed from all samples and all normal samples in a monitoring segment.

[0021] Preferably, the formula for calculating the water quality coefficient of the section is:

[0022]

[0023] In the formula, The sample to be tested is in the first... Water quality coefficient of each monitoring section , These are the symbolic functions, function, It is the water quality disturbance coefficient of the period to which the sample to be tested belongs. It refers to all the collection times in the sample to be tested. The sample to be tested Time of the first Water quality fluctuation coefficient of each monitoring section.

[0024] Preferably, the method for determining the water quality detection status by combining a preset water quality coefficient threshold is as follows:

[0025] when and At that time, the first Disinfection devices are used to disinfect each monitoring section;

[0026] when and At that time, the first The disinfection device in the first monitoring section is disinfected, and at the same time, the disinfection device in the second monitoring section is disinfected. Instructions for pipeline maintenance and water plant process adjustment were issued for each monitoring section of the water transmission pipeline.

[0027] in, The preset water quality coefficient threshold, For the sample to be tested in the first Water quality coefficient of each monitoring section.

[0028] This application has at least the following beneficial effects:

[0029] This application proposes a method for dynamic water quality monitoring of rural water supply pipelines. First, a water quality fluctuation coefficient is calculated based on changes in water quality data and flow rate within the pipeline. This coefficient quantifies water quality changes in each monitoring section from a spatiotemporal perspective, enhancing the accuracy of subsequent quantification of abrupt changes in water quality fluctuations. Then, based on these abrupt water quality fluctuations, a water quality disturbance coefficient is calculated. This coefficient analyzes and quantifies the water quality and causes of anomalies during the water supply process, thereby improving the accuracy of water quality monitoring and helping to determine the appropriate handling direction for subsequent coordinated control of the water supply system. This approach eliminates reliance on static data from monitoring points for water quality monitoring. Instead, it dynamically monitors water quality based on the spatiotemporal characteristics of water quality at each monitoring point throughout the entire water supply process and the characteristic analysis of the sources of water quality anomalies. This enhances the accuracy of subsequent coordinated control processes, resulting in a more accurate method for dynamic water quality monitoring of rural water supply pipelines. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart of the method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices provided in this application;

[0032] Figure 2 A flowchart illustrating the steps involved in constructing the water quality disturbance coefficient index provided in this application. Detailed Implementation

[0033] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0035] The following, in conjunction with the accompanying drawings, details the specific scheme of the method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices provided in this application.

[0036] One embodiment of this application provides a method for dynamic water quality monitoring of water supply pipelines to achieve linkage control of rural water supply devices.

[0037] Specifically, the following method for dynamic water quality monitoring of water supply pipelines to achieve coordinated control of rural water supply devices is provided. Please refer to [link / reference]. Figure 1 The method includes the following steps:

[0038] Step 1: Set up several monitoring points along the main pipeline and the end of the pipeline to monitor water quality data and water flow in real time.

[0039] To achieve full-process water quality monitoring of the water supply system, it is necessary to monitor the water quality at different locations during the water supply process. Since rural water supply pipelines are scattered, several monitoring points are set up along the main pipeline and the pipeline ends when collecting pipeline water and end water samples.

[0040] At each monitoring point along the water supply pipeline, the turbidity of the water is collected using an optical scattering turbidimeter; residual chlorine data is collected using a residual chlorine analyzer; the content of metals such as aluminum, iron, and manganese is collected using a metal analyzer; and the water flow rate in the pipeline is collected using a flow meter, with the timestamp of each collection point recorded.

[0041] In this embodiment, the real-time monitoring data includes water quality data and water flow rate; the water quality data includes turbidity, residual chlorine, and metal content.

[0042] Except for metal content, all other monitoring data are collected every 10 minutes. Since the detection and analysis of various metal contents is relatively slow, it is set to be collected once per hour.

[0043] The various monitoring data collected from each monitoring point are used to construct corresponding data sequences. Each monitoring point can obtain turbidity sequence, residual chlorine sequence, aluminum content sequence, iron content sequence, manganese content sequence, and water flow sequence. The maximum and minimum values ​​of each type of monitoring data are then normalized.

[0044] Step 2: Calculate the water quality fluctuation coefficient based on the changes in water quality data and water flow in the water supply pipeline; calculate the water quality disturbance coefficient based on sudden water quality fluctuations.

[0045] Please see the appendix Figure 2 It shows a flowchart of the steps for constructing the water quality disturbance coefficient index, specifically:

[0046] Step 1: Construct a monitoring segment by dividing the interval between any two adjacent monitoring points; based on the water quality fluctuation characteristics of any monitoring segment at each time and the differences in water quality fluctuation characteristics of any monitoring segment at each time when it flows through all its previous adjacent monitoring segments, construct the water quality fluctuation coefficient of any monitoring segment at each time.

[0047] During the water quality monitoring process of water supply pipelines, the water pollution situation at different locations is not entirely consistent due to differences in factors such as water flow and pipeline condition at different locations within the pipeline. Furthermore, as the water flows, the water quality at each location within the water supply pipeline is dynamically changing.

[0048] When conducting water quality testing in a water supply system, the causes of some water quality indicators are affected by the hydraulic operation of the water supply system, resulting in certain differences in the corresponding water quality indicators in different monitoring sections. Therefore, it is necessary to quantify the water quality fluctuations based on these changes in order to obtain the spatiotemporal fluctuations of water quality in each monitoring section, thereby improving the control of water quality in monitoring blind spots within the monitoring section.

[0049] First, turbidity reflects the content of particulate matter in water, and it is closely related to the age of drinking water in the water supply system. The age of drinking water refers to the time it remains in the water supply system, and turbidity is closely related to the flow velocity and transport distance during hydraulic operation within the water supply pipeline. Second, the aluminum content in the water mainly comes from the coagulants used, and particulate aluminum will deposit during the water supply process; this deposition is closely related to the water flow velocity. Iron in the water mainly comes from pipe corrosion; manganese mainly comes from chemical reduction reactions and also has deposition issues; and the particulate form of these substances further increases turbidity. Finally, residual chlorine in the water is mainly used to kill microorganisms, and its content is closely related to the water age. It can be observed that there is a high correlation between various detection indicators in the water quality testing process, and they are closely related to the scheduling during hydraulic operation. Therefore, this analysis can be used to improve the control of water quality fluctuations in monitoring blind spots within different monitoring sections.

[0050] During pipeline monitoring, the interval between any two adjacent monitoring points is defined as a monitoring section. In each monitoring section, the monitoring point at the end of the monitoring section collects various monitoring data during the time period when the current water quality flows through the monitoring section, which are used as the various monitoring data for each monitoring section.

[0051] Due to differences in flow velocity and pipe condition, water quality varies across monitoring sections. To analyze the correlation between monitoring data, this application uses the data sequences of various types of monitoring data from each monitoring section as input and employs the STL time series decomposition algorithm to obtain the trend sequences of the corresponding monitoring data. STL time series decomposition is a well-known technique and will not be elaborated further.

[0052] Based on the above analysis, this application constructs a water quality fluctuation coefficient for any monitoring segment at any time, based on the water quality fluctuation characteristics of any monitoring segment at any time and the differences in water quality fluctuation characteristics when the water quality of any monitoring segment at any time flows through all its previous adjacent monitoring segments, to measure the spatiotemporal fluctuation characteristics of water quality in each monitoring segment.

[0053] Specifically, the water quality fluctuation coefficient of any monitoring segment at any given time is positively correlated with the water quality fluctuation characteristics of that monitoring segment at any given time, and also positively correlated with the average difference in water quality fluctuation characteristics of that monitoring segment as it flows through all its preceding adjacent monitoring segments. That is, spatially, because monitoring segments with better water quality have fewer pollution sources during the water supply process, their corresponding water quality conditions are more consistent with the previous monitoring segment, resulting in a smaller water quality fluctuation coefficient.

[0054] It is understandable that a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. This is determined by the actual application and is not subject to any special restrictions in this application.

[0055] The water quality fluctuation characteristics are determined by the standard deviation and average value of various water quality data in any monitoring segment at any time.

[0056] Furthermore, the correlation between the trend sequence of water flow in any monitoring segment at any given time and the trend sequences of various water quality data can be used to weight the standard deviation and mean of the corresponding water quality data. That is, the better the water quality in a monitoring segment, the smaller the trend correlation between various water quality data and water flow. Using trend correlation for weighting helps to determine the propagation path of pollutants in the water supply network, i.e., to measure the pollution propagation in each segment. The correlation measurement can be calculated using methods such as Pearson correlation coefficient and cosine similarity. Pearson correlation coefficient and cosine similarity are well-known techniques and will not be elaborated further.

[0057] It should be noted that when the number of elements in the trend series of water flow is different from that in the trend series of various water quality data, the trend series with the most elements is moved averaged to ensure that the two trend series have the same number of elements when analyzing correlation measures.

[0058] Since it is necessary to analyze the correlation between the trend series of water flow and the trend series of metal content, and the sampling frequency of metal content is lower than that of water flow, if the trend series of water flow is not averaged, the noise introduced by the lower sampling frequency will be finer, resulting in more noise and making the assessment of the relationship between the two types of data inaccurate. Therefore, a moving average is performed on the trend series.

[0059] Accordingly, this application performs a moving average on the trend sequence of water flow, sets the sliding window to 6 data collection times of water flow, and obtains the average trend sequence of water flow, so that the number of elements in the average trend sequence is the same as the number of elements in the trend sequence of each metal content. Moving average is a well-known technique, and will not be described in detail here.

[0060] Specifically, in this embodiment, for Time of the first Water quality fluctuation coefficient of each monitoring section The calculation method is as follows:

[0061]

[0062] In the formula, yes Time of the first Water quality fluctuation coefficient of each monitoring section yes Time of the first Water quality fluctuation characteristics of each monitoring section It is the first The number of monitoring segments preceding each monitoring segment. It is the first The monitoring section and the first The distance between monitoring sections It is a moment In the The water quality monitored in each monitoring section corresponds to the water quality that flowed through the previous section. The corresponding time for each monitoring segment , They are Time of the first Water quality fluctuation characteristics of each monitoring section Time of the first Water quality fluctuation characteristics of each monitoring section.

[0063] in, ; It refers to the number of monitoring data types other than residual chlorine in the collected water quality data. , They are Time of the first The standard deviation and mean value of residual chlorine data for each monitoring section. yes Time of the first The Pearson correlation coefficient between the trend series of residual chlorine data and the trend series of water flow in each monitoring section. , They are Time of the first The first monitoring section Standard deviation and mean of water quality data yes Time of the first The first monitoring section The Pearson correlation coefficient between the trend series of water quality data and the average trend series of water flow.

[0064] It is understandable that changes in water quality during the water supply process can be reflected by corresponding detection indicators. The fluctuations of various water quality data are related to the hydraulic operation data (water flow rate, pipeline condition, etc.) in the water supply pipeline. Since water flow rate and pipeline condition are dynamic, this means that within a monitoring section, if the water quality of that monitoring section is better, the corresponding water quality data fluctuations will be smaller. Therefore, the water quality of that monitoring section is less affected by changes in hydraulic operation data, resulting in relatively smaller fluctuations in various water quality data within that monitoring section.

[0065] Step 2: Construct the feature vector of the data collection sample corresponding to each preset period by combining the sequence of all water quality fluctuation coefficients collected in all monitoring sections; analyze the abnormal scores of the feature vectors of all samples to screen out normal samples; obtain the fitting curves of all samples and all normal samples in each monitoring section constructed by the water quality fluctuation coefficients.

[0066] The aforementioned water quality fluctuation coefficient, by quantifying the spatiotemporal fluctuation characteristics of water quality, enhances the control over water quality fluctuations in monitoring blind areas within different monitoring sections. However, due to the diverse and complex sources of water for rural water supply devices, and the influence of factors such as groundwater, domestic sewage, and agricultural water use, especially during abnormal weather such as rainfall, the impact of these factors is amplified, causing sudden changes in the water quality of the source water. These factors are external factors for the water supply system, leading to a deterioration in water quality. However, this situation is caused by sudden changes in the water quality of the source water, making it difficult for the water supply plant to make real-time adjustments.

[0067] In the coordinated control of water supply systems, the primary purpose of water quality monitoring is to optimize the water supply and treatment processes based on water quality conditions. However, introducing sudden changes caused by external factors into normal regulation can actually reduce the quality of water supply and treatment processes. Furthermore, it may necessitate the addition of more chemical agents for regulation, which could lead to further water quality deterioration. Therefore, it is crucial to accurately identify abnormal fluctuations in water quality and measure their impact on the water supply system.

[0068] In response to changes in the water quality of the source water, which usually do not have a significant impact on the water quality in a short period of time, this embodiment takes one day as a cycle, takes all the data collected in one cycle as a sample, and takes all the water quality fluctuation coefficients of each monitoring segment in one cycle as a sequence. The sequence of all monitoring segments in one cycle constitutes the feature vector of the corresponding data collection sample in that cycle.

[0069] Furthermore, this application analyzes the abnormal scores of the feature vectors of all samples to filter out normal samples.

[0070] Specifically, the feature vectors of all samples are used as input, and the LOF anomaly detection algorithm is employed to output the LOF value of each sample's feature vector. Then, the LOF values ​​of all sample feature vectors are used as input, and the Otsu thresholding algorithm is employed to output a segmentation threshold. Samples with LOF values ​​greater than the segmentation threshold are marked as mutant samples, while samples with LOF values ​​less than or equal to the segmentation threshold are marked as normal samples. Both the LOF anomaly detection algorithm and the Otsu thresholding algorithm are well-known techniques and will not be described in detail here.

[0071] Then, the fitted curves of all samples and all normal samples in each monitoring section are obtained by constructing the water quality fluctuation coefficient.

[0072] Specifically, for each monitoring segment, the order number of the corresponding period for each sample is used as the independent variable, and the mean of the sequence elements of the feature vector of each sample in the same monitoring segment is used as the dependent variable. Polynomial fitting is performed on all samples in each monitoring segment to obtain the fitting curve of all samples in each monitoring segment. Polynomial fitting is a well-known technique and will not be elaborated further.

[0073] It should be noted that, as the amount of data increases with the operation of the water supply system, all samples and all normal samples can be selected from a period of time adjacent to the sample to be tested. At the same time, since the different types of pollution characteristics of water sources have certain periodicity or seasonality, such as the abundant rainfall during the plum rain season, which may introduce additional pollution, or the pollution from agricultural water use is also fixed within certain time periods when pesticides and fertilizers need to be sprayed, in order to ensure the comprehensiveness of the measurement of abnormal situations, it is recommended to select a time span of three months to six months. In this embodiment, samples within three months (90 days) adjacent to the sample to be tested are selected.

[0074] Step 3: Using the anomaly scores of the feature vectors of the corresponding samples in each period, as well as the difference measure between the fitted curves constructed from all samples and normal samples in each monitoring section, and the determination coefficient of the fitted curves, construct the water quality disturbance coefficient for each period.

[0075] Based on the above analysis, this application uses the abnormal score of the feature vector of the corresponding sample in each period, as well as the difference measure between the fitting curves constructed by all samples and normal samples in each monitoring section and the determination coefficient of the fitting curves, to construct the water quality disturbance coefficient for each period, which is used to measure the abnormal disturbance of non-pipeline and water treatment factors in the cause of abnormal water quality in water supply pipelines.

[0076] Specifically, in this embodiment, for the first Water quality disturbance coefficient for each cycle The calculation method is as follows:

[0077]

[0078] In the formula, It is the first Water quality disturbance coefficient for each cycle It is the first The LOF value of the feature vector of the sample corresponding to each period. It is the maximum LOF value among the feature vectors of all periodic corresponding samples. It is the segmentation threshold of the LOF value of the feature vectors of all corresponding samples in all periods. It is the total number of monitoring sections. , They are the first The coefficient of determination of the fitted curves constructed from all samples and all normal samples in each monitoring segment. It is the first The difference between the two fitted curves constructed from all samples and all normal samples in a monitoring segment is measured. In this embodiment, the difference between the two fitted curves is the difference in the integral area of ​​the two fitted curves.

[0079] Understandably, for water supply pipeline anomalies, if the anomaly is caused by the pipeline itself or water treatment, it will cause anomalies throughout the entire water supply process, and these anomalies will continue to occur across multiple samples. Secondly, anomalies caused by pipeline or water treatment factors, such as pipeline corrosion or pollutant accumulation, tend to develop slowly and systematically, and usually do not exhibit significant abrupt fluctuations throughout the entire process in a short period of time. Therefore, by constructing the water quality disturbance coefficient as described above, and by observing the anomalies in the LOF value, the overall degree of anomaly in the current sample can be measured. By observing the differences in the fitted curves of each monitoring section, local anomalies in each monitoring section during the water supply process can be measured. Thus, the greater the anomaly in the water supply pipeline, the greater the absolute value of the water quality disturbance coefficient.

[0080] It should also be noted that the water quality disturbance coefficient can be positive or negative, but the positive or negative sign here does not represent the magnitude of the value, but rather indicates the cause of the abnormal disturbance. When the abnormality is caused by the pipeline itself or water treatment, the water quality disturbance coefficient is negative; conversely, if the abnormality is caused by factors other than pipelines and water treatment, the water quality disturbance coefficient is positive.

[0081] Step 3: Using the water quality fluctuation coefficient and the water quality disturbance coefficient of the sample to be tested at each time and each period in each monitoring section, construct the water quality coefficient of the sample to be tested in each monitoring section, and combine it with the preset water quality coefficient threshold to judge the water quality detection status.

[0082] When testing the water quality of a sample, first obtain the water quality fluctuation coefficient and the water quality disturbance coefficient of the sample at each time and each period in each monitoring section in the manner described above, and then calculate the water quality coefficient of the sample in each monitoring section.

[0083] This application utilizes the water quality fluctuation coefficient and the water quality disturbance coefficient of the sample to be tested at each time and each period within each monitoring section to construct the water quality coefficient of the sample to be tested in each monitoring section, and combines it with a preset water quality coefficient threshold to determine the water quality detection status.

[0084] Specifically, in this embodiment, the sample to be detected is at the [number]th ...]. Water quality coefficient of each monitoring section The calculation formula is:

[0085]

[0086] In the formula, The sample to be tested is in the first... Water quality coefficient of each monitoring section , These are the symbolic functions, function, It is the water quality disturbance coefficient of the period to which the sample to be tested belongs. It refers to all the collection times in the sample to be tested. The sample to be tested Time of the first Water quality fluctuation coefficient of each monitoring section.

[0087] It is understandable that when the first The worse the water quality in a monitoring section, the greater the water quality fluctuation coefficient of that section, and the greater the difference between that section and historical samples. In other words, the greater the water quality disturbance coefficient, the closer the water quality coefficient is to 1, the worse the water quality, and the reason for the deterioration is not due to pipeline or water treatment factors. When the water quality coefficient is closer to -1, the worse the water quality, and the reason for the deterioration is due to pipeline problems or improper water treatment. When the water quality coefficient is closer to 0, the water quality is normal.

[0088] Using the method described above, the water quality coefficient of the sample to be tested in each monitoring section can be obtained, and the absolute value of the water quality coefficient can be compared with a preset water quality coefficient threshold. In this embodiment, a comparison is made. Take 0.75.

[0089] When the sum of the water quality coefficients of all monitoring sections in the sample to be tested is positive, it is determined that the cause of the water quality change in the sample is not due to pipeline or water treatment factors. Therefore, it is only necessary to activate the test if the absolute value of the water quality coefficient is greater than a certain value. Disinfection devices (such as ultraviolet or ozone modules) are used to disinfect the monitoring section;

[0090] When the sum of the water quality coefficients of all monitoring sections in the sample to be tested is negative, it is determined that the cause of the water quality change in the sample is due to pipeline problems or improper water treatment. In this case, it is necessary to activate the system where the absolute value of the water quality coefficient is greater than a certain value. The disinfection device within the monitoring section, while also monitoring water quality coefficients with an absolute value greater than [missing information]. Instructions for pipeline maintenance and water plant process adjustment are issued to the monitored sections of the water pipeline to remind staff to carry out maintenance and adjust the water treatment process of the water plant to optimize water quality from the source.

[0091] It should be noted that the specific water treatment adjustment methods are common technologies in the field of water treatment and will not be described in detail here.

[0092] The above technical features constitute the preferred embodiment of this application, which has strong adaptability and the best implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A method for dynamic water quality monitoring of water supply pipelines to achieve coordinated control of rural water supply devices, characterized in that, The method includes the following steps: Several monitoring points are set up along the main pipeline and the end of the pipeline to monitor water quality data and water flow in real time. A monitoring segment is formed by the interval between any two adjacent monitoring points. Based on the water quality fluctuation characteristics of any monitoring segment at each time and the difference in water quality fluctuation characteristics of any monitoring segment at each time when it flows through all its previous adjacent monitoring segments, the water quality fluctuation coefficient of any monitoring segment at each time is constructed. Construct a feature vector for the data collection sample corresponding to each preset period by assembling a sequence of all water quality fluctuation coefficients collected in all monitoring sections; analyze the abnormal scores of the feature vectors of all samples to screen out normal samples; obtain the fitting curves constructed from the water quality fluctuation coefficients for all samples and all normal samples in each monitoring section; The water quality disturbance coefficient for each period is constructed by using the anomaly score of the feature vector of the corresponding sample for each period, the difference measure between the fitted curves constructed by all samples and normal samples in each monitoring segment, and the determination coefficient of the fitted curves. By using the water quality fluctuation coefficient and the water quality disturbance coefficient of the sample to be tested at each time and each period in each monitoring section, the water quality coefficient of the sample to be tested in each monitoring section is constructed, and combined with the preset water quality coefficient threshold, the water quality detection status is judged.

2. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in claim 1, characterized in that, The water quality data includes turbidity, residual chlorine, and metal content.

3. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in claim 1, characterized in that, The water quality fluctuation coefficient of any monitoring section at any time is positively correlated with the water quality fluctuation characteristics of any monitoring section at any time, and is positively correlated with the average difference in water quality fluctuation characteristics of any monitoring section at any time when it flows through all its previous adjacent monitoring sections.

4. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in claim 3, characterized in that, The water quality fluctuation characteristics are determined by the standard deviation and average value of various water quality data in any monitoring segment at any time.

5. The method for dynamically detecting the water quality of a water supply pipeline for realizing the linkage control of a rural water supply device according to claim 4, characterized in that, Based on the determination of water quality fluctuation characteristics using standard deviation and mean, the standard deviation and mean of the corresponding water quality data are weighted by using the correlation measure between the trend sequence of water flow in any monitoring segment at any time and the trend sequence of various types of water quality data.

6. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in claim 5, characterized in that, When the number of elements in the trend series of water flow is different from that in the trend series of various water quality data, a moving average is performed on the trend series with the most elements so that the two trend series have the same number of elements when analyzing correlation measures.

7. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in any one of claims 3-6, characterized in that, In each monitoring segment, various monitoring data are collected from the monitoring point at the end of the monitoring segment during the time period when the water flows through the monitoring segment, and these data are used as the various monitoring data for each monitoring segment.

8. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in claim 1, characterized in that, The formula for calculating the water quality disturbance coefficient is as follows: In the formula, It is the first Water quality disturbance coefficient for each cycle It is the first The LOF value of the feature vector of the sample corresponding to each period. It is the maximum LOF value among the feature vectors of all periodic corresponding samples. It is the segmentation threshold of the LOF value of the feature vectors of all corresponding samples in all periods. It is the total number of monitoring sections. , They are the first The coefficient of determination of the fitted curves constructed from all samples and all normal samples in each monitoring segment. It is the first The difference measure between two fitted curves constructed from all samples and all normal samples in a monitoring segment.

9. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in claim 1, characterized in that, The formula for calculating the water quality coefficient of the aforementioned section is: In the formula, The sample to be tested is in the first... Water quality coefficient of each monitoring section , These are the symbolic functions, function, It is the water quality disturbance coefficient of the period to which the sample to be tested belongs. It refers to all the collection times in the sample to be tested. The sample to be tested Time of the first Water quality fluctuation coefficient of each monitoring section.

10. The method for dynamic water quality monitoring of water supply pipelines for realizing the linkage control of rural water supply devices as described in claim 9, characterized in that, The method for determining the water quality detection status by combining a preset water quality coefficient threshold is as follows: when and At that time, the first Disinfection devices are used to disinfect each monitoring section; when and At that time, the first The disinfection device in the first monitoring section is disinfected, and at the same time, the disinfection device in the second monitoring section is disinfected. Instructions for pipeline maintenance and water plant process adjustment were issued for each monitoring section of the water transmission pipeline. in, The preset water quality coefficient threshold, For the sample to be tested in the first Water quality coefficient of each monitoring section.

Images