A remote parameter self-regulation method based on a backwashing water filter

Abstract

The present invention relates to the technical field of water filtration treatment, and specifically relates to a remote parameter self-regulation method based on a backwashing water filter. First, according to the change of the inlet flow rate value in the preset influence time period at the current moment, the water flow influence degree at the current moment is obtained; according to the distribution difference of the flow velocity monitoring vectors at all monitoring positions at the current moment, the blockage measure at the current moment is obtained; by integrating the trend anomaly measure, the water flow influence degree and the blockage measure at the current moment, the adjustment requirement degree at the current moment is obtained, the preset pressure difference threshold is adjusted, the adjusted pressure difference threshold of the backwashing water filter at the current moment is obtained, and the backwashing water filter is controlled. By dynamically adjusting the preset pressure difference threshold of the backwashing water filter, the present invention meets the requirements of the real-time operation condition of the backwashing water filter, thereby ensuring the accuracy of backwashing.

Description

Technical Field

[0001] The present invention relates to the technical field of water filtration treatment, and particularly relates to a remote parameter self-regulation method based on a backwash water filter. Background Art

[0002] A water filter is a precision device that directly intercepts impurities in water by using a filter screen, removes impurities in the water body, purifies the water quality, and protects the normal operation of other equipment in the system. Backwashing is a maintenance method of the water filter, which clears the accumulated pollutants by flushing the filter screen in the reverse direction and restores the filtering ability of the filter screen. The backwash water filter intercepts impurities outside the filter screen through the filtering action of the filter screen, and the filtered clear water flows out from the other side of the filter screen. As the filtering time increases and impurities accumulate on the filter screen, backwashing treatment is required to ensure the filtering ability of the water filter.

[0003] The pressure difference of the backwash water filter is an important parameter, which can reflect the impurity content in the backwash water filter. The prior art determines whether to perform backwashing treatment on the backwash water filter by comparing the real-time pressure difference with a fixed preset pressure difference threshold. However, during the process of performing backwashing treatment on the backwash water filter using the prior art, the influence of the inlet water flow rate and the internal operation condition of the water filter on the pressure difference is not fully considered, resulting in the fixed preset pressure difference threshold being difficult to adapt to the requirements of the backwash water filter under different working conditions and difficult to ensure the backwashing accuracy of the water filter. Summary of the Invention

[0004] In order to solve the technical problem that the prior art is difficult to ensure the backwashing accuracy of the water filter, the purpose of the present invention is to provide a remote parameter self-regulation method based on a backwash water filter, and the specific technical solution adopted is as follows:

[0005] A remote parameter self-regulation method based on a backwash water filter, the method comprising:

[0006] Obtaining the pressure difference parameter, the inlet flow rate value of the backwash water filter at each sampling moment, and the flow velocity monitoring vector at each monitoring position inside the backwash water filter;

[0007] According to the change of the pressure difference parameter corresponding to the current moment over time, obtaining the trend anomaly metric at the current moment; according to the change of the inlet flow rate value in the preset influence time period at the current moment, obtaining the water flow influence degree at the current moment; according to the distribution difference of the flow velocity monitoring vectors at all monitoring positions at the current moment, obtaining the blockage metric at the current moment; fusing the trend anomaly metric, the water flow influence degree, and the blockage metric at the current moment to obtain the adjustment requirement degree at the current moment;

[0008] Adjust the preset pressure difference threshold according to the adjustment requirement degree at the current moment, obtain the adjusted pressure difference threshold of the backwash water filter at the current moment, and control the backwash water filter.

[0009] Further, the method for obtaining the trend anomaly metric includes:

[0010] Calculate the difference between the sampling moment and the corresponding pressure difference parameter at the previous sampling moment in time series to obtain the pressure difference rising trend value at the sampling moment;

[0011] Calculate the difference between the current moment and the corresponding pressure difference rising trend value at the previous sampling moment in time series, and perform a negative correlation mapping on the difference to obtain the trend anomaly metric at the current moment.

[0012] Further, the method for obtaining the water flow influence degree includes:

[0013] In the preset influence time period at the current moment, calculate the difference between each sampling moment and the previous inlet flow value to obtain the flow rising characteristic value at each sampling moment; calculate the mean value of the flow rising characteristic values at all sampling moments; when the mean value is not greater than 0, set the water flow influence degree at the current moment to 0; when the mean value is greater than 0, use the mean value as the water flow influence degree at the current moment.

[0014] Further, the method for obtaining the blockage metric includes:

[0015] Cluster all monitoring positions according to the difference between the flow velocity monitoring vectors at every two monitoring positions to obtain each clustering cluster;

[0016] Obtain the flow velocity distribution difference degree of the clustering cluster according to the difference between the flow velocity monitoring vectors between the clustering cluster and its adjacent clustering cluster;

[0017] Screen out the target clustering cluster from all clustering clusters according to the flow velocity distribution difference degree;

[0018] Obtain the blockage metric at the current moment according to the spatial distance between the target clustering cluster and the inlet of the backwash water filter.

[0019] Further, the method for obtaining the clustering cluster includes:

[0020] For the flow velocity monitoring vectors corresponding to any two monitoring positions, perform a negative correlation mapping on the cosine similarity of the two flow velocity monitoring vectors to obtain the direction difference metric; perform a normalization process on the difference between the magnitudes of the two flow velocity monitoring vectors to obtain the speed value difference metric; calculate the sum value of the direction difference metric and the speed value difference metric to obtain the distance metric value between every two monitoring positions;

[0021] Using the K-means clustering algorithm, all monitoring locations are clustered according to the distance metric values between every two monitoring locations to obtain each clustering cluster.

[0022] Further, the method for obtaining the flow velocity distribution difference degree includes:

[0023] Calculate the mean value of the flow velocity monitoring vectors of all monitoring locations in the clustering cluster to obtain the overall flow velocity vector of the clustering cluster;

[0024] Take any one clustering cluster as the cluster to be analyzed, and take all the clustering clusters adjacent to the cluster to be analyzed in the spatial distribution as the reference clusters of the cluster to be analyzed;

[0025] Perform a negative correlation mapping on the Euclidean distance between the cluster center points of the cluster to be analyzed and its reference clusters to obtain the distribution weight; calculate the modulus of the difference between the overall flow velocity vectors of the cluster to be analyzed and its reference clusters to obtain the distribution difference parameter; calculate the product of the distribution weight and the distribution difference parameter to obtain the local distribution difference index between the cluster to be analyzed and its reference clusters;

[0026] Calculate the mean value of the local distribution difference indexes between the cluster to be analyzed and all its reference clusters to obtain the flow velocity distribution difference degree of the cluster to be analyzed.

[0027] Further, the method for obtaining the blockage metric at the current moment according to the spatial distance between the target clustering cluster and the inlet of the backwash water filter includes:

[0028] Calculate the Euclidean distance between the cluster center point of each target clustering cluster and the inlet of the backwash water filter to obtain the deviation from inlet metric of each target clustering cluster; calculate the mean value of the deviation from inlet metrics of all target clustering clusters and perform a negative correlation mapping to obtain the blockage metric at the current moment.

[0029] Further, the method for adjusting the demand degree includes:

[0030] Calculate the product of the trend anomaly metric, the water flow influence degree, and the blockage metric at the current moment and perform normalization processing to obtain the adjustment demand degree at the current moment.

[0031] Further, the method for the adjusted pressure difference threshold includes:

[0032] Calculate the product of the adjustment demand degree and the preset adjustment value to obtain the target adjustment value at the current moment; calculate the product of the target adjustment value at the current moment and the preset pressure difference threshold to obtain the adjusted pressure difference threshold of the backwash water filter at the current moment.

[0033] Further, the method for controlling the backwash water filter includes:

[0034] At the current moment, if the pressure difference parameter is not less than the adjusted pressure difference threshold, control the backwashing water filter to perform backwashing of impurities on the filter net; if the pressure difference parameter is less than the adjusted pressure difference threshold, control the backwashing water filter to perform water quality filtration treatment.

[0035] The present invention has the following beneficial effects:

[0036] In order to ensure the accuracy of backwashing, it is necessary to analyze the demand for adjusting the preset pressure difference threshold based on the real-time operation status of the backwashing water filter. Considering that the filter net material in the backwashing water filter will be affected by chemical reactions or physical deterioration, changing the filtration performance of the filter net, resulting in a change in the pressure difference change trend of the backwashing water filter, it is necessary to increase the preset pressure difference threshold to adapt to this change. First, the trend anomaly measure is used to reflect the demand for increasing the preset pressure difference threshold due to poor real-time filter net performance; considering that an increase in the inlet flow means that more water flows through the water filter, the pressure difference inside the water filter may quickly reach the preset pressure difference threshold, and it is necessary to increase the preset pressure difference threshold to adapt to this change. The water flow influence degree is used to reflect the demand for increasing the preset pressure difference threshold due to the increase in real-time water flow; considering that during the operation of the backwashing water filter, due to the difference in the degree of impurity accumulation at different positions of the filter net caused by the design and placement of the water filter, larger and heavier impurities may deposit at positions closer to the water inlet, and the impurities will block the water flow, resulting in a rapid increase in the pressure difference between the water inlet and the water outlet. It is necessary to increase the preset pressure difference threshold to adapt to this change. The blockage measure is used to reflect the demand for increasing the preset pressure difference threshold due to the real-time impurity distribution; integrate the trend anomaly measure, water flow influence degree, and blockage measure at the current moment to obtain the adjustment demand degree at the current moment. The adjustment demand degree reflects the demand for increasing the preset pressure difference threshold by the real-time operation status of the backwashing water filter. According to the adjustment demand degree at the current moment, dynamically adjust the preset pressure difference threshold to meet the requirements of the real-time operation of the backwashing water filter, thereby ensuring the accuracy of backwashing. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is a flowchart of a remote parameter self-regulation method based on a backwashing water filter provided by an embodiment of the present invention;

[0039] Figure 2Flowchart of a method for obtaining a blocking metric provided by an embodiment of the present invention;

[0040] Figure 3 Structural diagram of a remote parameter self-regulation system based on a backwashing water filter provided by an embodiment of the present invention. Detailed implementation manners

[0041] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details a remote parameter self-regulation method based on a backwashing water filter proposed according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0043] The following specifically describes the specific solution of a remote parameter self-regulation method based on a backwashing water filter provided by the present invention with reference to the accompanying drawings.

[0044] An embodiment of the present invention provides a remote parameter self-regulation method based on a backwashing water filter. Please refer to Figure 1 , which shows a flowchart of a remote parameter self-regulation method based on a backwashing water filter provided by an embodiment of the present invention. The method includes the following steps:

[0045] Step S1: Obtain the pressure difference parameter, inlet flow rate value of the backwashing water filter at each sampling moment, and the flow velocity monitoring vector at each monitoring position inside the backwashing water filter.

[0046] The main purpose of the present invention is to adjust a preset pressure difference threshold to meet the requirements of the real-time operation of the backwashing water filter, thereby ensuring the accuracy of backwashing. First, data needs to be obtained.

[0047] First, obtain the pressure difference parameter, inlet flow rate value of the backwashing water filter at each sampling moment, and the flow velocity monitoring vector at each monitoring position inside the backwashing water filter from the monitoring system of the backwashing water filter.

[0048] Specifically, in order to analyze the demand for backwashing of the water filter, the pressure values at the inlet and outlet of the backwashing water filter can be obtained respectively through the pressure sensors installed at the inlet and outlet of the backwashing water filter; the difference between the pressure value at the inlet and the pressure value at the outlet is used as the pressure difference parameter of the backwashing water filter. In order to analyze the water flow condition at the inlet, a water meter is installed at the inlet of the backwashing water filter to collect the water flow value, and the flow value is used as the inlet flow value of the backwashing water filter. Inside the backwashing water filter, multiple monitoring positions are evenly set to comprehensively monitor the flow velocity condition inside the water filter. At each monitoring position, the ultrasonic flowmeter is used to collect the magnitude and direction of the water flow velocity, and the flow velocity monitoring vector at each monitoring position inside the backwashing water filter can be obtained. The magnitude of the flow velocity monitoring vector is the water flow velocity value, and the direction of the flow velocity monitoring vector is the water direction.

[0049] According to the preset sampling frequency, synchronous sampling is carried out to obtain the pressure difference parameter, the inlet flow value of the backwashing water filter, and the flow velocity monitoring vectors at each monitoring position inside the backwashing water filter at each sampling moment. Among them, the last sampling moment is the current moment.

[0050] In an embodiment of the present invention, the preset sampling frequency is 10 seconds / time, and the implementer can set it according to the implementation scenario. It should be noted that for the convenience of calculation, all the index data involved in the operation in the embodiment of the present invention have undergone data preprocessing, thereby canceling the influence of the dimension. The specific means of canceling the dimension influence are well-known technical means to those skilled in the art and will not be limited here.

[0051] In the operation logic of the backwashing water filter, the pressure difference parameter is a crucial parameter, which directly reflects the accumulation degree of impurities in the water filter. Ideally, when the impurity content increases, the pressure difference parameter before and after the water filter will also increase accordingly. This change becomes the basis for triggering the backwashing operation. The traditional method is to set a fixed preset pressure difference threshold. When the real-time pressure difference parameter exceeds this threshold, the backwashing operation is executed to remove the impurities on the filter screen and restore the filtering ability. The fixed preset pressure difference threshold has limitations in the face of variable working conditions. Although frequent backwashing operations can remove the impurities on the filter screen, there are also problems, that is, because each backwashing operation requires a certain amount of time to complete, and during this period, the water filter cannot continue to filter the water flow, resulting in a loss of filtering efficiency and wear of the filter screen. To solve this problem, the present invention proposes a method for dynamically adjusting the preset pressure difference threshold. By analyzing the real-time operating conditions that mainly affect the pressure difference parameter, that is, the inlet flow, the aging of the filter screen, and the distribution of impurity blockages in the water filter, the preset pressure difference threshold is dynamically adjusted.

[0052] Step S2: Obtain the trend anomaly metric at the current moment according to the change of the pressure difference parameter corresponding to the current moment over time; obtain the water flow influence degree at the current moment according to the change of the inlet flow value in the preset influence time period at the current moment; obtain the blockage metric at the current moment according to the distribution difference of the flow velocity monitoring vectors at all monitoring positions at the current moment; fuse the trend anomaly metric, the water flow influence degree and the blockage metric at the current moment to obtain the adjustment requirement degree at the current moment.

[0053] To ensure the accuracy of backwashing, it is necessary to analyze the requirement degree for adjusting the preset pressure difference threshold based on the real-time operating conditions of the backwashing water filter. Considering that the filter screen material in the backwashing water filter will be affected by chemical reactions or physical deterioration, changing the filtering performance of the filter screen, resulting in a change in the pressure difference change trend of the backwashing water filter, it is necessary to increase the preset pressure difference threshold to adapt to this change. First, the trend anomaly metric is used to reflect the requirement degree for increasing the preset pressure difference threshold due to poor real-time filter screen performance; considering that an increase in the inlet flow means that more water flows through the water filter, and the pressure difference inside the water filter may quickly reach the preset pressure difference threshold, it is necessary to increase the preset pressure difference threshold to adapt to this change, and the water flow influence degree is used to reflect the requirement degree for increasing the preset pressure difference threshold due to the increase in real-time water flow; considering that during the operation of the backwashing water filter, due to the difference in the aggregation degree of impurities at different positions of the filter screen caused by the design and placement of the water filter, larger and heavier impurities may deposit at positions closer to the water inlet, and the impurities will block the water flow, resulting in a rapid increase in the pressure difference between the water inlet and the water outlet, it is necessary to increase the preset pressure difference threshold to adapt to this change, and the blockage metric is used to reflect the requirement degree for increasing the preset pressure difference threshold due to the real-time impurity distribution; fuse the trend anomaly metric, the water flow influence degree and the blockage metric at the current moment to obtain the adjustment requirement degree at the current moment, and the adjustment requirement degree reflects the requirement degree for increasing the preset pressure difference threshold by the real-time operating conditions of the backwashing water filter.

[0054] To ensure the accuracy of backwashing, it is necessary to analyze the requirement degree for adjusting the preset pressure difference threshold based on the real-time operating conditions of the backwashing water filter. First, consider that the filter screen material in the backwashing water filter will be affected by chemical reactions or physical deterioration, changing the filtering performance of the filter screen, resulting in a change in the impurity content and the pressure difference change trend. It is necessary to increase the preset pressure difference threshold to adapt to this change to ensure that the backwashing operation can still be triggered when the filter screen is blocked to a certain extent, so as not to waste resources or accelerate the damage of the filter screen due to excessive washing. Preferably, in an embodiment of the present invention, the method for obtaining the trend anomaly metric includes:

[0055] Obtain the trend anomaly metric at the current moment according to the difference in the change trend of the pressure difference parameter corresponding to the current moment and its previous sampling moment in time series.

[0056] Considering that when the filter screen has good performance, impurities will gradually accumulate on the filter screen during the filtration process, causing the pressure difference parameter of the water filter to show an obvious upward trend; at the same time, considering that the filter screen material in the water filter may undergo chemical reactions or physical deterioration after long-term use, resulting in poor filter screen performance, and the increase in the pressure difference parameter of the water filter is not obvious. By constructing a trend anomaly metric to reflect the need to increase the preset pressure difference threshold due to poor real-time filter screen performance.

[0057] Preferably, in an embodiment of the present invention, the method for obtaining the trend anomaly metric includes:

[0058] Calculate the difference between the pressure difference parameter corresponding to the sampling moment and the pressure difference parameter corresponding to the previous sampling moment in the time series to obtain the pressure difference rising trend value at the sampling moment; calculate the difference between the current moment and the pressure difference rising trend value corresponding to the previous sampling moment in the time series, and perform a negative correlation mapping on the difference to obtain the trend anomaly metric at the current moment. It should be noted that negative correlation mapping is a well-known technical means in the art, and the negative correlation mapping can be in the form of inverse proportion or negative exponential power, which is not limited here.

[0059] For the above steps, the pressure difference rising trend value reflects the degree of increase in the pressure difference parameter within the adjacent sampling time interval. The larger the value of the pressure difference rising trend value, the more obvious the degree of increase. Next, calculate the difference between the current sampling moment and the pressure difference rising trend value corresponding to the previous sampling moment in the time series. This difference reflects the change in the pressure difference rising trend. Perform a negative correlation mapping on the difference. When the difference is larger, the trend anomaly metric is also larger, indicating a higher degree of abnormal filter screen performance and a greater need to increase the preset pressure difference threshold.

[0060] In order to ensure the accuracy of backwashing, it is necessary to analyze the need to adjust the preset pressure difference threshold based on the real-time operating conditions of the backwashing water filter. Considering that an increase in the inlet flow rate in the backwashing water filter means that more water flows through the water filter. If the preset pressure difference threshold remains unchanged, then as the flow rate increases, the pressure difference inside the water filter may quickly reach the threshold, resulting in frequent backwashing operations. However, this frequent backwashing may interrupt the normal filtration process and reduce the filtration efficiency. Therefore, it is necessary to increase the preset pressure difference threshold, which can enable the water filter to maintain a certain filtration time at a higher flow rate, thereby maintaining the filtration efficiency. The water flow influence degree is used to reflect the need to increase the preset pressure difference threshold due to the increase in real-time water flow. Preferably, in an embodiment of the present invention, the method for obtaining the water flow influence degree includes:

[0061] Obtain the water flow influence degree at the current moment according to the overall distribution of the rising characteristics of the inlet flow rate values at all sampling moments in the preset influence time period at the current moment.

[0062] Specifically, in one embodiment of the present invention, the method for obtaining the water flow influence degree includes:

[0063] In the preset influence time period at the current moment, calculate the difference between each sampling moment and its previous inlet flow value to obtain the flow increase characteristic value of each sampling moment; in the preset influence time period at the current moment, calculate the average value of the flow increase characteristic values of all sampling moments; when the average value is not greater than 0, set the water flow influence degree at the current moment to 0; when the average value is greater than 0, use the average value as the water flow influence degree at the current moment.

[0064] In one embodiment of the present invention, the method for obtaining the preset influence time period at the current moment includes: taking the time period corresponding to the previous preset number of sampling moments at the current moment as the preset influence time period at the current moment. In one embodiment of the present invention, the preset number is 5, and the implementer can set it according to the implementation scenario. It should be noted that the present invention analyzes the demand degree of backwashing by analyzing the real-time operation situation, so as to control the backwashing water filter. If a backwashing operation occurs in the preset influence time period at the current moment, at this time, the filtering ability of the backwashing water filter is good, and there is no need to perform a backwashing operation, that is, there is no need to calculate the adjusted pressure difference threshold and perform backwashing of the filter mesh.

[0065] For the above steps, the flow increase characteristic value reflects the flow increase situation at the sampling moment. Calculate the average value of the flow increase characteristic values of all sampling moments, and this average value reflects the average flow increase situation within the preset influence time period. If the average value is not greater than 0, it means that the flow has not increased significantly during this period, and may even have decreased. Therefore, the water flow influence degree at the current moment is set to 0, indicating that there is no need to increase the preset pressure difference threshold due to water flow changes. If the average value is greater than 0, it means that the flow has increased during this period. At this time, use this average value as the water flow influence degree at the current moment. The larger this value is, the more obvious the flow increase is, and the higher the demand for increasing the preset pressure difference threshold is.

[0066] Considering that during the operation of the backwashing water filter, due to the difference in the degree of impurity aggregation at different positions of the filter mesh caused by the design and placement of the water filter, larger and heavier impurities may deposit at positions closer to the water inlet. The impurities will block the water flow, resulting in a rapid increase in the pressure difference between the water inlet and the water outlet. Adjusting the preset pressure difference threshold is to reduce the false triggering of backwashing operations caused by impurity deposition and improve the accuracy and resource utilization efficiency of backwashing operations. The blocking metric is used to reflect the demand degree for adjusting the preset pressure difference threshold due to the real-time impurity distribution. Please refer to Figure 2 which shows a flowchart of a method for obtaining a blocking metric in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining the blocking metric includes:

[0067] Step S201: Cluster all monitoring positions according to the differences between the flow velocity monitoring vectors of every two monitoring positions, and obtain each clustering cluster.

[0068] By comparing the differences between the flow velocity monitoring vectors of the monitoring positions, the monitoring positions with similar flow velocity characteristics are grouped into one category to form each clustering cluster. The monitoring positions within the clustering cluster have similarities in terms of the magnitude and direction of the flow velocity, indicating that the monitoring positions are affected by impurity deposition to a similar degree.

[0069] Preferably, in an embodiment of the present invention, the method for obtaining the clustering cluster includes:

[0070] For the flow velocity monitoring vectors corresponding to any two monitoring positions, perform a negative correlation mapping on the cosine similarity of the two flow velocity monitoring vectors to obtain a direction difference measure; normalize the difference between the magnitudes of the two flow velocity monitoring vectors to obtain a velocity value difference measure; calculate the sum value of the direction difference measure and the velocity value difference measure to obtain the distance measure value between every two monitoring positions;

[0071] Use the K-means clustering algorithm to cluster all monitoring positions according to the distance measure values between every two monitoring positions, and obtain each clustering cluster. It should be noted that the K-means clustering algorithm and the cosine similarity are well-known technical means to those skilled in the art and will not be elaborated here. It should be noted that both the negative correlation mapping and the normalization process are well-known technical means to those skilled in the art. The negative correlation mapping can adopt the form of inverse proportion or negative exponential power, and the normalization process can adopt linear normalization, etc., and there is no limitation here.

[0072] For the above steps, the cosine similarity measures the similarity degree in the direction of two vectors. Perform a negative correlation mapping on the cosine similarity to obtain a direction difference measure. The larger the direction difference measure, the greater the difference in the direction of the flow velocity monitoring vectors corresponding to the two monitoring positions; the larger the velocity value difference measure, the greater the difference in the numerical values of the flow velocity monitoring vectors corresponding to the two monitoring positions; the distance measure value reflects the comprehensive difference in the magnitude and direction of the flow velocity between the two monitoring positions. The clustering algorithm will group the monitoring positions with similar flow velocity characteristics into one category to form each clustering cluster. The monitoring positions within the clustering cluster have similarities in terms of the magnitude and direction of the flow velocity, indicating that they may be affected by impurity deposition to a similar degree.

[0073] Step S202: Obtain the flow velocity distribution difference degree of the clustering cluster according to the differences between the flow velocity monitoring vectors between the clustering cluster and its adjacent clustering clusters.

[0074] Calculate the difference of the flow velocity monitoring vectors between each cluster and its adjacent clusters, and this difference is called the flow velocity distribution difference degree. The larger the flow velocity distribution difference degree is, the greater the flow velocity difference between the cluster and its adjacent clusters is, and the more likely there is an impurity aggregation area in the cluster.

[0075] Preferably, in an embodiment of the present invention, the method for obtaining the flow velocity distribution difference degree includes:

[0076] Calculate the mean value of the flow velocity monitoring vectors at all monitoring positions in the cluster to obtain the overall flow velocity vector of the cluster;

[0077] Take any cluster as the cluster to be analyzed, and take all the adjacent clusters of the cluster to be analyzed in the spatial distribution as the reference clusters of the cluster to be analyzed;

[0078] Perform a negative correlation mapping on the Euclidean distance between the cluster center points of the cluster to be analyzed and its reference clusters to obtain the distribution weight; calculate the modulus of the difference between the overall flow velocity vectors of the cluster to be analyzed and its reference clusters to obtain the distribution difference parameter; calculate the product of the distribution weight and the distribution difference parameter to obtain the local distribution difference index between the cluster to be analyzed and its reference clusters;

[0079] Calculate the mean value of the local distribution difference indexes between the cluster to be analyzed and all its reference clusters and perform normalization processing to obtain the flow velocity distribution difference degree of the cluster to be analyzed. Among them, normalization is a well-known technical means in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0080] For the above steps, the overall flow velocity vector represents the average characteristics of the cluster in terms of the flow velocity magnitude and direction. Calculate the Euclidean distance between the cluster center point of the cluster to be analyzed and each of its reference clusters. The Euclidean distance measures the relative distance between two clusters in the spatial position. Perform a negative correlation mapping on the Euclidean distance to obtain the distribution weight. The distribution weight reflects the degree of proximity between two clusters in the spatial position. The distribution difference parameter represents the comprehensive difference between two clusters in terms of the flow velocity magnitude and direction. Multiply the distribution weight by the distribution difference parameter to obtain the local distribution difference index between the cluster to be analyzed and each of its reference clusters. The larger the local distribution difference index is, the greater the flow velocity difference between the cluster to be analyzed and the closer adjacent clusters is. Calculate the mean value of the local distribution difference indexes between the cluster to be analyzed and all its reference clusters to obtain the flow velocity distribution difference degree of the cluster to be analyzed. The flow velocity distribution difference degree represents the average difference degree of the flow velocity distribution between the cluster to be analyzed and its adjacent clusters. The larger the flow velocity distribution difference degree is, the greater the flow velocity difference between the cluster and its adjacent clusters is, and the more likely there is an impurity aggregation area in the cluster.

[0081] Step S203: Screen out the target cluster from all the clusters according to the flow velocity distribution difference degree.

[0082] The target cluster is the cluster with a relatively large flow velocity distribution difference degree, which means that there are significant differences in the magnitude and direction of the flow velocity inside these regions compared with the adjacent regions. This difference is usually caused by the significant aggregation and deposition of impurities. Therefore, the target cluster reflects the region where the flow velocity inside the filter tube changes most violently.

[0083] Preferably, in an embodiment of the present invention, among all the clusters, the clusters with a flow velocity distribution difference degree greater than a preset flow velocity threshold are marked as the target clusters. In an embodiment of the present invention, the preset flow velocity threshold is 0.57, and the implementer can set it according to the implementation scenario.

[0084] Step S204: Obtain the current blocking metric according to the spatial distance between the target cluster and the inlet of the backwashing water filter.

[0085] Calculate the current blocking metric according to the spatial distance between the target cluster and the inlet of the backwashing water filter. The blocking metric quantifies the degree of aggregation of impurities near the inlet of the backwashing water filter.

[0086] Preferably, in an embodiment of the present invention, calculate the Euclidean distance between the center point of each target cluster and the inlet of the backwashing water filter to obtain the deviation-from-inlet metric of each target cluster; calculate the mean value of the deviation-from-inlet metrics of all the target clusters and perform a negative correlation mapping to obtain the current blocking metric. It should be noted that the method for obtaining the center point of the cluster and the Euclidean distance are well-known prior arts to those skilled in the art and will not be elaborated here.

[0087] For the above steps, the deviation-from-inlet metric of the target cluster reflects the spatial distance between the target cluster and the inlet of the water filter. Calculate the mean value of the deviation-from-inlet metrics of all the target clusters. This mean value represents the average deviation degree of all the target clusters relative to the inlet of the water filter. Calculate the mean value of the deviation-from-inlet metrics of all the target clusters and perform a negative correlation mapping to obtain the current blocking metric. The blocking metric quantifies the degree of aggregation of impurities near the inlet of the backwashing water filter and at the same time takes into account the differences in the degree of aggregation of impurities at different positions of the filter screen due to the design and placement of the water filter. Larger and heavier impurities may deposit closer to the water inlet. The impurities will block the water flow, resulting in a rapid increase in the pressure difference between the water inlet and the water outlet. It is necessary to increase the preset pressure difference threshold to adapt to this change. The blocking metric reflects the demand for increasing the preset pressure difference threshold due to the real-time impurity distribution.

[0088] Consider the trend anomaly metric to reflect the demand for increasing the preset pressure difference threshold due to poor real-time filter performance; the water flow influence metric to reflect the demand for increasing the preset pressure difference threshold due to increasing real-time water flow; the blockage metric to reflect the demand for increasing the preset pressure difference threshold due to the real-time impurity distribution; fuse the trend anomaly metric, the water flow influence metric, and the blockage metric at the current moment to obtain the adjustment demand degree at the current moment, and the adjustment demand degree reflects the demand for increasing the preset pressure difference threshold by the real-time operating condition of the backwashing water filter. Preferably, in an embodiment of the present invention, the method for the adjustment demand degree includes:

[0089] Calculate the product of the trend anomaly metric, the water flow influence metric, and the blockage metric at the current moment and perform normalization processing to obtain the adjustment demand degree at the current moment.

[0090] Step S3: According to the adjustment demand degree at the current moment, adjust the preset pressure difference threshold, obtain the adjusted pressure difference threshold of the backwashing water filter at the current moment, and control the backwashing water filter.

[0091] The adjustment demand degree reflects the demand for increasing the preset pressure difference threshold by the real-time operating condition of the backwashing water filter. According to the adjustment demand degree at the current moment, dynamically adjust the preset pressure difference threshold to meet the requirements of the real-time operating condition of the backwashing water filter, thereby ensuring the accuracy of backwashing.

[0092] In order to dynamically adjust the preset pressure difference threshold so that the adjusted pressure difference threshold meets the requirements of the real-time operating condition of the backwashing water filter, preferably, in an embodiment of the present invention, the method for the adjusted pressure difference threshold includes:

[0093] Calculate the product of the adjustment demand degree and the preset adjustment value to obtain the target adjustment value at the current moment; calculate the product of the target adjustment value at the current moment and the preset pressure difference threshold to obtain the adjusted pressure difference threshold of the backwashing water filter at the current moment. In an embodiment of the present invention, the preset adjustment value is set to 1.2 based on an empirical value, and the preset pressure difference threshold is 30. The implementer can also set it according to the implementation scenario.

[0094] In order to control the backwashing water filter to perform backwashing treatment of impurities on the filter screen, meet the requirements of the real-time operating condition of the backwashing water filter, and thereby ensure the accuracy of backwashing. Preferably, in an embodiment of the present invention, the method for controlling the backwashing water filter includes:

[0095] At the current moment, if the pressure difference parameter is not less than the adjusted pressure difference threshold, control the backwashing water filter to perform backwashing of impurities on the filter screen; if the pressure difference parameter is less than the adjusted pressure difference threshold, control the backwashing water filter to perform water quality filtration treatment.

[0096] In summary, the embodiment of the present invention provides a remote parameter self-regulation method based on a backwashing water filter. First, according to the change of the pressure difference parameter corresponding to the current moment over time, the trend anomaly measure at the current moment is obtained; according to the change of the inlet flow rate value in the preset influence time period at the current moment, the water flow influence degree at the current moment is obtained; according to the distribution difference of the flow velocity monitoring vectors at all monitoring positions at the current moment, the blockage measure at the current moment is obtained; the trend anomaly measure, the water flow influence degree and the blockage measure at the current moment are fused to obtain the adjustment requirement degree at the current moment, the preset pressure difference threshold is adjusted, and the adjusted pressure difference threshold of the backwashing water filter at the current moment is obtained and the backwashing water filter is controlled. By dynamically adjusting the preset pressure difference threshold, the present invention meets the requirements of the real-time operation of the backwashing water filter, thereby ensuring the accuracy of backwashing.

[0097] The present invention also proposes a remote parameter self-regulation system based on a backwashing water filter. Please refer to Figure 3 , which shows the structure diagram of a remote parameter self-regulation system based on a backwashing water filter provided by an embodiment of the present invention. The system includes: a data acquisition module 101, an adjustment requirement analysis module 102, and an adjustment control module 103.

[0098] The data acquisition module 101 is used to acquire the pressure difference parameter, the inlet flow rate value of the backwashing water filter at each sampling moment, and the flow velocity monitoring vectors at each monitoring position inside the backwashing water filter.

[0099] The adjustment requirement analysis module 102 is used to obtain the trend anomaly measure at the current moment according to the change of the pressure difference parameter corresponding to the current moment over time; obtain the water flow influence degree at the current moment according to the change of the inlet flow rate value in the preset influence time period at the current moment; obtain the blockage measure at the current moment according to the distribution difference of the flow velocity monitoring vectors at all monitoring positions at the current moment; fuse the trend anomaly measure, the water flow influence degree and the blockage measure at the current moment to obtain the adjustment requirement degree at the current moment.

[0100] The adjustment control module 103 is used to adjust the preset pressure difference threshold according to the adjustment requirement degree at the current moment, obtain the adjusted pressure difference threshold of the backwashing water filter at the current moment, and control the backwashing water filter.

[0101] It should be noted that: for the system provided in the above embodiments, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a remote parameter self-regulation system based on a backwashing water filter and an embodiment of a remote parameter self-regulation method based on a backwashing water filter provided in the above embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0102] It should be noted that: the sequence of the above embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A remote parameter self-regulation method based on a backwashing water filter, characterized in that, The method includes: Obtaining the pressure difference parameter, the inlet flow rate value of the backwash water filter at each sampling moment, and the flow velocity monitoring vectors at each monitoring position inside the backwash water filter; According to the change of the pressure difference parameter corresponding to the current moment over time, obtaining the trend anomaly metric at the current moment; according to the change of the inlet flow rate value in the preset influence time period at the current moment, obtaining the water flow influence degree at the current moment; according to the distribution difference of the flow velocity monitoring vectors at all monitoring positions at the current moment, obtaining the blockage metric at the current moment; fusing the trend anomaly metric, the water flow influence degree, and the blockage metric at the current moment to obtain the adjustment requirement degree at the current moment; According to the adjustment requirement degree at the current moment, adjusting the preset pressure difference threshold, obtaining the adjusted pressure difference threshold of the backwash water filter at the current moment, and controlling the backwash water filter; The method for obtaining the blockage metric includes: Clustering all monitoring positions according to the difference between the flow velocity monitoring vectors of every two monitoring positions to obtain each clustering cluster; Obtaining the flow velocity distribution difference degree of the clustering cluster according to the difference between the flow velocity monitoring vectors between the clustering cluster and its adjacent clustering clusters; Screening out the target clustering cluster from all clustering clusters according to the flow velocity distribution difference degree; Obtaining the blockage metric at the current moment according to the spatial distance between the target clustering cluster and the inlet of the backwash water filter.

2. The remote parameter self-regulation method based on a backwashing water filter according to claim 1, characterized in that The method for obtaining the trend anomaly metric includes: Calculating the difference between the pressure difference parameter corresponding to the sampling moment and the pressure difference parameter corresponding to the previous sampling moment in time series to obtain the pressure difference rising trend value at the sampling moment; Calculating the difference between the current moment and the pressure difference rising trend value corresponding to the previous sampling moment in time series, and performing a negative correlation mapping on the difference to obtain the trend anomaly metric at the current moment.

3. The remote parameter self-regulation method based on a backwashing water filter according to claim 1, wherein The method for obtaining the water flow influence degree includes: In the preset influence time period at the current moment, calculating the difference between each sampling moment and the previous inlet flow rate value to obtain the flow rate rising characteristic value at each sampling moment; calculating the mean value of the flow rate rising characteristic values at all sampling moments; when the mean value is not greater than 0, setting the water flow influence degree at the current moment to 0; when the mean value is greater than 0, using the mean value as the water flow influence degree at the current moment.

4. The remote parameter self-regulation method based on a backwashing water filter according to claim 3, wherein, The method for obtaining the clustering cluster includes: For the flow velocity monitoring vectors corresponding to any two monitoring positions, performing a negative correlation mapping on the cosine similarity of the two flow velocity monitoring vectors to obtain the direction difference metric; normalizing the difference between the magnitudes of the two flow velocity monitoring vectors to obtain the speed value difference metric; calculating the sum value of the direction difference metric and the speed value difference metric to obtain the distance metric value between every two monitoring positions; Using the K-means clustering algorithm, clustering all monitoring positions according to the distance metric value between every two monitoring positions to obtain each clustering cluster.

5. The remote parameter self-regulation method based on a backwashing water filter according to claim 4, characterized in that The method for obtaining the flow velocity distribution difference degree includes: Calculating the mean value of the flow velocity monitoring vectors of all monitoring positions in the clustering cluster to obtain the overall flow velocity vector of the clustering cluster; Take any one of the clustering clusters as the cluster to be analyzed, and take all the clustering clusters adjacent to the cluster to be analyzed in terms of spatial distribution as the reference clusters of the cluster to be analyzed; Perform a negative correlation mapping on the Euclidean distance between the center point of the cluster to be analyzed and its reference clusters to obtain the distribution weight; calculate the modulus of the difference between the overall flow velocity vectors of the cluster to be analyzed and its reference clusters to obtain the distribution difference parameter; calculate the product of the distribution weight and the distribution difference parameter to obtain the local distribution difference index between the cluster to be analyzed and its reference clusters; Calculate the mean value of the local distribution difference indices between the cluster to be analyzed and all its reference clusters to obtain the flow velocity distribution difference degree of the cluster to be analyzed.

6. The remote parameter self-regulation method based on a backwashing water filter according to claim 4, characterized in that, The method for obtaining the blockage metric at the current moment according to the spatial distance between the target clustering cluster and the inlet of the backwashing water filter includes: Calculate the Euclidean distance between the center point of each target clustering cluster and the inlet of the backwashing water filter to obtain the deviation from inlet metric of each target clustering cluster; calculate the mean value of the deviation from inlet metrics of all target clustering clusters and perform a negative correlation mapping to obtain the blockage metric at the current moment.

7. The remote parameter self-regulation method based on a backwashing water filter according to claim 1, characterized in that, The method for adjusting the demand degree includes: Calculate the product of the trend anomaly metric, the water flow influence degree, and the blockage metric at the current moment and perform normalization processing to obtain the adjustment demand degree at the current moment.

8. A remote parameter self-regulation method based on a backwashing water filter according to claim 1, characterized in that, The method for adjusting the pressure difference threshold includes: Calculate the product of the adjustment demand degree and the preset adjustment value to obtain the target adjustment value at the current moment; calculate the product of the target adjustment value at the current moment and the preset pressure difference threshold to obtain the adjusted pressure difference threshold of the backwashing water filter at the current moment.

9. The remote parameter self-regulation method based on a backwashing water filter according to claim 1, wherein, The method for controlling the backwashing water filter includes: At the current moment, if the pressure difference parameter is not less than the adjusted pressure difference threshold, control the backwashing water filter to perform backwashing of the filter screen impurities; if the pressure difference parameter is less than the adjusted pressure difference threshold, control the backwashing water filter to perform water quality filtration treatment.

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