A smart water affair monitoring management system based on an internet of things

Abstract

The application relates to the technical field of monitoring management, in particular to a smart water affair monitoring management system based on the Internet of Things, which comprises a processor and a memory, the processor executes a computer program stored in the memory to realize the following steps: obtaining data correlation coefficients and differential correlation coefficients corresponding to different monitoring moments, obtaining target abnormality coefficients of a first clustering cluster and a second clustering cluster obtained by clustering the data correlation coefficients and the differential correlation coefficients corresponding to the monitoring moments according to data quantity proportions and variation coefficients in the first clustering cluster and the second clustering cluster, obtaining a target abnormality index value corresponding to a current monitoring moment according to target abnormality coefficients of a clustering cluster to which data correlation coefficients and differential correlation coefficients corresponding to the current monitoring moment belong, and monitoring a water affair system according to the target abnormality index value. The application can improve the timeliness and reliability of abnormality monitoring and early warning of the water affair system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring management, and particularly relates to a smart water monitoring management system based on Internet of Things. BACKGROUND

[0002] Since the smart monitoring management of the water system based on Internet of Things is closely related to disaster risk prevention, water supply stability guarantee and water resource management efficiency improvement, it is crucial to monitor and manage the water system based on Internet of Things at present. The smart monitoring management of the water system based on Internet of Things can collect multi-dimensional data in real time and upload them to the cloud platform or the local management center through the wireless communication network, so as to realize remote monitoring and management.

[0003] In the prior art, the abnormal monitoring and early warning of the water system are usually based on the relationship between the single-dimensional monitoring data related to the water system and the threshold value. However, this monitoring method has the problems of high lag or low timeliness, which may cause resource waste and economic loss, facility damage expansion and other problems. That is, when the abnormal monitoring and early warning of the water system are based on the relationship between the single-dimensional monitoring data and the threshold value, the abnormality can only be monitored when it occurs or lasts for a period of time. For example, when there is a potential pipe explosion risk in the pipe network, the relationship between the single-dimensional monitoring data and the threshold value cannot identify the early stage of the slight abnormal fluctuation of water pressure. The abnormal alarm is triggered only when the monitoring value exceeds the threshold value or the pipe explosion has occurred. Therefore, how to improve the timeliness of monitoring the water system becomes a problem to be solved. SUMMARY

[0004] In order to solve the above problems, the present application provides a smart water monitoring management system based on Internet of Things, which adopts the following technical solutions:

[0005] One embodiment of the present application provides a smart water monitoring management system based on Internet of Things, which comprises a processor and a memory. The processor executes the computer program stored in the memory to realize the following steps:

[0006] obtaining a target water pressure data sequence and a target water flow data sequence corresponding to a monitoring time of the water system;

[0007] According to the correlation between the target water pressure data sequence and the target water flow data sequence and the correlation between the differential sequence of the target water pressure data sequence and the differential sequence of the target water flow data sequence, data correlation coefficients and differential correlation coefficients corresponding to different monitoring moments are obtained, and according to the data amount proportion and the variation coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficients and the differential correlation coefficients corresponding to the monitoring moment, target anomaly coefficients of the first clustering cluster and target anomaly coefficients of the second clustering cluster are obtained.

[0008] According to the target anomaly coefficients of the clustering cluster to which the data correlation coefficients and the differential correlation coefficients corresponding to the current monitoring moment belong, a target anomaly index value corresponding to the current monitoring moment is obtained, and the water affair system is monitored according to the target anomaly index value.

[0009] Beneficial effects: The application first obtains the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring moment of the water affair system, then obtains the data correlation coefficients and the differential correlation coefficients corresponding to different monitoring moments according to the correlation between the target water pressure data sequence and the target water flow data sequence and the correlation between the differential sequence of the target water pressure data sequence and the differential sequence of the target water flow data sequence, obtains the target anomaly coefficients of the first clustering cluster and the target anomaly coefficients of the second clustering cluster according to the data amount proportion and the variation coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficients and the differential correlation coefficients corresponding to the monitoring moment, then obtains the target anomaly index value corresponding to the current monitoring moment according to the target anomaly coefficients of the clustering cluster to which the data correlation coefficients and the differential correlation coefficients corresponding to the current monitoring moment belong, and finally monitors the water affair system according to the target anomaly index value. The application can improve the timeliness and reliability of the abnormal monitoring and early warning of the water affair system according to the data correlation coefficients and the differential correlation coefficients. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, and the advantages thereof, below, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0011] Figure 1 The flowchart of the application is a smart water affair monitoring and management method based on the Internet of Things. DETAILED DESCRIPTION

[0012] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the embodiments of the present application.

[0013] 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 belongs.

[0014] The embodiment provides a smart water monitoring management system based on Internet of Things, comprising a processor and a memory, the processor executes a computer program stored in the memory to realize a smart water monitoring management method based on Internet of Things, as shown in the figure, the smart water monitoring management method based on Internet of Things comprises the following steps: Figure 1 As shown in the figure, the smart water monitoring management method based on Internet of Things comprises the following steps:

[0015] Step S001, obtaining a target water pressure data sequence and a target water flow data sequence corresponding to a monitoring moment of a water system.

[0016] The subsequent embodiment will be described by taking the abnormal monitoring and early warning process of any water system as an example. Since the water system is a comprehensive water resource management network and comprises multiple links, each link undertakes a specific function and cooperates with each other. In order to more comprehensively monitor and early warn the water system, multiple monitoring positions are usually arranged in each link of the water system, and the purpose of monitoring and early warning the water system is achieved by monitoring and analyzing each monitoring position, that is, the parameters at the monitoring position can reflect the condition of the water system. In addition, since the water system generally processes water in a water plant, pressurizes water in a pump station, transports water in a pipe network, and uses water at a user end, the links of the water system include water plant processing, pump station pressurization, pipe network transportation, and user end, and the arrangement rule of the monitoring positions of the water system is a known technology.

[0017] Since the parameters at the monitoring positions that can reflect the condition of the water system include water pressure and water flow, the embodiment next needs to achieve the purpose of detecting and warning the water system abnormity by monitoring and analyzing the water pressure and water flow at each monitoring position, that is, the embodiment next needs to first obtain the water pressure and water flow data of the monitoring position at different monitoring time by collecting the water pressure and water flow at the monitoring position by the sensor, that is, the water pressure and water flow at the same monitoring position are synchronously collected; since the monitoring and analyzing method of each detection position is the same, the monitoring and analyzing process of any detection position will be taken as an example for description in the following embodiment for the convenience of understanding and description, and is recorded as a target detection position, that is, the water pressure and water flow data appearing in the following will be the data of the target detection position. The data collected by the sensor will be uploaded to the cloud platform or the local control center through the wireless communication network to ensure the real-time, integrity and reliability of the collected data, thereby providing a basis for the subsequent intelligent analysis, monitoring and warning.

[0018] In addition, since the correlation between the multi-dimensional parameters of the water system in the abnormal state or the state with abnormal trend is different from the correlation between the multi-dimensional parameters of the water system in the normal state, the embodiment mainly analyzes the correlation between the multi-dimensional parameters related to the running state of the water system to improve the timeliness of the abnormal monitoring and early warning of the water system, that is, the correlation between the water pressure and water flow data of the target monitoring position at different monitoring moments is analyzed to improve the timeliness of the abnormal monitoring and early warning of the water system. In order to avoid the contingency of instantaneous data, the embodiment needs to select a period of data for correlation analysis. However, the local change characteristics at different moments are different, such as the different water consumption of users in different time periods, which leads to different water pressure data fluctuation characteristics in the water pipe at different moments. The local change characteristics are necessarily related to the abnormal detection and early warning, such as unstable changes to capture short-term abnormalities. Therefore, a longer data segment should be selected for correlation analysis. It can be known that if the same length of data is selected for correlation operation without considering the difference of local change characteristics, the timeliness of subsequent corresponding abnormal monitoring will be affected. Therefore, the embodiment needs to obtain the target water pressure data sequence and the target water flow data sequence for correlation analysis based on the local change characteristics of the target monitoring position at different monitoring moments, that is, the target water pressure data sequence and the target water flow data sequence corresponding to the target monitoring position at different monitoring moments are obtained, which can also be called the target water pressure data sequence and the target water flow data sequence corresponding to the water system at the monitoring moment. In addition, for the convenience of understanding, the specific obtaining process of the target water pressure data sequence and the target water flow data sequence corresponding to the target monitoring position at the monitoring moment t will be described in the following embodiment, that is, the specific obtaining process of the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring moment t is:

[0019] Firstly, a time sequence sequence of all water pressure data collected by the sensor at the target monitoring position within a preset initial time length before the monitoring time t and at the monitoring time t is obtained, and is recorded as an initial water pressure data sequence corresponding to the monitoring time t of the target monitoring position. A time sequence sequence of all water flow data collected by the sensor at the target monitoring position within a preset initial time length before the monitoring time t and at the monitoring time t is obtained, and is recorded as an initial water flow data sequence corresponding to the monitoring time t of the target monitoring position. The initial water pressure data sequence and the initial water flow data sequence are mainly used to analyze the local change characteristics at the monitoring time t, and subsequent scale adjustment is based on the analyzed results, that is, the preset initial time length is adjusted. In specific applications, the implementer needs to set the preset initial time length according to historical experience and other actual situations. If the water pressure or water flow at the target monitoring position usually fluctuates greatly in history, the preset initial time length can be set to be shorter. If the water pressure or water flow at the target monitoring position usually fluctuates less in history, the preset initial time length can be set to be longer, so as to ensure the accuracy and reliability of the analysis of the local change characteristics. For example, the preset initial time length in this embodiment can be set to 10 minutes, so that the initial water pressure data sequence and the initial water flow data sequence corresponding to the monitoring time t of the target monitoring position correspond to a time period of ten minutes before the monitoring time t and at the monitoring time t. After obtaining the initial water pressure data sequence and the initial water flow data sequence corresponding to the monitoring time t, the change characteristic value is analyzed based on the difference between the initial water pressure data sequence and the initial water flow data sequence. The preset initial time length is adjusted based on the analysis result, the target data selection time length at the monitoring time t is obtained, and the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring time t are obtained based on the obtained target data selection time length.

[0020] Based on the above analysis, the next embodiment needs to obtain the change characteristic value first, that is, the next embodiment needs to perform first-order difference on the initial water pressure data sequence and the initial water flow data sequence corresponding to the monitoring time t respectively, to obtain the first-order difference sequence of the initial water pressure data sequence corresponding to the monitoring time t and the first-order difference sequence of the initial water flow data sequence corresponding to the monitoring time t, and the rth difference value in any sequence is the result of the r+1th data minus the rth data in the sequence; Since the difference value difference in the difference sequence can reflect the change characteristics, then according to the difference value difference in the first-order difference sequence of the initial water pressure data sequence corresponding to the monitoring time t, the water pressure change characteristic value at the monitoring time t is obtained, according to the difference value difference in the first-order difference sequence of the initial water flow data sequence corresponding to the monitoring time t, the water flow change characteristic value at the monitoring time t is obtained, and then the preset initial time length is adjusted according to the water pressure change characteristic value and the water flow change characteristic value at the monitoring time t, to obtain the target data selection time length at the monitoring time t, and according to the target data selection time length at the monitoring time t, the target water pressure data sequence and the target water flow data sequence corresponding to the target monitoring position at the monitoring time t are obtained, and the time period corresponding to any sequence is the time period formed from the time when the first data in the corresponding sequence is collected to the time when the last data in the corresponding sequence is collected. The acquisition method of the water flow change characteristic value is the same as that of the water pressure change characteristic value, so the subsequent process of obtaining the water flow change characteristic value will not be described in detail.

[0021] According to the difference value difference in the first-order difference sequence of the initial water pressure data sequence corresponding to the monitoring time t, the water pressure change characteristic value at the monitoring time t is obtained.

[0022] First, the first-order difference sequence of the initial water pressure data sequence corresponding to the monitoring time t is denoted as sequence A, and the weighted change index value of the difference value in sequence A is obtained according to the difference between each difference value and the adjacent difference value and the time interval between the collection time of the water pressure data corresponding to each difference value and the monitoring time t, and it needs to be noted that the first difference value and the last difference value in sequence A do not calculate the weighted change index value. Then, the weighted change index values of all difference values belonging to sequence A except the first difference value and the last difference value in sequence A are accumulated, and the accumulation result is denoted as an accumulation index value. The accumulation index value is negatively correlated mapped, and the mapping result is denoted as the water pressure change characteristic value at the monitoring time t. Here, a negative exponential function with constant e as the base is used for negative correlation mapping.

[0023] The obtaining process of the weighted change indicator value is as follows: for the i-th difference value in sequence A, i is greater than 1 and less than N, that is, i is not equal to 1 and N, N is the total amount of data in sequence A, the absolute value of the difference between the (i-1)-th difference value and the i-th difference value in sequence A is calculated and recorded as a first difference value, the absolute value of the difference between the i-th difference value and the (i+1)-th difference value in sequence A is calculated and recorded as a second difference value, the sum of the first difference value and the second difference value is recorded as a comprehensive difference value, the absolute value of the difference between the (i-1)-th difference value and the (i+1)-th difference value is recorded as a neighboring difference value, and the ratio of the comprehensive difference value to the neighboring difference value is recorded as the change indicator value of the i-th difference value. The time interval between the collection time of the water pressure data corresponding to the i-th difference value and the current monitoring time is obtained and recorded as the time difference value corresponding to the i-th difference value. The time difference value corresponding to the i-th difference value is negatively correlated and mapped, and the mapping result is recorded as the negative mapping value corresponding to the i-th difference value. The negative mapping value corresponding to the i-th difference value is normalized, and the result of the normalization is recorded as the time distance weight factor of the i-th difference value. The product of the change indicator value of the i-th difference value and the time distance weight factor of the i-th difference value is calculated and recorded as the weighted change indicator value of the i-th difference value. The water pressure data corresponding to the i-th difference value in sequence A is the (i+1)-th water pressure data in the initial water pressure data sequence corresponding to the monitoring time t, that is, the water pressure data corresponding to the first difference value in sequence A is the second water pressure data in the initial water pressure data sequence corresponding to the monitoring time t.

[0024] In addition, the calculation expression of the water pressure change characteristic value at the monitoring time t is:

[0025]

[0026] Wherein, W1 is the water pressure change characteristic value at the monitoring time t, N is the total amount of data in sequence A, is the i-th difference value, is the (i-1)-th difference value, is the (i+1)-th difference value, is the comprehensive difference value, is the neighboring difference value, exp() is the exponential function with constant e as the base, is the time difference value corresponding to the i-th difference value, is the negative mapping value corresponding to the i-th difference value, normalizes , and the negative mapping value corresponding to any difference value is obtained in the same way as the negative mapping value corresponding to the i-th difference value; and The phase difference value can reflect the relative difference between the ith difference value and the neighborhood difference value, and can eliminate the difference in calculation results caused by different difference values. Since the change index value closer to the monitoring time t can reflect the change at the monitoring time t, the change index value closer to the monitoring time t can reflect the change of the water pressure at the monitoring time t. Therefore, the embodiment makes the change index value of the difference value closer to the monitoring time t more dominant in the result of the water pressure change characteristic value at the monitoring time t, and the closer the data collection time corresponding to the ith difference value is to the monitoring time t, The greater the product of the above and , the smaller W1 is. When W1 is smaller, it indicates that the local water pressure change at the monitoring time t is less stable. When W1 is larger, it indicates that the local water pressure change at the monitoring time t is more stable.

[0027] According to the water pressure change characteristic value at the monitoring time t and the water flow change characteristic value, the preset initial time length is adjusted to obtain the specific process of the target data selection time length at the monitoring time t. The product of the minimum value of the water pressure change characteristic value at the monitoring time t and the water flow change characteristic value at the monitoring time t and the preset initial time length is calculated, and is recorded as the target data selection time length at the monitoring time t. The expression for obtaining the target data selection time length at the monitoring time t is , wherein W2 is the water flow change characteristic value at the monitoring time t, min() is the minimum value function, and L is the preset initial time length. The smaller the water pressure change characteristic value or the water flow change characteristic value, the less stable the local change at the monitoring time t is, and the shorter the target data selection time length at the monitoring time t is, i.e., the shorter the length of the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring time t. Since the more unstable the local change at a certain monitoring time is, the shorter data length should be selected to capture the local sudden change more sensitively. If a longer data length is used at this time, the short-time anomaly may be covered up or the local sudden change may not be captured, which affects the final abnormal monitoring and early warning result. Conversely, when the local change at a certain monitoring time is more stable, a longer data length should be selected to capture the overall trend and improve the timeliness of abnormal monitoring and early warning. Since the water pressure change characteristic value and the water flow change characteristic value can reflect the stability of the local change, the embodiment adjusts the selected data length based on the water pressure change characteristic value and the water flow change characteristic value, and makes the water pressure change characteristic value or the water flow change characteristic value smaller, and the target data selection time length at the monitoring time t shorter.

[0028] The specific process of obtaining the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring moment t of the target monitoring position is as follows: the target data selection time length at the monitoring moment t is denoted as T, the time period with a length of T before the monitoring moment t is denoted as the target time period before the monitoring moment t, the target time period before the monitoring moment t is adjacent to the monitoring moment t, all the water pressure data collected in the target time period before the monitoring moment t and the water pressure data collected at the monitoring moment t are denoted as a time sequence and are denoted as the target water pressure data sequence corresponding to the monitoring moment t, all the water flow data collected in the target time period before the monitoring moment t and the water flow data collected at the monitoring moment t are denoted as a time sequence and are denoted as the target water flow data sequence corresponding to the monitoring moment t. For example, if the target data selection time length is 15 minutes, all the water pressure data collected at the target monitoring position in the 15 minutes before the monitoring moment t and the water pressure data collected at the target monitoring position at the monitoring moment t form a time sequence, which is the target water pressure data sequence corresponding to the monitoring moment t, and the target water flow data sequence corresponding to the monitoring moment t is the same.

[0029] Therefore, the target water pressure data sequence and the target water flow data sequence corresponding to different monitoring moments of the target monitoring position can be obtained through the above process.

[0030] In step S002, the data correlation coefficients and the difference correlation coefficients corresponding to different monitoring moments are obtained according to the correlation between the target water pressure data sequence and the target water flow data sequence and the correlation between the difference sequence of the target water pressure data sequence and the difference sequence of the target water flow data sequence. The target anomaly coefficient of the first clustering cluster and the target anomaly coefficient of the second clustering cluster are obtained according to the data amount proportion and the variation coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficients and the difference correlation coefficients corresponding to the monitoring moment.

[0031] Since water pressure and water flow have a close relationship with each other, for example, when the water consumption of a certain area in the pipe network suddenly increases, the water flow will rise, and due to the resistance loss of the pipeline, the local water pressure will tend to fall, and vice versa, when the water consumption decreases, the water flow decreases, and the water pressure of the pipe network will relatively increase, so it can be known that the correlation between the changes of water pressure and water flow or the differential correlation between water pressure and water flow can be used to identify abnormalities or abnormal trends, that is, the correlation between the multi-dimensional parameters of the water system in the abnormal state or the state with abnormal trend is different from the correlation between the multi-dimensional parameters of the water system in the normal state, that is, the correlation between the multi-dimensional parameters of the water system in the abnormal state or the state with abnormal trend will usually deviate from the correlation calculated in the normal state, and since the duration of the normal state is generally longer, the embodiment will first calculate the correlation coefficients at each monitoring time, then cluster, and analyze the abnormal coefficients of the clustering clusters based on the number ratio, coefficient of variation, etc. in the clustering cluster, and subsequently based on the abnormal coefficient of the clustering cluster to which the correlation coefficient at the current monitoring time belongs to obtain the abnormal index value, which can reflect the possibility of existence of abnormality or abnormal trend; based on the above description, it can be known that the correlation coefficients at multiple monitoring times need to be calculated first, that is, the correlation between the target water pressure data sequence and the target water flow data sequence at different monitoring times and the correlation between the first order difference sequence of the target water pressure data sequence and the first order difference sequence of the target water flow data sequence at the target detection position will be obtained next.

[0032] For the sake of understanding and description, the embodiment will be described next with the acquisition process of the data correlation coefficient and the differential correlation coefficient corresponding to the monitoring time t as an example, t cannot be the time after the current monitoring time, nor can it be the initial stage of monitoring the target detection position. The historical data in the initial stage is too small to have little reference value, for example, t can be set to not be the first ten minutes before the target detection position starts to be monitored, and the specific acquisition process of the data correlation coefficient and the differential correlation coefficient corresponding to the monitoring time t is as follows:

[0033] The Spearman rank correlation coefficient between the target water pressure data sequence corresponding to the monitoring time t and the target water flow data sequence corresponding to the monitoring time t is calculated and used as the data correlation coefficient corresponding to the monitoring time t, and the Spearman rank correlation coefficient between the first order difference sequence of the target water pressure data sequence corresponding to the monitoring time t and the first order difference sequence of the target water flow data sequence corresponding to the monitoring time t is calculated and used as the differential correlation coefficient corresponding to the monitoring time t. The acquisition process of the Spearman rank correlation coefficient is known.

[0034] Then the HDBSCAN clustering algorithm is used to cluster the data correlation coefficients corresponding to the monitoring time, and the cluster obtained by clustering is recorded as the first clustering cluster. The HDBSCAN clustering algorithm is used to cluster the differential correlation coefficients corresponding to the monitoring time, and the cluster obtained by clustering is recorded as the second clustering cluster. The time after the current monitoring time and the time in the initial stage of monitoring the target detection position do not participate in the clustering at this time. The HDBSCAN clustering algorithm is a density-based hierarchical clustering algorithm that combines the advantages of DBSCAN and hierarchical clustering. The clustering process of the HDBSCAN clustering algorithm is known.

[0035] After clustering, the data amount ratio and the coefficient of variation in the first clustering cluster and the second clustering cluster are obtained to obtain the target anomaly coefficient of each first clustering cluster and the target anomaly coefficient of each second clustering cluster. Since the target anomaly coefficient of the second clustering cluster and the target anomaly coefficient of the first clustering cluster are obtained in the same way, the subsequent description of the target anomaly coefficient of the first clustering cluster is only described, and the specific acquisition process of the target anomaly coefficient of the second clustering cluster is not described. The acquisition process of the target anomaly coefficient of the first clustering cluster is as follows:

[0036] For any first clustering cluster R, first, the ratio of the total number of data correlation coefficients in the first clustering cluster R to the total number of all data correlation coefficients participating in the clustering of the first clustering cluster is obtained, and is recorded as the quantity ratio. Then, the coefficient of variation of the first clustering cluster R is obtained, which is the ratio of the standard deviation to the average value of all data in the first clustering cluster R. The result of the normalized processing of the coefficient of variation of the first clustering cluster R and the negative correlation mapping is recorded as the mapping coefficient. The product of the quantity ratio and the mapping coefficient is taken as the normal representation value of the first clustering cluster R. The expression of the normal representation value of the first clustering cluster R is as follows:

[0037]

[0038] wherein, is the normal representation value of the first clustering cluster R, MR is the total number of data correlation coefficients in the first clustering cluster R, NR is the total number of all data correlation coefficients participating in the clustering of the first clustering cluster, Norm() is a normalization function, and YR is the coefficient of variation of the first clustering cluster R. When the MR ratio is larger, the change relationship between most of the water pressure and the water flow is in the state of the cluster, so the reference reliability of the cluster is larger or the probability that the cluster is a normal cluster is higher. When YR is smaller, it indicates that the water pressure and water flow relationship characteristics of the cluster are more stable, so the reference reliability of the cluster is larger or the probability that the cluster is a normal cluster is higher. Since is larger, YR is smaller, is larger, so when The greater the value is, the greater the reference credibility of the cluster is or the higher the probability that the cluster is a normal cluster is, and vice versa. The smaller the value is, the smaller the reference credibility of the cluster is or the lower the probability that the cluster is a normal cluster is.

[0039] Then, the target abnormality coefficient of the first clustering cluster R is obtained according to the normal representation value of the first clustering cluster R and the normal representation values of the other remaining first clustering clusters except the first clustering cluster R, and the specific process is as follows:

[0040] Firstly, a set composed of all the first clustering clusters except the first clustering cluster R is recorded as a remaining cluster set corresponding to the first clustering cluster R, the sum of the normal representation values of all the first clustering clusters in the remaining cluster set corresponding to the first clustering cluster R is recorded as a first remaining normal representation value corresponding to the first clustering cluster R, and the ratio of the normal representation value of each first clustering cluster in the remaining cluster set corresponding to the first clustering cluster R to the first remaining normal representation value is recorded as a weight value corresponding to the first clustering cluster, that is, the weight value of any first clustering cluster in the remaining cluster set is the ratio of the normal representation value of the first clustering cluster to the first remaining normal representation value corresponding to the first clustering cluster R. Then, a weighted ratio set corresponding to the first clustering cluster R is obtained, the jth weighted ratio in the weighted ratio set is the ratio of the normal representation value of the jth first clustering cluster in the remaining cluster set corresponding to the first clustering cluster R to the normal representation value of the first clustering cluster R multiplied by the weight value of the jth first clustering cluster. After that, the cumulative result of all the weighted ratios in the weighted ratio set is calculated and recorded as the target abnormality coefficient of the first clustering cluster R. The specific calculation expression of the target abnormality coefficient of the first clustering cluster R is as follows:

[0041]

[0042] wherein, is the target abnormality coefficient of the first clustering cluster R, J is the number of clustering clusters in the remaining cluster set corresponding to the first clustering cluster R, is the normal representation value of the jth first clustering cluster in the remaining cluster set corresponding to the first clustering cluster R, and the higher the abnormality degree of a certain first clustering cluster is, the greater the difference between the normal representation value of the first clustering cluster and the normal representation values of the other first clustering clusters is, so that The greater the value is, the greater the abnormality degree of the first clustering cluster R is, and since is determined by comparison with the clustering clusters in the remaining cluster set, so when The greater the value is, the greater the reference value of should be, which dominates the result of ​In order to Normalization is performed, therefore The larger and When it is larger, The larger, and The larger the value, the higher the anomaly level of the first cluster R. A higher anomaly level of the first cluster R indicates that the data belonging to the first cluster R deviates more from the normal state, or that the first cluster R is more likely to be an anomalous cluster. Conversely, when the value decreases... The smaller the value, the higher the normality of the first cluster R. The higher the normality of the first cluster R, the closer the data belonging to the first cluster R is to a normal state, or the more likely the first cluster R is to be a normal cluster.

[0043] Therefore, this embodiment can obtain the target anomaly coefficient of the first cluster and the target anomaly coefficient of the second cluster through the above process.

[0044] Step S003: Based on the target anomaly coefficient of the cluster to which the data correlation coefficient and differential correlation coefficient belong at the current monitoring time, obtain the target anomaly index value corresponding to the current monitoring time, and monitor the water system based on the target anomaly index value.

[0045] Since the target anomaly coefficient of a cluster can reflect the deviation of data belonging to the corresponding cluster from normal or the probability of anomaly, this embodiment, after obtaining the target anomaly coefficient of the cluster, obtains the target anomaly index value corresponding to the current monitoring time based on the target anomaly coefficient of the cluster to which the data correlation coefficient and differential correlation coefficient of the target monitoring node belong at the current monitoring time. Specifically, it obtains the target anomaly coefficient of the first cluster to which the data correlation coefficient of the target monitoring node belongs at the current monitoring time, plus the anomaly coefficient of the second cluster to which the differential correlation coefficient of the target monitoring node belongs at the current monitoring time, and records the result as the target anomaly index value corresponding to the target monitoring node at the current monitoring time. Furthermore, the larger the target anomaly index value corresponding to the target monitoring node at the current monitoring time, the more abnormal the change in the water pressure-flow relationship coefficient at the target monitoring node is, and the greater the probability of anomalies or abnormal trends in the water system.

[0046] Therefore, the embodiment can obtain the target abnormal index value corresponding to each monitoring position at the current monitoring moment through the above process. Then, the water system is monitored according to the target abnormal index value corresponding to the current monitoring moment. The specific process is as follows: it is judged whether the target abnormal index value corresponding to all monitoring positions of the water system at the current monitoring moment is located in the preset normal interval corresponding to the monitoring position at the current monitoring moment. If not, it is determined that the water system at the current monitoring moment has an operation abnormality or an abnormal trend, and an alarm needs to be given immediately, and maintenance and troubleshooting need to be performed in time. If yes, it is determined that the water system at the current monitoring moment is normal, and can continue to operate normally. The preset normal interval is obtained through a three-sigma detection method. The specific determination process of the preset normal interval corresponding to any monitoring position at the current monitoring moment is as follows: the target abnormal index value corresponding to the monitoring position at all historical monitoring moments is obtained, and is recorded as the historical target abnormal index value corresponding to the monitoring position. The method for obtaining the target abnormal index value in the embodiment is the same. The standard deviation and the mean value of all historical target abnormal index values corresponding to the monitoring position are calculated. The mean value is taken as a central reference point, and a range of plus or minus three times the standard deviation is taken as the preset normal interval corresponding to the monitoring position at the current monitoring moment. That is, the preset normal interval corresponding to the monitoring position at the current monitoring moment is , is the mean value of all historical target abnormal index values corresponding to the monitoring position, is the standard deviation of all historical target abnormal index values corresponding to the monitoring position.

[0047] Thus, the embodiment completes the abnormal monitoring and early warning of the water system.

[0048] In summary, the embodiment first obtains the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring moment of the water system. Then, the data correlation coefficient and the difference correlation coefficient corresponding to different monitoring moments are obtained according to the correlation between the target water pressure data sequence and the target water flow data sequence and the correlation between the difference sequence of the target water pressure data sequence and the difference sequence of the target water flow data sequence. The target abnormal coefficient of the first clustering cluster and the target abnormal coefficient of the second clustering cluster are obtained according to the data amount proportion and the variation coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficient and the difference correlation coefficient corresponding to the monitoring moment. Then, the target abnormal index value corresponding to the current monitoring moment is obtained according to the target abnormal coefficient of the clustering cluster to which the data correlation coefficient and the difference correlation coefficient corresponding to the current monitoring moment belong. Finally, the water system is monitored according to the target abnormal index value. Moreover, the embodiment can improve the timeliness and reliability of the abnormal monitoring and early warning of the water system according to the data correlation coefficient and the difference correlation coefficient.

[0049] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An Internet of Things-based smart water monitoring management system, comprising a processor and a memory, characterized in that, The processor executes the computer program stored in the memory to implement the following steps: Obtain the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring time of the water system; According to the correlation between the target water pressure data sequence and the target water flow data sequence, and the correlation between the difference sequence of the target water pressure data sequence and the difference sequence of the target water flow data sequence, obtain the data correlation coefficient and the difference correlation coefficient corresponding to different monitoring times, and obtain the target anomaly coefficient of the first clustering cluster and the target anomaly coefficient of the second clustering cluster according to the data amount proportion and the variation coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficient and the difference correlation coefficient corresponding to the monitoring time; According to the target anomaly index value corresponding to the current monitoring time, the target anomaly index value is obtained according to the target anomaly index value of the clustering cluster to which the data correlation coefficient and the difference correlation coefficient corresponding to the current monitoring time belong, and the water system is monitored according to the target anomaly index value; The target water pressure data sequence and the target water flow data sequence are obtained by the following method: Obtain the initial water pressure data sequence and the initial water flow data sequence corresponding to the monitoring time of the water system; For any monitoring time, according to the difference between the difference values in the first order difference sequence of the initial water pressure data sequence corresponding to the monitoring time, obtain the water pressure change characteristic value under the monitoring time, according to the difference between the difference values in the first order difference sequence of the initial water flow data sequence corresponding to the monitoring time, obtain the water flow change characteristic value under the monitoring time, according to the water pressure change characteristic value and the water flow change characteristic value, obtain the target data selection time length under the monitoring time, and according to the target data selection time length under the monitoring time, obtain the target water pressure data sequence and the target water flow data sequence corresponding to the monitoring time, the water flow change characteristic value is obtained by the same method as the water pressure change characteristic value; The data correlation coefficient and the difference correlation coefficient are obtained by the following method: For any monitoring time, the Spearman rank correlation coefficient between the target water pressure data sequence corresponding to the monitoring time and the target water flow data sequence corresponding to the monitoring time is taken as the data correlation coefficient corresponding to the monitoring time, and the Spearman rank correlation coefficient between the first order difference sequence of the target water pressure data sequence corresponding to the monitoring time and the first order difference sequence of the target water flow data sequence corresponding to the monitoring time is taken as the difference correlation coefficient corresponding to the monitoring time.

2. The smart water monitoring management system based on the Internet of Things according to claim 1, wherein, The water pressure change characteristic value under the monitoring time is obtained by the following method: The first order difference sequence of the initial water pressure data sequence corresponding to the monitoring time is denoted as sequence A; For the i-th differential value in the sequence A, i is greater than 1 and less than N, N is the total amount of data in the sequence A, the absolute value of the difference between the i-1-th differential value and the i-th differential value in the sequence A is recorded as a first difference value, the absolute value of the difference between the i-th differential value and the i+1-th differential value in the sequence A is recorded as a second difference value, the sum of the first difference value and the second difference value is recorded as a comprehensive difference value, the absolute value of the difference between the i-1-th differential value and the i+1-th differential value is recorded as a neighboring difference value, the ratio of the comprehensive difference value to the neighboring difference value is recorded as a change indicator value of the i-th differential value, the result of the negative correlation mapping and the normalization processing of the time interval between the collection time of the water pressure data corresponding to the i-th differential value and the monitoring time is recorded as a time distance weight factor of the i-th differential value, and the product of the change indicator value and the time distance weight factor is recorded as a weighted change indicator value of the i-th differential value. The cumulative sum of the weighted change indicator values of all differential values in the sequence A is negatively correlated and mapped to obtain a water pressure change feature value at the monitoring time.

3. The smart water monitoring management system based on the Internet of Things according to claim 2, wherein, The target data selection time length at the monitoring time is the product of the minimum value of the water pressure change feature value and the water flow change feature value and the preset initial time length.

4. The smart water monitoring management system based on the Internet of Things according to claim 1, wherein, According to the target data selection time length at the monitoring time, a method for obtaining a target water pressure data sequence and a target water flow data sequence corresponding to the monitoring time comprises: For any monitoring time, the target data selection time length at the monitoring time is recorded as T, a time period before the monitoring time with a length of T is recorded as a target time period before the monitoring time, all water pressure data collected in the target time period before the monitoring time and water pressure data collected at the monitoring time are arranged in a time sequence to form a target water pressure data sequence corresponding to the monitoring time, and all water flow data collected in the target time period before the monitoring time and water flow data collected at the monitoring time are arranged in a time sequence to form a target water flow data sequence corresponding to the monitoring time.

5. The smart water monitoring management system based on the Internet of Things according to claim 1, wherein, A method for obtaining a target anomaly coefficient of a cluster comprises: For any first cluster, the ratio of the total number of data correlation coefficients in the first cluster to the total number of all data correlation coefficients participating in clustering is recorded as a quantity ratio, the product of the result of the negative correlation mapping of the normalized variation coefficient of the first cluster and the quantity ratio is taken as a normal representation value of the first cluster, and the target anomaly coefficient of the first cluster is obtained according to the normal representation value of the first cluster and the normal representation values of other first clusters except the first cluster. The method for obtaining the target anomaly coefficient of the second cluster is the same as that of the first cluster.

6. The smart water monitoring management system based on the Internet of Things according to claim 5, wherein, The method for obtaining the target anomaly coefficient of the first cluster according to the normal representation value of the first cluster and the normal representation values of other first clusters except the first cluster comprises: A set of other first clustering clusters except the first clustering cluster is denoted as a residual cluster set, a sum of normal representation values of all first clustering clusters in the residual cluster set is denoted as a first residual normal representation value, a ratio of a normal representation value of each first clustering cluster in the residual cluster set to the first residual normal representation value is denoted as a weight value of the corresponding first clustering cluster, a weighted ratio set corresponding to the first clustering cluster is obtained, a jth weighted ratio in the weighted ratio set is a result of multiplying a ratio of a normal representation value of a jth first clustering cluster in the residual cluster set to the normal representation value of the first clustering cluster by a weight value of the jth first clustering cluster, j is a position order of data or a clustering cluster in a corresponding set, j is in a range of 1 to J, J is a number of clustering clusters in the residual cluster set, and a cumulative result of weighted ratios in the weighted ratio set is denoted as a target abnormal coefficient of the first clustering cluster.

7. The smart water monitoring management system based on the Internet of Things according to claim 1, wherein, The method for obtaining the target abnormal index value corresponding to the current monitoring moment comprises the following steps: A sum of the target abnormal coefficient of the first clustering cluster to which the data correlation coefficient corresponding to the current monitoring moment belongs and the target abnormal coefficient of the second clustering cluster to which the differential correlation coefficient corresponding to the current monitoring moment belongs is denoted as the target abnormal index value corresponding to the current monitoring moment.

8. The smart water monitoring management system based on the Internet of Things according to claim 1, wherein, The method for monitoring the water affair system according to the target abnormal index value comprises the following steps: If the target abnormal index value does not belong to a preset normal interval, an abnormal alarm is given.

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