Intelligent Water Meter Control System and Method Based on Internet of Things

Abstract

The present application discloses an intelligent water meter control system and method based on the Internet of Things. By mining the temporal correlation features between the real-time water consumption curve and historical data of the same period, and establishing a dynamic comparison framework based on a time queue, the real-time data of the intelligent water meter is deeply queried and matched with the historical water consumption temporal features of the same period over multiple years, and the adaptive identification of abnormal water consumption is realized based on the query results. By adopting the method of time queue matching analysis, the risk of misjudgment caused by changes in user behavior patterns can be effectively reduced. At the same time, there is no need to rely on a large amount of historical data to train complex prediction models, making the operation of the entire system more efficient and sustainable.

Description

Technical Field

[0001] The present application relates to the field of intelligent control, and more specifically, to an intelligent water meter control system and method based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, smart water meters, as the core terminal equipment of smart water services, have gradually realized the real-time collection and remote transmission of water use data. In the existing technology, anomalies (such as water meter failure or pipeline leakage) are usually judged by comparing the statistical deviation of the total water consumption in the region with the data collected by the smart water meter. For example, by building a user water preference model, combining deep learning algorithms to analyze differences in water use behavior, or using Bayesian networks to predict the probability of water meter failure. However, such methods have significant limitations: on the one hand, the dynamic changes in user behavior patterns (such as seasonal water use fluctuations, new residents) may lead to model misjudgment, and rely on a large amount of historical data to train complex models, increasing the cost of system deployment and maintenance; on the other hand, threshold detection based on statistical deviations is difficult to accurately capture periodic or trend anomalies (such as a slight increase in daily average water consumption caused by slow pipeline leakage), and the response sensitivity to sudden short-term anomalies (such as instantaneous pipe bursts) is insufficient.

[0003] Therefore, an optimized IoT-based smart water meter control method is expected. Summary of the invention

[0004] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a smart water meter control system and method based on the Internet of Things, which mines the time series correlation characteristics of the real-time water usage curve and the historical data of the same period, and establishes a dynamic comparison framework based on the time queue, matches the real-time data of the smart water meter with the deep query of the time series characteristics of the historical water usage in the same period of multiple years, and realizes adaptive identification of water use anomalies based on the query results. By adopting the method of time queue matching analysis, the risk of misjudgment caused by changes in user behavior patterns can be effectively reduced. At the same time, there is no need to rely on a large amount of historical data to train complex prediction models, making the operation of the entire system more efficient and sustainable.

[0005] According to one aspect of the present application, a smart water meter control method based on the Internet of Things is provided, which includes:

[0006] Obtaining a set of a time queue of water consumption data of a first smart water meter and a time queue of historical water consumption data;

[0007] Based on the timestamp, a time series matching and a water consumption time series feature query matching analysis are performed between the time series of the water consumption data and the set of time series of the historical water consumption data to determine whether the first smart water meter has an abnormality;

[0008] In response to an abnormality in the first smart water meter, the first smart water meter is remotely controlled.

[0009] According to another aspect of the present application, there is provided a smart water meter control system based on the Internet of Things, which includes:

[0010] A data acquisition module, used to acquire a time queue of water consumption data of the first smart water meter and a collection of time queues of historical water consumption data;

[0011] A water consumption time series feature query matching analysis module is used to perform time series matching and water consumption time series feature query matching analysis between a time queue of water consumption data and a set of time queues of historical water consumption data based on timestamps to determine whether the first smart water meter has an abnormality;

[0012] The smart water meter control module is used to remotely control the first smart water meter in response to an abnormality in the first smart water meter.

[0013] Compared with the prior art, the present application provides an IoT-based smart water meter control system and method, which mines the temporal correlation characteristics of real-time water usage curves and historical data of the same period, and establishes a dynamic comparison framework based on time queues, matches the real-time data of smart water meters with the deep query of the temporal characteristics of historical water usage in the same period of multiple years, and realizes adaptive identification of water usage anomalies based on the query results. By adopting the time queue matching analysis method, the risk of misjudgment caused by changes in user behavior patterns can be effectively reduced, and at the same time, there is no need to rely on a large amount of historical data to train complex prediction models, making the operation of the entire system more efficient and sustainable. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1 It is a flow chart of a smart water meter control method based on the Internet of Things according to an embodiment of the present application;

[0016] Figure 2 A schematic diagram of data flow of a smart water meter control method based on the Internet of Things according to an embodiment of the present application;

[0017] Figure 3 Flow chart of sub-step S2 of the smart water meter control method based on the Internet of Things according to an embodiment of the present application;

[0018] Figure 4 It is a flowchart of sub-step S22 of the intelligent water meter control method based on the Internet of Things according to an embodiment of the present application;

[0019] Figure 5 It is a block diagram of the intelligent water meter control system based on the Internet of Things according to an embodiment of the present application. Detailed implementation manners

[0020] Next, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein.

[0021] As shown in the present application and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0022] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0023] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the operations before or below do not necessarily need to be executed precisely in order. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0024] Next, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein.

[0025] In the technical solution of the present application, an intelligent water meter control method based on the Internet of Things is proposed. Figure 1 It is a flowchart of the intelligent water meter control method based on the Internet of Things according to an embodiment of the present application. Figure 2 It is a schematic diagram of data flow of the intelligent water meter control method based on the Internet of Things according to an embodiment of the present application. As Figure 1 and Figure 2As shown, the Internet of Things-based intelligent water meter control method according to an embodiment of the present application includes the steps of: S1, obtaining a set of a time queue of water consumption data of a first intelligent water meter and a time queue of historical water consumption data; S2, based on timestamp-based temporal matching and water consumption temporal feature query matching analysis between the time queue of water consumption data and the set of time queues of historical water consumption data, determining whether there is an abnormality in the first intelligent water meter; S3, in response to the first intelligent water meter having an abnormality, remotely controlling the first intelligent water meter.

[0026] Specifically, in S1, a set of a time queue of water consumption data of a first intelligent water meter and a time queue of historical water consumption data is obtained. That is, in the embodiment of the present application, first, the collected water consumption data information is transmitted to the Internet of Things central control center through the communication module embedded in the first intelligent water meter. It should be understood that in traditional water management, manual meter reading or periodic data upload has defects of delay and discreteness, which are difficult to support the fine-grained analysis required for dynamic anomaly detection. The deployment of the embedded communication module essentially transforms the intelligent water meter from an isolated data collection node into an active sensing unit in the Internet of Things ecosystem. Through low-power wide-area Internet of Things protocols such as cellular networks (such as NB-IoT) and LoRa, multi-sensor data such as water meter flow, pressure, and valve status are encrypted and transmitted directionally at a preset time interval or event trigger mechanism. In addition, the real-time transmission mechanism enables the system to continuously capture the microscopic fluctuations of water use behaviors, such as the tiny leakage signals during low-flow periods at night, or the abnormal patterns of sudden increases in water use during holidays, thus breaking through the dependence of traditional threshold detection on a fixed time window. At the same time, the continuous injection of data streams creates conditions for the central control center to establish a dynamic baseline model - by comparing the real-time data stream with the multi-dimensional features in the same historical period, the system can automatically identify the pattern drift caused by normal variables such as seasonal factors and changes in the number of users, and avoid misjudgment caused by a static model.

[0027] Next, in the Internet of Things central control center, the water usage data information is sorted according to the timestamp to obtain a time queue of the water consumption data. Among them, the time queue of the water consumption data has a start time and an end time. It should be understood that in the prior art, water usage data is often simplified to discrete statistics or the accumulated value of a fixed time window (such as the water consumption per hour). Although this processing method is convenient for calculation, it severs the continuous association of water usage behavior on the time axis. For example, in the initial stage of pipeline leakage, it may be manifested as a small flow fluctuation lasting for several hours in the early morning. If only the total amount is statistically calculated in hours, its abnormal characteristics will be diluted by the zero-flow data in the normal period; while the continuous time queue generated by timestamp sorting can completely retain the start and end boundaries and the dynamic change trajectory of the water usage event, providing a data basis for capturing such progressive anomalies. It is worth mentioning that the start time represents the initial trigger point of a specific water usage behavior or abnormal state on the time axis (such as valve opening, sudden increase in flow), and the end time corresponds to its end point (such as the flow returning to zero or restoring the baseline value). Different from the traditional fixed time window (such as hourly division), the start and end times here are not artificially preset, but are automatically identified by analyzing the inflection points of the flow rate change in the data stream. For example, when it is detected that the flow rate continuously rises from zero, it is marked as the start time, and after the flow rate drops back to the stable baseline and maintains for a set duration, it is marked as the end time. This dynamic segmentation strategy can accurately depict the non-periodic characteristics of water usage events in the real scenario. In a specific example of this application, when the water usage data information enters the central control center, the preprocessing module first normalizes the timestamp to eliminate the time drift error caused by network delay and device clock asynchronization. Subsequently, a sliding window algorithm is combined with flow gradient detection to dynamically identify the start and end points of the water usage event: when the flow rate change rate exceeds the preset threshold, the start time is marked, and after the change rate returns to the stable interval, a certain protection period is delayed to confirm the end time. This mechanism can not only capture the instantaneous flow rate increase caused by sudden pipe bursts (the interval between the start time and the end time is extremely short), but also identify the long-term low-flow persistence caused by chronic leakage (the span between the start and end times reaches several days). The generated time queue of the water consumption data not only includes the total water consumption, but also implicitly encodes the water usage intensity change pattern through the timestamp density (such as the difference between high-frequency pulsed water usage and smooth continuous flow), providing a solid judgment basis for subsequent time series anomaly analysis.

[0028] Then, the historical water consumption data information of the first smart water meter is dispatched, and based on the start time and end time, the time queues of historical water consumption data of different years are extracted from the historical water consumption data information to obtain a collection of time queues of historical water consumption data. Since user water consumption behavior often has periodicity (such as peak water consumption during holidays), seasonality (such as a surge in water consumption for irrigation in summer), and long-term evolution characteristics (such as the inter-annual water consumption baseline migration caused by changes in the community population structure), simply relying on recent data or a single historical period data to build an analysis benchmark is prone to misjudgment due to ignoring the dynamic association across time dimensions. By extracting a historical contemporaneous data set with the same start / end time characteristics as the current time queue (such as the irrigation period queue from 3:00 to 5:00 in the morning of June to August in the past three years), the system can establish a dynamically evolving benchmark model, while identifying total anomalies, capturing the morphological distortion of the flow curve (such as the rise in the flow baseline caused by leakage), thereby effectively decoupling normal seasonal fluctuations from persistent abnormal leakage. Through a collection of historical queues aligned in time and space, the system can strip away long-term trend interference (such as the year-on-year increase in water consumption of a family) and focus on detecting abnormal mutations that deviate from historical fluctuation patterns.

[0029] In particular, the S2 determines whether there is an abnormality in the first smart water meter based on the time series matching between the time series of the water consumption data and the set of time series of the historical water consumption data and the water consumption time series feature query matching analysis based on the timestamp. In a specific example of the present application, Figure 3 As shown, the S2 includes: S21, respectively extracting the water consumption time series characteristics of the time queue of water consumption data and the set of time queues of historical water consumption data to obtain the query water consumption time series correlation characteristics and the set of historical water consumption time series correlation characteristics for the same period; S22, performing a dynamic query and analysis of the water consumption time series characteristics on the query water consumption time series correlation characteristics and the set of historical water consumption time series correlation characteristics for the same period to obtain the water consumption time series query dynamic response coding characteristics; S23, determining whether the first smart water meter has an abnormality based on the water consumption time series query dynamic response coding characteristics.

[0030] Specifically, in S21, the water consumption time series features of the time queue of water consumption data and the set of time queues of historical water consumption data are respectively extracted to obtain the set of query water consumption time series correlation features and historical same-period water consumption time series correlation features. That is, the time queue of water consumption data is input into the water consumption time series feature extractor based on the forward LSTM model to obtain the query water consumption time series correlation feature vector as the query water consumption time series correlation feature. Since traditional threshold detection or static statistical models are difficult to adapt to the non-stationarity of water use behavior, such as seasonal fluctuations, gradual changes in user habits, or short-term anomalies caused by sudden events. The forward LSTM, through its unique gating mechanism (input gate, forget gate, output gate) and cell state structure, can adaptively and selectively remember long-term historical information and filter out noise interference. In the forward time series processing process, the model sequentially analyzes the correlation between the current water consumption and the historical state at each time step, and establishes a global time series representation from the start time to the end time through continuous updates of the cell state. By mapping the multi-dimensional time queue into a low-dimensional dense query water consumption time series correlation feature vector, the system realizes the abstract expression of complex water use patterns. Compared with directly using the original time series data for statistical comparison, the feature vector encodes not only the water consumption values at each moment, but also the dynamic laws in the evolution process of water use behavior (such as implicit features such as cycle fluctuation phase and trend change acceleration). This high-order representation enables the subsequent anomaly detection module to go beyond simple threshold comparison and perform more accurate anomaly discrimination from the level of similarity measurement in the feature space, especially having better generalization ability for new anomaly patterns that have not appeared in historical data.

[0031] Similarly, the time queues of each historical water consumption data in the set of time queues of historical water consumption data are input into the water consumption time series feature extractor based on the forward LSTM model to obtain a set of historical simultaneous period water consumption time series correlation feature vectors as the set of historical simultaneous period water consumption time series correlations. Since it is difficult for traditional methods to cope with the time-varying characteristics of water use behavior through static thresholds or fixed-period pattern matching. For example, the summer water use peak in the same community may have a time series morphological shift in different years due to changes in population structure or climate anomalies. The forward LSTM model abstracts historical time series data layer by layer through its gated recurrent structure. Its input gate screens the importance weights of historical information through the sigmoid function, the forget gate dynamically adjusts the retention ratio of old memories in the cell state, and the output gate determines the contribution of the current state to feature expression. This mechanism enables the model to automatically identify common patterns in the water use curves during the same period of different years (such as the periodic fluctuations of the morning and evening peaks on weekdays) when processing cross-year historical data, while suppressing non-critical noises (such as temporary water cut-off events on a specific day in a certain year), thereby constructing a benchmark feature space with time robustness. During this process, when the forward LSTM processes each historical time queue, it accumulates time series context information along the positive direction of the time axis. The continuously updated memory units in its cell state not only record the absolute value changes of water consumption, but also capture the differential features in the evolution process of water use behavior (such as the flow rate change rate, the phase offset of periodic fluctuations, etc.). This deep feature expression enables the explicit encoding of the potential correlations between historical data. For example, the differences in the impact of pipeline aging stages on leakage rates in different years will be mapped to specific distribution patterns in the feature vector space. Compared with directly comparing the original data, this high-order feature abstraction significantly improves the system's ability to identify long-term trend anomalies.

[0032] Specifically, in step S22, a dynamic query analysis of water consumption time series features is performed on the set of query water consumption time series correlation features and historical water consumption time series correlation features in the same period to obtain water consumption time series query dynamic response coding features. It should be understood that it is difficult to effectively capture the multi-scale spatio-temporal correlation patterns hidden in water use behavior through simple feature comparison or threshold judgment. For example, the slightly increased daily water consumption caused by slow leakage and the dynamic deviation relationship with historical data in the same period, or the local mutation pattern formed by a sudden pipe burst event in the feature space and the non-linear difference from the historical distribution boundary. Therefore, to overcome the above problems and capture the global topological correlation between water consumption features to achieve an essential characterization of abnormal patterns, in the technical solution of this application, a dynamic query analysis of water consumption time series features is performed on the set of query water consumption time series correlation features and historical water consumption time series correlation features in the same period to obtain water consumption time series query dynamic response coding features. That is, by constructing an implicit graph structure, the interaction between the query feature vector and the historical feature set is mapped into the topological space, and the spectral graph theory is used to reveal the inherent manifold structure characteristics of the data. This processing mechanism can overcome the limitations of traditional methods in feature similarity measurement. Specifically, first, the query water consumption time series correlation features are used as "dynamic decision anchor points", and the historical feature set constitutes a "spatio-temporal reference system". The two are mapped in the implicit space through a non-linear decision response unit. This unit encodes the implicit interaction relationship between features (such as the periodic resonance, trend deviation or mutation correlation between the current feature and the historical feature) into a hidden coding vector of the decision point state through the non-linear mapping of a deep neural network. Then, the spectral decomposition of the Laplacian matrix further extracts the low-frequency components that reflect the essential structure of the data. Subsequently, by adaptively fusing these spectral domain feature components, the system can generate a water consumption time series query dynamic response coding vector that contains both local details and global structure. It not only encodes the deviation degree of the current water use pattern from the historical benchmark, but also reveals the potential patterns of abnormal types (such as the differential representation of leakage and pipe burst in the spectral domain), thereby providing multi-dimensional decision-making basis for subsequent abnormal determination. In this way, the spatio-temporal perception ability of the system for complex water use anomalies is significantly improved. In terms of the feature expression dimension, by constructing a decision point state neighborhood matrix and a Laplacian matrix, the system transforms the discrete data points in the high-dimensional time series feature space into nodes on a continuous manifold structure, so that the originally difficult-to-quantify "water use pattern similarity" is transformed into the connection strength in the graph structure and the feature vector distance in the spectral domain. For example, the short-term fluctuations caused by climate anomalies in historical data in the same period will be recognized as local structural perturbations by the neighborhood matrix, and the spectral decomposition of the Laplacian matrix filters such noise through low-dimensional embedding and retains the true long-term trend information. At the computational efficiency level, the spectral domain dimensionality reduction technology transforms the high-dimensional feature comparison into vector operations in a low-dimensional space, enabling the system to complete the pattern matching of massive historical data within milliseconds.More importantly, the adaptive fusion mechanism dynamically adjusts the contribution of different spectral components through attention weights, enabling the system to automatically select the most relevant feature combination for a specific anomaly type (such as periodic leakage or random pipe burst), thereby reducing the false alarm rate while improving the generalization ability of new anomaly patterns. In particular, in a specific example of the present application, such as. Figure 4 As shown, the S22 includes: S221, inputting the query water consumption time series correlation feature vector and each historical water consumption time series correlation feature in the set of historical water consumption time series correlation feature vectors into the historical-query water consumption nonlinear decision response unit to obtain a set of historical-query water consumption decision point state implicit coding vectors; S222, performing spectrum-guided historical-query water consumption decision point adaptive aggregation analysis on the set of historical-query water consumption decision point state implicit coding vectors to obtain a water consumption time series query dynamic response coding vector as a water consumption time series query dynamic response coding feature.

[0033] More specifically, in S221, each historical water consumption time series correlation feature in the set of query water consumption time series correlation feature vectors and historical water consumption time series correlation feature vectors in the same period is input into the historical-query water consumption nonlinear decision response unit to obtain a set of implicit coding vectors of the state of historical-query water consumption decision points. Since the user's water consumption behavior is time-varying and uncertain (such as seasonal fluctuations, sudden events), simple statistical thresholds or linear models are difficult to distinguish the subtle differences between normal pattern evolution and real anomalies (such as slow leakage or instantaneous pipe burst). By introducing a nonlinear decision response unit, the system can reconstruct the potential decision relationship between the current water consumption characteristics and the historical pattern in a high-dimensional latent space, and implicitly decouple the water consumption behavior features that were originally intertwined in the original feature space, thereby providing a clearer decision basis for subsequent anomaly detection. Through nonlinear mapping and feature interaction, the system not only considers the temporal similarity between the current water consumption sequence and historical data, but also deeply explores the association rules between the two in implicit dimensions such as state transition and mutation response. For example, in a slow pipeline leakage scenario, the system can capture the nonlinear amplification effect of the small trend offset of the current water consumption relative to the historical data of the same period in the latent space, while the traditional linear weighted method may ignore such progressive anomalies due to noise interference. This implicit state encoding enables the system to break through the reliance of traditional statistical models on explicit thresholds and instead achieve more refined anomaly discrimination through decision boundary optimization in the latent space. In a specific example of the present application, each historical water consumption time series correlation feature in the set of query water consumption time series correlation feature vectors and historical water consumption time series correlation feature vectors for the same period is input into the historical-query water consumption nonlinear decision response unit according to the following formula to obtain a set of implicit coding vectors of the historical-query water consumption decision point states; wherein, the formula is:

[0034]

[0035] Among them, is the set of time - series correlation feature vectors of water consumption in the same historical period, , , and are respectively the 1st, 2nd, th, and th time - series correlation feature vectors of water consumption in the same historical period in the set of time - series correlation feature vectors of water consumption in the same historical period, is the query water - consumption time - series correlation feature vector, is matrix multiplication, and are respectively the corresponding decision - response weight matrix and decision - response bias vector, is the activation function, , , , and are respectively the 1st, 2nd, th, th, and th historical - query water - consumption decision - point state implicit coding vectors in the set of historical - query water - consumption decision - point state implicit coding vectors, is the set of historical - query water - consumption decision - point state implicit coding vectors.

[0036] More specifically, the S222 performs a spectrum-guided adaptive aggregation analysis of the history-query water consumption decision point state implicit coding vectors on the set of history-query water consumption decision point states to obtain a water consumption time series query dynamic response coding vector as a water consumption time series query dynamic response coding feature. In an embodiment of the present application, first, a graph structure analysis based on the history-query water consumption implicit state is performed on the set of history-query water consumption decision point state implicit coding features to obtain a history-query water consumption decision point state class neighborhood matrix and a history-query water consumption decision point state class degree matrix. It should be understood that when the system generates a set of implicit coding vectors through a nonlinear decision response unit, although these high-dimensional feature vectors contain the dynamic decision state of water use behavior, the implicit information such as the strength of association between nodes and the local manifold structure is still not explicitly modeled. By constructing a graph structure analysis framework, the system can convert abstract implicit coding vectors into a data topology network with clear geometric meaning, thereby providing a structured decision basis for subsequent spectral analysis. Among them, the construction of the neighborhood matrix is essentially to convert the semantic correlation of decision points in the high-dimensional feature space into the edge weights of the graph structure. For example, when judging the abnormal water consumption in a certain period, the system can automatically identify the historical data nodes of the same period that are closest to the implicit state of the period through the neighborhood matrix (such as the water consumption pattern under similar temperature conditions in the same period last year), while excluding interference nodes caused by incidental factors such as holiday activities. The introduction of the degree matrix gives different nodes the ability to adjust weights differently. For example, when a decision point has a strong correlation with most historical nodes (such as a stable water consumption pattern caused by winter heating), the diagonal value of its degree matrix will be significantly higher than that of isolated abnormal points (such as sudden pipe burst events). This adaptive node importance quantification mechanism can effectively suppress the interference of noise nodes on the overall structural analysis, ensuring that the graph model more accurately reflects the distribution characteristics of real data. This graph structure analysis method significantly improves the adaptability and interpretability of the smart water meter system to complex water use scenarios. Through the synergy of the neighborhood matrix and the degree matrix, the system can not only capture the microscopic correlations between implicit states (such as identifying the hidden correlations between water consumption at multiple adjacent time nodes in the early stage of slow leakage), but also construct a dynamic topological map of the evolution of water use behavior at a macro level, so that the subsequent Laplace matrix spectral decomposition can more effectively extract discriminative core components, thereby accurately distinguishing normal pattern evolution from real abnormal events in real scenarios where complex variables such as seasonal fluctuations and infrastructure upgrades coexist, and providing reliable technical support for the refined management and predictive maintenance of smart water services. In a specific example of the present application, the set of implicit coding features of the historical-query water consumption decision point state is analyzed based on the graph structure of the historical-query water consumption implicit state using the following formula to obtain the historical-query water consumption decision point state class neighborhood matrix and the historical-query water consumption decision point state class degree matrix; wherein, the formula is:

[0037]

[0038]

[0039] Among them, is for calculating the two-norm of a vector, is the inverse hyperbolic cosine function, , , , and are the eigenvalues at each position in the historical-query water consumption decision point state class neighborhood matrix respectively, is the historical-query water consumption decision point state class neighborhood matrix, is for calculating the square of the one-norm of a vector, is one less than the number of vectors in , and are the eigenvalues at each position on the diagonal in the historical-query water consumption decision point state class degree matrix respectively, is the historical-query water consumption decision point state class degree matrix.

[0040] Next, based on the historical-query water consumption decision point status class neighborhood matrix and the historical-query water consumption decision point status class degree matrix, calculate the historical-query water consumption decision point status Laplacian matrix. It should be understood that although the neighborhood matrix can reflect the direct association strength between nodes (such as the similarity of the water use pattern in a certain period to the same period in history), and the degree matrix can characterize the connection density of nodes (such as the weak association between an abnormal decision point and most historical normal nodes), neither of them has formed an overall description of the dynamic balance state of the graph structure. By constructing the historical-query water consumption decision point status Laplacian matrix, the system algebraically fuses the local connection relationships encoded in the neighborhood matrix with the global importance of nodes quantified by the degree matrix. In this process, the Laplacian matrix encodes the continuity (such as the annual accumulation of leakage trends) and suddenness (such as the isolation of pipe burst events) in the process of water use behavior evolution into the frequency domain components of the graph signal through the local connection strength of the neighborhood matrix and the node centrality parameters of the degree matrix. For example, the slow leakage across years caused by pipeline aging forms a strongly connected node chain in the graph structure, and the corresponding eigenvector of the Laplacian matrix shows smooth fluctuations in the low frequency band, reflecting the persistence of the trend; while the sudden drop in water use caused by a temporary repair shows a spike pulse in the high frequency band. This frequency domain mapping mechanism enables the system to distinguish natural fluctuations (such as the periodic spectrum corresponding to seasonal water use changes) from real anomalies (such as irregular high-frequency disturbances), providing a feature basis with clear physical meaning for subsequent spectral decomposition. In this way, the system can break through the limitations of traditional time-domain analysis when facing multi-source heterogeneous water use scenarios, providing multi-dimensional decision-making basis for accurate anomaly diagnosis in smart water services. In a specific example of this application, based on the historical-query water consumption decision point status class neighborhood matrix and the historical-query water consumption decision point status class degree matrix, calculate the historical-query water consumption decision point status Laplacian matrix with the following formula; where the formula is:

[0041]

[0042] Wherein, is the historical-query water consumption decision point status Laplacian matrix.

[0043] Then, the historical-query water consumption decision point state Laplace matrix is spectrally decomposed to obtain a set of core component encoding vectors of the historical-query water consumption decision point. In an embodiment of the present application, first, the historical-query water consumption decision point state Laplace matrix is optimized based on the cut-cycle space closure of the historical-query water consumption decision point state matrix using a topological closure mechanism to obtain an optimized historical-query water consumption decision point state Laplace matrix. It should be understood that when water use behavior distortion occurs due to pipe network leakage or water meter failure, its abnormal signal is often not isolated in the timing fluctuation of a single node, but forms an implicit association pattern across nodes through the physical connection of the water supply network and the data topology. For example, a pipeline rupture may cause the water consumption timing of three adjacent water meters to resonate in a specific frequency band. The traditional Laplace matrix can only characterize the direct connection strength between nodes, but cannot capture such non-explicit topological associations formed by fluid dynamics conduction. At this time, the spectral space of the original historical-query water consumption decision point state Laplace matrix is susceptible to local noise interference, and its cut space component may mistakenly classify the real anomaly as seasonal fluctuations, while the cyclic space component may ignore the propagation path of the emergency. In the preferred example of the present application, a topological closure mechanism is used to optimize the historical-query water consumption decision point state Laplace matrix based on the cut-circular space closure to obtain the optimized historical-query water consumption decision point state Laplace matrix. That is, the cut space and cyclic space components of the historical-query water consumption decision point state Laplace matrix are separated by pseudo-inverse operation, which is essentially to decouple the static physical connection and dynamic data association of the water supply network at the algebraic topology level. The cut space closure strengthens the global correlation across time slices through exponential mapping (such as the cumulative effect of the slight increase in daily average water consumption caused by leakage in multi-year data), while the cyclic space closure constrains the propagation range of local disturbances through tensor product operations (such as the high-frequency oscillation of the burst pipe event only affects the adjacent nodes). For example, slow leakage is represented as a low-dimensional manifold in the cut space in the optimized Laplace matrix, and its spectral characteristics correspond to low-frequency energy concentration; while temporary water outage events form isolated high-frequency pulses in the cyclic space, which are significantly different from the real abnormal patterns. This optimization maps the spectral characteristics of the matrix to the physical evolution law of the water system, providing physical semantic support at the frequency domain level for abnormal diagnosis. This mechanism gives the system the ability to generalize and analyze unknown abnormalities: when a new type of fault occurs (such as progressive wear of the gears of smart water meters), its slowly developing characteristics will be mapped to the cut space, triggering a disposal strategy similar to that of known leakage; while sudden unknown faults (such as transient data anomalies caused by electromagnetic interference) fall into the high-frequency domain of the cyclic space, triggering immediate alarms. This frequency domain feature separation based on physical laws enables the system to achieve adaptive classification of diverse abnormal patterns without retraining the model, supporting the precise decision-making and rapid response of smart water systems in dynamic environments.

[0044] Preferably, the specific steps for optimizing the historical-query water consumption decision point state matrix based on cut-cycle space closure for the historical-query water consumption decision point state Laplacian matrix using the topological closure mechanism are as follows:

[0045] Specifically, first, pseudo-inverse operations are used to extract the connected component information of the historical-query water consumption decision point state class neighborhood matrix and the historical-query water consumption decision point state class degree matrix, that is, let:

[0046]

[0047] Among them, the matrix and can respectively represent the cut space and the cycle space of the historical-query water consumption decision point state Laplacian matrix , represents matrix multiplication;

[0048] Thus, the historical-query water consumption decision point state Laplacian matrix is optimized through cut space closure and cycle space closure, expressed as:

[0049]

[0050] Among them, is the exponential function value with the natural constant as the base, is addition by position point, is the optimized historical-query water consumption decision point state Laplacian matrix.

[0051] In this way, the global structural invariant in the structural information of the graph is extracted through the closure operation based on pseudo-inverse, making the spectral topological information representation of the historical-query water consumption decision point state Laplacian matrix more robust.

[0052] Furthermore, the optimized historical-query water consumption decision point state Laplace matrix is spectrally decomposed to obtain a set of core component encoding vectors of the historical-query water consumption decision points. It should be understood that although the Laplace matrix optimized by the cut-loop space closure has eliminated the topological holes caused by short-term disturbances, its essence is still a complex operator that characterizes node associations in a high-dimensional space. For example, the synergistic water consumption decline caused by a cross-regional pipeline leak is manifested in the optimization matrix as an enhancement of weak connections between multiple non-adjacent nodes, but this association pattern may be masked by the strong connection characteristics of adjacent nodes in the original feature space. Therefore, in the technical solution of the present application, the optimized historical-query water consumption decision point state Laplace matrix is spectrally decomposed to obtain a set of core component encoding vectors of the historical-query water consumption decision points. Here, through the spectral decomposition operation, the system decomposes the optimized Laplace matrix into a set of eigenvalues and eigenvectors, where the eigenvector corresponding to the smallest eigenvalue carries the low-frequency global trend of water use behavior (such as slow leakage across years caused by pipeline aging), while the larger eigenvalue maps high-frequency local events (such as pulse-like disturbances caused by instantaneous pipe bursts). The essence of this mathematical operation is to project high-dimensional time series data into a frequency domain space with clear physical meaning. For example, long-term leakage is manifested as low-frequency energy concentration, while emergencies are manifested as a sudden increase in high-frequency energy, thereby breaking through the limitations of traditional methods in single-dimensional analysis in the time domain. In a specific example of the present application, the optimized historical-query water consumption decision point state Laplace matrix is spectrally decomposed using the following formula to obtain a set of core component encoding vectors of the historical-query water consumption decision point; wherein, the formula is:

[0053]

[0054] in, For Perform spectral decomposition operation, is the set of core component encoding vectors of the history-query water consumption decision points, , , and are the first, second, and third core component encoding vectors of the history-query water consumption decision point. and The core component encoding vector of the history-query water consumption decision point, The diagonal elements of the matrix are , , and History - query water consumption eigenvalue diagonal matrix, , , and They are , , and the corresponding eigenvalue, is the historical - query water consumption eigenvalue diagonal matrix.

[0055] Furthermore, perform adaptive fusion on the set of historical - query water consumption decision point core component coding vectors to obtain the water consumption time - series query dynamic response coding vector. It should be understood that the set of historical - query water consumption decision point core component coding vectors, as the decoupled eigenbasis vectors in the spectral domain, although different - frequency abnormal signal components have been separated, in actual scenarios, heterogeneous abnormalities such as pipeline leaks and equipment failures often exhibit cross - frequency coupling characteristics. For example, the low - frequency trend drift caused by slow leakage is often accompanied by the medium - frequency micro - oscillation derived from pressure disturbances, and the failure of control valves may simultaneously trigger high - frequency pulses and the steady - state offset of specific spatial coding vectors. If a static fusion strategy is adopted, it is impossible to dynamically reconstruct the combined pattern of abnormal features according to real - time monitoring data, and it is easy to weaken the perception ability of the non - linear correlation between time - varying features due to fixed weight allocation. Through the adaptive fusion mechanism, the system can dynamically adjust the contribution degree of each core component according to the context of the current water use pattern, for example, using the attention mechanism to learn the weight allocation strategy of different - frequency features. In this process, by continuously learning the implicit mapping relationship between historical core components and real - time query inputs, a context - driven feature recombination mechanism is formed. When the abnormal pattern evolves over time (such as the increase in the low - frequency offset amplitude and the change in the pressure fluctuation frequency during the leakage development stage), the fusion network can dynamically track the migration trajectory of the contribution degree of each component and synchronously adjust the feature synthesis direction. This dynamic synthesis mechanism enables the finally generated response coding vector to have spatio - temporal generalization ability, which can not only represent the stable topological characteristics of the pipeline network structure but also capture the transient distortion characteristics of water use behavior. In a specific example of this application, the following formula is used to perform adaptive fusion on the set of historical - query water consumption decision point core component coding vectors to obtain the water consumption time - series query dynamic response coding vector; where, the formula is:

[0056]

[0057] Where, performs an adaptive fusion operation on , and are respectively the corresponding fusion weight matrix and fusion bias vector, is function, is the corresponding historical - query water consumption scoring weight vector, is the corresponding historical - query water consumption weight value, is the masking operation, is a preset threshold value, is the corresponding historical - query water consumption mask weight value, is the dynamic response encoding vector of the water consumption time - series query.

[0058] Specifically, in step S23, based on the dynamic response encoding features of the water consumption time - series query, it is determined whether the first intelligent water meter is abnormal. That is, in the technical solution of the present application, the dynamic response encoding vector of the water consumption time - series query is input into the water consumption data diagnosis module based on a classifier to obtain a diagnosis result, and the diagnosis result is used to indicate whether the first intelligent water meter is abnormal. It should be understood that the dynamic response encoding vector of the water consumption time - series query is essentially an implicit state fusion of historical and current water - using behaviors, but the abnormal discrimination information it contains needs to be separated by a non - linear decision boundary. However, due to the high - dimensional, non - linear, and pattern - mixed characteristics of the time - series features of water - using behaviors (such as the feature overlap between normal fluctuations and abnormal leaks), it is difficult to effectively distinguish abnormal states in complex scenarios simply relying on manual rules or static thresholds. By introducing a classifier, the system can map the dynamic response encoding vector of the water consumption time - series query after dynamic query analysis to an interpretable decision space, breaking through the shallow feature - correlation mining ability of traditional methods. In this way, the reliability and scenario adaptability of abnormal diagnosis are significantly improved.

[0059] Particularly, in step S3, in response to the first intelligent water meter being abnormal, remote control of the first intelligent water meter is performed. In the embodiment of the present application, first, in response to the first intelligent water meter being abnormal, an intelligent water meter control instruction is generated. That is, the water consumption data diagnosis module based on a classifier will analyze the dynamic response encoding vector of the water consumption time - series query. Once it is determined that the first intelligent water meter is abnormal, for example, it is found that the water - using pattern deviates from the normal range, which may be caused by potential leaks, equipment failures, etc., the system will automatically generate a warning signal as the control instruction. Among them, the warning signal contains detailed information about the abnormal situation, such as the specific type of abnormality (such as water leakage or illegal use), the severity, and the possible affected range, etc.

[0060] Subsequently, the IoT central control center sends the intelligent water meter control instruction to the remote control terminal of the first intelligent water meter to achieve remote control of the first intelligent water meter through the remote control terminal. That is, once the abnormality of the intelligent water meter is confirmed, the corresponding control instruction will be quickly sent to the remote control terminal of the intelligent water meter to achieve real-time control of the first intelligent water meter. Through rapid intervention by remote control means, it is ensured that the intelligent water meter and the related water supply system can resume normal working conditions as soon as possible. This not only helps to protect water resources from unnecessary waste, but also guarantees service quality and avoids inconveniencing users. In addition, by dealing with abnormal situations in a timely manner, the service life of the equipment can be extended and the maintenance cost can be reduced. After receiving the warning signal, each recipient can take corresponding measures according to their own responsibilities. For example, the maintenance team may arrange technicians to go to the site for inspection and repair; property management may contact the residents to remind them to pay attention to the recent water usage situation; and users can view the specific warning details through the application to understand the abnormal conditions of their own water meters. In this way, it is ensured that any potential problems can be handled in a timely and effective manner, minimizing losses to the greatest extent and ensuring the normal operation of the water supply system.

[0061] In summary, the intelligent water meter control method based on the Internet of Things according to the embodiments of the present application is elucidated. By mining the time-series correlation features between the real-time water usage curve and historical data of the same period, and establishing a dynamic comparison framework based on a time queue, the real-time data of the intelligent water meter is deeply queried and matched with the time-series features of historical water usage in multiple years of the same period, and adaptive identification of water usage abnormalities is realized based on the query results. By adopting the method of time queue matching analysis, the risk of misjudgment caused by changes in user behavior patterns can be effectively reduced. At the same time, there is no need to rely on a large amount of historical data to train a complex prediction model, making the operation of the entire system more efficient and sustainable.

[0062] Furthermore, an intelligent water meter control system based on the Internet of Things is also provided.

[0063] Figure 5 It is a block diagram of the intelligent water meter control system based on the Internet of Things according to the embodiments of the present application. As Figure 5 shown, the intelligent water meter control system 300 based on the Internet of Things according to the embodiments of the present application includes: a data acquisition module 310, configured to acquire a set of time queues of water consumption data of the first intelligent water meter and time queues of historical water consumption data; a water consumption time-series feature query and matching analysis module 320, configured to determine whether there is an abnormality in the first intelligent water meter based on the water consumption time-series feature query and matching analysis between the time queue of water consumption data and the set of time queues of historical water consumption data; an intelligent water meter control module 330, configured to remotely control the first intelligent water meter in response to the first intelligent water meter having an abnormality.

[0064] As described above, the Internet of Things-based intelligent water meter control system 300 according to the embodiments of the present application can be implemented in various wireless terminals, such as a server with an Internet of Things-based intelligent water meter control algorithm. In a possible implementation manner, the Internet of Things-based intelligent water meter control system 300 according to the embodiments of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the Internet of Things-based intelligent water meter control system 300 can be a software module in the operating system of the wireless terminal, or can be an application program developed for the wireless terminal; of course, the Internet of Things-based intelligent water meter control system 300 can also be one of the many hardware modules of the wireless terminal.

[0065] Alternatively, in another example, the Internet of Things-based intelligent water meter control system 300 and the wireless terminal can also be separate devices, and the Internet of Things-based intelligent water meter control system 300 can be connected to the wireless terminal through a wired and / or wireless network and transmit interaction information in accordance with a predefined data format.

[0066] The embodiments of the present disclosure have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skill in the art in the technical field to understand the embodiments disclosed herein.

Claims

1. An intelligent water meter control method based on the Internet of Things, characterized in that, Including: Obtaining a set of a time queue of water consumption data of a first intelligent water meter and a time queue of historical water consumption data; Based on timestamp, performing time series matching and water consumption time series feature query matching analysis between the time queue of water consumption data and the set of time queues of historical water consumption data to determine whether there is an abnormality in the first intelligent water meter; In response to the first intelligent water meter having an abnormality, remotely controlling the first intelligent water meter; Among them, based on timestamp, performing time series matching and water consumption time series feature query matching analysis between the time queue of water consumption data and the set of time queues of historical water consumption data to determine whether there is an abnormality in the first intelligent water meter, including: Respectively extracting water consumption time series features of the time queue of water consumption data and the set of time queues of historical water consumption data to obtain a set of query water consumption time series correlation features and historical same-period water consumption time series correlation features, including: inputting the time queue of water consumption data into a water consumption time series feature extractor based on a forward LSTM model to obtain a query water consumption time series correlation feature vector as the query water consumption time series correlation feature; inputting each time queue of historical water consumption data in the set of time queues of historical water consumption data into a water consumption time series feature extractor based on a forward LSTM model to obtain a set of historical same-period water consumption time series correlation feature vectors as the set of historical same-period water consumption time series correlation features; Performing water consumption time series feature dynamic query analysis on the set of query water consumption time series correlation features and historical same-period water consumption time series correlation features to obtain water consumption time series query dynamic response coding features, including: inputting each historical same-period water consumption time series correlation feature vector in the set of query water consumption time series correlation feature vectors and historical same-period water consumption time series correlation feature vectors into a historical-query water consumption non-linear decision response unit to obtain a set of historical-query water consumption decision point state hidden coding vectors; performing spectral graph-guided historical-query water consumption decision point adaptive aggregation analysis on the set of historical-query water consumption decision point state hidden coding vectors to obtain a water consumption time series query dynamic response coding vector as the water consumption time series query dynamic response coding feature; Based on the water consumption time series query dynamic response coding features, determining whether there is an abnormality in the first intelligent water meter.

2. The intelligent water meter control method based on the Internet of Things according to claim 1, characterized in that, Obtaining a set of a time queue of water consumption data and a time queue of historical water consumption data, including: Transmitting the collected water usage data information to the Internet of Things central control center through a communication module embedded in the first intelligent water meter; In the Internet of Things central control center, arranging the water usage data information according to the timestamp to obtain a time queue of water consumption data, where the time queue of water consumption data has a start time and an end time; Scheduling the historical water usage data information of the first intelligent water meter, and based on the start time and the end time, extracting time queues of historical water consumption data in different years from the historical water usage data information to obtain a set of time queues of historical water consumption data.

3. The intelligent water meter control method based on the Internet of Things according to claim 2, characterized in that, Performing spectrum graph-guided adaptive aggregation analysis on the set of latent encoding vectors of the historical-query water consumption decision point status to obtain a dynamic response encoding vector for water consumption time series queries, including: Performing graph structure analysis on the set of latent encoding features of the historical-query water consumption decision point status based on the implicit state of historical-query water consumption to obtain a historical-query water consumption decision point status class neighborhood matrix and a historical-query water consumption decision point status class degree matrix; Calculating the historical-query water consumption decision point status Laplacian matrix based on the historical-query water consumption decision point status class neighborhood matrix and the historical-query water consumption decision point status class degree matrix; Performing spectral decomposition on the historical-query water consumption decision point status Laplacian matrix to obtain a set of core component encoding vectors of the historical-query water consumption decision point; Performing adaptive fusion on the set of core component encoding vectors of the historical-query water consumption decision point to obtain a dynamic response encoding vector for water consumption time series queries.

4. The intelligent water meter control method based on the Internet of Things according to claim 3, characterized in that Performing spectral decomposition on the historical-query water consumption decision point status Laplacian matrix to obtain a set of core component encoding vectors of the historical-query water consumption decision point, including: Using a topological closure mechanism to perform optimization of the historical-query water consumption decision point status matrix based on cut-cycle space closure on the historical-query water consumption decision point status Laplacian matrix to obtain an optimized historical-query water consumption decision point status Laplacian matrix; Performing spectral decomposition on the optimized historical-query water consumption decision point status Laplacian matrix to obtain a set of core component encoding vectors of the historical-query water consumption decision point.

5. The intelligent water meter control method based on the Internet of Things according to claim 4, characterized in that Determining whether the first intelligent water meter is abnormal based on the dynamic response encoding features of water consumption time series queries, including: Inputting the dynamic response encoding vector of water consumption time series queries into a water consumption data diagnosis module based on a classifier to obtain a diagnosis result, and the diagnosis result is used to indicate whether the first intelligent water meter is abnormal.

6. The intelligent water meter control method based on the Internet of Things according to claim 1, characterized in that, In response to the first intelligent water meter being abnormal, performing remote control on the first intelligent water meter, including: Generating an intelligent water meter control instruction in response to the first intelligent water meter being abnormal; The Internet of Things central control center sends the intelligent water meter control instruction to the remote control terminal of the first intelligent water meter to achieve remote control of the first intelligent water meter through the remote control terminal.

7. An intelligent water meter control system based on the Internet of Things, characterized in that, Including: A data acquisition module for acquiring the set of time queues of water consumption data of the first intelligent water meter and the time queues of historical water consumption data; A water consumption time series feature query matching analysis module for determining whether the first intelligent water meter is abnormal based on time stamp-based temporal matching and water consumption time series feature query matching analysis between the set of time queues of water consumption data and the set of time queues of historical water consumption data; An intelligent water meter control module for performing remote control on the first intelligent water meter in response to the first intelligent water meter being abnormal; Among them, determining whether the first intelligent water meter is abnormal based on time stamp-based temporal matching and water consumption time series feature query matching analysis between the set of time queues of water consumption data and the set of time queues of historical water consumption data, including: Extract the water consumption time series features of the time queue of water consumption data and the set of time queues of historical water consumption data respectively to obtain the set of query water consumption time series correlation features and historical same - period water consumption time series correlation features, including: input the time queue of water consumption data into the water consumption time series feature extractor based on the forward LSTM model to obtain the query water consumption time series correlation feature vector as the query water consumption time series correlation feature; input each time queue of historical water consumption data in the set of time queues of historical water consumption data into the water consumption time series feature extractor based on the forward LSTM model to obtain the set of historical same - period water consumption time series correlation feature vectors as the set of historical same - period water consumption time series correlation features; Conduct dynamic query analysis of water consumption time series features on the set of query water consumption time series correlation features and historical same - period water consumption time series correlation features to obtain the water consumption time series query dynamic response coding feature, including: input each historical same - period water consumption time series correlation feature vector in the set of query water consumption time series correlation feature vectors and historical same - period water consumption time series correlation feature vectors into the historical - query water consumption non - linear decision response unit to obtain the set of historical - query water consumption decision point state implicit coding vectors; conduct spectrum - graph - guided adaptive aggregation analysis of historical - query water consumption decision point states on the set of historical - query water consumption decision point state implicit coding vectors to obtain the water consumption time series query dynamic response coding vector as the water consumption time series query dynamic response coding feature; Based on the water consumption time series query dynamic response coding feature, determine whether there is an abnormality in the first intelligent water meter.

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