A remote operation and maintenance method and system for secondary water supply pump stations based on the Internet of Things

Abstract

The application discloses a secondary water supply pump station remote operation and maintenance method and system based on Internet of Things, relates to the technical field of resource management, collects water supply related data, constructs a disturbance residual factor Dk, judges a water supply state, and obtains a misplacement characteristic index, realizes accurate identification of a "micro water hammer" implicit disturbance, makes up for the missed judgment problem of a short-time and low-amplitude misplacement behavior of a traditional method, and quantifies periodic risk fluctuations through autocorrelation analysis of a time interval sequence, finally, combines a disturbance countermeasure library, generates an adjustment strategy in real time and issues the adjustment strategy to a main control system, realizes closed-loop control and adaptive correction of a structural water hammer risk, and effectively improves the steady-state operation capability of the secondary water supply pump station.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of resource management, in particular to a remote operation and maintenance method and system for a secondary water supply pump station based on the Internet of Things. BACKGROUND

[0002] With the intelligent development of urban infrastructure, the Internet of Things technology is widely penetrated in the field of industrial control, especially in the smart water system. As a key link in the water supply chain, the secondary water supply pump station is widely used in high-rise residential areas, high-difference areas and commercial complexes to realize the re-pressurization and stable delivery of municipal raw water. In the Internet of Things environment, the system can dynamically optimize the pump group start-stop logic, frequency response and safety state by remotely collecting and intelligently controlling the pump station operation parameters. However, in this system, the water hammer disturbance caused by the pump station start-stop process is always a potential threat to the safe operation of the system, especially the hidden micro water hammer that is not monitored in real time, which is gradually becoming a key hidden danger leading to equipment fatigue and pipe network rupture.

[0003] In the Chinese invention patent with the application publication number CN119359058B, a secondary water supply pump station operation monitoring and management method is proposed, which includes the following steps: collecting performance records, pump speed and water pressure readings through water quality monitoring sensors and flow meters, performing data cleaning and format unification to obtain standardized data, applying regression analysis to predict the probability of risk events, and combining environmental monitoring data to generate regression analysis risk prediction results. The application uses regression analysis and variance analysis to deeply analyze the collected pump speed, water pressure and environmental data. This strategy not only makes risk prediction more accurate, but also can evaluate the specific impact of various operation variables on pump station performance. Through this method, managers can generate a risk point list based on real-time data and dynamically adjust operation parameters to realize real-time optimization of pump station operation status.

[0004] The above method generates a risk point list based on real-time data and dynamically adjusts operation parameters to realize real-time optimization of pump station operation status, but in addition, in the existing remote operation and maintenance system of the secondary water supply pump station, mechanical facilities such as slow closing valves and check valves are usually used to suppress water hammer for intelligent control, supplemented by soft start, constant pressure control and other operation strategies, which can theoretically alleviate the risk of water hammer impact.

[0005] However, although this method can alleviate the risk of water hammer impact, but in the actual operation, the "micro water hammer" event still frequently occurs, that is, the slight water flow impact which is not recognized by the sensor or control system, mainly manifested as: during the start-stop process of the pump station, the system monitoring data shows that the water pressure changes smoothly and the running state is normal, but the pump body load appears short-time jump, motor oscillation, abnormal running sound and other phenomena, which is often misjudged as normal fluctuation by the system, and then ignored recording and intervention, so that the physical impact of micro water hammer is difficult to archive, evaluate and predict the risk of recurrence, and the "water hammer cycle blind area" phenomenon exists for a long time, which is difficult to intervene or avoid in time through the existing control means.

[0006] Therefore, the application provides a secondary water supply pump station remote operation and maintenance method and system based on Internet of Things. SUMMARY

[0007] In view of the deficiencies of the prior art, the application provides a secondary water supply pump station remote operation and maintenance method and system based on Internet of Things, which collects water supply related data, constructs disturbance residual factor Dk, determines the water supply state, and obtains misplacement characteristic index, realizes accurate identification of "micro water hammer" implicit disturbance, makes up for the missed judgment problem of traditional method on short-time and low-amplitude misplacement behavior, and through autocorrelation analysis of time interval sequence, quantifies periodic risk fluctuation, finally combines disturbance countermeasures library, generates adjustment strategy in real time and issues to the main control system, realizes closed-loop control and adaptive correction of structural water hammer risk, effectively improves the steady-state operation ability of the secondary water supply pump station, and solves the problems in the above background art.

[0008] To achieve the above purpose, the application is implemented by the following technical scheme: a secondary water supply pump station remote operation and maintenance system based on Internet of Things, comprising a multi-dimensional data acquisition module, a local disturbance analysis module, a misplacement characteristic analysis module, a risk assessment module and an adaptive adjustment module;

[0009] The multi-dimensional data acquisition module is used for real-time acquisition of water supply related data of the secondary water supply pump station, and construction of a water supply related data set S;

[0010] The local disturbance analysis module is used for obtaining disturbance residual factor Dk according to the water supply related data set S, to determine the water supply state of the secondary water supply pump station, and triggering the misplacement characteristic analysis module to identify the misplacement sliding sampling window;

[0011] The misplacement characteristic analysis module is used for feature extraction according to the water supply related data set S, to obtain a characteristic data set and mark a water hammer trigger candidate segment, and construct a candidate time section set G;

[0012] The risk assessment module is used to construct a time interval sequence based on the candidate time interval set G, and perform autocorrelation analysis to obtain the periodic fluctuation risk index Fx in order to assess the risk level of micro water hammer phenomenon.

[0013] The adaptive adjustment module is used to generate strategy adjustment parameters from the disturbance countermeasure library when the risk level of micro water hammer phenomenon is the third risk level, and to perform adaptive adjustment of the secondary water supply pumping station.

[0014] Preferably, the multi-dimensional data acquisition module is used to set up data acquisition nodes in the secondary water supply pumping station, and to install intelligent sensor groups on the data acquisition nodes. A multi-channel high-precision synchronous ADC interface board is used to connect each sensor, and a sliding sampling window is set. ], and in the sliding sampling window [ The system collects water supply-related data from secondary water supply pumping stations to obtain water supply-related data from several sliding sampling windows, and constructs a water supply-related data set S. This water supply-related data includes current signals I... Voltage signal U Input power at the motor end and the mechanical impedance M at the load end ;

[0015] For the acquired current signal I and voltage signal U Using Fast Fourier Transform to identify the dominant frequency point and the collected current signal I and voltage signal U The frequency spectrum is transformed into a complex spectrum in the frequency domain through DFT transformation, and the dominant frequency point is obtained from the complex spectrum in the frequency domain. The complex spectrum value is used to calculate the phase difference between the current and voltage signals, and further obtain the phase synchronization factor Tb. The specific method for obtaining the phase synchronization factor Tb is as follows:

[0016] ;

[0017] In the formula, This indicates the voltage signal at the dominant frequency point. Complex spectral values ​​on This indicates the current signal at the dominant frequency point. Complex spectral values ​​on Indicates the dominant frequency point. Represents a complex phase angle function. This represents the phase angle of the voltage signal at the dominant frequency. denoted by , cos represents the phase angle of the current signal at the main frequency point, and cos represents the cosine function.

[0018] Preferably, the local disturbance analysis module comprises a pressure trajectory fitting unit and a residual disturbance identification unit;

[0019] The pressure trajectory fitting unit is configured to construct a periodic fitting pressure curve based on the voltage signal U and the main frequency point obtained within the sliding sampling window , wherein the periodic fitting pressure curve is expressed as:

[0020] ;

[0021] wherein, represents a constant term, i.e., an average pressure value of the fitting pressure trajectory curve, represents an amplitude coefficient of the nth harmonic, represents a main frequency angle frequency corresponding to the main frequency point , ωn represents a phase shift angle of the nth harmonic, , n represents a harmonic order index, represents a phase shift angle of the nth harmonic, represents a total harmonic order, and t represents a time variable within the sliding sampling window , t∈ .

[0022] Preferably, the residual disturbance identification unit is configured to compare the periodic fitting pressure curve with the voltage signal U , analyze a residual energy density per unit time, and obtain a disturbance residual factor Dk, wherein the disturbance residual factor Dk is obtained in the following manner:

[0023] ;

[0024] wherein, represents a pressure value at a time point t, represents a pressure value at the time point t in the periodic fitting pressure curve, represents a time length of the sliding sampling window .

[0025] A residual determination threshold Pd is preset, and the residual determination threshold Pd and the disturbance residual factor Dk are compared and analyzed to evaluate a water supply state of the secondary water supply pump station, and the specific evaluation content is as follows:

[0026] If the disturbance residual factor Dk is less than or equal to the residual determination threshold Pd, i.e., Dk≤Pd, it is determined that the water supply state of the secondary water supply pump station is a normal state, in which case the current operation strategy is maintained, and water supply related data is continuously monitored;

[0027] If the disturbance residual factor Dk is greater than the residual judgment threshold Pd, i.e. Dk>Pd, it is determined that the water supply state of the secondary water supply pump station is a structural disturbance state, at which time the misplacement feature analysis module is triggered immediately to perform structural fault analysis.

[0028] Preferably, the misplacement feature analysis module comprises a feature extraction unit and a misplacement evaluation unit.

[0029] The feature extraction unit is configured to perform feature extraction based on the water supply related data set S, and construct a feature data set H based on the extracted feature indicators.

[0030] The maximum instantaneous change rate of the sum of the current and the torque response is quantified to obtain an instantaneous load divergence index Fk, wherein the instantaneous load divergence index Fk is obtained in the following manner:

[0031]

[0032] In the formula, represents the input power of the motor end at time point t, represents the mechanical impedance of the load end at time point t, represents a gain coefficient, represents a phase synchronization factor, represents a phase synchronization factor of the equipment under a rated state, represents the maximum derivative value in the sliding sampling window .

[0033] The deviation degree of the pressure fluctuation in the current sliding sampling window is calculated to obtain a pressure stability residual factor Rk, wherein the pressure stability residual factor Rk is obtained in the following manner:

[0034]

[0035] In the formula, represents the pressure value at time point t, represents the pressure mean value of a plurality of sampling time points in the sliding sampling window, represents the pressure standard deviation in the sliding sampling window.

[0036] Preferably, the misplacement evaluation unit is configured to, for each sliding sampling window, perform summary calculation based on the feature data set H to obtain a misplacement coherence coefficient Ck, wherein the misplacement coherence coefficient Ck is obtained in the following manner:

[0037]

[0038] In the formula, represents the instantaneous load divergence index, represents the pressure stability residual factor, ​​​represents a pressure suppression control factor, represents a main frequency phase difference, represents a phase lag suppression coefficient, e represents a base number of a natural logarithm;

[0039] A misalignment threshold Ckyz is set, and for each sliding sampling window, the misalignment coherence coefficient Ck of the sliding sampling window is compared and analyzed with the misalignment threshold Ckyz. If the misalignment coherence coefficient Ck is greater than or equal to the misalignment threshold Ckyz, it is determined that the dynamic coupling misalignment behavior of the three sides of the electric control-load-hydraulic pressure occurs in the sliding sampling window. At this time, the sliding sampling window is marked as a water hammer trigger candidate window. If the misalignment coherence coefficient Ck is less than the misalignment threshold Ckyz, it is determined that the dynamic coupling misalignment behavior of the three sides of the electric control-load-hydraulic pressure does not occur in the sliding sampling window, and no processing is required.

[0040] All sliding sampling windows determined as water hammer trigger candidate windows are collected, and the center time points of each water hammer trigger candidate window are extracted to construct a candidate time section set G.

[0041] Preferably, the risk assessment module includes an autocorrelation analysis unit and a risk determination unit.

[0042] The autocorrelation analysis unit is configured to calculate the time intervals between the center time points of adjacent water hammer trigger candidate windows according to the candidate time section set G, to construct a time interval sequence, and to perform autocorrelation analysis according to the time interval sequence, to construct an autocorrelation function, wherein the autocorrelation function has the following specific form:

[0043] ;

[0044] In the formula, C(k) represents the autocorrelation function value when the lag step is k, C(k) represents the autocorrelation function value when the lag step is k, C(k) represents the autocorrelation function value when the lag step is k, C(k) represents the autocorrelation function value when the lag step is k, C(k) represents the autocorrelation function value when the lag step is k, C(k) represents the autocorrelation function value when the lag step is k, C(k) represents the autocorrelation function value when the lag step is k, C(k) represents the autocorrelation function value when the lag step is k,

[0045] For the autocorrelation function C(k), the autocorrelation function values at each lag step k are calculated, the maximum autocorrelation function value C(k) is extracted, and the lag step k corresponding to the maximum autocorrelation function value C(k) is recorded. =1, 2, 3,..., n- . .

[0046] ​​​​Preferably, the risk determination unit is configured to set a maximum autocorrelation function value is a periodic fluctuation risk index, and a first periodic fluctuation risk threshold value and a second periodic fluctuation risk threshold value are preset and a second periodic fluctuation risk threshold value The periodic fluctuation risk index Fx is compared with the first periodic fluctuation risk threshold value and the second periodic fluctuation risk threshold value to evaluate the risk level of the micro water hammer phenomenon. The specific evaluation content is as follows:

[0047] If the periodic fluctuation risk index Fx is less than or equal to the first periodic fluctuation risk threshold value , i.e. Fx≤ , it is determined that the risk level of the micro water hammer phenomenon is the first risk level, and there is no stable repetition feature in the time interval, and it is determined that the disturbance is not dominated by water hammer;

[0048] If the periodic fluctuation risk index Fx is greater than the first periodic fluctuation risk threshold value and less than the second periodic fluctuation risk threshold value , i.e. <Fx< , it is determined that the risk level of the micro water hammer phenomenon is the second risk level, and there is a potential water hammer risk in the secondary water supply pump station. The closing valve is delayed to start, and the starting rate is adjusted to 80% of the standard starting rate.

[0049] If the periodic fluctuation risk index Fx is greater than or equal to the second periodic fluctuation risk threshold value , i.e. Fx≥ , it is determined that the risk level of the micro water hammer phenomenon is the third risk level, and there is a structural water hammer risk in the secondary water supply pump station. The disturbance period is significant, and the secondary water supply pump station control system is triggered to perform adaptive adjustment of the secondary water supply pump station.

[0050] Preferably, the adaptive adjustment module is configured to, when the risk level of the micro water hammer phenomenon is the third risk level, automatically generate strategy adjustment parameters according to a disturbance countermeasure library in the secondary water supply pump station, package the strategy adjustment parameters into a strategy control instruction, and send the strategy control instruction to a PLC control platform through a standard interface protocol to drive the system components of the secondary water supply pump station to perform adaptive adjustment.

[0051] Preferably, a secondary water supply pump station remote operation and maintenance system method based on the Internet of Things comprises the following steps,

[0052] Step 1, real-time collection of water supply related data of the secondary water supply pump station to construct a water supply related data set S;

[0053] Step two, according to the water supply related data set S, the disturbance residual factor Dk is obtained to determine the water supply state of the secondary water supply pump station, and the misplacement feature analysis module is triggered to identify the misplacement sliding sampling window;

[0054] Step three, according to the water supply related data set S, feature extraction is carried out to obtain the feature data set, and the water hammer trigger candidate segment is marked, and the candidate time interval set G is constructed;

[0055] Step four, according to the candidate time interval set G, the time interval sequence is constructed, and the autocorrelation analysis is carried out, the periodic fluctuation risk index Fx is obtained to evaluate the micro water hammer phenomenon risk level;

[0056] Step five, when the micro water hammer phenomenon risk level is the third risk level, the disturbance countermeasure library is used to generate strategy adjustment parameters for adaptive adjustment of the secondary water supply pump station.

[0057] The application provides a secondary water supply pump station remote operation and maintenance method and system based on Internet of Things, which has the following beneficial effects:

[0058] (1) By constructing the "misplacement coherence coefficient Ck" and cooperating with the disturbance residual factor Dk, the response behavior of the "electric control-load-hydraulic" three sides in the sliding sampling window is cooperatively judged, the "micro water hammer" phenomenon which is difficult to be found by conventional technical means can be identified, the monitoring sensitivity of the system to the structural disturbance chain is significantly improved, the regulation lag problem caused by missed judgment and misjudgment is effectively avoided, and early perception and accurate locking of the water hammer risk of the secondary water supply pump station are realized.

[0059] (2) By constructing the candidate time interval set G and introducing the autocorrelation analysis method of the time interval sequence, the evaluation mechanism of the periodic fluctuation risk index Fx is established, the periodicity and regularity of the water hammer behavior can be accurately quantified and judged, the accidental disturbance and the structural periodic risk can be distinguished, the mechanism not only improves the scientificity of the risk level judgment, but also provides a criterion basis for the hierarchical response of the regulation strategy, and enhances the decision intelligence level of the system.

[0060] (3) By setting the disturbance countermeasure library and the PLC control interface linkage mechanism, when it is determined that the micro water hammer phenomenon risk level is the third level, the adjustment strategy parameters can be automatically generated and issued, the dynamic optimization of the key operation parameters such as start-stop rhythm and valve response time sequence can be realized, the adjustment process has closed-loop feedback ability, the adjustment logic can be continuously corrected according to the actual system operation state, so that the water hammer inducement can be effectively inhibited, the equipment life can be prolonged, and the system operation safety and energy efficiency can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 It is a remote operation and maintenance system block diagram of a secondary water supply pump station based on Internet of Things.

[0062] Figure 2 A flowchart of a remote operation and maintenance system for a secondary water supply pump station based on the Internet of Things is provided in the present application.

[0063] Figure 3 A flowchart of obtaining a candidate time segment set G is provided in the present application.

[0064] Figure 4 A self-correlation function is provided in the present application With a lag step A trend chart. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0066] Embodiment 1

[0067] Please refer to Figure 1 The present application provides a remote operation and maintenance system for a secondary water supply pump station based on the Internet of Things, which comprises a multidimensional data acquisition module, a local disturbance analysis module, a misplacement feature analysis module, a risk assessment module and a self-adaptive adjustment module.

[0068] The multidimensional data acquisition module is used to acquire real-time water supply related data of the secondary water supply pump station, and to construct a water supply related data set S.

[0069] The local disturbance analysis module is used to obtain a disturbance residual factor Dk according to the water supply related data set S, to determine the water supply state of the secondary water supply pump station, and to trigger the misplacement feature analysis module to identify a misplacement sliding sampling window.

[0070] The misplacement feature analysis module is used to perform feature extraction according to the water supply related data set S, to obtain a feature data set, to mark a water hammer trigger candidate segment, and to construct a candidate time segment set G.

[0071] The risk assessment module is used to construct a time interval sequence according to the candidate time segment set G, to perform autocorrelation analysis, to obtain a periodic fluctuation risk index Fx, and to assess the risk level of the micro water hammer phenomenon.

[0072] The self-adaptive adjustment module is used to generate strategy adjustment parameters using a disturbance countermeasure library when the risk level of the micro water hammer phenomenon is a third risk level, and to perform self-adaptive adjustment of the secondary water supply pump station.

[0073] In the embodiment, through the five-level closed-loop mechanism of "collection-analysis-identification-evaluation-regulation", the problem of "micro water hammer" being difficult to identify, evaluate and intervene in the prior art is effectively solved. The system uses multi-dimensional signal analysis to analyze the water supply state, combines the disturbance residual factor Dk and the misposition coherence coefficient Ck, realizes dynamic coupling misposition behavior identification of the electric control, load and hydraulic response, further extracts periodic disturbance characteristics through autocorrelation analysis of the time interval sequence, constructs a periodic fluctuation risk index Fx, solves the problems of micro water hammer behavior being difficult to archive in time and quantify in cycle, and after the periodic fluctuation risk index Fx reaches the third risk level, the adaptive adjustment module outputs control instructions combined with the disturbance countermeasure library, and the main control system is linked to realize accurate intervention and correction, realizes automatic identification and closed-loop regulation of structural water hammer risk, and significantly improves the operation stability of the pump station and the intelligent response capability of the control system.

[0074] Embodiment 2

[0075] Please refer to Figure 1 and Figure 3 , in particular: the multi-dimensional data collection module is used to set a data collection node in the secondary water supply pump station, and install an intelligent sensor group on the data collection node, use a multi-channel high-precision synchronous ADC interface board to connect each sensor, set a sliding sampling window , and collect the water supply related data of the secondary water supply pump station in the sliding sampling window , to obtain water supply related data of a plurality of sliding sampling windows, and construct a water supply related data set S, wherein the water supply related data includes current signal I , voltage signal U , input power of the motor end , and mechanical impedance M of the load end;

[0076] The current signal I is obtained by a Hall current sensor;

[0077] The voltage signal U is obtained by a strain pressure sensor;

[0078] The input power of the motor end is obtained by a multifunctional electric energy meter;

[0079] The mechanical resistance M of the load end is obtained by a torque sensor;

[0080] In the sliding sampling window , represents the starting time point of the sliding sampling window, represents the ending time point of the sliding sampling window, Indicates the duration of the sliding sampling window;

[0081] For the acquired current signal I and voltage signal U Using Fast Fourier Transform to identify the dominant frequency point and the collected current signal I and voltage signal U The frequency spectrum is transformed into a complex spectrum in the frequency domain through DFT transformation, and the dominant frequency point is obtained from the complex spectrum in the frequency domain. The complex spectrum value is used to calculate the phase difference between the current and voltage signals, and further obtain the phase synchronization factor Tb. The specific method for obtaining the phase synchronization factor Tb is as follows:

[0082] ;

[0083] In the formula, This indicates the voltage signal at the dominant frequency point. Complex spectral values ​​on This indicates the current signal at the dominant frequency point. Complex spectral values ​​on Indicates the dominant frequency point. Represents a complex phase angle function. This represents the phase angle of the voltage signal at the dominant frequency. denoted by , cos represents the phase angle of the current signal at the main frequency point, and cos represents the cosine function.

[0084] Complex phase angle function The phase angle used to extract the negative complex spectral value of a signal in the frequency domain;

[0085] The phase synchronization factor Tb represents the voltage signal and the current signal at the dominant frequency point. The phase difference cosine value is used to determine the phase difference between voltage and current signals when the system experiences disturbances, abnormal loads, or nonlinear responses. If Tb is close to 1, it indicates that the voltage and current are in phase, the system is operating stably, and the load changes linearly. If Tb approaches 0, it indicates that the phase asynchrony between voltage and current is higher, and the system is more likely to have structural, electrical, or hydraulic abnormalities. The phase synchronization factor Tb can be used to judge the pump's working efficiency and power factor trend. At the main frequency point, it is highly correlated with the instantaneous power factor and participates in the calculation of the instantaneous load divergence index Fk, reflecting whether the system coupling state is coordinated and whether the operating state is stable.

[0086] Here, DFT refers to Discrete Fourier Transform, a mathematical method for converting discrete-time signals from the time domain to the frequency domain. It is used to extract the complex spectrum values ​​of frequency points, by transforming the current signal I... and voltage signal U By transforming the data using the DFT, the data is converted into a complex spectrum in the frequency domain, allowing us to obtain the complex spectral values ​​at each discrete frequency point. The complex spectrum in the frequency domain refers to a dataset containing the complex spectral values ​​at all discrete frequency points.

[0087] In this embodiment, by deploying multi-dimensional data acquisition nodes at the secondary water supply pumping station and integrating high-precision synchronous ADCs and intelligent sensor groups, high-frequency and high-synchronous-precision sampling of pumping station operating parameters is achieved, providing a basic guarantee for subsequent feature extraction and risk identification. By real-time acquisition of key signals such as pump current, voltage, input power, and mechanical resistance, and combined with fast Fourier transform and DFT frequency domain analysis methods, the phase difference between electrical and power characteristics is accurately identified, thereby constructing a "phase synchronization factor Tb". This effectively captures the weak phase misalignment characteristics between control, load, and hydraulic pressure, and constructs a water supply data set S, providing a stable data foundation for subsequent disturbance identification and misalignment assessment. Overall, this improves the ability to perceive early abnormal signs of "micro water hammer", helps to break the blind zone limitation of water hammer cycle in traditional monitoring methods, and enhances the system's early perception and response capability to micro-disturbance dynamics.

[0088] Example 3

[0089] Please refer to Figure 1 Specifically: the local disturbance analysis module includes a pressure trajectory fitting unit and a residual disturbance identification unit;

[0090] The pressure trajectory fitting unit is used based on the sliding sampling window. Voltage signal U acquired within and main frequency Construct a periodic fitting pressure curve Among them, the periodically fitted pressure curve The specific manifestations are as follows:

[0091] ;

[0092] In the formula, This represents the constant term, namely the mean pressure value. This represents the amplitude coefficient of the nth harmonic. Indicates the main frequency point The corresponding dominant angular frequency, , where n represents the harmonic order index. This represents the phase shift angle of the nth harmonic. The total harmonic order is represented by t, and the sliding sampling window is represented by t. The time variable within ], t∈[ ].

[0093] The reconstructed term representing the periodic perturbation component of the pressure signal is defined within the sliding sampling window. The voltage signal U is fitted with sinusoidal waves of different frequencies, i.e. the main frequency and its harmonics, in the sliding sampling window , to obtain the periodic fluctuation-related part;

[0094] Total harmonic order Set by the customer according to historical experience, the amplitude coefficient of the nth harmonic By performing a fast Fourier transform (FFT) on the voltage signal in the sliding sampling window , the amplitude spectrum components at the main frequency point and the harmonic frequencies are extracted, and the amplitude value corresponding to the nth harmonic frequency is taken as the amplitude coefficient ;

[0095] wherein the constant term is obtained by performing an arithmetic mean value calculation on the voltage signal U in the sliding sampling window ;

[0096] The amplitude coefficient of the nth harmonic is used to reflect the amplitude intensity of the nth harmonic in the pressure fluctuation, and the greater the amplitude coefficient, the higher the energy proportion of the frequency component;

[0097] The phase shift angle of the nth harmonic refers to the time shift of the nth harmonic relative to the fundamental frequency, and is a key phase factor affecting the waveform trend, which is identified and obtained by FFT;

[0098] Harmonic refers to an integer multiple frequency component in a periodic signal other than the main frequency point, and the harmonic order n refers to the multiple relationship of a certain harmonic relative to the fundamental wave;

[0099] Periodic fitting pressure curve is a periodic estimation curve constructed based on the main frequency point and combined with multiple harmonics in the sliding sampling window, which is mainly used to construct an approximate model of the actual pressure signal as a reference baseline for disturbance identification, effectively stripping structural fluctuations and non-periodic disturbances, and laying a foundation for the calculation of the subsequent residual disturbance factor Dk. This fitting method has the advantages of strong interpretability and strong energy concentration description capability, which can significantly improve the robustness and accuracy of micro-disturbance identification. In specific examples, such as a secondary water supply system, the pressure sensor continuously collects pressure signals from the output end of the water pump. Due to the periodic change of motor speed, pipe network load disturbance and user water rhythmic behavior, the pressure signal often contains multiple harmonics and fluctuation components. At this time, by constructing a periodic fitting pressure curve , it can be determined whether there is a periodic pressure fluctuation in the water pump system, and the periodic fitting pressure curve Removing noise components such as electromagnetic interference and transient changes facilitates the subsequent calculation of the disturbance residual factor Dk.

[0100] The residual disturbance identification unit is used to fit the pressure curve based on the period. and voltage signal U By comparing and analyzing the residual energy density per unit time, the perturbation residual factor Dk is obtained. The specific method for obtaining the perturbation residual factor Dk is as follows:

[0101] ;

[0102] In the formula, This represents the pressure value at time point t. This represents the pressure value at time point t in the periodically fitted pressure curve. Indicates the sliding sampling window [ The duration of the [unclear];

[0103] The disturbance residual factor Dk is a quantitative indicator used to represent the degree of difference between the water supply pressure signal and its periodic fitting curve. It reflects the degree of deviation between the actual operating state of the water supply system and the ideal periodic fluctuation state per unit time. It can effectively identify abnormal pressure deviations in the water supply system caused by water hammer disturbances, mechanical feedback misalignment or other minor disturbances. It provides key basis for subsequent water hammer triggering judgment, structural misalignment identification and risk level assessment, and improves the system's perception accuracy and diagnostic capability for the latent behavior of "micro water hammer".

[0104] A preset residual judgment threshold Pd is established, and the residual judgment threshold Pd and the disturbance residual factor Dk are compared and analyzed to evaluate the water supply status of the secondary water supply pumping station. The specific evaluation content is as follows:

[0105] If the disturbance residual factor Dk is less than or equal to the residual judgment threshold Pd, i.e. Dk≤Pd, then the water supply status of the secondary water supply pump station is determined to be normal. The secondary water supply pump system can maintain stability through self-regulation and does not pose a threat to system safety and water supply stability. At this time, the current operation strategy is maintained and water supply-related data are continuously monitored.

[0106] If the disturbance residual factor Dk > the residual judgment threshold Pd, that is, Dk > Pd, then the water supply state of the secondary water supply pump station is determined to be a structural disturbance state. The secondary water supply pump system is difficult to maintain stability through self-adjustment and has exceeded the system self-balancing capability boundary. At this time, the misalignment feature analysis module is immediately triggered to perform structural fault analysis.

[0107] In this embodiment, a periodically fitted pressure curve is constructed. With voltage signal U By comparison, the micro and non-mutational disturbance behavior in the water supply system can be effectively identified. By using the frequency domain main frequency extraction and harmonic reconstruction method, a periodic theoretical pressure model is dynamically constructed, and the disturbance strength is evaluated by combining the residual energy density in unit time, so as to obtain the disturbance residual factor Dk and perform quantitative analysis. This mechanism improves the sensitivity of capturing the early characteristics of structural disturbance, and by setting the residual judgment threshold Pd, the micro disturbance and structural anomaly are automatically distinguished, thereby realizing early warning of abnormal water supply state, providing accurate and controllable trigger conditions for subsequent mislocation analysis and risk identification, and greatly enhancing the intelligent response and control foresight of the system.

[0108] Embodiment 4

[0109] Please refer to Figure 1 and Figure 3 , specifically: the mislocation feature analysis module includes a feature extraction unit and a mislocation evaluation unit;

[0110] The feature extraction unit is used to extract features according to the water supply related data set S, and construct a feature data set H according to the extracted feature indicators;

[0111] The maximum instantaneous change rate of the sum of the current and torque response is obtained to obtain the instantaneous load divergence index Fk, wherein the instantaneous load divergence index Fk is obtained in the following manner:

[0112] ;

[0113] In the formula, represents the input power of the motor end at time point t, represents the mechanical impedance of the load end at time point t, represents a gain coefficient, represents a phase synchronization factor, represents a phase synchronization factor of the device under rated state, represents the maximum derivative value in the sliding sampling window ;

[0114] wherein, represents a phase synchronization offset correction term, which is used to measure the deviation between the phase synchronization ability of the current state of the system and the synchronization reference of the rated state, so as to realize dynamic amplification or inhibition correction of the influence degree of load disturbance;

[0115] The gain coefficient is a modulation parameter for establishing a dimension mapping relationship between different physical quantity dimensions, which is used to map the mechanical impedance into an equivalent power disturbance form comparable to the input power of the motor end The specific value of the phase synchronization factor is obtained by experimental calibration, that is, when the device is running under a steady-state load condition, different amplitudes of load disturbance are applied, and the input power change value and the load resistance change value are recorded synchronously to construct a disturbance response sample set, and then the least square method is used to fit the linear relationship between the disturbances to extract the gain coefficient ;

[0116] The phase synchronization factor of the device under the rated state obtained by calibration during the device debugging and factory inspection stage;

[0117] The instantaneous load divergence index Fk is used to quantify the coupling consistency between the electric control driving side, that is, the motor input power and the execution load side, that is, the pump torque response within a sliding sampling window, which specifically reflects the degree of non-coordinated motion or short-term power imbalance phenomenon of the water supply pump group during operation. Considering the dynamic difference between the energy input and the mechanical resistance under the real-time running state, combined with the offset degree of the phase synchronization factor, the mechanical divergence characteristics existing in the transient disturbance process of the water supply system are extracted. In specific examples, such as high-load mechanical systems driven by variable frequency motors, such as water pump systems, mechanical load mutation, acceleration and deceleration, and impact vibration can cause the motor power response to change dramatically. If it cannot be responded in time, the phenomenon of out of step, mechanical fatigue and system stop running may occur. At this time, the instantaneous load divergence index Fk can identify the cooperative fluctuation peak value of power and mechanical resistance through the maximum derivative rate, quantify the dynamic impact strength of the system, and judge whether the current system is facing a high-risk load impact.

[0118] The deviation degree of pressure fluctuation in the current sliding sampling window is calculated to obtain the pressure smooth residual factor Rk, wherein the specific acquisition method of the pressure smooth residual factor Rk is as follows:

[0119] ;

[0120] In the formula, represents the pressure value at time point t, represents the pressure mean value of a plurality of sampling time points in the sliding sampling window, represents the standard deviation of pressure in the sliding sampling window.

[0121] The pressure smooth residual factor Rk represents the degree of deviation of pressure fluctuation from the pressure mean value in the sliding sampling window, which is used to reveal whether the local disturbance breaks through the structural response capability of the pressure regulating system. In the operation of the secondary water supply pump station, the peak water consumption period and the water hammer disturbance will cause the pressure to rise. However, the traditional monitoring method generally judges based on the instantaneous pressure value, which is easy to misjudge the micro water hammer as normal pressure fluctuation, resulting in difficulty in identifying the micro water hammer phenomenon.

[0122] The misalignment evaluation unit is configured to, for each sliding sampling window, perform a summary calculation based on the feature data set H to obtain a misalignment coherence coefficient Ck, where the misalignment coherence coefficient Ck is obtained in the following manner:

[0123] ;

[0124] In the formula, represents an instantaneous load divergence index, represents a pressure plateau residual factor, represents a pressure suppression regulation factor, represents a main frequency phase difference, represents a phase lag suppression coefficient, e represents the base number of a natural logarithm, i.e., a constant commonly used in mathematics, e≈2.71828...

[0125] represents a pressure steady-state compensation term, which is used to determine whether the pressure fluctuation of the sliding sampling window deviates from the pressure balance state, if is large, it indicates that the water pressure fluctuates violently;

[0126] represents a phase synchronization suppression term, which is used to measure the response synchronization of the control system. If there is a significant phase difference between the voltage signal and the current signal, it indicates that there is a command response delay or a regulation chain misadjustment, ≈0, it indicates that the electric drive response and the control input are consistent, >0, it indicates that the voltage control is misadjusted;

[0127] wherein the pressure suppression regulation factor is a nonlinear gain adjustment index, which is used to amplify or compress the effect of the pressure plateau residual factor Rk on the misalignment coherence coefficient Ck. The specific value of the pressure suppression regulation factor is obtained by an empirical initialization method, which is used for a motor-hydraulic coupling system;

[0128] The phase lag suppression coefficient is an electric control-execution response chain coupling credibility adjustment coefficient, which is used to measure the synchronization credibility between the voltage and the current. The specific value of the phase lag suppression coefficient is set by an expert according to historical experience;

[0129] In the prior art, although the secondary water supply pump station is monitored by pressure sensors, motor feedback, PLC control and other means, when low-amplitude and short-lag water hammer disturbance, i.e. "micro water hammer" occurs, the various modules of the system, including pressure monitoring, electric control feedback and mechanical response, still work independently, lack unified evaluation logic, making it difficult to determine whether the control-execution-fluid response of the secondary water supply pump station system is misaligned, difficult to identify the misalignment between fluid disturbance and control behavior, and lack of unified, multi-source fusion identification mechanism, thus leading to the key response chain after micro water hammer triggering not being tracked, forming hidden structural damage and inducing misjudgment and lag control. Using misalignment coherence coefficient Ck can solve the problems of difficult cross-dimensional identification of dynamic misalignment state, water hammer occurrence node difficult to calibrate and disturbance cause misjudgment in the traditional system, and is a key supporting index for building an intelligent micro-disturbance monitoring and regulation mechanism, ensuring the coordination and predictability between the operation logic and physical response of the pump station system.

[0130] A misalignment threshold Ckyz is set, and for each sliding sampling window, the misalignment coherence coefficient Ck of the sliding sampling window is compared and analyzed with the misalignment threshold Ckyz. If the misalignment coherence coefficient Ck is greater than or equal to the misalignment threshold Ckyz, it is determined that the dynamic coupling misalignment behavior of the electric control-load-hydraulic three sides occurs in the sliding sampling window. At this time, the sliding sampling window is marked as a water hammer triggering candidate window. If the misalignment coherence coefficient Ck is less than the misalignment threshold Ckyz, it is determined that the dynamic coupling misalignment behavior of the electric control-load-hydraulic three sides does not occur in the sliding sampling window, and no processing is required.

[0131] The sliding sampling windows determined as water hammer triggering candidate windows are collected, and the center time points of each water hammer triggering candidate window are extracted to construct a candidate time section set G.

[0132] In the embodiment, by fusing the three-dimensional information of motor input power, load mechanical resistance and pressure change, "instantaneous load divergence index Fk" and "pressure smooth residual factor Rk" are constructed, and then the misalignment coherence coefficient Ck is used to evaluate the dynamic matching state of the electric control-load-hydraulic three sides of the pump station in operation, which can effectively identify whether there is misalignment coupling behavior in the sliding sampling window, so as to realize accurate marking of the precursor behavior of "micro water hammer". Compared with the existing method of relying on single water pressure or current change for early warning, the perception ability of low-amplitude and short-lag disturbance is significantly improved, the problem that "micro water hammer" is difficult to be found is effectively solved, and the basis support for subsequent water hammer periodic reconstruction and risk classification judgment is provided, and the identification accuracy and response timeliness of the water hammer triggering chain are comprehensively improved.

[0133] Embodiment 5

[0134] Please refer to Figure 1 and Figure 4Specifically: the risk assessment module includes an autocorrelation analysis unit and a risk determination unit;

[0135] The autocorrelation analysis unit is used to calculate the time interval between the center time points of each adjacent water hammer triggering candidate window based on the candidate time interval set G, in order to construct a time interval sequence. Based on this time interval sequence, autocorrelation analysis is performed to construct an autocorrelation function, which is specifically expressed as follows:

[0136] ;

[0137] In the formula, Indicates the lag step size as The autocorrelation function value at time, This represents the k-th time interval in the time interval sequence. Indicates the average of time intervals. Indicates the lag step size. Indicates the first There are 1 time intervals, where n represents the total number of time intervals;

[0138] For autocorrelation function Calculate each lag step. Extract the autocorrelation function value, and then extract the maximum autocorrelation function value. And record the maximum autocorrelation function value. Corresponding lag step size ,in, =1, 2, 3, ..., n- , where n- Indicates the lag step size The upper limit calculation range, This indicates the minimum set hysteresis step size;

[0139] In the formula, This indicates how much the fluctuation in the k-th time interval deviates from the mean. It means that it has passed. The product of the time intervals after each step is positive and negative or both are negative, indicating a positive trend. If one is positive and the other is negative, the trend is opposite. By continuously accumulating such products, it is possible to identify how much the time interval sequence is being shifted. After the next step, will there still be a similar pattern to the original one? The larger the value, the stronger the periodic reproducibility of the disturbance interval, the higher the probability of the micro water hammer phenomenon, and the greater the risk.

[0140] Autocorrelation analysis is a statistical method used to determine the similarity between a signal or sequence and its "self" at different time intervals, i.e., at "lag". If a system's events recur, such as the micro water hammer phenomenon, it will show high autocorrelation at a certain lag step, thus determining whether the micro water hammer phenomenon has "periodic fluctuations".

[0141] The risk assessment unit is used to set the maximum autocorrelation function value. Let Fx be the cyclical volatility risk index, and let the first cyclical volatility risk threshold be preset. Second-cycle volatility risk threshold The cyclical volatility risk index Fx is compared with the first cyclical volatility risk threshold. Second-cycle volatility risk threshold A comparative analysis was conducted to assess the risk level of micro-water hammer phenomena. The specific assessment content is as follows:

[0142] If the cyclical volatility risk index Fx is less than or equal to the first cyclical volatility risk threshold That is, Fx≤ If the risk level of the micro water hammer phenomenon is determined to be the first risk level, then the time interval has no stable repeating characteristics and is determined to be a micro-occasional disturbance, not dominated by water hammer.

[0143] If the cyclical volatility risk index Fx is greater than the first cyclical volatility risk threshold And less than the second cycle volatility risk threshold ,Right now <Fx< If the risk level of the micro water hammer phenomenon is determined to be the second risk level, then there is a potential water hammer hazard in the secondary water supply pumping station. The valve should be shut off with a delayed start and the start rate should be adjusted to 80% of the standard start rate.

[0144] If the cyclical volatility risk index Fx is greater than or equal to the second cyclical volatility risk threshold That is, Fx≥ If the water hammer phenomenon is detected, the risk level is determined to be the third risk level. At this time, the secondary water supply pumping station has a structural water hammer risk and the disturbance period is significant, which triggers the main control system of the secondary water supply pumping station to perform adaptive adjustment of the secondary water supply pumping station.

[0145] In the embodiment, by setting the autocorrelation analysis unit and the risk judgment unit, the periodic risk of the micro water hammer phenomenon can be accurately identified and quantified. The core lies in constructing a time interval sequence and performing autocorrelation analysis, and then calculating a periodic fluctuation risk index Fx to reflect the periodic recurrence ability of the disturbance. This breaks through the limitations of traditional methods that rely on pressure or electrical signal abnormal value identification, and can identify the low-amplitude and high-repetitive disturbance pattern generated by the "micro water hammer". With the help of a double threshold mechanism, different risk levels can trigger differentiated response strategies, realizing a step-by-step control link from potential hazard identification to structural water hammer warning, significantly improving the monitoring sensitivity and response initiative of the system to micro periodic disturbances, effectively filling the identification gap in the "water hammer periodic blind area", and ensuring the safe operation and structural stability of the pump station.

[0146] Embodiment 6

[0147] Please refer to Figure 1 , specifically: the adaptive adjustment module is used for generating strategy adjustment parameters according to the disturbance countermeasure library in the secondary water supply pump station when the risk level of the micro water hammer phenomenon is the third risk level, packaging the strategy adjustment parameters into strategy control instructions, and sending the strategy control instructions to the PLC control platform through a standard interface protocol to drive the system components of the secondary water supply pump station to perform adaptive adjustment.

[0148] Among them, the disturbance countermeasure library refers to a set of pre-defined control strategy combination library in the system for different water hammer risk levels, periodic characteristics and disturbance forms. The strategy adjustment parameters are generated by calling the pre-defined control strategy template or rule.

[0149] The PLC platform refers to an automation control platform with a programmable logic controller as the core, which is widely used in industrial control systems for real-time monitoring and logic control of mechanical equipment, motors, water pumps, valves and other industrial components.

[0150] In the embodiment, by introducing the disturbance countermeasure library, after identifying the structural micro water hammer risk, the system components can be quickly linked to execute the targeted response control strategy. Compared with the traditional static control logic, the pre-defined control template is dynamically called according to the periodic fluctuation risk level to generate strategy adjustment parameters that match the current disturbance characteristics, greatly improving the perception sensitivity and response adaptability of the system to water hammer disturbances. The strategy adjustment parameters are packaged into standardized instructions in real time and sent to the PLC platform to realize adaptive operation adjustment of the closed-loop control chain, improving the overall operation safety and intelligent level of the pump station.

[0151] Embodiment 7

[0152] Please refer to Figure 2 , specifically, a secondary water supply pump station remote operation and maintenance system method based on the Internet of Things, comprising the following steps,

[0153] Step one, real-time collection of water supply related data of the secondary water supply pump station, construction of water supply related data set S;

[0154] Step two, according to the water supply related data set S, obtain the disturbance residual factor Dk to determine the water supply state of the secondary water supply pump station, and trigger the dislocation feature analysis module to identify the dislocation sliding sampling window;

[0155] Step three, according to the water supply related data set S, feature extraction is performed to obtain the feature data set, and the water hammer trigger candidate segment is marked, and the candidate time interval set G is constructed;

[0156] Step four, according to the candidate time interval set G, construct the time interval sequence, and perform autocorrelation analysis to obtain the periodic fluctuation risk index Fx to evaluate the risk level of micro water hammer phenomenon;

[0157] Step five, when the risk level of micro water hammer phenomenon is the third risk level, use the disturbance countermeasure library to generate strategy adjustment parameters for adaptive adjustment of the secondary water supply pump station.

[0158] In the embodiment, by collecting water supply related data, constructing disturbance residual factor Dk and dislocation feature index, the accurate identification of "micro water hammer" implicit disturbance is realized, the missing judgment problem of traditional method to short-time and low-amplitude dislocation behavior is made up, and through autocorrelation analysis of time interval sequence, the periodic risk fluctuation is quantified, the technical short board that water hammer cycle blind area is difficult to identify is solved, finally combined with the disturbance countermeasure library, the adjustment strategy is generated in real time and issued to the main control system, the closed loop control and adaptive correction of structural water hammer risk are realized, the steady-state operation ability and adjustment response time of the system are effectively improved.

[0159] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A remote operation and maintenance system for secondary water supply pumping stations based on the Internet of Things, characterized in that: It includes a multi-dimensional data acquisition module, a local disturbance analysis module, a misalignment feature analysis module, a risk assessment module, and an adaptive adjustment module; The multi-dimensional data acquisition module is used to collect water supply-related data from the secondary water supply pumping station in real time and construct a water supply-related data set S; The local disturbance analysis module is used to obtain the disturbance residual factor Dk based on the water supply related data set S, in order to determine the water supply status of the secondary water supply pumping station, and to trigger the misalignment feature analysis module to identify the misalignment sliding sampling window. The misalignment feature analysis module is used to extract features based on the water supply-related data set S to obtain a feature data set, mark water hammer triggering candidate segments, and construct a candidate time segment set G; The risk assessment module is used to construct a time interval sequence based on the candidate time interval set G, and perform autocorrelation analysis to obtain the periodic fluctuation risk index Fx in order to assess the risk level of micro water hammer phenomenon. The adaptive adjustment module is used to generate strategy adjustment parameters from the disturbance countermeasure library when the risk level of micro water hammer phenomenon is the third risk level, and to carry out adaptive adjustment of the secondary water supply pumping station. The misalignment feature analysis module includes a feature extraction unit and a misalignment evaluation unit; The feature extraction unit is used to extract features based on the water supply-related data set S, and to construct a feature data set H based on the extracted feature indicators; The maximum instantaneous rate of change of the sum of current and torque response is quantified to obtain the instantaneous load divergence index Fk. The specific method for obtaining the instantaneous load divergence index Fk is as follows: ； In the formula, This represents the input power at the motor terminals at time point t. This represents the mechanical impedance at the load end at time point t. Indicates the gain coefficient. Represents the phase synchronization factor. This indicates the phase synchronization factor of the equipment under rated conditions. Indicates the sliding sampling window The maximum derivative value within; Calculate the degree of deviation of pressure fluctuation within the current sliding sampling window to obtain the pressure stability residual factor Rk. The specific method for obtaining the pressure stability residual factor Rk is as follows: ； In the formula, This represents the pressure value at time point t. This represents the average pressure value across several sampling time points within the sliding sampling window. This represents the standard deviation of pressure within the sliding sampling window; The risk assessment unit is used to set the maximum autocorrelation function value. Let Fx be the cyclical volatility risk index, and let the first cyclical volatility risk threshold be preset. Second-cycle volatility risk threshold The cyclical volatility risk index Fx is compared with the first cyclical volatility risk threshold. Second-cycle volatility risk threshold A comparative analysis was conducted to assess the risk level of micro-water hammer phenomena. The specific assessment content is as follows: If the cyclical volatility risk index Fx is less than or equal to the first cyclical volatility risk threshold That is, Fx≤ If the risk level of the micro water hammer phenomenon is determined to be the first risk level, then the time interval has no stable repeating characteristics and is determined to be a micro-occasional disturbance, not dominated by water hammer. If the cyclical volatility risk index Fx is greater than the first cyclical volatility risk threshold And less than the second cycle volatility risk threshold ,Right now <Fx< If the water hammer phenomenon is detected, the risk level is determined to be the second risk level. At this time, there is a potential water hammer hazard in the secondary water supply pumping station. The valve should be shut off with a delayed start and the start rate should be adjusted to 80% of the standard start rate. If the cyclical volatility risk index Fx is greater than or equal to the second cyclical volatility risk threshold That is, Fx≥ If the risk level of the micro water hammer phenomenon is determined to be the third risk level, then the secondary water supply pumping station has a structural water hammer risk, the disturbance period is significant, triggering the main control system of the secondary water supply pumping station to perform adaptive adjustment of the secondary water supply pumping station.

2. The remote operation and maintenance system for a secondary water supply pumping station based on the Internet of Things as described in claim 1, characterized in that: The multi-dimensional data acquisition module is used to set up data acquisition nodes in secondary water supply pumping stations, and to install intelligent sensor groups on the data acquisition nodes. A multi-channel high-precision synchronous ADC interface board is used to connect each sensor, and a sliding sampling window is set. ], and in the sliding sampling window [ The system collects water supply-related data from secondary water supply pumping stations to obtain water supply-related data from several sliding sampling windows, and constructs a water supply-related data set S. This water supply-related data includes current signals I... Voltage signal U Input power at the motor end and the mechanical impedance M at the load end ; For the acquired current signal I and voltage signal U Using Fast Fourier Transform to identify the dominant frequency point and the collected current signal I and voltage signal U The frequency spectrum is transformed into a complex spectrum in the frequency domain through DFT transformation, and the dominant frequency point is obtained from the complex spectrum in the frequency domain. The complex spectrum value is used to calculate the phase difference between the current and voltage signals, and further obtain the phase synchronization factor Tb. The specific method for obtaining the phase synchronization factor Tb is as follows: ； In the formula, This indicates the voltage signal at the dominant frequency point. Complex spectral values ​​on This indicates the current signal at the dominant frequency point. Complex spectral values ​​on Indicates the dominant frequency point. Represents a complex phase angle function. This represents the phase angle of the voltage signal at the dominant frequency. denoted by , cos represents the phase angle of the current signal at the main frequency point, and cos represents the cosine function.

3. The remote operation and maintenance system for a secondary water supply pumping station based on the Internet of Things as described in claim 2, characterized in that: The local disturbance analysis module includes a pressure trajectory fitting unit and a residual disturbance identification unit; The pressure trajectory fitting unit is used based on the sliding sampling window. Voltage signal U acquired within and main frequency Construct a periodic fitting pressure curve Among them, the periodically fitted pressure curve The specific manifestations are as follows: ； In the formula, This represents the constant term, which is the average pressure value of the fitted pressure trajectory curve. This represents the amplitude coefficient of the nth harmonic. Indicates the main frequency point The corresponding dominant angular frequency, , where n represents the harmonic order index. This represents the phase shift angle of the nth harmonic. The total harmonic order is represented by t, and the sliding sampling window is represented by t. The time variable within ], t∈[ ].

4. The remote operation and maintenance system for a secondary water supply pumping station based on the Internet of Things as described in claim 3, characterized in that: The residual disturbance identification unit is used to fit the pressure curve based on the period. and voltage signal U By comparing and analyzing the residual energy density per unit time, the perturbation residual factor Dk is obtained. The specific method for obtaining the perturbation residual factor Dk is as follows: ； In the formula, This represents the pressure value at time point t. This represents the pressure value at time point t in the periodically fitted pressure curve. Indicates the sliding sampling window [ The duration of the [unclear]; A preset residual judgment threshold Pd is established, and the residual judgment threshold Pd and the disturbance residual factor Dk are compared and analyzed to evaluate the water supply status of the secondary water supply pumping station. The specific evaluation content is as follows: If the disturbance residual factor Dk is less than or equal to the residual judgment threshold Pd, i.e., Dk≤Pd, then the water supply status of the secondary water supply pumping station is determined to be normal. At this time, the current operation strategy is maintained and water supply-related data is continuously monitored. If the disturbance residual factor Dk > the residual judgment threshold Pd, that is, Dk > Pd, then the water supply state of the secondary water supply pumping station is determined to be a structural disturbance state. At this time, the misalignment feature analysis module is immediately triggered to perform structural fault analysis.

5. The remote operation and maintenance system for a secondary water supply pumping station based on the Internet of Things as described in claim 1, characterized in that: The misalignment evaluation unit is used to perform a summary calculation for each sliding sampling window based on the feature data set H to obtain the misalignment coherence coefficient Ck. The specific method for obtaining the misalignment coherence coefficient Ck is as follows: ； In the formula, Indicates the instantaneous load divergence index. This represents the pressure-stabilized residual factor. Indicates a stress-inhibiting regulatory factor. Indicates the phase difference of the main frequency. This represents the phase lag suppression coefficient, and e represents the base of the natural logarithm. Set a misalignment threshold Ckyz, and for each sliding sampling window, compare the misalignment coherence coefficient Ck of the sliding sampling window with the misalignment threshold Ckyz. If the misalignment coherence coefficient Ck is greater than or equal to the misalignment threshold Ckyz, it is determined that dynamic coupling misalignment behavior of the three sides of the electronic control, load and hydraulic system has occurred in the sliding sampling window. At this time, the sliding sampling window is marked as a water hammer trigger candidate window. If the misalignment coherence coefficient Ck is less than the misalignment threshold Ckyz, it is determined that dynamic coupling misalignment behavior of the three sides of the electronic control, load and hydraulic system has not occurred in the sliding sampling window, and no processing is required. The set of all judgment results is a sliding sampling window of the water hammer triggering candidate window. The center time point of each water hammer triggering candidate window is extracted to construct the candidate time segment set G.

6. The remote operation and maintenance system for a secondary water supply pumping station based on the Internet of Things as described in claim 5, characterized in that: The risk assessment module includes an autocorrelation analysis unit and a risk determination unit; The autocorrelation analysis unit is used to calculate the time interval between the center time points of each adjacent water hammer triggering candidate window based on the candidate time interval set G, in order to construct a time interval sequence. Based on this time interval sequence, autocorrelation analysis is performed to construct an autocorrelation function, which is specifically expressed as follows: ； In the formula, Indicates the lag step size as The autocorrelation function value at time, This represents the k-th time interval in the time interval sequence. Indicates the average of time intervals. Indicates the lag step size. Indicates the first There are 1 time intervals, where n represents the total number of time intervals; For autocorrelation function Calculate each lag step. Extract the autocorrelation function value, and then extract the maximum autocorrelation function value. And record the maximum autocorrelation function value. Corresponding lag step size ,in, =1, 2, 3, ..., n- .

7. The remote operation and maintenance system for a secondary water supply pumping station based on the Internet of Things as described in claim 1, characterized in that: The adaptive adjustment module is used to automatically generate strategy adjustment parameters based on the disturbance countermeasure library in the secondary water supply pumping station when the risk level of micro water hammer phenomenon is the third risk level. The strategy adjustment parameters are packaged into strategy control instructions and sent to the PLC control platform through the standard interface protocol to drive the system components of the secondary water supply pumping station to perform adaptive adjustment.

8. A method for a remote operation and maintenance system for a secondary water supply pumping station based on the Internet of Things (IoT), used to implement the remote operation and maintenance system for a secondary water supply pumping station based on the IoT as described in any one of claims 1 to 7, characterized in that: Includes the following steps, Step 1: Collect water supply-related data from the secondary water supply pumping station in real time and construct a water supply-related data set S; Step 2: Based on the water supply-related data set S, obtain the disturbance residual factor Dk to determine the water supply status of the secondary water supply pumping station and trigger the misalignment feature analysis module to identify the misalignment sliding sampling window. Step 3: Based on the water supply-related data set S, perform feature extraction to obtain the feature data set, mark the candidate segments for water hammer triggering, and construct the candidate time segment set G; Step 4: Based on the candidate time interval set G, construct a time interval sequence and perform autocorrelation analysis to obtain the periodic fluctuation risk index Fx, so as to assess the risk level of the micro water hammer phenomenon. Step 5: When the risk level of micro-water hammer phenomenon is the third risk level, use the disturbance countermeasure library to generate strategy adjustment parameters and carry out adaptive adjustment of the secondary water supply pumping station.

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