An integrated energy-saving control system for water pumps

Abstract

The present invention belongs to the technical field of fluid machinery control, and specifically relates to an integrated water pump energy-saving control system, including: a heterogeneous sensing array, an intelligent optimization controller, a dual-mode energy recovery device, a multi-scale feature analysis module, and a cloud platform diagnosis system; the heterogeneous sensing array includes a pressure gradient sensor, a multi-frequency acoustic fingerprint sensor, and an electromagnetic-ultrasonic composite flowmeter. The pressure gradient sensors are arranged at intervals of 0.5D along the axial direction of the pipeline. The acoustic fingerprint sensor is configured with a Helmholtz resonance cavity. The composite flowmeter automatically switches between electromagnetic / ultrasonic measurement modes according to the Reynolds number. In the present invention, a dual-mode energy recovery device is adopted: control optimization energy saving and energy recovery energy saving, and the comprehensive energy-saving rate reaches 33.7%. The flywheel-piezoelectric composite energy storage system achieves an energy conversion efficiency of 82%, which is 19.6% higher than that of the traditional single mode.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid machinery control, and particularly to an integrated energy-saving control system for water pumps. Background Technique

[0002] An integrated water pump is a fluid transportation device that highly integrates a water pump, a driving motor, a control system, and auxiliary functional components. Its core feature is to break through the structural limitations of traditional single-function water pumps through modular design and intelligent control, and is widely used in fields such as automotive thermal management, industrial circulation systems, and heating, ventilation, and air conditioning.

[0003] With the acceleration of urbanization, the energy consumption of water pump systems has accounted for more than 20% of the global electricity consumption and is increasing annually. Currently, the existing integrated water pump energy-saving control systems generally adopt simple linkage control and rely on preset algorithms. Such technologies have the following defects:

[0004] 1. Poor adaptability of control algorithms: For the publicly disclosed PID control system, the adjustment time exceeds 5 seconds under sudden flow change conditions and cannot handle non-linear coupling variables. Experiments show that when the pressure fluctuation exceeds 15%, the overshoot reaches 32%;

[0005] 2. Lack of energy recovery mechanism: Traditional water pump systems are not equipped with energy recovery devices. Tests show that under valve throttling conditions, up to 28% of hydraulic energy is wasted in the form of heat loss;

[0006] 3. Insufficient accuracy of fault prediction: The commonly used single-dimensional vibration monitoring scheme has an early warning accuracy rate of only 76.3% for early bearing faults, and the false alarm rate is as high as 17%;

[0007] 4. Low system coordination efficiency: Each module in the conventional system uses an independent communication protocol, resulting in a data delay of 200 - 500 ms. In the test of the municipal water supply scenario, the multi-device collaborative response error exceeds 12%;

[0008] 5. Weak protection against extreme working conditions: It is pointed out in industry reports that 83% of water hammer accidents are caused by the untimely response of the control system, and the action time of traditional pressure relief valves exceeds 1.2 seconds.

[0009] Therefore, we propose an integrated energy-saving control system for water pumps. Summary of the Invention

[0010] The present invention aims to solve one of the technical problems existing in the prior art or related technologies.

[0011] For this reason, the technical solutions adopted by the present invention are as follows:

[0012] An integrated energy-saving control system for water pumps, comprising: a heterogeneous sensing array, an intelligent optimization controller, a dual-mode energy recovery device, a multi-scale feature analysis module, and a cloud platform diagnosis system;

[0013] The heterogeneous sensing array includes a pressure gradient sensor, a multi-frequency acoustic fingerprint sensor, and an electromagnetic-ultrasonic composite flowmeter. The pressure gradient sensors are arranged at intervals of 0.5D along the axial direction of the pipeline. The acoustic fingerprint sensor is configured with a Helmholtz resonance cavity. The composite flowmeter automatically switches between electromagnetic / ultrasonic measurement modes according to the Reynolds number;

[0014] The intelligent optimization controller is built-in with an improved vulture optimization algorithm, integrated with a quantum behavior decision unit and a hydrodynamic constraint module, and establishes an OPC UA data channel with a period of 20 ms with the sensing array;

[0015] The dual-mode energy recovery device includes a carbon fiber flywheel energy storage component and a piezoelectric power generation array, and automatically switches the energy storage mode through the pressure fluctuation coefficient;

[0016] The multi-scale feature analysis module adopts a 3D-CNN + Transformer hybrid architecture to realize feature fusion at a spatial scale of 5 mm - 200 mm and a time scale of 10 ms - 10 min;

[0017] The cloud platform diagnosis system constructs a fault propagation model based on tensor chain decomposition and outputs the fault probability distribution for the next 30 minutes;

[0018] Each module realizes data interaction through the industrial Internet of Things protocol.

[0019] In a preferred example of the present invention, it can be further configured as follows: In the heterogeneous sensing array:

[0020] The pressure gradient sensors are arranged at intervals of 0.5D along the axial direction of the pipeline, with a measurement range of 0 - 2.5 MPa and a temperature compensation range of -40°C to 125°C, where D is the pipe diameter;

[0021] The acoustic fingerprint sensor is configured with a Helmholtz resonance cavity, and the frequency domain resolution is ≤1 Hz;

[0022] The composite flowmeter enables the ultrasonic mode when the Reynolds number Re > 2300 and the electromagnetic mode when Re ≤ 2300.

[0023] In a preferred example of the present invention, it can be further configured as follows: The improved vulture optimization algorithm includes a quantum tunneling search strategy, and its parameter update formula is: , where σ is the perturbation coefficient, is the flow velocity, is the barrier height, k is the Boltzmann constant, and T is the ambient temperature;

[0024] σ is dynamically adjusted according to the pressure volatility, and the adjustment rule is: 。

[0025] The quantum improved optimization algorithm formula has the following functions:

[0026] 1. The tanh function converts the fluid kinetic energy into the search step size to achieve physical constraint optimization;

[0027] 2. The sigmoid function dynamically adjusts the perturbation amplitude to balance exploration and exploitation.

[0028] Effect: When the pressure volatility ΔP / Δt > 2 MPa / s, σ automatically increases to 0.3, enhancing the ability of the algorithm to jump out of the local optimum.

[0029] In a preferred example, the present invention can be further configured as: The dual-mode energy recovery device satisfies:

[0030] Flywheel energy storage component: Made of carbon fiber, the rotational speed is adjustable from 0 to 30,000 rpm, and the vacuum chamber pressure ≤ 1× Pa;

[0031] Piezoelectric power generation array: 128 d33-type piezoelectric units, arranged in a honeycomb pattern in the turbulent area of the pipe wall;

[0032] Mode switching condition: When the pressure fluctuation coefficient CV > 0.15, the piezoelectric mode is enabled; when CV ≤ 0.15, the flywheel mode is enabled.

[0033] In a preferred example, the present invention can be further configured as: The multi-scale feature analysis module includes:

[0034] Spatial scale: 5 mm local flow field feature (3×3×3 convolution kernel) and 200 mm global feature (dilated convolution dilation = 4);

[0035] Temporal scale: 10 ms dynamic feature (LSTM unit) and 10 min trend feature (self-attention mechanism);

[0036] Feature fusion method: Deformable convolution for dynamic weighting.

[0037] In a preferred example, the present invention can be further configured as: The fault propagation model satisfies the spatio-temporal evolution equation: , where is the fault feature tensor, is the device degradation source term;

[0038] is the spatio-temporal diffusion coefficient matrix, and the calculation method is: , where is the material attenuation coefficient, is the time decay factor, is the vibration spectrum matrix.

[0039] Using the fault propagation equation and diffusion coefficient has the following effects:

[0040] 1. Tensor operations characterize the propagation path of faults in multi-dimensional space;

[0041] 2. The ReLU function filters out invalid vibration spectrum components.

[0042] Function: When ‖∇V‖2 > 5 m / s², automatically enhance the diffusion coefficient calculation frequency (from 1 Hz to 10 Hz).

[0043] In a preferred example, the present invention can be further configured as: The forward propagation formula of the multi-scale feature analysis module is: , where ⊕ represents channel attention weighted splicing, and DWT is the discrete wavelet transform layer.

[0044] Using the dynamic optimization weight mechanism has the following functions:

[0045] 1. Focus on energy efficiency during peak periods and extend the equipment life during off-peak periods;

[0046] 2. Linear gradient avoids sudden changes in control instructions.

[0047] Effect: When the weight is switched, the energy recovery device synchronously adjusts the flywheel inertia (inertia change rate ≤ 5% / min).

[0048] In a preferred example, the present invention can be further configured as: The cloud platform diagnosis system achieves:

[0049] Digital twin mirror delay < 80 ms;

[0050] Parallel deduction of 16 fault scenarios;

[0051] Virtual sensor calibration error compensation rate ≥ 92%.

[0052] In a preferred example, the present invention can be further configured as: It further includes abnormal condition handling, including:

[0053] (1) When a water hammer effect is detected (pressure rise rate > 10 MPa / s):

[0054] Start the pufferfish-inspired expansion protection mechanism within 0.5 s

[0055] The flywheel speed is increased to 28,000 rpm to absorb the impact energy;

[0056] (2) Activate the sharkskin-inspired micro-groove flow field regulation under cavitation conditions.

[0057] The adoption of a bio - bionic emergency control strategy has the following functions:

[0058] 1. Form a pressure buffer layer by imitating the pufferfish mechanism (the peak pressure is reduced by 42%);

[0059] 2. Inhibit the development of cavitation by imitating the shark - skin structure (the cavitation volume is reduced by 67%).

[0060] Effect: In the emergency state, the energy recovery device switches to the maximum power mode (the energy storage capacity is instantaneously increased by 15%).

[0061] In a preferred example of the present invention, it can be further configured as follows: The application of the above - mentioned system in intelligent water service is integrated with the SCADA system through the IEC 61850 standard protocol and supports the OPC UA data encapsulation format.

[0062] The above - mentioned technical solution of the present invention has the following beneficial technical effects:

[0063] 1. A dual - mode energy recovery device is adopted in the present invention: controlling optimization energy conservation and energy recovery energy conservation, with a comprehensive energy - saving rate of 33.7%. The flywheel - piezoelectric composite energy storage system achieves an energy conversion efficiency of 82%, which is 19.6% higher than that of the traditional single - mode.

[0064] 2. An intelligent optimization controller is adopted in the present invention to shorten the strategy generation time from the traditional 120 ms to 45 ms. And the 100 - Hz high - speed sampling of the heterogeneous sensing array, combined with the edge - computing module, realizes a full - link response at the 238 - ms level.

[0065] 3. A cloud - platform diagnosis system is adopted in the present invention to give an early warning of mechanical failures 35 ± 8 minutes in advance, with an accuracy rate of 98.3% (83.5% for the traditional scheme). The multi - scale feature analysis reduces the false - alarm rate from 8.6% to 1.3%.

[0066] 4. The integration using the OPC UA protocol in the present invention makes the communication delay between modules < 80 ms, and the digital - twin mirror realizes the collaborative error of multiple devices ≤ 2.7%.

[0067] 5. A heterogeneous sensing array is adopted in the present invention. By using the bionic control mechanism, the peak value of the water - hammer pressure is reduced by 42% (from 8.7 MPa to 5.1 MPa), and the cavitation volume is reduced by 67% (from 12.5 cm³ to 4.1 cm³).

[0068] 6. In the multi - scale feature analysis module of the present invention, the LSTM prediction model extends the equipment life by more than 40% (the bearing replacement cycle is extended from 12 months to 17 months), and the self - calibration system reduces the manual inspection frequency by 60% (from 3 times a week to 1.2 times). Brief Description of the Drawings

[0069] Figure 1 This is the integrated water pump energy-saving control system diagram of the present invention. Specific implementation manners

[0070] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with specific implementation manners and with reference to the accompanying drawings. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0071] It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention.

[0072] The following describes an integrated water pump energy-saving control system provided by some embodiments of the present invention with reference to the accompanying drawings.

[0073] Combined Figure 1 As shown, an integrated water pump energy-saving control system provided by the present invention includes: a heterogeneous sensing array, an intelligent optimization controller, a dual-mode energy recovery device, a multi-scale feature analysis module, and a cloud platform diagnosis system;

[0074] The heterogeneous sensing array includes a pressure gradient sensor, a multi-frequency acoustic fingerprint sensor, and an electromagnetic-ultrasonic composite flowmeter. Among them, the pressure gradient sensors are arranged at intervals of 0.5D along the axial direction of the pipeline, the acoustic fingerprint sensors are configured with Helmholtz resonators, and the composite flowmeter automatically switches between electromagnetic / ultrasonic measurement modes according to the Reynolds number;

[0075] The intelligent optimization controller is built-in with an improved vulture optimization algorithm, integrates a quantum behavior decision-making unit and a fluid dynamics constraint module, and establishes an OPC UA data channel with a 20ms cycle with the sensing array;

[0076] The dual-mode energy recovery device includes a carbon fiber flywheel energy storage component and a piezoelectric power generation array, and automatically switches the energy storage mode through the pressure fluctuation coefficient;

[0077] The multi-scale feature analysis module adopts a 3D-CNN + Transformer hybrid architecture to achieve feature fusion at a spatial scale of 5mm - 200mm and a time scale of 10ms - 10min;

[0078] The cloud platform diagnosis system constructs a fault propagation model based on tensor chain decomposition and outputs the fault probability distribution for the next 30 minutes;

[0079] Each module realizes data interaction through the industrial Internet of Things protocol.

[0080] Specifically, by constructing an intelligent control system including the entire chain of perception - decision - execution - diagnosis, this solution has the following functions:

[0081] 1. Heterogeneous sensing enables collaborative sensing of multiple physical fields (pressure field + sound field + flow field);

[0082] 2. Quantum optimization algorithm improves the adaptability to complex working conditions;

[0083] 3. Dual-mode energy recovery breaks through the efficiency bottleneck of traditional single energy storage.

[0084] Effect: When the voiceprint sensor detects an abnormal frequency, it triggers the optimization controller to adjust the flywheel speed, and at the same time, the cloud platform starts directional fault diagnosis.

[0085] Furthermore, in the heterogeneous sensing array:

[0086] The pressure gradient sensors are arranged at intervals of 0.5D along the pipe axis, with a measurement range of 0 - 2.5 MPa and a temperature compensation range of -40°C to 125°C, where D is the pipe diameter;

[0087] The voiceprint sensor is configured with a Helmholtz resonance cavity, and the frequency domain resolution is ≤1 Hz;

[0088] The composite flowmeter enables the ultrasonic mode when the Reynolds number Re > 2300, and enables the electromagnetic mode when Re ≤ 2300.

[0089] This scheme has the following functions by defining the specific implementation parameters of the heterogeneous sensing array:

[0090] 1. Arrangement at intervals of 0.5D ensures the measurement accuracy of the pressure gradient (error < 0.8%);

[0091] 2. The Helmholtz resonance cavity enhances the signal-to-noise ratio in the 20 - 200 Hz frequency band (an increase of 15 dB);

[0092] 3. Reynolds number adaptive measurement eliminates turbulent interference;

[0093] Effect: The composite flowmeter cross-verifies data with the pressure sensor, and when the deviation > 5%, it triggers system self-calibration.

[0094] Furthermore, the improved vulture optimization algorithm includes a quantum tunneling search strategy, and its parameter update formula is: , where represents the latest parameter update value, represents the optimal parameter value, σ is the perturbation coefficient, V is the flow velocity, is the barrier height, k is the Boltzmann constant, and T is the ambient temperature;

[0095] σ is dynamically adjusted according to the pressure volatility, and the adjustment rule is: , where △P is the pressure change amount, and △ is the time change amount.

[0096] The adoption of the quantum improved optimization algorithm formula has the following effects:

[0097] 1. The tanh function converts the fluid kinetic energy into the search step size to achieve physical constraint optimization;

[0098] 2. The sigmoid function dynamically adjusts the perturbation amplitude to balance exploration and exploitation.

[0099] Effect: When the pressure volatility ΔP / Δt > 2 MPa / s, σ automatically increases to 0.3, enhancing the ability of the algorithm to jump out of the local optimum.

[0100] Furthermore, the dual-mode energy recovery device satisfies:

[0101] Flywheel energy storage component: Made of carbon fiber, with adjustable rotational speed of 0 - 30,000 rpm and vacuum chamber pressure ≤ 1× Pa;

[0102] Piezoelectric power generation array: 128 d33-type piezoelectric units, arranged in a honeycomb pattern in the turbulent flow area of the pipe wall;

[0103] Mode switching condition: When the pressure fluctuation coefficient CV > 0.15, the piezoelectric mode is enabled; when CV ≤ 0.15, the flywheel mode is enabled.

[0104] 1. The carbon fiber flywheel realizes high-density energy storage (120 Wh / kg);

[0105] 2. The honeycomb structure of the piezoelectric array improves the turbulent energy capture efficiency (18% higher than the traditional arrangement).

[0106] Effect: When CV > 0.15, the piezoelectric module and the feature analysis module cooperate to improve the power generation efficiency through vibration frequency matching.

[0107] Furthermore, the multi-scale feature analysis module includes:

[0108] Spatial scale: 5 mm local flow field features (3×3×3 convolution kernel) and 200 mm global features (dilated convolution with dilation = 4);

[0109] Temporal scale: 10 ms dynamic features (LSTM unit) and 10 min trend features (self-attention mechanism);

[0110] Feature fusion method: Deformable convolution for dynamic weighting.

[0111] By establishing a multi-scale feature fusion architecture, it has the following effects:

[0112] 1. Dilated convolution captures long-range flow field correlations (the receptive field is expanded by 4 times);

[0113] 2. Deformable convolution adaptively focuses on key regions (computing resources saved by 32%);

[0114] Effect: When a transient anomaly is detected in the LSTM cell, the sampling density of the dilated convolution is automatically increased.

[0115] Furthermore, the fault propagation model satisfies the spatio-temporal evolution equation: , where is the fault evolution rate, is the diffusion term, is the fault feature tensor, is the device degradation source term;

[0116] is the spatio-temporal diffusion coefficient matrix, and the calculation method is: , where is the material attenuation coefficient, is the time attenuation factor, is the vibration spectrum matrix.

[0117] Adopting the fault propagation equation and diffusion coefficient calculation has the following effects:

[0118] 1. Tensor operations represent the propagation path of faults in multi-dimensional space;

[0119] 2. The ReLU function filters out invalid vibration spectrum components.

[0120] Function: When ‖∇V‖2 > 5 m² / s, the diffusion coefficient calculation frequency is automatically increased (from 1 Hz to 10 Hz).

[0121] Furthermore, the forward propagation formula of the multi-scale feature analysis module is: , using the time series algorithm, is the input data at time t, where ⊕ represents channel attention weighted splicing, and DWT is the discrete wavelet transform layer.

[0122] Adopting the dynamic optimization weight mechanism has the following functions:

[0123] 1. Focus on energy efficiency during peak periods and extend the device life during off-peak periods;

[0124] 2. Linear gradient to avoid sudden changes in control instructions.

[0125] Effect: When the weight is switched, the energy recovery device synchronously adjusts the flywheel inertia (inertia change rate ≤ 5% / min).

[0126] Furthermore, the cloud platform diagnostic system achieves:

[0127] Digital twin mirror delay < 80 ms;

[0128] Deduce 16 fault scenarios in parallel;

[0129] The calibration error compensation rate of the virtual sensor is ≥ 92%.

[0130] The diagnostic function parameters through the cloud platform have the following functions:

[0131] 1. The digital twin mirror realizes real-time status visualization;

[0132] 2. The multi-scenario deduction anticipates system risks.

[0133] Effect: When the virtual sensor detects an anomaly, it automatically schedules on-site robots for physical verification.

[0134] Furthermore, it also includes the disposal of abnormal working conditions, including:

[0135] (1) When the water hammer effect is detected (pressure rise rate > 10 MPa / s):

[0136] Start the pufferfish-inspired inflation protection mechanism within 0.5 s

[0137] Increase the flywheel speed to 28,000 rpm to absorb the impact energy;

[0138] (2) Activate the sharkskin-inspired micro-groove flow field regulation under cavitation conditions;

[0139] The biological bionic emergency control strategy has the following functions:

[0140] 1. The pufferfish-inspired mechanism forms a pressure buffer layer (peak pressure reduced by 42%);

[0141] 2. The sharkskin-inspired structure inhibits the development of cavitation (cavity volume reduced by 67%).

[0142] Effect: In the emergency state, the energy recovery device switches to the maximum power mode (instantly increasing the energy storage capacity by 15%).

[0143] Furthermore, the application of this system in intelligent water services is integrated with the SCADA system through the IEC 61850 standard protocol and supports the OPC UA data encapsulation format.

[0144] The integration using industrial standard protocols has the following functions:

[0145] 1. IEC 61850 ensures seamless connection with the power grid dispatching system;

[0146] 2. OPC UA enables cross-platform data intercommunication.

[0147] Effect: When the power grid demand response instruction is issued, the system can complete the operation mode switch within 30 s.

[0148] In a specific embodiment, such as the implementation in the municipal water supply scenario:

[0149] 1. Deploy 3 groups of sensing arrays on the DN800 main pipe, with a spacing of 300m;

[0150] 2. Optimize the controller parameter settings:

[0151] Number of qubits: 128

[0152] Population size: 50

[0153] Maximum number of iterations: 200;

[0154] 3. Fault prediction test results:

[0155] Fault type Early warning lead Accuracy rate Bearing wear 41 min 98.2% Impeller cavitation 28 min 96.7%

[0156] Experimental results:

[0157] 33.7% more energy-efficient than the traditional PID system (annual power saving of 182,000 kWh);

[0158] The false alarm rate of faults is reduced to 1.3% (industry average of 8.6%);

[0159] The response time under extreme working conditions is shortened to 238 ms (850 ms for the traditional system).

[0160] The functions of each module of this application are as follows:

[0161] Heterogeneous sensing array: Real-time detection of abnormal working condition characteristic signals;

[0162] Intelligent optimization controller: Generate bionic control strategies;

[0163] Dual-mode energy recovery device: Execute emergency response at the physical level;

[0164] Multi-scale feature analysis module: Verify the disposal effect and dynamically optimize;

[0165] Cloud platform diagnostic system: Provide long-term strategy support.

[0166] The technical synergy effect is as follows:

[0167] Detect water hammer effect (pressure rise rate > 10 MPa / s) Heterogeneous sensing array (pressure gradient sensor) Increase the sampling rate of the pressure sensor to 10 kHz to ensure transient capture Activate the pufferfish-inspired expansion protection mechanism Intelligent optimization controller (algorithm model library) Call the computational fluid dynamics simulation model to generate expansion buffer layer parameters Increase the flywheel speed to 28,000 rpm Dual-mode energy recovery device (flywheel assembly) The inertial moment J of the carbon fiber flywheel is 12.5 kg·m², which can absorb impact energy E = ½Jω² ≈ 1.2 MJ Sharkskin-inspired micro-groove regulation Multi-scale feature analysis module (flow field reconstruction) Identify the cavitation area through 3D-CNN and generate the groove arrangement density function ρ(x,y,z) = a·exp(-b·∇P²)

[0168] Comprehensive technical effect verification data:

[0169] Index Traditional system This invention Improvement rate Test standard Unit water power consumption 0.48 kWh / m³ 0.32 kWh / m³ 33.3%↓ GB / T 30256-2013 Fault early warning lead time 18 min 35 min 94.4%↑ ISO 13379-2012 Extreme condition recovery time 5.8s 2.1s 63.8%↓ ANSI / HI 9.6.7 System availability 92.7% 99.1% 6.4%↑ IEC 61010-1

[0170] Targeted classification data is as follows:

[0171] 1. Energy efficiency improvement effect:

[0172] Effect description Experimental data Test method Comprehensive energy saving rate of 33.7% Traditional system: 0.48 kWh / m³ This invention: 0.32 kWh / m³ Continuous operation for 720 hours according to the GB / T 30256 standard Energy recovery efficiency of 82% Piezoelectric mode: 68% Flywheel mode: 75% Dual-mode cooperation: 82% Measure using a FLUKE 438-II power quality analyzer

[0173] 2. Control Response Optimization:

[0174] Effect description Experimental data Test method Strategy generation time of 45 ms Traditional PID: 120 ms Fuzzy control: 85 ms This invention: 45 ms Use an oscilloscope to capture the time delay from the input step to the output response Extreme condition response of 238 ms Detection → Decision → Execution full-link time delay: Traditional system 850 ms → This invention Simulate the water hammer condition (pressure rise rate of 15 MPa / s)

[0175] 3. Fault Prediction Breakthrough:

[0176] Effect description Experimental data Test method Fault early warning advance of 35 min Bearing wear early warning: Traditional solution 18 min → This invention 41 min Inject a 0.1 mm gap fault simulation Accuracy rate of 98.3% Traditional model 83.5% → This invention 98.3% (17 false alarms → 2 false alarms in 1000 tests) CNAS certification by the National Pump and Valve Testing Center

[0177] 4. System Synergy Enhancement:

[0178] Effect description Experimental data Test method Communication latency < 80 ms Inter-module data transmission time delay: Traditional CAN bus 210 ms → This invention OPC UA 76 ms Wireshark packet capture analysis Cooperation error ≤ 2.7% Flow rate deviation during multi-pump parallel operation: Traditional 12% → This invention 2.7% Multi-point synchronous measurement using an ultrasonic flowmeter

[0179] 5. Extreme Working Condition Protection:

[0180] Effect description Experimental data Test method Water hammer peak pressure drop of 41.4% Traditional system 8.7 MPa → This invention 5.1 MPa Record using a transient pressure sensor (Kistler 601C) Cavitation bubbles reduced by 67% Bubble volume: Traditional 12.5 cm³ → This invention 4.1 cm³ Observation by high-speed camera (Photron SA-Z)

[0181] 6. Operation and Maintenance Cost Reduction:

[0182] Effect description Experimental data Test method The equipment life is extended by 40% Bearing replacement cycle: traditional 12 months → 17 months in this invention Industrial site tracking record (12 pumps in 3 water plants) Manual inspection is reduced by 60% Inspection frequency: traditional 3 times / week → 1.2 times / week in this invention Statistical analysis of operation and maintenance logs

[0183] Commercial Value:

[0184] Index Calculation basis Estimated annual income Electricity cost savings Water plant with a daily output of 100,000 tons × price difference of 0.16 kWh / m³ × ￥0.8 / kWh ￥1.26 million Reduction of maintenance cost Reduction of downtime loss of ￥2.3 million / year + labor cost of ￥0.58 million / year ￥2.88 million

[0185] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. An integrated energy-saving control system for a water pump, characterized in that, Including: Heterogeneous sensing array, intelligent optimization controller, dual-mode energy recovery device, multi-scale feature analysis module and cloud platform diagnosis system; The heterogeneous sensing array includes a pressure gradient sensor, a multi-frequency acoustic fingerprint sensor and an electromagnetic-ultrasonic composite flowmeter. The pressure gradient sensors are arranged at intervals of 0.5D along the axial direction of the pipeline, where D is the pipe diameter. The acoustic fingerprint sensor is configured with a Helmholtz resonator. The composite flowmeter automatically switches between electromagnetic / ultrasonic measurement modes according to the Reynolds number; The intelligent optimization controller is built-in with an improved vulture optimization algorithm, integrated with a quantum behavior decision unit and a hydrodynamic constraint module, and establishes an OPC UA data channel with a period of 20 ms with the sensing array; The dual-mode energy recovery device includes a carbon fiber flywheel energy storage component and a piezoelectric power generation array, and automatically switches the energy storage mode through the pressure fluctuation coefficient. The dual-mode energy recovery device satisfies: Flywheel energy storage component: made of carbon fiber, adjustable rotational speed from 0 to 30,000 rpm, vacuum chamber pressure ≤ 1× Pa; Piezoelectric power generation array: 128 d33-type piezoelectric units, arranged in a honeycomb pattern in the turbulent zone of the pipe wall; Mode switching condition: When the pressure fluctuation coefficient CV > 0.15, the piezoelectric mode is enabled; when CV ≤ 0.15, the flywheel mode is enabled; The multi-scale feature analysis module adopts a 3D-CNN+Transformer hybrid architecture to achieve feature fusion at spatial scales of 5 mm - 200 mm and time scales of 10 ms - 10 min. The multi-scale feature analysis module includes: Spatial scale: 5 mm local flow field features and 200 mm global features; Time scale: 10 ms dynamic features and 10 min trend features; Feature fusion method: deformable convolution with dynamic weighting; when the LSTM unit detects a transient anomaly, automatically increase the sampling density of the dilated convolution; the forward propagation formula of the multi-scale feature analysis module is: , where ⊕ represents channel attention weighted splicing, and DWT is the discrete wavelet transform layer; The cloud platform diagnosis system constructs a fault propagation model based on tensor chain decomposition and outputs the fault probability distribution for the next 30 min; Each module realizes data interaction through the industrial Internet of Things protocol.

2. The integrated water pump energy-saving control system according to claim 1, characterized in that, In the heterogeneous sensing array: The pressure gradient sensors are arranged at intervals of 0.5D along the axial direction of the pipeline, with a measurement range of 0 - 2.5 MPa and a temperature compensation range of -40°C to 125°C, where D is the pipe diameter; The acoustic fingerprint sensor is configured with a Helmholtz resonator, and the frequency domain resolution ≤ 1 Hz; The composite flowmeter enables the ultrasonic mode when the Reynolds number Re > 2300 and the electromagnetic mode when Re ≤ 2300.

3. An integrated water pump energy-saving control system according to claim 1, characterized in that, The improved vulture optimization algorithm includes a quantum tunneling search strategy, and its parameter update formula is: = ·(1 + σ·tanh( )) where σ is the perturbation coefficient, is the flow velocity, is the barrier height, k is the Boltzmann constant, and T is the ambient temperature; σ is dynamically adjusted according to the pressure volatility, and the adjustment rule is: σ = 0.1 + 0.2 · sigmoid(5 - 2).

4. An integrated water pump energy-saving control system according to claim 1, characterized in that The described fault propagation model satisfies the spatio-temporal evolution equation: , where is the fault feature tensor, is the device degradation source term; is the spatio-temporal diffusion coefficient matrix, and the calculation method is: , where is the time decay factor, is the vibration spectrum matrix.

5. An integrated water pump energy-saving control system according to claim 1, characterized in that, The cloud platform diagnosis system realizes: Digital twin mirror delay < 80 ms; Parallel deduction of 16 fault scenarios; Virtual sensor calibration error compensation rate ≥ 92%.

6. An integrated water pump energy-saving control system according to claim 1, characterized in that, It also includes abnormal condition handling, including: (1) When the water hammer effect is detected and the pressure rise rate > 10 MPa / s: Start the imitation pufferfish inflation protection mechanism within 0.5 s; The flywheel speed is increased to 28,000 rpm to absorb the impact energy; (2) Activate the imitation shark skin micro-groove flow field regulation under cavitation conditions.

7. Use of the system according to any one of claims 1-6 in intelligent water services, characterized in that Integrated with the SCADA system through the IEC 61850 standard protocol, supporting the OPC UA data encapsulation format.

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