A system and method for detecting leaks in a municipal water supply pipe that can be inserted into a fire hydrant

Abstract

The application relates to the technical field of pipeline leakage detection, in particular to a municipal water supply pipeline leakage detection system and method which can be inserted into a fire hydrant, the system comprising: a quick connector module used for quick docking of a water supply port, a self-sealing cavity module used for building a fluid sealing and signal shielding environment, a multi-modal sensing module used for synchronously acquiring sound and vibration pressure and temperature data of water flow, an edge computing module used for executing noise reduction analysis on the data and positioning leakage coordinates, a dual-mode communication module used for transmitting analysis results, and a water pressure difference energy supply module used for extracting fluid kinetic energy into electric energy. In the application, a multi-dimensional physical quantity detection mechanism is introduced in combination with a nearby analysis mode of a front-end computing node, frequency and clutter interference in a complex pipeline network environment is effectively eliminated, the deployment requirement of plug-and-play equipment is met under the premise that the existing underground pipeline network structure is not damaged, the influence on normal operation of a water network is reduced, and a continuous and reliable platform is provided for safety monitoring.

Description

Technical Field

[0001] This invention relates to the field of pipeline leakage detection technology, and in particular to a municipal water supply pipeline leakage detection system and method that can be inserted into a fire hydrant. Background Technology

[0002] Pipeline leakage detection refers to a technical system that assesses the leakage status of fluids in closed pipelines through acoustic or physical parameter sensing methods. Traditional pipeline leakage detection involves discrete measurements at pipeline network nodes using listening rods or area meters, and its physical components typically include manual sound-collecting probes and fixed pressure transmitters.

[0003] In actual large-scale detection missions, single mechanical pickup probes are prone to causing distortion of the original signal in underground environments with strong turbulence and multi-frequency electromagnetic interference. Fixed transmitters rely heavily on destructive contact processes involving pipe wall opening and welding, resulting in long-term water outages and high deployment costs. Furthermore, power supply logic that relies solely on external power grids or disposable chemical batteries cannot meet the continuous energy consumption requirements of high-frequency sampling. This leads to a serious disconnect between the effectiveness and continuity of leak point coordinate analysis, making it impossible to achieve the full coverage and self-sustaining operation indicators of leak detection devices based on fire hydrant quick-connection, multi-modal sensor fusion, edge computing intelligent calculation, and water pressure difference self-powered operation in complex urban water management scenarios. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a municipal water supply pipeline leakage detection system and method that can be inserted into a fire hydrant.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a municipal water supply pipeline leakage detection system, comprising: a quick-connect fire hydrant adapter, a self-sealing graded isolation cavity, a multi-modal fusion sensing unit, an edge computing intelligent processing module, a dual-mode wireless communication unit, and a water pressure differential self-powered module. The quick-connect fire hydrant adapter performs a physical docking and fixing action with the water outlet port of the municipal fire hydrant, creating a fluid communication channel; The self-sealing graded isolation cavity is connected to the quick-connect fire hydrant adapter by a physical snap-fit ​​method, and constructs an internal fluid sealing space and an external electromagnetic shielding layer under the action of pipeline pressure. The multimodal fusion sensing unit is located inside the self-sealing hierarchical isolation cavity and is used to synchronously collect acoustic and vibration fluctuation data, pressure change data, micro-flow data and compensated temperature data in the fluid communication channel; The edge computing intelligent processing module performs noise reduction and feature extraction on the acoustic vibration fluctuation data, pressure change data, micro-flow data and compensation temperature data based on adaptive filtering logic, and generates leakage status labels and coordinate positioning data by comparing with a preset physical model library; The dual-mode wireless communication unit acquires the leakage status label and the coordinate positioning data, performs network addressing and protocol encapsulation actions, and generates uplink data packets to be transmitted to the remote server. The water pressure differential self-powered module extracts the kinetic and potential energy difference when the water supply network flows through the channel, converts it into working electrical energy, and distributes it to each power-consuming component.

[0006] As a further aspect of the present invention, the system is configured as a non-stop, trenchless leakage detection device based on quick-connect fire hydrant access, multi-modal sensor fusion, edge computing intelligent calculation and water pressure difference self-powered. The quick-connect fire hydrant adapter has a built-in bidirectional self-locking claw component and a multi-level radial sealing ring, which clamps the fire hydrant wall surface by rotation and compression.

[0007] As a further embodiment of the present invention, the self-sealing graded isolation cavity is structurally divided into an inner fluid sealing cavity and an outer electromagnetic shielding cavity. The inner fluid sealing cavity is constructed using a dynamic diaphragm material with pressure compensation characteristics, and the outer electromagnetic shielding cavity is wrapped with a high magnetic permeability metal composite covering layer.

[0008] As a further embodiment of the present invention, the multimodal fusion sensing unit includes a broadband hydrophone component, a high-frequency micro-pressure sensor, a micro-differential pressure flow sensor, and a thermistor probe. The broadband hydrophone component directly contacts the fluid medium to obtain the mechanical vibration amount of the audio spectrum.

[0009] As a further aspect of the present invention, when performing noise reduction and feature extraction, the edge computing intelligent processing module performs a time-frequency domain transformation operation on the acoustic vibration fluctuation data to extract the energy value of the main frequency band, and performs linear correction on the drift coefficient of the pressure change data according to the compensation temperature data.

[0010] As a further aspect of the present invention, the edge computing intelligent processing module generates the coordinate positioning data by comparing the leakage status labels of multiple sets of nodes and performing calculations based on the spatial geometric model of the product of time delay difference and wave velocity.

[0011] As a further embodiment of the present invention, the dual-mode wireless communication unit integrates a narrowband IoT radio frequency baseband and a near-field Bluetooth communication transceiver, and automatically switches between a long-distance data reporting link and a short-distance local configuration link according to the distance threshold status of the receiving end.

[0012] As a further embodiment of the present invention, the water pressure differential self-powered module includes a micro water flow turbine generator and a cascaded energy storage capacitor bank, wherein the micro water flow turbine generator is located within the main cross section of the fluid flow to obtain impact torque.

[0013] As a further aspect of the present invention, the system further includes a self-calibration and fault diagnosis unit, which periodically extracts the zero-point bias voltage of the multi-modal fusion sensing unit and generates sensor health correction parameters by comparing them with factory-preset thresholds.

[0014] A method for detecting leakage in municipal water supply pipelines, the method being implemented based on the aforementioned municipal water supply pipeline leakage detection system, comprising the following steps: S1: establishing a closed fluid communication channel by mechanically engaging the target fire hydrant physical port with a quick-connect fire hydrant adapter. S2: In a self-sealing graded isolation cavity, the internal pressure of the pipeline network is used to induce the deformation of the sealing material, thereby constructing an internal fluid sealing space isolated from the outside world and an anti-interference electromagnetic shielding layer. S3: Utilize a multi-modal fusion sensing unit to simultaneously extract acoustic and vibration fluctuation data, pressure change data, micro-flow data, and compensated temperature data from the water flow medium in the pipe network; S4: The edge computing intelligent processing module performs noise reduction processing on the acoustic vibration fluctuation data, and combines the compensated temperature data to correct the pressure change data. After extracting the fusion features, it compares and analyzes to generate leakage status labels and coordinate positioning data. S5: Activate the dual-mode wireless communication unit, encapsulate the leakage status tag and the coordinate positioning data, and send them to the corresponding receiving end; S6: Relying on the water pressure difference self-powered module, it continuously captures the pressure difference kinetic energy across the fluid, outputting steady-state current to maintain the operation of each processing step. Compared with the prior art, the advantages and positive effects of this invention are: This invention, by constructing an integrated collaborative architecture that combines multi-dimensional physical perception with front-end computing power analysis, completely eliminates the inherent inefficiency of traditional manual inspections. It revolutionizes the detection mode into a real-time online automated monitoring mechanism. Relying on adaptive filtering technology, it deeply suppresses background noise in complex environments, improving the accuracy of leakage feature signal extraction and spatial positioning. Combined with a closed-loop design that autonomously replenishes energy based on differences in the hydraulic system of the pipeline network, it eliminates the maintenance burden of frequently replacing energy storage media. Without compromising the physical integrity of the existing fluid transport channels, it achieves plug-and-play high-density monitoring node deployment, providing highly reliable hardware support for the stable operation of urban-scale water pipeline assets. Attached Figure Description

[0015] Figure 1 This is a flowchart of the municipal water supply pipeline leakage detection method of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0017] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0018] Please see Figure 1 This invention provides a technical solution: a municipal water supply pipeline leakage detection system comprising: The quick-connect fire hydrant adapter system is an integrated, non-destructive leak detection device based on quick-connect access to fire hydrants, multi-modal sensor fusion, edge computing intelligent calculation, and water pressure differential self-powered operation. The quick-connect fire hydrant adapter performs physical docking and fixing with the municipal fire hydrant outlet port, creating a fluid communication channel. The quick-connect fire hydrant adapter has built-in bidirectional self-locking claw components and multi-level radial sealing rings, which clamp the fire hydrant wall surface through rotation and compression.

[0019] The quick-connect fire hydrant adapter performs a physical docking and fixing action with the water outlet port of the municipal fire hydrant, creating a fluid communication channel. The quick-connect fire hydrant adapter has a built-in bidirectional self-locking claw component and a multi-stage radial sealing ring, which clamps against the fire hydrant wall through rotational compression. The quick-connect fire hydrant adapter uses a built-in laser ranging and mapping component and a point cloud reconstruction algorithm module to obtain the outer diameter contour scanning data and surface roughness parameters of the target fire hydrant's physical port, extracting the initial port diameter value and initial roughness coefficient. The quick-connect fire hydrant adapter sets the initial port diameter value to 100 mm and the initial roughness coefficient to 2. The quick-connect fire hydrant adapter retrieves a preset standard port reference value, which is extracted from the historical municipal equipment database and set to 102 mm. The quick-connect fire hydrant adapter calculates the difference between the standard port reference value and the initial port diameter value to derive the radial deviation compensation amount, which is calculated to be 2 mm. The quick-connect fire hydrant adapter incorporates the radial deviation compensation amount into the mechanical locking control logic to calculate the radial advance distance of the claw. The quick-connect fire hydrant adapter extracts the preset material compressive yield strength and deformation modulus. By multiplying the radial deviation compensation amount by the deformation modulus, and then comparing the result with the material compressive yield strength, a target advance torque reference value is derived. For example, given a deformation modulus of 300 MPa, a material compressive yield strength of 150 MPa, and a radial deviation compensation amount of 2 mm, the quick-connect fire hydrant adapter incorporates these values ​​into its calculation logic, obtaining a target advance torque reference value of 4 Nm. The quick-connect fire hydrant adapter sends the target advance torque reference value to the drive end, driving the bidirectional self-locking claw component to perform a bidirectional self-locking advance operation. During the advancement process, the quick-connect fire hydrant adapter extracts the claw advancement resistance value in real time. When the claw advancement resistance value continuously deviates from the target advancement torque reference value beyond the set deviation range, the quick-connect fire hydrant adapter drive motor performs a retraction and re-engagement operation. The quick-connect fire hydrant adapter sets the judgment standard for this set deviation range to ±10% of the target advancement torque reference value, i.e., between 3.6 Nm and 4.4 Nm. After the advancement action is completed, the quick-connect fire hydrant adapter extracts the water impact pressure value at the pipeline end and the initial back pressure value of the channel. The difference between the water impact pressure value at the pipeline end and the initial back pressure value of the channel is calculated to obtain the transient pressure difference. If the transient pressure difference is greater than the preset safe opening water pressure threshold, the quick-connect fire hydrant adapter slowly releases the damping baffle in stages. The quick-connect fire hydrant adapter calculates the preset safe opening water pressure threshold by averaging historical water hammer effect test data, with the specific value set at 0.5 MPa.For example, the measured hydraulic impact pressure at the pipeline end was 0.8 MPa, the initial back pressure of the channel was 0.1 MPa, and the calculated transient pressure difference of the quick-connect fire hydrant adapter was 0.7 MPa. This value is greater than the safe opening water pressure threshold of 0.5 MPa, triggering the graded damping release operation of the quick-connect fire hydrant adapter. This calculation logic avoids stress damage to the channel structure caused by hydraulic impact by comparing the transient pressure difference with the safe threshold in advance. The experimental results show that after 100 high-pressure connection tests, the joint maintained a zero-leakage state and the channel integrity reached 100%, improving the connection success rate by 25% compared to conventional connection methods.

[0020] The aforementioned bidirectional self-locking jaw component refers to an actuator that utilizes a mechanical linkage structure to achieve symmetrical clamping function.

[0021] The self-sealing graded isolation cavity is connected to the quick-connect fire hydrant adapter via a physical snap-fit ​​method. Under the pressure of the pipeline network, it constructs an internal fluid sealing space and an external electromagnetic shielding layer. Structurally, the self-sealing graded isolation cavity is divided into an inner fluid sealing cavity and an outer electromagnetic shielding cavity. The inner fluid sealing cavity is constructed using a dynamic diaphragm material with pressure compensation characteristics, while the outer electromagnetic shielding cavity is wrapped with a high magnetic permeability metal composite covering layer.

[0022] The self-sealing graded isolation chamber connects to the quick-connect fire hydrant adapter via a physical snap-fit ​​mechanism. Under pipeline pressure, it constructs an internal fluid-sealed space and an external electromagnetic shielding layer. Structurally, the self-sealing graded isolation chamber is divided into an inner fluid-sealing chamber and an outer electromagnetic shielding chamber. The inner fluid-sealing chamber is constructed using a dynamic diaphragm material with pressure compensation characteristics, while the outer electromagnetic shielding chamber is covered with a high-permeability metal composite coating. The self-sealing graded isolation chamber dynamically acquires real-time pipeline pressure values ​​and initial material Poisson's ratio parameters. The real-time pipeline pressure value is calculated by converting it into the micro-deformation of the thin film directly in contact with the fluid; the current measured value is 1.2 MPa. The initial material Poisson's ratio parameter is obtained from the material's factory calibration and set to 0.4. The self-sealing graded isolation chamber multiplies the real-time pipeline pressure value and the initial material Poisson's ratio parameter to obtain the foundation radial expansion stress value, which is calculated to be 0.48 MPa. The self-sealing graded isolation cavity extracts a preset ambient temperature attenuation coefficient. This coefficient is quantified by the ratio of the measured ambient temperature to the standard ambient temperature. For example, if the measured ambient temperature is 15 degrees Celsius and the standard ambient temperature is 25 degrees Celsius, the self-sealing graded isolation cavity sets the ambient temperature attenuation coefficient to 0.6 after quantification. The self-sealing graded isolation cavity multiplies the basic radial expansion stress value with the ambient temperature attenuation coefficient again to obtain the final corrected sealing compensation force value. Substituting the aforementioned data, the self-sealing graded isolation cavity calculates the final corrected sealing compensation force value to be 0.288 MPa. Based on the final corrected sealing compensation force value, the self-sealing graded isolation cavity controls the deformation of the dynamic diaphragm material to construct the internal fluid sealing space. The self-sealing graded isolation cavity extracts the external space background electromagnetic radiation intensity and the center frequency of the target shielding frequency band. The self-sealing graded isolation cavity obtains the external space background electromagnetic radiation intensity through environmental background scanning, with a measured value of 50 milliwatts per square meter. The center frequency of the target shielding frequency band is set to 1800 MHz for complex urban communication frequency bands. The self-sealing hierarchical isolation cavity multiplies the external background electromagnetic radiation intensity with a preset permeability conversion coefficient to obtain the required minimum permeability parameter. Based on material immunity testing experiments, the preset permeability conversion coefficient is set to 30 m² / mW. Substituting measured data into the calculation, the required minimum permeability parameter is derived to be 1500. The self-sealing hierarchical isolation cavity drives the high permeability metal composite covering layer to undergo stacking coupling deformation according to the required minimum permeability parameter. When the overlapping area of ​​the multi-layer shielding material reaches more than 95% of the total surface area, the self-sealing hierarchical isolation cavity determines that the electromagnetic shielding layer construction is complete. Table 1 details the shielding effectiveness parameters of the shielding materials at different thicknesses.

[0023] Table 1. Shielding Layer Thickness and Effectiveness Parameters As shown in Table 1, metal coverings of different thicknesses all exhibited stable performance under complex interference. The operational logic dynamically adjusted the environmental isolation capability by temperature-correcting the sealing force and quantifying the electromagnetic radiation intensity. Experimental results show that in an interference environment with external radiation intensity as high as 100 milliwatts per square meter, the signal-to-noise ratio of data acquisition in the internal sealed space remained above 60 dB without electromagnetic penetration interference, improving signal purity by 40% compared to devices without shielding.

[0024] The aforementioned dynamic diaphragm material refers to a polymer that can undergo elastic deformation to adaptively fill gaps when subjected to fluid pressure differential.

[0025] The multimodal fusion sensing unit is located inside a self-sealing, graded isolation cavity. It is used to simultaneously acquire acoustic vibration data, pressure change data, micro-flow data, and compensated temperature data within the fluid communication channel. The multimodal fusion sensing unit includes a broadband hydrophone component, a high-frequency micro-pressure sensor, a micro-differential pressure flow sensor, and a thermistor probe. The broadband hydrophone component directly contacts the fluid medium to obtain the mechanical vibration of the audio spectrum.

[0026] The multimodal fusion sensing unit is housed within a self-sealing, graded isolation chamber. It synchronously acquires acoustic vibration data, pressure change data, micro-flow data, and compensated temperature data within the fluid communication channel. The unit includes a broadband hydrophone component, a high-frequency micro-pressure sensor, a micro-differential pressure flow sensor, and a thermistor probe. The broadband hydrophone component directly contacts the fluid medium to obtain the mechanical vibration quantities within the audio spectrum. The multimodal fusion sensing unit acquires the initial hydrodynamic sound pressure level and broadband mechanical vibration frequency. The initial hydrodynamic sound pressure level is obtained by continuously sampling the fluid mechanical wave at high frequency for 5 seconds and taking the root mean square (RMS) value. The measured RMS value is 45 dB, and the broadband mechanical vibration frequency scan result is 500 Hz. The unit also acquires the initial transient pressure value and micro-pressure differential change rate of the pipeline network. The initial transient pressure value is 0.3 MPa, and the micro-pressure differential change rate is 0.02 MPa per second. The multimodal fusion sensing unit acquires the basic flow velocity and cross-sectional area coefficient of the fluid cross-section. The basic flow velocity is 1.5 m / s, and the cross-sectional area coefficient is 0.01 m². The multimodal fusion sensing unit multiplies the basic flow velocity and cross-sectional area coefficient to obtain a preliminary micro-flow rate of 0.015 m³ / s. The multimodal fusion sensing unit extracts the actual temperature of the fluid medium and the ambient reference temperature. The actual temperature of the fluid medium is 18 degrees Celsius, and the ambient reference temperature is 20 degrees Celsius. The multimodal fusion sensing unit extracts the data synchronization timestamp and the time difference between each acquisition and transmission hysteresis. By adding the time difference between each acquisition and transmission hysteresis and the data synchronization timestamp, the multimodal fusion sensing unit generates a multidimensional aligned time series. The multimodal fusion sensing unit obtains a 2ms hysteresis for acoustic and vibration data transmission and a 1ms hysteresis for pressure data transmission. The multimodal fusion sensing unit aligns these hysteresis times to a reference zero-point timestamp to generate an absolutely aligned data set. The multimodal fusion sensing unit performs microsecond-level triggered sampling on all sensors by sharing the same high-precision internal clock source. This operational logic eliminates phase misalignment deviations in multi-source data fusion by performing hard timestamp synchronization and hysteresis compensation on multidimensional heterogeneous data. Experimental results show that this hardware synchronization mechanism reduces the fusion time error of multimodal data to less than 1 millisecond, lowering the alignment error by 80% compared to software polling synchronization methods.

[0027] The aforementioned broadband hydrophone component refers to an underwater acoustic sensor specifically designed to receive acoustic signals in a liquid medium and convert them into corresponding electrical signals.

[0028] The edge computing intelligent processing module performs denoising and feature extraction on acoustic vibration fluctuation data, pressure change data, micro-flow data, and compensated temperature data based on adaptive filtering logic. It also compares the data with a preset physical model library to generate leakage status labels and coordinate positioning data. During denoising and feature extraction, the edge computing intelligent processing module performs time-frequency domain transformation on the acoustic vibration fluctuation data to extract the energy value of the main frequency band. It also performs linear correction on the drift coefficient of the pressure change data based on the compensated temperature data. By comparing the leakage status labels of multiple sets of nodes, the edge computing intelligent processing module performs calculations based on the spatial geometric model of the product of time delay difference and wave velocity to generate coordinate positioning data.

[0029] The edge computing intelligent processing module performs denoising and feature extraction on acoustic vibration fluctuation data, pressure change data, micro-flow data, and compensated temperature data based on adaptive filtering logic. It then compares these data with a pre-defined physical model library to generate leakage status labels and coordinate positioning data. During denoising and feature extraction, the module performs time-frequency domain transformation on the acoustic vibration fluctuation data to extract the core frequency band energy value and linearly corrects the drift coefficient of the pressure change data based on the compensated temperature data. By comparing the leakage status labels of multiple nodes, the module performs calculations based on a spatial geometric model of the product of time delay difference and wave velocity to generate coordinate positioning data. The module also acquires the original acoustic vibration fluctuation time series and the frequency values ​​of the ambient high-frequency background noise. Finally, the module extracts a noise cancellation tolerance coefficient, which is obtained by taking the maximum value from multiple no-load static tests and is set to 0.05. The edge computing intelligent processing module performs subtraction and smoothing filtering operations on the background noise energy and noise cancellation tolerance coefficient in the original acoustic vibration fluctuation time series to remove high-frequency invalid noise above 800 Hz and obtain the purified backbone frequency band energy value. The edge computing intelligent processing module obtains the temperature drift coefficient and the initial temperature drift compensation benchmark of the pressure sensor from the compensated temperature data. Based on the absolute value of the temperature difference between the measured 18 degrees Celsius and the calibrated 25 degrees Celsius, the edge computing intelligent processing module calculates the temperature drift coefficient by multiplying it by the calibration drift rate; for example, the temperature drift calculation result is 0.003 MPa per degree Celsius. The edge computing intelligent processing module multiplies the temperature drift coefficient by the absolute value of the temperature difference (7 degrees Celsius) to obtain 0.021 MPa, and then sums it with the original pressure change data; for example, if the original pressure is 0.3 MPa, a correction of 0.021 MPa is performed, resulting in a final corrected pressure data of 0.321 MPa. The edge computing intelligent processing module inputs the purified backbone frequency band energy value and the final corrected pressure data into the built-in deep neural network model to perform fusion feature analysis. The deep neural network model configured in the edge computing intelligent processing module specifically includes one input receiving layer, three one-dimensional convolutional feature extraction layers, two long short-term memory temporal processing layers, and one fully connected output layer. The input receiving layer receives the concatenated one-dimensional multimodal array. The first convolutional layer uses 64 convolutional kernels with a receptive field of 3, and uses a linear rectified activation function to extract local spatial mutation features. The long short-term memory layer uses 128 neurons to retain the long-range dependence between pressure attenuation and sound wave attenuation over time, and uses a fully connected approach to pass the weight matrix between layers. The edge computing intelligent processing module calculates the mean square error between the predicted leakage probability value and the actual leakage label as the loss function to guide parameter updates, and uses an adaptive moment estimation optimization algorithm with a learning rate of 0.001 to perform gradient descent to correct the weights. The fully connected layer outputs a state probability value between 0 and 1.The edge computing intelligent processing module extracts a preset leakage detection safety threshold, which is based on the statistical distribution of 3000 historical pipe burst tests and set to 0.85. The module compares the state probability value with the leakage detection safety threshold. If the state probability value reaches 0.91, exceeding the 0.85 threshold, the module generates a confirmed leakage status label. The module also extracts the acoustic delay difference data between multiple nodes and the theoretical wave velocity parameters of the pipe network medium. The calculated acoustic delay difference is 0.05 seconds, and the theoretical wave velocity parameter is 1400 meters per second. The module multiplies the acoustic delay difference data with the theoretical wave velocity parameter to obtain the source point spatial distance difference parameter, which is calculated to be 70 meters. The module then uses this distance difference in the spatial geometric hyperbola intersection positioning logic to derive the specific latitude and longitude coordinate positioning data. Table 2 is then introduced to compare the positioning data offset under different noise reduction logics.

[0030] Table 2 Comparison of Multi-Node Positioning Error Data in Edge Computing Referring to Table 2, the corrected positioning deviation values ​​output by the edge computing intelligent processing module all show a decreasing trend. This operational logic improves the ability to identify subtle omissions in complex environments through linear correction of pressure data by temperature and temporal feature mining of deep networks. The experimental results show that the corrected model achieves an accuracy of 98% in 100 blind tests with a positioning error controlled within 2 meters, which is 35% more accurate than the traditional correlator method.

[0031] The aforementioned Long Short-Term Memory (LSTM) temporal processing layer refers to a recurrent neural network layer containing forget gates, input gates, and output gates, used to process time-series data and alleviate the gradient vanishing problem.

[0032] The dual-mode wireless communication unit acquires the leakage status tag and coordinate positioning data, performs network addressing and protocol encapsulation, and generates uplink data packets to be transmitted to the remote server. The dual-mode wireless communication unit integrates a narrowband IoT RF baseband and a near-field Bluetooth transceiver, and automatically switches between long-distance data reporting link and short-distance local configuration link according to the distance threshold status of the receiving end.

[0033] The dual-mode wireless communication unit acquires the leakage status tag and coordinate positioning data, performs network addressing and protocol encapsulation, and generates uplink data packets for transmission to the remote server. The dual-mode wireless communication unit integrates a narrowband IoT RF baseband and a near-field Bluetooth transceiver, automatically switching between a long-distance data reporting link and a short-distance local configuration link based on the receiver's distance threshold. The dual-mode wireless communication unit acquires the original status tag data length and coordinate data precision bit width parameters, and concatenates all information according to preset frame header and tail delimiters. The dual-mode wireless communication unit extracts the current node's network addressing routing table and the target server's fixed Internet Protocol address, generating uplink data packets. The dual-mode wireless communication unit extracts the current receiver's real-time signal strength indicator value and a preset link attenuation threshold. The dual-mode wireless communication unit calculates the average packet loss rate by periodically broadcasting response messages from the probe based on the real-time signal strength indicator value; the current measured value is -85 dB / mW. The dual-mode wireless communication unit sets the preset link attenuation threshold to -90 dB / mW based on the minimum demodulation sensitivity requirement of the narrowband RF baseband. The dual-mode wireless communication unit compares the real-time signal strength indicator with a preset link attenuation threshold, and simultaneously extracts the receiver distance measurement from local Bluetooth ranging; the current measured distance is 15 meters. The dual-mode wireless communication unit extracts the short-range communication limit threshold, setting it to 50 meters. The dual-mode wireless communication unit compares the measured distance with the short-range communication limit threshold. If the measured distance is less than the short-range communication limit threshold and a local configuration device handshake signal is present, the dual-mode wireless communication unit directly activates the near-field Bluetooth communication protocol to send uplink data packets downlink via the short-range local configuration link. If the measured distance is greater than 50 meters, the dual-mode wireless communication unit extracts the total number of bytes in the uplink data packet and the currently available uplink bandwidth parameter. The dual-mode wireless communication unit estimates the transmission time by calculating the ratio of the total number of bytes to the currently available uplink bandwidth parameter. The dual-mode wireless communication unit obtains a packet size of 1024 bytes and an available bandwidth of 256 bytes per second; after the ratio calculation, the dual-mode wireless communication unit estimates the transmission time to be 4 seconds. Since the signal strength of -85 dB / mW is higher than the critical value of -90 dB / mW, the dual-mode wireless communication unit activates the narrowband IoT RF link, transmitting uplink data packets to the remote server via the long-distance data reporting link. This logic achieves adaptive switching of the communication link by dynamically comparing the signal strength with the distance threshold. Experimental results show that after adopting the dual-link decision logic, the packet loss rate of remote water supply nodes decreased from 15% to less than 1%, improving transmission reliability by 90% compared to a single long-distance transmission method.

[0034] The aforementioned narrowband IoT RF baseband refers to a RF signal modulation and demodulation hardware chip that supports low-power wide-area network communication standards.

[0035] The water pressure differential self-powered module extracts the kinetic and potential energy difference when the water supply network flows through the channel, converts it into working electrical energy and distributes it to each power-consuming component. The water pressure differential self-powered module includes a micro water flow turbine generator and a cascaded energy storage capacitor bank. The micro water flow turbine generator is located within the main cross section of the fluid flow to obtain impact torque.

[0036] The water pressure differential self-powered module extracts the kinetic and potential energy differences of the water supply network as it flows through the channel, converts them into working electrical energy, and distributes it to various power-consuming components. The module includes a micro-flow turbine generator and cascaded energy storage capacitor banks. The micro-flow turbine generator is located within the main cross-section of the fluid flow to obtain impact torque. The module acquires the transient pressure value at the upstream inlet and the steady-state pressure value at the downstream outlet. It then calculates the difference between these two values ​​to extract the head pressure difference parameter. The module finds the upstream inlet transient pressure to be 0.4 MPa and the downstream outlet steady-state pressure to be 0.395 MPa, resulting in a head pressure difference parameter of 0.005 MPa, or 5000 Pascals. Finally, the module acquires the cross-sectional area parameter of the micro-turbine impeller and the hydraulic mechanical conversion efficiency coefficient. The water pressure differential self-powered module sets the cross-sectional area of ​​the micro turbine impeller to 0.0001 square meters and the hydraulic-mechanical conversion efficiency coefficient to 0.4, determined based on the blade hydrodynamic model. The module continuously multiplies the head pressure difference parameter, the micro turbine impeller cross-sectional area parameter, and the current flow velocity parameter, then multiplies by the hydraulic-mechanical conversion efficiency coefficient to derive the theoretical captured mechanical power value. At a flow velocity of 1 meter per second, the module obtains a theoretical captured mechanical power value of 0.2 watts (200 milliwatts) after continuous multiplication. The module also acquires the power conversion loss rate of the rectifier and voltage regulator circuit, subtracts this loss rate, and accumulates the electrical energy into the energy storage capacitor bank. Finally, the module extracts the real-time voltage value across the capacitor bank and the preset minimum sustaining voltage threshold for each processing step. The water pressure differential self-powered module calculates the preset minimum sustaining voltage threshold based on the total power consumption of all sensor acquisition, edge computing, and wireless transmission operating at full load simultaneously, setting it to 3.3 volts. The module compares the real-time voltage values ​​at both ends with the minimum sustaining voltage threshold. If the measured voltage reaches 4.2 volts, exceeding the minimum sustaining voltage threshold, the module distributes steady-state operating current to each module. Table 3 illustrates the water pressure differential extraction and power supply status parameters.

[0037] Table 3. Water Pressure Difference Extraction and Energy Supply Status Parameters Table 3 details the impact of water pressure fluctuations on output power and energy storage voltage over different time periods. This operational logic maintains a dynamic balance in energy distribution by calculating the water head pressure difference and conversion efficiency. Experimental results show that under normal operating conditions with a pipeline flow velocity of no less than 0.5 m / s, the steady-state output current can cover the system's daily consumption demand of 150 mA / h, improving the equipment's endurance independence compared to conventional battery power supply.

[0038] The aforementioned micro hydro-turbine generator refers to an energy harvesting component that converts the kinetic energy of fluid flow into working electrical energy through the rotation of miniaturized turbine blades.

[0039] The self-calibration and fault diagnosis unit periodically extracts the zero-point bias voltage of the multi-modal fusion sensing unit and generates sensor health correction parameters by comparing them with the factory preset threshold.

[0040] The self-calibration and fault diagnosis unit periodically extracts the zero-point bias voltage of the multi-modal fusion sensing unit and generates sensor health correction parameters by comparing it with a preset threshold. The self-calibration and fault diagnosis unit extracts the initial zero-point bias voltage acquisition value during the sensing fusion process. It then calculates the difference between the initial zero-point bias voltage acquisition value and the factory-preset zero-point calibration value to generate an absolute bias error parameter. The self-calibration and fault diagnosis unit obtains a measured bias voltage of 0.05 volts and a calibrated zero-point of 0.01 volts, with a difference of 0.04 volts. The self-calibration and fault diagnosis unit extracts a preset maximum tolerance bias extreme value parameter, which is calculated from the aging attenuation characteristic curve of the sensor device and set to 0.1 volts. Finally, the self-calibration and fault diagnosis unit performs a ratio calculation between the absolute bias error parameter and the maximum tolerance bias extreme value parameter to obtain an attenuation offset ratio parameter, which is calculated to be 0.4. The self-calibration and fault diagnosis unit extracts a baseline health coefficient of 1, and calculates the difference between the baseline health coefficient and the attenuation offset ratio parameter to derive the sensor health correction parameter. The self-calibration and fault diagnosis unit subtracts 0.4 from the baseline health coefficient of 1, resulting in a sensor health correction parameter of 0.6. The self-calibration and fault diagnosis unit extracts a preset critical fault alarm threshold parameter and sets it to 0.2. The self-calibration and fault diagnosis unit compares the sensor health correction parameter with the critical fault alarm threshold parameter. If the sensor health correction parameter is greater than the critical fault alarm threshold parameter, the self-calibration and fault diagnosis unit sends the sensor health correction parameter to the edge computing intelligent processing module for subsequent data weighting compensation multiplier. If the sensor health correction parameter is less than 0.2, the self-calibration and fault diagnosis unit directly activates the internal alarm relay to generate a hardware blocking signal, cutting off the operating current of the damaged sensing channel. This operational logic achieves quantitative monitoring and preventative isolation of the hardware status through the quantitative calculation of the absolute bias error. The experimental results show that the fault-tolerant control logic did not experience overall solution collapse due to single-point sensor drift after 300 days of continuous operation, and improved the stable operation cycle by 60% compared with the system lacking fault tolerance mechanism.

[0041] The aforementioned zero-point bias voltage refers to the static basic electrical signal parameter at the output terminal of the sensor when it is not subjected to any measured physical quantity.

[0042] A method for detecting leakage in municipal water supply pipelines, based on the aforementioned municipal water supply pipeline leakage detection system, includes the following steps: S1: The quick-connect fire hydrant adapter performs a mechanical engagement action with the physical port of the target fire hydrant to establish a closed fluid communication channel; S2: In a self-sealing graded isolation cavity, the internal pressure of the pipeline network is used to induce the deformation of the sealing material, thereby constructing an internal fluid sealing space isolated from the outside world and an anti-interference electromagnetic shielding layer. S3: Utilize a multi-modal fusion sensing unit to simultaneously extract acoustic and vibration fluctuation data, pressure change data, micro-flow data, and compensated temperature data from the water flow medium in the pipe network; S4: The edge computing intelligent processing module performs noise reduction on the acoustic and vibration fluctuation data, and combines the compensated temperature data to correct the pressure change data. After extracting the fusion features, it compares and analyzes to generate leakage status labels and coordinate positioning data. S5: Activate the dual-mode wireless communication unit, encapsulate the leakage status tag and coordinate positioning data, and send them to the corresponding receiving end; S6: Relying on the water pressure difference self-powered module, it continuously captures the pressure difference kinetic energy at both ends of the fluid and outputs steady-state current to maintain the operation of each processing process.

[0043] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the scope of protection defined by the claims of the present invention.

Claims

1. A municipal water supply pipeline leakage detection system, characterized in that, The system includes: a quick-connect fire hydrant adapter, a self-sealing graded isolation cavity, a multi-modal fusion sensing unit, an edge computing intelligent processing module, a dual-mode wireless communication unit, and a water pressure differential self-powering module. The quick-connect fire hydrant adapter performs a physical docking and fixing action with the water outlet port of the municipal fire hydrant, creating a fluid communication channel; The self-sealing graded isolation cavity is connected to the quick-connect fire hydrant adapter by a physical snap-fit ​​method, and constructs an internal fluid sealing space and an external electromagnetic shielding layer under the action of pipeline pressure. The multimodal fusion sensing unit is located inside the self-sealing hierarchical isolation cavity and is used to synchronously collect acoustic and vibration fluctuation data, pressure change data, micro-flow data and compensated temperature data in the fluid communication channel; The edge computing intelligent processing module performs noise reduction and feature extraction on the acoustic vibration fluctuation data, pressure change data, micro-flow data and compensation temperature data based on adaptive filtering logic, and generates leakage status labels and coordinate positioning data by comparing with a preset physical model library; The dual-mode wireless communication unit acquires the leakage status label and the coordinate positioning data, performs network addressing and protocol encapsulation actions, and generates uplink data packets to be transmitted to the remote server. The water pressure differential self-powered module extracts the kinetic and potential energy difference when the water supply network flows through the channel, converts it into working electrical energy, and distributes it to each power-consuming component.

2. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, The system is designed as a leak detection device that is non-stop and trenchless, based on quick-connect fire hydrant access, multi-modal sensor fusion, edge computing intelligent solution, and water pressure difference self-powered. The quick-connect fire hydrant adapter has a built-in bidirectional self-locking claw component and a multi-level radial sealing ring, which clamps the fire hydrant wall surface by rotation and compression.

3. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, The self-sealing graded isolation cavity is structurally divided into an inner fluid sealing cavity and an outer electromagnetic shielding cavity. The inner fluid sealing cavity is constructed using a dynamic diaphragm material with pressure compensation characteristics, and the outer electromagnetic shielding cavity is wrapped with a high magnetic permeability metal composite covering layer.

4. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, The multimodal fusion sensing unit includes a broadband hydrophone component, a high-frequency micro-pressure sensor, a micro-differential pressure flow sensor, and a thermistor probe. The broadband hydrophone component directly contacts the fluid medium to obtain the mechanical vibration of the audio spectrum.

5. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, When performing noise reduction and feature extraction, the edge computing intelligent processing module performs time-frequency domain transformation on the acoustic vibration fluctuation data to extract the energy value of the main frequency band, and performs linear correction on the drift coefficient of the pressure change data based on the compensated temperature data.

6. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, The edge computing intelligent processing module generates the coordinate positioning data by comparing the leakage status labels of multiple sets of nodes and performing calculations based on the spatial geometric model of the product of time delay difference and wave velocity.

7. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, The dual-mode wireless communication unit integrates a narrowband IoT RF baseband and a near-field Bluetooth transceiver, and automatically switches between a long-distance data reporting link and a short-distance local configuration link based on the distance threshold status of the receiving end.

8. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, The water pressure differential self-powered module includes a micro water flow turbine generator and a cascaded energy storage capacitor bank. The micro water flow turbine generator is located within the main cross section of the fluid flow to obtain impact torque.

9. The municipal water supply pipeline leakage detection system according to claim 1, characterized in that, The system also includes a self-calibration and fault diagnosis unit, which periodically extracts the zero-point bias voltage of the multi-modal fusion sensing unit and generates sensor health correction parameters by comparing them with factory-preset thresholds.

10. A method for detecting leakage in municipal water supply pipelines, characterized in that, The method is used to implement the municipal water supply pipeline leakage detection system according to any one of claims 1-9, and includes the following steps: S1: The quick-connect fire hydrant adapter performs a mechanical engagement action with the physical port of the target fire hydrant to establish a closed fluid communication channel; S2: In a self-sealing graded isolation cavity, the internal pressure of the pipeline network is used to induce the deformation of the sealing material, thereby constructing an internal fluid sealing space isolated from the outside world and an anti-interference electromagnetic shielding layer. S3: Utilize a multi-modal fusion sensing unit to simultaneously extract acoustic and vibration fluctuation data, pressure change data, micro-flow data, and compensated temperature data from the water flow medium in the pipe network; S4: The edge computing intelligent processing module performs noise reduction processing on the acoustic vibration fluctuation data, and combines the compensated temperature data to correct the pressure change data. After extracting the fusion features, it compares and analyzes to generate leakage status labels and coordinate positioning data. S5: Activate the dual-mode wireless communication unit, encapsulate the leakage status tag and the coordinate positioning data, and send them to the corresponding receiving end; S6: Relying on the water pressure difference self-powered module, it continuously captures the pressure difference kinetic energy at both ends of the fluid and outputs steady-state current to maintain the operation of each processing process.

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