Pipe network health real-time monitoring system and evaluation method

Abstract

The invention discloses a pipe network health real-time monitoring system and an evaluation method. According to the invention, through real-time health monitoring and dynamic deduction capability, the flexibility and safety of energy supply are significantly improved. The healthy twin module constructs a digital mirror image of the full life cycle of energy equipment, solar hydrogen production efficiency fluctuation, hydrogen storage tank pressure change and fuel cell attenuation trend can be accurately tracked, and risk points such as electrolytic cell abnormal hydrogen evolution and proton exchange membrane aging can be recognized in advance. The all-weather transparent monitoring enables the system to dynamically adjust the ratio of hydrogen production to power generation, the fuel cell is automatically switched to supply power in cloudy and rainy days, continuous energy supply is guaranteed, and the system can generate a coping plan in advance by simulating extreme scenes such as sudden drop of photovoltaic output and hydrogen leakage, so that the system is convenient to use. The energy interruption risk caused by sudden equipment failure is greatly reduced, meanwhile, the service life of key components is prolonged, the maintenance cost of a distributed energy network is reduced, and meanwhile the overall energy efficiency utilization rate is also improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipeline network monitoring, and specifically relates to a real-time pipeline network health monitoring system and an evaluation method. Background Art

[0002] Pipeline network health monitoring refers to the process of using advanced sensing technologies, data acquisition systems, analysis software, and communication networks to monitor and evaluate the structural safety, operating status, and environmental factors of various underground pipeline networks such as urban water supply, gas, heat, and drainage in real-time or regularly. This system continuously collects key parameters by deploying sensors for pressure, flow rate, water quality, corrosion, leakage, deformation, etc. on the pipeline network, and combines geographic information system (GIS) and Internet of Things technologies to transmit the data to the monitoring center. Through the analysis and processing of a large amount of monitoring data, abnormal conditions of the pipeline network, such as potential risks like leakage, corrosion, deformation, and pressure fluctuations, can be detected in a timely manner, the pipeline life can be predicted, the system reliability can be evaluated, and scientific basis can be provided for the maintenance, repair, and renovation of the pipeline network, thereby effectively preventing accidents, ensuring the safe and stable operation of the urban lifeline, extending the service life of the pipeline network, and optimizing the operation and maintenance management efficiency.

[0003] However, the existing technologies mainly rely on fixed threshold alarms and regular manual inspections, are unable to perceive subtle changes in the device status in real-time, lack the ability to dynamically model the coupling relationship of the entire hydrogen production - hydrogen storage - power generation link, resulting in a lag in abnormal response; the traditional operation and maintenance mode is difficult to simulate the impact of multi-device chain failures in extreme scenarios, and energy allocation depends on manual experience, prone to supply interruptions and energy efficiency losses. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time pipeline network health monitoring system and an evaluation method to solve the problems mentioned above.

[0005] The technical solution adopted by the present invention is as follows: A real-time pipeline network health monitoring system, the system includes: a data acquisition and perception module, a data transmission and communication module, a data processing and storage module, an intelligent analysis and diagnosis module, a visualization and interaction module, and a health twin and simulation deduction module; The internal of the health twin and simulation deduction module is provided with: a digital twin modeling and mapping module, a multi-dimensional simulation deduction engine, a dynamic synchronization and real-time feedback module, and a visualization decision support module; The sensor data output end of the data acquisition and perception module accesses the industrial protocol conversion input end of the data transmission and communication module through an RS485 / LoRa dual-mode channel, The data transmission and communication module is connected to the Kafka streaming data access end of the data processing and storage module through an optical fiber network output end; The feature vector output end of the data processing and storage module is directly connected to the time series data analysis input end of the intelligent analysis and diagnosis module. The fault diagnosis result output end of the intelligent analysis and diagnosis module is synchronously connected to the real-time alarm analysis interface of the visualization and interaction module and the model parameter update end of the health twin and simulation deduction module. The simulation instruction output end of the health twin and simulation deduction module is transmitted back to the control instruction input end of the dynamic synchronization and real-time feedback module through the OPC UA protocol. The optimized parameter output end of the module is finally fed back to the edge computing strategy configuration end of the data acquisition and perception module through the MQTT protocol, forming a closed-loop data link.

[0006] In a preferred embodiment, a high-density heterogeneous sensor array and an edge preprocessing unit are provided inside the data acquisition and perception module.

[0007] In a preferred embodiment, a ring topology optical fiber network is provided in the backbone layer of the data transmission and communication module to achieve gigabit-level data transmission, an NB-IoT base station is provided in the wireless coverage layer to form a cellular low-power wide-area network, and a Beidou short message module is provided in the emergency communication layer to ensure data transmission in areas without public networks.

[0008] In a preferred embodiment, the data processing and storage module is provided with a stream-batch fusion processing engine and a hierarchical storage pool.

[0009] In a preferred embodiment, the intelligent analysis and diagnosis module is provided with a multi-modal hybrid intelligent model system; the dynamic threshold calculation unit sets the alarm threshold according to the historical data sliding percentile, and the anomaly detection layer fuses the isolation forest algorithm and the variational autoencoder to achieve unsupervised anomaly localization.

[0010] In a preferred embodiment, the following are provided inside the visualization and interaction module: The operation and maintenance large screen displays the whole network pressure heat map and the risk level topology. The GIS engine integrates the BIM model to achieve transparent perspective of the underground pipe network. The mobile terminal pushes hierarchical alarm information and associates with the emergency plan document.

[0011] In a preferred embodiment, the digital twin modeling and mapping module performs multi-source data fusion on the real-time sensor data and the model prediction value through the Kalman filter algorithm; the core formula for the dynamic data fusion of the digital twin modeling and mapping module is: ; In the formula: represents the pipe network state vector after fusion at time t; Kt represents the Kalman gain matrix, which is composed of the sensor noise covariance RR and the model prediction error covariance P t∣t−1 Calculated: K t =P t |t−1(P t∣t−1 +R)−1; y t represents the sensor observation value vector at time t; M(x t−1 ,θ) represents the state x at time t-1 t−1 and the model predicted value of the physical parameter θ of the pipeline network.

[0012] In a preferred embodiment, the core structure of the multi-dimensional simulation engine includes a fault path prediction algorithm module, a multi-physics field coupling solver, a random scenario generator and an interactive simulation controller; The corrosion-stress synergistic effect equation of the multi-dimensional simulation engine is: Where: ΔSt represents the cumulative amount of pipe wall material damage at time t (dimensionless); α represents the stress sudden change sensitivity coefficient, which is calibrated by the fatigue property experiment of the pipeline material; σ represents the pipeline hoop stress tensor; β represents the corrosion-strain synergy factor, which depends on the pH value of the medium and the electrochemical properties of the material; C(τ) represents the dynamic corrosion rate function, which is updated by the online corrosion sensor data; ϵ(τ) represents the local strain rate, which is calculated in real time from the distributed fiber optic sensing data.

[0013] In a preferred embodiment, the architecture of the dynamic synchronization and real-time feedback module consists of a bidirectional data pipeline, a delay compensation algorithm, a confidence evaluator, and a command encoder; The edge-cloud collaboration weight allocation equation of the dynamic synchronization and real-time feedback module is: Where: we represents the data fusion weight of edge nodes; σ m Represents the standard deviation of the model prediction error, which is calculated online through historical residuals; σ s Indicates the standard deviation of the sensor measurement error, which is obtained from the equipment calibration certificate; Δ t Represents the transmission delay of data from edge to cloud; λ represents the delay attenuation coefficient, which is dynamically adjusted according to the network QoS index; η represents the gradient compensation factor, which is positively correlated with the gradient magnitude of the objective function J; The functional carriers of the visualization decision support module include a 3D rendering engine, a spatio-temporal data mapper, a decision matrix generator, and a multi-terminal adapter.

[0014] In a preferred embodiment, a method for evaluating the health of a pipe network includes the following steps: S1: The data acquisition and perception module collects multiple physical parameters such as pipe network pressure, flow rate, vibration, and corrosion degree in real time through a distributed sensor network, and combines with edge computing nodes for local noise filtering and initial screening of abnormal events to form a high-precision original data set; S2: The data transmission and communication module uses a hybrid optical fiber and wireless communication architecture to transmit encrypted data packets, and realizes data normalization of heterogeneous devices through a protocol conversion gateway to ensure that low-latency and highly reliable data channels are transmitted to the cloud processing center; S3: The data processing and storage module uses a stream-batch integrated engine to clean the data and extract spatio-temporal features, and stores them in a time-series database, a relational database, and an object storage pool at different levels to construct a standardized analysis data set; S4: The intelligent analysis and diagnosis module applies a dynamic threshold algorithm and an isolation forest model to achieve real-time anomaly detection, models the hydraulic coupling relationship of the pipe network through a graph neural network, combines an attention-enhanced temporal convolutional network to predict the remaining life of pipe segments, and outputs the confidence level of fault types and risk levels; S5: The pipe network health twin and simulation deduction module embeds fluid mechanics equations and corrosion-stress synergistic effect equations based on a digital twin model, simulates the spatio-temporal propagation paths of faults such as pipe bursts and leaks, and quantitatively evaluates the long-term impact of different maintenance strategies on the pipe network health index; S6: The visualization and interaction module dynamically displays the pipe network health heat map, fault simulation deduction animation, and maintenance decision matrix through a 3D GIS engine and AR technology, generates a multi-dimensional evaluation report, and feeds back optimization instructions to the physical pipe network control system in reverse to form a closed-loop management.

[0015] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are: 1. In the present invention, through real-time health monitoring and dynamic deduction capabilities, the flexibility and safety of energy supply are significantly improved. The health twin module constructs a digital mirror of the entire life cycle of energy equipment, which can accurately track the fluctuations in the efficiency of solar hydrogen production, the pressure changes in hydrogen storage tanks, and the attenuation trends of fuel cells, and can identify risk points such as abnormal hydrogen evolution in electrolyzers and aging of proton exchange membranes in advance. This all-weather transparent monitoring enables the system to dynamically adjust the ratio of hydrogen production to power generation, automatically switch to fuel cell power supply in rainy weather, and ensure continuous energy supply.

[0016] 2. In the present invention, by simulating extreme scenarios such as sudden drop in photovoltaic output and hydrogen leakage, the system can pre-generate response plans, such as automatically starting a standby hydrogen storage tank or switching to a grid complementary mode. This predictive operation and maintenance significantly reduces the risk of energy interruption caused by sudden equipment failures, while extending the lifespan of key components, reducing the maintenance cost of the distributed energy network, and improving the overall energy efficiency utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the overall system block diagram of the present invention; Figure 2 is the system block diagram of the health twin and simulation deduction module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] Embodiment: Referring to Figure 1-2 , a pipeline network health real-time monitoring system and evaluation method, the system includes: a data acquisition and perception module, a data transmission and communication module, a data processing and storage module, an intelligent analysis and diagnosis module, a visualization and interaction module, and a health twin and simulation deduction module; Inside the health twin and simulation deduction module, there are: a digital twin modeling and mapping module, a multi-dimensional simulation deduction engine, a dynamic synchronization and real-time feedback module, and a visualization decision support module.

[0020] Inside the data acquisition and perception module, there is a high-density heterogeneous sensor array and an edge preprocessing unit The sensor array covers key nodes of the pipeline network, including a pressure sensor to capture the peak value of the internal pressure fluctuation in the pipeline in real time, a flow sensor to obtain the fluid volume change through turbine or electromagnetic measurement technology, a leakage sensor to capture abnormal vibration spectra above 5 kHz using a broadband acoustic wave array, a vibration sensor to monitor third-party construction impact signals based on MEMS technology, and a corrosion sensor to calculate the wall thickness attenuation rate using ultrasonic echo time difference. The edge preprocessing unit is equipped with an adaptive Kalman filter algorithm to eliminate environmental noise, runs a lightweight LSTM model to perform initial screening of local abnormal events, and compresses the original data by 12 times through Huffman coding and uploads it through an RS485 / LoRa dual-mode channel.

[0021] Inside the data transmission and communication module, there are: The backbone layer uses a ring topology fiber optic network to achieve gigabit-level data transmission. The wireless coverage layer deploys NB-IoT base stations to form a cellular low-power wide-area network. The emergency communication layer integrates a Beidou short message module to ensure data transmission in areas without public network coverage. The protocol conversion gateway is built with a Modbus-TCP / MQTT dual protocol stack to achieve interoperability between multi-vendor devices. The physical layer is configured with a lightning and surge protector and a dual-power redundancy system. The transmission security layer applies quantum key distribution technology to establish an end-to-end encrypted tunnel. The network quality monitoring system analyzes packet loss rate and delay metrics in real time and dynamically switches to the optimal transmission path.

[0022] The data processing and storage module is equipped with a stream-batch fusion processing engine and a hierarchical storage pool. The stream processing layer uses Apache Flink to achieve millisecond-level window aggregation calculation. The data cleaning component uses cubic spline interpolation to repair missing values and detects and removes multi-dimensional outliers based on Mahalanobis distance. The time series storage layer uses a TDengine cluster to store millions of data points per second. The relational storage layer uses PostgreSQL sharding to store the pipe network topology relationship and maintenance worker unit data. The unstructured storage layer archives 3D laser point clouds and voiceprint spectrograms based on Ceph object storage. The feature engineering module automatically generates 32-dimensional spatio-temporal feature vectors such as pressure fluctuation variance and flow accumulation deviation for use in downstream machine learning model training.

[0023] The intelligent analysis and diagnosis module is equipped with a multi-modal hybrid intelligent model system. The dynamic threshold calculation unit sets the alarm threshold according to the sliding percentile of historical data. The anomaly detection layer integrates the isolation forest algorithm and variational autoencoder to achieve unsupervised anomaly localization. The fault diagnosis engine constructs a graph neural network model to capture the hydraulic coupling effect of the pipe network, combines with an XGBoost multi-classifier to output the confidence of nine-level fault types, and the prediction module uses a time convolutional network enhanced with an attention mechanism to predict the pipe segment failure probability 72 hours in advance with an accuracy of 89%. The root cause tracing system uses a Bayesian network to reverse the fault propagation path and generates a weighted influence factor tree diagram.

[0024] The visualization and interaction module is internally equipped with a multi-terminal collaborative decision-making platform. The operation and maintenance large screen displays the network-wide pressure heat map and risk level topology. The AR inspection system uses Hololens glasses to superimpose a 3D model of pipe wall corrosion onto the real scene. The GIS engine integrates a BIM model to achieve transparent perspective of the underground pipe network and supports generating hydraulic gradient isosurfaces for the selected area. The mobile terminal pushes hierarchical alarm information and associates with emergency plan documents. The decision-making cockpit is built with a Monte Carlo simulator to visualize the ten-year cost-benefit curves of different maintenance strategies. The permission control system implements the RBAC model and dynamic watermark tracking to meet the ISO27001 information security standard.

[0025] The internal settings of the digital twin modeling and mapping module include a three-dimensional physical modeling unit, a multi-source data fusion layer, a dynamic behavior model library, and a real-time synchronization interface; The digital twin modeling and mapping module constructs a high-fidelity virtual model by fusing the physical characteristics of the pipe network and real-time perception data, specifically including: generating a three-dimensional geometric model based on the pipe network CAD drawings, material properties, and geological information to accurately restore the pipeline topology and mechanical parameters; integrating fluid mechanics equations (such as the simplified form of the Navier-Stokes equation) to describe the pressure-flow relationship, and combining with a corrosion rate model (such as the power-law corrosion model) to dynamically calculate the attenuation of the pipe wall thickness; performing multi-source data fusion on the real-time sensor data (pressure, flow rate, corrosion degree) and the model prediction values through the Kalman filter algorithm, and dynamically adjusting the model weights using the covariance matrix to achieve millisecond-level state synchronization between the physical pipe network and the virtual model; at the same time, embedding an abnormal propagation model, such as the pressure wave diffusion equation caused by leakage, to realize the spatio-temporal deduction of the impact of faults. The core formula for dynamic data fusion is: ; In the formula: represents the state vector of the pipe network after fusion at time t (including dimensions such as pressure and flow rate); K t represents the Kalman gain matrix, which is calculated from the sensor noise covariance RR and the model prediction error covariance P t∣t−1 as follows: K t = P t ∣t−1 (P t∣t−1 + R)−1; yt represents the sensor observation value vector at time t; M(x t−1 , θ) represents the model prediction value based on the state x t−1 at time t-1 and the physical parameters θ of the pipe network; The innovation of this formula lies in extending the traditional Kalman filter to a data fusion framework driven by a physical model, and achieving the following balance by dynamically adjusting K_tKt: when the reliability of the sensor data is high (RR is small), the measured value is preferred, and when the model prediction accuracy is high, it depends on physical laws to calculate, so as to maintain the robustness of state estimation in a noisy environment.

[0026] The core components of the multi-dimensional simulation and deduction engine include a fault path prediction algorithm, a multi-physics field coupling solver, a random scenario generator, and an interactive deduction controller; The multi-dimensional simulation and deduction engine solves the fluid dynamics equations based on the finite element method to simulate the propagation of pressure waves and the leakage diffusion path, and calculates the remaining strength of the defective pipeline using the improved B31G standard to evaluate the bursting risk; introduces the Dynamic Bayesian Network (DBN) to integrate multi-source uncertainties (such as sensor noise, randomness of soil corrosion rate), and iteratively updates the failure probability through the Markov Chain Monte Carlo method; constructs a Spatio-Temporal Convolutional Neural Network (ST-CNN) to extract features from historical failure modes, generates multi-scale spatio-temporal attention weights to optimize the deduction boundary conditions; at the same time, integrates the climate-load coupling model to quantify the impact of the alternating thermal stress of the pipeline caused by extreme temperature cycles on the fatigue life The corrosion-stress synergistic effect equation is: In the formula: ΔSt represents the cumulative amount of damage to the pipe wall material at time t (dimensionless); α represents the stress mutation sensitivity coefficient, which is calibrated by the pipe material fatigue characteristic experiment; σ represents the circumferential stress tensor of the pipeline; β represents the corrosion-strain synergistic factor, which depends on the pH value of the medium and the electrochemical characteristics of the material; C(τ) represents the dynamic corrosion rate function, which is updated by the data of the on-line corrosion sensor; ϵ(τ) represents the local strain rate, which is calculated in real time through the distributed fiber optic sensing data; The innovation of this formula lies in the integral coupling of the time-varying corrosion rate and the dynamic mechanical stress, breaking through the limitations of the traditional single-factor corrosion or stress model, and more accurately characterizing the material degradation process under complex working conditions.

[0027] The architecture of the dynamic synchronization and real-time feedback module consists of a bidirectional data pipeline, a delay compensation algorithm, a confidence evaluator, and an instruction encoder; The dynamic synchronization and real-time feedback module compensates for the sensor data acquisition and transmission delay based on the adaptive timestamp alignment algorithm, and uses the sliding window mechanism with weight decay to eliminate the timing disorder caused by network jitter; designs a bidirectional confidence evaluator to dynamically adjust the data fusion weight according to the sensor health score (such as signal-to-noise ratio, drift rate) and the model prediction residual, ensuring that the synchronization accuracy can still be maintained in a high-noise environment; deploys a lightweight feedback controller to encode the optimized parameters (such as the target pressure threshold, valve opening suggestion) deduced by the twin model into a low-bit instruction stream and transmits it back to the on-site actuator through a redundant channel; at the same time, constructs an abnormal propagation blocking model, which automatically triggers the regional isolation and multi-modal data re-calibration process when detecting that the local synchronization deviation exceeds the critical value Among them: The edge-cloud collaborative weight allocation equation is: Where: we represents the data fusion weight of edge nodes; σ m Represents the standard deviation of the model prediction error, which is calculated online through historical residuals; σ s Indicates the standard deviation of the sensor measurement error, which is obtained from the equipment calibration certificate; Δ t Represents the transmission delay of data from edge to cloud; λ represents the delay attenuation coefficient, which is dynamically adjusted according to the network QoS index; η represents the gradient compensation factor, which is positively correlated with the gradient amplitude of the objective function J; The functional carriers of the visual decision support module include a three-dimensional rendering engine, a spatiotemporal data mapper, a decision matrix generator, and a multi-terminal adapter. The three-dimensional rendering engine dynamically renders the corrosion heat map and stress distribution cloud map of the pipeline network based on WebGL technology, and supports free cutting under gesture control; the spatiotemporal data mapper converts time series data such as pressure fluctuations and flow changes into vector animations to intuitively display the fault diffusion path; the decision matrix generator aggregates simulation results through a clustering algorithm and outputs a multi-dimensional decision table containing maintenance priorities and cost-benefit ratios; the multi-terminal adapter realizes data synchronization between large screens, PCs, and mobile terminals, and supports AR glasses to view the real status of underground pipelines.

[0028] A method for evaluating the health of a pipe network comprises the following steps: S1: The data acquisition and perception module collects multiple physical parameters such as pipeline pressure, flow, vibration, corrosion, etc. in real time through a distributed sensor network, and combines edge computing nodes to perform local noise filtering and initial screening of abnormal events to form a high-precision original data set.

[0029] S2: The data transmission and communication module uses a hybrid optical fiber and wireless communication architecture to transmit encrypted data packets, and normalizes heterogeneous device data through a protocol conversion gateway to ensure low-latency, high-reliability data channel transmission to the cloud processing center.

[0030] S3: The data processing and storage module uses a batch-stream engine to clean data and extract spatiotemporal features, and stores them in a hierarchical manner in a time series database, relational database, and object storage pool to build a standardized analysis data set.

[0031] S4: The intelligent analysis and diagnosis module applies the dynamic threshold algorithm and the isolation forest model to achieve real-time anomaly detection, models the hydraulic coupling relationship of the pipeline network through the graph neural network, combines the attention-enhanced temporal convolutional network to predict the remaining life of the pipe section, and outputs the confidence and risk level of the fault type.

[0032] S5: The pipeline network health digital twin and simulation module embeds the hydrodynamic equation and the corrosion-stress synergy effect equation based on the digital twin model, simulates the spatio-temporal propagation paths of faults such as pipe bursts and leaks, and quantitatively evaluates the long-term impact of different maintenance strategies on the pipeline network health index.

[0033] S6: The visualization and interaction module dynamically displays the pipeline network health heat map, the fault simulation animation, and the maintenance decision matrix through a 3D GIS engine and AR technology, generates a multi-dimensional evaluation report, and feeds back optimization instructions to the physical pipeline network control system in reverse to form a closed-loop management.

[0034] As can be seen from the above: In the present invention, through the real-time health monitoring and dynamic deduction capabilities, the flexibility and safety of energy supply are significantly improved. The health digital twin module constructs a digital mirror of the entire life cycle of energy equipment, can accurately track the fluctuations in the hydrogen production efficiency of solar energy, the pressure changes in hydrogen storage tanks, and the decay trend of fuel cells, and can identify risk points such as abnormal hydrogen evolution in electrolyzers and aging of proton exchange membranes in advance. This all-weather transparent monitoring enables the system to dynamically adjust the ratio of hydrogen production to power generation, automatically switch to fuel cell power supply in rainy weather, and ensure continuous energy supply.

[0035] In the present invention, by simulating extreme scenarios such as sudden drops in photovoltaic power output and hydrogen leaks, the system can pre-generate response plans, such as automatically starting backup hydrogen storage tanks or switching to a grid complementary mode. This predictive operation and maintenance significantly reduces the risk of energy interruption caused by sudden equipment failures, extends the life of key components, reduces the maintenance cost of the distributed energy network, and improves the overall energy efficiency utilization rate.

[0036] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0037] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time monitoring system for the health of pipe networks, characterized in that: The system includes: a data acquisition and perception module, a data transmission and communication module, a data processing and storage module, an intelligent analysis and diagnosis module, a visualization and interaction module, and a health twin and simulation deduction module; Inside the health twin and simulation deduction module, there are: a digital twin modeling and mapping module, a multi-dimensional simulation deduction engine, a dynamic synchronization and real-time feedback module, and a visualization decision support module; The sensor data output end of the data acquisition and perception module accesses the industrial protocol conversion input end of the data transmission and communication module through an RS485 / LoRa dual-mode channel, The data transmission and communication module is connected to the Kafka streaming data access end of the data processing and storage module through its optical fiber network output end; The feature vector output end of the data processing and storage module is directly connected to the time series data analysis input end of the intelligent analysis and diagnosis module, The fault diagnosis result output end of the intelligent analysis and diagnosis module is synchronously connected to the real-time alarm analysis interface of the visualization and interaction module and the model parameter update end of the health twin and simulation deduction module; The simulation instruction output end of the health twin and simulation deduction module is transmitted back to the control instruction input end of the dynamic synchronization and real-time feedback module through the OPC UA protocol; The optimized parameter output end of the module is finally fed back to the edge computing strategy configuration end of the data acquisition and perception module through the MQTT protocol.

2. The real-time pipeline network health monitoring system according to claim 1, characterized in that: Inside the data acquisition and perception module, there is a high-density heterogeneous sensor array and an edge preprocessing unit.

3. The real-time pipeline network health monitoring system according to claim 1, characterized in that: In the backbone layer of the data transmission and communication module, a ring topology optical fiber network is set up to achieve gigabit-level data transmission. In the wireless coverage layer, an NB-IoT base station is set up to form a cellular low-power wide-area network. In the emergency communication layer, a Beidou short message module is set up.

4. The real-time pipeline network health monitoring system according to claim 1, characterized in that: The data processing and storage module is set up with a stream-batch fusion processing engine and a hierarchical storage pool.

5. The real-time pipeline network health monitoring system according to claim 1, wherein: The intelligent analysis and diagnosis module is set up with a multi-modal hybrid intelligent model system; the dynamic threshold calculation unit sets the alarm threshold according to the sliding percentile of historical data, and the anomaly detection layer fuses the isolation forest algorithm and the variational autoencoder to achieve unsupervised anomaly location.

6. The real-time pipeline network health monitoring system according to claim 1, characterized in that: Inside the visualization and interaction module, there are: an operation and maintenance large screen, a GIS engine, and a mobile terminal that pushes hierarchical alarm information and associates with emergency plan documents.

7. The real-time pipeline network health monitoring system according to claim 1, characterized in that: The digital twin modeling and mapping module performs multi-source data fusion on real-time sensor data and model prediction values through the Kalman filtering algorithm; the core formula for dynamic data fusion of the digital twin modeling and mapping module is: ; Wherein: represents the fused pipeline network state vector at time t; K t represents the Kalman gain matrix, which is calculated from the sensor noise covariance RR and the model prediction error covariance P t∣t−1 as follows: K t =P t ∣t−1(P t∣t−1 +R)−1; y t represents the sensor observation value vector at time t. M(x t−1 , θ) represents the model prediction value based on the state x at time t-1 t−1 and the physical parameters θ of the pipe network.

8. The real-time pipeline network health monitoring system according to claim 1, characterized in that: The core components of the multi-dimensional simulation deduction engine include a fault path prediction algorithm module, a multi-physical field coupling solver, a random scenario generator, and an interactive deduction controller; The corrosion-stress synergy effect equation of the multi-dimensional simulation deduction engine is: In the formula: ΔSt represents the cumulative damage of the pipe wall material at time t (dimensionless); α represents the stress mutation sensitivity coefficient, calibrated by the pipe material fatigue characteristic experiment; σ represents the circumferential stress tensor of the pipe; β represents the corrosion-strain synergy factor, depending on the medium pH value and the material electrochemical characteristics; C(τ) represents the dynamic corrosion rate function, updated by the online corrosion sensor data; ϵ(τ) represents the local strain rate, which is calculated in real time through distributed optical fiber sensing data.

9. The real-time pipeline network health monitoring system according to claim 1, wherein: The architecture of the dynamic synchronization and real-time feedback module consists of a bidirectional data pipeline, a delay compensation algorithm, a confidence evaluator, and an instruction encoder; The edge-cloud collaborative weight allocation equation of the dynamic synchronization and real-time feedback module is: In the formula: we represents the data fusion weight of the edge node; σ m represents the standard deviation of the model prediction error, which is calculated online through historical residuals; σ s Indicates the standard deviation of the sensor measurement error, obtained from the device calibration certificate; Δ t represents the transmission delay of data from the edge to the cloud; λ represents the delay attenuation coefficient, which is dynamically adjusted according to the network QoS index; η represents the gradient compensation factor, which is positively correlated with the gradient amplitude of the objective function J; The functional carriers of the visualization decision support module include a 3D rendering engine, a spatio-temporal data mapper, a decision matrix generator, and a multi-terminal adapter.

10. A method for evaluating the health of a pipe network, characterized in that: The evaluation method uses the real-time pipeline network health monitoring system described in any one of claims 1 to 9 for evaluation during evaluation; The method includes the following steps: S1: The data acquisition and perception module collects physical parameters such as pipeline network pressure, flow rate, vibration, and corrosion degree in real time through a distributed sensor network, combines with edge computing nodes for local noise filtering and initial screening of abnormal events, and forms a high-precision original data set; S2: The data transmission and communication module uses a hybrid optical fiber and wireless communication architecture to transmit encrypted data packets, and realizes data normalization of heterogeneous devices through a protocol conversion gateway, ensuring that low-latency and highly reliable data channels are transmitted to the cloud processing center; S3: The data processing and storage module uses a stream-batch integrated engine to clean data and extract spatio-temporal features, and stores them in a time series database, a relational database, and an object storage pool in a hierarchical manner to construct a standardized analysis data set; S4: The intelligent analysis and diagnosis module applies a dynamic threshold algorithm and an isolation forest model to achieve real-time anomaly detection, models the hydraulic coupling relationship of the pipeline network through a graph neural network, combines with an attention-enhanced temporal convolutional network to predict the remaining life of the pipe section, and outputs the confidence of the fault type and the risk level; S5: The pipeline network health digital twin and simulation deduction module embeds hydrodynamic equations and corrosion-stress synergistic effect equations based on the digital twin model, simulates the spatio-temporal propagation paths of pipe burst and leakage faults, and quantitatively evaluates the long-term impact of different maintenance strategies on the pipeline network health index; S6: The visualization and interaction module dynamically displays the pipeline network health heat map, fault simulation deduction animation, and maintenance decision matrix through a 3D GIS engine and AR technology, generates a multi-dimensional evaluation report, and feeds back optimization instructions to the physical pipeline network control system in reverse to form a closed-loop management.

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