Thermoelectric pipe network fault inspection system and method based on deep learning

Abstract

The invention relates to the technical field of thermoelectric pipe network inspection, and discloses a thermoelectric pipe network fault inspection system and method based on deep learning. The system comprises a pipe network partition module, a multi-mode sensing module, a fault prediction module, a grading response module and a dynamic control module. The pipe network partition module is divided into a high-temperature steam pipeline area, a medium-temperature hot water pipeline area, a low-temperature water return pipeline area and a key valve connecting area; the multi-modal sensing module deploys temperature, pressure and sonic sensor arrays in each area; the fault prediction module extracts spatial-temporal characteristics through a deep belief network model and outputs a fault probability value; the hierarchical response module triggers different levels of response instructions according to a comparison result of the fault probability value and a preset threshold interval; and the dynamic control module controls a moving path of the inspection robot and detection equipment parameters according to the response instruction. According to the system, accurate prediction and dynamic response of the thermoelectric pipe network fault can be realized, and the inspection efficiency and the pipe network operation reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heat pipe network inspection, in particular to a system and method for heat pipe network fault inspection based on deep learning. BACKGROUND

[0002] As an important infrastructure for urban energy supply, heat pipe networks bear the key function of heat energy transportation, and their operation status is directly related to the stability of industrial production and the guarantee of residents' life. With the expansion of urban scale and the growth of energy demand, the coverage of heat pipe networks is continuously extended, and the types of pipelines are increasingly complex, covering high-temperature steam, medium-temperature hot water, low-temperature return water and other media. The structural particularity of the key valve connection area further increases the risk of failure. Traditional heat pipe network fault inspection mainly relies on a combination of periodic manual inspection and single-point sensor monitoring. Manual inspection is limited by the experience level of the inspectors and the working environment, making it difficult to achieve full-time, full-coverage monitoring, and the ability to predict potential faults is weak, often requiring treatment after the fault occurs, resulting in increased maintenance costs and interruptions in energy transportation. Although single-point sensor monitoring can provide operation data for local areas, due to differences in medium characteristics and pressure states in different areas of the heat pipe network, a single type of sensor cannot fully capture the fault characteristics of different areas, leading to missed or false alarms. Existing inspection systems lack effective integration and analysis capabilities for multi-source data, making it difficult to extract valuable fault information from massive monitoring data, resulting in inaccurate fault positioning and response classification. When the pipe network is abnormal, it is difficult to quickly allocate inspection resources according to the severity of the fault, affecting the efficiency of fault handling. Therefore, it is an urgent need to develop an inspection system that can adapt to different pipe network area characteristics, integrate multi-modal sensor data, and achieve accurate fault prediction and dynamic response, to improve the reliability of heat pipe network operation. SUMMARY

[0003] The present application aims to provide a system and method for heat pipe network fault inspection based on deep learning to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides a system for heat pipe network fault inspection based on deep learning, which comprises: a pipe network partition module, a multi-modal sensor module, a fault prediction module, a hierarchical response module, and a dynamic control module; The pipe network partition module identifies the heat and power pipe network topology based on geographic information system data, divides high-temperature steam pipe area, medium-temperature hot water pipe area, low-temperature return water pipe area and key valve connection area; the multi-modal sensing module distributes temperature sensor arrays, pressure sensor arrays and acoustic sensor arrays in each area divided by the pipe network partition module; The fault prediction module receives the multi-dimensional time series data stream collected by the multi-modal sensing module, extracts space-time features through a deep belief network model and outputs fault probability values; The hierarchical response module triggers response instructions of different levels according to the comparison result of the fault probability values generated by the fault prediction module and the preset probability threshold interval; The dynamic control module controls the moving path of the inspection robot and the detection equipment parameters according to the response instructions issued by the hierarchical response module.

[0005] Preferably, the pipe network partition module specifically performs: analyzing the pipe coordinate distribution graph and pipe diameter parameter table in the heat supply pipe network geographic information database through a convolutional neural network to identify the spatial connection relationship between the main pipe and the branch pipe; extracting pipe material characteristics and service life characteristics, and calculating the heat loss coefficient of each pipe section in combination with a thermodynamic conduction model; and dividing the pipe network into high-temperature steam pipe area, medium-temperature hot water pipe area and low-temperature return water pipe area according to the heat loss coefficient threshold; identifying valve position coordinates and pressure adjustment ranges, and generating a polygon boundary coordinate set of the key valve connection area.

[0006] Preferably, the multi-modal sensing module includes: deploying an infrared thermal imager array and a vibration accelerometer array in the high-temperature steam pipe area to synchronously collect pipe wall temperature distribution graphs and mechanical vibration frequency spectra; deploying a distributed fiber-optic temperature sensing array and a pressure transmitter array in the medium-temperature hot water pipe area to real-time acquire pipe axial temperature gradient curves and pressure fluctuation time series data; deploying an ultrasonic thickness gauge array and an acoustic emission sensor array in the low-temperature return water pipe area to continuously record pipe wall thickness change values and cavitation noise signals; The multi-modal sensing module marks and compressively transmits the collected original data stream according to the area number divided by the pipe network partition module.

[0007] Preferably, the fault prediction module specifically implements: receiving the multi-dimensional data packet with area marking sent by the multi-modal sensing module, performing three-dimensional convolution operation on the infrared thermal imaging data to extract spatial hot spot features, and performing long short-term memory network processing on the vibration frequency spectrum data to extract time-frequency domain abnormal patterns; fusing temperature gradient curves and pressure fluctuation time series data, and weighting and distributing feature weights through an attention mechanism; The fused multi-source features are input into a deep belief network, and a fault probability prediction value of each pipe network partition is output and marked with a time stamp.

[0008] Preferably, the fault prediction module further comprises: establishing a pipe network health state knowledge base to store historical fault feature vectors, and synthesizing training samples by using a generative adversarial network; The correlation between the temperature change rate and the pressure change rate of the pipeline is captured by the gated recurrent unit, and a pipe segment stress fatigue index is calculated; The number of hidden layer nodes of the deep belief network is dynamically adjusted according to the stress fatigue index, and the parameter weight matrix of the fault prediction model is updated.

[0009] Preferably, the hierarchical response module executes the following process: receiving the fault probability prediction value with a time stamp sent by the fault prediction module; A low-risk threshold interval, a medium-risk threshold interval, and a high-risk threshold interval are set; when the fault probability prediction value falls within the low-risk threshold interval, a first-level response instruction is generated, requiring to increase the sampling frequency of the sensor; When the fault probability prediction value falls within the medium-risk threshold interval, a second-level response instruction is generated, requiring to start the inspection robot of the specified pipe area; When the fault probability prediction value falls within the high-risk threshold interval, a third-level response instruction is generated, requiring to close the associated valve and activate the emergency cooling device.

[0010] Preferably, the dynamic control module executes the following process in response to the second-level response instruction: obtaining the abnormal pipe segment coordinate set output by the fault prediction module, and generating a moving trajectory of the inspection robot by a path planning algorithm; According to the actual pipe wall temperature value provided by the multi-modal sensing module, the scanning resolution of the thermal imager carried by the robot is adjusted; According to the noise decibel value collected by the acoustic sensor array, the detection depth parameter of the ultrasonic flaw detector of the mechanical arm is modified in real time.

[0011] Preferably, the system further comprises a model optimization module: collecting instruction execution records of the hierarchical response module and actual pipe network state data, and constructing a feedback data set; By comparing the theoretical probability value output by the fault prediction module with the actual fault occurrence, a prediction deviation degree is calculated; when the prediction deviation degree exceeds the allowable error limit, the generative adversarial network is triggered to reconstruct the training sample distribution; The optimized parameter weights are synchronously updated to the deep belief network in the fault prediction module by using a transfer learning algorithm.

[0012] Preferably, the model optimization module is specifically implemented: a double-channel communication link is established to receive fault confirmation information input by the field maintenance personnel; The fault confirmation information and the original data stream collected by the multi-modal sensing module are spatio-temporally aligned; Locate the weak layer of the deep belief network through the characteristic inversion algorithm, insert the residual connection structure to reconstruct the network architecture; Use the reinforcement learning algorithm to dynamically adjust the heat loss coefficient calculation rules of the pipe network partition module.

[0013] Preferably, the application also includes a deep learning-based fault inspection method for a heat and power pipe network, applied to a deep learning-based fault inspection system for a heat and power pipe network as described above, the method comprising: Process the geographic information data of the heat supply pipe network through a convolutional neural network, and divide the high-temperature steam pipe area, the medium-temperature hot water pipe area, the low-temperature return water pipe area, and the key valve connection area; Distribute a multi-modal sensor array within the partition, and collect temperature distribution maps, pressure fluctuation curves, and acoustic spectrum data; Fuse multi-source spatio-temporal features through a deep belief network, and output a timestamped fault probability prediction value; Set a multi-level probability threshold interval, and trigger a response instruction according to the threshold interval in which the fault probability prediction value is located; Control the movement path of the inspection robot and the detection equipment parameters according to the response instruction; Collect on-site fault confirmation information and equipment response data, and optimize the fault prediction model through a generative adversarial network.

[0014] Compared with the prior art, the application has the following advantages: Through the pipe network partition module, the heat and power pipe network is accurately partitioned according to geographic information system data, and the high-temperature steam pipe area, the medium-temperature hot water pipe area, the low-temperature return water pipe area, and the key valve connection area are distinguished, so that subsequent sensing and monitoring can be carried out according to the medium characteristics and structural features of different areas, avoiding the problem of insufficient pertinence caused by the uniform method used in traditional monitoring. The multi-modal sensing module distributes temperature, pressure, and acoustic sensor arrays within each partition, and can comprehensively collect multi-dimensional operation data in different areas. Temperature data can reflect the heat loss or abnormal temperature rise of the pipeline, pressure data can reflect the stability of medium transportation, and acoustic data can help capture the subtle sound of pipeline leakage or valve abnormalities. The combination of the three types of data realizes comprehensive perception of the operation state of the pipe network, and compared with single sensor monitoring, it can more comprehensively capture potential fault clues. The fault prediction module uses a deep belief network model to process the multi-dimensional time series data stream collected by the multi-modal sensing module. This model has strong spatio-temporal feature extraction capability, can mine fault correlation information in different areas and different time dimensions from complex data, and then output fault probability values. This deep learning-based prediction method can break through the limitations of traditional data analysis methods, more accurately identify early fault signs, and realize early perception of faults. The hierarchical response module triggers different levels of response instructions according to the comparison result of the fault probability value and the preset threshold interval, so that the processing of the fault can take corresponding measures according to its severity. For low-probability potential faults, a regular inspection plan can be started; for high-probability emergency faults, an emergency troubleshooting process is immediately started. This differentiated response mode avoids waste of resources and ensures rapid response to serious faults. The dynamic control module controls the movement path of the inspection robot and the detection equipment parameters according to the instructions of the hierarchical response module, so that the inspection resources can be flexibly allocated according to the fault condition. When a certain area has a higher fault probability, the inspection robot can be guided to the area for intensive detection, and the parameters of the detection equipment are adjusted to obtain more accurate data, improving the efficiency of fault positioning and confirmation, and making the entire inspection process more targeted and flexible. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The working principle diagram of the system for thermal power pipe network fault inspection based on deep learning according to the present application; Figure 2 The flowchart executed by the pipe network partition module; Figure 3 The flowchart deployed by the multi-modal sensing module; Figure 4 The flowchart of data processing of the fault prediction module; Figure 5 The flowchart of the dynamic control module responding to the second-level instruction. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0017] Please refer to Figure 1 The present application provides a system for thermal power pipe network fault inspection based on deep learning, which comprises a pipe network partition module, a multi-modal sensing module, a fault prediction module, a hierarchical response module and a dynamic control module. The specific implementation steps are as follows: The pipe network partition module first processes the heat pipe network data in the geographic information system, identifies the pipe network topology, and divides the entire pipe network into high-temperature steam pipe area, medium-temperature hot water pipe area, low-temperature return water pipe area and key valve connection area.

[0018] The multi-modal sensor module deploys a distributed sensor array within each sub-zone, including a temperature sensor array, a pressure sensor array, and an acoustic wave sensor array, for collecting multi-dimensional time-series data streams.

[0019] The fault prediction module receives and processes these data streams, extracts spatio-temporal features using a deep belief network model, and outputs timestamped fault probability prediction values.

[0020] The hierarchical response module triggers different levels of response instructions based on the comparison results of the prediction values and the preset probability threshold intervals.

[0021] The dynamic control module adjusts the movement path of the inspection robot and the operating parameters of the detection equipment carried by it according to the response instructions, forming a closed-loop control.

[0022] Embodiment 1: Refer to Figure 2 The pipe network sub-zone module constructs a heat pipe network topology model by structuring geographic spatial data, and the processing procedure includes three stages. The first stage analyzes the original data in the heat pipe network geographic information system database, including the three-dimensional coordinate distribution map of each pipe segment, the pipe diameter numerical table, and the installation location description file. The spatial direction regularity of the pipe is identified through a feature extraction algorithm. This algorithm uses a neural network structure containing multiple convolution kernels to process the input data. Different sizes of convolution kernels capture key information such as the extension direction of the main pipe, the connection points of branch pipes, and the pipe diameter mutation positions. Then, the data volume is compressed through dimension reduction operation, and finally, a matrix model describing the spatial connection relationship of each pipe is output. This matrix accurately records the number and spatial orientation of the physical connection nodes between the main pipe and branch pipe.

[0023] The second stage combines the physical properties of the pipe for thermodynamic analysis and completes the sub-zone. The pipe material composition parameters and service life records are extracted from the engineering database, including multiple types such as carbon steel, stainless steel, and composite materials. A dynamic heat transfer analysis model is constructed, which introduces environmental temperature variables and pipe wall insulation material thermal conductivity parameters. According to the preset time step, the heat conduction loss of the pipe unit is calculated node by node. Each pipe segment node is compared with the preset sub-zone boundary condition according to the heat transfer characteristic quantitative value. When the heat loss parameter of the node is greater than or equal to the set reference value α, the pipe segment is marked as a high-temperature steam pipe zone unit; when the heat loss parameter is between the set reference values α and β, it is classified into a medium-temperature hot water pipe zone; and when the heat loss parameter is lower than the reference value β, it is classified into a low-temperature return water pipe zone. All sub-zone boundaries are stored in the form of a spatial coordinate set, and the sub-zone result synchronously generates a corresponding number and sub-zone attribute table.

[0024] The third stage is to build the spatial area of the key valve of the pipe network. The coordinate position list and pressure regulation range dataset of the valve management system are obtained, and the spatial triangulation algorithm is used to establish the valve position association network. The algorithm first identifies the spatial distance relationship of adjacent valve nodes, generates a triangular facet coverage area based on the valve influence radius, and each triangular facet vertex contains the accurate latitude and longitude coordinates of the valve node. To build a complete valve control influence area, a polygon boundary is constructed based on the triangular facet vertex set, and the polygon expansion scale is dynamically determined according to the set proportion of the upper limit of the valve pressure regulation range. The boundary buffer distance calculation formula considers the pressure wave propagation characteristics, and the generated closed polygon area covers all valve equipment positions and their action ranges. Finally, the valve partition spatial coordinate set with topological relationship is output, which is stored in the spatial database together with the temperature partition dataset.

[0025] In the partition model visualization link, the spatial data of high temperature area, medium temperature area, low temperature area and valve area are input into the three-dimensional rendering engine. The engine uses different color schemes to distinguish various partition models. The high temperature area pipeline is presented with a red curved surface model, the medium temperature area is displayed with an orange gradient geometric body, the low temperature area pipeline is visualized with a blue grid structure, and the valve area generates a translucent yellow polyhedron coverage area. The three-dimensional scene supports topological relationship query function, engineers can obtain the partition attribute and associated valve control area information of the selected pipe segment. The partition database synchronously establishes the space-time index structure, the index key value includes partition number, generation timestamp, regional boundary coordinate characteristic value and other elements. This design ensures that the subsequent modules can quickly locate the target area data.

[0026] The partition parameter calibration mechanism continuously optimizes the model accuracy. The system regularly retrieves newly added pipe network reconstruction construction records, and when the pipe direction change or valve position adjustment is detected, the partition recalculation program is automatically triggered. The calibration process prioritizes preserving the original partition logic framework, and only re-divides the local pipe segment within the changed influence range. Each calibration operation generates a version record log, which includes change position coordinates, original partition attributes, updated partition attributes, and change trigger time parameters. The partition historical version data is archived and saved through a time axis storage structure, which facilitates partition strategy backtracking analysis.

[0027] The partition module output interface defines a standardized data structure package, and each partition entity in the data package contains a region type identifier, a polygon boundary point sequence, an associated device list, and a set of thermodynamic characteristic parameters. The set of thermodynamic characteristic parameters stores the reference temperature range of the partition, the pressure fluctuation threshold, the material fatigue parameters, and other physical properties. The data package is output in binary and JSON dual formats synchronously to adapt to the data reading needs of different downstream modules. The partition module operating cycle is executed according to a preset schedule, and the partition model is updated once a day during routine inspection. When changes in the pipe network structure are sensed, the real-time update mode is started. The partition execution status indicator light is displayed in green on the control system operation panel, indicating that the model is running normally, orange indicates that partition calculation is being performed, and red indicates that the partition data is abnormal and requires manual intervention.

[0028] After the valve correlation network is established, the cooperation logic of the main control valve and the standby valve needs to be determined. The system analyzes the coverage range of the pressure regulation ability of each valve. When a single pipe unit is located in the influence area of multiple valves, the valve with the largest pressure regulation range is marked as the main control node, and the rest are auxiliary nodes. This cooperation rule table of multiple valve coverage areas is written into the attribute field of the partition database, and the cooperation strategy is dynamically corrected according to the changes in valve operating parameters. The valve partition data stream and the temperature partition data set are physically isolated during transmission, and only when the system issues a pipe network emergency response instruction is the logical association query operation performed.

[0029] The module is deployed on a data processing server equipped with parallel computing units. The server is configured with high-frequency processors and large-capacity high-speed storage arrays to ensure the efficiency of partition calculation. The system resource monitoring unit records the memory usage and processor resource consumption curve during partition calculation. When the resource usage exceeds the predetermined threshold, the algorithm parameters are automatically optimized. The fault-tolerant mechanism starts the pre-plan mode when data acquisition is abnormal. The pre-plan mode uses historical partition template data to fill in the missing pipe segment position information. After the data channel is restored, data consistency verification is performed. Partition log files are archived and compressed daily, and the storage period meets the data security standards of industrial control systems.

[0030] Example 2: see Figure 3 , the multi-modal sensing module implements differentiated sensing strategies according to the region categories divided by the pipe network partition module. The region sensing device deployment scheme and data acquisition process are as follows: In the high-temperature steam pipeline area, monitoring stations are arranged at fixed intervals along the pipeline axis. Each station is equipped with an infrared thermal imaging device and a three-axis vibration sensor. The infrared thermal imaging device lens is aimed at the pipe wall surface, and temperature distribution images are acquired at set time intervals. The image resolution meets the requirements of industrial detection standards, and the temperature acquisition range covers the typical working condition interval of the steam pipeline. The vibration sensor is fixed to the outer wall of the pipeline through a magnetic base and continuously collects mechanical vibration waveform data. The sampling frequency meets the fault feature extraction requirements of rotating equipment. All acquisition devices have built-in temperature compensation units to eliminate the influence of environmental temperature fluctuations on measurement results.

[0031] The monitoring scheme for the medium-temperature hot water pipeline area uses distributed optical fiber temperature measurement technology and pressure sensing technology. A special optical fiber sensing line is laid along the outer wall of the pipeline. Temperature demodulation nodes are set at specific lengths along the optical fiber line, and the node spacing is determined based on the heat conduction characteristics of the pipeline. The demodulation nodes emit modulated light signals and receive backscattered light. By analyzing the time domain of the light signals, an axial temperature distribution curve of the pipeline is generated. Pressure sensing devices are installed at key node flanges of the pipeline. The sensing probe physically contacts the pipeline internal medium through an isolation diaphragm and converts it into a standard electrical signal output in real time. The pressure data acquisition rate is dynamically adjusted according to the fluid mechanics characteristics, and the sampling frequency is automatically increased when the flow rate is high.

[0032] The monitoring focus of the low-temperature return water pipeline area is on pipeline wall structural integrity evaluation and fluid state analysis. Ultrasonic wave transmitting and receiving device pairs are uniformly arranged on the pipeline surface, maintaining a fixed angular relationship between the device pairs. The transmitting unit periodically generates high-frequency mechanical waves, and the receiving unit captures the sound waves that penetrate the pipe wall. The thickness change is calculated based on the time difference of the sound waves. Meanwhile, broadband acoustic sensing devices are installed at pipe bends and reducers. These devices are equipped with anti-interference filter circuits to effectively collect specific frequency band noise signals generated by cavitation inside the pipeline. All sensing units are equipped with moisture-proof housings and electromagnetic shielding layers to adapt to the high-humidity environment of underground pipe galleries.

[0033] The original data collected in each region are embedded with the region identification code generated by the pipeline partition module. The identification code includes the partition type, geographic coordinate segment number, and device serial number. Temperature distribution image data are converted to grayscale matrix format, vibration waveform data retain the original voltage range, pressure data are stored as floating point arrays, and acoustic data are in the form of waveform segments with time markers. The data compression unit performs differentiated encoding schemes on each type of data stream: image data use lossy compression algorithms to retain temperature feature region details, and waveform data use lossless encoding to maintain time domain integrity. The compressed data packets are attached with cyclic redundancy check codes and transmitted to the central processing unit through the industrial field bus network.

[0034] The central processing unit sets up a multi-channel data receiving buffer, which automatically classifies and stores data streams according to regional identification codes. Each data channel is configured with an independent clock synchronization unit, which receives satellite timing signals and applies a unified timestamp to each regional data packet. The raw data storage area uses a ring buffer structure, with new data covering old data that exceeds the storage period. The data preprocessing unit performs standardization operations on the raw data to eliminate range differences caused by different batches of sensors.

[0035] The sensor network management system monitors the operating status of each regional device in real time. Device status polling instructions are sent to each sensor node at a predetermined period, and the node returns diagnostic information including battery voltage, signal strength, environmental temperature, and other parameters. When the diagnostic information is abnormal, the system automatically marks the location of the faulty device and alerts in the form of a flashing icon on the topology map. The device maintenance interface supports remote configuration of sensor parameters, and engineers can modify the sampling frequency, range, and transmission protocol of specific regional sensors through the human-machine interface.

[0036] In terms of data quality assurance, the system implements multiple verification mechanisms. The transmission layer verification mechanism verifies the integrity of the data packet, and the application layer verification mechanism analyzes the reasonableness of the data. When detecting that the temperature value exceeds the tolerance limit of the pipeline material or the pressure value presents a non-physical jump, the system discards the abnormal data segment and triggers the data re-sampling process. All abnormal events are recorded in the device operation log, which includes fields such as the time of abnormal occurrence, device number, abnormal type, and disposal measures.

[0037] The sensor data distribution interface provides a standard format input stream for the fault prediction module. This interface generates a data structure body containing time series, which is divided into three logical layers: the first layer stores regional attribute descriptors, the middle layer stores multi-dimensional sensor data arrays, and the bottom layer stores data quality identification bits. The interface protocol defines two calling modes: synchronous mode for real-time prediction tasks and asynchronous mode for historical data backtracking analysis. The data stream transmission process uses an industrial encryption protocol, and the key is periodically updated according to the device group.

[0038] The system maintenance module performs periodic sensor device calibration tasks. The calibration program selects standard sources according to device types: thermal imaging devices use blackbody radiation sources for calibration, vibration sensing devices use standard vibration tables for calibration, and pressure sensing devices connect to precision pressure generators for calibration. Calibration data is recorded in the device lifecycle archive, which includes factory parameters, historical calibration data, and error correction curves. Unqualified devices are automatically placed in isolation, and the system switches to backup sensor nodes to continue working.

[0039] For critical valve connection areas, the sensing scheme adds pressure regulation characteristic monitoring. A displacement sensor and a torque sensor are installed at the valve actuator, the displacement sensor records the valve opening trajectory, and the torque sensor detects the valve operating torque fluctuations. The data acquisition terminal is integrated inside the valve controller, and real-time communication is maintained with the central processing unit through the device bus. The valve state data stream and the pipeline monitoring data stream are strictly aligned in the time dimension, providing complete data chain for analyzing the impact of valve action on pipeline operating conditions.

[0040] During the module deployment phase, strict equipment installation specifications must be established. High-temperature area sensing devices use high-temperature resistant alloy housings and thermal insulation layers, and cables are wrapped with mineral insulation materials. Underground pipeline sensing devices are equipped with waterproof junction boxes and mechanical protective covers. All cable laying paths avoid high-temperature pipelines and sharp metal edges, and sufficient curvature radius is maintained at turns. The installation location is sprayed with reflective identification codes, which completely correspond to the equipment number in the pipeline partition database. Channel testing and background noise testing are performed during the installation and acceptance stage, and the test data is stored in the system initialization parameter library as a reference value.

[0041] The clock synchronization system uses a hierarchical time correction architecture. The master clock receives dual-mode time signals from Beidou satellite and GPS, the secondary clock is distributed in each regional data acquisition station, and the tertiary clock is integrated in the sensing device microprocessor. The time correction protocol includes initial time correction, periodic calibration, and deviation compensation. When the network is interrupted, each device maintains a local time reference based on the accuracy of the built-in crystal oscillator, and automatically performs time correction after the network is restored. The time marker is accurate to the millisecond level, meeting the time accuracy requirements of multi-source data fusion analysis.

[0042] The sensing network power supply system is designed with multiple redundancy structures. The main network uses the industrial power over Ethernet standard, and transmits DC power through data cables. Key monitoring points are equipped with uninterruptible power supply modules, and the standby power supply can work continuously for more than the maximum fault recovery time limit of the system. Solar power supply devices are used in passive areas, and the energy storage unit capacity meets the normal operation demand under continuous rainy weather. The power state monitoring unit displays real-time voltage and current parameters of each node, and triggers automatic switching operation of the power supply path when abnormal.

[0043] The data storage management system implements a multi-level archiving strategy. Real-time data retention period covers the entire process of typical fault development, medium-term storage data retention time meets the system optimization analysis requirements, and long-term archived data is saved according to the pipeline life cycle management requirements. The data retrieval interface supports multi-condition combined query, which can quickly extract specific time period, specific area category or specific sensor type original data records. The data export format is compatible with mainstream industrial analysis software, supporting binary stream and structured text dual output mode.

[0044] The fault diagnosis interface receives the anomaly localization request of the prediction module. When the prediction module detects a specific area data anomaly, the diagnosis interface instructs the corresponding area sensor device to start the enhanced monitoring mode. In the enhanced mode, the sampling frequency is automatically increased, the backup sensing channel is activated, and the data collection time is extended. The diagnosis data packet is additionally added with a fault analysis identifier, and is given the highest priority in the transmission queue. The system maintains a passive response mode and only performs specific diagnostic operations when receiving explicit instructions, avoiding unnecessary data overload.

[0045] Embodiment 3: refer to Figure 4 The fault prediction module implements multi-source data fusion analysis and dynamic model optimization, and the processing flow includes four stages of data feature extraction, space-time feature fusion, fault probability calculation and model adaptive adjustment. The data packet with area label transmitted by the multi-modal sensing module first enters the preprocessing channel. The infrared thermal imaging data is processed by spatial standardization to generate a uniform size temperature distribution matrix. The matrix is input into a three-dimensional convolution architecture to extract thermal anomaly features. The convolution architecture is set with three groups of continuous convolution operation layers, each group of convolution kernel size is fixed, the number of convolution kernels in the first layer is set to a specific value, and the feature map size decreases layer by layer but the depth increases. The vibration spectrum data stream is divided into equal length time segments using a sliding window, each segment is input into a bidirectional long short-term memory network unit, the number of network hidden layer neurons is set according to the spectrum width of the pipeline vibration historical data, and the output includes feature vectors containing time domain waveform anomalies and frequency domain energy aggregation.

[0046] The temperature gradient curve and the pressure fluctuation time series data perform time axis alignment operation, and the alignment reference adopts the clock pulse count of the sensing data collection terminal. The aligned data stream is normalized to eliminate the influence of different physical dimensions. The normalized data is input into a multi-head attention fusion architecture, the number of attention heads is fixed to an integer value, and each head independently calculates the correlation weight of different sensing features. The weight distribution coefficient is dynamically adjusted by the correlation measure value of the feature and the fault type, and the correlation measure is obtained by statistical analysis of historical fault data. The fusion feature vector dimension is compressed to a preset length, and the vector elements contain the comprehensive expression of thermodynamic state, mechanical vibration characteristics and fluid pressure characteristics.

[0047] The deep belief network receives the fusion feature vector to make a probability prediction. The network structure includes a stack of three layers of restricted Boltzmann machines, and the number of nodes in each layer is in a decreasing distribution: the number of nodes in the first layer corresponds to the dimension of the fusion feature, the number of nodes in the middle layer is halved, and the number of nodes in the last layer is halved again. The restricted Boltzmann machine updates the weight parameters using the contrast divergence algorithm, and the initial values of the connection weights between the visible layer and the hidden layer follow a Gaussian distribution. The output layer of the network uses a nonlinear activation function to map the calculation results to the [0, 1] interval, and the output value represents the probability of failure occurring at the current time in the region, and the timestamp is taken from the exact millisecond when the data packet arrives. The probability prediction value is attached with the region identification code to form a complete prediction record, and the record storage format includes the partition number in the header, the probability value and the timestamp in the middle, and the check code in the tail.

[0048] The model dynamic optimization mechanism is based on real-time adjustment of the stress state of the pipeline. The system continuously calculates the coordinated fluctuation index of the temperature change rate and the pressure change rate, which is input into the gate recurrent unit to generate a pipe segment stress fatigue index. The index calculation formula is:

[0049] where F represents the stress fatigue index (dimensionless), σ is the temperature change weight coefficient (°C / s), τ is the pressure change weight coefficient (MPa / s), -1 -1 represents the temperature change per unit time (°C / s), represents the pressure change per unit time (MPa / s). When the F value exceeds the preset threshold, the number of nodes in the first layer of the deep belief network is automatically expanded to a set multiple of the original value, and the new node weight is injected into the network using the orthogonal initialization method. The network parameter update uses the gradient descent method with momentum, and the momentum coefficient remains a fixed value, and the learning rate decays exponentially with the training round.

[0050] The pipeline health state knowledge base stores structured fault feature records. Each record includes the time of failure, the location of the region, the fault type code, and a multi-dimensional sensor feature snapshot. The knowledge base uses a graph database to store entity relationships, with fault types as central nodes and associated sensor feature subgraphs. When the generative adversarial network synthesizes training samples, the generator inputs a random vector in the latent space and outputs a synthesized feature vector through a four-layer fully connected network; the discriminator uses a three-convolutional-layer structure to judge the authenticity of the features. The training process sets an early stopping mechanism to prevent overfitting, and terminates training when the discrimination accuracy of the synthesized samples is less than the threshold for a continuous number of iterations. The upper limit of the proportion of synthesized samples injected into the training data set is set to a specific percentage of the original data amount.

[0051] ​​The online learning module establishes a model performance monitoring closed loop. The actual working condition of the to-be-predicted area is compared with the prediction result in real time, and when the prediction deviation exceeds the allowed range for a plurality of times in succession, a local retraining process is triggered. The retraining data window length is set according to the physical inertia characteristics of the pipe network, the training process freezes the network bottom feature extraction layer, and only the upper layer weight parameters are fine-tuned. The model version management adopts a rolling update strategy, and the new verified model weight gradually replaces the old version online, and the replacement process adopts a gray release mechanism: the first phase switches a small number of area prediction tasks to the new model, and expands to the whole network after confirming that there is no error.

[0052] The computing resource scheduling unit optimizes the processing efficiency. The deep belief network forward propagation task is assigned to a graphics processor cluster, and the cluster is set to a master-slave architecture: the master node assigns the computing task, and the slave node executes the matrix operation. The data loading adopts a double buffering technology: when the processor executes the current batch prediction, the next batch data is preloaded into the cache. The network parameters are stored in a high-speed non-volatile memory, so that the system can quickly recover the prediction state after power failure and restart.

[0053] The abnormality processing mechanism includes data abnormality and model abnormality double paths. When the data abnormality detects that the feature value exceeds the physically possible range, the data packet is discarded and the retransmission is requested from the sensing module. When the model abnormality detects that the probability output is continuously zero or the probability distribution is abnormal, the system automatically rolls back to the historical stable version model. The abnormal event triggers a diagnostic data acquisition mode, which saves the intermediate calculation results in the whole process of fault prediction, including the convolution layer feature activation map, the attention weight distribution matrix and the output values of each layer of the network. The diagnostic data packet is stored in binary format, and the file naming includes the event triggering timestamp and the area code.

[0054] The prediction result output interface supports multiple calling protocols. The real-time data stream interface adopts a publish-subscribe mode, and the subscriber registers the concerned area list to obtain continuous probability push. The batch query interface supports time range retrieval of historical prediction records, and the returned result set includes the probability curve of the time sequence. The emergency alarm interface actively pushes the alarm message when the probability value jumps, and the message body includes the area location, the probability change amount and the recommended response level. All interface communication data implements end-to-end encryption, and the key negotiation adopts an elliptic curve digital signature algorithm.

[0055] The model explanation component generates a visual analysis report. The report shows the main feature contribution degree ranking, and lists the top several sensing features and their weight values that affect the current prediction probability. A three-dimensional heat map displays the global probability distribution of the pipe network, and different probability intervals are rendered with a gradient color scale. The key area provides feature attribution analysis, which shows the correlation between the high temperature area in the infrared thermal image and the abnormal frequency band in the vibration spectrum. The report update frequency is synchronized with the prediction task, and the storage period covers the complete maintenance year.

[0056] The version control system records all model change trajectories. Each weight update generates a difference report, which quantifies the change in connection weights and bias parameters of the new model relative to the old model. The model performance evaluation dataset is independent of the training set and contains test samples labeled with true fault states. The test samples are imported through isolated acquisition channels, and the evaluation results generate trend charts of indicators such as precision and recall. The indicator data is stored in the model lifecycle database, which retains the evaluation results of all historical version models.

[0057] The hardware acceleration unit integrates a field programmable gate array module. This module solidifies the forward propagation computation logic of the deep belief network, enabling parallel matrix multiplication operations. The programmable gate array configuration cache bypasses the central processing unit and connects directly to the memory pool for data transmission. The power consumption control unit dynamically adjusts the clock frequency of the computation unit and automatically enters energy-saving mode when the predicted load decreases. The cooling system adjusts the fan speed based on the processor core temperature, and temperature sensors are distributed in key heating areas on the circuit board.

[0058] The prediction service monitoring panel displays the running status in real time. The core area of the panel shows the current fault probability values of each partition, with risk levels represented by color depth. The side bar displays the computation resource occupancy curve, data processing throughput statistics, and model version information. The alarm information area displays recent prediction anomaly events in a scrolling manner, supporting filtering and display by severity level. The historical record query area supports playback of probability changes by time dimension, with time resolution adjustable to the minute level. The panel is configured with multiple screen output interfaces to meet the needs of large-screen display in industrial control centers.

[0059] Embodiment 4: refer to Figure 5 The hierarchical response module and the dynamic control module work together to form a closed-loop control chain from risk identification to physical execution. After the fault prediction module outputs the time-stamped fault probability value, the hierarchical response module starts the threshold comparison process. The system presets three risk judgment intervals: probability values in the 0 to 0.3 interval are low risk, 0.3 to 0.7 are medium risk, and 0.7 to 1.0 are high risk. Each level triggers a differentiated response instruction set, with instruction parameters dynamically fine-tuned according to the specific value of the probability value.

[0060] The response instructions are transmitted to the dynamic control module through industrial communication protocols. In low-risk scenarios, level one response instructions are generated, with the core operation being to increase the monitoring density of abnormal areas. The system retrieves the sensor device number list of the target area and sends instructions to the temperature sensor nodes to increase the sampling frequency from the regular value to a specific multiple, and the pressure sensor synchronously increases the number of sampling points. The sensor data return period is shortened to a set proportion of the original interval, and the duration is automatically calculated according to the probability value change rate. In this state, the control center operation screen displays a yellow warning symbol, and the historical data record is marked as an enhanced monitoring mode.

[0061] The medium-risk scenario triggers a secondary response instruction, which contains both mechanical response and data analysis tasks. The system first parses the abnormal pipe segment coordinate set output by the prediction module, which is in the format of a sequence of latitude-longitude coordinate pairs. The path planning engine calls the pipe network topology map and uses the improved A* algorithm to calculate the optimal movement path for the robot. The path weight calculation introduces actual pipe working condition parameters: when the pipe wall temperature exceeds the safety threshold, the path cost weight of this pipe segment increases; when multiple abnormal points exist, a serial detection path is automatically generated. At the same time, a wake-up instruction is sent to the inspection robot deployed in the target area, and the robot self-checks pass and moves along the planned path.

[0062] The robot dynamically adjusts the detection parameters during movement. The central processor receives the actual pipe wall temperature readings from the multi-modal sensing module in real time, and when the temperature reading breaks through the critical threshold, it automatically upgrades the resolution level of the infrared thermal imager carried by the robot. The resolution adjustment rule uses a ladder strategy: the basic resolution is maintained in the normal temperature range, the medium resolution mode is switched to when the temperature rises to a certain range, and the highest resolution scanning is enabled when the temperature reaches the high-risk temperature. The acoustic parameter adjustment unit works synchronously, and according to the noise decibel value returned by the acoustic emission sensor, the detection depth parameter of the ultrasonic flaw detector of the mechanical arm is modified according to the preset mapping relationship. The detection depth and noise decibel value change positively, and when the noise increases, the detection depth is automatically increased to capture internal defects.

[0063] The high-risk scenario generates a tertiary response instruction, which activates the device linkage protection mechanism. The system first locates the associated valve position and sends a valve closing sequence instruction through the control bus. The closing sequence is determined according to the pipe network hydraulic model calculation: upstream valves are closed before downstream valves, and high-pressure valves are closed before low-pressure valves. The closing instruction contains the opening degree change rate parameter, which is dynamically set according to the pipe pressure change gradient. The emergency cooling device is started simultaneously, and the cooling water flow is calculated according to the product of the pipe diameter and the prediction probability value, and the spray angle is automatically calibrated according to the pipe direction. The control center switches to the red emergency state interface and automatically generates an accident impact range simulation diagram.

[0064] The response instruction execution process is recorded in the operation log, which contains time markers and parameter correction trajectories. A typical execution record is as follows: Timestamp Region Number Predicted Probability Value Response Level Action Summary Control Parameter Adjustment Detail 2025-03-1214:05:23 HS_Z7 0.25 Level 1 Increase Sampling Frequency Temperature Sensor Sampling Interval from 5 s -> 2 s; Pressure Sensor Sampling Point from 200 points / s -> 500 points / s 2025-03-1218:30:41 MW_Z3 0.52 Level 2 Start Robot Inspection Path Length 83 meters; Thermal Imager Resolution 640x480 -> 1280x960; Ultrasonic Detection Depth 8 mm -> 12 mm 2025-03-1309:17:05 LW_Z9 0.86 Level 3 Close Valves HV-209 / HV-211; Start Cooling Unit Valve Closing Rate 15% / s; Cooling Water Flow 350 L / min; Spray Angle 32° The robot control subsystem contains a motion coordination mechanism. When multiple robots are used for collaborative inspection, the central controller assigns detection areas and establishes a collision avoidance safety distance. When the robot approaches a high-temperature pipe segment, the movement speed is automatically reduced to a safety value. The mechanical arm operation logic sets spatial avoidance rules: when the temperature of the surface of the adjacent pipe exceeds the warning value, the mechanical arm automatically maintains a minimum safety distance. The detection data is transmitted back to the fault prediction module in real time, forming a feedback data stream to update the prediction model.

[0065] The valve operation feedback system monitors the execution effect. The valve opening sensor returns the position signal in real time, and when the actual closing rate deviates from the command value, the system automatically compensates the subsequent valve operation parameters. The cooling device water flow sensor verifies the actual flow, and when the deviation exceeds the set percentage, the pressure compensation pump adjusts the flow. All execution data are time series correlation analyzed with predicted probability values, and the analysis results are used to optimize the response threshold boundary.

[0066] The emergency response termination process sets a double confirmation mechanism. When the predicted probability value falls back to the safety interval, the system first maintains the current response state for a certain period of time. When no abnormal fluctuations are detected in the new sensor data during the confirmation period, the hierarchical response module sends a release instruction. The release instruction performs reverse operations: reducing the sampling frequency to the regular value, recalling the inspection robot, restoring the original state of the valve, and closing the cooling device. The operation interface switches to the green normal state, and an event analysis report is generated and archived.

[0067] The control instruction transmission adopts multiple check mechanisms. Key instructions such as valve closing commands need to be sent through three independent communication channels, and the receiving end uses a voting mechanism to confirm the validity of the instructions. The instruction data packet contains a cyclic redundancy check code and a sequence number, and the receiving end automatically requests retransmission when the check fails. The historical instruction storage area retains the operation records of the last few times, supporting instruction sequence playback and problem tracing.

[0068] The dynamic control module hardware adopts a triple modular redundancy architecture. The main controller performs real-time control tasks, and the standby controller synchronously receives all data streams, automatically switching when the main controller fails. The control instruction generation unit and the execution unit are physically isolated, and only transmit signals through optical coupling devices. The power system is configured with dual independent power supply and battery pack, maintaining the continuous operation of the core control system during power interruption.

[0069] The human-machine collaborative interface allows engineers to intervene in the control process. The control console is equipped with a manual priority knob, which suspends automatic response instructions when switched to manual mode and waits for manual decision. The operation screen displays recommended operation schemes and parameter suggestions, and engineers can modify the response level or adjust the control parameters. All manual operation records are attached with electronic signatures and stored separately from automatic control records.

[0070] The response effect evaluation module generates analysis reports regularly. The report compares the predicted probability curve with the response instruction timeline, and marks the probability change trend after the instruction is triggered. The execution delay time, parameter adjustment accuracy, and fault suppression effect of different response levels are statistically analyzed, which are used to optimize the response threshold interval division rules. The report generation cycle is synchronized with the pipeline maintenance plan, serving as an important input for system optimization.

[0071] Example 5: The model optimization module establishes a closed-loop mechanism for continuous improvement of prediction performance, driven by multi-dimensional feedback data to iteratively upgrade the system. Field maintenance personnel use a dedicated handheld terminal to submit fault confirmation information, which includes fault occurrence location coordinates, fault type code, severity rating, and treatment measures record. The terminal establishes a dual-channel connection with the central database through an encrypted wireless link, with the main channel transmitting structured form data and the auxiliary channel uploading on-site pipe state image data. The system automatically extracts the time stamp and spatial coordinates from the form and performs time-space alignment with the raw data stream collected by the multi-modal sensing module: time alignment uses a dynamic scaling algorithm to compensate for clock drift between devices, and spatial alignment is completed based on the pipe network partition coordinate mapping table.

[0072] After the fault confirmation information is associated with the sensing raw data, a feedback data set is formed. The data set storage format includes three layers of structure: the basic layer records the fault event number, occurrence time stamp, and partition location; the intermediate layer stores the sensing raw data array within the corresponding time window; and the analysis layer labels the difference between the actual fault type and the prediction module output probability value. The data retrieval interface supports filtering the data set by fault type, occurrence area, or time range, and the query results generate comparison curve graphs with a time axis.

[0073] The prediction deviation calculation unit periodically scans the feedback data set. The calculation process extracts the prediction probability value at a specific time point before the fault occurs, and compares it with the actual fault state to measure the probability distribution difference. The measurement result is quantitatively represented by the divergence index in information theory, which reflects the confidence deviation of the prediction model. The system sets an upper limit for the deviation threshold, and when multiple consecutive events exceed the threshold, the model reconstruction process is triggered. The deviation trend graph is updated in real time on the optimization monitoring interface, with different color blocks marking the model reliability level of each partition.

[0074] The adversarial generative network starts a specific workflow when reconstructing training samples. The generative network receives a random noise vector input and outputs a synthetic feature vector through a four-layer fully connected neural network. The activation function of the network's hidden layer uses a smooth nonlinear transformation, and the output layer dimension strictly matches the real sensing feature dimension. The discriminative network is configured with three convolutional layers to process input features, with the convolution kernel size decreasing with network depth, and the final output layer judges the authenticity of the sample through a logistic function. The training process uses an alternating optimization strategy: in the first round, the generative network weights are frozen to update the discriminative network, and in the second round, the discriminative network is frozen to update the generative network. The proportion of synthetic samples injected into the training set is dynamically adjusted according to the current model deviation, with an upper limit not exceeding the set value of the proportion of original data.

[0075] The architecture reconstruction of deep belief network is based on feature inversion analysis. The system loads the intermediate calculation results of fault misjudgment samples, and reversely traces the activation value distribution of each layer of the network. When it is detected that the variance of a certain hidden layer activation value is continuously lower than the critical level, it is determined that the layer is a feature expression weak layer. The reconstruction operation inserts a residual connection structure at the output end of the weak layer: the residual block contains two convolution layers and batch normalization layers, and the convolution kernel size remains a certain specification. The new layer weights use the transfer initialization method, and part of the parameters inherit the original network weight at the corresponding position. The reconstructed network performs end-to-end fine-tuning training, and the training data includes a mixed set of original samples and synthetic samples.

[0076] The heat loss coefficient calculation rule of the pipe network partition module is dynamically optimized through reinforcement learning. The state space is defined as the combination encoding of pipe material type and service life, and the action space includes the selection items of the three key coefficients in the heat conduction equation. The reward function is designed based on the partition accuracy: when the partition result matches the actual fault distribution well, a positive reward is given, and when the matching degree is low, a negative penalty is applied. The agent uses a table-based learning algorithm to update the strategy, and the expected return value of the state-action pair is stored in an incremental update table. The optimized heat loss coefficient is written into the partition module parameter storage through the configuration management interface.

[0077] The parameter migration process adopts a hierarchical update strategy. The optimized weights of the deep belief network are divided into feature extraction layer and decision layer: the feature extraction layer weights are directly overwritten after stability verification; the decision layer weights use a gradual update method, and the new and old weights are mixed at a certain proportion and gradually transition to the new weights. The migration operation is performed during the low load period of the system, and the process priority is set to the second highest level. Each migration generates a version change record, which includes the parameter modification position, value change amount and associated optimization task number.

[0078] The hardware resource allocation unit configures a special computing partition for the optimization task. The graphics processor cluster is divided into logically isolated areas, and the optimization task exclusively occupies a certain number of computing cores. The memory allocation uses a reserved pool mechanism to ensure that the training process is not interrupted due to resource competition. The task progress monitoring interface displays the current optimization stage, remaining time estimate and resource consumption. When the system detects that the temperature of the host computer exceeds the standard, it automatically reduces the calculation frequency of the optimization task.

[0079] The fault tolerance recovery mechanism establishes multiple safeguards. The training process saves intermediate model snapshots every fixed number of iterations, and the snapshot file contains the current weights and optimizer state. When the training is interrupted unexpectedly, the system automatically loads the latest snapshot to continue execution. The synthetic sample generation task sets an output verification link: when the authenticity score of the generated samples by the discriminator network is lower than the threshold, the batch of samples is automatically discarded and regenerated. The original model file is automatically backed up before the network architecture is reconstructed, and the backup is retained until the new model passes the verification test.

[0080] The version verification process includes offline testing and online trial running. The offline testing uses an independent verification dataset to evaluate the performance of the optimization model, and the test indicators include fault detection rate and false alarm rate. In the online trial running stage, the new model is deployed in a representative subarea of the pipe network, and the prediction task in this area is run simultaneously with the old model. The comparative analysis interface shows the difference between the probability curves of the outputs of the two models. When the prediction accuracy of the new model is better than that of the old model for several consecutive times, the system approves the full update. The verification report is attached with the index of all test data samples, supporting subsequent audit tracing.

[0081] The model knowledge base implements version management. The derived data generated by each optimization is stored as an independent knowledge sub-base, and a blood relationship map is established between the sub-bases. The query interface supports cross-version comparative analysis, which can compare the prediction ability changes of different optimization stages for the same type of faults. The knowledge base compression and archiving strategy converts historical version data into columnar storage format, greatly reducing the storage space occupation. The data lifecycle management strategy automatically cleans up the intermediate optimization data that exceeds the retention period.

[0082] The fault diagnosis interface responds to the optimization request of the prediction module. When the prediction module detects that there is persistent prediction uncertainty in a specific area, it sends a feature analysis request to the optimization module. The optimization module starts the targeted sample generation program to enhance the sample diversity of the fault features in that area. After generating the samples and injecting them into the training set, it triggers a local retraining task to update only the network connection weights related to the target area. The diagnosis log records all optimization intervention operations, and the operation trajectory forms a decision path graph through the visualization tool.

[0083] It should be noted that, in the present text, the relationship 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 such actual relationship or order between the entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0084] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A system for fault inspection of thermal power pipeline networks based on deep learning, characterized in that, It includes a pipeline zoning module, a multimodal sensing module, a fault prediction module, a graded response module, and a dynamic control module; The pipeline zoning module identifies the topology of the thermal power pipeline network based on geographic information system data, and divides it into high-temperature steam pipeline area, medium-temperature hot water pipeline area, low-temperature return water pipeline area, and key valve connection area; the multimodal sensing module distributes temperature sensor arrays, pressure sensor arrays, and acoustic wave sensor arrays in each area divided by the pipeline zoning module. The fault prediction module receives the multi-dimensional time-series data stream collected by the multimodal sensing module, extracts spatiotemporal features through a deep belief network model, and outputs a fault probability value. The graded response module triggers different levels of response commands based on the comparison results between the fault probability value generated by the fault prediction module and the preset probability threshold range. The dynamic control module controls the movement path of the inspection robot and the parameters of the detection equipment based on the response instructions issued by the hierarchical response module.

2. The system for fault inspection of thermal power pipelines based on deep learning according to claim 1, characterized in that, The pipeline zoning module specifically performs the following: using a convolutional neural network to analyze the pipeline coordinate distribution map and pipe diameter parameter table in the thermal pipeline geographic information database, and identifying the spatial connection relationship between the main pipeline and the branch pipeline. Extract the material characteristics and service life characteristics of the pipeline, and calculate the heat loss coefficient of each pipe section by combining the thermodynamic conduction model; divide the pipeline network into high-temperature steam pipeline area, medium-temperature hot water pipeline area and low-temperature return water pipeline area according to the heat loss coefficient threshold. Identify valve position coordinates and pressure regulation range, and generate a polygonal boundary coordinate set for the key valve connection area.

3. The system for fault inspection of thermal power pipelines based on deep learning according to claim 1, characterized in that, The multimodal sensing module includes: deploying an infrared thermal imager array and a vibration accelerometer array in the high-temperature steam pipeline area to simultaneously acquire pipe wall temperature distribution maps and mechanical vibration spectra; In the medium-temperature hot water pipeline area, a distributed fiber optic temperature sensor array and a pressure transmitter array are deployed to acquire the pipeline axial temperature gradient curve and pressure fluctuation time series data in real time. An array of ultrasonic thickness gauges and an array of acoustic emission sensors were deployed in the low-temperature return water pipeline area to continuously record the changes in pipe wall thickness and cavitation noise signals. The multimodal sensing module marks the collected raw data stream according to the area numbering divided by the pipeline partitioning module and compresses it for transmission.

4. The system for fault inspection of thermal power pipelines based on deep learning according to claim 3, characterized in that, The fault prediction module specifically implements the following: receiving multidimensional data packets with region markings sent by the multimodal sensing module, performing three-dimensional convolution operations on infrared thermal imaging data to extract spatial hot spot features, and performing long short-term memory network processing on vibration spectrum data to extract time-frequency domain abnormal patterns. By fusing temperature gradient curves and time-series data of pressure fluctuations, feature weights are assigned using an attention mechanism. The fused multi-source features are input into a deep belief network, which outputs the predicted fault probability values ​​for each pipeline section and adds a timestamp.

5. A system for fault inspection of thermal power pipelines based on deep learning according to claim 4, characterized in that, The fault prediction module also includes: establishing a knowledge base of pipeline health status to store historical fault feature vectors, and using adversarial generative networks to synthesize training samples; The correlation between the rate of change of pipe temperature and the rate of change of pipe pressure is captured by a gated circulation unit, and the stress fatigue index of the pipe section is calculated. The number of hidden layer nodes in the deep belief network is dynamically adjusted based on the stress fatigue index, and the parameter weight matrix of the fault prediction model is updated.

6. The system for fault inspection of thermal power pipelines based on deep learning according to claim 1, characterized in that, The hierarchical response module executes the following process: receiving a timestamped fault probability prediction value sent by the fault prediction module; Set low-risk, medium-risk, and high-risk threshold ranges; when the predicted fault probability falls into the low-risk threshold range, generate a first-level response command that requires an increase in the sensor sampling frequency. When the predicted failure probability falls into the medium-risk threshold range, a level-two response command is generated, requiring the activation of the inspection robot in the designated area. When the predicted failure probability falls into the high-risk threshold range, a level 3 response command is generated, requiring the associated valves to be closed and the emergency cooling device to be activated.

7. A system for fault inspection of thermal power pipelines based on deep learning according to claim 6, characterized in that, When the dynamic control module responds to the secondary response command, it performs the following: obtains the set of abnormal pipe segment coordinates output by the fault prediction module, and generates the inspection robot's movement trajectory through a path planning algorithm; Adjust the scanning resolution of the thermal imager mounted on the robot based on the actual pipe wall temperature value provided by the multimodal sensing module; Based on the noise decibel value collected by the acoustic sensor array, the detection depth parameters of the robotic arm's ultrasonic flaw detector are adjusted in real time.

8. The system for fault inspection of thermal power pipelines based on deep learning according to claim 1, characterized in that, It also includes a model optimization module: collecting instruction execution records from the hierarchical response module and actual pipeline network status data to construct a feedback dataset; The prediction bias is calculated by comparing the theoretical probability value output by the fault prediction module with the actual fault occurrence. When the prediction bias exceeds the allowable error limit, the adversarial generative network is triggered to reconstruct the distribution of training samples. The optimized parameter weights are synchronously updated to the deep belief network in the fault prediction module using a transfer learning algorithm.

9. A system for fault inspection of thermal power pipelines based on deep learning according to claim 8, characterized in that, The specific implementation of the model optimization module is as follows: a dual-channel communication link is established to receive fault confirmation information input by on-site maintenance personnel; The fault confirmation information is spatiotemporally aligned with the raw data stream collected by the multimodal sensing module; Weak layers in a deep belief network are located using a feature inversion algorithm, and the network architecture is reconstructed by inserting residual connection structures. The calculation rules for the heat loss coefficient of the pipeline network partition module are dynamically adjusted using reinforcement learning algorithms.

10. A method for fault inspection of thermal power pipeline networks based on deep learning, applied to a system for fault inspection of thermal power pipeline networks based on deep learning as described in any one of claims 1-9, characterized in that, The method includes: By processing geographic information data of thermal pipeline networks through convolutional neural networks, the high-temperature steam pipeline area, medium-temperature hot water pipeline area, low-temperature return water pipeline area and key valve connection area are divided. A multimodal sensor array is deployed in a distributed manner within the partition to collect temperature distribution maps, pressure fluctuation curves, and acoustic spectrum data; By fusing multi-source spatiotemporal features through a deep belief network, a time-stamped fault probability prediction value is output. Set multiple probability threshold ranges and trigger response commands based on the threshold range in which the predicted fault probability value falls; The movement path of the inspection robot and the parameters of the detection equipment are controlled according to the response commands; Collect on-site fault confirmation information and equipment response data, and optimize the fault prediction model through adversarial generative networks.

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