Gas pipeline inspection and maintenance method based on augmented reality technology

Abstract

The invention provides a gas pipeline inspection and maintenance method based on an augmented reality technology, and relates to a method for connecting an intelligent inspection system and an edge computing platform by wearing augmented reality equipment, and generating a three-dimensional model by utilizing a multi-modal sensor network scanning environment. The pipeline state is checked in real time, fault points are recognized by combining the quantum image processing technology, and data are processed through a distributed Turing learning algorithm. And marking fault points, identifying faults by using a quantum causal reasoning model, and displaying personalized maintenance suggestions. In the maintenance process, real-time operation guidance is provided, data are automatically recorded and uploaded to the cloud through the Internet of Things, fault prediction and maintenance optimization are carried out through big data and machine learning, and expert guidance is achieved through remote communication.

Description

Technical Field

[0001] The present invention relates to the field of augmented reality technology, and in particular to a gas pipeline inspection and maintenance method based on augmented reality technology. Background Art

[0002] With the continuous development of modern industry and the acceleration of urbanization, gas pipelines, as an important part of urban infrastructure, have attracted much attention for their safety and reliability. Traditional gas pipeline inspection and maintenance methods mainly rely on manual operation and regular inspections, which have many shortcomings.

[0003] First, manual inspections are inefficient and inaccurate. Traditional inspections rely on manual visual inspections of pipelines and simple detection tools for measurement. Limited by manual experience and tool limitations, it is difficult to fully and accurately identify potential fault points. In addition, manual inspections have a long cycle and often fail to detect and handle sudden faults in a timely manner, posing safety risks.

[0004] Secondly, traditional maintenance methods rely on manual operation, which limits maintenance efficiency and accuracy. Due to the complexity of the maintenance process, manual operation is difficult to ensure the accuracy of each step. Especially in the face of complex maintenance scenarios, the experience and skills of maintenance personnel become important factors in determining the quality of maintenance. This method is not only time-consuming and labor-intensive, but also prone to misoperation, resulting in unstable maintenance quality.

[0005] Third, existing inspection and maintenance methods make it difficult to achieve real-time data collection and analysis. Traditional methods mainly rely on paper records and simple spreadsheets for data management, which cannot achieve real-time data collection, transmission and analysis. The lag and inaccuracy of data affect the accurate judgment of the status of gas pipelines and the implementation of preventive maintenance.

[0006] In response to the above problems, some new technologies and methods have emerged in recent years, such as drone inspections and robot inspections. These methods have improved the efficiency and accuracy of inspections to a certain extent, but there are still some shortcomings. For example, drone inspections are limited by flight altitude and environmental conditions, and it is difficult to fully cover all pipelines; robot inspections are limited by their mobility and environmental adaptability, and their application in complex environments is limited. In addition, these methods still rely on traditional technologies in data processing and analysis, making it difficult to achieve real-time analysis and processing of large-scale, multi-source data, and cannot effectively support preventive maintenance and intelligent decision-making. Therefore, how to provide a gas pipeline inspection and maintenance method based on augmented reality technology is an urgent problem that technicians in this field need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose a gas pipeline inspection and maintenance method based on augmented reality technology. One purpose of the present invention is to propose a gas pipeline inspection and maintenance method based on augmented reality technology. By integrating multimodal sensor networks, quantum image processing technology, distributed Turing learning algorithm and Internet of Things technology, comprehensive monitoring and intelligent analysis of the gas pipeline status are realized, and it has the advantages of high efficiency, precision, safety and intelligence.

[0008] A gas pipeline inspection and maintenance method based on augmented reality technology according to an embodiment of the present invention includes the following steps:

[0009] S1. Wear an augmented reality device, connect the augmented reality device to the intelligent inspection system and edge computing platform, use the multimodal sensor network to perform initial environment and equipment status scanning, and generate three-dimensional environment model data;

[0010] S2. Use augmented reality devices to view gas pipeline status information in real time, including pipeline layout, direction and connection points, and combine 3D environmental model data and quantum image processing technology to identify and locate potential fault points in real time;

[0011] S3, using augmented reality technology to overlay and display pipeline status data in real time, combining historical data for multi-dimensional anomaly detection and trend analysis, using augmented reality annotation and virtual assistance technology to mark fault points, and using distributed Turing learning algorithms for data processing and analysis;

[0012] S4. Automatically or manually mark the fault points found during the inspection based on the data processing and analysis results, use the fault identification algorithm of the quantum causal reasoning model, and display personalized maintenance suggestions and steps;

[0013] S5. During the maintenance process, augmented reality devices are used to provide real-time operation guidance, including specific maintenance steps, required tools and materials;

[0014] S6, automatically record inspection and maintenance data, and upload it to the cloud in real time through the Internet of Things technology;

[0015] S7. Use big data analysis and machine learning algorithms to intelligently analyze uploaded maintenance data, combine environmental models and real-time sensor data to predict fault trends and multi-level warnings, and optimize maintenance plans;

[0016] S8. Through remote communication technology, on-site inspection and maintenance personnel communicate with remote experts in real time, and experts provide professional guidance through a shared augmented reality perspective.

[0017] Optionally, the S1 includes the following steps:

[0018] S11, wearing an augmented reality device, where the augmented reality device includes a display unit, a camera unit, and a data processing unit;

[0019] S12. Connecting the augmented reality device to the intelligent inspection system and the edge computing platform through wireless connection or wired connection;

[0020] S13, intelligent inspection system and edge computing platform: composed of a multimodal sensor network, a data processing unit and a communication module, a data preprocessing unit, a real-time computing unit and a storage unit;

[0021] S14. Deploy a multimodal sensor network, where the multimodal sensor includes a temperature sensor, a pressure sensor, a flow sensor, and a gas composition sensor, and the multimodal sensor is connected to the intelligent inspection system via a wireless network or a wired network;

[0022] S15, performing an initial environment scan, using the camera unit of the augmented reality device and the multimodal sensor network to obtain environment images and device status data, performing fusion processing through the data processing unit, and generating an initial environment model;

[0023] S16, based on the initial environment model, applying a three-dimensional reconstruction algorithm to generate dynamic three-dimensional environment model data;

[0024] S17. Annotate the pipeline layout, direction and connection points in the three-dimensional environment model data, and synchronize with the augmented reality device in real time.

[0025] Optionally, S2 includes the following steps:

[0026] S21. Viewing gas pipeline status information in real time through a display unit of an augmented reality device, including the layout, direction, and connection points of the pipeline, wherein the display unit includes a high-definition display screen and a high-resolution camera;

[0027] S22. Acquire multimodal sensor data through the intelligent inspection system, including temperature sensor data, pressure sensor data, flow sensor data, and gas composition sensor data, and transmit the multimodal sensor data to the edge computing platform through the wireless communication module;

[0028] S23, the edge computing platform preprocesses the multimodal sensor data, including data cleaning, filtering and preliminary anomaly detection, to generate a preliminary preprocessed data set D sensor :

[0029] Data cleaning: Multimodal sensor data is D = {d1, d2, ..., d n}, by removing noise and outliers, we get the cleaned data set D clean ;

[0030] Dclean ={d i ∣|d i -μ|≤3σ};

[0031] Among them, d i is the i-th multimodal sensor data point, μ is the mean of the multimodal sensor data, and σ is the standard deviation of the multimodal sensor data;

[0032] Filtering: Use Kalman filtering to smooth the data:

[0033]

[0034] P k|k-1 =AP k-1|k-1 A T +Q;

[0035] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ;

[0036]

[0037] P k|k =(IK k H)P k|k-1 ;

[0038] in, For the predicted state, is the update state, P is the covariance matrix, K is the Kalman gain, A is the state transfer matrix, B is the control input matrix, u k is the control input, Q is the process noise covariance, H is the observation matrix, R is the observation noise covariance, z k is the observed value;

[0039] Preliminary anomaly detection: Detect and mark outliers through statistical analysis methods and generate preprocessed dataset D sensor ;

[0040] S24, using the camera unit of the augmented reality device to obtain the pipeline environment image data I, combined with the multimodal sensor data D sensor , image enhancement and analysis are performed by quantum image processing technology, the quantum image processing technology includes:

[0041] The quantum Fourier transform converts image data into the frequency domain, enhancing image details:

[0042]

[0043] Among them, I(x,y) is the original image data, I QFT (u,v) is the frequency domain image data after quantum Fourier transform, M and N are the width and height of the image, and u and v are the coordinates in the frequency domain;

[0044] The Quantum Edge Detection algorithm identifies edges and contours in images:

[0045]

[0046] Among them, E(x,y) is the image after edge detection, and are the gradients of the image in the x and y directions respectively;

[0047] S25. Fusion preprocessing dataset D sensor and quantum image processing results I QFT , through the improved convolutional neural network to process and analyze data, generate the fault point identification model M fault :

[0048] The training process of the improved convolutional neural network includes optimizing the following objective function:

[0049]

[0050] Where N is the number of samples, Indicates the category with the highest probability of selecting e natural constant, σ activation function, max k,l Indicates taking the maximum value within the pooling window in the pooling operation. The mth preprocessed data, w mj Convolution kernel weight, b j Convolutional layer bias, w jk Fully connected layer weights, b k Fully connected layer bias, y i The true label of the i-th sample;

[0051] The specific convolutional neural network model structure is as follows:

[0052] Input layer: accepts preprocessed data D sensor and quantum image processing results I QFT ;

[0053] Convolutional layer: Apply convolution kernel W to extract features:

[0054]

[0055] in, is the output of the jth neuron in the lth layer, σ is the activation function, M j is the receptive field of the jth neuron, is the convolution kernel weight, is the bias term;

[0056] Pooling layer: downsamples the convolutional layer output:

[0057]

[0058] Among them, P j is the pooling window;

[0059] Fully connected layer: Connect the output of the pooling layer to the fully connected layer to identify the fault point:

[0060] z=W fc h+b fc ;

[0061] Among them, z is the output of the fully connected layer, W fc is the weight of the fully connected layer, b fc is the bias term;

[0062] S26, through the fault point identification model M fault Identify and locate potential fault points in real time fault , including the location coordinates (x, y, z) of the fault point and the fault type:

[0063] P fault ={(x i ,y i ,z i ,T i )|i=1,2,…,m};

[0064] Among them, (x i ,y i ,z i ) is the location coordinate of the i-th fault point, T i is the fault type of the i-th fault point;

[0065] S27, the fault point information P fault Superimposed on the 3D environmental model data of the pipeline, it is presented to the inspectors in real time through the display unit of the augmented reality device, and generates real-time alarms and detailed fault information for further processing by the inspectors.

[0066] Optionally, S3 includes the following steps:

[0067] S31. Use augmented reality devices to overlay and display pipeline status data in real time, including multi-modal sensor data such as temperature, pressure, flow and gas composition. sensor , the data is transmitted from the multimodal sensor network to the augmented reality device via the wireless communication module;

[0068] S32. Combined with historical data Dhistorical Perform multi-dimensional anomaly detection and trend analysis, where the historical data includes past multi-modal sensor data records and known fault point information:

[0069] D historical ={(t i ,d i )|i=1,2,…,n};

[0070] Among them, t i Indicates the timestamp, d i Represents multimodal sensor data corresponding to time;

[0071] S33, real-time data D sensor and historical data D historical Perform multi-dimensional anomaly detection using a dimensionality reduction method based on manifold learning:

[0072] Z = φ(X);

[0073] Among them, X is the standardized data matrix, φ is the manifold learning mapping function, and Z is the data matrix after dimensionality reduction. The specific steps include:

[0074] Data Standardization:

[0075]

[0076] Where D is the original data matrix, μ is the data mean, and σ is the data standard deviation;

[0077] Manifold Learning Mapping:

[0078] Use a locally linear embedding algorithm to map high-dimensional data to a low-dimensional manifold:

[0079]

[0080] Where W is the weight matrix, I is the identity matrix, represents pseudo-inverse;

[0081] S34. Use quantum Bayesian networks to perform trend analysis and predict future fault trends. Quantum Bayesian networks combine quantum computing and Bayesian reasoning to construct quantum states for fault prediction:

[0082]

[0083] Among them, |ψ> is the quantum state, α i is the amplitude of the state, |x i > is the base state, N is the number of samples in the multimodal sensor dataset;

[0084] S35. Use augmented reality annotation and virtual assistance technology to mark the detected fault points, and use augmented reality equipment to display the fault point location information and detailed fault description in real time, including fault type, severity and recommended maintenance measures;

[0085] S36. Use the distributed Turing learning algorithm to process and analyze the fused data to generate a fault point identification and prediction model M fault , the specific steps include:

[0086] S361. Define the input data matrix X and label vector Y:

[0087] X=[D sensor ,D historical ];

[0088] Y=[y1,y2,…,y N ];

[0089] Among them, y i is the fault label of the i-th sample;

[0090] S362, initializing model parameters θ, including weight matrix and bias vector;

[0091] S363, the parameters are updated through the distributed Turing learning algorithm, and each distributed node i calculates the local gradient:

[0092]

[0093] Among them, m i is the number of samples on the i-th node, is the predicted value of the jth sample, X ij is the input feature of the jth sample;

[0094] S364, global parameter update adopts distributed synchronization mechanism, each node transfers local gradient to the central node for aggregation:

[0095]

[0096] Among them, θ t is the model parameter of the tth iteration, η is the learning rate, and N is the number of distributed nodes;

[0097] S365, repeat steps S363 and S364 until convergence;

[0098] S37, according to the fault point identification and prediction model M fault , identify and locate potential fault points in real time, and superimpose the fault point information on the three-dimensional environmental model data of the pipeline through augmented reality equipment, and provide it to inspection personnel for further processing.

[0099] Optionally, S4 includes the following steps:

[0100] S41, automatically or manually marking the fault points found during the inspection, wherein the marking includes extracting fault features from multimodal sensor data and image data to generate a preliminary fault point set P fault :

[0101] P fault ={(x i ,y i ,z i ,t i )|i=1,2,…,m};

[0102] Among them, (x i ,y i ,z i ) is the three-dimensional coordinate of the fault point, t i The timestamp of the failure point;

[0103] S42, the initial fault point set P fault Perform multimodal sensor data fusion and use extended Kalman filter for data fusion and state estimation:

[0104] The prediction steps of the extended Kalman filter include:

[0105]

[0106]

[0107] in, is the predicted state, P k|k-1 is the prediction covariance matrix, f is the nonlinear state transfer function, F k is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix;

[0108] The update steps of the extended Kalman filter include:

[0109]

[0110]

[0111] P k|k =(IK k H k ) k|k-1 ;

[0112] Among them, K k is the Kalman gain, H k is the Jacobian matrix of the observation matrix, R kis the observation noise covariance matrix, h is the nonlinear observation function, z k is the observed value;

[0113] S43, using a quantum causal reasoning model to identify faults on the fused data, the quantum causal reasoning model includes the following steps:

[0114] Construct a quantum state space where each state represents a specific configuration of multimodal sensor data and define the initial quantum state |ψ0>:

[0115]

[0116] Among them, α i is the amplitude of the state, satisfying |x i > is the base state of multimodal sensor data;

[0117] Define the quantum causality operator O causal Represent causal relationships between multimodal sensor data:

[0118]

[0119] Among them, β ij Represents data x i and x j The weight of the causal relationship between

[0120] Use quantum gate operations to construct a causal network. The specific steps include:

[0121] Use Hadamard gate H to initialize the quantum state:

[0122]

[0123] Using CNOT gate U CNOT Constructing causal relationships:

[0124] U CNOT (|x i >|x j >)=|x i >|x i ⊕x j >

[0125] Among them, ⊕ represents modulo 2 addition;

[0126] Solve the causal influence through quantum computing and apply the quantum state |ψ0> to the causal relationship operator O causal On the quantum state |ψ causal >:

[0127] |ψ causal >=O causal |>

[0128] Calculate the observation operator O for the quantum state |ψ causal > the expected value of the fault point, and obtain the causal impact measure I c :

[0129]

[0130] Among them, I c is the causal influence measure, O is the observation operator, is the complex conjugate of the amplitude of the state, <x i ||x j > represents the causal relationship ground state |x i > and |x j >causal relationship between them;

[0131] Reconstructing quantum states using quantum state tomography:

[0132]

[0133] Among them, ρ is the density matrix, which represents the statistical description of the quantum state, ρ ij is the probability amplitude of the quantum state;

[0134] S44、Calculate the quantum state |ψ causal >The normalization constant:

[0135]

[0136] S45. Generate personalized maintenance suggestions and steps based on the fault identification results, including required tools, materials and specific operation instructions, and display them to maintenance personnel through augmented reality devices.

[0137] The beneficial effects of the present invention are:

[0138] (1) The present invention proposes a gas pipeline inspection and maintenance method based on augmented reality technology and quantum image processing technology. The method uses a multimodal sensor network to perform an initial environmental scan and combines quantum image processing technology to identify and locate potential fault points in real time. The distributed Turing learning algorithm is used to process and analyze data, which effectively improves the accuracy of fault point identification and location, especially in complex environments.

[0139] (2) The present invention provides real-time operation guidance through augmented reality equipment, including specific maintenance steps, required tools and materials, and combines gesture recognition and voice interaction technology to achieve real-time interaction and feedback with users and dynamically adjust the guidance content. This intelligent real-time guidance not only improves the efficiency and accuracy of maintenance, but also reduces the errors of manual operation.

[0140] (3) The present invention realizes the automatic recording of inspection and maintenance data and real-time uploading through the Internet of Things technology, uses big data analysis and machine learning algorithms to perform intelligent analysis on the uploaded data, and combines environmental models and real-time sensor data to predict fault trends and multi-level early warnings. Through these means, maintenance plans can be optimized to ensure the long-term safe operation of gas pipelines.

[0141] (4) The present invention uses remote communication technology to achieve real-time communication between on-site inspection and maintenance personnel and remote experts. Experts provide professional guidance through a shared augmented reality perspective and combine virtual reality technology to simulate complex maintenance scenarios to achieve collaborative work and training. This remote assistance mechanism improves the efficiency of on-site problem solving and enhances the ability to handle complex maintenance tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0142] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0143] Figure 1 This is a flow chart of a gas pipeline inspection and maintenance method based on augmented reality technology proposed by the present invention;

[0144] Figure 2 A schematic diagram of the three-dimensional environment model data and quantum image processing technology proposed by the present invention;

[0145] Figure 3 Schematic diagram of data processing and analysis for the distributed Turing learning algorithm proposed in the present invention. DETAILED DESCRIPTION

[0146] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0147] refer to Figure 1-3 , a gas pipeline inspection and maintenance method based on augmented reality technology, comprising the following steps:

[0148] S1. Wear an augmented reality device, connect the augmented reality device to the intelligent inspection system and edge computing platform, use the multimodal sensor network to perform initial environment and equipment status scanning, and generate three-dimensional environment model data;

[0149] S2. Use augmented reality devices to view gas pipeline status information in real time, including pipeline layout, direction and connection points, and combine 3D environmental model data and quantum image processing technology to identify and locate potential fault points in real time;

[0150] S3, using augmented reality technology to overlay and display pipeline status data in real time, combining historical data for multi-dimensional anomaly detection and trend analysis, using augmented reality annotation and virtual assistance technology to mark fault points, and using distributed Turing learning algorithms for data processing and analysis;

[0151] S4. Automatically or manually mark the fault points found during the inspection based on the data processing and analysis results, use the fault identification algorithm of the quantum causal reasoning model, and display personalized maintenance suggestions and steps;

[0152] S5. During the maintenance process, augmented reality devices are used to provide real-time operation guidance, including specific maintenance steps, required tools and materials;

[0153] S6, automatically record inspection and maintenance data, and upload it to the cloud in real time through the Internet of Things technology;

[0154] S7. Use big data analysis and machine learning algorithms to intelligently analyze uploaded maintenance data, combine environmental models and real-time sensor data to predict fault trends and multi-level warnings, and optimize maintenance plans;

[0155] S8. Through remote communication technology, on-site inspection and maintenance personnel communicate with remote experts in real time, and experts provide professional guidance through a shared augmented reality perspective.

[0156] In this implementation, S1 includes the following steps:

[0157] S11, wearing an augmented reality device, where the augmented reality device includes a display unit, a camera unit, and a data processing unit;

[0158] S12. Connecting the augmented reality device to the intelligent inspection system and the edge computing platform through wireless connection or wired connection;

[0159] S13, intelligent inspection system and edge computing platform: composed of a multimodal sensor network, a data processing unit and a communication module, a data preprocessing unit, a real-time computing unit and a storage unit;

[0160] S14. Deploy a multimodal sensor network, where the multimodal sensor includes a temperature sensor, a pressure sensor, a flow sensor, and a gas composition sensor, and the multimodal sensor is connected to the intelligent inspection system via a wireless network or a wired network;

[0161] S15, performing an initial environment scan, using the camera unit of the augmented reality device and the multimodal sensor network to obtain environment images and device status data, performing fusion processing through the data processing unit, and generating an initial environment model;

[0162] S16, based on the initial environment model, applying a three-dimensional reconstruction algorithm to generate dynamic three-dimensional environment model data;

[0163] S17. Annotate the pipeline layout, direction and connection points in the three-dimensional environment model data, and synchronize with the augmented reality device in real time.

[0164] In this implementation, S2 includes the following steps:

[0165] S21. Viewing gas pipeline status information in real time through a display unit of an augmented reality device, including the layout, direction, and connection points of the pipeline, wherein the display unit includes a high-definition display screen and a high-resolution camera;

[0166] S22. Acquire multimodal sensor data through the intelligent inspection system, including temperature sensor data, pressure sensor data, flow sensor data, and gas composition sensor data, and transmit the multimodal sensor data to the edge computing platform through the wireless communication module;

[0167] S23, the edge computing platform preprocesses the multimodal sensor data, including data cleaning, filtering and preliminary anomaly detection, to generate a preliminary preprocessed data set D sensor :

[0168] Data cleaning: Multimodal sensor data is D = {d1, d2, ..., d n}, by removing noise and outliers, we get the cleaned data set D clean ;

[0169] D clean ={d i ∣|d i -μ|≤3σ};

[0170] Among them, d i is the i-th multimodal sensor data point, μ is the mean of the multimodal sensor data, and σ is the standard deviation of the multimodal sensor data;

[0171] Filtering: Use Kalman filtering to smooth the data:

[0172]

[0173] P k|k-1 =AP k-1|k-1 A T +Q;

[0174] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ;

[0175]

[0176] P k|k =(IK k H)P k|k-1 ;

[0177] in, For the predicted state, is the update state, P is the covariance matrix, K is the Kalman gain, A is the state transfer matrix, B is the control input matrix, u k is the control input, Q is the process noise covariance, H is the observation matrix, R is the observation noise covariance, z k is the observed value;

[0178] Preliminary anomaly detection: Detect and mark outliers through statistical analysis methods and generate preprocessed dataset D sensor ;

[0179] S24, using the camera unit of the augmented reality device to obtain the pipeline environment image data I, combined with the multimodal sensor data D sensor , image enhancement and analysis are performed by quantum image processing technology, the quantum image processing technology includes:

[0180] The quantum Fourier transform converts image data into the frequency domain, enhancing image details:

[0181]

[0182] Among them, I(x,y) is the original image data, I QFT (u,v) is the frequency domain image data after quantum Fourier transform, M and N are the width and height of the image, and u and v are the coordinates in the frequency domain;

[0183] The Quantum Edge Detection algorithm identifies edges and contours in images:

[0184]

[0185] Among them, E(x,y) is the image after edge detection, and are the gradients of the image in the x and y directions respectively;

[0186] S25. Fusion preprocessing dataset D sensor and quantum image processing results I QFT , through the improved convolutional neural network to process and analyze data, generate the fault point identification model M fault :

[0187] The training process of the improved convolutional neural network includes optimizing the following objective function:

[0188]

[0189] Where N is the number of samples, Indicates the category with the highest probability of selecting e natural constant, σ activation function, max k,l Indicates taking the maximum value within the pooling window in the pooling operation. The mth preprocessed data, w mj Convolution kernel weight, b j Convolutional layer bias, w jk Fully connected layer weights, b k Fully connected layer bias, y i The true label of the i-th sample;

[0190] The specific convolutional neural network model structure is as follows:

[0191] Input layer: accepts preprocessed data D sensor and quantum image processing results I QFT ;

[0192] Convolutional layer: Apply convolution kernel W to extract features:

[0193]

[0194] in, is the output of the jth neuron in the lth layer, σ is the activation function, M j is the receptive field of the jth neuron, is the convolution kernel weight, is the bias term;

[0195] Pooling layer: downsamples the convolutional layer output:

[0196]

[0197] Among them, P j is the pooling window;

[0198] Fully connected layer: Connect the output of the pooling layer to the fully connected layer to identify the fault point:

[0199] z=W fc h+b fc ;

[0200] Among them, z is the output of the fully connected layer, W fc is the weight of the fully connected layer, b fc is the bias term;

[0201] S26, through the fault point identification model M fault Identify and locate potential fault points in real time fault, including the location coordinates (x, y, z) of the fault point and the fault type:

[0202] P fault ={(x i ,y i ,z i ,T i )|i=1,2,…,m};

[0203] Among them, (x i ,y i ,z i ) is the location coordinate of the i-th fault point, T i is the fault type of the i-th fault point;

[0204] S27, the fault point information P fault Superimposed on the 3D environmental model data of the pipeline, it is presented to the inspectors in real time through the display unit of the augmented reality device, and generates real-time alarms and detailed fault information for further processing by the inspectors.

[0205] In this implementation, S3 includes the following steps:

[0206] S31. Use augmented reality devices to overlay and display pipeline status data in real time, including multi-modal sensor data such as temperature, pressure, flow and gas composition. sensor , the data is transmitted from the multimodal sensor network to the augmented reality device via the wireless communication module;

[0207] S32. Combined with historical data D historical Perform multi-dimensional anomaly detection and trend analysis, where the historical data includes past multi-modal sensor data records and known fault point information:

[0208] D historical ={(t i ,d i )|i=1,2,…,n};

[0209] Among them, t i Indicates the timestamp, d i Represents multimodal sensor data corresponding to time;

[0210] S33, real-time data D sensor and historical data D historical Perform multi-dimensional anomaly detection using a dimensionality reduction method based on manifold learning:

[0211] Z = φ(X);

[0212] Among them, X is the standardized data matrix, φ is the manifold learning mapping function, and Z is the data matrix after dimensionality reduction. The specific steps include:

[0213] Data Standardization:

[0214]

[0215] Where D is the original data matrix, μ is the data mean, and σ is the data standard deviation;

[0216] Manifold Learning Mapping:

[0217] Use a locally linear embedding algorithm to map high-dimensional data to a low-dimensional manifold:

[0218]

[0219] Where W is the weight matrix, I is the identity matrix, represents pseudo-inverse;

[0220] S34. Use quantum Bayesian networks to perform trend analysis and predict future fault trends. Quantum Bayesian networks combine quantum computing and Bayesian reasoning to construct quantum states for fault prediction:

[0221]

[0222] Among them, |ψ> is the quantum state, α i is the amplitude of the state, |x i > is the base state, N is the number of samples in the multimodal sensor dataset;

[0223] S35. Use augmented reality annotation and virtual assistance technology to mark the detected fault points, and use augmented reality equipment to display the fault point location information and detailed fault description in real time, including fault type, severity and recommended maintenance measures;

[0224] S36. Use the distributed Turing learning algorithm to process and analyze the fused data to generate a fault point identification and prediction model M fault , the specific steps include:

[0225] S361. Define the input data matrix X and label vector Y:

[0226] X=[D sensor ,D historical ];

[0227] Y=[y1,y2,…,y N ];

[0228] Among them, y i is the fault label of the i-th sample;

[0229] S362, initializing model parameters θ, including weight matrix and bias vector;

[0230] S363, the parameters are updated through the distributed Turing learning algorithm, and each distributed node i calculates the local gradient:

[0231]

[0232] Among them, m i is the number of samples on the i-th node, is the predicted value of the jth sample, X ij is the input feature of the jth sample;

[0233] S364, global parameter update adopts distributed synchronization mechanism, each node transfers local gradient to the central node for aggregation:

[0234]

[0235] Among them, θ t is the model parameter of the tth iteration, η is the learning rate, and N is the number of distributed nodes;

[0236] S365, repeat steps S363 and S364 until convergence;

[0237] S37, according to the fault point identification and prediction model M fault , identify and locate potential fault points in real time, and superimpose the fault point information on the three-dimensional environmental model data of the pipeline through augmented reality equipment, and provide it to inspection personnel for further processing.

[0238] In this implementation, S4 includes the following steps:

[0239] S41, automatically or manually marking the fault points found during the inspection, wherein the marking includes extracting fault features from multimodal sensor data and image data to generate a preliminary fault point set P fault :

[0240] P fault ={(x i ,y i ,z i ,t i )|i=1,2,…,m};

[0241] Among them, (x i ,y i ,z i ) is the three-dimensional coordinate of the fault point, t i The timestamp of the failure point;

[0242] S42, the initial fault point set P fault Perform multimodal sensor data fusion and use extended Kalman filter for data fusion and state estimation:

[0243] The prediction steps of the extended Kalman filter include:

[0244]

[0245]

[0246] in, is the predicted state, P k|k-1 is the prediction covariance matrix, f is the nonlinear state transfer function, F k is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix;

[0247] The update steps of the extended Kalman filter include:

[0248]

[0249]

[0250] P k|k =(IK k H k ) k|k-1 ;

[0251] Among them, K k is the Kalman gain, H k is the Jacobian matrix of the observation matrix, R k is the observation noise covariance matrix, h is the nonlinear observation function, z k is the observed value;

[0252] S43, using a quantum causal reasoning model to identify faults on the fused data, the quantum causal reasoning model includes the following steps:

[0253] Construct a quantum state space where each state represents a specific configuration of multimodal sensor data and define the initial quantum state |ψ0>:

[0254]

[0255] Among them, α i is the amplitude of the state, satisfying |x i > is the base state of multimodal sensor data;

[0256] Define the quantum causality operator O causal Represent causal relationships between multimodal sensor data:

[0257]

[0258] Among them, β ij Represents data xi and x j The weight of the causal relationship between

[0259] Use quantum gate operations to construct a causal network. The specific steps include:

[0260] Use Hadamard gate H to initialize the quantum state:

[0261]

[0262] Using CNOT gate U CNOT Constructing causal relationships:

[0263] U CNOT (|x i >|x j >)=|x i >|x i ⊕x j >

[0264] Among them, ⊕ represents modulo 2 addition;

[0265] Solve the causal influence through quantum computing and apply the quantum state |ψ0> to the causal relationship operator O causal On the quantum state |ψ causal >:

[0266] |ψ causal >=O causal |>

[0267] Calculate the observation operator O for the quantum state |ψ causal > the expected value of the fault point, and obtain the causal impact measure I c :

[0268]

[0269] Among them, I c is the causal influence measure, O is the observation operator, is the complex conjugate of the amplitude of the state, <x i ||x j > represents the causal relationship ground state |x i > and |x j >causal relationship between them;

[0270] Reconstructing quantum states using quantum state tomography:

[0271]

[0272] Among them, ρ is the density matrix, which represents the statistical description of the quantum state, ρ ij is the probability amplitude of the quantum state;

[0273] S44、Calculate the quantum state |ψ causal >The normalization constant:

[0274]

[0275] S45. Generate personalized maintenance suggestions and steps based on the fault identification results, including required tools, materials and specific operation instructions, and display them to maintenance personnel through augmented reality devices.

[0276] Embodiment 1:

[0277] In a gas company in a large city, the safe operation of gas pipelines is of vital importance. Due to the complexity of the urban pipeline network, inspection and maintenance work face great challenges. Traditional manual inspection and maintenance methods have problems such as low efficiency, poor accuracy, and many safety hazards. In order to solve these problems, the company decided to adopt a gas pipeline inspection and maintenance method based on augmented reality technology.

[0278] In actual application, the company equips each inspector with an augmented reality (AR) device, which includes a display unit, a camera unit, and a data processing unit. The inspector first wears the AR device and connects it to the intelligent inspection system and the edge computing platform. The initial environment and equipment status scan is performed through a multimodal sensor network to generate three-dimensional environmental model data. The AR device can view the status information of the gas pipeline in real time, including the layout, direction, and connection points of the pipeline.

[0279] During the inspection process, AR devices combine 3D environmental model data and quantum image processing technology to identify and locate potential fault points in real time. Quantum image processing technology enhances image details and accurately identifies tiny cracks and corrosion points in pipelines through methods such as quantum Fourier transform and quantum edge detection. After identifying the fault point, the system will superimpose and display pipeline status data in real time, and combine historical data for multi-dimensional anomaly detection and trend analysis, use augmented reality annotation and virtual assistance technology to mark the fault point, and use distributed Turing learning algorithms for data processing and analysis.

[0280] When a fault point is found, the system will automatically or manually mark the fault point found during the inspection, use the fault identification algorithm of the quantum causal reasoning model, and display personalized maintenance suggestions and steps. During the maintenance process, the AR device provides real-time operation guidance, including specific maintenance steps, required tools and materials. The inspector completes the maintenance operation quickly and safely according to the guidance of the AR device.

[0281] All inspection and maintenance data will be automatically recorded and uploaded to the cloud in real time through IoT technology. Using big data analysis and machine learning algorithms, the uploaded data is intelligently analyzed, and environmental models and real-time sensor data are combined to predict fault trends and multi-level warnings to optimize maintenance plans. In addition, inspectors and maintenance personnel can communicate with remote experts in real time through remote communication technology. Remote experts provide professional guidance through shared AR perspectives to solve complex problems.

[0282] Concrete data proves our beneficial effects

[0283] In one application, the company conducted a one-month inspection and maintenance test on a section of gas pipeline in the city center. The test results are as follows:

[0284] Comparison of meter inspection and maintenance efficiency

[0285]

[0286] Comparison of table fault identification and repair accuracy

[0287]

[0288] Table data processing and transmission comparison

[0289]

[0290] These data fully demonstrate the superiority of the present invention in practical applications. By adopting augmented reality technology and quantum image processing technology, combined with distributed Turing learning algorithm and Internet of Things technology, not only the efficiency and accuracy of inspection and maintenance are significantly improved, but also the safety and reliability of gas pipelines are greatly improved.

[0291] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A gas pipeline inspection and maintenance method based on augmented reality technology, comprising the following steps: S1. Wear an augmented reality device, connect the augmented reality device to the intelligent inspection system and edge computing platform, use the multimodal sensor network to perform initial environment and equipment status scanning, and generate three-dimensional environment model data; S2. Use augmented reality devices to view gas pipeline status information in real time, including pipeline layout, direction and connection points, and combine 3D environmental model data and quantum image processing technology to identify and locate potential fault points in real time; S3, using augmented reality technology to overlay and display pipeline status data in real time, combining historical data for multi-dimensional anomaly detection and trend analysis, using augmented reality annotation and virtual assistance technology to mark fault points, and using distributed Turing learning algorithms for data processing and analysis; S4. Automatically or manually mark the fault points found during the inspection based on the data processing and analysis results, use the fault identification algorithm of the quantum causal reasoning model, and display personalized maintenance suggestions and steps; S5. During the maintenance process, augmented reality devices are used to provide real-time operation guidance, including specific maintenance steps, required tools and materials; S6, automatically record inspection and maintenance data, and upload it to the cloud in real time through the Internet of Things technology; S7. Use big data analysis and machine learning algorithms to intelligently analyze uploaded maintenance data, combine environmental models and real-time sensor data to predict fault trends and multi-level warnings, and optimize maintenance plans; S8. Through remote communication technology, on-site inspection and maintenance personnel communicate with remote experts in real time, and experts provide professional guidance through a shared augmented reality perspective.

2. The gas pipeline inspection and maintenance method based on augmented reality technology according to claim 1 is characterized in that: The S1 comprises the following steps: S11, wearing an augmented reality device, where the augmented reality device includes a display unit, a camera unit, and a data processing unit; S12. Connecting the augmented reality device to the intelligent inspection system and the edge computing platform through wireless connection or wired connection; S13, intelligent inspection system and edge computing platform: composed of a multimodal sensor network, a data processing unit and a communication module, a data preprocessing unit, a real-time computing unit and a storage unit; S14. Deploy a multimodal sensor network, where the multimodal sensor includes a temperature sensor, a pressure sensor, a flow sensor, and a gas composition sensor, and the multimodal sensor is connected to the intelligent inspection system via a wireless network or a wired network; S15, performing an initial environment scan, using the camera unit of the augmented reality device and the multimodal sensor network to obtain environment images and device status data, performing fusion processing through the data processing unit, and generating an initial environment model; S16, based on the initial environment model, applying a three-dimensional reconstruction algorithm to generate dynamic three-dimensional environment model data; S17. Annotate the pipeline layout, direction and connection points in the three-dimensional environment model data, and synchronize with the augmented reality device in real time.

3. The gas pipeline inspection and maintenance method based on augmented reality technology according to claim 1 is characterized in that: The S2 comprises the following steps: S21. Viewing gas pipeline status information in real time through a display unit of an augmented reality device, including the layout, direction, and connection points of the pipeline, wherein the display unit includes a high-definition display screen and a high-resolution camera; S22. Acquire multimodal sensor data through the intelligent inspection system, including temperature sensor data, pressure sensor data, flow sensor data, and gas composition sensor data, and transmit the multimodal sensor data to the edge computing platform through the wireless communication module; S23, the edge computing platform preprocesses the multimodal sensor data, including data cleaning, filtering and preliminary anomaly detection, to generate a preliminary preprocessed data set D sensor : Data cleaning: Multimodal sensor data is D = {d1, d2, ..., d n }, by removing noise and outliers, we get the cleaned data set D clean ; D clean ={d i ∣|d i -μ|≤3σ}; Among them, d i is the i-th multimodal sensor data point, μ is the mean of the multimodal sensor data, and σ is the standard deviation of the multimodal sensor data; Filtering: Use Kalman filtering to smooth the data: P k|k-1 =AP k-1|k-1 From T +Q; K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ; P k|k =(I-K k H)P k|k-1 ; in, For the predicted state, is the update state, P is the covariance matrix, K is the Kalman gain, A is the state transfer matrix, B is the control input matrix, u k is the control input, Q is the process noise covariance, H is the observation matrix, R is the observation noise covariance, z k is the observed value; Preliminary anomaly detection: Detect and mark outliers through statistical analysis methods and generate preprocessed dataset D sensor ; S24, using the camera unit of the augmented reality device to obtain the pipeline environment image data I, combined with the multimodal sensor data D sensor , image enhancement and analysis are performed by quantum image processing technology, the quantum image processing technology includes: The quantum Fourier transform converts image data into the frequency domain, enhancing image details: Among them, I(x,y) is the original image data, I QFT (u,v) is the frequency domain image data after quantum Fourier transform, M and N are the width and height of the image, and u and v are the coordinates in the frequency domain; The Quantum Edge Detection algorithm identifies edges and contours in images: Among them, E(x,y) is the image after edge detection, and are the gradients of the image in the x and y directions respectively; S25. Fusion preprocessing dataset D sensor and quantum image processing results I QFT , through the improved convolutional neural network to process and analyze data, generate the fault point identification model M fault : The training process of the improved convolutional neural network includes optimizing the following objective function: Where N is the number of samples, Indicates the category with the highest probability of selecting e natural constant, σ activation function, max k,l Indicates taking the maximum value within the pooling window in the pooling operation, D sensor,m The mth preprocessed data, w mj Convolution kernel weight, b j Convolutional layer bias, w jk Fully connected layer weights, b k Fully connected layer bias, y i The true label of the i-th sample; The specific convolutional neural network model structure is as follows: Input layer: accepts preprocessed data D sensor and quantum image processing results I QFT ; Convolutional layer: Apply convolution kernel W to extract features: in, is the output of the jth neuron in the lth layer, σ is the activation function, M j is the receptive field of the jth neuron, is the convolution kernel weight, is the bias term; Pooling layer: downsamples the convolutional layer output: Among them, P j is the pooling window; Fully connected layer: Connect the output of the pooling layer to the fully connected layer to identify the fault point: z=W fc h+b fc ; Among them, z is the output of the fully connected layer, W fc is the weight of the fully connected layer, b fc is the bias term; S26, through the fault point identification model M fault Identify and locate potential fault points in real time fault , including the location coordinates (x, y, z) of the fault point and the fault type: P fault ={(x i ,y i ,z i ,T i )∣i=1,2,…,m}; Among them, (x i ,y i ,z i ) is the location coordinate of the i-th fault point, T i is the fault type of the i-th fault point; S27, the fault point information P fault Superimposed on the 3D environmental model data of the pipeline, it is presented to the inspectors in real time through the display unit of the augmented reality device, and generates real-time alarms and detailed fault information for further processing by the inspectors.

4. The gas pipeline inspection and maintenance method based on augmented reality technology according to claim 1 is characterized in that: The S3 comprises the following steps: S31. Use augmented reality devices to overlay and display pipeline status data in real time, including multi-modal sensor data such as temperature, pressure, flow and gas composition. sensor , the data is transmitted from the multimodal sensor network to the augmented reality device via the wireless communication module; S32. Combined with historical data D historical Perform multi-dimensional anomaly detection and trend analysis, where the historical data includes past multi-modal sensor data records and known fault point information: D historical ={(t i ,d i )∣i=1,2,…,n}; Among them, t i Indicates the timestamp, d i Represents multimodal sensor data corresponding to time; S33, real-time data D sensor and historical data D historical Perform multi-dimensional anomaly detection using a dimensionality reduction method based on manifold learning: Z = φ(X); Among them, X is the standardized data matrix, φ is the manifold learning mapping function, and Z is the data matrix after dimensionality reduction. The specific steps include: Data Standardization: Where D is the original data matrix, μ is the data mean, and σ is the data standard deviation; Manifold Learning Mapping: Use a locally linear embedding algorithm to map high-dimensional data to a low-dimensional manifold: Where W is the weight matrix, I is the identity matrix, represents pseudo-inverse; S34. Use quantum Bayesian networks to perform trend analysis and predict future fault trends. Quantum Bayesian networks combine quantum computing and Bayesian reasoning to construct quantum states for fault prediction: Among them, |ψ> is the quantum state, α i is the amplitude of the state, |x i > is the base state, N is the number of samples in the multimodal sensor dataset; S35. Use augmented reality annotation and virtual assistance technology to mark the detected fault points, and use augmented reality equipment to display the fault point location information and detailed fault description in real time, including fault type, severity and recommended maintenance measures; S36. Use the distributed Turing learning algorithm to process and analyze the fused data to generate a fault point identification and prediction model M fault , the specific steps include: S361. Define the input data matrix X and label vector Y: X=[D sensor ,D historical ]; Y=[y1,y2,…,y N ]; Among them, y i is the fault label of the i-th sample; S362, initializing model parameters θ, including weight matrix and bias vector; S363, the parameters are updated through the distributed Turing learning algorithm, and each distributed node i calculates the local gradient: Among them, m i is the number of samples on the i-th node, is the predicted value of the jth sample, X ij is the input feature of the jth sample; S364, global parameter update adopts distributed synchronization mechanism, each node transfers local gradient to the central node for aggregation: Among them, θ t is the model parameter of the tth iteration, η is the learning rate, and N is the number of distributed nodes; S365, repeat steps S363 and S364 until convergence; S37, according to the fault point identification and prediction model M fault , identify and locate potential fault points in real time, and superimpose the fault point information on the three-dimensional environmental model data of the pipeline through augmented reality equipment, and provide it to inspection personnel for further processing.

5. The gas pipeline inspection and maintenance method based on augmented reality technology according to claim 1 is characterized in that: The S4 comprises the following steps: S41, automatically or manually marking the fault points found during the inspection, wherein the marking includes extracting fault features from multimodal sensor data and image data to generate a preliminary fault point set P fault : P fault ={(x i ,y i ,z i ,t i )∣i=1,2,…,m}; Among them, (x i ,y i ,z i ) is the three-dimensional coordinate of the fault point, t i The timestamp of the failure point; S42, the initial fault point set P fault Perform multimodal sensor data fusion and use extended Kalman filter for data fusion and state estimation: The prediction steps of the extended Kalman filter include: in, is the predicted state, P k|k-1 is the prediction covariance matrix, f is the nonlinear state transfer function, F k is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix; The update steps of the extended Kalman filter include: P k|k =(I-K k H k )P k|k-1 ; Among them, K k is the Kalman gain, H k is the Jacobian matrix of the observation matrix, R k is the observation noise covariance matrix, h is the nonlinear observation function, z k is the observed value; S43, using a quantum causal reasoning model to identify faults on the fused data, the quantum causal reasoning model includes the following steps: Construct a quantum state space where each state represents a specific configuration of multimodal sensor data and define the initial quantum state |ψ0>: Among them, α i is the amplitude of the state, satisfying is the base state of multimodal sensor data; Define the quantum causality operator O causal Represent causal relationships between multimodal sensor data: Among them, β ij Represents data x i and x j The weight of the causal relationship between Use quantum gate operations to construct a causal network. The specific steps include: Use Hadamard gate H to initialize the quantum state: Using CNOT gate U CNOT Constructing causal relationships: Among them, ⊕ represents modulo 2 addition; Solve the causal influence through quantum computing and apply the quantum state |ψ0> to the causal relationship operator O causal On the quantum state |ψ causal >: |ψ causal >=O causal |>; Calculate the observation operator O for the quantum state |ψ causal > the expected value of the fault point, and obtain the causal impact measure I c : Among them, I c is the causal influence measure, O is the observation operator, is the complex conjugate of the amplitude of the state, <x i ||x j > represents the causal relationship ground state |x i > and |x j >causal relationship between them; Reconstructing quantum states using quantum state tomography: Among them, ρ is the density matrix, which represents the statistical description of the quantum state, ρ ij is the probability amplitude of the quantum state; S44、Calculate the quantum state |ψ causal >The normalization constant: S45. Generate personalized maintenance suggestions and steps based on the fault identification results, including required tools, materials and specific operation instructions, and display them to maintenance personnel through augmented reality devices.

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