Ultrahigh temporal-spatial resolution multidimensional pipe network monitoring and intelligent diagnosis system

Abstract

The invention discloses an ultrahigh temporal-spatial resolution multidimensional pipe network monitoring and intelligent diagnosis system, which relates to the technical field of intelligent monitoring and data processing, and is characterized in that an ultrahigh density monitoring network is constructed based on an MEMS (Micro Electro Mechanical System) micro sensor array and an optical fiber acoustic wave sensor, and monitoring of multidimensional parameters such as pressure, flow, acoustic wave, vibration and the like is integrated; self-adaptive signal processing and dynamic digital twinning technologies are combined to realize holographic state reconstruction and high-precision anomaly detection of pipe network operation; a distributed block chain and a homomorphic encryption technology are adopted to ensure efficient storage and analysis of data in an encryption state, and a consensus mechanism is optimized to support large-scale real-time processing; a multi-modal AI model and a self-adaptive evolutionary algorithm are fused, intelligent diagnosis and long-term prediction of water leakage are achieved, the positioning precision is better than 3 meters, and the diagnosis accuracy rate reaches 99%; the method provides innovative technical support for intelligent management of the pipe network, has the remarkable advantages of high sensitivity, high safety and high predictive capacity, and is suitable for complex pipe network systems for urban water supply, heat supply and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring and data processing, and in particular relates to an ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system. Background Art

[0002] In the field of urban underground pipeline monitoring and management, foreign countries developed technology earlier and have established a relatively mature system, particularly in high-density monitoring, acoustic detection, data security, and intelligent analysis. The United States, Europe, and Japan, as technological leaders, each focus on their own areas. The United States excels in pipeline monitoring technology, particularly acoustic monitoring and multi-source data analysis. Acoustic monitoring technology, promoted by the American Water Works Association (AWWA), uses acoustic wave sensors to detect abnormal acoustic signals in pipelines and has been widely used to locate water leaks. Its sensitivity can reach 0.5 liters per second, but it is limited by sensor density (typically 2 nodes per kilometer), making full network coverage difficult. Furthermore, multi-source time series analysis technology developed by IBM utilizes a big data platform to integrate multi-dimensional data such as pressure and flow, identifying pipeline anomalies through machine learning algorithms, significantly improving diagnostic efficiency. However, this technology still has shortcomings in real-time performance and data security, particularly in large-scale distributed pipeline networks, resulting in high computational latency. In recent years, the United States has also explored the application of IoT technologies in pipeline networks, such as interconnecting sensors through the LoRaWAN network. However, the high overall deployment cost has limited its widespread adoption.

[0003] Europe focuses on technology integration and data management in the field of pipeline network monitoring. Vitens Water, a Dutch water company, pioneered the use of blockchain technology for the distributed storage and management of pipeline network data. Using the Hyperledger framework to build a ledger, they ensure data immutability and integrate smart contracts to enable automated anomaly alerts. This technology excels in data security and transparency, but its throughput is limited by traditional consensus mechanisms, processing only a few hundred data points per second, making it difficult to meet the real-time demands of ultra-large-scale pipeline networks. Furthermore, the European LeakFinder project has developed anomaly detection-based water leak location technology, leveraging statistical analysis and acoustic signal processing to reduce location error to approximately 5 meters. However, this technology relies on a single signal source and lacks the ability to integrate multidimensional data, resulting in a high false alarm rate in complex environments. Europe has also experimented with digital twin technology for pipeline network management, using 3D modeling to reflect pipeline network status. However, limitations in sensor density and signal processing algorithms limit the frequency of dynamic updates, making it difficult to achieve high spatial and temporal resolution.

[0004] Japan's pipeline network monitoring technology is characterized by high-density deployment and predictive analysis. The Tokyo Waterworks Bureau's high-density sensor network (5 nodes / km) has significantly improved monitoring coverage. This network uses pressure and flow sensors to collect real-time data, combined with simple threshold analysis to detect leaks with a sensitivity of up to 0.3 liters / second. This technology performs well in densely populated urban areas, but the sensors are expensive and lack multi-dimensional signal integration capabilities. NTT Japan has explored distributed management technology, using edge computing nodes to disperse data processing, reducing pressure on the cloud and improving system response speed. Furthermore, NEC Japan has achieved breakthroughs in predictive technology, using time series analysis and machine learning models to predict pipeline aging trends with a prediction period of up to six months. However, this technology relies on large amounts of historical data for data generation and model training, making it less adaptable to new pipelines or unusual scenarios. Japan has also experimented with fiber optic sensing technology for long-distance monitoring, but limitations due to signal attenuation and processing complexity have limited its practical application.

[0005] In contrast, domestic pipeline monitoring technology started relatively late, and its overall level lags behind internationally advanced technologies. However, it has gradually developed distinctive features through university research and enterprise practice. At the university level, Tsinghua University has made progress in fiber optic sensing technology, developing a monitoring system based on distributed temperature measurement (DTS) and acoustic wave (DAS). This system can detect temperature and vibration over a range of several kilometers, making it suitable for thermal pipeline networks. However, this technology has a low sampling rate (typically 1Hz), making it difficult to capture transient anomalies, and its deployment costs are high, making it difficult to achieve large-scale application. Tongji University has explored the field of digital twins, attempting to construct pipeline network models using BIM technology and combining them with finite element analysis to simulate operational conditions. However, due to limitations in data input accuracy and real-time performance, the model update cycle is long. Zhejiang University has focused on AI diagnostics, using convolutional neural networks (CNNs) to process acoustic signals. Initial leak detection has achieved an accuracy rate of approximately 90%, but the positioning error is large (approximately 10 meters) and long-term prediction capabilities are lacking.

[0006] At the enterprise level, domestic technological development is primarily focused on the Internet of Things (IoT) and data processing. Huawei, based on its IoT platform, has launched a pipeline monitoring solution. This solution utilizes NB-IoT technology to connect sensors and support remote collection of pressure and flow data. However, this solution has a low monitoring density (approximately 1 node per kilometer), insufficient sensitivity to detect even minor leaks, and relies on traditional encryption for data security, without incorporating a distributed management mechanism. Hikvision has developed video- and sound-based monitoring equipment, leveraging edge computing for preliminary analysis, but has made limited progress in multi-dimensional data fusion and intelligent diagnosis. Energy companies such as CNPC are experimenting with applying fiber optic sensing technology to oil and gas pipelines to detect pipeline vibration and leaks, but the system's low level of integration has hindered its application to urban water supply. Furthermore, some small and medium-sized domestic enterprises have introduced low-cost sensors, but their technical specifications (such as sampling rate and detection distance) fall far below international standards, making them difficult to meet the demands of complex pipeline networks.

[0007] Overall, foreign technologies offer advantages in high-density deployment, acoustic monitoring, data security, and predictive analytics. However, they face challenges such as insufficient monitoring sensitivity, poor real-time performance, and weak multi-dimensional integration capabilities. While domestic technologies have achieved breakthroughs in fiber optic sensing, AI diagnostics, and IoT applications, they generally face challenges such as low monitoring density, weak data security, limited intelligence, and poor system integration. Existing technologies struggle to simultaneously meet the requirements for ultra-high spatiotemporal resolution, high security, and intelligent prediction. Innovative solutions are urgently needed to drive the transformation of pipeline network management towards refined and intelligent management.

[0008] At present, the density and sensitivity of traditional pipeline network monitoring are insufficient: Traditional pipeline network monitoring technology is limited by the low density of sensor deployment (usually less than 2 nodes / km) and poor detection sensitivity (the lower limit of water leakage detection is about 0.5 liters / second), making it difficult to capture minor anomalies or reflect the status of the pipeline network in real time.

[0009] The collection and fusion of multi-dimensional parameters are difficult: existing technologies are mostly limited to the monitoring of a single parameter (such as pressure or flow), lacking the ability to collaboratively analyze multi-dimensional data, resulting in an incomplete description of the pipeline network status.

[0010] Insufficient data security and privacy protection: Traditional pipeline network data is easily tampered with or leaked, and lacks efficient encryption and distributed management mechanisms.

[0011] Low efficiency in large-scale real-time data processing: When processing large-scale pipeline network data, existing systems are limited by computing throughput and consensus mechanism efficiency, making it difficult to meet real-time requirements.

[0012] Insufficient accuracy in water leakage diagnosis and prediction: Traditional methods have large errors in water leakage positioning and long-term prediction (positioning error exceeds 10 meters, and the prediction period is less than 6 months), making it difficult to support intelligent decision-making.

[0013] Poor system self-learning and adaptability: Existing technologies lack dynamic optimization and self-evolution capabilities, making it difficult to adapt to changes in the pipeline network operating environment. Summary of the Invention

[0014] The technical problem to be solved by the present invention is to provide an ultra-high temporal and spatial resolution multi-dimensional pipeline network monitoring and intelligent diagnosis system to address the shortcomings of the background technology, provide innovative technical support for intelligent management of pipeline networks, and have the significant advantages of high sensitivity, high safety and high predictive ability, and are suitable for complex pipeline network systems such as urban water supply and heating.

[0015] The present invention adopts the following technical solutions to solve the above technical problems:

[0016] Ultra-high spatiotemporal resolution multi-dimensional pipe network monitoring and intelligent diagnosis system, specifically including ultra-high density multi-dimensional sensor network construction, adaptive signal processing and dynamic digital twin, distributed blockchain data management, homomorphic encryption data protection, multi-modal AI leak diagnosis and adaptive evolutionary algorithm optimization;

[0017] Among them, ultra-high-density multi-dimensional sensor network construction: used to build an ultra-high-density monitoring network by integrating micro-electromechanical system MEMS micro sensor arrays and distributed fiber optic acoustic wave sensors to achieve multi-dimensional parameter acquisition of pressure, flow, acoustic waves and vibration;

[0018] Adaptive signal processing and dynamic digital twins: used to combine empirical mode decomposition (EMD) and Doppler analysis to process multidimensional signals, extract pipeline network operation characteristics, and build dynamic digital twin models;

[0019] Distributed blockchain data management: used to build a distributed ledger to record pipeline monitoring data, using the practical Byzantine Fault Tolerance (PBFT) consensus mechanism to ensure data consistency and real-time performance;

[0020] Homomorphic encryption data protection: Used to encrypt, store, and calculate monitoring data using homomorphic encryption technology, supporting AI analysis in an encrypted state;

[0021] Multimodal AI water leak diagnosis: used to integrate convolutional neural networks (CNN), long short-term memory (LSTM) networks, and Transformer models to build a multimodal AI system;

[0022] Adaptive evolutionary algorithm optimization: used to dynamically optimize AI model parameters through an adaptive evolutionary algorithm, enabling the system to self-learn and adapt to changes in the pipe network environment.

[0023] As a further preferred solution of the ultra-high spatiotemporal resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention, the calculation model involved in the construction of the ultra-high density multi-dimensional sensor network is as follows:

[0024] Sensor node density: Among them, N is the total number of sensor nodes, L is the total length of the pipe network, and D represents the number of nodes per kilometer, which is used to measure the network coverage capacity;

[0025] Water leakage detection sensitivity: Where ΔQ is the minimum detectable flow change, T is the sampling period, and S reflects the system's ability to sense small leaks;

[0026] Sound wave propagation distance: R = v·t; where v is the propagation speed of the sound wave in the medium, t is the sound wave propagation time, and R represents the effective detection range of the fiber optic sensor.

[0027] As a further preferred solution of the ultra-high spatiotemporal resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention, the empirical mode decomposition (EMD) is used to decompose non-stationary signals into intrinsic mode functions (IMFs) to separate noise from valid signals; Doppler analysis calculates fluid velocity and acoustic frequency shift to locate abnormal sources; and digital twin technology generates a holographic state diagram of the pipe network through real-time data mapping, enabling visualization and dynamic updating of operating status.

[0028] EMD decomposition: Among them, x(t) is the original signal, IMF i (t) is the i-th intrinsic mode function, r(t) is the residual, reflecting the multi-scale characteristics of the signal;

[0029] Doppler shift: Where vf is the fluid velocity, λ is the wavelength of the sound wave, and f d Indicates frequency shift, used to locate leaks;

[0030] Status update frequency: Among them, Δt is the update interval of the digital twin model, and F reflects the real-time performance of the system.

[0031] As a further preferred solution of the ultra-high spatiotemporal resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention, the distributed blockchain data management involves the following computing model:

[0032] Data throughput: Among them, N d is the number of data processed per unit time, t c is the consensus time, T reflects the system processing capacity;

[0033] Block generation time: Among them, S b is the block size, R t is the network transmission rate, T b Indicates the time required to generate a block;

[0034] Node synchronization delay: Among them, D n is the physical distance between nodes, v n is the network transmission speed, and L reflects the synchronization efficiency.

[0035] As a further preferred solution of the ultra-high spatiotemporal resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention, the Paillier algorithm is used to implement additive homomorphism, ensuring data privacy while retaining computing power. The specific calculation model is as follows:

[0036] Encryption time: Among them, S d is the data size, R e is the encryption rate, T e Indicates encryption time consumption;

[0037] Computational complexity: C = O(n k ); where n is the amount of data, k is the algorithm order, and C reflects the encryption computation overhead;

[0038] Decryption verification: V = D (E (x)) = x; where E is the encryption function, D is the decryption function, x is the original data, and V verifies the correctness of the encryption.

[0039] As a further preferred solution of the ultra-high spatiotemporal resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention, the fused convolutional neural network (CNN) is used to extract the spatial features of sound waves and vibrations, the long short-term memory (LSTM) network is used to capture the temporal dependency of pressure and flow, and the Transformer model is used to enhance global correlation to achieve high-precision water leakage diagnosis and location. The specific calculation model is as follows:

[0040] CNN convolution output: Among them, W is the input width, K is the convolution kernel size, P is the padding, S is the stride, and O represents the output feature map size;

[0041] LSTM state update: h t =o t tanh(c t ); where o t is the output gate, c t is the cell state, h t It is a hidden state, reflecting the temporal characteristics;

[0042] Transformer Attention: Among them, Q is the query matrix, K is the key matrix, d k is the dimension, and A represents the attention weight.

[0043] As a further preferred solution of the ultra-high spatiotemporal resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention, the adaptive evolutionary algorithm is based on the principle of genetic evolution. Through fitness evaluation, crossover and mutation operations, iteratively updates parameters to improve diagnostic and prediction capabilities. The specific calculation model is as follows:

[0044] Fitness function: Among them, E is the positioning error, and F reflects the model performance;

[0045] Mutation rate: M = M0·e -αt ; Where M0 is the initial mutation rate, α is the decay coefficient, t is the number of iterations, and M controls the randomness of the parameters;

[0046] Parameter update: Among them, P t is the current parameter, η is the learning rate, is the fitness gradient, P t+1 To update the parameters.

[0047] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0048] 1. Ultra-high spatiotemporal resolution monitoring capability: Through an ultra-high-density sensor network of 6 nodes per kilometer and a 100Hz sampling rate, the spatiotemporal resolution of pipeline network status is significantly improved. Traditional technologies are limited by low density (2 nodes / km) and low sampling rate (1Hz), making it difficult to detect subtle anomalies. This system integrates MEMS and fiber optic sensors, with a detection sensitivity of 0.03 liters / second and a positioning error of less than 3 meters. It can reflect subtle changes in pipeline network operation in real time, providing high-precision data support for anomaly detection, and is particularly suitable for complex urban pipeline networks.

[0049] 2. The present invention's multidimensional data fusion and holographic state reconstruction: By integrating multidimensional parameters such as pressure, flow, sound waves, and vibration, and through adaptive signal processing (EMD decomposition and Doppler analysis) and dynamic digital twin technology, a holographic state diagram of the pipeline network operation is generated. Compared with traditional single-parameter monitoring (such as pressure only), this technology comprehensively depicts the pipeline network status and reduces the false alarm rate of anomaly detection to less than 5%. The holographic state diagram is presented in a three-dimensional visual form with an update frequency of 1Hz, providing managers with intuitive and real-time decision-making basis.

[0050] 3. Data security and privacy protection of the present invention: Using distributed blockchain (Hyperledger Fabric) and homomorphic encryption (Paillier algorithm) technology, the present invention ensures that data cannot be tampered with and has strong privacy. Traditional systems are vulnerable to data leakage and tampering, while this system supports AI analysis in an encrypted state, with a key length of 2048 bits and an encryption time of only 10ms. Data is stored in a distributed ledger with a throughput of 5,000 records per second, meeting the needs of large-scale pipeline networks and significantly improving the security and reliability of data management.

[0051] 4. High-efficiency real-time data processing: By optimizing the PBFT consensus mechanism, the present invention achieves a processing capacity of 5,000 data items per second, far exceeding the traditional blockchain system (hundreds of items / second). The edge computing unit pre-processes data, reducing cloud load and controlling synchronization delay to less than 50ms. This high-efficiency real-time processing capability ensures instant analysis and feedback of large-scale pipeline network data, which is suitable for dynamic scenarios such as urban water supply and heating, and avoids the response lag caused by delays in traditional technologies.

[0052] 5. High-precision water leak diagnosis and long-term prediction: This invention combines a multimodal AI model with adaptive evolution to reduce leak location error to 3 meters, achieve 99% diagnostic accuracy, and extend the prediction period to 12 months. Traditional methods have a location error exceeding 10 meters and a prediction period of less than 6 months. This technology uses GAN-generated virtual data for enhanced training and dynamic parameter optimization, enabling the system to adapt to changes in the pipe network environment and providing reliable support for intelligent maintenance.

[0053] 6. Self-learning and environmental adaptability of the system: The adaptive evolutionary algorithm gives the system self-learning capabilities, dynamically adjusts AI model parameters, and adapts to complex conditions such as pipeline aging and geological changes. Traditional technologies lack self-optimization mechanisms, and their performance degrades over time. The present invention updates parameters monthly, maintaining prediction accuracy above 95%, ensuring long-term operational stability and reliability. It is particularly suitable for areas with changeable geological conditions, such as Yining City, Xinjiang. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the ultra-high density sensor network architecture of the present invention;

[0055] Figure 2 is a flow chart of adaptive signal processing of the present invention;

[0056] Figure 3 This is a schematic diagram of the distributed blockchain network structure of the present invention;

[0057] Figure 4 It is a schematic diagram of the homomorphic encryption data processing process of the present invention;

[0058] Figure 5Schematic diagram of the structure of the multimodal AI diagnostic model of the present invention;

[0059] Figure 6 It is the optimization flow chart of the adaptive evolutionary algorithm of the present invention;

[0060] Figure 7 This is a method principle diagram of the ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention. DETAILED DESCRIPTION

[0061] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings:

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments. The purpose and effect of the present invention will become more clear. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0063] This ultra-high spatiotemporal resolution, multi-dimensional pipe network monitoring and intelligent diagnosis system relates to the field of intelligent monitoring and data processing technology, specifically focusing on multi-dimensional state perception, data security, and intelligent diagnosis and prediction technologies for urban underground pipe networks. From an industry perspective, this technology is applicable to the operation and management of urban water supply, heating, and other infrastructure pipe networks, addressing the shortcomings of traditional monitoring methods in density, sensitivity, and security. From a technical perspective, this system integrates cutting-edge advances in multiple disciplines, including microelectromechanical systems (MEMS), fiber optic sensing, distributed computing, encryption algorithms, and artificial intelligence, to establish a complete technology chain from physical perception to data analysis. Applications include smart city construction, infrastructure maintenance, and disaster prevention and control, with particular potential in leak detection, pipe network aging assessment, and operational optimization. By utilizing ultra-high-density sensor deployment (six nodes per kilometer) and multi-dimensional signal acquisition, combined with adaptive signal processing and digital twin technology, this system generates a holographic state map of pipe network operation, providing high-precision data support for AI-driven anomaly detection and prediction. Distributed blockchain and homomorphic encryption ensure data integrity and privacy, while multimodal AI and evolutionary algorithms enable intelligent decision support. This technology not only improves the temporal and spatial resolution of pipeline network monitoring, but also promotes the paradigm shift of pipeline network management from passive maintenance to active prediction, and has significant engineering application value and social benefits.

[0064] Specifically, it includes six parts: ultra-high-density multi-dimensional sensor network construction, adaptive signal processing and dynamic digital twins, distributed blockchain data management, homomorphic encryption data protection, multimodal AI water leakage diagnosis, and adaptive evolutionary algorithm optimization;

[0065] Among them, ultra-high density sensor network architecture such as Figure 1 As shown, the ultra-high-density multi-dimensional sensor network is constructed by integrating micro-electromechanical system (MEMS) microsensor arrays and distributed fiber-optic acoustic wave sensors to build an ultra-high-density monitoring network (6 nodes per kilometer), enabling multi-dimensional parameter acquisition of pressure, flow, acoustic waves, and vibration. MEMS sensors capture transient signals at a high sampling rate (100Hz), while fiber-optic sensors utilize the principle of acoustic wave reflection to detect anomalies over long distances (over 1 kilometer). Working together, these two sensors overcome the limitations of traditional low-density monitoring, improve the spatiotemporal resolution of pipeline network status perception, and provide a high-precision data foundation for subsequent analysis.

[0066] The computational models involved in building an ultra-high-density multi-dimensional sensor network are as follows:

[0067] Sensor node density: Among them, N is the total number of sensor nodes, L is the total length of the pipe network, and D represents the number of nodes per kilometer, which is used to measure the network coverage capacity;

[0068] Water leakage detection sensitivity: Where ΔQ is the minimum detectable flow change, T is the sampling period, and S reflects the system's ability to sense small leaks;

[0069] Sound wave propagation distance: R = v·t; where v is the propagation speed of the sound wave in the medium, t is the sound wave propagation time, and R represents the effective detection range of the fiber optic sensor.

[0070] Implementation plan:

[0071] (1) Sensor design and selection:

[0072] ① A MEMS sensor less than 5mm in size was selected, with built-in pressure, flow, and vibration detection modules. The sampling rate was set to 100Hz to ensure the capture of transient signals. The pressure module uses silicon-based piezoresistive technology with a range of 0-10MPa; the flow module is based on the micro-thermal film principle and has a range of 0-50L / min; and the vibration module uses an accelerometer with a sensitivity of 0.01g.

[0073] ② The fiber optic acoustic wave sensor adopts distributed acoustic sensing (DAS) technology and uses the principle of Rayleigh scattering to detect acoustic wave disturbances along the line. A single optical fiber can cover a distance of over 1 km and has a resolution of 1 meter.

[0074] (2) Node deployment planning:

[0075] 1. Calculate the required number of nodes based on the length of the pipeline network. For example, a 100-kilometer pipeline network requires 600 nodes (D = 6). MEMS sensors are installed at key locations on the pipeline wall (such as valves and elbows) and secured with bolts. Fiber optic sensors are laid parallel to the pipeline and fixed to the outer wall or buried in the nearby soil.

[0076] ② During deployment, ensure that the node spacing is uniform (about 166 meters), draw the pipeline network topology map through the GIS system, and mark the coordinates of each node.

[0077] (3) Communication network construction:

[0078] ① Each node is interconnected through a low-power wide area network (LoRa) module. The communication frequency is set to 915MHz, the transmission distance is up to 2 kilometers, and the data packet size is controlled within 50 bytes.

[0079] ② Set up a gateway at the edge of the pipeline network, with one gateway configured every 10 kilometers, responsible for aggregating node data and uploading it to the cloud server through the 4G / 5G network.

[0080] (4) Data collection and preprocessing:

[0081] ①MEMS sensors collect multidimensional data at a frequency of 100 Hz, generating 100 sets of samples per second. The data format is {timestamp, pressure, flow, vibration}.

[0082] ② The fiber optic sensor monitors the acoustic wave signal in real time, generates continuous waveform data with a sampling rate of 10kHz, and performs Fourier transform through the edge computing unit to extract the characteristic frequency.

[0083] ③ The edge node has a built-in filtering algorithm (such as Kalman filtering) to remove noise, compress the data, and upload it to the cloud with a compression rate of 80%.

[0084] (5) System testing and calibration:

[0085] ① In the laboratory, a pipe network environment was simulated and 0.03 liters / second of water leakage was injected to verify the sensitivity of the MEMS sensor. The detection distance and positioning accuracy of the fiber optic sensor were tested by generating sound waves by knocking on the pipe.

[0086] ② After calibration is completed, deploy it to the actual pipeline network and record the initial operating data as a benchmark.

[0087] Adaptive signal processing flow chart is as follows Figure 2As shown, adaptive signal processing and dynamic digital twins are used to combine empirical mode decomposition (EMD) and Doppler analysis to process multidimensional signals, extract pipeline network operation characteristics, and build a dynamic digital twin model. The empirical mode decomposition (EMD) is used to decompose non-stationary signals into intrinsic mode functions (IMFs) to separate noise from valid signals. Doppler analysis calculates fluid velocity and acoustic frequency shift to locate abnormal sources. Digital twin technology generates a holographic state diagram of the pipeline network through real-time data mapping, enabling visualization and dynamic updating of the operation status.

[0088] EMD decomposition: Among them, x(t) is the original signal, IMF i (t) is the i-th intrinsic mode function, r(t) is the residual, reflecting the multi-scale characteristics of the signal;

[0089] Doppler shift: Where vf is the fluid velocity, λ is the wavelength of the sound wave, and f d Indicates frequency shift, used to locate leaks;

[0090] Status update frequency: Among them, Δt is the update interval of the digital twin model, and F reflects the real-time performance of the system.

[0091] Implementation plan:

[0092] (1) Signal acquisition and preprocessing:

[0093] ① Obtain multidimensional data from the sensor network, including pressure, flow, vibration and acoustic signals, and store them in time series format.

[0094] ② Apply high-pass filtering (cut-off frequency 1 Hz) to the original signal to remove low-frequency interference and ensure the accuracy of subsequent decomposition.

[0095] (2) EMD decomposition implementation:

[0096] ① Run the EMD algorithm on the edge node to decompose the signal into multiple IMFs. The number of iterations is set to 10, and the stopping criterion is that the standard deviation is less than 0.01.

[0097] ② Extract the first three IMFs as the main features, corresponding to high-frequency vibration, medium-frequency sound waves and low-frequency pressure changes, and eliminate the noise components in the residual.

[0098] (3) Doppler analysis and calculation:

[0099] ① Perform fast Fourier transform (FFT) on the sound wave signal with a window size of 1024 points, calculate the spectrum, and extract the main frequency f0.

[0100] ② Calculate the frequency shift fd according to the Doppler formula, and combine it with the speed of sound (about 1480 m / s in water medium) to infer the fluid velocity and abnormal position.

[0101] (4) Construction of digital twin model:

[0102] ① Build a 3D pipeline network model based on Unity3D on the cloud server, import the pipeline network GIS data, and define the material and geometric parameters.

[0103] ② Map the processed signal features to the model, with pressure represented by a color gradient (red high, green low), vibration displayed as a ripple animation, and acoustic anomalies marked with flashing dots.

[0104] (5) Dynamic update and storage:

[0105] ① Set the update interval to 1 second and push the status graph to the front-end interface via the WebSocket protocol.

[0106] ② Generate a snapshot every minute and store it in a distributed database (such as MongoDB), supporting historical backtracking and analysis.

[0107] (6) Verification and optimization: Inject known anomalies (such as a water leak of 0.05 L / s) into the simulation environment, compare the state diagram with the actual position, and adjust the EMD decomposition parameters to ensure that the positioning error is less than 3 meters.

[0108] The schematic diagram of the distributed blockchain network structure is as follows Figure 3 As shown in the figure, distributed blockchain data management: Based on the Hyperledger Fabric framework, a distributed ledger is built to record pipeline monitoring data, and the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism is used to ensure data consistency and real-time performance. Each node independently stores a copy of the ledger, and through optimized consensus processes, throughput is increased to meet the needs of large-scale pipeline network data processing.

[0109] The computing model involved in distributed blockchain data management is as follows:

[0110] Data throughput: Among them, N d is the number of data processed per unit time, t c is the consensus time, T reflects the system processing capacity;

[0111] Block generation time: Among them, S b is the block size, R t is the network transmission rate, T b Indicates the time required to generate a block;

[0112] Node synchronization delay: Among them, D n is the physical distance between nodes, v n is the network transmission speed, and L reflects the synchronization efficiency.

[0113] Implementation plan:

[0114] (1) Blockchain network construction:

[0115] ① Deploy the Fabric network, configure 5 peer nodes (Peer), 1 sorting service node (Orderer) and 1 certificate authority (CA).

[0116] ② Each peer node is equipped with 8GB of memory, a 4-core CPU, runs the Ubuntu 20.04 system, and has a storage capacity of 1TB.

[0117] (2) Data format definition:

[0118] ① The monitoring data is encapsulated in JSON format, including {timestamp, node ID, pressure, flow, vibration, acoustic wave characteristics}, and each piece of data is about 200 bytes in size.

[0119] ② Set the block capacity to 500 data items, with a total size of approximately 100KB.

[0120] (3) Consensus mechanism optimization:

[0121] ① Use the PBFT algorithm to optimize the pre-preparation phase, reduce the number of message broadcasts, and reduce the consensus rounds from 3 to 2.

[0122] ② Set the batch size to 100 data items, control the consensus time within 50ms, and ensure a throughput of 5000 items per second.

[0123] (4) Data on-chain process:

[0124] ① After the edge node generates data, it calculates the hash value through the SHA-256 algorithm as a unique identifier.

[0125] ② The data is submitted to the nearest Peer node, and the Orderer generates blocks after sorting and broadcasts them to all nodes for synchronization.

[0126] (5) Ledger storage and verification:

[0127] ① Each node maintains a complete ledger, uses LevelDB to store key-value pairs, and supports fast queries.

[0128] ② Sample and verify 10 blocks every hour to check hash consistency and ensure that the data has not been tampered with.

[0129] (6) Performance testing: In a 100-kilometer pipeline network simulation environment, 6,000 data items were injected per second, throughput and latency were recorded, and the number of nodes and network bandwidth were adjusted to optimize to the target performance.

[0130] The schematic diagram of homomorphic encryption data processing process is as follows Figure 4 Homomorphic encryption data protection: Utilizes homomorphic encryption technology to encrypt the storage and computation of monitoring data, supporting AI analysis in an encrypted state. The Paillier algorithm implements additive homomorphism, ensuring data privacy while retaining computing power, addressing issues that traditional encryption methods cannot directly analyze.

[0131] The calculation model is as follows:

[0132] Encryption time: Among them, S d is the data size, R e is the encryption rate, T e Indicates encryption time consumption;

[0133] Computational complexity: C = O(n k ); where n is the amount of data, k is the algorithm order, and C reflects the encryption computation overhead;

[0134] Decryption verification: V = D (E (x)) = x; where E is the encryption function, D is the decryption function, x is the original data, and V verifies the correctness of the encryption.

[0135] 2. Implementation Plan

[0136] (1) Encryption algorithm configuration:

[0137] ① Select the Paillier algorithm, set the length of the public key and private key to 2048 bits, generate a key pair and store it on the CA node.

[0138] ② Encrypt each piece of monitoring data (about 200 bytes) to generate a ciphertext size of about 4KB.

[0139] (2) Data encryption process:

[0140] ① After receiving the original data, the edge node calls the encryption module, inputs the public key and data, and generates ciphertext.

[0141] ② The encryption process is processed in parallel, processing 1,000 pieces of data per second, and the time consumption is controlled within 10ms.

[0142] (3) Encrypted storage:

[0143] ① The ciphertext is uploaded to the blockchain network and stored in the ledger of the peer node, marked as {block ID, data ID, ciphertext}.

[0144] ② Set access permissions to authorize only the AI ​​analysis module to read ciphertext.

[0145] (4) Encryption status calculation:

[0146] ① The AI ​​model loads encrypted data in the cloud and uses Paillier’s additive homomorphic property to execute anomaly detection algorithms (such as mean shift).

[0147] ② The calculation result is still encrypted and is transmitted to the management end for decryption to verify the abnormal location.

[0148] (5) Decryption and verification:

[0149] ① The management end uses the private key to decrypt the result and compare the original data with the calculated output to ensure consistency.

[0150] ② Randomly sample 10 data items every hour, record the decryption time and error, and optimize the encryption parameters.

[0151] (6) Security testing: Simulate man-in-the-middle attacks, attempt to tamper with ciphertext, verify the system's anti-attack capabilities, and ensure data privacy.

[0152] The schematic diagram of the multimodal AI diagnostic model structure is as follows Figure 5 As shown in the figure, multimodal AI water leak diagnosis is used to integrate convolutional neural networks (CNN), long short-term memory networks (LSTM), and transformer models to build a multimodal AI system. The fused convolutional neural network (CNN) is used to extract the spatial features of sound waves and vibrations, the long short-term memory network (LSTM) is used to capture the temporal dependency of pressure and flow, and the transformer model is used to enhance global correlation to achieve high-precision water leak diagnosis and positioning. The specific calculation model is as follows:

[0153] CNN convolution output: Among them, W is the input width, K is the convolution kernel size, P is the padding, S is the stride, and O represents the output feature map size;

[0154] LSTM state update: h t =o t tanh(c t ); where o t is the output gate, c t is the cell state, h t It is a hidden state, reflecting the temporal characteristics;

[0155] Transformer Attention: Among them, Q is the query matrix, K is the key matrix, d k is the dimension, and A represents the attention weight.

[0156] 3. Implementation Plan

[0157] (1) Data preparation and preprocessing:

[0158] ① Collect multidimensional data (pressure, flow, vibration, sound waves), normalize it to the [0,1] interval, and divide it into a training set (80%) and a test set (20%).

[0159] ② Perform short-time Fourier transform (STFT) on the acoustic signal to generate a time-frequency map as CNN input.

[0160] (2) CNN model construction:

[0161] ① Design a 3-layer CNN with a convolution kernel size of 3×3, a stride of 1, padding of 0, and the number of output channels of 32, 64, and 128 respectively.

[0162] ②Add a maximum pooling layer (2×2) to extract spatial features and reduce the output dimension to 1 / 4 of the original.

[0163] (3) LSTM model training:

[0164] ① Input pressure and flow sequence, set the hidden layer to 128 units, time step 50, use Adam optimizer, and learning rate 0.001.

[0165] ② Train for 100 rounds, use the mean square error (MSE) as the loss function, and record the time series dependency features.

[0166] (4) Transformer integration:

[0167] ① Set 8 attention heads, input dimension 512, and adopt multi-head self-attention mechanism to enhance global correlation.

[0168] ②The output layer is connected to the fully connected layer and mapped to the coordinates and probability of the leakage location.

[0169] (5) Model fusion and optimization:

[0170] ① Concatenate the outputs of CNN, LSTM, and Transformer, and calculate the diagnosis results through the softmax function. The positioning error target is less than 3 meters.

[0171] ② Use GAN to generate virtual leakage data, expand the training set, and improve model robustness.

[0172] (6) Deployment and verification:

[0173] ①The model is deployed on a cloud-based GPU server (NVIDIA A100), processing 100 sets of data per second.

[0174] ②Injecting 0.05 liters / second of leaking water into the actual pipe network to verify that the diagnostic accuracy rate reached 99%.

[0175] Adaptive evolutionary algorithm optimization flow chart is as follows Figure 6As shown in the figure; Adaptive Evolutionary Algorithm Optimization: It is used to dynamically optimize AI model parameters through an adaptive evolutionary algorithm, enabling the system to self-learn and adapt to changes in the pipe network environment. The algorithm is based on the principles of genetic evolution and iteratively updates parameters through fitness evaluation, crossover, and mutation operations to improve diagnostic and predictive capabilities. The calculation model is as follows:

[0176] Fitness function: Among them, E is the positioning error, and F reflects the model performance;

[0177] Mutation rate: M = M0·e -αt ; Where M0 is the initial mutation rate, α is the decay coefficient, t is the number of iterations, and M controls the randomness of the parameters;

[0178] Parameter update: Among them, P t is the current parameter, η is the learning rate, is the fitness gradient, P t+1 To update the parameters.

[0179] 4. Implementation Plan

[0180] (1) Algorithm initialization:

[0181] ① Set the population size to 100 individuals, each of which is a set of AI model parameters (CNN weights, LSTM biases, etc.).

[0182] ② Initial mutation rate M0 = 0.1, decay coefficient α = 0.01, learning rate η = 0.001.

[0183] (2) Fitness evaluation:

[0184] ① Input test data, calculate the positioning error (E) of each individual, and take the inverse as the fitness (F).

[0185] ② Sort the population and retain the top 20% of individuals as elites to directly enter the next generation.

[0186] (3) Crossover and mutation:

[0187] ① Perform single-point crossover on the remaining individuals, randomly select parameter positions, exchange parent genes, and generate new individuals.

[0188] ② Randomly adjust the parameter value according to the mutation rate (M) in the range of [-0.01, 0.01] to increase diversity.

[0189] (4) Parameter update iteration:

[0190] ① Calculate the fitness gradient and update the parameters by gradient descent. The number of iterations is set to 50.

[0191] ② Record the best individual performance every 10 iterations and adjust (M) and η to ensure convergence.

[0192] (5) Model deployment and self-learning:

[0193] ① Load the optimized parameters into the AI ​​model, deploy it in the cloud, and run the evolutionary algorithm once a month to update the parameters.

[0194] ②Record the forecast period and accuracy, ensuring 12 months and 99%.

[0195] (6) Effect verification: Test in different pipeline network environments (such as aging pipelines and newly built pipelines) to verify the adaptability of the algorithm and adjust the initial parameters to optimize the convergence speed.

[0196] The method principle diagram of the ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system of the present invention is as follows: Figure 7 As shown,

[0197] 1. Ultra-high density sensor network deployment:

[0198] 1. Pipeline network survey and planning:

[0199] (1) Conduct a field survey of the target pipeline network (assuming a length of 100 km) to obtain the pipeline length, diameter (DN200-DN1000), material (cast iron, PE), and location of key nodes (such as valves and joints).

[0200] (2) Use GIS software to draw the pipeline network topology map, calculate the node deployment density (6 nodes / km), and determine the total number of nodes to be 600, including 400 MEMS sensors and 200 monitoring points covered by fiber optic sensors along the line.

[0201] 2.Sensor selection and installation:

[0202] (1) MEMS sensor: A 5mm x 5mm micromodule with integrated pressure (0-10MPa), flow (0-50L / min), and vibration (0-10g) detection functions is used. It has a sampling rate of 100Hz and consumes less than 50mW. It is bolted to the inside of the pipe wall during installation and has an IP68 sealing rating.

[0203] (2) Fiber Optic Sensor: This sensor uses single-mode fiber (G.652) and is based on distributed acoustic sensing (DAS) technology. It has a detection range of 1 km and a resolution of 1 meter. It is laid parallel to the pipeline and fixed to the outer wall of the pipeline, with protective sleeves installed every 50 meters.

[0204] 3. Communication network configuration:

[0205] (1) Each MEMS node is equipped with a LoRa module (frequency 915 MHz, transmission distance 2 km), data packet size 50 bytes, and sending interval 1 second.

[0206] (2) Deploy one gateway (supporting 4G / 5G) every 10 kilometers, aggregate data through the MQTT protocol and upload it to the cloud server.

[0207] 4. Initial testing:

[0208] (1) In the laboratory, a 10-meter pipe section was simulated and a water leak of 0.03 liters / second was injected to verify the sensitivity of the MEMS sensor. The pipe was struck to generate sound waves and the positioning accuracy of the fiber optic sensor was tested (the error was less than 3 meters).

[0209] (2) After deployment, run for 24 hours and record the initial data as a baseline.

[0210] 2. Multi-dimensional signal acquisition and preprocessing:

[0211] 1. Data collection settings:

[0212] (1) The MEMS sensor collects pressure, flow, and vibration data at a sampling rate of 100 Hz, generating 100 sets of samples per second in the format of {timestamp, node ID, pressure, flow, vibration}.

[0213] (2) The fiber optic sensor collects acoustic wave signals at a sampling rate of 10 kHz, generates continuous waveforms, and stores them as 16-bit floating point numbers.

[0214] 2. Edge preprocessing:

[0215] (1) Run the Kalman filter algorithm on the edge node (Raspberry Pi 4, 4GB memory) to remove high-frequency noise (cutoff frequency 50Hz) and retain the valid signal.

[0216] (2) Perform fast Fourier transform (FFT) on the acoustic signal with a window size of 1024 points, extract the characteristic frequency (0-5kHz), and compress the data to 10% of the original.

[0217] 3. Data transmission: The pre-processed data is transmitted to the gateway via LoRa. The gateway aggregates 600 sets of data (about 30KB) per second and uploads them to the cloud via the 4G network, with latency controlled within 100ms.

[0218] 4. Quality check: After the cloud receives the data, it checks the packet loss rate (target is less than 1%) and timestamp consistency. Abnormal data is marked as "pending verification" and re-collected.

[0219] 3. Adaptive Signal Processing and Digital Twin Construction:

[0220] 1. Signal decomposition:

[0221] (1) Empirical mode decomposition (EMD) is applied to the multidimensional data, decomposing it into five intrinsic mode functions (IMFs), with 10 iterations and a stopping criterion of standard deviation less than 0.01.

[0222] (2) Extract the first three IMFs, which correspond to vibration (high frequency), sound wave (medium frequency), and pressure (low frequency) features, and remove the residual noise.

[0223] 2. Doppler analysis: Calculate the Doppler frequency shift of the acoustic signal, set the sound speed to 1480 m / s (water medium), and use a window sliding step of 0.1 seconds to locate the anomaly source (with an error of less than 3 meters).

[0224] 3. Digital Twin Implementation:

[0225] (1) Build a 3D pipe network model based on Unity3D in the cloud (AWS EC2, 16 cores and 32GB), import GIS data, and define pipe diameters and material parameters.

[0226] (2) Mapping signal features to the model: pressure is displayed as a color gradient (red high, green low), vibration is represented by ripple animation, and sound wave anomalies are marked with flashing red dots.

[0227] 4. Dynamic Updates:

[0228] (1) Set the update frequency to 1Hz and push the status graph to the front-end interface (HTML5) via WebSocket to support real-time monitoring.

[0229] (2) Generate snapshots every minute and store them in MongoDB, supporting historical queries.

[0230] 4. Distributed blockchain and homomorphic encryption implementation:

[0231] 1. Blockchain network deployment:

[0232] (1) Use Hyperledger Fabric 2.5 to build a network, configure 5 peer nodes (Peer) and 1 orderer node (Orderer), and run it in a Docker container.

[0233] (2) Each peer node stores a complete ledger, using LevelDB to store key-value pairs with a capacity of 1TB.

[0234] 2. Data on-chain:

[0235] (1) The preprocessed data is encapsulated into JSON (200 bytes / item), hashed using SHA-256, and submitted to the peer node.

[0236] (2) Orderer packs 500 items per block, optimizes PBFT consensus, takes 50ms, and has a throughput of 5000 items per second.

[0237] 3. Homomorphic encryption implementation:

[0238] (1) The Paillier algorithm is selected, the key length is 2048 bits, the public key is used to encrypt the data, the ciphertext size is 4KB / item, and the encryption time is 10ms.

[0239] (2) The ciphertext is stored in the ledger, marked as {block ID, data ID, ciphertext}, and only the AI ​​module is authorized to access it.

[0240] 4. Verification and backup: Sample 10 blocks every hour to verify hash consistency; back up the ledger to cold storage (S3) daily and retain it for 30 days.

[0241] 5. Multimodal AI water leak diagnosis:

[0242] 1. Data preparation:

[0243] (1) Encrypted data is extracted from the blockchain, decrypted and normalized to [0, 1], and divided into a training set (100,000 records) and a test set (20,000 records).

[0244] (2) Use GAN to generate 50,000 virtual water leakage data to enhance the robustness of the model.

[0245] 2. Model construction:

[0246] (1) CNN: 3 layers of convolution (kernel 3×3, channels 32-64-128), processing the sound wave time-frequency map and outputting spatial features.

[0247] (2) LSTM: 128 units, time step 50, processes pressure and flow sequences, and outputs time series features.

[0248] (3) Transformer: 8 attention heads, dimension 512, integrates global features, and outputs leakage probability and location.

[0249] 3. Training and optimization:

[0250] (1) Use NVIDIA A100 GPU to train for 100 rounds, loss function MSE, learning rate 0.001, and accuracy target 99%.

[0251] (2) Save the model every 10 rounds and test the positioning error (less than 3 meters).

[0252] 4. Deployment and inference: The model is deployed in the cloud, inputs real-time data, processes 100 groups per second, and pushes the output results to the management end.

[0253] 6. Adaptive evolutionary algorithm optimization and result output:

[0254] 1. Algorithm initialization: Set the population size to 100, the initial mutation rate to 0.1, and the fitness function to F = 1 / E ((E) is the positioning error).

[0255] 2. Iterative optimization:

[0256] (1) The fitness is calculated in each iteration, the top 20% individuals are retained, and the remaining individuals are crossover (single point) and mutation (range [-0.01, 0.01]).

[0257] (2) Iterate 50 times, update the parameters, and the prediction period target is 12 months.

[0258] 3. Model update: Load the optimized parameters into the AI ​​model, run the algorithm once a month, and record the performance improvement.

[0259] 4. Result output and feedback:

[0260] (1) Output the leak location (latitude and longitude), probability (>95%) and prediction period, generate a report and push it to the management platform. Adjust the threshold based on the feedback and optimize the diagnosis strategy.

[0261] Example 1: Background: The total length of the Yining City water supply network is approximately 150 kilometers. Some pipelines are severely aged and have a high annual leakage rate. The company undertook the city's smart water project and applied the technology of this invention to improve water leakage detection capabilities.

[0262] Implementation process:

[0263] Sensor deployment: Six nodes per kilometer are deployed on the pipeline network, including MEMS sensors and fiber optic sensor monitoring points. MEMS sensors are installed at key nodes (such as aging cast iron pipe sections), and fiber optic sensors are laid along the main pipelines.

[0264] Data acquisition and processing: MEMS sensors collect pressure, flow, and vibration data at a 100Hz sampling rate, while fiber optic sensors detect acoustic signals. Edge nodes extract features through EMD decomposition and generate a holographic state map.

[0265] Blockchain and encryption: Data is homomorphically encrypted and stored in the Hyperledger Fabric distributed ledger, processing 5,000 data items per second to ensure security and real-time performance.

[0266] AI diagnosis: A multimodal AI model (CNN+LSTM+Transformer) analyzes data to locate leaks. An adaptive evolutionary algorithm optimizes model parameters to improve diagnostic accuracy.

[0267] Ultra-High-Density Multi-Dimensional Monitoring Network Design: This proposes an ultra-high-density sensor network with six nodes per kilometer, integrating MEMS microsensors (100Hz sampling) and fiber-optic acoustic sensors (with a detection range exceeding 1 kilometer). This breaks through the limitations of traditional low-density (2 nodes / km) and single-parameter monitoring. Its innovation lies in the coordinated collection of multi-dimensional parameters (pressure, flow, acoustic waves, and vibration) with high temporal and spatial resolution, achieving a detection sensitivity of 0.03 liters / second and laying the foundation for refined pipe network management.

[0268] Adaptive signal processing and digital twin integration: By combining EMD decomposition with Doppler analysis, we develop adaptive signal processing technology to extract multidimensional signal features and generate a holographic state diagram through a dynamic digital twin. This innovation combines non-stationary signal decomposition with real-time state mapping, achieving a 1Hz update frequency and positioning accuracy of less than 3 meters. This technology transcends the limitations of traditional static modeling and single-signal analysis, enhancing the comprehensiveness and dynamism of pipeline network status perception.

[0269] Distributed blockchain and homomorphic encryption converge: This distributed ledger, built on Hyperledger Fabric, incorporates homomorphic encryption technology to achieve encrypted data storage and computation. Innovations include optimizing the PBFT consensus mechanism (throughput 5,000 records per second) and supporting AI analysis in encrypted environments. This addresses the conflict between traditional technologies in data security and real-time processing, ensuring the privacy and efficiency of large-scale pipeline network data.

[0270] High-precision diagnosis using a multimodal AI model: By integrating CNN (spatial features), LSTM (temporal dependencies), and Transformer (global correlation), a multimodal AI model was developed, achieving 99% leak diagnosis accuracy and a positioning error of less than 3 meters. The innovation lies in the combination of multimodal feature extraction and GAN data enhancement, breaking through the accuracy bottleneck of traditional single models and providing an intelligent solution for pipe network anomaly detection.

[0271] Dynamic Optimization with an Adaptive Evolutionary Algorithm: This system incorporates an adaptive evolutionary algorithm to dynamically optimize AI model parameters, enabling self-learning and a 12-month prediction cycle. This innovation leverages fitness assessment and mutation operations to achieve real-time parameter adjustment, transcending the limitations of traditional fixed-parameter models. This allows the system to adapt to changes in the pipeline network environment and ensure high performance over the long term.

[0272] Edge Computing and Cloud Collaborative Architecture: This architecture integrates edge computing and cloud computing, with edge nodes performing data preprocessing (filtering and compression) and the cloud performing in-depth analysis and storage. This innovation leverages efficient distributed computing, reducing cloud load and achieving synchronization latency of less than 50ms, improving the scalability and responsiveness of large-scale real-time monitoring and management of pipeline networks.

[0273] Those skilled in the art will understand that the above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will still be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the invention shall be included within the scope of protection of the invention. All technical features in this embodiment may be freely combined according to actual needs.

[0274] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system, characterized by: Specifically, it includes six parts: ultra-high-density multi-dimensional sensor network construction, adaptive signal processing and dynamic digital twins, distributed blockchain data management, homomorphic encryption data protection, multimodal AI water leakage diagnosis, and adaptive evolutionary algorithm optimization; Among them, ultra-high-density multi-dimensional sensor network construction: used to build an ultra-high-density monitoring network by integrating micro-electromechanical system MEMS micro sensor arrays and distributed fiber optic acoustic wave sensors to achieve multi-dimensional parameter acquisition of pressure, flow, acoustic waves and vibration; Adaptive signal processing and dynamic digital twins: used to combine empirical mode decomposition (EMD) and Doppler analysis to process multidimensional signals, extract pipeline network operation characteristics, and build dynamic digital twin models; Distributed blockchain data management: used to build a distributed ledger to record pipeline monitoring data, using the practical Byzantine Fault Tolerance (PBFT) consensus mechanism to ensure data consistency and real-time performance; Homomorphic encryption data protection: Used to encrypt, store, and calculate monitoring data using homomorphic encryption technology, supporting AI analysis in an encrypted state; Multimodal AI water leak diagnosis: used to integrate convolutional neural networks (CNN), long short-term memory (LSTM) networks, and Transformer models to build a multimodal AI system; Adaptive evolutionary algorithm optimization: used to dynamically optimize AI model parameters through an adaptive evolutionary algorithm, enabling the system to self-learn and adapt to changes in the pipe network environment.

2. The ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system according to claim 1 is characterized by: The computational models involved in building an ultra-high-density multi-dimensional sensor network are as follows: Sensor node density: Among them, N is the total number of sensor nodes, L is the total length of the pipe network, and D represents the number of nodes per kilometer, which is used to measure the network coverage capacity; Water leakage detection sensitivity: Where ΔQ is the minimum detectable flow change, T is the sampling period, and S reflects the system's ability to sense small leaks; Sound wave propagation distance: R = v·t; where v is the propagation speed of the sound wave in the medium, t is the sound wave propagation time, and R represents the effective detection range of the fiber optic sensor.

3. The ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system according to claim 1 is characterized by: The empirical mode decomposition (EMD) is used to decompose the non-stationary signal into the intrinsic mode function (IMF) to separate the noise from the effective signal; the Doppler analysis calculates the fluid velocity and the sound wave frequency shift to locate the abnormal source; Digital twin technology generates a holographic status diagram of the pipeline network through real-time data mapping, enabling visualization and dynamic updates of operating status; EMD decomposition: Among them, x(t) is the original signal, IMF i (t) is the i-th intrinsic mode function, r(t) is the residual, reflecting the multi-scale characteristics of the signal; Doppler shift: Where vf is the fluid velocity, λ is the wavelength of the sound wave, and f d Indicates frequency shift, used to locate leaks; Status update frequency: Among them, Δt is the update interval of the digital twin model, and F reflects the real-time performance of the system.

4. The ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system according to claim 1 is characterized by: The computing model involved in distributed blockchain data management is as follows: Data throughput: Among them, N d is the number of data processed per unit time, t c is the consensus time, T reflects the system processing capacity; Block generation time: Among them, S b is the block size, R t is the network transmission rate, T b Indicates the time required to generate a block between; Node synchronization delay: Among them, D n is the physical distance between nodes, v n is the network transmission speed, and L reflects the synchronization efficiency.

5. The ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system according to claim 1 is characterized by: The Paillier algorithm is used to implement additive homomorphism, ensuring data privacy while retaining computing power. The calculation model is as follows: Encryption time: Among them, S d is the data size, R e is the encryption rate, T e Indicates encryption time consumption; Computational complexity: C = O(n k ); where n is the amount of data, k is the algorithm order, and C reflects the encryption computation overhead; Decryption verification: V = D (E (x)) = x; where E is the encryption function, D is the decryption function, x is the original data, and V verifies the correctness of the encryption.

6. The ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system according to claim 1 is characterized by: The fused convolutional neural network (CNN) is used to extract the spatial features of sound waves and vibrations, the long short-term memory (LSTM) network is used to capture the temporal dependencies of pressure and flow, and the Transformer model is used to enhance global correlation to achieve high-precision water leak diagnosis and location. The specific calculation model is as follows: CNN convolution output: Among them, W is the input width, K is the convolution kernel size, P is the padding, S is the stride, and O represents the output feature map size; LSTM state update: h t =o t tanh(c t ); where o t is the output gate, c t is the cell state, h t It is a hidden state, reflecting the temporal characteristics; Transformer Attention: Among them, Q is the query matrix, K is the key matrix, d k is the dimension, and A represents the attention weight.

7. The ultra-high temporal and spatial resolution multi-dimensional pipe network monitoring and intelligent diagnosis system according to claim 1 is characterized by: The adaptive evolutionary algorithm is based on the principle of genetic evolution. Through fitness evaluation, crossover and mutation operations, it iteratively updates parameters to improve diagnostic and predictive capabilities. The calculation model is as follows: Fitness function: Among them, E is the positioning error, and F reflects the model performance; Mutation rate: M = M0·e -αt ; Where M0 is the initial mutation rate, α is the decay coefficient, t is the number of iterations, and M controls the randomness of the parameters; Parameter update: Among them, P t is the current parameter, η is the learning rate, is the fitness gradient, P t+1 To update the parameters.

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