Artificial intelligence water supply and drainage system based on 3D CNN multi-mode mixed reality technology and block chain technology

Abstract

The invention aims to introduce an intelligent water supply and drainage system combining a 3D convolutional neural network (3D CNN), a multi-modal mixed reality technology and a block chain technology. According to the system, multi-dimensional data of a water supply and drainage system is analyzed by utilizing a 3D CNN through an advanced deep learning algorithm, and real-time monitoring and prediction of parameters such as water flow, pressure and quality are realized. Meanwhile, by means of the multi-mode mixed reality technology, the system can visualize data and provide a visual user interface, so that an operator can better understand and manage water resources. The introduction of the block chain technology provides guarantee for the data security and transparency of the system. Through a decentration mode, the non-tampering and traceability of the data are ensured, and the credibility of the system is improved. The intelligent water supply and drainage system not only can improve the management efficiency of water resources, but also can provide support for sustainable development of cities.

Description

Technical Field

[0001] Applications of 3D CNN in image processing:

[0002] 3D CNNs can be used to process and analyze three-dimensional data in water supply and drainage systems, such as pipe structure and fluid flow. Deep learning of 3D image data can improve the accuracy of fault detection and predictive maintenance.

[0003] Multimodal Mixed Reality Technology:

[0004] By using augmented reality (AR) or virtual reality (VR) technology, engineers and technicians can overlay digital information into a real-world environment to monitor the operational status of water supply and drainage systems in real time. This interactive approach can improve training efficiency and the accuracy of on-site maintenance.

[0005] Applications of blockchain technology:

[0006] Blockchain can ensure the security and transparency of water supply and drainage system data. Through decentralized data storage, all sensor data, maintenance records, and operation logs can be securely recorded and tracked, ensuring data integrity and immutability. Intelligent Management:

[0007] By combining the above technologies, intelligent management of water supply and drainage systems can be achieved. For example, through real-time data analysis and predictive models, the allocation and use of water resources can be optimized, and energy consumption and operating costs can be reduced.

[0008] Decision support system:

[0009] By integrating various types of data (such as sensor data, historical maintenance records, environmental data, etc.), a decision support system is built to help decision-makers formulate more scientific management strategies. Background Technology

[0010] 1. 3D CNN (Convolutional Neural Network) Background: 3D CNN is a deep learning model that extends the traditional 2D convolutional neural network, enabling it to process three-dimensional data. This technology has been widely used in fields such as medical imaging, video analysis, and 3D object recognition.

[0011] In water supply and drainage systems, 3D CNNs can be used to analyze the three-dimensional structure of pipes, dynamic changes in fluid flow, etc., to help identify potential faults and optimize system design.

[0012] Fault detection: 3D CNN is used to analyze image data inside the pipeline to identify problems such as cracks and corrosion.

[0013] Fluid dynamics simulation: Predicting water flow behavior through 3D modeling of fluid motion. 2. Multimodal Mixed Reality Technology Background: Mixed reality (MR) combines virtual reality (VR) and augmented reality (AR) technologies, enabling the integration of virtual information with the real environment to provide an immersive user experience.

[0014] In water supply and drainage systems, multimodal mixed reality technology can help engineers conduct real-time monitoring and maintenance on-site.

[0015] On-site training: Through AR technology, engineers can see virtual operation instructions and data analysis in a real environment, improving training effectiveness.

[0016] Remote collaboration: Technicians can use MR technology to share information with other experts in real time, enabling remote diagnosis and problem solving.

[0017] 3. Blockchain Technology Background: Blockchain is a distributed ledger technology characterized by decentralization, transparency, and immutability. It has been widely applied in fields such as finance and supply chain management.

[0018] In water supply and drainage systems, blockchain technology can be used for data management and security.

[0019] Data recording: Through blockchain technology, sensor data, maintenance records, and operation logs are recorded in real time to ensure data integrity and security.

[0020] Contract Management: In water supply and drainage projects, blockchain can be used to implement smart contracts to ensure that the responsibilities and obligations of all parties are fulfilled.

[0021] 4. Background of comprehensive application of artificial intelligence: The development of artificial intelligence (AI) technology has made data analysis and decision support more intelligent and efficient.

[0022] In water supply and drainage systems, AI can be combined with 3D CNN and blockchain technology to achieve intelligent monitoring and automated management. Application: Intelligent Monitoring: Through real-time data analysis, AI can predict equipment failures and provide maintenance recommendations.

[0023] Optimized management: Utilize AI algorithms to optimize water resource allocation and improve system operating efficiency. Summary of the Invention

[0024] 1. Overall System Architecture:

[0025] This system integrates 3D CNN for data processing and analysis, multimodal mixed reality technology for user interaction, and blockchain for data security and transparency. The overall architecture includes a data acquisition layer, a data processing layer, a user interaction layer, and a data management layer. 2. 3D CNN Application Data Processing:

[0026] 3D Image Analysis: 3D CNN is used to process 3D images of water supply and drainage systems to identify cracks, corrosion and other potential faults in the pipes.

[0027] Fluid dynamics simulation: By modeling fluid flow in 3D, predicting water flow behavior and optimizing pipeline design and flow control. 3. Multimodal mixed reality technology user interaction:

[0028] Augmented Reality Support: Technicians can use AR devices on-site to view the pipeline's operational status, fault locations, and historical maintenance records in real time. The overlay of virtual information with the actual environment makes maintenance work more efficient.

[0029] Training and guidance: New employees can receive on-site training through mixed reality technology, and receive real-time operational guidance and troubleshooting assistance.

[0030] 4. Integration of blockchain technology with data security and transparency:

[0031] Decentralized data storage: All sensor data, maintenance records, and operation logs are stored using blockchain technology to ensure the immutability and transparency of the data.

[0032] Smart contracts: In water supply and drainage projects, the smart contract function of blockchain can be used to automate contract execution and ensure the fulfillment of the responsibilities of all parties.

[0033] 5. Artificial intelligence decision support and intelligent analysis:

[0034] Fault prediction and maintenance: Combining the analysis results of 3D CNN with real-time data, machine learning algorithms are used to predict faults, provide maintenance suggestions, and reduce maintenance costs.

[0035] Resource optimization management: Through AI algorithms, optimize the allocation and use of water resources to improve the overall efficiency of the system.

[0036] 6. Implementation Results and Advantages: System Advantages:

[0037] Improve fault detection rate: Significantly improve the accuracy of fault detection through deep learning and 3D analysis.

[0038] Enhanced user experience: Multimodal mixed reality technology improves the convenience and efficiency of on-site operations.

[0039] Data security: Blockchain technology ensures the integrity and security of data, increasing user trust.

[0040] Brief description of the attached diagram

[0041] Figure 1 For multimodal mixed reality technology user interaction

[0042] Reference Figure 1 This invention relates to an AR device used by technicians on-site to view the real-time operating status, fault location, and historical maintenance records of pipelines. The overlay of virtual information with the actual environment makes maintenance work more efficient.

Claims

1. This invention relates to an artificial intelligence-based water supply and drainage system based on 3D CNN multimodal mixed reality technology and blockchain technology, particularly a novel MR / AR and blockchain-based artificial intelligence design system capable of visualizing water supply and drainage design data. The system includes the following modules: (1) Data acquisition module Sensors and devices: Collect data from various sensors (flow meters, pressure sensors, temperature sensors, etc.) and imaging devices (cameras for visual monitoring). Multimodal integration: Integrating data from different modalities (e.g., numerical data from sensors and visual data from cameras) for comprehensive analysis. (2) Data preprocessing module Data cleaning: Remove noise and outliers from the collected data to ensure data quality. Normalization: Normalizing data so that different features reach a similar scale is crucial for effective model training. Feature extraction: 3D CNN is used to extract spatiotemporal features from video and sensor data to capture the dynamic changes of the drainage system. (3) Analysis and classification module Machine learning algorithms: Implementing algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), or neural network classification tasks. 3D CNN Processing: Analyze video data using 3D CNN to identify patterns and anomalies in drainage systems. Multimodal fusion: Integrating features from different modalities to improve classification accuracy and robustness. (4) Decision Support Module Real-time monitoring: Provides real-time analysis and alarms based on classification results to help operators make timely decisions. Predictive analytics: Based on historical and real-time data, implement predictive models to anticipate potential problems in drainage systems. (5) Mixed Reality Visualization Module Augmented Reality (AR): Using AR to visualize data overlays onto the physical environment, allowing users to view real-time data and system status. User interaction: Allows users to interact with visualized data and explore different scenarios and system states. (6) Blockchain security module Data integrity: Utilize blockchain technology to ensure the integrity and security of data collected from the drainage system. Smart contracts: Implement smart contracts to automate processes, such as triggering alarms or maintenance requests based on specific conditions detected in the system. (7) User Interface Module Dashboard: Creates an intuitive dashboard for users to monitor system status, view analytics, and receive alerts. Reporting tools: Provides tools for generating reports on system performance, events, and maintenance activities. (8) Feedback and Learning Module Continuous learning: A mechanism that enables a system to learn from new data and improve its predictive ability over time. User feedback loop: Establish a feedback loop where users can provide insights into system performance, thereby enabling continuous improvement.

2. The artificial intelligence water supply and drainage system based on 3D CNN multimodal mixed reality technology and blockchain technology as described in claim 1, characterized in that, The Support Vector Machine (SVM) classification task includes: Step a identifies and selects relevant features extracted from the 3D CNN, which may include spatiotemporal features from multimodal data sources, and divides the dataset into training and testing subsets to ensure that the model can generalize well to unseen data. Step b involves selecting a suitable kernel function (such as linear basis, polynomial basis, or radial basis) based on the nature of the data and the complexity of the classification task, and optimizing hyperparameters, such as the regularization parameter (C) and kernel parameters (e.g., gamma of the RBF kernel), using techniques such as grid search or random search. Step c uses the training dataset to train the SVM model, where the model learns to distinguish different categories (e.g., normal, warning, critical state of the drainage system), and implements k-fold cross-validation to evaluate the model's performance and reduce the risk of overfitting. Step d uses the trained SVM model to classify the input data in real time, provides immediate feedback on the state of the drainage system, analyzes the decision boundary created by the support vector machine, and understands the contribution of different features to the classification. Step e uses metrics such as accuracy, precision, recall, F1 score, and ROC-AUC to evaluate the model's performance to ensure it meets the standards required by the application, visualizes the classification results using a confusion matrix, and identifies areas for improvement. Step f integrates the SVM classification results into the mixed reality environment, allowing users to visualize the status of the drainage system and any alarms or warnings in a more intuitive way. This enables users to interact with the classification results and provides options for more in-depth analysis or further action based on the system status. Step g implements a feedback mechanism where users can provide insights about the classification results, allowing for model adjustments and improvements. The SVM model is periodically updated and retrained with new data to adapt to the constantly changing conditions in the drainage system.

3. The artificial intelligence water supply and drainage system based on 3D CNN multimodal mixed reality technology and blockchain technology as described in claim 2, characterized in that, Extracting spatiotemporal features from video and sensor data using 3D CNNs includes: Step a uses 3D convolutional layers to process video frames, allowing the model to capture spatial (width and height) and temporal (time) information. This helps to understand how the state of the drainage system changes over time, extracting features representing motion and change in the scene, such as flow patterns, water level changes, or the presence of blockages. Step b integrates data from various sensors (such as flow meters, pressure sensors, and temperature sensors) with video data to create a comprehensive feature set. This fusion enhances the model's ability to make accurate predictions, normalizes the sensor data, and ensures that different scales do not affect the feature extraction process. Step c generates 3D feature maps that encapsulate the relevant spatial and temporal features of the input data. These feature maps can highlight significant changes in the drainage system over time. Pooling operations (e.g., max pooling or average pooling) are applied to reduce the dimensionality of the feature maps while preserving the most salient information, which helps improve computational efficiency. Step d: After feature extraction, PCA or similar techniques can be used to reduce the dimensionality of the feature set while preserving variance, making it easier to use in subsequent classification tasks. This technique selects the features with the most information from the extracted set, thereby improving the performance of the classification algorithm. Step e integrates a recurrent layer (such as LSTM or GRU) to capture long-term dependencies in temporal data, especially when the system needs to analyze sequences over a longer period of time. The extracted features are used to detect specific events or anomalies in the drainage system, such as sudden changes in flow or pressure that may indicate a problem. Step f uses technology to visualize the extracted spatiotemporal features, helping operators understand key aspects of the drainage system and facilitating decision-making. The extracted features and analysis results are overlaid on a mixed reality interface to provide a more intuitive understanding of the system's status and dynamics.

4. The artificial intelligence water supply and drainage system based on 3D CNN multimodal mixed reality technology and blockchain technology as described in claim 3, characterized in that, Features from different modalities include: Step a combines the features from different modalities before inputting them into the model. This may involve concatenating feature vectors from video and sensor data. Step b processes each modality separately using a dedicated model, and then combines the prediction or feature representation in the output layer to make the final decision. Step c uses a hierarchical approach, where the characteristics of one pattern inform the processing of another, allowing for a more granular understanding of the data. Step d implements a system that can process and fuse data from various patterns in real time, providing immediate insights and alerts regarding the status of the drainage system.

5. The artificial intelligence water supply and drainage system based on 3D CNN multimodal mixed reality technology and blockchain technology as described in claim 4, characterized in that, Using blockchain technology to ensure the integrity and security of data collected from drainage systems includes: Step a utilizes the immutable ledger functionality of blockchain to record all data collected from the drainage system. Once data is written to the blockchain, it cannot be altered or deleted, ensuring the integrity of historical data. Cryptographic hashing is implemented to protect data entries. Each data block contains a hash of the previous block, thus creating a secure chain that prevents tampering. Step b stores data across a distributed network of nodes, rather than in a single centralized location. This reduces the risk of data loss or unauthorized access due to single points of failure, and uses smart contracts to define and enforce access control policies. Only authorized users and systems can access or modify data, ensuring that sensitive information is protected. Step c involves using a consensus algorithm (e.g., Proof-of-Work, Proof-of-Stake) to verify new data entries before adding them to the blockchain. This ensures that only accurate and verified data is recorded, and utilizes machine learning techniques to detect anomalies in the data collection process. If inconsistencies are found, the system can flag the data for review before recording it on the blockchain. Step d involves multi-factor authentication for users accessing the system to enhance security, prevent unauthorized access, and implement role-based access control to ensure that users can only access the data and functions required by their roles. Step e involves creating redundant backups of blockchain data across multiple nodes to ensure data availability and recovery capabilities in the event of system failures, and developing and implementing a disaster recovery plan to restore data and system functionality in the event of catastrophic failures or data loss.

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