Intelligent building water system comprehensive monitoring and performance optimization system

Abstract

The invention discloses a comprehensive monitoring and performance optimization system for an intelligent building water system, belongs to the field of building water systems, and aims to improve the safety, reliability and efficiency of the building water system. The system integrates advanced technologies such as a digital twinborn platform framework, an artificial intelligence technology, an intelligent identification model, a knowledge graph technology, an urban rainstorm waterlogging numerical model, an intelligent water supply system, an intelligent drainage system and an underground space waterlogging monitoring and early warning model. Water quality, water pressure, flow and other data are collected in real time through a sensor network, and rapid transmission and processing of the data are achieved through the Internet of Things technology. The system can simulate and predict the performance of a water system under extreme conditions, early warns the waterlogging risk in advance, optimizes water resource distribution, and effectively prevents water quality pollution and odor overflow. The modularized design improves the expandability and flexibility of the system, reduces the operation cost, enhances the user experience, and promotes the sustainable development.

Description

Technical Field

[0001] The present invention belongs to the field of building water systems, and particularly relates to an integrated monitoring and performance optimization system for intelligent building water systems. Background Art

[0002] I. International Perspective

[0003] Internationally, the development of smart water has gone beyond traditional management models and shifted to using integrated intelligent systems to improve the efficiency and responsiveness of water systems. This shift marks the transformation of water management from experience-based to data-driven decision-making. Research and applications in the field of smart water abroad cover a wide range of technologies, including but not limited to advanced metering infrastructure (AMI), leak detection technologies, water quality monitoring sensors, and cloud-based data analysis platforms. The application of these technologies not only improves the management efficiency of water resources but also enhances the adaptability to environmental changes and the resilience of the system. For example, some countries have implemented smart water network projects to achieve real-time monitoring and optimization of urban water supply systems, reduce water resource waste, and improve water supply security.

[0004] II. Domestic Perspective

[0005] The development of smart water is in a rapid growth stage, and both the government and enterprises are actively exploring how to use modern information technology to improve the intelligence level of water management. The domestic research focus is on developing smart water solutions suitable for China's national conditions, including but not limited to the intelligent control of building water supply and drainage systems, real-time monitoring of water quality and quantity, and a decision-making support system for water resource management based on big data. China's water management is gradually shifting from single water supply management to comprehensive water cycle management, emphasizing the sustainable use of water resources and environmental protection. In addition, innovations in the field of smart water in China also include the development of energy-saving and consumption-reducing reclaimed water reuse systems and intelligent control technologies for improving the energy efficiency of building water systems.

[0006] III. Development Trends

[0007] The development trend of global smart water indicates that this field is moving towards a more integrated, intelligent, and sustainable direction. With the progress of technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence, smart water systems can achieve more precise water resource management and improve the ability to respond to extreme weather and environmental changes. In the future, smart water will pay more attention to data integration and analysis, make data-driven decisions, and achieve the optimal allocation of water resources. At the same time, with the increasing global attention to environmental protection and climate change, smart water will play an increasingly important role in ensuring water supply security, improving water resource utilization efficiency, and promoting sustainable development.

[0008] Existing Technical Problems:

[0009] 1. Difficulties in technology integration: The transformation to intelligent water services involves systematic changes and requires the integration of various cutting-edge technologies such as the Internet of Things, cloud computing, and artificial intelligence. This poses high requirements for an enterprise's organizational structure, management capabilities, and technical level. During the actual implementation process, enterprises often face a series of practical problems such as poor compatibility between hardware devices and system software, long technical debugging cycles, and high integration difficulties.

[0010] 2. Lack of unified technical specifications: The construction and upgrading of intelligent water service systems lack unified technical specifications, which pose significant obstacles to cross-platform integration and data sharing for enterprises. This not only affects the system stability and interoperability of intelligent water services but also increases the difficulty of customer selection and weakens the effectiveness of overall solutions.

[0011] 3. Insufficient mining of data resources: An unclear understanding of the data inventory limits the overall efficiency of the system and its decision-making support capabilities, leading to the emergence of information silos and making it difficult to achieve comprehensive analysis and decision-making.

[0012] 4. Existence of data silo phenomenon: Data is stored separately in different systems or departments, resulting in difficulties in integrating and analyzing information and limiting the system's comprehensive understanding of the overall water service situation and comprehensive decision-making.

[0013] 5. Incomplete top-level design: The lack of a comprehensive top-level design leads to a lack of consistency and integrity among various parts of the system, affecting the overall efficiency and synergy of the system.

[0014] 6. Imperfect standard specifications: The lack of consistent standard specifications may lead to data compatibility problems and chaotic data management, increasing the costs of system design and maintenance. Summary of the Invention

[0015] The technical problem to be solved by the present invention is to provide an integrated monitoring and performance optimization system for intelligent building water systems in view of the deficiencies in the background technology, which is used to monitor, diagnose, and optimize the operation efficiency of water supply, drainage, and rainwater systems in existing buildings, and provide real-time warnings to address potential water system problems.

[0016] The present invention adopts the following technical solutions to solve the above technical problems:

[0017] An integrated monitoring and performance optimization system for intelligent building water systems, comprising a digital twin platform framework, an artificial intelligence module, an intelligent recognition model, a knowledge graph module, a numerical model of urban rainstorm waterlogging, an intelligent water supply system, an intelligent drainage system, and an underground space waterlogging monitoring and warning model;

[0018] Among them, the digital twin platform framework is a comprehensive technical system for integrating data collection, model construction, simulation analysis, and decision support. By creating a digital copy of a physical entity, it realizes real-time monitoring, analysis, and optimization of the entity;

[0019] The artificial intelligence module is used for real-time monitoring of the water system status, real-time transmission and processing of data, as well as comprehensive monitoring and analysis;

[0020] The intelligent recognition model is used to automatically identify the characteristics of the water system and predict trends based on machine learning and deep learning;

[0021] The knowledge graph module is used to organize and store knowledge in a graphical way, organizing things, concepts, and their relationships in the real world in the form of a graph;

[0022] The urban rainstorm waterlogging numerical model is used to predict the water accumulation depth and the evolution of water accumulation by simulating the main hydro-hydraulic physical processes such as urban surface, open channel, and drainage pipe network;

[0023] The intelligent water supply system is a water supply management solution that integrates multiple sensors and intelligent systems for monitoring, analyzing, and managing water resources;

[0024] The intelligent drainage system is a new type of drainage system that integrates modern information technology, Internet of Things technology, cloud computing, artificial intelligence, etc. By installing devices such as sensors and data collectors, it realizes real-time monitoring and data analysis of the urban drainage pipe network to improve the efficiency and reliability of the drainage system;

[0025] The underground space waterlogging monitoring and early warning model is a system that integrates high-precision geographic information data, hydro-hydraulic models, and real-time monitoring technology. It simulates and predicts the water accumulation depth and evolution in the underground space under urban rainstorm waterlogging conditions, and realizes the simulation of the water accumulation depth and evolution by analyzing the main hydro-hydraulic physical processes such as urban surface, open channel, and drainage pipe network.

[0026] Compared with the prior art, the present invention adopts the above technical solutions and has the following technical effects:

[0027] An intelligent system integrating performance improvement technologies and monitoring and early warning functions, which improves efficiency and reliability: By integrating intelligent sensing technologies and communication technologies, the system can monitor the operating status of the water system in real time, respond quickly to various situations, and improve the efficiency and reliability of the water supply and drainage systems; Enhance the resilience and adaptability of the system: The application of the digital twin platform framework and intelligent recognition models enables the system to simulate and predict its performance under various extreme conditions, enhancing the system's adaptability and resilience to extreme weather and emergencies; Optimize resource allocation: Knowledge graph technologies and intelligent decision support systems help managers better understand the operating rules of the water system and optimize the allocation and management of water resources; Improve water quality and environmental safety: Intelligent water supply systems and intelligent drainage systems effectively prevent water pollution and odor spillage through real-time monitoring and automatic control, improving water quality and environmental safety; Reduce operating costs: The automation and intelligence of the system reduce the need for manual monitoring and maintenance, lowering operating costs; Improve emergency response capabilities: Urban rainstorm waterlogging numerical models and underground space waterlogging monitoring and early warning models can predict waterlogging risks in advance, provide decision support for emergency response, and reduce disaster losses; Enhance user experience: The friendly design of the user interface enables users to intuitively view the operating status of the water system, improving the user experience; Promote sustainable development: The system supports energy conservation, emission reduction, and sustainable development by optimizing the use of water resources; Improve the scalability and flexibility of the system: The modular design enables the system to be expanded and upgraded according to future needs, adapting to changing requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is the architecture diagram of the integrated monitoring and performance optimization system for the intelligent building water system of the present invention;

[0029] Figure 2 is the data flow diagram of the digital twin platform framework of the present invention;

[0030] Figure 3 is the application flow chart of the artificial intelligence technology of the present invention;

[0031] Figure 4 is the algorithm flow chart of the intelligent recognition model of the present invention;

[0032] Figure 5 is the structural diagram of the knowledge graph technology of the present invention;

[0033] Figure 6 is the simulation diagram of the urban rainstorm waterlogging numerical model of the present invention;

[0034] Figure 7 is the schematic diagram of the principle of the intelligent water supply system of the present invention;

[0035] Figure 8 is the structural schematic diagram of the intelligent drainage system of the present invention

[0036] Figure 9 is the logic diagram of the underground space waterlogging monitoring and early warning model of the present invention

[0037] Figure 10 is the design drawing of the flood-resistant water supply equipment of the present invention

[0038] Figure 11 is the flow chart of system integration and implementation steps of the present invention;

[0039] Figure 12 is the flow chart of system operation and maintenance management and optimization of the present invention Specific implementation manners

[0040] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings:

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. The present invention will be described in detail below according to the accompanying drawings and preferred embodiments, and the purpose and effect of the present invention will become more apparent. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] As Figures 1 to 12 shown, an intelligent building water system comprehensive monitoring and performance optimization system, as Figure 1 shown, shows each component of the system integration and their connection relationships; includes a digital twin platform framework, an artificial intelligence module, an intelligent recognition model, a knowledge graph module, an urban rainstorm waterlogging numerical model, an intelligent water supply system, an intelligent drainage system, and an underground space waterlogging monitoring and early warning model;

[0043] Among them, the digital twin platform framework is a comprehensive technical system for integrating data collection, model construction, simulation analysis, and decision support. By creating a digital copy of a physical entity, it realizes real-time monitoring, analysis, and optimization of the entity;

[0044] The artificial intelligence module is used for real-time monitoring of the water system state, real-time transmission and processing of data, and comprehensive monitoring and analysis;

[0045] The intelligent recognition model is used to automatically identify and predict trends of water system characteristics based on machine learning and deep learning;

[0046] The knowledge graph module is used to organize and store knowledge in a graphical way, and organize the things and concepts in the real world and their relationships in the form of a graph;

[0047] A numerical model for urban rainstorm waterlogging, which is used to predict the waterlogging depth and the evolution of waterlogging by simulating the main hydro - hydrodynamic physical processes such as urban surface, open - channel river, and drainage pipe network;

[0048] An intelligent water supply system, which is used for a water supply management solution integrating multiple sensors and intelligent systems to monitor, analyze, and manage water resources;

[0049] An intelligent drainage system, which is a new - type drainage system integrating modern information technology, Internet of Things technology, cloud computing, artificial intelligence and other technologies. By installing devices such as sensors and data collectors, it realizes real - time monitoring and data analysis of urban drainage pipe networks to improve the efficiency and reliability of the drainage system;

[0050] An underground space waterlogging monitoring and early - warning model, a system integrating high - precision geographic information data, hydro - hydrodynamic models, and real - time monitoring technologies. It simulates and predicts the waterlogging depth and evolution in the underground space under urban rainstorm waterlogging conditions, and realizes the simulation of waterlogging depth and evolution by analyzing the main hydro - hydrodynamic physical processes such as urban surface, open - channel river, and drainage pipe network.

[0051] As Figure 2 shown, the data - flow diagram of the digital twin platform framework details the entire process of data from collection to analysis and then to decision - making support. The principles of the digital twin platform framework specifically include:

[0052] Data collection: Collect real - time data of the building water system through sensors and monitoring systems, including water quality parameters, water pressure, and flow rate;

[0053] Mathematical modeling: Use the collected data to build mathematical models of physical entities, including physical models, statistical models, and machine - learning models;

[0054] Virtual simulation: Simulate the behavior and performance of physical entities in a virtual environment to predict possible problems and optimization solutions;

[0055] Real - time monitoring and feedback: Update the digital model through real - time data to achieve real - time monitoring and feedback adjustment of the state of physical entities.

[0056] As Figure 3 shown, the application flow chart of artificial intelligence technology shows the working processes of intelligent sensing technology, communication technology, and water conservancy sensing network. The principles of the artificial intelligence module specifically include:

[0057] Intelligent sensing technology: Deploy a sensor network to collect real - time data on the physical state of the water system, including water level, flow velocity, and water quality parameters;

[0058] Communication technology: Using Internet of Things technology, the data collected by sensors is transmitted to the central processing system in real time to achieve instant processing and analysis of the data;

[0059] Water conservancy perception network: Build an integrated "sky-ground-underground" monitoring network, integrate data from the air (such as satellite remote sensing), the ground (such as ground sensor networks), and underground (such as groundwater monitoring equipment) to achieve all-round monitoring of the water system.

[0060] Computing models are as follows:

[0061] Machine learning model training: When training a machine learning model, loss functions and optimizer update rules of various algorithms are used; in a neural network, the loss function is expressed as: where L is the loss function, N is the number of samples, y i is the true value, is the predicted value;

[0062] Decision tree construction: When constructing a decision tree, metrics such as information gain or Gini impurity are used to select the best splitting attribute; information gain is expressed as: IG(S,A) = H(S) - H(S|A); where IG is the information gain, H is the entropy, S is the sample set, and A is the attribute.

[0063] As Figure 4 shown, the intelligent recognition model algorithm flowchart describes the application of algorithms such as reinforcement learning and transfer learning in water system feature recognition. The intelligent recognition model is based on machine learning and deep learning technologies and includes advanced algorithms of reinforcement learning and transfer learning to achieve automatic recognition and trend prediction of water system features;

[0064] Reinforcement learning is used to learn how to make decisions by interacting with the environment and is applicable to scenarios that require continuous decision-making;

[0065] Transfer learning is used to apply the knowledge learned in one domain to another domain and is applicable to water system feature recognition;

[0066] Among them, the computing models are as follows:

[0067] Loss function: In deep learning, the loss function is used to measure the difference between the model prediction and the actual result. Common loss functions include mean squared error MSE and cross-entropy error;

[0068] where L is the loss function, N is the number of samples, y i is the true value, is the predicted value;

[0069] Gradient descent: Used to optimize model parameters and update model weights by minimizing the loss function;

[0070] Among them, w′ is the updated model weight, w is the model weight, and η is the learning rate, and is the partial derivative of the loss function with respect to the weight.

[0071] As Figure 5 shown, the knowledge graph technology structure diagram shows the structured representation of water conservancy knowledge in the knowledge graph; the knowledge graph module organizes and stores knowledge in a graphical way, organizing the things and concepts in the real world and the relationships between them in the form of a graph; the nodes in the knowledge graph represent things or concepts, and the edges represent the relationships between them; the calculation model is as follows:

[0072] Representation of entities and relationships: In the knowledge graph, entities and relationships are usually represented in the form of triples, that is, subject, predicate, and object; in RDF, knowledge can be represented in the form of triples, where each entity and relationship is assigned a unique URI;

[0073] Similarity calculation: In semantic analysis, calculating the similarity between two entities is a common requirement; the cosine similarity is used to calculate the similarity between two word embeddings: where V1 and V2 are the vector representations of two entities, · represents the dot product, and || || represents the norm of the vector;

[0074] Knowledge reasoning: Knowledge reasoning is to infer the possible relationships or attribute values between entities from the existing knowledge; logical rules or neural network models are used for reasoning.

[0075] As Figure 6 shown: The numerical model simulation diagram of urban rainstorm waterlogging simulates the water depth and evolution situation under urban rainstorm waterlogging. The principle of the urban rainstorm waterlogging numerical model specifically includes:

[0076] Hydrodynamic model: Construct a hydrodynamic-based hydraulic model to simulate urban rainstorm waterlogging, including one-dimensional and two-dimensional hydraulic models;

[0077] Machine learning algorithms: Combine machine learning algorithms, including random forest, XGBoost, K-nearest neighbor, and long short-term memory LSTM neural network, to construct a fast prediction model;

[0078] Dataset construction: Use the high-precision data obtained from numerical simulation as the dataset, combined with rainfall conditions, underground information, and spatial characteristic data of the drainage capacity of the drainage pipe network;

[0079] The calculation model is as follows:

[0080] Manning's formula: Used to calculate the water flow velocity, and the formula is: Among them, V is the water flow velocity, n is the Manning roughness coefficient, and R h is the hydraulic radius, and S0 is the channel slope;

[0081] Momentum equation: Considering the water flow resistance and the net rainfall intensity, the momentum equation is expressed as:

[0082] Among them, k is the unit conversion coefficient, d is the water depth, q is the net rainfall intensity, and g is the acceleration due to gravity;

[0083] Water depth change equation: The equation describing the change of water depth over time is expressed as:

[0084] Among them, Q w , Q S , Q E , Q N are the flow rates in the east, west, south, and north directions respectively, θ is the surface saturation, and q is the net rainfall intensity.

[0085] As Figure 7 shown, the schematic diagram of the intelligent water supply system shows the working principle and component layout of the intelligent water supply system. The intelligent water supply system is a water supply management solution integrating multiple sensors and intelligent systems. Its core lies in monitoring, analyzing, and managing water resources through these sensors and systems. The system detects parameters such as water quality, water level, and flow rate through sensors and transmits the data to the controller. The controller analyzes the water usage, waste situation, and potential leakage problems based on the data and controls the flow valves to allocate and control the water flow reasonably. At the same time, the intelligent water supply system can also achieve automatic adjustment of water quality. According to the monitored data, it takes various measures such as automatically supplementing oxygen to the water and adding water treatment agents to improve the water quality; the specific calculations are as follows:

[0086] Flow rate calculation formula: Q = v·A; where v is the flow velocity, Q is the flow rate, and A is the cross-sectional area of the pipe;

[0087] Pressure calculation formula: ΔP = ρ·g·h; ΔP is the pressure change, ρ is the fluid density, g is the acceleration due to gravity, and h is the height difference;

[0088] Pump efficiency formula: Among them, η is the efficiency, P out is the output power, and P in is the input power.

[0089] As Figure 8 shown, the structural schematic diagram of the intelligent drainage system shows the structure and key components of the intelligent drainage system.

[0090] The intelligent drainage system is a new type of drainage system that integrates modern information technology, Internet of Things technology, cloud computing, artificial intelligence and other technologies. The system realizes real-time monitoring and data analysis of urban drainage pipe networks by installing devices such as sensors and data collectors, so as to improve the efficiency and reliability of the drainage system.

[0091] Sensor technology: Install sensors such as water level, flow rate, and pressure at key positions of the drainage pipe network for real-time monitoring of pipe network data.

[0092] Data collection and transmission: The data collector is responsible for transmitting the data collected by the sensors to a computer or cloud server for processing and storage, which can be completed through wireless communication (such as 4G / 5G / NB-IoT) or wired means.

[0093] Data analysis platform: The computer or data analysis platform conducts real-time analysis on the transmitted data. By comparing with preset thresholds, it determines whether there are abnormal situations, such as too rapid water level rise, abnormal flow rate, etc., and transmits the results to the early warning platform.

[0094] Early warning and management platform: Through data analysis and early warning processing, the system early warns of possible problems such as pipe network blockage and leakage, ensuring the safe operation of the urban drainage system.

[0095] The intelligent drainage system realizes real-time monitoring and data analysis of urban drainage pipe networks by installing sensors and data collector devices. The specific calculations are as follows:

[0096] Flow velocity calculation formula: Among them, v is the flow velocity, Q is the flow rate, and A is the cross-sectional area of the pipe;

[0097] Flow rate calculation formula: Q = v·A;

[0098] Water level change calculation: Among them, Δh is the water level change, and △t is the time interval.

[0099] As Figure 9 shown: The logic diagram of the underground space waterlogging monitoring and early warning model describes the logic and working process of the underground space waterlogging monitoring and early warning model.

[0100] The underground space waterlogging monitoring and early warning model is a system that integrates high-precision geographic information data, hydrodynamics models and real-time monitoring technologies, aiming to simulate and predict the water accumulation depth and evolution in the underground space under urban rainstorm waterlogging conditions. The model realizes the simulation of the water accumulation depth and evolution by analyzing the main hydrodynamics physical processes such as urban surface, open channels and drainage pipe networks.

[0101] High-precision geographic information data: Using high-precision elevation, road network, river network, drainage pipe network, engineering facilities, and flood control scheduling data, various spatial information is dissected into unstructured irregular grids and corresponding channels.

[0102] Hydrological and hydrodynamic model: Construct a numerical model for urban rainstorm waterlogging to simulate the water accumulation depth and evolution. The model considers the main hydrological and hydrodynamic physical processes such as urban surface, open-channel river, and drainage pipe network.

[0103] Real-time monitoring technology: Through an integrated Internet of Things sensing network monitoring device, it ensures the richness, accuracy, and real-time nature of information acquisition such as rainfall monitoring, water level monitoring, low-lying point monitoring, and key facility monitoring.

[0104] The principle of the underground space waterlogging monitoring and early warning model specifically includes:

[0105] High-precision geographic information data: Using high-precision elevation, road network, river network, drainage pipe network, engineering facilities, and flood control scheduling data, various spatial information is dissected into unstructured irregular grids and corresponding channels;

[0106] Hydrological and hydrodynamic model: Construct a numerical model for urban rainstorm waterlogging to simulate the water accumulation depth and evolution; The specific calculation is as follows:

[0107] Flow continuity equation:

[0108] Where, M and N are the flow rates in the x and y directions respectively, H is the water depth, Z is the elevation, u and v are the flow velocities in the x and y directions, and n is the Manning roughness coefficient;

[0109] Special channel flow calculation: Where, Q is the flow rate, m is the channel flow coefficient, σ s is the downstream flooding coefficient of the channel, and H is the water depth;

[0110] Urban drainage system simulation: H p is the water depth of the pipeline, q1 is the drainage intensity of the pipeline, N is the number of channels passed by the drainage pipeline, and A′ p is the equivalent bottom area of the pipeline in the grid;

[0111] Real-time monitoring technology: Through an integrated Internet of Things sensing network monitoring device, it ensures the richness, accuracy, and real-time nature of information acquisition such as rainfall monitoring, water level monitoring, low-lying point monitoring, and key facility monitoring.

[0112] Monitoring and early warning model establishment: Establish an underground space waterlogging monitoring and early warning model to monitor the water accumulation situation in the underground space in real time. The model is based on high-precision geographic information data and data such as drainage pipe network, engineering facilities, and flood control scheduling to simulate the water accumulation depth and evolution.

[0113] Flood-resistant water supply equipment: Utilize highly efficient flood-resistant water supply equipment to ensure continuous and safe operation under extreme conditions.

[0114] System integration and application: Integrate the monitoring and early warning model with the flood-resistant water supply equipment into a unified platform to achieve comprehensive monitoring and management of waterlogging in underground spaces. Through GIS technology, standardize the database management of drainage facilities and equipment assets, and provide real-time monitoring and data integration. As Figure 10 shown: The design drawing of the flood-resistant water supply equipment shows the design details and working principle of the flood-resistant water supply equipment.

[0115] Early warning and alarm function: Utilize an intelligent alarm system. When abnormal data or equipment failures are detected, the system automatically alarms and notifies relevant personnel through multiple methods (such as text messages, emails, sound and light, WeChat official account information, etc.).

[0116] As Figure 11 and Figure 12 shown: The flow chart of system integration and implementation steps details the entire implementation steps from equipment selection to system integration.

[0117] System integration architecture: The integrated system will include a data acquisition layer, a network transmission layer, a data processing layer, an application support layer, and an application software layer. The data acquisition layer is responsible for collecting key parameters of the building water system. The network transmission layer is responsible for real-time data transmission. The data processing layer stores and analyzes data. The application support layer provides basic services and database support. The application software layer realizes specific business applications.

[0118] Data acquisition and monitoring: Intelligent sensing technology: Deploy a sensor network to monitor parameters such as water quality, water pressure, and flow rate in real time. Communication technology: Utilize Internet of Things technology to achieve real-time data transmission and processing. Water conservancy sensing network: Build an "air-ground-space" integrated water conservancy sensing network to achieve all-round monitoring and analysis.

[0119] Intelligent identification and early warning: Intelligent identification model: Adopt algorithms such as reinforcement learning and transfer learning to achieve automatic feature identification and trend prediction. Knowledge graph technology: Utilize knowledge graph technology for water conservancy knowledge representation to realize the visualization of knowledge reasoning results. Urban rainstorm waterlogging numerical model: Build a numerical model to simulate the water accumulation depth and evolution situation to achieve waterlogging risk early warning.

[0120] Intelligent control and optimization: Intelligent water supply system: Integrate intelligent flood-proof water supply equipment and intelligent secondary water supply systems to provide online monitoring and safety early warning functions. Intelligent drainage system: Develop intelligent drainage anti-odor precise detection technology and traceability blocking technology, and develop a new type of micro-negative pressure building drainage system and an external building drainage system.

[0121] In - space waterlogging monitoring and early warning: In - space waterlogging monitoring and early warning model: Establish a model to monitor the water accumulation situation in the underground space in real - time. Flood - resistant water supply equipment: Utilize highly efficient flood - resistant water supply equipment to ensure continuous and safe operation under extreme conditions.

[0122] System implementation and optimization: Equipment selection and procurement: According to the system design requirements, select appropriate sensors, data transmission equipment, control equipment, etc., and conduct procurement and installation.

[0123] System integration and commissioning: Integrate various equipment and systems, conduct commissioning and testing to ensure the stability and reliability of the system.

[0124] Data acquisition and processing: Establish a stable and reliable data transmission network, and transmit the data collected by sensors to the data center for processing and analysis in real - time.

[0125] Intelligent management and control: Based on the data analysis results, achieve intelligent management and control of the drainage system.

[0126] Operation and maintenance management and optimization: Conduct continuous operation and maintenance management of the system, optimize the system performance, and improve the efficiency and response speed.

[0127] Example 1: Application of the intelligent building water system comprehensive monitoring and performance optimization system in a commercial complex

[0128] Background:

[0129] In a large commercial complex in Jiangsu, the building water system includes complex water supply, drainage, and rainwater collection systems. Due to the large scale of the building, it is difficult to manage the water system, and problems such as unstable water supply and waterlogging often occur.

[0130] Implementation steps:

[0131] 1. Requirement analysis: Analyze the water system requirements of the commercial complex to determine the key parameters to be monitored and the optimization goals.

[0132] 2. Equipment selection and procurement: Select suitable sensors, data collectors, communication modules, and central processing equipment.

[0133] 3. Installation and deployment: Install sensors at key positions in the building, and deploy data collectors and communication modules.

[0134] 4. System integration: Integrate all components into a central processing system to achieve real - time monitoring and analysis of data.

[0135] 5. System testing and optimization: Conduct system testing and optimize the performance according to the test results.

[0136] 6. User training and system launch: Provide system usage training for property management staff and officially launch the system.

[0137] 7. Daily operation, maintenance and optimization: After the system is launched, conduct daily monitoring and maintenance, and continuously optimize according to user feedback and system performance.

[0138] Result: By implementing the intelligent building water system comprehensive monitoring and performance optimization system, the water supply stability in the commercial complex has been significantly improved, the waterlogging problem has been effectively controlled, and user satisfaction has been enhanced.

[0139] Embodiment 2: Application of intelligent drainage system in urban residential areas

[0140] Background: In a large residential area in Jianye District, Nanjing, due to the aging of the drainage system, waterlogging often occurs during heavy rains, seriously affecting the lives of residents.

[0141] Implementation steps:

[0142] 1. Requirement analysis: Collaborate with the residential area management department to analyze the problems and requirements of the drainage system.

[0143] 2. Equipment selection and procurement: Purchase sensors, pumping station control equipment and communication equipment required for the intelligent drainage system.

[0144] 3. Installation and deployment: Install sensors and control equipment in the drainage pipe network of the residential area.

[0145] 4. System integration and testing: Integrate all equipment into an intelligent drainage system and conduct system testing.

[0146] 5. Performance optimization: Optimize the system performance according to the test results and residents' feedback.

[0147] 6. Emergency response plan: Develop an emergency response plan to address the waterlogging problem during heavy rains.

[0148] 7. System launch and monitoring: After the system is officially launched, conduct real-time monitoring and maintenance to ensure the normal operation of the drainage system.

[0149] Result: The implementation of the intelligent drainage system has significantly improved the efficiency and response ability of the drainage system in the residential area, reduced waterlogging incidents during heavy rains, and improved the quality of life of residents.

[0150] Those of ordinary skill in the art can understand that the above are only preferred examples of the invention and are not used to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, etc. made within the spirit and principle of the invention shall be included within the protection scope of the invention. All technical features in this embodiment can be freely combined according to actual needs.

[0151] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A comprehensive monitoring and performance optimization system for intelligent building water systems, characterized by: It includes a digital twin platform framework, artificial intelligence module, intelligent recognition model, knowledge graph module, urban rainstorm waterlogging numerical model, intelligent water supply system, intelligent drainage system and underground space waterlogging monitoring and early warning model; Among them, the digital twin platform framework is a comprehensive technical system for integrating data collection, model building, simulation analysis and decision support. By creating a digital copy of a physical entity, it can achieve real-time monitoring, analysis and optimization of the entity; Artificial intelligence module for real-time monitoring of water system status, real-time transmission and processing of data, and comprehensive monitoring and analysis; Intelligent recognition model for automatic identification and trend prediction of water system characteristics based on machine learning and deep learning; The knowledge graph module is used to organize and store knowledge in a graphical way, organizing the objects and concepts in the real world and their relationships in the form of graphs; The urban rainstorm waterlogging numerical model is used to predict the depth and evolution of waterlogging by simulating the main hydrological and hydrodynamic physical processes such as the urban surface, open channel river, and drainage network; Smart water supply system, which is used to integrate multiple sensors and intelligent systems to monitor, analyze and manage water resources; Intelligent drainage system is a new type of drainage system that integrates modern information technology, Internet of Things technology, cloud computing, artificial intelligence and other technologies. By installing sensors, data collectors and other equipment, it can realize real-time monitoring and data analysis of urban drainage networks to improve the efficiency and reliability of the drainage system. The underground space waterlogging monitoring and early warning model is a system that integrates high-precision geographic information data, hydrological and hydrodynamic models, and real-time monitoring technology. It simulates and predicts the depth and evolution of water accumulation in underground spaces under the condition of urban rainstorm waterlogging. By analyzing the main hydrological and hydrodynamic physical processes such as the urban surface, open channel rivers, and drainage networks, it can simulate the depth and evolution of water accumulation.

2. According to claim 1, the intelligent building water system comprehensive monitoring and performance optimization system is characterized by: The principles of the digital twin platform framework specifically include: Data collection: Collect real-time data of the building water system through sensors and monitoring systems, including water quality parameters, water pressure, and flow; Mathematical modeling: using the collected data to build mathematical models of physical entities, including physical models, statistical models, and machine learning models; Virtual simulation: simulate the behavior and performance of physical entities in a virtual environment to predict possible problems and optimization solutions; Real-time monitoring and feedback: Update the digital model through real-time data to achieve real-time monitoring and feedback adjustment of the physical entity status.

3. The intelligent building water system comprehensive monitoring and performance optimization system according to claim 1 is characterized by: The principles of the artificial intelligence module specifically include: Intelligent sensing technology: By deploying a sensor network, the physical state data of the water system, including water level, flow rate, and water quality parameters, can be collected in real time; Communication technology: Using the Internet of Things technology, the data collected by sensors is transmitted to the central processing system in real time to achieve instant data processing and analysis; Water Conservancy Sensing Network: Build an integrated air-ground monitoring network to integrate air, ground and underground data to achieve all-round monitoring of the water system; The calculation model is as follows: Machine learning model training: When training a machine learning model, various algorithms’ loss functions and optimizer update rules are used; in neural networks, the loss function is expressed as: Among them, L is the loss function, N is the number of samples, and y i is the true value, is the predicted value; Decision tree construction: When building a decision tree, indicators such as information gain or Gini impurity are used to select the best splitting attribute; information gain is expressed as: IG(S,A)=H(S)-H(S|A); where IG is information gain, H is entropy, S is the sample set, and A is the attribute.

4. The intelligent building water system comprehensive monitoring and performance optimization system according to claim 1 is characterized by: The intelligent recognition model is based on machine learning and deep learning technologies, including reinforcement learning and transfer learning advanced algorithms, to achieve automatic recognition of water system characteristics and trend prediction; Reinforcement learning, which is used to learn how to make decisions through interaction with the environment and is suitable for scenarios that require continuous decision-making; Transfer learning, which is used to apply knowledge learned in one field to another, is applicable to water system feature identification; The calculation model is as follows: Loss function: In deep learning, the loss function is used to measure the difference between the model prediction and the actual result. Common loss functions include mean square error (MSE) and cross entropy error. Among them, L is the loss function, N is the number of samples, and y i is the true value, is the predicted value; Gradient descent: used to optimize model parameters and update model weights by minimizing the loss function; Where w′ is the updated model weight, w is the model weight, η is the learning rate, is the partial derivative of the loss function with respect to the weights.

5. The intelligent building water system comprehensive monitoring and performance optimization system according to claim 1 is characterized by: The knowledge graph module organizes and stores knowledge in a graphical way, organizing the objects and concepts in the real world and their relationships in the form of a graph; the nodes in the knowledge graph represent objects or concepts, and the edges represent the relationships between them; the specific calculation model is as follows: Representation of entities and relationships: In knowledge graphs, entities and relationships are usually represented in the form of triples, i.e., subject, predicate, object; in RDF, knowledge can be represented in the form of triples, where each entity and relationship is assigned a unique URI; Similarity calculation: In semantic analysis, calculating the similarity between two entities is a common requirement; use cosine similarity to calculate the similarity between two word embeddings: Where V1 and V2 are vector representations of two entities, · represents the dot product, and || || represents the modulus of the vector; Knowledge reasoning: Knowledge reasoning is to infer the possible relationships or attribute values ​​between entities from existing knowledge; use logical rules or neural network models to perform reasoning.

6. The intelligent building water system comprehensive monitoring and performance optimization system according to claim 1, characterized in that: The principles of the urban rainstorm waterlogging numerical model specifically include: Hydrodynamic model: construct a hydraulic model based on hydrodynamics to simulate urban flooding caused by heavy rain, including one- and two-dimensional hydraulic models; Machine Learning Algorithms: Combine machine learning algorithms, including random forest, XGBoost, K-nearest neighbor, and long short-term memory (LSTM) neural networks, to build a fast prediction model; Dataset construction: Use high-precision data obtained from numerical simulation as the dataset, combined with rainfall conditions, underground information, and spatial characteristic data of drainage capacity of the drainage network; The calculation model is as follows: Manning formula: used to calculate water flow rate, the formula is Where V is the water velocity, n is the Manning roughness coefficient, R h is the hydraulic radius, S0 is the channel slope; Momentum equation: Considering the water flow resistance and net rainfall intensity, the momentum equation is expressed as: Where k is the unit conversion factor, d is the water depth, q is the net rainfall intensity, and g is the gravitational acceleration; Water depth variation equation: The equation describing the variation of water depth over time is expressed as: Among them, Q w ,Q S ,Q E ,Q N are the flow rates in the four directions of east, west, south and north, θ is the surface saturation, and q is the net rainfall intensity.

7. The intelligent building water system comprehensive monitoring and performance optimization system according to claim 1 is characterized by: The intelligent water supply system detects water quality, water level, and flow parameters through sensors and transmits the data to the controller; the controller analyzes water usage, waste, and potential water leakage based on the data and reasonably distributes and controls the water flow by controlling the flow valve: The specific calculation is as follows: Flow calculation formula: Q = v·A; where v is the flow rate, Q is the flow rate, and A is the cross-sectional area of ​​the pipe; Pressure calculation formula: ΔP = ρ·g·h; ΔP is the pressure change, ρ is the fluid density, g is the gravitational acceleration, and h is the height difference; Pump efficiency formula: Where η is the efficiency, P out is the output power, P in is the input power.

8. The intelligent building water system comprehensive monitoring and performance optimization system according to claim 1 is characterized by: The intelligent drainage system realizes real-time monitoring and data analysis of the urban drainage network by installing sensors and data acquisition equipment. The specific calculation is as follows: Flow rate calculation formula: Where v is the flow velocity, Q is the flow rate, and A is the cross-sectional area of ​​the pipe; Flow calculation formula: Q = v·A; Water level change calculation: Among them, Δh is the water level change and △t is the time interval.

9. The intelligent building water system comprehensive monitoring and performance optimization system according to claim 1, characterized in that: The principles of the underground space waterlogging monitoring and early warning model specifically include: High-precision geographic information data: Use high-precision elevation, road network, river network, drainage network, engineering facilities and flood control dispatch data to divide various types of spatial information into unstructured irregular grids and corresponding channels; Hydrological and hydrodynamic model: Construct a numerical model of urban rainstorm waterlogging to simulate the depth and evolution of waterlogging; the specific calculation is as follows: Continuity equation for water flow: Where M and N are the flow rates in the x and y directions, H is the water depth, Z is the elevation, u and v are the flow velocities in the x and y directions, and n is the Manning roughness coefficient; Special channel flow calculation: Where Q is the flow rate, m is the channel flow coefficient, σ s is the flooding coefficient of the downstream channel, and H is the water depth; Urban drainage system simulation: H p is the water depth of the pipeline, q1 is the drainage strength of the pipeline, N is the number of channels through which the drainage pipeline passes, and A′ p is the equivalent bottom area of ​​the pipe in the grid; Real-time monitoring technology: Through the integrated Internet of Things sensing network monitoring equipment, the richness, accuracy and real-time nature of rainfall monitoring, water level monitoring, low-lying point monitoring and key facility monitoring information acquisition are guaranteed.

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