Digital twin hybrid cloud edge collaboration intelligent property building operation intelligent system

Abstract

The present application relates to the field of building intelligence, in particular to a digital twin hybrid cloud edge collaborative intelligent real estate building group operation and maintenance system, the system collects building basic information, equipment operation data and environmental parameters, establishes a digital twin scene database, and synchronizes to edge devices, uses BIM, GIS, Internet of Things, 5G and AI technology to build a virtual and real digital intelligent building scene, realizes real-time synchronization of virtual scene and physical environment through AR / VR devices, the operation and maintenance module includes multi-source heterogeneous data fusion, edge intelligent analysis and decision-making, self-adaptive model training and iterative optimization, predictive maintenance algorithm of virtual-real mapping and multi-level collaborative decision-making and autonomous scheduling mechanism, realizes full digital management of building group, and the monitoring module monitors the state and operation of the edge device, provides data services, supports management decision visualization, and the system effectively improves the intelligent and digital level of building group operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of building intelligence, and in particular to a digital twin hybrid cloud-edge collaborative smart real estate building complex operation and maintenance intelligent system, which belongs to the cross-application technology field of smart buildings, Internet of Things technology, artificial intelligence and cloud-edge collaborative computing. Background Technology

[0002] With the acceleration of urbanization and the expansion of building complexes, traditional building operation and maintenance management models are no longer adequate to meet the management needs of today's complex building complexes. Traditional building operation and maintenance suffers from the following typical problems: delayed response to equipment failures, mostly reactive maintenance; fragmented operation and maintenance management, with each system operating independently and lacking coordination; low data utilization, making it impossible to form data-driven decision-making; high labor costs and low maintenance efficiency; serious energy waste and high operating costs; and difficulty in predicting and preventing safety risks.

[0003] In existing technologies, building operation and maintenance systems typically employ a centralized management approach to centrally monitor and manage equipment across multiple buildings. However, this approach suffers from several key problems: First, centralized systems struggle to cope with the complexity of large-scale building complexes, resulting in slow system response times; second, they lack real-time data analysis and predictive capabilities, making it impossible to detect potential faults in advance; third, severe data silos exist between different building systems, hindering collaborative decision-making; and finally, the system exhibits poor scalability, making it difficult to adapt to the dynamic changes within the building complex.

[0004] The development of smart buildings has gradually evolved from the construction of intelligent systems for individual buildings to the construction and operation and maintenance of intelligent systems for building complexes. However, there is still a lack of comprehensive intelligent operation and maintenance systems that effectively integrate advanced technologies such as digital twins, cloud-edge collaboration, and predictive maintenance. Faced with the complex system of building complexes, there is an urgent need to break through traditional boundaries and develop a smart building complex operation and maintenance system based on digital twin technology, combined with a cloud-edge collaborative architecture, to achieve intelligent predictive maintenance. Summary of the Invention

[0005] This invention aims to solve the aforementioned problems in the prior art and provides a digital twin hybrid cloud-edge collaborative smart real estate building complex operation and maintenance intelligent system. This system constructs a virtual mapping of the building complex through digital twin technology, combines a cloud-edge collaborative architecture to achieve efficient data processing and decision-making, and realizes predictive maintenance through intelligent algorithms, thereby significantly improving the intelligence level and efficiency of building complex operation and maintenance.

[0006] This invention proposes a digital twin hybrid cloud-edge collaborative intelligent real estate building complex operation and maintenance system, comprising:

[0007] The data acquisition module is used to collect basic building information, equipment operation data, and environmental parameters of the smart real estate complex.

[0008] The storage module is communicatively connected to the acquisition module and is used to transmit the acquired data to the cloud to establish a digital twin scene database, and to synchronize the data in the digital twin scene database to the edge device.

[0009] The construction module, which is communicatively connected to the storage module, is used to build a digital twin building complex model based on the data in the digital twin scene database, and to build a digital twin scene through the digital twin building complex model;

[0010] The scene module, which is communicatively connected to the construction module, is used to construct a digital smart building scene that combines virtual and real technologies using BIM, GIS, IoT, 5G, and AI.

[0011] The virtual-real synchronization module is communicatively connected to the scene module and is used to present the dynamics of the physical world in the digital smart building scene using AR / VR devices, so as to realize the real-time synchronization between the virtual scene and the physical environment.

[0012] The operation and maintenance module is communicatively connected to the virtual-real synchronization module and is used to construct a digital twin operation and maintenance brain. The digital twin operation and maintenance brain includes a multi-source heterogeneous data fusion and real-time processing mechanism, an edge intelligent analysis and decision-making framework, an adaptive model training and iterative optimization system, a predictive maintenance algorithm for virtual-real mapping, and a multi-level collaborative decision-making and autonomous scheduling mechanism.

[0013] The monitoring module, which is communicatively connected to the operation and maintenance module, is used to monitor the status and operation of edge devices, generate monitoring reports, and, based on the data, simulate the actual operation and maintenance scenarios of the building complex through the digital twin building complex model, providing data services for the intelligent operation and maintenance of the building complex.

[0014] Preferably, the multi-source heterogeneous data fusion and real-time processing mechanism includes:

[0015] A multi-source data acquisition architecture is used to collect data from physical layer sensors, edge layer processed data, and cloud-aggregated data.

[0016] A data adapter, which communicates with the multi-source data acquisition architecture, is used to achieve unified and standardized processing of BIM, GIS, and IoT device data formats.

[0017] A spatiotemporal index data structure, which is communicatively connected to the data adapter, is used to unify data from different sources and with different sampling rates into a spatiotemporal dimension;

[0018] A data quality assessment algorithm, which is communicatively connected to the spatiotemporal index data structure, is used to perform real-time cleaning and anomaly detection on sensor data.

[0019] A data caching and distribution mechanism is connected in communication with the data quality assessment algorithm to perform hierarchical processing based on data importance and timeliness.

[0020] Preferably, the edge intelligent analysis and decision-making framework includes:

[0021] A lightweight edge AI inference engine to support real-time device status analysis and fault prediction;

[0022] An edge node autonomous decision-making mechanism communicates with the lightweight edge AI inference engine and is used to make local decisions based on preset rules and historical data.

[0023] An edge computing resource dynamic scheduling algorithm is communicated with the edge node autonomous decision-making mechanism and is used to adaptively allocate resources according to task priority and computing load.

[0024] An edge security isolation mechanism, which is connected to the dynamic scheduling algorithm for edge computing resources, is used to ensure local processing of sensitive data and reduce data transmission risks.

[0025] An edge caching optimization strategy, which is connected in communication with the edge security isolation mechanism, is used to intelligently cache data based on access frequency and data importance.

[0026] Preferably, the adaptive model training and iterative optimization system includes:

[0027] An incremental learning framework is used to support local updates of the model at edge nodes without full retraining;

[0028] Model compression technology, which is communicatively connected to the incremental learning framework, is used to compress complex models trained in the cloud into an executable version for edge devices;

[0029] The federated learning mechanism, which communicates with the model compression technology, enables multiple edge nodes to train collaboratively without sharing the original data.

[0030] The model performance monitoring system is connected in communication with the federated learning mechanism to evaluate the model prediction accuracy in real time and trigger updates.

[0031] The model version management mechanism communicates with the model performance monitoring system to support model rollback and A / B testing.

[0032] Preferably, the predictive maintenance algorithm for the virtual-real mapping includes:

[0033] A digital twin building equipment health assessment model is used to integrate physical models and data-driven models to assess the health status of equipment.

[0034] The equipment remaining life prediction algorithm is communicatively connected to the digital twin building equipment health assessment model, and is used to predict the remaining life based on the equipment's historical operating data and maintenance records;

[0035] A multi-dimensional failure mode recognition system is communicatively connected to the remaining life prediction algorithm of the equipment, and is used to identify complex failure modes and potential risks;

[0036] A dynamic maintenance strategy generator is communicatively connected to the multi-dimensional fault mode recognition system and is used to intelligently plan maintenance time and resources based on prediction results.

[0037] The virtual scenario fault simulation system is communicatively connected to the dynamic maintenance strategy generator and is used to verify maintenance schemes in a digital twin environment.

[0038] Preferably, the multi-level collaborative decision-making and autonomous scheduling mechanism includes:

[0039] A hierarchical decision-making framework is used to clarify the decision-making authority and collaboration mechanism of the cloud layer, edge layer, and terminal layer.

[0040] A conflict resolution algorithm, which is communicatively connected to the hierarchical decision framework, is used to automatically coordinate when there are conflicts in decisions at different levels.

[0041] The task splitting and distribution system, which is connected in communication with the conflict resolution algorithm, is used to decompose complex operation and maintenance tasks into sub-tasks that can be executed at different levels.

[0042] Resource pooling management, which is communicatively connected to the task splitting and distribution system, is used to dynamically allocate computing, storage and network resources according to task requirements;

[0043] An autonomous scheduling optimizer, which is connected to the resource pooling management, is used to optimize scheduling strategies based on task priority, resource availability, and energy consumption targets.

[0044] Preferably, the system further includes:

[0045] The communication module is connected to the operation and maintenance module and the monitoring module. It is used to connect with the various system devices in the smart building and transmit the obtained smart building equipment operation and maintenance data and building group equipment operation data to the cloud-edge collaborative architecture to achieve data synchronization.

[0046] The communication module enables the collection and transmission of building complex operation and maintenance data by establishing an API connection.

[0047] Preferably, the building infrastructure information, equipment operation data, and environmental parameters of the smart residential complex include:

[0048] Basic building information, including the building's floor plan, elevation, structural drawings, and equipment distribution diagram;

[0049] Building operation and maintenance information, including equipment operating parameters, energy consumption data, and maintenance records;

[0050] Building equipment information, including equipment model, specifications, and operating status;

[0051] Information on public areas, including pedestrian flow, environmental parameters, and safety status;

[0052] Building complex information, including the overall layout of the building complex, its interrelationships, and data on shared facilities.

[0053] Preferably, the cloud architecture of the system includes:

[0054] The cloud infrastructure service layer provides basic computing, storage, and network resources;

[0055] The platform service layer communicates with the cloud infrastructure service layer and is used to provide data processing, model training and knowledge base management.

[0056] The application service layer communicates with the platform service layer and is used to provide operation and maintenance decision support, resource scheduling, and user interaction interface.

[0057] Preferably, the edge layer architecture of the system includes:

[0058] The intelligent access control layer is used to control personnel access and authenticate identities;

[0059] The intelligent machine control layer, which is communicatively connected to the intelligent access control layer, is used to manage intelligent devices and robots;

[0060] The robot brain layer is communicatively connected to the intelligent machine control layer and is used to process visual information and control robot behavior;

[0061] The data processing and service layer is communicatively connected to the robot brain database layer and is used to perform data preprocessing and edge analysis;

[0062] The intelligent access control layer, the intelligent machine control layer, the robot brain database layer, and the data processing and service layer are all communicatively connected to the cloud layer and the building module.

[0063] The beneficial effects of this invention include:

[0064] 1. Achieve fully digital management of building complexes: Through digital twin technology, construct a digital model of the building complex to achieve real-time mapping and interaction between the physical and virtual worlds, providing visual support for management decisions.

[0065] 2. Improve operation and maintenance efficiency: Through predictive maintenance algorithms, the system can predict potential equipment failures in advance, transforming the traditional passive response into proactive prevention, reducing equipment failure rate by 35% and maintenance costs by 30%.

[0066] 3. Reduce energy consumption: Through intelligent control and management, optimize energy use strategies to reduce building energy consumption by 15-30% and reduce peak load by 20%.

[0067] 4. Enhance security: Through intelligent security and access control management, comprehensive monitoring of personnel, equipment and environment can be achieved, reducing security incidents by 50% and shortening emergency response time by 60%.

[0068] 5. Optimize resource allocation: Through a multi-level collaborative decision-making and autonomous scheduling mechanism, optimize the allocation of human and material resources, improve resource utilization by 35%, and enhance operation and maintenance quality by 25%.

[0069] 6. Promote wealth creation: Reduce operation and maintenance costs through intelligent operation and maintenance systems, enabling high-quality construction services to benefit a wider range of people; at the same time, create high-quality jobs, enhance the overall value of the industrial chain, and provide technical support for achieving common wealth creation. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the overall architecture of the digital twin hybrid cloud-edge collaborative smart real estate building complex operation and maintenance intelligent system of the present invention;

[0071] Figure 2 This is a data flow flowchart of the system of the present invention;

[0072] Figure 3 This is a schematic diagram of the multi-source heterogeneous data fusion and real-time processing mechanism in the system of the present invention;

[0073] Figure 4 This is a schematic diagram of the edge intelligent analysis and decision-making framework in the system of the present invention;

[0074] Figure 5 This is a flowchart of the adaptive model training and iterative optimization system in the present invention.

[0075] Figure 6 This is a schematic diagram of the predictive maintenance algorithm for virtual-real mapping in the system of the present invention;

[0076] Figure 7 This is a schematic diagram of the multi-level collaborative decision-making and autonomous scheduling mechanism in the system of the present invention;

[0077] Figure 8 This is a schematic diagram illustrating an embodiment of the system of the present invention applied to energy management in intelligent buildings;

[0078] Figure 9This is a schematic diagram illustrating an embodiment of the system of the present invention applied to equipment fault prediction and maintenance;

[0079] Figure 10 This is a schematic diagram illustrating an embodiment of the system of the present invention applied to intelligent security and access control management. Detailed Implementation

[0080] Please refer to the attached document. Figure 1-10 The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0081] like Figure 1 As shown, the intelligent operation and maintenance system for smart real estate building complexes provided by this invention includes a data acquisition module 1, a storage module 2, a construction module 3, a scene module 4, a virtual-real synchronization module 5, an operation and maintenance module 6, a monitoring module 7, and a communication module 8. The system of this invention adopts a three-layer collaborative architecture of "cloud-edge-device," with a digital twin operation and maintenance brain at its core, to realize intelligent operation and maintenance management of smart building complexes.

[0082] The cloud architecture of the system of the present invention includes: cloud basic service layer 91, platform service layer 92 and application service layer 93.

[0083] The cloud infrastructure service layer 91 provides basic computing, storage, and network resources. In one embodiment of the invention, the cloud infrastructure service layer 91 adopts a distributed computing architecture, including a computing resource pool, a storage resource pool, and a network resource pool. The computing resource pool consists of multiple high-performance servers, supporting elastic scaling; the storage resource pool includes block storage, file storage, and object storage to meet the storage needs of different data types; and the network resource pool provides high-speed, secure network connections to ensure rapid data transmission.

[0084] The platform service layer 92 communicates with the cloud infrastructure service layer 91 to provide data processing, model training, and knowledge base management. Preferably, the platform service layer 92 includes a big data processing engine, an artificial intelligence training platform, and a knowledge graph management system. The big data processing engine supports batch and stream processing and can process more than 100,000 data records per second; the artificial intelligence training platform provides model training, optimization, and management functions, and supports deep learning and machine learning algorithms; the knowledge graph management system stores knowledge in the construction field and equipment maintenance experience, forming a queryable knowledge base.

[0085] The application service layer 93 is communicatively connected to the platform service layer 92, and is used to provide operation and maintenance decision support, resource scheduling, and user interface. In an embodiment of the present invention, the application service layer 93 includes a decision support system, a resource scheduling system, and a user interface system. The decision support system provides operation and maintenance decision suggestions based on data analysis results; the resource scheduling system optimizes the allocation of human and material resources; and the user interface system provides web and mobile access interfaces, supporting visualized data display and interactive operations.

[0086] The edge architecture of the system of the present invention includes: intelligent access control layer 101, intelligent machine control layer 102, robot brain database layer 103, and data processing and service layer 104.

[0087] The intelligent access control layer 101 is used to implement personnel access control and identity authentication. In a specific embodiment of the present invention, the intelligent access control layer 101 includes a multimodal identity recognition system and a visitor management system. The multimodal identity recognition system integrates multiple identity verification methods such as facial recognition, IC card recognition, and fingerprint recognition, with an accuracy rate of 99.7%; the visitor management system supports appointment registration, temporary permission allocation, and access trajectory recording functions.

[0088] The intelligent machine control layer 102 is communicatively connected to the intelligent access control layer 101 and is used to manage intelligent devices and robots. Preferably, the intelligent machine control layer 102 includes an equipment control system and a robot management system. The equipment control system enables intelligent control of building equipment such as air conditioners, lighting, and elevators; the robot management system is responsible for coordinating the work of multiple robots, including task allocation, path planning, and status monitoring.

[0089] The robot brain layer 103 is communicatively connected to the intelligent machine control layer 102, and is used to process visual information and control robot behavior. In one embodiment of the present invention, the robot brain layer 103 includes a visual analysis system and a behavior control system. The visual analysis system processes image data acquired by the robot's camera and identifies equipment status and environmental changes; the behavior control system generates control commands based on the analysis results to guide the robot to perform tasks such as inspection and measurement.

[0090] The data processing and service layer 104 is communicatively connected to the robot brain database layer 103, and is used to perform data preprocessing and edge analysis. Furthermore, the data processing and service layer 104 includes a data preprocessing engine and an edge analysis engine. The data preprocessing engine filters, standardizes, and times-aligns the collected raw data; the edge analysis engine executes lightweight analysis algorithms at edge nodes to generate preliminary analysis results.

[0091] Meanwhile, the intelligent access control layer 101, the intelligent machine control layer 102, the robot brain layer 103, and the data processing and service layer 104 are all connected to the cloud layer and the building module 3 to realize the bidirectional flow of data and control information.

[0092] The system of this invention includes multiple functional modules, the functions and implementation methods of each module will be described in detail below:

[0093] The data acquisition module 1 is used to collect basic building information, equipment operation data, and environmental parameters of the smart real estate complex. In a specific implementation of this invention, the data acquisition module 1 includes a sensor network, a data acquisition gateway, and a data quality control unit. The sensor network consists of sensors distributed throughout the complex, such as temperature, humidity, pressure, flow rate, current, and voltage sensors. The sampling frequency can be dynamically adjusted according to the importance of the data, typically 1-10 times / second. The data acquisition gateway uses a lightweight Internet of Things protocol (MQTT / CoAP) to collect sensor data. The data quality control unit performs preliminary verification of the collected data and filters out obviously erroneous data.

[0094] Storage module 2 is communicatively connected to acquisition module 1, used to transmit acquired data to the cloud to establish a digital twin scene database, and to synchronize data in the digital twin scene database to edge devices. Preferably, storage module 2 adopts a hierarchical storage strategy, storing frequently accessed data in high-speed storage devices and transferring cold data to low-cost storage devices. Data synchronization uses an incremental synchronization method, synchronizing only the data that has changed, reducing the network transmission burden.

[0095] The construction module 3 is communicatively connected to the storage module 2 and is used to build a digital twin building complex model based on data from the digital twin scene database, and to build a digital twin scene using the digital twin building complex model. In this embodiment of the invention, the construction module 3 integrates BIM (Building Information Modeling) technology and GIS (Geographic Information System) technology to construct a comprehensive digital model containing geometric information, semantic information, and relational information. The model construction process includes three levels: spatial structure modeling, equipment system modeling, and operational status modeling.

[0096] Scene module 4 communicates with construction module 3 to construct a digital smart building scene that integrates virtual and physical elements, utilizing BIM, GIS, IoT, 5G, and AI technologies. Furthermore, scene module 4 enables multi-dimensional scene construction, including physical structure scenes, equipment system scenes, personnel activity scenes, and environmental parameter scenes. Scene construction employs a modular design, supporting rapid scene updates and reconstruction.

[0097] The virtual-real synchronization module 5 is communicatively connected to the scene module 4, and is used to present the dynamics of the physical world in a digital smart building scene using AR / VR devices, achieving real-time synchronization between the virtual scene and the physical environment. In one embodiment of the present invention, the virtual-real synchronization module 5 adopts real-time data stream processing technology to synchronize changes in the physical world to the virtual scene with low latency (typically less than 100 milliseconds); at the same time, it adopts spatial positioning technology to achieve precise positioning of AR / VR devices in physical space, supporting interactive operation and information overlay display.

[0098] The operation and maintenance module 6 is communicatively connected to the virtual-real synchronization module 5, and is used to construct a digital twin operation and maintenance brain. This digital twin operation and maintenance brain includes a multi-source heterogeneous data fusion and real-time processing mechanism, an edge intelligent analysis and decision-making framework, an adaptive model training and iterative optimization system, a predictive maintenance algorithm for virtual-real mapping, and a multi-level collaborative decision-making and autonomous scheduling mechanism. The operation and maintenance module 6 is the core of the system of this invention and will be described in detail in subsequent chapters.

[0099] The monitoring module 7 communicates with the operation and maintenance module 6 to monitor the status and operation of edge devices, generate monitoring reports, and, based on data, simulate the actual operation and maintenance scenarios of the building complex through a digital twin building complex model, provide data services for the intelligent operation and maintenance of the building complex. Preferably, the monitoring module 7 includes an equipment status monitoring unit, an operation data analysis unit, and a report generation unit. The equipment status monitoring unit monitors the working status and health status of the equipment in real time; the operation data analysis unit performs statistical analysis on historical operation data to identify abnormal patterns and optimization opportunities; and the report generation unit automatically generates daily, weekly, and monthly reports, supporting custom report templates and indicators.

[0100] The communication module 8 communicates with the operation and maintenance module 6 and the monitoring module 7, and is used to connect with various system devices in the smart building. It transmits the obtained smart building equipment operation and maintenance data and building group equipment operation data to the cloud-edge collaborative architecture to achieve data synchronization. In this embodiment of the invention, the communication module 8 establishes an API connection to collect and send building group operation and maintenance data. The communication module 8 supports multiple communication protocols, including MQTT, HTTP, WebSocket, etc., ensuring compatibility with different systems and devices; it also implements communication encryption and authentication to ensure data transmission security.

[0101] The building infrastructure information, equipment operation data, and environmental parameters of a smart real estate complex include: building infrastructure information, building operation and maintenance information, building equipment information, public area information, and building complex information.

[0102] Basic building information includes the building's floor plans, elevations, structural drawings, and equipment layout diagrams. This information is typically stored in the form of CAD drawings, BIM models, or 3D scan data, serving as the foundational data for building a digital twin model.

[0103] Building operation and maintenance information includes equipment operating parameters, energy consumption data, and maintenance records. Equipment operating parameters include physical quantities such as temperature, pressure, flow rate, voltage, and current, which are usually stored in the form of time series data; energy consumption data includes the consumption of electricity, water, gas, etc., and is statistically analyzed by time dimensions such as hours, days, and months; maintenance records include information such as maintenance time, maintenance content, maintenance personnel, and maintenance results.

[0104] Building equipment information includes equipment model, specifications, and operating status. Equipment model and specifications are stored as static information in the equipment information database; operating status is updated in real time as dynamic information, reflecting the current working status of the equipment.

[0105] Public area information includes pedestrian flow, environmental parameters, and security status. Pedestrian flow data is collected through pedestrian sensors, cameras, and access control systems to analyze public area usage; environmental parameters include indicators such as temperature, humidity, light intensity, noise, and air quality; security status includes the status information of fire protection, monitoring, and alarm systems.

[0106] Building complex information includes the overall layout, interrelationships, and shared facilities data. The overall layout describes the spatial location and relative relationships of the buildings; the interrelationships between buildings include information on functional connections and resource sharing; shared facilities data includes information on shared roads, plazas, green spaces, parking lots, and other public facilities.

[0107] The core of this invention is the digital twin operation and maintenance brain, which integrates multiple innovative technologies to achieve intelligent operation and maintenance management of building complexes. The five core technologies of the digital twin operation and maintenance brain will be explained in detail below.

[0108] The multi-source heterogeneous data fusion and real-time processing mechanism includes: multi-source data acquisition architecture, data adapter, spatiotemporal index data structure, data quality assessment algorithm, and data caching and distribution mechanism.

[0109] A multi-source data acquisition architecture is used to collect physical layer sensor data, edge layer processed data, and cloud-aggregated data. In embodiments of this invention, physical layer data acquisition adopts a hierarchical and partitioned deployment strategy, optimizing the sensor network layout based on building structural characteristics and device distribution. The sensor sampling frequency is dynamically adjusted according to data importance and rate of change, with key devices sampling at 5-10 times / second and general devices at 1-3 times / second. Edge layer data preprocessing is performed at the data acquisition gateway, including range checks, consistency verification, and temporal continuity analysis, effectively reducing invalid data transmission. Cloud data aggregation uses a distributed timestamp protocol to ensure time consistency of data from different sources, with errors controlled within 10 milliseconds.

[0110] The data adapter communicates with the multi-source data acquisition architecture to achieve unified and standardized processing of BIM, GIS, and IoT device data formats. Preferably, the data adapter includes multiple data conversion modules, supporting mutual conversion between common data formats such as JSON, XML, and CSV. For BIM data, it supports parsing and conversion of formats such as IFC and RVT; for GIS data, it supports processing formats such as Shapefile and GeoJSON; and for IoT device data, it supports parsing protocol data such as MQTT and CoAP. The data adapter also implements unit standardization, unifying different units of measurement into the International System of Units (ISU).

[0111] The spatiotemporal index data structure communicates with the data adapter to unify data from different sources and with different sampling rates into a spatiotemporal dimension. In embodiments of this invention, the spatiotemporal index data structure employs a combination of multi-granularity time indexing and hierarchical spatial indexing. The time dimension index supports time granularity queries at different levels such as seconds, minutes, hours, and days. The spatial dimension index is designed with a hierarchical index structure based on the physical structure of buildings, including multiple levels such as building groups, buildings, floors, regions, and equipment. The spatiotemporal composite index structure can be represented as follows:

[0112] ,

[0113] in: For spatiotemporal joint index, For time indexing, Indicates the time granularity level. For spatial indexing, Indicates spatial hierarchy, For data values, Indicates the data type.

[0114] The data quality assessment algorithm communicates with the spatiotemporal index data structure to perform real-time cleaning and anomaly detection on sensor data. Preferably, the data quality assessment algorithm includes three steps: integrity check, accuracy verification, and consistency analysis. Integrity check identifies missing and incomplete records; accuracy verification identifies outliers through range checks and pattern matching; and consistency analysis ensures data consistency by cross-validating related data from different sources. The data quality score is calculated using the following formula:

[0115] ,

[0116] in: Score the data quality. For completeness score, To score for accuracy, For consistency score, , , Let be the weight coefficient, and satisfy... In embodiments of the present invention, it is typically set , , Because accuracy has a greater impact on subsequent analysis.

[0117] The data caching and distribution mechanism communicates with the data quality assessment algorithm to perform tiered processing based on data importance and timeliness. In embodiments of this invention, the data caching employs a multi-level caching strategy, including memory caching, local storage caching, and cloud storage. The data importance score is calculated based on device importance, data change rate, and application requirements.

[0118] ,

[0119] in: Score the importance of the data. Rate the importance of the equipment (1-10, 10 being the most important). For the rate of change of data, The frequency at which data is requested by the application. , , Let be the weight coefficient, and satisfy... Based on practical experience, it is usually set to... =0.5, =0.3, =0.2, because the importance of the device has the greatest impact on the importance of the data.

[0120] The edge intelligent analysis and decision-making framework includes: a lightweight edge AI inference engine, an edge node autonomous decision-making mechanism, an edge computing resource dynamic scheduling algorithm, an edge security isolation mechanism, and an edge caching optimization strategy.

[0121] A lightweight edge AI inference engine is used to support real-time device status analysis and fault prediction. In embodiments of this invention, the lightweight edge AI inference engine is based on model compression technology, compressing complex models trained in the cloud into an executable version for edge devices. Model compression employs parameter quantization, structural pruning, and knowledge distillation techniques to significantly reduce model size and computational complexity while maintaining model accuracy. Parameter quantization converts 32-bit floating-point numbers into 8-bit integers, reducing storage space by 75%; structural pruning reduces model parameters by 30-50% by removing unimportant network connections; knowledge distillation transfers knowledge from large teacher models to small student models, reducing model size while maintaining performance.

[0122] An edge node autonomous decision-making mechanism communicates with a lightweight edge AI inference engine to make local decisions based on preset rules and historical data. Preferably, the edge node autonomous decision-making mechanism includes a rule engine and a lightweight decision tree evaluation system. The rule engine makes rapid decisions based on preset business rules, suitable for well-defined scenarios; the decision tree evaluation system constructs multi-layered decision trees based on device characteristics and the operating environment, supporting more complex decision logic. During the decision-making process, the system clearly defines the scope of local decision-making and the situations requiring reporting to the cloud, ensuring the security and effectiveness of the decisions.

[0123] The edge computing resource dynamic scheduling algorithm communicates with the edge node autonomous decision-making mechanism to adaptively allocate resources based on task priority and computing load. In embodiments of this invention, the edge computing resource dynamic scheduling algorithm employs a priority-based multi-level queue scheduling strategy combined with a load-aware resource allocation mechanism. Task priority evaluation considers task importance, urgency, and resource requirements, and the calculation formula is as follows:

[0124] ,

[0125] in: As a task priority, Assign tasks based on their importance (1-10, 10 being the most important). Task urgency level (0-1, 1 being the most urgent). The resource requirement ratio for the task is (0-1). , , Let be the weight coefficient, and satisfy... Based on practical experience, it is usually set to... This means that importance and urgency have the same impact on priority, and are greater than the impact of resource demand.

[0126] An edge security isolation mechanism communicates with an edge computing resource dynamic scheduling algorithm to ensure local processing of sensitive data and reduce data transmission risks. Preferably, the edge security isolation mechanism employs application sandboxing technology and access control mechanisms. Application sandboxing technology ensures secure isolation between different functional modules, preventing malicious code or erroneous operations from affecting system stability; the access control mechanism uses role-based fine-grained permission control to ensure that only authorized users and programs can access sensitive data. Furthermore, the system automatically identifies and marks data containing sensitive information, prioritizing processing at edge nodes and transmitting only the necessary processing results, not the original data.

[0127] The edge caching optimization strategy communicates with the edge security isolation mechanism to intelligently cache data based on access frequency and data importance. In embodiments of this invention, the edge caching optimization strategy employs an improved LRU-K algorithm, considering data access frequency, access patterns, and data importance. The cache priority calculation formula is as follows:

[0128] ,

[0129] in: For cache priority, For access frequency, This represents the number of historical visits. For the importance of data, This is the importance weighting coefficient. Practical verification has shown that setting it to... It achieves a good balance between access efficiency and prioritizing the caching of important data. The caching is more efficient.

[0130] The adaptive model training and iterative optimization system includes: an incremental learning framework, model compression technology, federated learning mechanism, model performance monitoring system, and model version management mechanism.

[0131] Incremental learning frameworks are used to support local model updates at edge nodes without requiring full retraining. In embodiments of this invention, the incremental learning framework employs a parameter difference learning method, updating only the parameters relevant to the new data, rather than all parameters. This method significantly reduces computational and memory requirements, enabling edge devices to perform local model updates. The incremental learning process includes three steps: local sample collection, difference parameter calculation, and local model update. Local sample collection is based on a data value assessment mechanism, prioritizing samples with high information content; difference parameter calculation employs gradient sparsity techniques, calculating and transmitting only significantly changed parameters; and local model update uses a low learning rate strategy to avoid new data excessively impacting model performance.

[0132] Model compression technology communicates with the incremental learning framework to compress complex models trained in the cloud into an executable version for edge devices. Preferably, the model compression technology includes three aspects: network structure simplification, weight quantization, and computational optimization. Network structure simplification reduces model size by removing redundant connections and layers; weight quantization converts 32-bit floating-point numbers into 8-bit or lower integers, reducing storage space and computational complexity; computational optimization optimizes the computation process based on the characteristics of the target hardware, improving execution efficiency. In embodiments of this invention, the model compression ratio typically reaches 10:1 to 20:1, and the accuracy of the compressed model remains above 95% of the original model.

[0133] The federated learning mechanism communicates with model compression technology to enable multiple edge nodes to train collaboratively without sharing the original data. In embodiments of this invention, the federated learning mechanism employs a secure aggregation algorithm to aggregate model updates while protecting the privacy of the original data. The federated learning process includes five steps: node selection, local training, gradient encryption, parameter aggregation, and model update. Node selection chooses participating nodes based on data quality and computing power; local training uses local data on each node to train the model; gradient encryption uses homomorphic encryption technology to protect gradient information; parameter aggregation is completed in the cloud, aggregating encrypted gradients from multiple nodes; and model update applies the aggregation results to the global model. The objective function of federated learning can be expressed as:

[0134] ,

[0135] in: For the global loss function, For model parameters, The number of participating nodes. For nodes The number of samples, This represents the total number of samples. For nodes The local loss function.

[0136] The model performance monitoring system communicates with the federated learning mechanism to evaluate model prediction accuracy in real time and trigger updates. Preferably, the model performance monitoring system includes three functional modules: performance metric tracking, anomaly detection, and update triggering. Performance metric tracking continuously records the model's accuracy, precision, recall, and other metrics; anomaly detection identifies situations where model performance significantly declines; and update triggering determines whether to initiate the model update process based on preset rules. Performance evaluation uses a sliding window mechanism to calculate the average performance of the most recent N predictions, avoiding unnecessary updates due to short-term fluctuations. Update triggering conditions are typically set as follows: a performance metric decline exceeding 10% for more than 3 days, or a cumulative number of incorrect predictions exceeding a preset threshold.

[0137] The model version management mechanism communicates with the model performance monitoring system to support model rollback and A / B testing. In embodiments of this invention, the model version management mechanism employs a distributed version control system to record the version and performance metrics of all model updates. Version management includes four aspects: version identification, performance recording, deployment tracking, and a rollback mechanism. Version identification uses semantic version numbers to easily identify major and minor updates of the model; performance recording stores the performance metrics of each model version under different scenarios; deployment tracking records the deployment status and usage of the model; and the rollback mechanism supports rapid restoration to a previous stable version when the performance of a new model is unsatisfactory. The A / B testing framework allows for the simultaneous deployment of different model versions for comparative testing to evaluate the actual effectiveness of the new model.

[0138] Predictive maintenance algorithms that map virtual and real data include: digital twin building equipment health assessment models, equipment remaining life prediction algorithms, multi-dimensional failure mode recognition systems, dynamic maintenance strategy generators, and virtual scenario failure simulation systems.

[0139] A digital twin building equipment health assessment model is used to evaluate the health status of equipment by integrating physical models and data-driven models. In embodiments of this invention, the health assessment model adopts a dual-model fusion architecture, combining the advantages of physical law models and data-driven models. The physical model is based on the working principles and physical laws of the equipment to establish a mathematical model describing the normal working state of the equipment; the data-driven model is based on historical data to learn the statistical laws and abnormal patterns of equipment operation. The health score calculation formula is as follows:

[0140] ,

[0141] in: Rate the device's health (0-100, 100 being the healthiest). Rate the physics model. Scoring for data-driven models and Let be the weight coefficient, and satisfy... Based on practical experience, different weights are set for different types of equipment. For mechanical equipment, the weights are typically set as follows: Because physical models provide a more accurate description of mechanical equipment; for electrical equipment, they are typically set... This is because data-driven models are more sensitive to anomaly detection in electrical equipment.

[0142] The remaining life prediction algorithm communicates with the digital twin building equipment health assessment model to predict the remaining life based on historical operating data and maintenance records. Preferably, the remaining life prediction algorithm employs a degradation model-based life prediction method, combining equipment type characteristics and usage environment to establish a life model. The prediction process includes three steps: key parameter identification, degradation rate assessment, and life prediction. Key parameter identification determines the key parameters affecting equipment life through correlation analysis and expert knowledge; degradation rate assessment quantifies the degradation rate of key parameters through time series analysis; life prediction calculates the remaining life based on the current state and degradation trend. The remaining life calculation formula is:

[0143] ,

[0144] in: For the remaining service life, The fault threshold, The current parameter value. For degradation rate, This refers to the environmental impact factor. The environmental impact factor considers the impact of environmental conditions such as temperature, humidity, and load on equipment lifespan, and typically ranges from 0.8 to 1.2. A value greater than 1 indicates accelerated environmental degradation, while a value less than 1 indicates slowed environmental degradation.

[0145] A multi-dimensional fault mode recognition system communicates with an equipment remaining life prediction algorithm to identify complex fault modes and potential risks. In embodiments of this invention, the multi-dimensional fault mode recognition system includes three functional modules: a fault mode library, anomaly mode detection, and fault correlation analysis. The fault mode library establishes a feature pattern database of common equipment faults, containing typical characteristics and evolution processes of various faults; anomaly mode detection, based on multi-dimensional time series analysis, identifies abnormal behaviors deviating from normal operating modes; fault correlation analysis, through graph models and causal reasoning, identifies the correlation relationships between different equipment faults. Anomaly detection employs a multi-dimensional anomaly detection algorithm based on an autoencoder, with the reconstruction error calculation formula as follows:

[0146] ,

[0147] in: For reconstruction error, For feature dimension, These are the original eigenvalues. To reconstruct feature values. When the reconstruction error exceeds a preset threshold, the system determines it to be in an abnormal state. The threshold is typically set to three times the standard deviation of the reconstruction error in the normal state, i.e. ,in This represents the mean of the reconstruction error under normal conditions. The standard deviation is denoted as .

[0148] The dynamic maintenance strategy generator communicates with the multi-dimensional fault mode recognition system to intelligently plan maintenance time and resources based on prediction results. Preferably, the dynamic maintenance strategy generator includes three functional modules: preventative maintenance plan, maintenance task priority, and maintenance scheme recommendation. The preventative maintenance plan is formulated based on equipment status and prediction results; the maintenance task priority determines the maintenance order based on risk level and resource constraints; and the maintenance scheme recommendation recommends the most suitable maintenance methods and steps for the current situation. The maintenance priority calculation formula is:

[0149] ,

[0150] in: To maintain priority, For failure costs, This represents the probability of failure. For the remaining service life, For maintenance cycle, For resource costs, For resource availability. The first term in the formula, taking the maximum value of 0, ensures that new or recently maintained equipment will not generate negative risk values. The second term... Basic maintenance costs remain constant. Priority calculations comprehensively consider failure risks and resource constraints, balancing maintenance needs and resource utilization efficiency.

[0151] A virtual scenario fault simulation system communicates with a dynamic maintenance strategy generator to verify maintenance plans in a digital twin environment. In embodiments of this invention, the virtual scenario fault simulation system employs a hybrid simulation method based on physical laws and historical data to simulate equipment faults and maintenance processes within the digital twin environment. The simulation process includes three steps: initial state setting, fault evolution simulation, and maintenance effect evaluation. Initial state setting is based on the current actual state of the equipment; fault evolution simulation is based on evolution patterns in a fault mode library; and maintenance effect evaluation compares changes in the equipment state before and after maintenance. The simulation system supports comparative testing of multiple maintenance plans, evaluating the effectiveness and cost of different plans, and providing a scientific basis for maintenance decisions.

[0152] The multi-level collaborative decision-making and autonomous scheduling mechanism includes: a hierarchical decision-making framework, a conflict resolution algorithm, a task splitting and distribution system, resource pooling management, and an autonomous scheduling optimizer.

[0153] A hierarchical decision-making framework is used to clarify the decision-making authority and collaboration mechanism of the cloud layer, edge layer, and endpoint layer. In the embodiments of this invention, the hierarchical decision-making framework adopts a three-layer decision-making architecture based on responsibility and capability. The cloud layer is responsible for global strategy formulation, resource planning, and long-term optimization decisions, typically handling decision problems with large data volumes, computational complexity, and long time spans; the edge layer is responsible for regional coordination, short-term scheduling, and equipment collaborative control, handling decision problems involving multi-device collaboration, moderate complexity, and response time requirements in the second range; the endpoint layer is responsible for device-level real-time control and emergency response, handling decision problems involving single-device control, low complexity, and response time requirements in the millisecond range. The decision-making process includes four stages: information collection, scheme generation, evaluation and selection, and execution supervision, ensuring the scientific nature and effectiveness of the decisions.

[0154] The conflict resolution algorithm communicates with the hierarchical decision-making framework to automatically coordinate decisions at different levels. Preferably, the conflict resolution algorithm includes two functional modules: conflict detection and conflict coordination. Conflict detection identifies decision conflicts between different levels or parallel nodes through decision result comparison and impact analysis; conflict coordination resolves conflicts through preset priority rules and negotiation mechanisms. Conflict types include resource conflicts, time conflicts, and objective conflicts, and different resolution strategies are adopted for different types of conflicts. The conflict priority score calculation formula is as follows:

[0155] ,

[0156] in: Scoring the conflict priority Decision-making level (cloud layer = 3, edge layer = 2, end layer = 1) The urgency level is categorized as follows (0-1, 1 being the most urgent). The scope of influence (O-1, 1 represents the widest impact), , , Let be the weight coefficient, and satisfy... 1. In security-related decisions, settings are typically configured... Priority is given to urgency; in resource allocation decisions, a system is typically set... Priority should be given to the level and scope of influence.

[0157] The task splitting and distribution system communicates with the conflict resolution algorithm to decompose complex operation and maintenance tasks into subtasks that can be executed at different levels. In embodiments of the present invention, the task splitting and distribution system adopts a task decomposition strategy based on function and timing to split complex tasks into subtasks that can be executed in parallel or sequentially. The task splitting process includes three steps: functional decomposition, timing arrangement, and parallel optimization. Functional decomposition breaks down complex tasks into subtasks according to functional modules; timing arrangement determines the execution order and dependencies of subtasks; parallel optimization identifies subtasks that can be executed in parallel to improve efficiency. Task distribution is based on execution capability assessment and matching degree calculation, assigning subtasks to the most suitable execution units. The execution capability score calculation formula is:

[0158] ,

[0159] in: To score performance capabilities, For computing power, The current load rate, This refers to task similarity (the degree of similarity to historical tasks). , , Let be the weight coefficient, and satisfy... Based on practical experience, it is usually set to... This means that computing power and workload have the same impact on execution capability, and are greater than the impact of task similarity.

[0160] The resource pooling management system communicates with the task splitting and distribution system to dynamically allocate computing, storage, and network resources based on task requirements. Preferably, resource pooling management includes three functional modules: a computing resource pool, a storage resource pool, and a network resource pool. The computing resource pool uniformly manages available computing resources at all levels, including cloud servers, edge computing nodes, and end-device processors; the storage resource pool coordinates and manages distributed storage resources, including cloud databases, edge caches, and local storage; and the network resource pool optimizes network bandwidth and connection resource allocation to ensure the priority of critical data transmission. Resource allocation employs a demand forecasting-based advance allocation strategy, predicting future resource demands based on historical patterns and scheduling additional resources in advance for anticipated peak periods.

[0161] The autonomous scheduling optimizer communicates with the resource pooling management system to optimize scheduling strategies based on task priority, resource availability, and energy consumption targets. In embodiments of this invention, the autonomous scheduling optimizer employs a multi-objective optimization scheduling algorithm to balance performance, energy consumption, and cost objectives. The scheduling optimization objective function can be expressed as:

[0162] ,

[0163] in To comprehensively optimize the objectives, For the scheduling scheme, The performance objective function is... Let the energy consumption objective function be... The objective function is cost. , , Let be the weight coefficient, and satisfy... In critical business scenarios, it is typically set up Prioritize performance; in routine operation and maintenance scenarios, it is usually set to... Priority is given to energy consumption and cost. The implementation process of the scheduling strategy includes three steps: scheme generation, scheme evaluation, and scheme execution, to ensure the scientific nature and feasibility of the scheduling scheme.

[0164] The system of this invention is applicable to various smart building complex operation and maintenance scenarios. The following three typical application scenarios are used as examples to illustrate the application effect of the system.

[0165] The system of this invention is applied to the energy consumption management scenario of intelligent buildings, realizing energy consumption monitoring and analysis, energy efficiency optimization control, and abnormal energy consumption detection.

[0166] In terms of energy consumption monitoring and analysis, the system collects energy consumption data from various systems within the building complex through acquisition module 1, including the consumption of electricity, water, and gas. Data is collected every 15 minutes, meeting the time granularity requirements for energy consumption analysis. The collected data undergoes multi-source heterogeneous data fusion and real-time processing to form a unified energy consumption dataset. Based on a digital twin model, the system correlates energy consumption data with information such as building structure and usage to establish a multi-dimensional energy consumption analysis model. Energy consumption analysis includes time-dimensional analysis (hours, days, weeks, months, years), spatial-dimensional analysis (floors, areas, equipment), and usage-dimensional analysis (lighting, air conditioning, power, etc.), forming a comprehensive energy consumption profile.

[0167] In terms of energy efficiency optimization and control, the system automatically adjusts equipment operating parameters based on building usage and environmental conditions through an edge intelligent analysis and decision-making framework. Optimization control strategies include optimizing air conditioning temperature setpoints, dynamically adjusting lighting brightness, and optimizing equipment operating time. The system employs reinforcement learning algorithms to continuously optimize control strategies, minimizing energy consumption while ensuring comfort. Control commands are sent to the corresponding devices via communication module 8 to achieve automatic control.

[0168] In terms of abnormal energy consumption detection, the system establishes an energy consumption benchmark model through a predictive maintenance algorithm that maps virtual and real data, compares actual energy consumption with expected energy consumption in real time, and identifies abnormal energy consumption and its causes. Anomaly detection employs a hybrid approach based on statistics and machine learning, capable of identifying various causes of abnormal energy consumption, such as equipment malfunctions, improper parameter settings, and abnormal user behavior.

[0169] Application Results: By applying this invention's system, building energy consumption is reduced by 15-30%, peak load is reduced by 20%, and opportunities for energy-saving retrofits in buildings are identified, providing a basis for further optimization. Taking a commercial building complex with a total floor area of ​​100,000 square meters as an example, after applying this system, annual electricity savings of approximately 2 million kilowatt-hours are achieved, cost savings exceeding 1.2 million yuan are achieved, and carbon emissions are reduced by 1,600 tons.

[0170] The system of this invention is applied to equipment fault prediction and maintenance scenarios, realizing core equipment monitoring, fault prediction analysis, and maintenance plan optimization.

[0171] In terms of core equipment monitoring, the system comprehensively monitors important equipment through acquisition module 1, collecting parameters including physical quantities such as temperature, pressure, flow rate, voltage, current, and vibration. The sampling frequency for critical equipment is 5-10 times / second, while for general equipment it is 1-3 times / second. The collected data is pre-processed at the edge and then transmitted to the cloud to form an equipment operation database. Based on digital twin technology, the system constructs virtual models of the equipment, displaying its operating status and health condition in real time.

[0172] In terms of fault prediction and analysis, the system constructs a fault prediction model through adaptive model training and iterative optimization. Based on historical fault data and expert knowledge, the model learns the precursory characteristics and evolution patterns of equipment faults. Predictive analysis includes health status assessment, fault risk prediction, and remaining life estimation. The system can identify various common fault types, such as bearing wear, motor overheating, and pump leakage, with a prediction accuracy exceeding 85%. Early warning time is typically 1-7 days, providing maintenance personnel with ample preparation time.

[0173] In terms of maintenance plan optimization, the system utilizes a multi-level collaborative decision-making and autonomous scheduling mechanism to optimize maintenance time and resource allocation based on equipment status and forecast results. Maintenance plan optimization considers factors such as equipment importance, failure risk, maintenance cost, and resource availability to generate the optimal maintenance plan. The system supports multiple maintenance strategies, including status-based maintenance, risk-based maintenance, and opportunity-based maintenance, allowing for flexible selection of the most suitable strategy based on actual conditions.

[0174] Application Results: Through the application of this invention system, equipment failure rate is reduced by 35%, maintenance costs are reduced by 30%, and equipment lifespan is extended by 20-40%. Taking a building complex containing 500 critical pieces of equipment as an example, after applying this system, the average number of failures per year decreased from 150 to 98, the number of emergency repairs decreased from 60 to 15, maintenance costs decreased from 2 million yuan to 1.4 million yuan, and the average equipment lifespan was extended from 10 years to 13 years.

[0175] The system of this invention is applied to intelligent security and access control scenarios to realize personnel access control, abnormal behavior detection and emergency response management.

[0176] In terms of personnel access control, the system achieves precise management of personnel entry and exit permissions through the intelligent access control layer 101 in the edge architecture. The system employs multimodal identity recognition technology, integrating multiple verification methods such as facial recognition, IC card recognition, and fingerprint recognition, achieving an accuracy rate of 99.7%. The visitor management system supports appointment registration, temporary permission allocation, and access trajectory recording, ensuring that only authorized personnel can enter the corresponding areas. The system sets differentiated access permissions based on time, area, and role to meet the needs of different security levels.

[0177] In terms of abnormal behavior detection, the system uses an edge intelligent analysis and decision-making framework to analyze surveillance video in real time and identify suspicious or abnormal behaviors. Based on deep learning algorithms, the abnormal behavior detection can identify abnormal behavior patterns such as loitering, running, fighting, and climbing. The system employs a layered detection strategy, performing initial detection at edge nodes, and then uploading suspicious behaviors to the cloud for precise analysis, balancing real-time performance and accuracy. The detection results trigger corresponding security responses through a multi-level collaborative decision-making and autonomous scheduling mechanism.

[0178] In terms of emergency response management, the system utilizes a predictive maintenance algorithm that maps virtual and real environments, along with a multi-level collaborative decision-making and autonomous scheduling mechanism, to achieve coordinated responses to emergencies. The system pre-sets various emergency plans, including scenarios such as fires, intrusions, and equipment failures. When a security threat is detected, the system automatically activates the corresponding plan, coordinating security equipment, notifying relevant personnel, and guiding evacuation routes. Through digital twin technology, the system simulates event development and response effects in a virtual environment, providing support for decision-making.

[0179] Application Results: Through the application of this invention's system, security incidents are reduced by 50%, response time is shortened by 60%, and management efficiency is improved by 40%. Taking a building complex with an average daily foot traffic of 10,000 people as an example, after applying this system, the average number of security incidents per year has decreased from 80 to 40, the average response time for security incidents has been shortened from 15 minutes to 6 minutes, the number of access control personnel has been reduced from 10 to 6, and annual security cost savings exceed 500,000 yuan.

[0180] In practical applications, this invention significantly improves the intelligence level and efficiency of building complex operation and maintenance by integrating digital twin, cloud-edge collaboration, and artificial intelligence technologies. The system's main value is reflected in the following aspects:

[0181] First, the system realizes the transformation from passive response to proactive prediction. Through predictive maintenance algorithms, it can predict potential equipment failures in advance, transforming the traditional passive response into proactive prevention, greatly reducing sudden failures and emergency repairs, and improving equipment reliability and service life.

[0182] Secondly, the system optimizes resource allocation and utilization efficiency. Through multi-level collaborative decision-making and autonomous scheduling mechanisms, it achieves optimized allocation of human and material resources and improves resource utilization. Through intelligent control and management, it optimizes energy use strategies and reduces building energy consumption and operating costs.

[0183] Furthermore, the system enhances the scientific rigor and timeliness of management decisions. Through digital twin technology, it constructs a digital model of the building complex, enabling real-time mapping and interaction between the physical and virtual worlds, providing visual support for management decisions. Through data-driven analysis, it uncovers the value of operational data, providing a scientific basis for decision-making.

[0184] Finally, the system promotes the digital transformation and shared prosperity of the building operations and maintenance industry. By reducing maintenance costs through intelligent systems, high-quality building services can benefit a wider population; simultaneously, it creates high-quality jobs and enhances the overall value of the industry chain. The widespread application of this system will drive the digital and intelligent transformation of the building operations and maintenance industry, improve the overall level of the industry, and achieve a balance between technological progress, economic benefits, and social value.

[0185] This invention provides a digital twin hybrid cloud-edge collaborative intelligent real estate building complex operation and maintenance system. By integrating digital twin, cloud-edge collaboration, artificial intelligence, and Internet of Things technologies, it constructs a comprehensive, intelligent, and efficient building complex operation and maintenance management platform. The core of the system is the digital twin operation and maintenance brain, which integrates five innovative technologies: a multi-source heterogeneous data fusion and real-time processing mechanism, an edge intelligent analysis and decision-making framework, an adaptive model training and iterative optimization system, a virtual-real mapping predictive maintenance algorithm, and a multi-level collaborative decision-making and autonomous scheduling mechanism. This enables fully digital management, predictive maintenance, intelligent decision-making, and optimized resource allocation for the building complex. The system has shown significant application effects in multiple scenarios, including intelligent building energy consumption management, equipment fault prediction and maintenance, and intelligent security and access control management. It has greatly improved operation and maintenance efficiency, reduced operating costs, extended equipment lifespan, and provided scientific decision support for building managers. This system not only possesses advanced technology but also practicality and scalability, representing the future development direction of the intelligent building operation and maintenance field.

Claims

1. A digital twin hybrid cloud-edge collaborative intelligent real estate building complex operation and maintenance intelligent system, characterized in that: include: The data acquisition module is used to collect basic building information, equipment operation data, and environmental parameters of the smart real estate complex. The storage module is communicatively connected to the acquisition module and is used to transmit the acquired data to the cloud to establish a digital twin scene database, and to synchronize the data in the digital twin scene database to the edge device. The construction module, which is communicatively connected to the storage module, is used to build a digital twin building complex model based on the data in the digital twin scene database, and to build a digital twin scene through the digital twin building complex model; The scene module, which is communicatively connected to the construction module, is used to construct a digital smart building scene that combines virtual and real technologies using BIM, GIS, IoT, 5G, and AI. The virtual-real synchronization module is communicatively connected to the scene module and is used to present the dynamics of the physical world in the digital smart building scene using AR / VR devices, so as to realize the real-time synchronization between the virtual scene and the physical environment. The operation and maintenance module, which communicates with the virtual-real synchronization module, is used to construct a digital twin operation and maintenance brain. This digital twin operation and maintenance brain includes a multi-source heterogeneous data fusion and real-time processing mechanism, an edge intelligent analysis and decision-making framework, an adaptive model training and iterative optimization system, a predictive maintenance algorithm for virtual-real mapping, and a multi-level collaborative decision-making and autonomous scheduling mechanism. The edge intelligent analysis and decision-making framework includes a lightweight edge AI inference engine, an edge node autonomous decision-making mechanism, an edge computing resource dynamic scheduling algorithm, an edge security isolation mechanism, and an edge cache optimization strategy. The multi-level collaborative decision-making and autonomous scheduling mechanism includes a conflict resolution algorithm, a task splitting and distribution system, resource pooling management, and an autonomous scheduling optimizer. The multi-source heterogeneous data fusion and real-time processing mechanism includes: a multi-source data acquisition architecture for acquiring physical layer sensor data, edge layer processing data, and cloud-aggregated data; a data adapter, which is communicatively connected to the multi-source data acquisition architecture, for achieving unified and standardized processing of BIM, GIS, and IoT device data formats; and a spatiotemporal index data structure, which is communicatively connected to the data adapter, for unifying data from different sources and with different sampling rates into a spatiotemporal dimension. The predictive maintenance algorithm for the virtual-real mapping includes: a digital twin building equipment health assessment model, which adopts a dual-model fusion architecture, integrating a physical model and a data-driven model to assess the health status of the equipment; The equipment remaining life prediction algorithm is communicatively connected to the digital twin building equipment health assessment model. Based on historical equipment operating data and maintenance records, it predicts the remaining lifespan using a degradation model-based lifespan prediction method. The remaining lifespan calculation formula is as follows: ， in: For the remaining service life, The fault threshold, The current parameter value. For degradation rate, Environmental impact factors; A multi-dimensional fault mode identification system, communicatively connected to the equipment remaining life prediction algorithm, is used to identify complex fault modes and potential risks; a dynamic maintenance strategy generator, communicatively connected to the multi-dimensional fault mode identification system, is used to intelligently plan maintenance time and resources based on the prediction results, and the maintenance priority calculation formula is: ， in: To maintain priority, For failure costs, This represents the probability of failure. For the remaining service life, For maintenance cycle, For resource costs, For resource availability; The virtual scenario fault simulation system is communicatively connected to the dynamic maintenance strategy generator and is used to verify maintenance schemes in a digital twin environment. The multi-level collaborative decision-making and autonomous scheduling mechanism includes: a hierarchical decision-making framework, which clarifies the decision-making authority and collaborative mechanism of the cloud layer, edge layer and terminal layer, wherein the cloud layer is responsible for global strategy formulation, resource planning and long-term optimization decision-making, the edge layer is responsible for regional coordination, short-term scheduling and equipment collaborative control, and the terminal layer is responsible for equipment-level real-time control and emergency response.

2. The intelligent operation and maintenance system for digital twin hybrid cloud-edge collaborative smart real estate building complexes according to claim 1, characterized in that, The lightweight edge AI inference engine is used to support real-time device status analysis and fault prediction; The edge node autonomous decision-making mechanism is communicatively connected to the lightweight edge AI inference engine and is used to make local decisions based on preset rules and historical data. The edge computing resource dynamic scheduling algorithm is communicatively connected to the edge node autonomous decision-making mechanism, and is used to adaptively allocate resources according to task priority and computing load. The edge security isolation mechanism is communicatively connected to the edge computing resource dynamic scheduling algorithm to ensure local processing of sensitive data and reduce data transmission risks. The edge caching optimization strategy is communicatively connected to the edge security isolation mechanism and is used to intelligently cache data based on access frequency and data importance.

3. The intelligent operation and maintenance system for digital twin hybrid cloud-edge collaborative smart real estate building complexes according to claim 1, characterized in that, The adaptive model training and iterative optimization system includes: An incremental learning framework is used to support local updates of the model at edge nodes without full retraining; Model compression technology, which is communicatively connected to the incremental learning framework, is used to compress complex models trained in the cloud into an executable version for edge devices; The federated learning mechanism, which communicates with the model compression technology, enables multiple edge nodes to train collaboratively without sharing the original data. The model performance monitoring system is connected in communication with the federated learning mechanism to evaluate the model prediction accuracy in real time and trigger updates. The model version management mechanism communicates with the model performance monitoring system to support model rollback and A / B testing.

4. The intelligent operation and maintenance system for digital twin hybrid cloud-edge collaborative smart real estate building complexes according to claim 1, characterized in that, The conflict resolution algorithm is communicatively connected to the hierarchical decision framework and is used to automatically coordinate when there are conflicts in decisions at different levels. The task splitting and distribution system is communicatively connected to the conflict resolution algorithm and is used to decompose complex operation and maintenance tasks into sub-tasks that can be executed at different levels. The resource pooling management is communicatively connected to the task splitting and distribution system, and is used to dynamically allocate computing, storage and network resources according to task requirements; The autonomous scheduling optimizer is communicatively connected to the resource pooling management system and is used to optimize scheduling strategies based on task priority, resource availability, and energy consumption targets.

5. The intelligent operation and maintenance system for digital twin hybrid cloud-edge collaborative smart real estate building complexes according to claim 1, characterized in that, The system also includes: The communication module is connected to the operation and maintenance module and the monitoring module. It is used to connect with the various system devices in the smart building and transmit the obtained smart building equipment operation and maintenance data and building group equipment operation data to the cloud-edge collaborative architecture to achieve data synchronization. The communication module enables the collection and transmission of building complex operation and maintenance data by establishing an API connection.

6. The intelligent operation and maintenance system for digital twin hybrid cloud-edge collaborative smart real estate building complexes according to claim 1, characterized in that, The building infrastructure information, equipment operation data, and environmental parameters of the smart residential complex include: Basic building information, including the building's floor plan, elevation, structural drawings, and equipment distribution diagram; Building operation and maintenance information, including equipment operating parameters, energy consumption data, and maintenance records; Building equipment information, including equipment model, specifications, and operating status; Information on public areas, including pedestrian flow, environmental parameters, and safety status; Building complex information, including the overall layout of the building complex, its interrelationships, and data on shared facilities.

7. The intelligent operation and maintenance system for digital twin hybrid cloud-edge collaborative smart real estate building complexes according to claim 1, characterized in that, The system's edge-layer architecture includes: The intelligent access control layer is used to control personnel access and authenticate identities; The intelligent machine control layer, which is communicatively connected to the intelligent access control layer, is used to manage intelligent devices and robots; The robot brain layer is communicatively connected to the intelligent machine control layer and is used to process visual information and control robot behavior; The data processing and service layer is communicatively connected to the robot brain database layer and is used to perform data preprocessing and edge analysis; The intelligent access control layer, the intelligent machine control layer, the robot brain database layer, and the data processing and service layer are all communicatively connected to the cloud layer and the building module.

8. The intelligent operation and maintenance system for digital twin hybrid cloud-edge collaborative smart real estate building complexes according to claim 1, characterized in that, It also includes a monitoring module, which is communicatively connected to the operation and maintenance module. The monitoring module is used to monitor the status and operation of edge devices, generate monitoring reports, and, based on the data, simulate the actual operation and maintenance scenarios of the building complex through the digital twin building complex model, providing data services for the intelligent operation and maintenance of the building complex.

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