Intelligent building operation and maintenance system and method based on BIM and indoor positioning technology

Abstract

The present invention relates to the field of smart building operation and maintenance technology, and in particular to a smart building operation and maintenance system and method based on BIM and indoor positioning technology. It comprises a building information acquisition module, a positioning and tracking module, an intelligent operation and maintenance decision module, a path planning and navigation module and a control feedback module. The building information acquisition module collects building data in real time based on the BIM model; the positioning and tracking module combines the BIM model and indoor positioning technology to achieve accurate positioning and trajectory backtracking of personnel and equipment; the intelligent operation and maintenance decision module predicts abnormal situations through AI algorithms and puts forward operation and maintenance suggestions; the path planning and navigation module generates the optimal operation and maintenance path using random geometric graphs and graph embedding algorithms; and the control feedback module executes and monitors operation and maintenance instructions. The present invention constructs an efficient and intelligent smart building operation and maintenance system through the deep integration of BIM technology and indoor positioning technology, improves the intelligence level and operation and maintenance efficiency of building operation and maintenance management, and improves user experience.

Description

Technical Field

[0001] The present invention relates to the field of smart building operation and maintenance technology, and in particular to a smart building operation and maintenance system and method based on BIM and indoor positioning technology. Background Art

[0002] With the continuous development of modern building technology, the complexity and refined management requirements of modern building operation and maintenance are gradually increasing. At the same time, the application of building information modeling (BIM) in building design, construction and operation and maintenance management is becoming more and more extensive. BIM technology covers the geometric information, physical information and functional information of the building by establishing a digital model of the building, so that each stage of the building life cycle (such as design, construction, operation and maintenance) can achieve higher efficiency and accuracy through information sharing and collaborative work. At present, there are still the following problems: traditional building operation and maintenance systems often rely on manual experience for decision-making, lack effective data analysis and intelligent algorithm support, and cannot predict potential abnormal situations in advance, which easily leads to delays and uncontrollability of operation and maintenance work, increasing operation and maintenance costs and risks; existing technologies are difficult to generate the optimal operation and maintenance path in a complex building environment, and the navigation system lacks effective integration with positioning technology, resulting in low execution efficiency of operation and maintenance personnel or automation equipment, increasing the time and cost of operation and maintenance. Summary of the invention

[0003] To solve the above problems, the present invention provides a smart building operation and maintenance system and method based on BIM and indoor positioning technology, which solves the problem of how to improve the abnormal prediction ability and path planning efficiency of the building operation and maintenance system by integrating BIM technology, intelligent algorithms and precise positioning technology, thereby improving the intelligence level and operation and maintenance efficiency of building operation and maintenance management and improving user experience.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] On the one hand, a smart building operation and maintenance system based on BIM and indoor positioning technology includes a building information collection module, a positioning tracking module, an intelligent operation and maintenance decision module, a path planning and navigation module, and a control feedback module which are sequentially connected in communication;

[0006] The building information collection module is used to collect building information in real time based on the BIM model;

[0007] The positioning and tracking module is used to obtain and record the location information of personnel and equipment in real time through indoor positioning technology, realize accurate positioning and historical trajectory backtracking in space in combination with BIM model, and realize high-precision positioning and tracking in non-static environment based on graph neural network;

[0008] The intelligent operation and maintenance decision module is used to predict potential abnormal situations in the building operation and maintenance process based on the collected building information and positioning data, combined with artificial intelligence algorithms, and generate corresponding operation and maintenance strategy recommendations;

[0009] The path planning and navigation module is used to build a path planning model based on the BIM model, positioning information and operation and maintenance strategy recommendations, combined with random geometric graphs and graph embedding algorithms, to generate the optimal operation and maintenance path planning, and guide the operation and maintenance personnel or automation equipment through indoor positioning technology;

[0010] The control feedback module is used to send operation and maintenance instructions to the corresponding equipment of building operation and maintenance, realize real-time control and adjustment, and monitor and feedback the execution results.

[0011] Furthermore, the building information includes building structure data, equipment installation information, facility operation status, environmental monitoring data, energy consumption data and historical maintenance records.

[0012] Furthermore, the operation process of the positioning tracking module includes the following steps:

[0013] Use indoor positioning technology to initially locate people and equipment in the building, and use multi-sensor fusion technology to calibrate the positioning data;

[0014] Continuously collect real-time location information of personnel and equipment, dynamically update the positioning data according to environmental changes, and analyze and process the positioning data through graph neural network;

[0015] Record the target's movement trajectory in real time and store historical location information in the BIM model, provide a trajectory backtracking function based on the time dimension, and support the analysis and optimization of the historical operation and maintenance process;

[0016] In non-static environments, indoor positioning technology and graph neural network algorithms are combined to locate and track moving targets with high precision.

[0017] The processed positioning information is transmitted to the intelligent operation and maintenance decision module and the path planning and navigation module.

[0018] Furthermore, the indoor positioning technology specifically includes one or more of ultra-wideband technology, Bluetooth beacon technology, Wi-Fi positioning technology, visual positioning technology and inertial navigation technology.

[0019] Furthermore, the operation process of the intelligent operation and maintenance decision module includes the following steps:

[0020] Acquire raw data from the building information acquisition module and the positioning tracking module, and perform cleaning, denoising and anomaly correction;

[0021] Based on the preprocessed data, the random forest nested random effect model is used to build the operation and maintenance prediction model;

[0022] Based on the operation and maintenance prediction model, real-time data monitoring is carried out to identify and locate abnormal situations, trigger the early warning mechanism, and transmit the early warning information to relevant operation and maintenance personnel;

[0023] Automatically generate and optimize operation and maintenance strategies based on detected anomalies.

[0024] Furthermore, the formula of the operation and maintenance prediction model is as follows:

[0025] ;

[0026] in, It represents the operation and maintenance prediction indicators at time t, including equipment failure rate, energy consumption prediction or other building operation status; Indicates the baseline level or initial operation and maintenance status; represents the value of building data provided by the i-th building information collection module at time t; Represents the i-th input data provided by the building information acquisition module The relevant regression coefficients; Indicates that the value of the positioning data provided by the j-th positioning and tracking module at time t includes personnel position, equipment position, motion trajectory and dynamic environment data; Represents the jth input data provided by the positioning tracking module The relevant regression coefficients; represents the random error term; n and m represent the number of data variables used in the building information acquisition module and the positioning tracking module, respectively.

[0027] Furthermore, the operation process of the path planning and navigation module includes the following steps:

[0028] Acquire the building information, and construct a topological map including the building space information based on the BIM model;

[0029] Based on the topological graph, a path planning model is constructed using a random geometric graph and a graph embedding algorithm to generate an optimal path planning solution;

[0030] Based on the generated path planning solution, the operation and maintenance path is updated in real time, and real-time navigation guidance is provided to the operation and maintenance personnel or automation equipment through indoor positioning technology;

[0031] Continuously monitor the path execution status, dynamically adjust the path planning according to the actual situation, and feed back the path planning and execution results to the control feedback module.

[0032] Furthermore, the formula of the path planning model is as follows:

[0033] ;

[0034] in, Represents the optimal path planning solution generated at time t; represents the path vector to be optimized in path planning; K represents the number of influencing factors considered in the path planning model, including path length, energy consumption, time, risk, and building facility operation status; represents the weight coefficient associated with the kth influencing factor; represents the ideal path or reference path sequence associated with the kth influencing factor; Indicates the path The objective function value associated with the kth influencing factor; Represents the regularization coefficient, which is used to balance multiple objectives in path planning; Indicates the path The regularization term is used to constrain the complexity of the path.

[0035] On the other hand, a smart building operation and maintenance method based on BIM and indoor positioning technology includes the following steps:

[0036] Collect building information in real time based on BIM model;

[0037] Use indoor positioning technology to obtain and record the location information of people and equipment in real time, combine with BIM models to achieve precise positioning in space and historical trajectory backtracking, and use graph neural networks to achieve high-precision positioning and tracking in non-static environments;

[0038] Based on the collected building information and positioning data, combined with artificial intelligence algorithms, potential abnormal situations in the building operation and maintenance process are predicted, and corresponding operation and maintenance strategy recommendations are generated;

[0039] Based on BIM model, positioning information and operation and maintenance strategy recommendations, a path planning model is built by combining random geometric graphs and graph embedding algorithms to generate the optimal operation and maintenance path planning, and guide operation and maintenance personnel or automated equipment through indoor positioning technology;

[0040] Send operation and maintenance instructions to the corresponding equipment for building operation and maintenance to achieve real-time control and adjustment, and monitor and provide feedback on the execution results.

[0041] The beneficial effects of the present invention are:

[0042] In the present invention, through the building information acquisition module, the system can obtain and update the building information based on the BIM model in real time, ensure the accuracy and timeliness of the data in the operation and maintenance process, and improve the efficiency and accuracy of building management. The positioning and tracking module combines indoor positioning technology and graph neural network to achieve high-precision positioning and tracking in non-static environments, and can record and trace back the location information of personnel and equipment in real time, improving the safety and traceability of operation and maintenance management. The intelligent operation and maintenance decision module uses artificial intelligence algorithms to analyze the collected building information and positioning data, which can predict potential abnormal situations and provide corresponding operation and maintenance strategy suggestions, significantly improving the scientificity and foresight of operation and maintenance decisions, and reducing the losses caused by sudden problems. The path planning and navigation module is based on the BIM model, positioning information and intelligent operation and maintenance suggestions, and generates the optimal operation and maintenance path planning through random geometric graphs and graph embedding algorithms, which can effectively guide operation and maintenance personnel or automation equipment, reduce operation and maintenance costs, and improve execution efficiency. The control feedback module can dynamically adjust the operation and maintenance strategy by sending operation and maintenance instructions in real time and monitoring the execution results, ensuring the efficient execution and closed-loop management of building operation and maintenance work, and further improving the reliability and response speed of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a module schematic diagram of a smart building operation and maintenance system based on BIM and indoor positioning technology of the present invention.

[0044] Figure 2 It is a flowchart of the operation process of the intelligent operation and maintenance decision module provided by one embodiment of the present invention.

[0045] Figure 3 It is a flow chart of a smart building operation and maintenance method based on BIM and indoor positioning technology of the present invention. DETAILED DESCRIPTION

[0046] See also Figure 1-3 As shown, the present invention relates to a smart building operation and maintenance system and method based on BIM and indoor positioning technology.

[0047] Example 1

[0048] A smart building operation and maintenance system based on BIM and indoor positioning technology, comprising a building information collection module, a positioning tracking module, an intelligent operation and maintenance decision module, a path planning and navigation module and a control feedback module which are sequentially connected in communication;

[0049] The building information collection module is used to collect building information in real time based on the BIM model; the building information includes building structure data, equipment installation information, facility operation status, environmental monitoring data, energy consumption data and historical maintenance records.

[0050] It should be noted that when the system is initialized, the building information is first digitally modeled by importing the building structure, equipment installation, facility location and other data in the building information model (BIM). The BIM model will be updated regularly or in real time to reflect the actual building structure and equipment changes to ensure the accuracy of the model data.

[0051] A large number of sensors are deployed in buildings, covering environmental monitoring, equipment status monitoring, energy consumption metering and other aspects. For example, temperature and humidity sensors and differential pressure sensors are installed in the air-conditioning system, and sensors such as electric meters and water meters are integrated in the energy management system. These sensors are connected to the building information acquisition module through wired or wireless networks to upload data in real time. The system not only collects static building structure data (such as wall material and space layout), but also collects dynamic data in real time, such as ambient temperature, humidity, air quality, energy consumption (electricity, water, gas) and equipment operation status (switch status, operation mode, fault alarm, etc.). The frequency of dynamic data collection can be set according to specific needs, for example, environmental monitoring data is collected once a minute, and equipment status data is collected once a second. The building information acquisition module is also connected to the historical maintenance record database in the building management system to obtain the historical operation data and maintenance logs of the equipment. Through the integration and analysis of historical data, the system can identify common failure modes and maintenance cycles of equipment, and provide data support for predictive maintenance.

[0052] The positioning and tracking module is used to obtain and record the location information of personnel and equipment in real time through indoor positioning technology, realize accurate positioning and historical trajectory backtracking in space in combination with BIM model, and realize high-precision positioning and tracking in non-static environment based on graph neural network;

[0053] The operation process of the positioning and tracking module includes the following steps:

[0054] Initially locate people and equipment in the building through indoor positioning technology, and calibrate the positioning data using multi-sensor fusion technology; the indoor positioning technology specifically includes one or more of ultra-wideband technology, Bluetooth beacon technology, Wi-Fi positioning technology, visual positioning technology and inertial navigation technology;

[0055] It should be noted that indoor positioning technologies specifically include the following:

[0056] Ultra-wideband (UWB) technology: Utilizes the high bandwidth and high temporal resolution of ultra-wideband signals to achieve centimeter-level high-precision positioning. It is suitable for scenarios that require extremely high positioning accuracy, such as positioning of precision equipment and precise tracking of personnel.

[0057] Bluetooth beacon technology: a large number of Bluetooth beacons are distributed in the building, and the RSSI (received signal strength indication) value is used for positioning. It is suitable for positioning personnel and equipment in large-scale and multi-story buildings.

[0058] Wi-Fi positioning technology: Utilizes the existing Wi-Fi network in the building and performs positioning through signal triangulation or fingerprint recognition, which is suitable for scenarios with existing Wi-Fi coverage.

[0059] Visual positioning technology: Through the installation of cameras and computer vision algorithms, key areas can be visually identified and positioned. Suitable for environments that require visually assisted positioning, such as storage areas or important channels.

[0060] Inertial navigation technology: uses the built-in accelerometer, gyroscope and other sensors of the device, combined with the Kalman filter algorithm, to calculate the movement trajectory of the device. It is usually used in combination with other technologies to enhance the continuity and stability of positioning.

[0061] Continuously collect real-time location information of personnel and equipment, dynamically update the positioning data according to environmental changes, and analyze and process the positioning data through graph neural network;

[0062] It should be noted that the positioning data is analyzed and processed by the graph neural network (GNN). The GNN model can capture the complex spatial relationship of the positioning data and represent the locations of different devices and personnel and the relationship between them through the nodes and edges of the graph. When processing these complex positioning data, GNN can adaptively correct and predict positioning errors, thereby improving the positioning accuracy in non-static environments.

[0063] Record the target's movement trajectory in real time and store historical location information in the BIM model, provide a trajectory backtracking function based on the time dimension, and support the analysis and optimization of the historical operation and maintenance process;

[0064] In non-static environments, indoor positioning technology and graph neural network algorithms are combined to locate and track moving targets with high precision.

[0065] Specifically, in the positioning and tracking module, GNN is used to construct a graph structure that reflects the complex environment inside the building. The nodes of the graph represent the positions of people, equipment, sensors, or obstacles, and the edges represent the spatial relationship or signal strength relationship between these nodes. GNN generates a model that can predict the target location by learning the characteristics of these nodes and edges. The GNN model has adaptive capabilities and can dynamically update the graph structure and weight distribution based on real-time environmental data. For example, in a highly dynamic environment (such as a corridor where people move frequently), GNN can automatically adjust the connectivity and weights between nodes, give priority to the positioning data of key areas, and reduce the data processing burden of non-key areas. The module corrects the errors of sensor data through GNN, especially in an environment with large signal interference. GNN identifies and corrects abnormal data points by analyzing historical data to improve positioning accuracy. In addition, GNN will continuously optimize its own model and adapt to different environmental conditions through online learning mechanisms, such as the activity patterns of people on different floors and in different time periods.

[0066] The processed positioning information is transmitted to the intelligent operation and maintenance decision module and the path planning and navigation module.

[0067] It should be noted that the module transmits the processed positioning information to the intelligent operation and maintenance decision module and the path planning and navigation module through a standard data interface (such as API or MQTT protocol). These interfaces support high-frequency data transmission and low-latency communication to ensure the real-time response of the system. The positioning information is encrypted during the transmission process to ensure the security and integrity of the data in the communication link to prevent malicious theft or tampering. The precise location information provided by the positioning and tracking module supports the prediction and decision-making process of the intelligent operation and maintenance decision module, such as personnel scheduling and equipment maintenance plan formulation. The positioning data is converted into specific action instructions through the path planning and navigation module to guide the operation and maintenance personnel or robots to perform operation and maintenance tasks in the building to ensure the optimality and efficiency of the path.

[0068] The intelligent operation and maintenance decision module is used to predict potential abnormal situations in the building operation and maintenance process based on the collected building information and positioning data, combined with artificial intelligence algorithms, and generate corresponding operation and maintenance strategy recommendations;

[0069] The operation process of the intelligent operation and maintenance decision module includes the following steps:

[0070] Acquire raw data from the building information acquisition module and the positioning tracking module, and perform cleaning, denoising and anomaly correction;

[0071] Based on the preprocessed data, the random forest nested random effect model is used to build the operation and maintenance prediction model;

[0072] Specifically, the intelligent operation and maintenance decision module uses the Random Forest model to model the preprocessed data. Random Forest is an integrated model composed of multiple decision trees, each of which is generated through different random samples and feature subsets. The purpose of this is to improve the generalization ability and prediction accuracy of the model and prevent overfitting of a single model. When constructing the random forest model, the system selects important features according to the specific needs of building operation and maintenance. These features may include equipment operating parameters (such as temperature, pressure, current, etc.), environmental monitoring data, historical fault records, personnel locations, and equipment movement trajectories. Feature selection is performed through feature importance scoring to ensure that the model can capture key variables for prediction. The random effect model is nested in the random forest model to handle the random effects and multi-level structures in building operation and maintenance. For example, there may be operating differences between different devices (equipment effects), and the performance of the same device in different time periods may also be different (time effects). The nested random effect model can capture these differences and provide more accurate prediction results. The system trains the random forest nested random effect model through supervised learning. The training data set consists of historical operation and maintenance data and positioning data. The system uses cross-validation technology to optimize model parameters to ensure the model's predictive performance in different scenarios.

[0073] Based on the operation and maintenance prediction model, real-time data monitoring is carried out to identify and locate abnormal situations, trigger the early warning mechanism, and transmit the early warning information to relevant operation and maintenance personnel;

[0074] Specifically, the module uses the operation and maintenance prediction model to predict and analyze the data in real time. The model can detect abnormal operation of equipment, abnormal environmental parameters (such as temperature exceeding the standard, deterioration of air quality, etc.), abnormal personnel behavior (such as intrusion into unauthorized areas), etc. The system also supports multivariate anomaly detection, identifying potential problems by analyzing the joint change pattern of multiple parameters. Depending on the severity of the abnormal situation, the system will trigger different levels of warnings. The warning levels include low, medium and high, corresponding to potential problems, serious problems and emergencies respectively. Each warning level will trigger different response measures. Once an abnormal situation is detected, the system will promptly transmit the warning information to the relevant operation and maintenance personnel through multiple channels (such as SMS, email, mobile phone push). The warning information includes a description of the abnormal situation, the time of occurrence, the affected equipment or area, and the recommended treatment measures.

[0075] Automatically generate and optimize operation and maintenance strategies based on detected anomalies.

[0076] Specifically, the intelligent operation and maintenance decision module automatically generates corresponding operation and maintenance strategies based on the type of abnormality detected. Strategy generation is based on a predefined rule base and operation and maintenance knowledge base. For example: If a device failure is detected, the system will generate a detailed fault diagnosis report and recommend specific repair steps, such as replacing parts, adjusting parameters, or restarting the system. When abnormal environmental parameters are detected (such as excessive temperature), the system will recommend adjusting the HVAC system, optimizing air circulation, or turning off the power supply to specific areas to reduce the load. For behaviors such as intrusion into unauthorized areas, the system will generate security strategies, such as notifying security personnel, activating alarms, or locking the access control system of relevant areas.

[0077] Furthermore, the formula of the operation and maintenance prediction model is as follows:

[0078] ;

[0079] in, It represents the operation and maintenance prediction indicators at time t, including equipment failure rate, energy consumption prediction or other building operation status; Indicates the baseline level or initial operation and maintenance status; represents the value of building data provided by the i-th building information collection module at time t; Represents the i-th input data provided by the building information acquisition module The relevant regression coefficients; Indicates that the value of the positioning data provided by the j-th positioning and tracking module at time t includes personnel location, equipment location, motion trajectory and dynamic environment data; Represents the jth input data provided by the positioning tracking module The relevant regression coefficients; represents the random error term; n and m represent the number of data variables used in the building information acquisition module and the positioning tracking module, respectively.

[0080] The path planning and navigation module is used to build a path planning model based on the BIM model, positioning information and operation and maintenance strategy recommendations, combined with random geometric graphs and graph embedding algorithms, to generate the optimal operation and maintenance path planning, and guide the operation and maintenance personnel or automation equipment through indoor positioning technology;

[0081] The operation process of the path planning and navigation module includes the following steps:

[0082] Acquire the building information, and construct a topological map including the building space information based on the BIM model;

[0083] Specifically, the path planning and navigation module first obtains the detailed spatial information of the building from the BIM model. The BIM model contains the three-dimensional structure of the building, room layout, floor connections (such as elevators, stairs, passages), equipment locations, fixed obstacles (such as walls, columns), etc. This information is extracted from the BIM database through the API interface and converted into the format required for path planning. The extracted building information is standardized and converted into topological structure data that can be used for path planning. This includes space partitions (such as rooms, corridors, public areas), nodes (such as doors, corners, stairways), edges (such as passages, floor connections), etc. Based on the spatial information of the building, the system constructs a topological graph containing all key locations. The nodes of the graph represent specific locations in the building (such as equipment, room entrances, stairways, etc.), and the edges represent the accessible paths between these locations. The attributes of the edges include path length, pass width, obstacle information, etc. The topological graph reflects the multi-level structure of the building, such as the connections between floors (elevators, stairs), and the access relationship between different areas. The system uses a hierarchical approach to represent complex spatial structures to ensure that path planning can adapt to the specific requirements of different floors and areas.

[0084] Based on the topological graph, a path planning model is constructed using a random geometric graph and a graph embedding algorithm to generate an optimal path planning solution;

[0085] Specifically, based on the topological graph, the path planning module uses random geometric graph (RGG) technology to simulate the possible path network inside the building. RGG generates nodes through random sampling methods and connects these nodes according to certain distance criteria to form a network graph covering the entire building space. The system evaluates the accessibility between various areas inside the building by calculating the connectivity of the nodes in RGG. For areas with low connectivity, the system automatically increases the node density or optimizes the path to ensure that all key locations can be effectively covered. The path planning module further transforms the path planning problem in high-dimensional space into an optimization problem in low-dimensional space through a graph embedding algorithm. The graph embedding algorithm uses machine learning technology to embed the nodes in the topological graph into a low-dimensional vector space, so that in this space, the distance between nodes can effectively reflect the travel cost of the actual path (such as distance, time, and energy consumption). In the graph embedding space, the system calculates the shortest path or optimal path between each node, considering factors including path length, obstacles, and the priority of the current operation and maintenance task. By solving the shortest path problem in the embedded space, the system generates the optimal path planning solution.

[0086] Based on the generated path planning solution, the operation and maintenance path is updated in real time, and real-time navigation guidance is provided to the operation and maintenance personnel or automation equipment through indoor positioning technology;

[0087] Specifically, the path planning module monitors the environmental changes and the execution of operation and maintenance tasks in the building in real time based on the generated path planning scheme. If dynamic changes on the path are detected (such as temporary obstacles, changes in equipment status, changes in personnel density, etc.), the system will immediately recalculate the path planning and adjust the original operation and maintenance route. By integrating with indoor positioning technologies (such as UWB, Bluetooth, and Wi-Fi), the system can obtain the current location of the operation and maintenance personnel or automated equipment in real time, and provide accurate navigation guidance in combination with the path planning scheme. Positioning data is continuously updated to ensure the real-time and accuracy of navigation guidance. Operation and maintenance personnel can receive real-time navigation guidance through mobile devices (such as smartphones and tablets). The system will provide path navigation information in various forms such as graphical interface, voice prompts, and text notifications. For automated equipment (such as robots), the system directly sends navigation instructions to the control system of the equipment to ensure that the equipment moves according to the planned path. According to the roles, task priorities, and path preferences (such as the shortest path, avoiding crowded areas, etc.) of different operation and maintenance personnel, the system provides personalized path suggestions. During the navigation process, the system adjusts the navigation guidance in real time to prevent operation and maintenance personnel or equipment from entering congested or dangerous areas.

[0088] Continuously monitor the path execution status, dynamically adjust the path planning according to the actual situation, and feed back the path planning and execution results to the control feedback module.

[0089] Specifically, the system uses indoor positioning technology to monitor the path execution of operation and maintenance personnel or equipment in real time. Path tracking includes recording the actual moving path, deviation analysis from the planned path, execution speed and status monitoring of the equipment, etc. If an abnormal situation is detected during the path execution process (such as path deviation, equipment stagnation, sudden obstacles, etc.), the system will immediately trigger the emergency response mechanism. The system can automatically adjust the path or issue reminders to the operation and maintenance personnel to guide them to take appropriate actions. During the path execution process, the system will dynamically adjust the path planning according to the actual situation. For example, when the system detects a new obstacle on the predetermined path, it will calculate the alternative path in real time and issue an update instruction to the operation and maintenance personnel or equipment as soon as possible. All path execution and dynamic adjustment records will be fed back to the control feedback module in real time. The control feedback module uses this data for subsequent operation and maintenance decisions and strategy optimization to further improve the intelligent level of building operation and maintenance.

[0090] Furthermore, the formula of the path planning model is as follows:

[0091] ;

[0092] in, It represents the optimal path planning solution generated at time t, which is used to guide the action path of operation and maintenance personnel or automation equipment in the building space; represents the path vector to be optimized in path planning, specifically including the path node sequence of operation and maintenance personnel or equipment in the building space, which is generated based on the BIM model and real-time positioning data; K represents the number of influencing factors considered in the path planning model, including path length, energy consumption, time, risk, and building facility operation status; Represents the weight coefficient associated with the kth influencing factor. The weight coefficient is set according to actual operation and maintenance requirements or strategy recommendations and is used to balance the impact of various factors in path planning. represents the ideal path or reference path sequence associated with the kth influencing factor, which is determined based on the BIM model and historical data of the building; Indicates the path The objective function value associated with the kth influencing factor; Represents the regularization coefficient, which is used to balance multiple objectives in path planning; Indicates the path The regularization term is used to constrain the complexity of the path.

[0093] The control feedback module is used to send operation and maintenance instructions to the corresponding equipment of building operation and maintenance, realize real-time control and adjustment, and monitor and feedback the execution results.

[0094] Specifically, after receiving the operation and maintenance instructions from the intelligent operation and maintenance decision module, the control feedback module will parse and optimize these instructions. The parsing process includes converting high-level operation and maintenance strategies into specific equipment control instructions, such as starting equipment and adjusting operating parameters. The optimization process is to sort and merge instructions based on the current equipment status, system load and execution priority to maximize execution efficiency and reduce system resource consumption. In large-scale smart buildings, the control feedback module usually adopts a distributed architecture to send instructions to sub-controllers located on different floors or areas. These sub-controllers can be PLCs (programmable logic controllers), DDCs (direct digital controllers) or other intelligent control devices, which are responsible for specific execution tasks, such as air conditioning temperature adjustment, lighting control, access control management, etc. After receiving the instructions, the sub-controllers will operate according to the preset logic and feedback the execution status to the central control module in real time.

[0095] The control feedback module continuously monitors the operating status of the equipment through an integrated sensor network. For example, the temperature sensor and wind speed sensor of the air conditioning system, the light sensor of the lighting system, the camera and access control sensor of the security system, etc. The data collected by these sensors will be uploaded to the control feedback module in real time. The system will analyze the collected data in real time and detect whether the equipment is operating normally through preset thresholds and rules. If the equipment operation deviates from the set value or an abnormality occurs (such as excessive temperature, abnormal increase in energy consumption, etc.), the system will immediately issue an alarm and try to automatically adjust the equipment operating parameters to restore normal status. If a more serious fault is detected, the control feedback module will start the fault diagnosis process. The system quickly locates the fault based on the historical operation data, sensor data and diagnostic model of the equipment, and tries to repair the fault by adjusting the equipment parameters or restarting the equipment. If it cannot be repaired automatically, the system will notify the operation and maintenance personnel to take manual intervention.

[0096] The control feedback module dynamically adjusts the control strategy based on multivariable factors in the building, such as real-time load, occupancy distribution, external weather conditions, etc. For example, during the hot summer months, the system automatically adjusts the operation mode of the air conditioning system to prioritize cooling needs in crowded areas while optimizing energy consumption. The module uses adaptive control algorithms to adjust the control parameters of the equipment in real time. For example, based on historical data and real-time monitoring, the air conditioning system can dynamically adjust the temperature set point to balance comfort and energy consumption. The lighting system can automatically adjust the light brightness and switch status based on natural light changes and room usage.

[0097] Example 2

[0098] A smart building operation and maintenance method based on BIM and indoor positioning technology, comprising the following steps:

[0099] Collect building information in real time based on BIM model;

[0100] Use indoor positioning technology to obtain and record the location information of people and equipment in real time, combine with BIM models to achieve precise positioning in space and historical trajectory backtracking, and use graph neural networks to achieve high-precision positioning and tracking in non-static environments;

[0101] Based on the collected building information and positioning data, combined with artificial intelligence algorithms, potential abnormal situations in the building operation and maintenance process are predicted, and corresponding operation and maintenance strategy recommendations are generated;

[0102] Based on BIM model, positioning information and operation and maintenance strategy recommendations, a path planning model is built by combining random geometric graphs and graph embedding algorithms to generate the optimal operation and maintenance path planning, and guide operation and maintenance personnel or automated equipment through indoor positioning technology;

[0103] Send operation and maintenance instructions to the corresponding equipment for building operation and maintenance to achieve real-time control and adjustment, and monitor and provide feedback on the execution results.

[0104] In this embodiment, a smart building operation and maintenance method based on BIM and indoor positioning technology is applied to a smart building operation and maintenance system based on BIM and indoor positioning technology described in Example 1, which will not be repeated here.

[0105] In summary, the present invention adopts a variety of indoor positioning technologies (such as ultra-wideband, Bluetooth beacons, Wi-Fi positioning, visual positioning, etc.) combined with graph neural networks (GNN) to achieve high-precision positioning and real-time tracking of people and equipment in buildings. Especially in non-static environments, GNN can significantly improve positioning accuracy and ensure the accuracy and real-time nature of positioning data through adaptive positioning error correction and prediction. Through the combination of BIM model and building information acquisition module, the system can realize all-round information collection of buildings, including static structural data and dynamic environmental data (such as equipment operating status, environmental parameters, etc.). These data can not only reflect the current operating status of the building, but also provide strong support for predictive maintenance of the building through historical data analysis.

[0106] Based on the collected building information and positioning data, the system can accurately predict abnormal situations that may occur during the operation and maintenance process through the random forest nested random effect model, and generate intelligent operation and maintenance strategy recommendations. Through real-time monitoring and multivariate anomaly detection, the system can timely identify and locate potential problems, trigger early warning mechanisms, and effectively prevent the impact of sudden failures on building operation and maintenance. The path planning and navigation module combines random geometric graphs with graph embedding algorithms to generate optimal operation and maintenance path planning based on BIM models and real-time positioning data. The system uses indoor positioning technology to provide real-time guidance to operation and maintenance personnel or automated equipment to ensure the optimality of the path and the efficient execution of operation and maintenance tasks. The dynamic adjustment function can cope with real-time changes in the internal environment of the building and ensure the accuracy and timeliness of navigation guidance.

[0107] The control feedback module in the present invention can dynamically adjust the operating parameters of the equipment by real-time monitoring of the equipment operating status and environmental parameters to ensure the stable operation of the building system. Through distributed control and adaptive control algorithms, the system can effectively respond to complex and changeable operation and maintenance needs and maximize the execution efficiency and response speed of the system. At the same time, the real-time feedback mechanism can record and analyze the operation of the equipment to provide data support for subsequent operation and maintenance decisions and strategy optimization. The system uses encryption processing during data transmission to ensure the security and integrity of positioning information and control instructions in the communication link. In addition, the system has powerful fault diagnosis and repair capabilities. Through automatic adjustment or manual intervention, it can effectively reduce the occurrence of equipment failures and improve the overall safety and stability of the system.

[0108] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A smart building operation and maintenance system based on BIM and indoor positioning technology, characterized in that: It includes a building information collection module, a positioning tracking module, an intelligent operation and maintenance decision module, a path planning and navigation module, and a control feedback module which are connected in sequence; The building information collection module is used to collect building information in real time based on the BIM model; The positioning and tracking module is used to obtain and record the location information of personnel and equipment in real time through indoor positioning technology, realize accurate positioning and historical trajectory backtracking in space in combination with BIM model, and realize high-precision positioning and tracking in non-static environment based on graph neural network; The intelligent operation and maintenance decision module is used to predict potential abnormal situations in the building operation and maintenance process based on the collected building information and positioning data, combined with artificial intelligence algorithms, and generate corresponding operation and maintenance strategy recommendations; The path planning and navigation module is used to build a path planning model based on the BIM model, positioning information and operation and maintenance strategy recommendations, combined with random geometric graphs and graph embedding algorithms, to generate the optimal operation and maintenance path planning, and guide the operation and maintenance personnel or automation equipment through indoor positioning technology; The control feedback module is used to send operation and maintenance instructions to the corresponding equipment of building operation and maintenance, realize real-time control and adjustment, and monitor and feedback the execution results; The operation process of the intelligent operation and maintenance decision module includes the following steps: Acquire raw data from the building information acquisition module and the positioning tracking module, and perform cleaning, denoising and anomaly correction; Based on the preprocessed data, the random forest nested random effect model is used to build the operation and maintenance prediction model; Based on the operation and maintenance prediction model, real-time data monitoring is carried out to identify and locate abnormal situations, trigger the early warning mechanism, and transmit the early warning information to relevant operation and maintenance personnel; Automatically generate and optimize operation and maintenance strategies based on detected anomalies; The formula of the operation and maintenance prediction model is as follows: ； in, It represents the operation and maintenance prediction indicators at time t, including equipment failure rate, energy consumption prediction or other building operation status; Indicates the baseline level or initial operation and maintenance status; represents the value of building data provided by the i-th building information collection module at time t; Represents the i-th input data provided by the building information acquisition module The relevant regression coefficients; Indicates that the value of the positioning data provided by the j-th positioning and tracking module at time t includes personnel position, equipment position, motion trajectory and dynamic environment data; Represents the jth input data provided by the positioning tracking module The relevant regression coefficients; represents the random error term; n and m represent the number of data variables used in the building information acquisition module and the positioning tracking module, respectively.

2. The intelligent building operation and maintenance system based on BIM and indoor positioning technology according to claim 1 is characterized in that: The building information includes building structure data, equipment installation information, facility operation status, environmental monitoring data, energy consumption data and historical maintenance records.

3. The intelligent building operation and maintenance system based on BIM and indoor positioning technology according to claim 1 is characterized in that: The operation process of the positioning tracking module includes the following steps: Use indoor positioning technology to initially locate people and equipment in the building, and use multi-sensor fusion technology to calibrate the positioning data; Continuously collect real-time location information of personnel and equipment, dynamically update the positioning data according to environmental changes, and analyze and process the positioning data through graph neural network; Record the target's movement trajectory in real time and store historical location information in the BIM model, provide a trajectory backtracking function based on the time dimension, and support the analysis and optimization of the historical operation and maintenance process; In non-static environments, indoor positioning technology and graph neural network algorithms are combined to locate and track moving targets with high precision. The processed positioning information is transmitted to the intelligent operation and maintenance decision module and the path planning and navigation module.

4. The intelligent building operation and maintenance system based on BIM and indoor positioning technology according to claim 3 is characterized in that: The indoor positioning technology specifically includes one or more of ultra-wideband technology, Bluetooth beacon technology, Wi-Fi positioning technology, visual positioning technology and inertial navigation technology.

5. The intelligent building operation and maintenance system based on BIM and indoor positioning technology according to claim 1 is characterized in that: The operation process of the path planning and navigation module includes the following steps: Acquire the building information, and construct a topological map including the building space information based on the BIM model; Based on the topological graph, a path planning model is constructed using a random geometric graph and a graph embedding algorithm to generate an optimal path planning solution; Based on the generated path planning solution, the operation and maintenance path is updated in real time, and real-time navigation guidance is provided to the operation and maintenance personnel or automation equipment through indoor positioning technology; Continuously monitor the path execution status, dynamically adjust the path planning according to the actual situation, and feed back the path planning and execution results to the control feedback module.

6. The intelligent building operation and maintenance system based on BIM and indoor positioning technology according to claim 1 is characterized in that: The formula of the path planning model is as follows: ； in, Represents the optimal path planning solution generated at time t; represents the path vector to be optimized in path planning; K represents the number of influencing factors considered in the path planning model, including path length, energy consumption, time, risk, and building facility operation status; represents the weight coefficient associated with the kth influencing factor; represents the ideal path or reference path sequence associated with the kth influencing factor; Indicates the path The objective function value associated with the kth influencing factor; Represents the regularization coefficient, which is used to balance multiple objectives in path planning; Indicates the path The regularization term is used to constrain the complexity of the path.

7. A smart building operation and maintenance method based on BIM and indoor positioning technology, characterized in that: The system is applied to a smart building operation and maintenance system based on BIM and indoor positioning technology as described in any one of claims 1 to 6, comprising the following steps: Collect building information in real time based on BIM model; Use indoor positioning technology to obtain and record the location information of people and equipment in real time, combine with BIM models to achieve precise positioning in space and historical trajectory backtracking, and use graph neural networks to achieve high-precision positioning and tracking in non-static environments; Based on the collected building information and positioning data, combined with artificial intelligence algorithms, potential abnormal situations in the building operation and maintenance process are predicted, and corresponding operation and maintenance strategy recommendations are generated; Based on BIM model, positioning information and operation and maintenance strategy recommendations, a path planning model is built by combining random geometric graphs and graph embedding algorithms to generate the optimal operation and maintenance path planning, and guide operation and maintenance personnel or automated equipment through indoor positioning technology; Send operation and maintenance instructions to the corresponding equipment for building operation and maintenance to achieve real-time control and adjustment, and monitor and provide feedback on the execution results.

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