A smart building operation and maintenance management system and method based on BIM platform

Abstract

The present invention relates to the field of smart building management technology, and in particular to a smart building operation and maintenance management system and method based on a BIM platform. The method comprises the following steps: constructing a device connection topology network according to the acquired target BIM building model data to generate device physical connection topology data; automatically eliminating logical conflicts in the device physical connection topology data to generate device connection topology data; performing fault impact link analysis on the device connection topology data to generate fault propagation link data; performing fault severity assessment on the fault propagation link data to generate device fault severity data; performing intelligent operation and maintenance path planning according to the equipment fault severity data to obtain intelligent device operation and maintenance path data. The present invention realizes intelligent scheduling of fault operation and maintenance tasks through refined division of operation and maintenance equipment management space and analysis of equipment fault propagation links, which significantly improves operation and maintenance efficiency and the accuracy of resource allocation.

Description

Technical Field

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

[0002] As a building form that integrates advanced technology and management concepts, smart buildings can effectively improve the safety, comfort, energy saving and sustainability of buildings, and have become an important trend in the development of future buildings. The BIM platform can effectively represent the physical, functional and spatial relationships of buildings by integrating multi-dimensional information such as buildings, structures, and electromechanical equipment. There are many devices in smart buildings, especially electromechanical equipment in buildings. They are usually distributed in different spatial areas of the building and have different functions. However, there are complex physical connections and logical relationships between them. In the actual operation and maintenance process, the BIM platform can provide three-dimensional models and their location information for the management of equipment. However, due to the complex topological relationship of equipment connection and its spatiotemporal characteristics, the physical connection relationship between equipment is often easily misidentified during the management process. The management of this relationship between equipment is crucial for the normal operation and maintenance of smart buildings. However, the current management system based on the BIM platform mostly relies on manual troubleshooting and experience judgment when dealing with equipment failures, which makes it difficult to accurately identify the connection relationship between equipment, especially in the logical relationship processing of electromechanical equipment. It is difficult to effectively identify the fault propagation link between equipment, and it is very easy to have misjudgment and omission problems, which makes it impossible to carry out intelligent fault management and operation and maintenance planning. Summary of the invention

[0003] Based on this, the present invention provides a smart building operation and maintenance management system and method based on the BIM platform to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a smart building operation and maintenance management method based on BIM platform includes the following steps:

[0005] Step S1: Acquire target BIM building model data; construct a BIM three-dimensional space index according to the target BIM building model data to obtain equipment three-dimensional space index data; perform a neighboring search for the same type of electromechanical equipment according to the equipment three-dimensional space index data, and construct an equipment connection topology network to generate equipment physical connection topology data;

[0006] Step S2: annotating the device node function semantics of the device physical connection topology data to generate device function node semantic data; automatically eliminating logical conflicts of the device physical connection topology data using the device function node semantic data to generate device connection topology data;

[0007] Step S3: performing real-time device status pattern recognition based on the device connection topology data to obtain device operation status pattern data; performing device fault status detection based on the device operation status pattern data to generate device fault status data; using the device fault status data to perform fault impact link analysis on the device connection topology data to generate fault propagation link data;

[0008] Step S4: perform fault severity assessment on the fault propagation link data to generate equipment fault severity data; perform intelligent operation and maintenance path planning based on the equipment fault severity data to obtain intelligent equipment operation and maintenance path data.

[0009] The present invention can more accurately capture and represent the spatial distribution of equipment and its physical connection relationship by constructing a BIM three-dimensional spatial index and constructing a device connection topology network, thereby avoiding the problem of misidentification caused by the complexity of the device connection relationship in traditional manual management. By annotating the functional semantics of the device nodes and automatically eliminating logical conflicts, the generated device connection topology data is not only more accurate, but also effectively avoids the misjudgment and omission of faults caused by improper handling of logical relationships. In addition, the real-time device status pattern recognition and fault status detection functions enable the system to timely discover and locate the fault point, and further clarify the fault propagation path through the fault impact link analysis, thereby improving the accuracy and efficiency of fault diagnosis. The fault severity assessment and intelligent operation and maintenance path planning based on the fault propagation link data can not only quantify the impact range and severity of the fault, but also formulate a more scientific and reasonable operation and maintenance strategy, reduce unnecessary waste of resources, and improve the overall efficiency and quality of operation and maintenance work. Therefore, the present invention provides a smart building operation and maintenance management method based on the BIM platform. Through the automated logic conflict elimination mechanism, the physical connection and logical association of electromechanical equipment are accurately identified, which solves the problem that the traditional operation and maintenance management system is prone to misjudging the equipment connection when processing the logical relationship of electromechanical equipment, thereby improving the efficiency of troubleshooting. At the same time, combined with the operating status of the equipment and the prediction of the spatial impact range of the fault, the incidence of erroneous connection is effectively reduced, and the accuracy of fault identification is improved. Intelligent analysis of equipment failure risks and optimization planning of operation and maintenance paths based on the BIM platform significantly improve the efficiency of equipment management and maintenance, and ensure the intelligence and precision of smart building operation and maintenance.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire target BIM building model data;

[0012] Step S12: using the IFC standard protocol to identify the electromechanical equipment object of the target BIM building model data, and generating the electromechanical equipment object metadata;

[0013] Step S13: constructing a BIM three-dimensional spatial index for the metadata of the electromechanical equipment object to obtain the three-dimensional spatial index data of the equipment;

[0014] Step S14: performing a proximity search for similar-type devices on the electromechanical device object metadata through the device three-dimensional spatial index data, and performing device spatial proximity analysis according to the preset spatial range data to generate device spatial relationship data;

[0015] Step S15: identifying potential connection device pairs according to the device spatial relationship data to obtain potential connection device pair data;

[0016] Step S16: extracting the pipeline / cable path direction according to the target BIM building model data to generate pipeline / cable path data;

[0017] Step S17: Based on the pipeline / cable path data, the physical connection relationship of the potential connection device pair data is verified, and the device connection topology network is constructed to generate device physical connection topology data.

[0018] The present invention performs object recognition on electromechanical equipment in the BIM building model based on the IFC standard protocol, ensures data standardization and interoperability, enhances the system's compatibility with data from different sources, and reduces the risk of information loss and misunderstanding. Then, the BIM three-dimensional spatial index is used to perform spatial positioning of equipment and proximity search of equipment of the same type, which can achieve accurate distribution management of equipment in the building space, especially in large buildings. On the basis of spatial proximity analysis and identification of potential connected equipment pairs, the system can identify potential physical connections between devices in advance, avoid errors in the manual judgment process, and thus reduce the complexity and uncertainty in equipment connection management. At the same time, the extraction of pipeline and cable path data ensures that the actual connection between devices is based on real physical lines, rather than relying solely on spatial position inference, further improving the reliability and accuracy of connection relationship identification. Through physical connection relationship verification, the system can truly verify the connection of potential connected equipment pairs, avoiding operation and maintenance risks caused by misjudgment of connection relationships.

[0019] Preferably, step S13 comprises the following steps:

[0020] Step S131: standardizing the BIM coordinate system of the electromechanical equipment object metadata, and performing equipment coordinate positioning to generate standard equipment space coordinate data;

[0021] Step S132: identifying the operation and maintenance management space of the target BIM building model data by using the standard equipment space coordinate data to obtain the operation and maintenance equipment management space data;

[0022] Step S133: extracting device size from the metadata of the electromechanical device object to obtain device size data;

[0023] Step S134: performing regular three-dimensional geometric abstraction processing according to the device size data, and calculating the device space volume to generate device space volume data;

[0024] Step S135: Use the operation and maintenance equipment management space data to construct a spatial index for the equipment space volume data to generate equipment three-dimensional space index data.

[0025] In the present invention, the standardization of the BIM coordinate system and the positioning of the equipment coordinates eliminate the error problems caused by the inconsistency of the coordinate systems between different systems or software, so that the physical position of the equipment can be accurately identified and managed. Secondly, the extraction of equipment size data and the abstract processing of regular three-dimensional geometric bodies simplify the geometric characteristics of the equipment, reduce the occupation of computing resources by complex models, and ensure the accurate assessment of the physical space occupied by the equipment through spatial volume calculation, thereby improving the rationality of the equipment layout and space utilization inside the building. The identification of the operation and maintenance management space helps to combine the spatial information of the equipment with the actual management needs, ensure that each device can be effectively classified and managed, and improve the accuracy of space management. Finally, the spatial index is constructed using these data to optimize the speed of equipment positioning and calling, greatly reducing the time cost of equipment search and management.

[0026] Preferably, step S135 includes the following steps:

[0027] Step S1351: performing root node index range processing on the operation and maintenance equipment management space data to obtain Octree space root node range data;

[0028] Step S1352: performing spatial node division on the operation and maintenance equipment management space data based on the Octree spatial root node range data, dividing the spatial range of the root node into two equal parts along the three coordinate axes, and allocating the equipment to the corresponding subspace to obtain node division data;

[0029] Step S1353: Calculate the node space occupancy rate of the equipment space volume data using the node partitioning data to obtain the node space occupancy rate data;

[0030] Step S1354: recursively process the node partition data using the node space occupancy rate data, and construct an Octree index tree, thereby obtaining a preliminary searchable spatial index;

[0031] Step S1355: Count the number of node space devices on the preliminary searchable spatial index to generate node density distribution data;

[0032] Step S1356: using a preset device density threshold to perform device density identification on the node density distribution data, and obtaining high-density device node data and low-density device node data respectively;

[0033] Step S1357: performing R-Tree index local optimization on high-density device node data, and using device coordinates as leaf nodes for index association to obtain high-density device index optimization data;

[0034] Step S1358: generating an ordered list of device coordinates for low-density device node data, and performing one-dimensional index link optimization to generate low-density device index optimization data;

[0035] Step S1359: Optimize the preliminary searchable spatial index using the low-density device index optimization data and the high-density device index optimization data to generate device three-dimensional spatial index data.

[0036] The present invention adopts Octree structure to carry out fine division of operation and maintenance equipment management space, so that equipment can achieve more efficient distribution management in space. Through root node index range processing, a basic spatial framework is established to ensure that the spatial data of the equipment can be reasonably organized in a three-dimensional environment. The calculation of node space occupancy rate not only provides density information for equipment management, but also lays the foundation for subsequent space optimization. This node recursive processing method helps to form a queryable spatial index and improves the response speed of the query. Through the classification and identification of equipment density, the scheme realizes targeted management of high-density and low-density areas. After the high-density equipment nodes are optimized by R-Tree, the access and scheduling of equipment in the area where the equipment is concentrated are faster, which effectively reduces the management bottleneck caused by the excessive concentration of equipment. At the same time, for low-density areas, through the generation of ordered lists and one-dimensional index optimization, the query efficiency is maintained even in areas with a small number of equipment, ensuring the balance and efficiency of the overall system, effectively avoiding the problem of low efficiency of equipment query and space resource utilization caused by improper management methods in different density areas, making equipment management more accurate and efficient.

[0037] Preferably, step S17 comprises the following steps:

[0038] Step S171: identifying the connection endpoints of the pipeline / cable path data through the target BIM building model data, and extracting the connection coordinates to obtain the pipeline / cable path endpoint coordinate data;

[0039] Step S172: extracting device connection point information from the potential connection device pair data to obtain device connection point data;

[0040] Step S173: Calculate the spatial distance of the connection points according to the pipeline / cable path endpoint coordinate data and the equipment connection point data to generate connection point distance data;

[0041] Step S174: Acquire a mechatronic device connection matching rule library;

[0042] Step S175: traversing the equipment connection point data, and using the electromechanical equipment connection matching rule library and the connection point distance data to match the path connection points of the pipeline / cable path endpoint coordinate data, and generating connection point matching result data;

[0043] Step S176: verifying the connection relationship of the potential connection device pair data based on the connection point matching result data, and generating verified connection device pair data when the connection points of the potential connection device pair all match the connection points of the same path; otherwise, generating non-connection device pair data;

[0044] Step S177: searching for connection paths for the non-connected device pair data and performing indirect connection relationship analysis. If it is an indirect connection relationship, marking the non-connected device pair data as indirect device connection pair data; otherwise, marking the non-connected device pair data as unconnected device pair and feeding back to the terminal device for manual verification;

[0045] Step S178: Perform device physical connection topology processing on the verified connection device pair data, indirect device connection pair data, and pipeline / cable path data, and mark the connection direction, thereby obtaining device physical connection topology data.

[0046] The present invention identifies the connection endpoints of pipelines and cables and extracts coordinates, ensures the accuracy of connection path data, and provides clear starting and end point information for device connection. Then, the connection point information of potential connection devices is extracted to lay the foundation for subsequent connection analysis. By calculating the spatial distance between the connection points, the connectability between devices can be better evaluated. The electromechanical equipment connection matching rule library is used to make the connection point matching process more systematic and standardized. By traversing the connection point data and combining the spatial distance data, the device pairs that meet the connection rules can be quickly identified to ensure the validity of the connection relationship. For devices that fail to match directly, connection path search and indirect connection relationship analysis are implemented to identify indirectly connected devices. By topologically processing the verified connection device pair data, indirect connection pair data, and pipeline / cable paths, the clarity and accuracy of the device connection relationship are ensured. This process not only improves the efficiency of device connection, but also enhances the system's ability to control the device status, and ultimately promotes the collaborative work and intelligent management of the entire electromechanical system.

[0047] Preferably, step S2 comprises the following steps:

[0048] Step S21: annotating device node function semantics on device physical connection topology data to generate device function node semantic data;

[0049] Step S22: performing device function group association analysis on the device physical connection topology data through the device function node semantic data to generate device group function association data;

[0050] Step S23: constructing a semantic network according to the semantic data of the device function nodes and the device group function association data to obtain device function semantic network data;

[0051] Step S24: searching for a function path according to the device function semantic network data, and mapping the function path to the corresponding device to obtain device logical connection relationship data;

[0052] Step S25: performing logic conflict detection on the device physical connection topology data through the device logical connection relationship data to obtain logic conflict detection data;

[0053] Step S26: performing logic conflict type identification on the logic conflict detection data to generate logic conflict type data;

[0054] Step S27: performing a logic conflict confidence evaluation according to the logic conflict type data to obtain logic conflict confidence data;

[0055] Step S28: Automatically eliminate logical conflicts in the device physical connection topology data using the logic conflict confidence data to generate device connection topology data.

[0056] The present invention significantly improves the intelligence and accuracy of device connection through multi-level analysis and processing of device physical connection topology data. First, by performing functional semantic annotation on device nodes, the specific role and function of each device in the system are clarified, laying the foundation for subsequent association analysis. The association analysis of device function groups further explores the functional relationship between devices, allowing the system to understand the synergy of devices at a higher level, thereby enhancing the overall coordination between devices. In the process of detecting logical conflicts, potential connection problems are identified in a timely manner through the detection of device logical connection relationships, thereby reducing conflicts and misconnections between devices. The type identification and confidence assessment of logical conflicts enable the system to handle conflict problems in a more refined manner, ensuring that the final generated device connection topology data is not only accurate but also reasonable. In addition, the automatic elimination mechanism of logical conflicts greatly reduces the need for manual intervention, improves work efficiency, and reduces the possibility of manual errors.

[0057] Preferably, step S3 comprises the following steps:

[0058] Step S31: Based on the device connection topology data, the preset smart building Internet of Things sensor network is used to collect the real-time operation data of the device to obtain the real-time operation status data of the device;

[0059] Step S32: transmitting the real-time operation status data of the equipment to the BIM platform, and performing data preprocessing to generate equipment operation status monitoring data;

[0060] Step S33: performing real-time equipment status pattern recognition according to the equipment operation status monitoring data to obtain equipment operation status pattern data;

[0061] Step S34: performing dynamic connection relationship analysis on the real-time operation status data of the device, and performing dynamic connection strength evaluation according to the operation status mode data of the device to generate device connection strength evaluation data;

[0062] Step S35: Perform device fault status detection on the device connection topology data to generate device fault status data; perform fault impact link analysis on the device fault status data using the device connection strength assessment data to generate fault propagation link data.

[0063] The present invention realizes the efficient collection of real-time operation data of equipment by combining equipment connection topology data with the smart building Internet of Things sensor network. Transmitting real-time operation status data to the BIM platform and preprocessing it can effectively remove noise and unnecessary information, thereby improving the quality of data and the accuracy of analysis. In the process of identifying equipment status patterns, with the help of real-time status monitoring data, the system can timely understand the operation trends and abnormal conditions of the equipment, which is conducive to the implementation of more flexible and targeted maintenance strategies. At the same time, the combination of dynamic connection relationship analysis and connection strength assessment can reveal the dependencies and changes between devices. Through the combination of equipment fault status detection and fault propagation link analysis, the system can not only identify faults in a timely manner, but also track the propagation path of faults, so as to formulate more effective countermeasures.

[0064] Preferably, step S35 includes the following steps:

[0065] Step S351: identifying device function dependency relationships on device connection topology data according to device operation status mode data, and generating device function dependency relationship data;

[0066] Step S352: performing equipment fault state detection on the equipment operation state mode data to generate equipment fault state data;

[0067] Step S353: dividing the equipment operation status monitoring data into fault time windows according to the equipment fault status data to obtain fault time window data;

[0068] Step S354: performing fault device timing correlation analysis according to the fault time window data and the device connection strength assessment data to generate fault-related device data;

[0069] Step S355: Preliminary determination of the fault impact range is performed on the fault-associated device data using the device function dependency data to generate preliminary fault impact range data;

[0070] Step S356: Calculate the equipment space distance for the fault-related equipment data, and perform fault space impact range analysis to generate equipment fault space impact data;

[0071] Step S357: dynamically expand the affected devices on the preliminary fault impact range data using the device fault spatial impact data, and construct a fault propagation path for the device connection topology data to generate fault propagation link data.

[0072] The present invention identifies the functional dependency of the equipment according to the equipment operation status mode data, laying the foundation for the subsequent fault state detection and fault impact analysis. The clarification of this dependency helps to quickly locate the source of the fault and improve the efficiency of troubleshooting. In the fault state detection link, the system can timely identify the abnormal state of the equipment, ensuring that the management personnel can take effective measures at the early stage of the fault, reducing the equipment downtime and potential losses. Subsequently, by dividing the fault time window, the system can accurately grasp the time range of the fault occurrence, providing a basis for the timing correlation analysis of the fault, which is particularly important in a complex equipment network. The timing correlation analysis of the faulty equipment can reveal the mutual influence between different equipment and provide an important reference for the formulation of targeted maintenance strategies. Combined with the equipment function dependency data, the data analysis of the fault-related equipment can realize the preliminary determination of the fault impact range, helping the management personnel to timely identify the affected equipment groups. Through spatial distance calculation and fault spatial impact range analysis, the system can accurately assess the potential impact of the fault on the surrounding equipment and reduce hidden dangers.

[0073] Preferably, step S4 comprises the following steps:

[0074] Step S41: perform equipment criticality assessment according to the fault propagation link data to obtain equipment criticality assessment data; perform equipment operation state stability analysis using the equipment operation state mode data and the fault propagation link data to obtain equipment operation state stability data; perform equipment safety risk assessment on the fault propagation link data using the equipment fault state data to generate equipment safety risk assessment data;

[0075] Step S42: using a preset equipment failure probability prediction model to predict the equipment failure probability of the equipment criticality assessment data, the equipment operating status stability data, and the equipment safety risk assessment data, and to perform a failure severity assessment to generate equipment failure severity data;

[0076] Step S43: sorting the equipment processing priorities according to the equipment fault severity data, and performing operation and maintenance resource solution configuration processing to generate equipment operation and maintenance solution data;

[0077] Step S44: estimating the equipment maintenance time for the equipment operation and maintenance plan data to generate equipment maintenance time estimation data;

[0078] Step S45: performing operation and maintenance personnel allocation processing according to the equipment operation and maintenance plan data and the equipment maintenance time estimation data to obtain operation and maintenance personnel scheduling data;

[0079] Step S46: constructing a building internal navigation network based on the target BIM building model data; using the building internal navigation network to map the equipment operation and maintenance solution data to operation and maintenance equipment, and obtaining equipment location mapping data;

[0080] Step S47: Based on the equipment location mapping data, equipment operation and maintenance plan data, and operation and maintenance personnel scheduling data, the internal navigation network of the building is used to perform multi-objective operation and maintenance path planning to obtain intelligent equipment operation and maintenance path data.

[0081] The present invention can clarify the importance of each device in the overall system based on the criticality assessment of the fault propagation link data, which helps to prioritize the failure of key equipment, thereby reducing the impact on the operation of the entire building. At the same time, through the stability analysis of the equipment operation status mode data, potential unstable factors can be identified in time to ensure that the equipment operates in an efficient state. The safety risk assessment combined with the equipment failure status data provides an important reference for decision-making, so that managers can clearly understand the safety hazards of different equipment. This comprehensive risk assessment mechanism helps to formulate preventive measures in advance before the failure occurs and reduce the probability of failure. The criticality, stability and safety risk data are analyzed using the equipment failure probability prediction model, which can not only evaluate the probability of failure, but also determine the severity of the failure. This comprehensive assessment provides a basis for the processing priority sorting of equipment, ensures the reasonable configuration of operation and maintenance resources, and makes resource allocation more accurate and effective. The estimation of equipment maintenance time and the reasonable scheduling of operation and maintenance personnel can effectively improve the efficiency of maintenance work, ensure that maintenance personnel can reach the fault site in the shortest time, and reduce the downtime and loss of equipment. By constructing an internal navigation network in the building and accurately mapping the location of equipment, operation and maintenance personnel can efficiently formulate multi-target operation and maintenance paths, further improving the flexibility and responsiveness of the entire operation and maintenance process.

[0082] Preferably, the present invention further provides a smart building operation and maintenance management system based on the BIM platform, which executes the smart building operation and maintenance management method based on the BIM platform as described above, and the smart building operation and maintenance management system based on the BIM platform includes:

[0083] The BIM topology construction module is used to obtain the target BIM building model data; construct the BIM three-dimensional space index according to the target BIM building model data to obtain the equipment three-dimensional space index data; perform the proximity search of the same type of electromechanical equipment according to the equipment three-dimensional space index data, and construct the equipment connection topology network to generate the equipment physical connection topology data;

[0084] The device connection logic verification module is used to annotate the device node function semantics of the device physical connection topology data to generate device function node semantic data; the device function node semantic data is used to automatically eliminate logical conflicts in the device physical connection topology data to generate device connection topology data;

[0085] The equipment fault monitoring module is used to perform real-time equipment status pattern recognition based on the equipment connection topology data to obtain equipment operation status pattern data; perform equipment fault status detection based on the equipment operation status pattern data to generate equipment fault status data; use the equipment fault status data to perform fault impact link analysis on the equipment connection topology data to generate fault propagation link data;

[0086] The intelligent operation and maintenance path planning module is used to evaluate the fault severity of the fault propagation link data and generate equipment fault severity data; intelligent operation and maintenance path planning is performed based on the equipment fault severity data to obtain intelligent equipment operation and maintenance path data. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A schematic diagram of the steps of a smart building operation and maintenance management method based on a BIM platform according to the present invention;

[0088] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0089] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0090] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0091] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0092] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0093] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0094] To achieve this, please refer to Figures 1 to 3 The present invention provides a smart building operation and maintenance management method based on the BIM platform, comprising the following steps:

[0095] Step S1: Acquire target BIM building model data; construct a BIM three-dimensional space index according to the target BIM building model data to obtain equipment three-dimensional space index data; perform a neighboring search for the same type of electromechanical equipment according to the equipment three-dimensional space index data, and construct an equipment connection topology network to generate equipment physical connection topology data;

[0096] Step S2: annotating the device node function semantics of the device physical connection topology data to generate device function node semantic data; automatically eliminating logical conflicts of the device physical connection topology data using the device function node semantic data to generate device connection topology data;

[0097] Step S3: performing real-time device status pattern recognition based on the device connection topology data to obtain device operation status pattern data; performing device fault status detection based on the device operation status pattern data to generate device fault status data; using the device fault status data to perform fault impact link analysis on the device connection topology data to generate fault propagation link data;

[0098] Step S4: perform fault severity assessment on the fault propagation link data to generate equipment fault severity data; perform intelligent operation and maintenance path planning based on the equipment fault severity data to obtain intelligent equipment operation and maintenance path data.

[0099] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a smart building operation and maintenance management method based on a BIM platform of the present invention. In this embodiment, the smart building operation and maintenance management method based on a BIM platform includes the following steps:

[0100] Step S1: Acquire target BIM building model data; construct a BIM three-dimensional space index according to the target BIM building model data to obtain equipment three-dimensional space index data; perform a neighboring search for the same type of electromechanical equipment according to the equipment three-dimensional space index data, and construct an equipment connection topology network to generate equipment physical connection topology data;

[0101] In the embodiment of the present invention, taking the air conditioning system of a certain office building as an example, firstly, the target BIM building model data is exported using BIM modeling software such as Revit, and the data includes geometric information, material information, air conditioning system equipment information, etc. of the building. Then, the BIM data server is built using the open source BIMServer platform, and the exported BIM model data is uploaded to the server. The API interface provided by BIMServer is used to obtain the location coordinates, size, type and other information of all air conditioning system equipment in the model, for example, to obtain the information of all air conditioning units, fan coil units, water pumps, pipes and other equipment. And the three-dimensional spatial index data of the equipment is constructed based on the spatial index algorithm such as Octree to realize the spatial fast retrieval of the equipment. For example, the entire office building space can be divided into several cubic units, each unit stores the air conditioning equipment information contained therein, and when it is necessary to find the air conditioning equipment in a certain area, it is only necessary to traverse the unit corresponding to the area. Then, according to the three-dimensional spatial index data of the equipment, the neighboring search is performed for the air conditioning equipment of the same type. For example, a distance threshold can be set to search for equipment with a distance less than the threshold and the same type, for example, the search distance is less than 5 meters and the type is all "fan coil units". Finally, based on the search results, combined with the actual installation location of the equipment and the pipeline connection relationship, the graph theory algorithm is used to build a device connection topology network and generate device physical connection topology data. For example, each air-conditioning device is taken as a node, and the pipeline connection relationship between devices is taken as an edge to construct a directed graph to represent the physical connection relationship between air-conditioning devices.

[0102] Step S2: annotating the device node function semantics of the device physical connection topology data to generate device function node semantic data; automatically eliminating logical conflicts of the device physical connection topology data using the device function node semantic data to generate device connection topology data;

[0103] In an embodiment of the present invention, based on the physical connection topology data of the air-conditioning equipment generated in step S1, the functional semantic annotation of the equipment nodes is performed using the attribute information in the BIM model and external databases, such as equipment product manuals, national standards, etc. For example, the "fan coil unit" can be labeled as the "terminal air supply equipment", the "chiller" can be labeled as the "refrigeration equipment", and the "water pump" can be labeled as the "transportation equipment". After the semantic data of the equipment function nodes are generated, the logical conflicts of the physical connection topology data of the air-conditioning equipment are automatically eliminated using expert rules and machine learning algorithms. For example, according to the rule that the "chilled water pump" can only be connected to the "chiller", the erroneous connection relationship of the "chilled water pump" connected to the "hot water unit" can be eliminated. In addition, machine learning algorithms, such as Bayesian networks, can also be used to learn the normal connection patterns between air-conditioning equipment, identify and eliminate abnormal connection relationships. Finally, the connection topology data of the air-conditioning equipment after logical conflict elimination is generated.

[0104] Step S3: performing real-time device status pattern recognition based on the device connection topology data to obtain device operation status pattern data; performing device fault status detection based on the device operation status pattern data to generate device fault status data; using the device fault status data to perform fault impact link analysis on the device connection topology data to generate fault propagation link data;

[0105] In an embodiment of the present invention, based on the connection topology data of the air-conditioning equipment generated in step S2, the building automatic control system or the Internet of Things platform is connected to collect the operating parameters of the air-conditioning equipment in real time, such as temperature, pressure, flow, switch status, etc. The operating state mode of the air-conditioning equipment is identified by using methods such as time series analysis and cluster analysis. For example, according to the supply and return water temperature of the chiller, the compressor current and other parameters, it can be identified that it is in different operating state modes such as "normal operation", "partial load" and "high load", and the identification result is stored as the operating state mode data of the air-conditioning equipment. Then, according to a pre-set threshold or machine learning model, such as a support vector machine, the operating state mode data of the air-conditioning equipment is detected for the equipment fault state. For example, when the supply and return water temperature difference of the chiller exceeds the set threshold, or when the switch state of a fan coil unit is continuously closed for a period of time, it is judged that it is in a "fault" state, and the detection result is stored as the fault state data of the air-conditioning equipment. Finally, the fault state data of the air-conditioning equipment and the connection topology data of the air-conditioning equipment are used to perform fault impact link analysis. For example, when a chiller fault is detected, the downstream devices affected by the fault, such as fan coil units and air conditioning boxes, can be analyzed based on the device connection topology data to generate fault propagation link data.

[0106] Step S4: perform fault severity assessment on the fault propagation link data to generate equipment fault severity data; perform intelligent operation and maintenance path planning based on the equipment fault severity data to obtain intelligent equipment operation and maintenance path data.

[0107] In the embodiment of the present invention, based on the air-conditioning system fault propagation link data generated in step S3, the fault severity is evaluated in combination with factors such as the importance of the air-conditioning equipment, the severity of the fault, and the scope of influence. For example, different weights can be set according to the functional level, usage frequency, fault type, etc. of the air-conditioning equipment, and the comprehensive severity score of the fault can be calculated, and the result can be stored as the fault severity data of the air-conditioning equipment. For example, the severity score of the chiller fault is higher than the score of the fan coil fault, and the score of the fan coil fault affecting multiple floors is higher than the score of the fan coil fault affecting only a single room. Finally, according to the fault severity data of the air-conditioning equipment, combined with the geographical location of the air-conditioning equipment, the availability of maintenance personnel, etc., a graph theory algorithm or a heuristic algorithm, such as a Dijkstra algorithm, a genetic algorithm, etc., is used to perform intelligent operation and maintenance path planning, for example, planning the inspection route and maintenance plan of the maintenance personnel, etc., and generating intelligent equipment operation and maintenance path data, for example, giving priority to arranging maintenance personnel to handle faults with high severity scores, and planning the shortest maintenance path, so as to improve the operation and maintenance efficiency and reduce the operation and maintenance cost.

[0108] Preferably, step S1 comprises the following steps:

[0109] Step S11: Acquire target BIM building model data;

[0110] Step S12: using the IFC standard protocol to identify the electromechanical equipment object of the target BIM building model data, and generating the electromechanical equipment object metadata;

[0111] Step S13: constructing a BIM three-dimensional spatial index for the metadata of the electromechanical equipment object to obtain the three-dimensional spatial index data of the equipment;

[0112] Step S14: performing a proximity search for similar-type devices on the electromechanical device object metadata through the device three-dimensional spatial index data, and performing device spatial proximity analysis according to the preset spatial range data to generate device spatial relationship data;

[0113] Step S15: identifying potential connection device pairs according to the device spatial relationship data to obtain potential connection device pair data;

[0114] Step S16: extracting the pipeline / cable path direction according to the target BIM building model data to generate pipeline / cable path data;

[0115] Step S17: Based on the pipeline / cable path data, the physical connection relationship of the potential connection device pair data is verified, and the device connection topology network is constructed to generate device physical connection topology data.

[0116] In an embodiment of the present invention, the BIM model data of the office building is exported by BIM modeling software such as Revit and ArchiCAD. The data is stored in IFC format and includes building structure, spatial information, and geometric information, attribute information, and location information of electromechanical equipment such as air-conditioning units, fan coils, pipes, and valves. The BIM model data obtained in step S11 is parsed using an open source IFC parsing library, such as xBIM, IFCOpenShell, etc. According to the entity type and attribute information defined in the IFC standard protocol, IFC objects belonging to electromechanical equipment are identified, such as IfcPump (pump), IfcChiller (chiller), IfcAirTerminalBox (fan coil), etc. The attribute information of these electromechanical equipment objects, such as equipment name, model, specification, location coordinates, etc., is extracted to generate electromechanical equipment object metadata, such as stored in JSON format or in the form of a database table. The BIM three-dimensional spatial index is constructed using the spatial location information of the equipment object, such as the center point coordinates of the equipment, the size of the bounding box, etc. Octree, Kd-Tree and other spatial indexing algorithms can be used to divide the building space into multiple subspaces, and assign the equipment objects to the corresponding subspaces, so as to quickly retrieve the equipment objects within a specific spatial range. For example, each floor of an office building can be divided into multiple cubic units, and each air conditioning device can be assigned to a corresponding unit. The metadata of the electromechanical equipment object is searched for the same type of equipment proximity. For example, a search radius, such as 5 meters, can be set to search for all other fan coils within 5 meters of a certain fan coil. At the same time, according to the preset spatial range data, such as room boundaries, floor boundaries, etc., the equipment space proximity analysis is performed. For example, it is determined whether two devices are located in the same room or on the same floor. The proximity search results and the spatial proximity analysis results are combined to generate equipment space relationship data, for example, two spatially adjacent and same type of equipment are marked as "potentially connected equipment pairs". According to the equipment space relationship data generated in step S14, potential connected equipment pairs are identified, such as identifying two fan coils located in the same room and close to each other, or identifying air conditioning units and water pumps located on the same floor and close to each other. The information of these potential connection device pairs is stored as potential connection device pair data, for example, the device ID of each potential connection device pair is stored in a list. The geometric information of the pipe and cable objects, such as IfcPipe (pipe), IfcCableSegment (cable segment), etc., is extracted from the BIM model data obtained in step S11 using the IFC parsing library. According to the geometric information of these objects, such as the starting point coordinates, the end point coordinates, the diameter, etc., the pipe / cable path data is constructed, for example, the path direction of the pipe / cable is represented in the form of a line segment sequence. For example, it is determined whether there is a pipe / cable connection between two potential connection devices.A spatial topological relationship judgment algorithm can be used, for example, to determine whether the starting point or end point of a pipe / cable is within the bounding box of a device. If there is a pipe / cable connection between two devices, it is confirmed that there is a physical connection relationship between the two devices. Based on the verification results, a device connection topology network is constructed, for example, each device is taken as a node, and the physical connection relationship between devices is taken as an edge to construct a directed graph. Device physical connection topology data is generated, for example, the device connection topology network is stored as an adjacency matrix or adjacency table.

[0117] Preferably, step S13 comprises the following steps:

[0118] Step S131: standardizing the BIM coordinate system of the electromechanical equipment object metadata, and performing equipment coordinate positioning to generate standard equipment space coordinate data;

[0119] Step S132: identifying the operation and maintenance management space of the target BIM building model data by using the standard equipment space coordinate data to obtain the operation and maintenance equipment management space data;

[0120] Step S133: extracting device size from the metadata of the electromechanical device object to obtain device size data;

[0121] Step S134: performing regular three-dimensional geometric abstraction processing according to the device size data, and calculating the device space volume to generate device space volume data;

[0122] Step S135: Use the operation and maintenance equipment management space data to construct a spatial index for the equipment space volume data to generate equipment three-dimensional space index data.

[0123] In an embodiment of the present invention, the local coordinates of the device are converted into standard coordinates under a unified BIM coordinate system. For example, if the BIM model adopts a world coordinate system, the local coordinates of the device need to be converted into world coordinates. The coordinate conversion can be achieved through a coordinate conversion matrix or a related API function. Then, according to the standard coordinates, the precise position of the device in the BIM model is determined, and standard device space coordinate data is generated. The standard device space coordinate data generated in step S131 is combined with the spatial information in the BIM model, such as rooms, floors, etc., to identify the operation and maintenance management space where each device is located. For example, the room or floor in which the device is located is determined based on the coordinates of the device. The device is associated with the corresponding operation and maintenance management space to generate operation and maintenance device management space data. The size information of the device, such as length, width, height, etc., is extracted from the metadata of the electromechanical device object. The device is abstracted into a regular three-dimensional geometric body, such as a cuboid, a cylinder, etc. For example, a fan coil unit can be abstracted into a cuboid, and a pipe can be abstracted into a cylinder. Then, according to the size information of the device, the space volume occupied by the device is calculated. For example, the volume calculation formula of a cuboid is: volume = length × width × height. The BIM model is divided into different operation and maintenance management space areas. Then, according to the equipment space volume data generated in step S134, the space volume information of the equipment is associated with the corresponding operation and maintenance management space area. For example, the space volume information of the equipment located in a room is stored in the space area corresponding to the room. Finally, a spatial index is constructed for the equipment space volume data in each operation and maintenance management space area, for example, using a spatial index structure such as Octree, Kd-tree or R-tree to generate equipment three-dimensional space index data.

[0124] Preferably, step S135 includes the following steps:

[0125] Step S1351: performing root node index range processing on the operation and maintenance equipment management space data to obtain Octree space root node range data;

[0126] Step S1352: performing spatial node division on the operation and maintenance equipment management space data based on the Octree spatial root node range data, dividing the spatial range of the root node into two equal parts along the three coordinate axes, and allocating the equipment to the corresponding subspace to obtain node division data;

[0127] Step S1353: Calculate the node space occupancy rate of the equipment space volume data using the node partitioning data to obtain the node space occupancy rate data;

[0128] Step S1354: recursively process the node partition data using the node space occupancy rate data, and construct an Octree index tree, thereby obtaining a preliminary searchable spatial index;

[0129] Step S1355: Count the number of node space devices on the preliminary searchable spatial index to generate node density distribution data;

[0130] Step S1356: using a preset device density threshold to perform device density identification on the node density distribution data, and obtaining high-density device node data and low-density device node data respectively;

[0131] Step S1357: performing R-Tree index local optimization on high-density device node data, and using device coordinates as leaf nodes for index association to obtain high-density device index optimization data;

[0132] Step S1358: generating an ordered list of device coordinates for low-density device node data, and performing one-dimensional index link optimization to generate low-density device index optimization data;

[0133] Step S1359: Optimize the preliminary searchable spatial index using the low-density device index optimization data and the high-density device index optimization data to generate device three-dimensional spatial index data.

[0134] In an embodiment of the present invention, for example, the operation and maintenance equipment management space data includes the spatial range information of all rooms, such as the minimum and maximum coordinate values ​​of each room. First, it is necessary to determine the root node range of the Octree space index, which needs to be able to include the spatial range of all rooms. The Octree space root node range data can be obtained by calculating the union of the minimum and maximum coordinate values ​​of all rooms. Based on the Octree space root node range data obtained in step S1351, the spatial range of the root node is divided into two equal parts along the three coordinate axes of x, y, and z to form 8 subspaces. Then, the operation and maintenance equipment management space data, such as room information, is traversed to determine the subspace to which each room belongs, and the room is assigned to the corresponding subspace. For example, if the center point of a room is located in the subspace in front of the upper left corner of the root node, the room is assigned to the subspace, for example, stored in a tree structure, each node represents a spatial area, and the leaf node stores the room information contained in the area. Using the node partitioning data obtained in step S1352, each subspace is traversed to calculate the sum of the spatial volumes of all equipment in the subspace and the total volume of the subspace. Then, calculate the node space occupancy rate, that is, the ratio of the sum of the device space volume to the total volume of the subspace. According to the node space occupancy rate data obtained in step S1353, determine whether each subspace needs to be further divided. For example, if the space occupancy rate of a subspace exceeds a preset threshold, such as 0.5, it is considered that the device density of the subspace is high and needs to be further divided. Otherwise, stop the division. For the subspace that needs to be further divided, repeat steps S1352 and S1353 and perform recursive processing until all subspaces do not need to be further divided or reach the preset Octree depth. Finally, build an Octree index tree to obtain a preliminary queryable spatial index. Traverse each leaf node of the preliminary queryable spatial index constructed in step S1354 and count the number of devices contained in the node. According to the preset device density threshold, such as 10, divide the nodes in the node density distribution data into high-density device nodes and low-density device nodes. For example, if the number of devices contained in a node is greater than 10, it is divided into a high-density device node; otherwise, it is divided into a low-density device node. The high-density device node data obtained in step S1356 is traversed, and each high-density device node is locally optimized using the R-Tree index. For example, the node space range is used as the root node of the R-Tree, and the coordinates of the device are used as leaf nodes to construct the R-Tree index. The low-density device node data obtained in step S1356 is traversed, and for each low-density device node, the device coordinates contained in the node are sorted according to the x, y, and z coordinate values ​​to generate an ordered list of device coordinates. Then, the ordered list is optimized for one-dimensional index links, such as constructing a skip list index.For example, the index of high-density device nodes is replaced with R-Tree index, and the index of low-density device nodes is replaced with ordered list and skip list index. Finally, the three-dimensional spatial index data of the device is generated, for example, stored in a tree structure, each node represents a spatial area, and the leaf node stores the device information contained in the area, as well as the corresponding index structure (R-Tree index or ordered list and skip list index).

[0135] Preferably, step S17 comprises the following steps:

[0136] Step S171: identifying the connection endpoints of the pipeline / cable path data through the target BIM building model data, and extracting the connection coordinates to obtain the pipeline / cable path endpoint coordinate data;

[0137] Step S172: extracting device connection point information from the potential connection device pair data to obtain device connection point data;

[0138] Step S173: Calculate the spatial distance of the connection points according to the pipeline / cable path endpoint coordinate data and the equipment connection point data to generate connection point distance data;

[0139] Step S174: Acquire a mechatronic device connection matching rule library;

[0140] Step S175: traversing the equipment connection point data, and using the electromechanical equipment connection matching rule library and the connection point distance data to match the path connection points of the pipeline / cable path endpoint coordinate data, and generating connection point matching result data;

[0141] Step S176: verifying the connection relationship of the potential connection device pair data based on the connection point matching result data, and generating verified connection device pair data when the connection points of the potential connection device pair all match the connection points of the same path; otherwise, generating non-connection device pair data;

[0142] Step S177: searching for connection paths for the non-connected device pair data and performing indirect connection relationship analysis. If it is an indirect connection relationship, marking the non-connected device pair data as indirect device connection pair data; otherwise, marking the non-connected device pair data as unconnected device pair and feeding back to the terminal device for manual verification;

[0143] Step S178: Perform device physical connection topology processing on the verified connection device pair data, indirect device connection pair data, and pipeline / cable path data, and mark the connection direction, thereby obtaining device physical connection topology data.

[0144] In an embodiment of the present invention, the connection endpoints of the pipeline / cable path, such as the equipment or pipe fittings connected at both ends of the pipeline, are identified through the target BIM building model data, such as the IFC file. Then, the coordinate information of the connection endpoint, such as the starting point coordinates and the end point coordinates, is extracted to generate the pipeline / cable path endpoint coordinate data. The data of the potential connection device is traversed, such as (fan coil unit A, fan coil unit B). According to the device object metadata and BIM model information, such as the connection port information of the device, the connection point information of the device is extracted, such as the location coordinates of the connection port, the type of the connection port, etc. According to the pipeline / cable path endpoint coordinate data obtained in step S171 and the device connection point data obtained in step S172, the spatial distance between each path endpoint and each device connection point is calculated. For example, the Euclidean distance formula can be used to calculate the distance between two points, for example, stored in the form of a matrix, the rows of the matrix represent the path endpoints, the columns represent the device connection points, and the matrix elements represent the corresponding distance values. A predefined electromechanical device connection matching rule base is obtained, which defines how to connect and match different types of devices. For example, the rule base may define that the air outlet of a fan coil unit can only be connected to a certain type of interface of a pipe, or that the water outlet of a chiller can only be connected to a pipe of a certain diameter, etc. The device connection point data obtained in step S172 is traversed, such as the air outlet connection point of fan coil unit A. According to the type of device connection point, such as the air outlet, the corresponding matching rule is searched from the electromechanical equipment connection matching rule base. Then, according to the matching rule, for example, the air outlet can only be connected to the pipe interface, the connection point distance data generated in step S173 is searched for the path endpoint that meets the rule closest to the connection point, such as the nearest pipe interface endpoint. If a matching path endpoint is found, the connection point and the path endpoint are matched to generate connection point matching result data. Based on the connection point matching result data generated in step S175, the connection relationship of the potential connection device pair data is verified. For example, for a potentially connected device pair (fan coil unit A, fan coil unit B), if the connection point of fan coil unit A and the connection point of fan coil unit B both match the two endpoints of the same path, the potentially connected device pair is considered to have a connection relationship, and the device pair is added to the verified connected device pair data. For example, for a non-connected device pair (fan coil unit A, fan coil unit B), a search is performed in the device connection topology network to see if there is a path that can connect fan coil unit A and fan coil unit B. If such a path exists, it is considered that there is an indirect connection relationship between fan coil unit A and fan coil unit B, and the device pair is marked as an indirect device connection pair data, for example, a marking field is added to the non-connected device pair data to indicate that the device pair is an indirect connection relationship. Otherwise, the device pair is marked as an unconnected device pair and feedback is given to the terminal device, for example, a warning message is displayed on the BIM platform to prompt the user to perform a manual check.The verified connected device pair data generated in step S176, the indirect device connection pair data generated in step S177, and the pipeline / cable path data are processed to construct a device physical connection topology network. For example, the device is used as a node and the pipeline / cable path is used as an edge to construct a graph structure. Then, according to the matching relationship between the device connection point and the path endpoint, the connection direction is marked. For example, the air outlet of the fan coil unit is connected to the starting point of the pipeline, and the return air outlet of the fan coil unit is connected to the end point of the pipeline. Finally, the device physical connection topology data is obtained.

[0145] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0146] Step S21: annotating device node function semantics on device physical connection topology data to generate device function node semantic data;

[0147] In the embodiment of the present invention, the constructed air conditioning domain ontology library, such as HVACOntology, is used to perform functional semantic annotation on the device nodes. Specifically, according to the IFC type or attribute information of the device, such as "IfcAirTerminalBox" for fan coil units and "IfcChiller" for chillers, it is mapped to the corresponding concepts in the ontology library, such as "terminal device" and "cold source device". At the same time, the functional description information of the device, such as the air supply temperature of the fan coil unit and the cooling capacity of the chiller, is extracted as supplementary information for the functional semantic annotation.

[0148] Step S22: performing device function group association analysis on the device physical connection topology data through the device function node semantic data to generate device group function association data;

[0149] In the embodiment of the present invention, the physical connection topology of the device is traversed, and the device nodes with the same function type or closely related functions are divided into the same function group according to the functional semantic annotation information of the nodes. For example, a chiller, the pipes and valves connected to it, and all the fan coils connected to these pipes are divided into a function group, indicating that they together constitute an independent air conditioning and air supply system.

[0150] Step S23: constructing a semantic network according to the semantic data of the device function nodes and the device group function association data to obtain device function semantic network data;

[0151] In an embodiment of the present invention, a graph database such as Neo4j is used to construct a device function semantic network based on the device function node semantic data generated in step S21 and the device group function association data generated in step S22. Specifically, each device node is taken as a node in the graph database, and the functional semantic information of the device is taken as the attribute of the node. The physical connection relationship between devices is taken as the edge in the graph database, and the functional group information to which the device belongs is taken as the attribute of the edge. For example, if there is a connection relationship between the chiller and the pipeline, an edge is created in the graph database to connect the two nodes, and the attribute of the edge is set to the functional group ID to which they belong.

[0152] Step S24: searching for a function path according to the device function semantic network data, and mapping the function path to the corresponding device to obtain device logical connection relationship data;

[0153] In the embodiment of the present invention, based on the device function semantic network constructed in step S23, the function path search is performed using the query language provided by the graph database, such as Cypher. Specifically, according to a predefined function path pattern, such as "cold source device-transportation device-terminal device", all paths that meet the pattern are queried. For example, all paths starting from the cold source device and ending at the terminal device, which pass through the transportation device in the middle, are queried. The queried path result is mapped to the corresponding device ID to obtain the device logical connection relationship data.

[0154] Step S25: performing logic conflict detection on the device physical connection topology data through the device logical connection relationship data to obtain logic conflict detection data;

[0155] In an embodiment of the present invention, a logical conflict detection is performed on the device physical connection topology data according to the device logical connection relationship data obtained in step S24. Specifically, each path in the device logical connection relationship data is traversed to check whether the device connection relationship on the path complies with the logical rules of the air-conditioning system. For example, check whether there is a situation where a cold source device is directly connected to a terminal device, or whether there is a situation where a direct connection relationship exists between two cold source devices. For example, (conflict type_1, [device ID_1, device ID_3]) indicates that there is a "cold source terminal direct connection" conflict between device ID_1 (cold source device) and device ID_3 (terminal device).

[0156] Step S26: performing logic conflict type identification on the logic conflict detection data to generate logic conflict type data;

[0157] In an embodiment of the present invention, the logical conflicts detected in step S25 are classified to identify different types of logical conflicts. For example, logical conflicts can be divided into types such as "missing connection", "connection error", "control logic error", etc. According to the device connection relationship and device function semantic information recorded in the logical conflict detection data, the conflict type pattern defined in the rule base is matched. For example, if there is a direct connection relationship between the cold source device and the terminal device in the logical conflict detection data, it is identified as a "cold source terminal direct connection" conflict. The identification result is stored in the logical conflict type data, for example (conflict ID_1, cold source terminal direct connection).

[0158] Step S27: performing a logic conflict confidence evaluation according to the logic conflict type data to obtain logic conflict confidence data;

[0159] In an embodiment of the present invention, a Bayesian network can be used to construct a logic conflict confidence assessment model. The logic conflict type is used as a node of the Bayesian network, and the correlation between different logic conflict types is used as an edge of the Bayesian network. The probability of occurrence of different logic conflict types is statistically analyzed based on historical operation and maintenance data as the prior probability of the Bayesian network node. Based on expert experience, the influence relationship between different logic conflict types is determined, for example, the "direct connection of the cold source terminal" conflict will lead to the "reduced system energy efficiency" conflict, and these influence relationships are converted into conditional probabilities of the Bayesian network edges. The Bayesian network inference algorithm is used to calculate the confidence of each logic conflict and generate logic conflict confidence data.

[0160] Step S28: Automatically eliminate logical conflicts in the device physical connection topology data using the logic conflict confidence data to generate device connection topology data.

[0161] In an embodiment of the present invention, a logical conflict confidence threshold is set, for example, 0.8. The logical conflict confidence data is traversed, and the device connection relationship corresponding to the logical conflict with a confidence higher than the threshold is removed from the device physical connection topology data. For example, if the confidence of conflict ID_1 is 0.9, which is higher than the threshold 0.8, the connection relationship between device ID_1 and device ID_3 is deleted from the device physical connection topology graph. Finally, logically reasonable device connection topology data is generated, for example, the physical connection relationship between devices is represented in the form of a graph, where nodes in the graph represent devices and edges represent connection pipes or cables between devices.

[0162] Preferably, step S3 comprises the following steps:

[0163] Step S31: Based on the device connection topology data, the preset smart building Internet of Things sensor network is used to collect the real-time operation data of the device to obtain the real-time operation status data of the device;

[0164] Step S32: transmitting the real-time operation status data of the equipment to the BIM platform, and performing data preprocessing to generate equipment operation status monitoring data;

[0165] Step S33: performing real-time equipment status pattern recognition according to the equipment operation status monitoring data to obtain equipment operation status pattern data;

[0166] Step S34: performing dynamic connection relationship analysis on the real-time operation status data of the device, and performing dynamic connection strength evaluation according to the operation status mode data of the device to generate device connection strength evaluation data;

[0167] Step S35: Perform device fault status detection on the device connection topology data to generate device fault status data; perform fault impact link analysis on the device fault status data using the device connection strength assessment data to generate fault propagation link data.

[0168] In an embodiment of the present invention, a corresponding relationship between a device and a sensor is established according to the device node information recorded in the device connection topology data and the IoT sensor network pre-deployed in the smart building. The Modbus protocol is used to read the data such as the supply and return water temperature and the chilled water flow of the air conditioning unit, the temperature and humidity sensor is used to collect the data such as the supply air temperature, the return air temperature, the indoor temperature of the fan coil, the pressure sensor is used to collect the pressure data in the pipeline, and the solenoid valve switch state sensor is used to collect the switch state data of the valve. The collected data is transmitted to the data acquisition gateway through the wireless sensor network to generate the real-time operation status data of the device. The real-time operation status data of the device collected in step S31 is transmitted to the server side of the BIM platform through the MQTT protocol. After the server side receives the data, it first pre-processes the data, such as data cleaning, data conversion, data normalization, etc. For example, different types of sensor data are converted into a unified unit, such as converting degrees Celsius into degrees Fahrenheit, and converting the pressure unit into Pascal. The operation state of the device is divided into different modes, such as "cooling mode", "heating mode", "standby mode" and the like of the air conditioning unit. According to the historical operation data, the parameters of the hidden Markov model are trained, such as the state transition probability matrix and the observation probability matrix. The trained hidden Markov model is used to perform pattern recognition on the real-time operation status monitoring data of the equipment, for example, according to the data such as the supply and return water temperature and the chilled water flow of the air conditioning unit, to determine which operation mode it is currently in. For example, according to the supply air temperature of the fan coil unit and the temperature data of the pipe connected to it, it is determined whether the fan coil unit is delivering cold air or warm air to the pipe. Then, according to the equipment operation status mode data obtained in step S33, a dynamic connection strength evaluation is performed. For example, if the fan coil unit is in "cooling mode" and the temperature of the pipe connected to it continues to decrease, it is considered that the strength of the connection relationship is high; if the fan coil unit is in "standby mode", it is considered that the strength of the pipe connection relationship connected to it is low. For example, if the chilled water flow of the air conditioning unit is continuously lower than the set value, it is determined that a "chilled water pump failure" occurs. The detected fault information is stored in the equipment fault status data, such as (equipment ID, fault type). Then, according to the equipment connection strength evaluation data generated in step S34, the equipment fault status data is subjected to fault impact link analysis. For example, if a "chilled water pump failure" occurs in an air-conditioning unit, the pipes, valves, and fan coils that are strongly connected to it will be affected, thus forming a fault propagation link.

[0169] Preferably, step S35 includes the following steps:

[0170] Step S351: identifying device function dependency relationships on device connection topology data according to device operation status mode data, and generating device function dependency relationship data;

[0171] Step S352: performing equipment fault state detection on the equipment operation state mode data to generate equipment fault state data;

[0172] Step S353: dividing the equipment operation status monitoring data into fault time windows according to the equipment fault status data to obtain fault time window data;

[0173] Step S354: performing fault device timing correlation analysis according to the fault time window data and the device connection strength assessment data to generate fault-related device data;

[0174] Step S355: Preliminary determination of the fault impact range is performed on the fault-associated device data using the device function dependency data to generate preliminary fault impact range data;

[0175] Step S356: Calculate the equipment space distance for the fault-related equipment data, and perform fault space impact range analysis to generate equipment fault space impact data;

[0176] Step S357: dynamically expand the affected devices on the preliminary fault impact range data using the device fault spatial impact data, and construct a fault propagation path for the device connection topology data to generate fault propagation link data.

[0177] In an embodiment of the present invention, the connection relationship between devices, such as the connection relationship between an air conditioning unit and a fan coil unit, is analyzed based on the device connection topology data. The functional dependency relationship between devices is identified in combination with the operation status mode of the device. For example, if the operation mode of the fan coil unit depends on the operation mode of the air conditioning unit, for example, the fan coil unit can be in the "cooling mode" only when the air conditioning unit is in the "cooling mode", then it is considered that the function of the fan coil unit depends on the air conditioning unit. The device operation status mode data is detected for the device fault state. For example, if the operation mode of the air conditioning unit is in the "standby mode" for a long time, and the indoor temperature is high at this time, it is determined that the air conditioning unit has an "unable to start" fault. Alternatively, if the air supply temperature of the fan coil unit is lower than the set value for a long time, it is determined that the fan coil unit has a "poor cooling effect" fault. Centered on the time point when the device fails, a period of time, such as 1 hour, is extended forward and backward to form a time window. For example, if the air conditioning unit fails at 10:00, the data between 9:00 and 11:00 is divided into the time window of the fault. According to the fault time window data generated in step S353, the equipment operation status monitoring data within the fault time window is extracted. The data correlation between the faulty equipment and other equipment is calculated using methods such as the Pearson correlation coefficient. For example, the correlation between the water supply temperature of the faulty air-conditioning unit and the air supply temperature of the fan coil connected to it is calculated. Combined with the equipment connection strength evaluation data generated in step S34, for example, the higher the connection strength, the higher the correlation weight, the faulty equipment timing correlation analysis is performed. For example, if there is a strong correlation between the water supply temperature of the faulty air-conditioning unit and the air supply temperature of a certain fan coil, and the connection strength between them is high, it is considered that the fan coil has an associated relationship with the fault of the air-conditioning unit. For example, if the faulty air-conditioning unit has an associated relationship with a certain fan coil, and the function of the fan coil depends on the air-conditioning unit, it is considered that the fan coil is within the fault impact range. The spatial coordinate information of the faulty equipment and the associated equipment is obtained, for example, the coordinate information of the equipment is extracted from the BIM model. The spatial distance between the faulty equipment and the associated equipment is calculated using the Euclidean distance formula. Then, according to a pre-set spatial distance threshold, such as 5 meters, it is determined whether the associated equipment is within the spatial impact range of the fault. For example, if the distance between the faulty device and an associated device is less than 5 meters, the associated device is considered to be within the spatial impact range of the fault. For example, if an associated device is within the spatial impact range of the fault, but the device does not exist in the preliminary fault impact range data, the device is added to the fault impact range. Then, based on the device connection topology data, a fault propagation path is constructed. For example, starting from the faulty device, all devices within the fault impact range are found along the device connection topology diagram to form one or more fault propagation paths.

[0178] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S4 in the flowchart, in this example, step S4 includes:

[0179] Step S41: perform equipment criticality assessment according to the fault propagation link data to obtain equipment criticality assessment data; perform equipment operation state stability analysis using the equipment operation state mode data and the fault propagation link data to obtain equipment operation state stability data; perform equipment safety risk assessment on the fault propagation link data using the equipment fault state data to generate equipment safety risk assessment data;

[0180] In an embodiment of the present invention, the criticality score of each device is calculated using the PageRank algorithm based on fault propagation link data, such as a fault propagation path represented in the form of a graph. For example, if a device is in a central position in the fault propagation path and is connected to multiple other devices, its criticality score is high. The calculation result is stored in the device criticality assessment data, for example, in the form of a list: (device ID, criticality score). The stability of the device operating state is analyzed using the device operating state mode data, such as the operating mode history record of the air-conditioning unit, and the fault propagation link data. For example, if the operating mode of a device is frequently switched, or its fault propagation path is long, its operating state stability is low. The analysis result is stored in the device operating state stability data, for example, in the form of a list: (device ID, stability score). According to the device fault state data, such as the historical fault record of the device, and the fault propagation link data, the safety risk of the device is evaluated. For example, if a device has a large number of historical faults, or the devices on its fault propagation path have safety hazards, its safety risk is high.

[0181] Step S42: using a preset equipment failure probability prediction model to predict the equipment failure probability of the equipment criticality assessment data, the equipment operating status stability data, and the equipment safety risk assessment data, and to perform a failure severity assessment to generate equipment failure severity data;

[0182] In an embodiment of the present invention, a pre-trained equipment failure probability prediction model based on a random forest algorithm is used to predict the equipment failure probability of the equipment criticality assessment data, equipment operating status stability data, and equipment safety risk assessment data generated in step S41. For example, the criticality score, stability score, and safety risk level of the equipment are used as input features of the model to predict the probability of failure of the equipment in the future. Then, based on the predicted failure probability and the functional importance of the equipment, the type of failure, and other factors, such as the failure of an air-conditioning unit is more serious than the failure of a fan coil unit, a fault severity assessment is performed. For example, the failure probability and the failure type can be mapped to a pre-defined fault severity level table, for example, a fault with a failure probability greater than 0.8 and a fault type of "unable to start" is assessed as a "serious" level.

[0183] Step S43: sorting the equipment processing priorities according to the equipment fault severity data, and performing operation and maintenance resource solution configuration processing to generate equipment operation and maintenance solution data;

[0184] In an embodiment of the present invention, the equipment is prioritized for processing according to the equipment fault severity data generated in step S42. For example, equipment with a fault severity level of "serious" is placed in front, and equipment with a fault severity level of "minor" is placed in the back. Then, according to the type of equipment, the type of fault, the required maintenance tools and materials, and other information, such as the replacement of the condenser of the air-conditioning unit requires professional tools and materials, the operation and maintenance resource solution configuration process is performed. For example, the maintenance information of the equipment can be matched with a predefined operation and maintenance resource library, such as finding maintenance personnel who can repair the condenser of the air-conditioning unit and the required tools and materials.

[0185] Step S44: estimating the equipment maintenance time for the equipment operation and maintenance plan data to generate equipment maintenance time estimation data;

[0186] In an embodiment of the present invention, the maintenance time of each device is estimated based on the fault type, maintenance steps, required resources and other information recorded in the equipment operation and maintenance plan data, combined with historical operation and maintenance data and expert experience. For example, the average repair time of the same type of faults can be calculated based on historical fault repair records, or the repair time estimate given by experts can be referenced, and the estimated result can be associated with the equipment operation and maintenance plan data to generate equipment maintenance time estimation data.

[0187] Step S45: performing operation and maintenance personnel allocation processing according to the equipment operation and maintenance plan data and the equipment maintenance time estimation data to obtain operation and maintenance personnel scheduling data;

[0188] In the embodiment of the present invention, the operation and maintenance personnel are assigned according to the information such as the number of operation and maintenance personnel required and the skill requirements recorded in the equipment operation and maintenance plan data, as well as the current working status and skill level of the operation and maintenance personnel. For example, the operation and maintenance personnel who are closer to the faulty equipment, have matching skills and are currently idle can be assigned to the task. The assignment results, such as which operation and maintenance personnel are responsible for the maintenance of which equipment, are stored in the operation and maintenance personnel scheduling data.

[0189] Step S46: constructing a building internal navigation network based on the target BIM building model data; using the building internal navigation network to map the equipment operation and maintenance solution data to operation and maintenance equipment, and obtaining equipment location mapping data;

[0190] In an embodiment of the present invention, based on the target BIM building model data, such as an IFC file, the spatial information of the rooms, corridors, stairs, etc., as well as the component information of doors, windows, etc., inside the building are extracted. A graph search algorithm such as the Dijkstra algorithm or the A* algorithm is used to construct an internal navigation network of the building, for example, in the form of a graph, where the nodes in the graph represent the center points of the rooms or corridors, the edges represent the connection relationship between the rooms or corridors, and the weight of the edges represents the length of the connection path or the travel time. Then, according to the equipment operation and maintenance plan data, such as the equipment ID, the equipment that needs to be operated and maintained is mapped to the corresponding node in the internal navigation network of the building. For example, if a certain device is located in a certain room, the device is mapped to the node corresponding to the room.

[0191] Step S47: Based on the equipment location mapping data, equipment operation and maintenance plan data, and operation and maintenance personnel scheduling data, the internal navigation network of the building is used to perform multi-objective operation and maintenance path planning to obtain intelligent equipment operation and maintenance path data.

[0192] In the embodiment of the present invention, based on the equipment location mapping data generated in step S46, such as the equipment ID and the corresponding node ID, the equipment operation and maintenance plan data, such as the equipment priority, the maintenance personnel ID and other information, and the operation and maintenance personnel scheduling data, such as the maintenance personnel's starting position and working hours and other information, the multi-objective operation and maintenance path planning is performed using the internal navigation network of the building. For example, an optimization algorithm such as a genetic algorithm or an ant colony algorithm can be used to plan an optimal operation and maintenance path for each maintenance personnel, so that all equipment that needs maintenance can be repaired within the specified time, and the walking distance of each maintenance personnel is the shortest. The planning results are sent to the terminal platform of the relevant operation and maintenance personnel to remind the operation and maintenance personnel to perform maintenance work in real time.

[0193] Preferably, the present invention further provides a smart building operation and maintenance management system based on the BIM platform, which executes the smart building operation and maintenance management method based on the BIM platform as described above, and the smart building operation and maintenance management system based on the BIM platform includes:

[0194] The BIM topology construction module is used to obtain the target BIM building model data; construct the BIM three-dimensional space index according to the target BIM building model data to obtain the equipment three-dimensional space index data; perform the proximity search of the same type of electromechanical equipment according to the equipment three-dimensional space index data, and construct the equipment connection topology network to generate the equipment physical connection topology data;

[0195] The device connection logic verification module is used to annotate the device node function semantics of the device physical connection topology data to generate device function node semantic data; the device function node semantic data is used to automatically eliminate logical conflicts in the device physical connection topology data to generate device connection topology data;

[0196] The equipment fault monitoring module is used to perform real-time equipment status pattern recognition based on the equipment connection topology data to obtain equipment operation status pattern data; perform equipment fault status detection based on the equipment operation status pattern data to generate equipment fault status data; use the equipment fault status data to perform fault impact link analysis on the equipment connection topology data to generate fault propagation link data;

[0197] The intelligent operation and maintenance path planning module is used to evaluate the fault severity of the fault propagation link data and generate equipment fault severity data; intelligent operation and maintenance path planning is performed based on the equipment fault severity data to obtain intelligent equipment operation and maintenance path data.

[0198] The beneficial effect of this application is that the BIM model is parsed using the IFC standard protocol, the electromechanical equipment objects are identified, and a three-dimensional spatial index of the equipment is constructed. Through the spatial index and the preset spatial range, the spatial proximity analysis of the equipment is performed to identify the potential connected equipment pairs. Then, the pipeline / cable path information is extracted, the physical connection relationship of the potential connected equipment pairs is verified, and finally the accurate physical connection topology data of the equipment is constructed, which avoids the subjectivity and inefficiency of manual investigation and ensures the accurate identification of the equipment connection relationship. The functional semantics of the equipment nodes are annotated using the domain ontology library, and the equipment function group association analysis is performed to construct the equipment functional semantic network. Through functional path search and logical conflict detection, the logical conflicts in the equipment connection relationship are identified and eliminated, such as unreasonable situations such as the direct connection of the cold source equipment to the terminal equipment. The functional semantics and logical relationship of the equipment are combined to effectively improve the accuracy and rationality of the equipment connection relationship. The real-time operation data of the equipment is collected using the smart building Internet of Things sensor network, and transmitted to the BIM platform for data preprocessing and state pattern recognition. By analyzing the equipment operation status mode and dynamic connection relationship, evaluating the equipment connection strength, and combining the preset fault diagnosis rules or machine learning models to detect the equipment fault status, the real-time monitoring of the equipment operation status and the automatic diagnosis of the fault are realized, avoiding the misjudgment and omission caused by relying on manual experience judgment. According to the equipment operation status mode, the equipment functional dependency is identified, and the fault time window data and the equipment connection strength evaluation data are used to perform the fault equipment timing correlation analysis to preliminarily determine the fault impact range. Then, the fault spatial impact range is analyzed in combination with the equipment spatial distance, and the fault impact range is dynamically expanded to finally build the fault propagation link. The functional dependency, timing correlation and spatial relationship of the equipment are comprehensively considered to achieve accurate analysis of the fault impact range and effectively identify the fault propagation link. The equipment failure probability prediction model is used to predict the equipment failure probability and fault severity. The equipment processing priority is sorted according to the fault severity, and the operation and maintenance resource plan is configured. Combined with the equipment maintenance time estimation and the operation and maintenance personnel scheduling information, the internal navigation network of the building is used for multi-objective operation and maintenance path planning, realizing the intelligent scheduling and path planning of operation and maintenance tasks, effectively improving the operation and maintenance efficiency and reducing the operation and maintenance cost.

[0199] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0200] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A smart building operation and maintenance management method based on BIM platform, characterized in that: The following steps are involved: Step S1: Acquire target BIM building model data; construct a BIM three-dimensional space index according to the target BIM building model data to obtain equipment three-dimensional space index data; perform a neighboring search for the same type of electromechanical equipment according to the equipment three-dimensional space index data, and construct an equipment connection topology network to generate equipment physical connection topology data; Step S2: annotating the device node function semantics of the device physical connection topology data to generate device function node semantic data; automatically eliminating logical conflicts of the device physical connection topology data using the device function node semantic data to generate device connection topology data; Step S3: performing real-time device status pattern recognition based on the device connection topology data to obtain device operation status pattern data; performing device fault state detection based on the device operation status pattern data to generate device fault state data; Use the equipment fault status data to analyze the fault-affected links on the equipment connection topology data and generate fault propagation link data; Step S4: evaluating the fault severity of the fault propagation link data to generate equipment fault severity data; Intelligent operation and maintenance path planning is performed based on the equipment fault severity data to obtain intelligent equipment operation and maintenance path data.

2. The smart building operation and maintenance management method based on the BIM platform according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire target BIM building model data; Step S12: using the IFC standard protocol to identify the electromechanical equipment object of the target BIM building model data, and generating the electromechanical equipment object metadata; Step S13: constructing a BIM three-dimensional spatial index for the metadata of the electromechanical equipment object to obtain the three-dimensional spatial index data of the equipment; Step S14: performing a proximity search for similar-type devices on the electromechanical device object metadata through the device three-dimensional spatial index data, and performing device spatial proximity analysis according to the preset spatial range data to generate device spatial relationship data; Step S15: identifying potential connection device pairs according to the device spatial relationship data to obtain potential connection device pair data; Step S16: extracting the pipeline / cable path direction according to the target BIM building model data to generate pipeline / cable path data; Step S17: Based on the pipeline / cable path data, the physical connection relationship of the potential connection device pair data is verified, and the device connection topology network is constructed to generate device physical connection topology data.

3. The smart building operation and maintenance management method based on the BIM platform according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: standardizing the BIM coordinate system of the electromechanical equipment object metadata, and performing equipment coordinate positioning to generate standard equipment space coordinate data; Step S132: identifying the operation and maintenance management space of the target BIM building model data by using the standard equipment space coordinate data to obtain the operation and maintenance equipment management space data; Step S133: extracting device size from the metadata of the electromechanical device object to obtain device size data; Step S134: performing regular three-dimensional geometric abstraction processing according to the device size data, and calculating the device space volume to generate device space volume data; Step S135: Use the operation and maintenance equipment management space data to construct a spatial index for the equipment space volume data to generate equipment three-dimensional space index data.

4. The smart building operation and maintenance management method based on the BIM platform according to claim 3 is characterized in that: Step S135 includes the following steps: Step S1351: performing root node index range processing on the operation and maintenance equipment management space data to obtain Octree space root node range data; Step S1352: performing spatial node division on the operation and maintenance equipment management space data based on the Octree spatial root node range data, dividing the spatial range of the root node into two equal parts along the three coordinate axes, and allocating the equipment to the corresponding subspace to obtain node division data; Step S1353: Calculate the node space occupancy rate of the equipment space volume data using the node partitioning data to obtain the node space occupancy rate data; Step S1354: recursively process the node partition data using the node space occupancy rate data, and construct an Octree index tree, thereby obtaining a preliminary searchable spatial index; Step S1355: Count the number of node space devices on the preliminary searchable spatial index to generate node density distribution data; Step S1356: using a preset device density threshold to perform device density identification on the node density distribution data, and obtaining high-density device node data and low-density device node data respectively; Step S1357: performing R-Tree index local optimization on high-density device node data, and using device coordinates as leaf nodes for index association to obtain high-density device index optimization data; Step S1358: generating an ordered list of device coordinates for low-density device node data, and performing one-dimensional index link optimization to generate low-density device index optimization data; Step S1359: Optimize the preliminary searchable spatial index using the low-density device index optimization data and the high-density device index optimization data to generate device three-dimensional spatial index data.

5. The smart building operation and maintenance management method based on the BIM platform according to claim 1 is characterized in that: Step S17 includes the following steps: Step S171: identifying the connection endpoints of the pipeline / cable path data through the target BIM building model data, and extracting the connection coordinates to obtain the pipeline / cable path endpoint coordinate data; Step S172: extracting device connection point information from the potential connection device pair data to obtain device connection point data; Step S173: Calculate the spatial distance of the connection points according to the pipeline / cable path endpoint coordinate data and the equipment connection point data to generate connection point distance data; Step S174: Acquire a mechatronic device connection matching rule library; Step S175: traversing the equipment connection point data, and using the electromechanical equipment connection matching rule library and the connection point distance data to match the path connection points of the pipeline / cable path endpoint coordinate data, and generating connection point matching result data; Step S176: verifying the connection relationship of the potential connection device pair data based on the connection point matching result data, and generating verified connection device pair data when the connection points of the potential connection device pair all match the connection points of the same path; otherwise, generating non-connection device pair data; Step S177: searching for connection paths for the non-connected device pair data and performing indirect connection relationship analysis. If it is an indirect connection relationship, marking the non-connected device pair data as indirect device connection pair data; otherwise, marking the non-connected device pair data as unconnected device pair and feeding back to the terminal device for manual verification; Step S178: Perform device physical connection topology processing on the verified connection device pair data, indirect device connection pair data, and pipeline / cable path data, and mark the connection direction, thereby obtaining device physical connection topology data.

6. The smart building operation and maintenance management method based on the BIM platform according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: annotating device node function semantics on device physical connection topology data to generate device function node semantic data; Step S22: performing device function group association analysis on the device physical connection topology data through the device function node semantic data to generate device group function association data; Step S23: constructing a semantic network according to the semantic data of the device function nodes and the device group function association data to obtain device function semantic network data; Step S24: searching for a function path according to the device function semantic network data, and mapping the function path to the corresponding device to obtain device logical connection relationship data; Step S25: performing logic conflict detection on the device physical connection topology data through the device logical connection relationship data to obtain logic conflict detection data; Step S26: performing logic conflict type identification on the logic conflict detection data to generate logic conflict type data; Step S27: performing a logic conflict confidence evaluation according to the logic conflict type data to obtain logic conflict confidence data; Step S28: Automatically eliminate logical conflicts in the device physical connection topology data using the logic conflict confidence data to generate device connection topology data.

7. The smart building operation and maintenance management method based on the BIM platform according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Based on the device connection topology data, the preset smart building Internet of Things sensor network is used to collect the real-time operation data of the device to obtain the real-time operation status data of the device; Step S32: transmitting the real-time operation status data of the equipment to the BIM platform, and performing data preprocessing to generate equipment operation status monitoring data; Step S33: performing real-time equipment status pattern recognition according to the equipment operation status monitoring data to obtain equipment operation status pattern data; Step S34: performing dynamic connection relationship analysis on the real-time operation status data of the device, and performing dynamic connection strength evaluation according to the operation status mode data of the device to generate device connection strength evaluation data; Step S35: Perform device fault status detection on the device connection topology data to generate device fault status data; perform fault impact link analysis on the device fault status data using the device connection strength assessment data to generate fault propagation link data.

8. The smart building operation and maintenance management method based on the BIM platform according to claim 1 is characterized in that: Step S35 includes the following steps: Step S351: identifying device function dependency relationships on device connection topology data according to device operation status mode data, and generating device function dependency relationship data; Step S352: performing equipment fault state detection on the equipment operation state mode data to generate equipment fault state data; Step S353: dividing the equipment operation status monitoring data into fault time windows according to the equipment fault status data to obtain fault time window data; Step S354: performing fault device timing correlation analysis according to the fault time window data and the device connection strength assessment data to generate fault-related device data; Step S355: Preliminary determination of the fault impact range is performed on the fault-associated device data using the device function dependency data to generate preliminary fault impact range data; Step S356: Calculate the equipment space distance for the fault-related equipment data, and perform fault space impact range analysis to generate equipment fault space impact data; Step S357: dynamically expand the affected devices on the preliminary fault impact range data using the device fault spatial impact data, and construct a fault propagation path for the device connection topology data to generate fault propagation link data.

9. The smart building operation and maintenance management method based on the BIM platform according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: perform equipment criticality assessment according to the fault propagation link data to obtain equipment criticality assessment data; perform equipment operation state stability analysis using the equipment operation state mode data and the fault propagation link data to obtain equipment operation state stability data; perform equipment safety risk assessment on the fault propagation link data using the equipment fault state data to generate equipment safety risk assessment data; Step S42: using a preset equipment failure probability prediction model to predict the equipment failure probability of the equipment criticality assessment data, the equipment operating status stability data, and the equipment safety risk assessment data, and to perform a failure severity assessment to generate equipment failure severity data; Step S43: sorting the equipment processing priorities according to the equipment fault severity data, and performing operation and maintenance resource solution configuration processing to generate equipment operation and maintenance solution data; Step S44: estimating the equipment maintenance time for the equipment operation and maintenance plan data to generate equipment maintenance time estimation data; Step S45: performing operation and maintenance personnel allocation processing according to the equipment operation and maintenance plan data and the equipment maintenance time estimation data to obtain operation and maintenance personnel scheduling data; Step S46: constructing a building internal navigation network based on the target BIM building model data; using the building internal navigation network to map the equipment operation and maintenance solution data to operation and maintenance equipment, and obtaining equipment location mapping data; Step S47: Based on the equipment location mapping data, equipment operation and maintenance plan data, and operation and maintenance personnel scheduling data, the internal navigation network of the building is used to perform multi-objective operation and maintenance path planning to obtain intelligent equipment operation and maintenance path data.

10. A smart building operation and maintenance management system based on BIM platform, characterized in that: Used to execute the smart building operation and maintenance management method based on the BIM platform as claimed in claim 1, the smart building operation and maintenance management system based on the BIM platform includes: The BIM topology construction module is used to obtain the target BIM building model data; construct the BIM three-dimensional space index according to the target BIM building model data to obtain the equipment three-dimensional space index data; perform the proximity search of the same type of electromechanical equipment according to the equipment three-dimensional space index data, and construct the equipment connection topology network to generate the equipment physical connection topology data; The device connection logic verification module is used to annotate the device node function semantics of the device physical connection topology data to generate device function node semantic data; the device function node semantic data is used to automatically eliminate logical conflicts in the device physical connection topology data to generate device connection topology data; The equipment fault monitoring module is used to perform real-time equipment status pattern recognition based on the equipment connection topology data to obtain equipment operation status pattern data; perform equipment fault status detection based on the equipment operation status pattern data to generate equipment fault status data; use the equipment fault status data to perform fault impact link analysis on the equipment connection topology data to generate fault propagation link data; The intelligent operation and maintenance path planning module is used to evaluate the fault severity of the fault propagation link data and generate equipment fault severity data; intelligent operation and maintenance path planning is performed based on the equipment fault severity data to obtain intelligent equipment operation and maintenance path data.

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