A BIM-based building operation and maintenance supervision method and system

Abstract

The present invention discloses a building operation and maintenance supervision method and system based on BIM, including S1: acquiring building operation and maintenance data, and dividing the building operation and maintenance data into n data partitions according to the layout and structural system of the building; S2: establishing a corresponding data partition index for each data partition, and outputting a prediction index table using a building component graph model; S3: determining the initial data partition of the building operation and maintenance data based on the prediction index table, and introducing a random forest model to predict the next data partition where a collision will occur; S4: performing collision detection on the next data partition where a collision will occur using an optimized collision detection algorithm, judging whether the component space bounding boxes intersect, and obtaining collision detection results. When performing collision detection, the data partitions where a collision may occur are first screened, and then the collision detection algorithm is executed in the screened data partitions, thereby reducing unnecessary calculation amount and improving collision detection efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data operation and maintenance supervision, and in particular to a BIM-based building operation and maintenance supervision method and system. Background Art

[0002] This data covers a large amount of information throughout the building's entire life cycle, including geometric information (such as component shape, size, and spatial location), physical information (such as component weight, density, and strength), and material information (such as material type and model). As buildings grow in size and over time, the amount of data continues to expand. For example, large commercial complexes or high-rise office buildings contain a large number of components, and new data (such as equipment operation data and maintenance records) is constantly generated during building operations, resulting in a sharp increase in data volume.

[0003] Among them, collision detection plays an important role in ensuring the safety and functionality of building facilities in building operation and maintenance. However, the expansion of data volume prolongs the collision detection time. Because when performing collision detection, the system needs to compare the spatial position relationship of various components in the building (such as pipes, lines, structural frames, etc.). The excessive amount of data leads to an exponential increase in the amount of calculation, which makes it impossible to timely discover and resolve potential collision risks, and increases the risk of building operation and maintenance supervision. Therefore, a BIM-based building operation and maintenance supervision method and system are proposed to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing technology that the amount of data is too large, resulting in an exponential increase in the amount of calculation, which makes it impossible to timely discover and resolve potential collision risks, and to propose a building operation and maintenance supervision method and system based on BIM.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A BIM-based building operation and maintenance supervision method, the steps of the method are as follows:

[0007] S1: Obtain building operation and maintenance data and divide the building operation and maintenance data into n data partitions according to the layout and structural system of the building;

[0008] S2: Establish a corresponding data partition index for each data partition, and use the building component graph model to output a prediction index table;

[0009] S3: Determine the initial data partition of the building operation and maintenance data based on the prediction index table, and introduce the random forest model to predict the next data partition where the collision will occur;

[0010] S4: Use the optimized collision detection algorithm to perform collision detection on the next data partition where the collision occurs, determine whether the component space bounding boxes intersect, and obtain the collision detection results. Based on the collision detection results, a real-time warning is issued for the detected collision event on the building operation and maintenance supervision interface.

[0011] Preferably, the process of dividing the building operation and maintenance data into multiple data partitions is:

[0012] Obtain a large amount of information data covering the entire life cycle of the building from the real-time building operation and maintenance database, including geometric information, physical information, and material information. Let the acquired building operation and maintenance data be D;

[0013] According to the functional layout and spatial layout of the building, the data set is divided into n data partitions, denoted as D = (P1...P2, .., P n );

[0014] Preferably, the data set is divided into n data partitions by functional area division and spatial layout.

[0015] Preferably, the prediction index table includes a data partition component set, a component quantity, and a set of adjacent data partitions.

[0016] Preferably, the implementation process of outputting the corresponding prediction index table through the building component graph model is:

[0017] Construct a building component diagram model;

[0018] The preferred construction process of the building component diagram model is:

[0019] Extract all the relevant information of components from the building operation and maintenance data, including the geometric information, physical information, spatial location information and the connection relationship between components. Let the component set be C = (c1, c2, .., c N ), where N is the number of components;

[0020] Treat each component as a node in a graph model, and the attributes of the component as the attributes of the node;

[0021] Assume that the basic framework of the building component graph model is G = (C, E), where E is the edge set, expressed as E = {e jk │c j , c k}, specific, e jk Composed of two components c j , c k The connection relationship between them is determined, and 1≤j≤N, 1≤k≤N, j≠k;

[0022] Preferably, the output process of the prediction index table is:

[0023] Traverse all nodes in the building component graph model and obtain the component type t of each node;

[0024] Through a data partition Traversing data partitions All nodes of the node are counted, and the component types of all nodes are counted to obtain a component type set T = (t1, t2, .., t N );

[0025] For a data partition P v , let the corresponding graph model node set be C v , traverse the node set C v Nodes in will be with the node The data partition P where all nodes connected by the edge are located α , added to a set of adjacent data partitions A, which contains a series of data partitions connected by edges;

[0026] The component type set, component quantity, and adjacent partition set are integrated to form a prediction index table, expressed as θ = {N, T, A}.

[0027] Preferably, the steps of determining the initial data partition of the building operation and maintenance data based on the prediction index table are:

[0028] Traverse the prediction index table and set the component type set involved in the collision detection request as T C For each partition and its corresponding prediction index table, check whether each component type in the component type set T belongs to T C ;

[0029] Define a matching function F T (P i ):

[0030]

[0031] in, Indicates the first Type of component, when F T (P i )>0, the corresponding data partition P i As the initial data partition, an initial data partition set S = {P i1 , P i2 ,..,P in}.

[0032] Preferably, the implementation process of predicting the next data partition where a collision occurs is:

[0033] A random forest model is introduced, and the collision data in the historical operation and maintenance data is used as training data for model training;

[0034] Starting from the initial data partition, for the new data partition, the component type and the components in the adjacent partition are input into the trained random forest model. Extract the characteristic values of the initial data partition, that is, the component type in the partition and the component type in the adjacent partition, and obtain a series of characteristic data The feature data Input into the trained random forest model to obtain the prediction result Y;

[0035] Output the final collision probability based on the prediction results Among them, the final collision probability prediction result is the average value of all decision tree prediction results. It represents the predicted probability value of the collision between component type t and components in adjacent data partition P. After traversing all the initial data partition inputs, a collision threshold σ is set. When , the adjacent data partition of the initial data partition in the input data that is larger than the collision threshold σ is marked as the next data partition where collision occurs.

[0036] Preferably, the steps for obtaining the collision detection result are:

[0037] Define a data partition P S ∈S next Partition, data partition P S The component set in C S =(c S1 , c S2 ,..,c Sq ), where q represents the data partition P S The number of components within;

[0038] By C S Any two components c Sα , c Sβ , α∈q, β∈q and α≠β, let component c Sα The bounding box is B Sα =(x maxα , x minα ,y maxα ,y minα , z maxα , z minα ), where for component c Sα , its spatial bounding box B Sα Defined by six parameters, x maxα and x minα Respectively represent the minimum and maximum coordinate values of the component in the axial direction; ymaxα and y minα Respectively represent the minimum and maximum coordinate values of the component in the axial direction; z maxα and z minα Respectively represent the minimum and maximum coordinate values of the component in the axial direction;

[0039] Component c Sβ The bounding box is B Sβ =(x maxβ , x minβ ,y maxβ ,y minβ , z maxβ , z minβ ), where x maxβ and x minβ Respectively represent the minimum and maximum coordinate values of the component in the axial direction; y maxβ and y minβ Respectively represent the minimum and maximum coordinate values of the component in the axial direction; z maxβ and z minαβ Respectively represent the minimum and maximum coordinate values of the component in the axial direction;

[0040] A formula is used to determine whether two spatial bounding boxes intersect, expressed as:

[0041]

[0042] When all conditions are met, the judgment result is that the spatial bounding boxes of the two components intersect, otherwise they do not intersect.

[0043] A BIM-based building operation and maintenance supervision system, including:

[0044] Data partitioning module: obtains building operation and maintenance data and divides the building operation and maintenance data into n data partitions according to the layout and structural system of the building;

[0045] Data index establishment module: establishes corresponding data partition index for each data partition, and outputs a prediction index table using the building component graph model;

[0046] Data partition prediction module: Determines the initial data partition of building operation and maintenance data based on the prediction index table, and introduces a random forest model to predict the next data partition where a collision will occur;

[0047] Data detection module: Use the optimized collision detection algorithm to perform collision detection on the next data partition where a collision occurs, determine whether the component space bounding boxes intersect, and obtain the collision detection results. Based on the collision detection results, a real-time warning is issued for the detected collision event on the building operation and maintenance supervision interface.

[0048] The present invention has the following beneficial effects:

[0049] 1. In the present invention, through data partition management and prediction index table, areas where collisions may occur can be quickly located, reducing unnecessary data processing. In large buildings, only data partitions related to specific component types or initial collision areas need to be focused on, without the need for comprehensive detection of the entire building data, significantly shortening the detection time. At the same time, the random forest model is used to predict the next data partition where a collision will occur, further narrowing the detection range. The model is based on patterns learned from historical data and can accurately predict potential collision areas, avoiding one-by-one detection of all data partitions, thereby improving detection efficiency. By optimizing the collision detection algorithm, a preliminary screening of spatial bounding boxes is first performed to quickly exclude a large number of non-intersecting components, and then accurate detection is performed on components that may collide, avoiding complex geometric calculations for all components and greatly improving the speed of collision detection. Based on the prediction and detection results, building operation and maintenance managers can make more scientific and reasonable decisions. When a data partition with high collision risk is predicted, detailed data conditions can be obtained in advance through BIM building operation and maintenance data to take preventive measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a method step diagram of a BIM-based building operation and maintenance supervision method and system proposed by the present invention.

[0051] Figure 2 This is a system block diagram of a BIM-based building operation and maintenance supervision method and system proposed by the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Example 1

[0054] like Figures 1 to 2 As shown, the present invention proposes a BIM-based building operation and maintenance supervision method and system, including:

[0055] S1: Obtain building operation and maintenance data and divide the building operation and maintenance data into n data partitions according to the layout and structural system of the building;

[0056] The process of dividing building operation and maintenance data into multiple data partitions is as follows:

[0057] Obtain a large amount of information data covering the entire life cycle of the building from the real-time building operation and maintenance database, including geometric information, physical information, and material information. Let the acquired building operation and maintenance data be D;

[0058] Among them, the building operation and maintenance database is the main source of data, continuously collecting and storing various types of data throughout the life cycle of the building, including but not limited to:

[0059] Geometric information: such as the shape, size, spatial position of building components and the coordinates of each component in three-dimensional space;

[0060] Physical information: physical properties of building materials, such as weight, density, strength, etc. of components;

[0061] Material information: including detailed data of various materials used in the building, such as the type of material (concrete, steel, wood, etc.) and model;

[0062] According to the functional layout and spatial layout of the building, the data set is divided into n data partitions, denoted as D = (P1...P2, .., P n );

[0063] Among them, according to the functional area division: buildings are usually composed of areas with different functions, such as office areas, commercial areas, residential areas, equipment areas, etc. The building components in these functional areas have similar functions and operation and maintenance requirements;

[0064] For example, the office area may contain a large number of office furniture, electrical equipment, and network lines; the equipment area is concentrated with large equipment such as air conditioning units and elevator rooms. Based on functional characteristics, the building component data belonging to the same functional area is divided into the same data partition;

[0065] Partitioning by spatial layout: Data partitioning is performed based on the spatial structure of the building, for example, by floor. For multi-story buildings, the building components of each floor are relatively independent in the vertical direction, and the usage and operation and maintenance requirements of each floor may be different. Suppose the building has m floors, and each floor corresponds to a data partition P. i (i=1,2,..,m),P i Represents the i-th partition.

[0066] S2: Establish a corresponding data partition index for each data partition, and use the building component graph model to output a prediction index table;

[0067] The prediction index table includes a data partition component set, a component quantity, and an adjacent data partition set;

[0068] The implementation process of outputting the corresponding prediction index table through the building component graph model is as follows:

[0069] Construct a building component diagram model;

[0070] The construction process of the building component diagram model is as follows:

[0071] Extract all the relevant information of components from the building operation and maintenance data, including the geometric information (such as shape and size), physical information (such as material and weight), spatial position information (3D coordinates) and the connection relationship between components. Let the component set be C = (c1, c2, .., c N ), where N is the number of components;

[0072] Treat each component as a node in a graph model, and the attributes of the component as the attributes of the node;

[0073] For example, the geometric shape of component c1 is G1 and the spatial position is (x1, y1, z1), then the attributes of the node in the graph model can be expressed as c1 = (G1, x1, y1, z1);

[0074] Assume that the basic framework of the building component graph model is G = (C, E), where E is the edge set, expressed as E = {e jk │c j , c k}, specific, e jk Composed of two components c j , c k The connection relationship between them is determined, and 1≤j≤N, 1≤k≤N, j≠k;

[0075] Among them, the connection relationship mainly refers to the physical connection and logical association between building components;

[0076] The output process of the prediction index table is:

[0077] Traverse all nodes in the building component graph model and obtain the component type t of each node;

[0078] Through a data partition Traversing data partitions All nodes of the node are counted, and the component types of all nodes are counted to obtain a component type set T = (t1, t2, .., t N );

[0079] A counter array count[N] is set through the component set. N represents the number of all possible component types in the building. The array is used to record the number of times each component type appears. The initial value of each element in the array is set to 0. Before starting to count, the number of times each component type appears is considered to be 0. All components in the data partition are traversed. When a component of type t is encountered, i When a component is created, the corresponding counter is increased by 1;

[0080] Specifically, it is represented in the program as count[i]++, where i is the same as the component type t i The corresponding index,Finally, the component type set T is determined by checking which elements in the counter array have values greater than 0. For each element in the counter array count, if count[j]>0, then the corresponding component type belongs to the component type set T;

[0081] For a data partition P v , let the corresponding graph model node set be C v , traverse the node set C v Nodes in will be with the node The data partition P where all nodes connected by the edge are located α , added to a set of adjacent data partitions A, which contains a series of data partitions connected by edges;

[0082] The component type set, component quantity, and adjacent partition set are integrated to form a prediction index table, which is expressed as θ = {N, T, A}.

[0083] S3: Determine the initial data partition of the building operation and maintenance data based on the prediction index table, and introduce the random forest model to predict the next data partition where the collision will occur;

[0084] The steps for determining the initial data partition of building operation and maintenance data based on the prediction index table are as follows:

[0085] Traverse the prediction index table and set the component type set involved in the collision detection request as T C For each partition and its corresponding prediction index table, check whether each component type in the component type set T belongs to T C ;

[0086] Define a matching function F T (P i ):

[0087]

[0088] in, Indicates the first Type of component, when F T (P i )>0, the corresponding data partition P i As the initial data partition, an initial data partition set S = {P i1 , P i2 ,..,P in};

[0089] For each partition P i and its corresponding prediction index table, by checking the component type set in the data partition and the specific component type set T C The relationship between the components determines the initial data partition. This method based on component type matching can quickly screen out the initial data partition from the perspective of data structure.

[0090] For example, suppose there is BIM operation and maintenance data of a large commercial building. The building is divided into multiple data partitions, including shopping malls, office areas, etc. We want to inspect the fire protection system related components in the building to ensure the normal operation of the fire protection system. In the fire protection system inspection, the specific component type set C T Including: fire hoses, fire sprinklers, fire hydrants, fire alarms. In the data partition P1, the first floor of the shopping mall is included. The corresponding component type set T1 includes: floor tiles, lighting fixtures, fire hoses, shop counters, and the number of component types m = 4. In the data partition P2, the second floor of the office is included. The component types include: office desks and chairs, network lines, air conditioning ducts, and the number of component types m = 3. Through the matching function F T (P i ), substitute P1 and P2 into it, and get F T (P1)>1,F T (P2)=0, so the corresponding data partition P1 is used as the initial partition. Through the above calculation, the initial partition set will include the data partition mall layer, because these data partitions contain component types related to the fire protection system (fire water pipes and fire hydrants). In this way, when conducting a fire protection system inspection, priority is given to the initial data partition, and there is no need to conduct a comprehensive inspection of the entire system first.

[0091] The implementation process of predicting the next data partition where a collision occurs is as follows:

[0092] A random forest model is introduced, and the collision data in the historical operation and maintenance data is used as training data for model training;

[0093] The random forest consists of multiple decision trees. Let the number of decision trees be i (the same as the number of data partitions). For each decision tree, samples are randomly extracted from the training data set with replacement to form a training subset of the decision tree. At each node, a portion of features is randomly selected from all features, and then the best feature is selected from these features for splitting. The decision tree is continuously grown until the stopping condition is met (such as the depth of the tree reaches a preset value), and finally a random forest model is obtained.

[0094] Starting from the initial data partition, for the new data partition, the component type and the components in the adjacent partition are input into the trained random forest model. Extract the characteristic values of the initial data partition, that is, the component type in the partition and the component type in the adjacent partition, and obtain a series of characteristic data The feature data Input into the trained random forest model, each decision tree makes a prediction based on the input features, obtains a prediction result Y, and outputs the final collision probability based on the prediction result Among them, the final collision probability prediction result is the average value of all decision tree prediction results. It represents the predicted probability value of the collision between component type t (initial data partition) and components in adjacent data partition P. After traversing the collision probability of all initial data partitions, a collision threshold σ is set. When the initial data partition in the input data is larger than the collision threshold σ, the adjacent data partition is marked as the next data partition S that will collide next .

[0095] S4: Use the optimized collision detection algorithm to perform collision detection on the next data partition where the collision occurs, determine whether the component space bounding boxes intersect, and obtain the collision detection results. Based on the collision detection results, a real-time warning is issued for the detected collision event on the building operation and maintenance supervision interface;

[0096] The steps to obtain collision detection results are:

[0097] According to the prediction results, get the next data partition set S where the collision occurs next , which is predicted by the random forest model mentioned above, and the data partitions that may collide have been marked.

[0098] Define a data partition P S ∈S next Partition, data partition P S The component set in C S =(c S1 , c S2 ,..,c Sq ), where q represents the data partition P S The number of components within;

[0099] By C S Any two components c Sα , c Sβ , α∈q, β∈q and α≠β, let component c Sα The bounding box is B Sα =(x maxα , x minα ,y maxα ,y minα , z maxα , z minα), where for component c Sα , its spatial bounding box B Sα Defined by six parameters, x maxα and x minα Respectively represent the minimum and maximum coordinate values of the component in the axial direction; y maxα and y minα Respectively represent the minimum and maximum coordinate values of the component in the axial direction; z maxα and z minα Respectively represent the minimum and maximum coordinate values of the component in the axial direction;

[0100] Component c Sβ The bounding box is B Sβ =(x maxβ , x minβ ,y maxβ ,y minβ , z maxβ , z minβ ), where x maxβ and x minβ Respectively represent the minimum and maximum coordinate values of the component in the axial direction; y maxβ and y minβ Respectively represent the minimum and maximum coordinate values of the component in the axial direction; z maxβ and z minαβ Respectively represent the minimum and maximum coordinate values of the component in the axial direction;

[0101] The formula used to determine whether two spatial bounding boxes intersect is:

[0102]

[0103] When all conditions are met, the judgment result is that the spatial bounding boxes of the two components intersect, that is, the two components collide; otherwise, no collision occurs;

[0104] The judgment is based on finding a constructed spatial bounding box that satisfies all the formula conditions. The spatial bounding box is a circumscribed cuboid of the component. If the spatial bounding boxes of two components intersect, it means that the two components have overlapping areas in three-dimensional space, and then the two components have collided.

[0105] Specifically, in the x-axis direction, the maximum value of one bounding box must be greater than or equal to the minimum value of the other bounding box, and its minimum value must be less than or equal to the maximum value of the other bounding box, so that the two bounding boxes have overlapping parts in the axial direction. Similarly, the conditions must also be met in the y-axis and z-axis directions. Only when the conditions are met in all three directions, the two bounding boxes intersect.

[0106] Specifically, after knowing that two components have collided, a real-time warning of the detected collision event is issued through the building operation and maintenance supervision interface, and the relevant operation and maintenance personnel are informed through system interface prompts, notifications, etc. The main idea of this solution is to manage the BIM model data in data partitions and adopt an optimized collision detection algorithm. By dividing the building model into multiple data partitions according to functional areas or floors, when performing collision detection, the data partitions that may collide are first screened, and then the collision detection algorithm is executed in the screened data partitions, thereby reducing unnecessary calculations and improving collision detection efficiency.

[0107] Example 2

[0108] like Figures 1 to 2 As shown, based on the first embodiment,

[0109] Data partitioning module: obtains building operation and maintenance data and divides the building operation and maintenance data into n data partitions according to the layout and structural system of the building;

[0110] Data index establishment module: establishes corresponding data partition index for each data partition, and outputs a prediction index table using the building component graph model;

[0111] Data partition prediction module: Determines the initial data partition of building operation and maintenance data based on the prediction index table, and introduces a random forest model to predict the next data partition where a collision will occur;

[0112] Data detection module: Use the optimized collision detection algorithm to perform collision detection on the next data partition where a collision occurs, determine whether the component space bounding boxes intersect, and obtain the collision detection results. Based on the collision detection results, a real-time warning is issued for the detected collision event on the building operation and maintenance supervision interface.

[0113] In the application, the several formulas involved are all calculated by taking their numerical values after removing the dimensions, and the formulas are established by collecting a large amount of data and performing software simulation to obtain a formula for the most recent real situation. Some coefficients or weights in the formulas are set by technical personnel in this field according to actual conditions, so they will not be elaborated here.

[0114] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0115] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A BIM-based building operation and maintenance supervision method, characterized in that: The steps of this method are as follows: S1: Obtain building operation and maintenance data and divide the building operation and maintenance data into n data partitions according to the layout and structural system of the building; S2: Establish a corresponding data partition index for each data partition, and use the building component graph model to output a prediction index table; The implementation process of outputting the corresponding prediction index table through the building component graph model is as follows: Constructing architectural component diagram models; The construction process of the building component graph model is as follows: Extract all the relevant information of components from the building operation and maintenance data, including the geometric information, physical information, spatial location information and the connection relationship between components. Let the component set be ,in, is the number of components; Treat each component as a node in a graph model, and the attributes of the component as the attributes of the node; Assume that the basic framework of the building component diagram model is ,in, is an edge set, represented by , specifically, Consists of two components The connection relationship between them is determined, and , , ; The output process of the prediction index table is: Traverse all nodes in the building component graph model and obtain the component type of each node ; Through a data partition , , traverse the data partition All nodes of the component type of all nodes are counted to obtain a component type set ; For a data partition , let the corresponding graph model node set be , traverse the node set Nodes in , will be with the node The data partition where all nodes connected by the edge are located , added to a set of adjacent data partitions In, collection Contains data partitions connected by edges; The component type set, component quantity, and adjacent partition set are integrated to form a prediction index table, which is expressed as ; S3: Determine the initial data partition of the building operation and maintenance data based on the prediction index table, and introduce the random forest model to predict the next data partition where the collision will occur; S4: Use the optimized collision detection algorithm to perform collision detection on the next data partition where a collision occurs, determine whether the component space bounding boxes intersect, and obtain the collision detection results. Based on the collision detection results, a real-time warning is issued for the detected collision event on the building operation and maintenance supervision interface.

2. A BIM-based building operation and maintenance supervision method according to claim 1, characterized in that: The process of dividing the building operation and maintenance data into multiple data partitions is as follows: Obtain information data covering the entire life cycle of the building from the building operation and maintenance database, including geometric information, physical information and material information. Suppose the acquired building operation and maintenance data is ; According to the functional layout and spatial layout of the building, the data set is divided into data partitions, denoted as ; The data set is divided into Each data partition is divided by functional area and spatial layout.

3. The BIM-based building operation and maintenance supervision method according to claim 1, characterized in that: The prediction index table includes a data partition component set, a component quantity, and an adjacent data partition set.

4. The BIM-based building operation and maintenance supervision method according to claim 1, characterized in that: The step of determining the initial data partition of the building operation and maintenance data based on the prediction index table is as follows: Traverse the prediction index table and set the component type set involved in the collision detection request as , for each partition and its corresponding prediction index table, check the component type set Does each component type in ; Define a matching function : ； in, Indicates the first Type of component, when When the corresponding data partition As the initial data partition, it eventually forms an initial data partition set .

5. The BIM-based building operation and maintenance supervision method according to claim 4 is characterized in that: The implementation process of predicting the next data partition where a collision occurs is as follows: A random forest model is introduced, and the collision data in the historical operation and maintenance data is used as training data for model training; Starting from the initial data partition, for each new data partition, its related feature data is input into the trained random forest model. , , extract the characteristic value of the initial data partition, that is, the component type in the partition and the component type in the adjacent partition, and obtain the characteristic data , the feature data Input into the trained random forest model to obtain the prediction result Y; Output the final collision probability based on the prediction results , where the final collision probability prediction result is the average value of all decision tree prediction results, Indicates component type Partition with adjacent data The probability prediction value of the internal component collision, the collision probability after traversing all the initial data partition inputs, and setting a collision threshold ,when When the initial data partition in the input data is greater than the collision threshold The adjacent data partition is marked as the next data partition where the collision occurs .

6. The BIM-based building operation and maintenance supervision method according to claim 5 is characterized in that: The steps for obtaining the collision detection result are: Define a data partition Partition, data partition The component set within is ,in, Indicates data partition The number of components within; pass Any two components , and , set components The bounding box of , where for the component , its spatial bounding box Defined by six parameters, and Respectively represent the minimum and maximum coordinate values of the component in the X-axis direction; and Respectively represent the minimum and maximum coordinate values of the component in the Y-axis direction; and Respectively represent the minimum and maximum coordinate values of the component in the Z-axis direction; member The bounding box of ,in, and Respectively represent the minimum and maximum coordinate values of the component in the X-axis direction; and Respectively represent the minimum and maximum coordinate values of the component in the Y-axis direction; and Respectively represent the minimum and maximum coordinate values of the component in the Z-axis direction; The following formula is used to determine whether two spatial bounding boxes intersect: ； When all conditions are met, the judgment result is that the spatial bounding boxes of the two components intersect, otherwise they do not intersect.

7. The system of a BIM-based building operation and maintenance supervision method according to claim 1 is characterized in that: include: Data partitioning module: obtains building operation and maintenance data and divides the building operation and maintenance data into n data partitions according to the layout and structural system of the building; Data index establishment module: establishes corresponding data partition index for each data partition, and outputs a prediction index table using the building component graph model; Data partition prediction module: Determines the initial data partition of building operation and maintenance data based on the prediction index table, and introduces a random forest model to predict the next data partition where a collision will occur; Data detection module: Use the optimized collision detection algorithm to perform collision detection on the next data partition where a collision occurs, determine whether the component space bounding boxes intersect, and obtain the collision detection results. Based on the collision detection results, a real-time warning is issued for the detected collision event on the building operation and maintenance supervision interface.

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