CN112560152A - Intelligent detection method of BIM geometric model based on GIS - Google Patents

Abstract

The invention discloses an intelligent detection method of a GIS-based BIM geometric model, which uses a coordinate of a component primitive as a search index to help identify the component primitive and a primitive adjacent to the component primitive and detect a point set collection mode of the primitive and the adjacent primitive. And detecting the subsystem as a group of the images, and judging the relation between the detection component image element and the adjacent component image element by using the intersection mode of the detection body in the feasible region of the detection component image element and the infeasible region of the adjacent component image element of the detection component image element. The scheme provides a judgment mode of correctness for the intersection result of the detection component primitive and the neighboring primitive thereof and a detection mode of re-judgment when the judgment is unclear. When the detection is unclear, the point set range is enlarged by using an octal method of the BIM three-dimensional graphic primitive so as to obtain a more accurate detection result.

Description

Intelligent detection method of BIM geometric model based on GIS

Technical Field

The invention belongs to the field of building software, and particularly relates to an intelligent detection method of a BIM geometric model based on a GIS.

Background

Large bim (building information modeling) models are typically involved in many industries such as building, construction, plumbing, and electromechanical, and often contain thousands of components (e.g., doors, walls, posts …, etc.). These components, from design to use, can produce errors of intervention and even significant errors. About 40% of defects in BIM are associated with errors in the design process.

Errors or mistakes in the BIM model can be corrected by detection. The conventional detection method for the large BIM Model is commonly used in Solibri Model Checker (SMC), Autodesk Navisvarks, Tekla BIMinsight (Hjelseth,2015) and the like. SMC is validated based on Java in ifc (industry Foundation classes) data format. Vccl (visual Code Checking language) uses visual language and automatically checks the compliance of BIM building information model.

The BIM model is detected from information requirement, rule structure, rule logic and structure from top to bottom based on RDF/XML (resource Description framework) of BMC (BIM-based packaging). The use of RDF graphical link data is based on the logical relationship between BMC concepts, which link data enables machine readability/machine interpretability and enables automatic classification, thereby enabling higher precision searches related to BMC. Representing concepts as link data can be used to test relationships within and between different concepts.

The BIM model can be detected by using the Revit transformed IFC (industry Foundation classes) standard. The data model of the IFC is defined according to the object definition of the IFC, the relationship among the objects, the attributes of the objects and the open specification of the objects, so that the data model becomes a proper representation form of the BIM data. The BIM software application extracts the data from the IFC model file and performs the mapping of the IFC data according to the coding specifications so that the code generates an accurate compliance report describing which rules of the building model are valid or invalid.

The IFC data model component method for design specification detection extracts entity objects, attribute constraints and incidence relations defined in design specification texts, and establishes a specification text system; comparing the specification system with the entity object, the attribute constraint and the incidence relation defined by the IFC standard, and extracting and recording the undefined part of the IFC standard according to the comparison result as the specification information expanded in the IFC standard; and establishing an IFC data model framework based on design specification detection, so that the IFC data model can completely express information required by the design specification detection.

BIM detection can search the attribute elements of the component primitive in a model by creating a filtering condition for detecting the component attribute, put all the found attribute element parameters into a set, and traverse each parameter of each attribute element in the set. The BIM automatic detection method comprises the steps of setting a filter, filtering all types of data out through the filter, putting the filtered data into another set, traversing all information in the set, and detecting errors or mistakes which do not meet the standard.

The BIM geometric model test is essentially identical to the BIM automatic filtering test. Geometry-based inspection-collision detection is essentially a conditional process filtering, and since the geometry is expressed using spatial coordinates, the BIM geometry detection and coordinate detection have consistency.

For BIM geometry, checking for compliance by looking at the 3D model from different angles and sections and then flagging a fault report, the checking process is highly transparent. A virtual prototype BIM model may be created based on BIM techniques. And by combining the virtual BIM model, the simulation animation of the virtual BIM model is compiled, so that the aim of visual detection can be fulfilled.

The detection of BIM is allowable for reasonable errors, so that the detection of BIM can be subdivided. The three-dimensional model of the BIM building blocks of the 3D solid can be realized using an octant method. The octant method is to divide a cube unit into eight smaller cubes. The purpose of the octant is to further subdivide the primitives to further detect the correctness of the more accurate primitives of the BIM building blocks and components, the geometric information of the model and the associated geometric data.

Intelligent detection of the BIM model has been a focus of research in BIM. How to accurately and automatically detect the geometric accuracy of the BIM model, particularly the geometric accuracy of the fitting and connection between the components is a technical problem to be solved.

At present, the geometric intelligent detection of the BIM based on the GIS is more and more emphasized on the detection, the standard interpretation, the model preparation, the model detection, the detection report and the like of the BIM. The detection establishes a basic research framework of compliance automatic detection on the basis of literature investigation and research summary, analyzes research directions of rule expression, information modeling, rule reasoning, result expression and the like of design detection, and also points out the requirements and development directions of future intelligent design detection.

By analyzing IFC data and combining with BIM technology, an algorithm for automatically generating detection points based on BIM is established: firstly, traversing IFC data, acquiring the relation between components and abstract attributes such as layers, construction sections, axes and the like required by dividing inspection lots through IfcRelationship and subclasses thereof, returning IfcProduct sets under different layers and axes, reorganizing data through IfcObjectType, returning the inspection lot sets, and allocating inspection items in specifications for each inspection lot. And traversing the inspection batch set again, sampling according to different inspection items and inspection quantity rules, returning to inspection and displaying on the BIM. The compliance detection of the BIM still faces the challenges of information model isomerism, semantic gap, automatic rule interpretation and opening, flexible sharing, complex spatial relationship analysis, large-scale reasoning and the like.

The purpose of smart object inspection is to let the object itself observe its environment and adapt it automatically by means of embedded predefined rules or algorithms. The use of smart objects continually facilitates the updating of designs. The BIM geometric compliance test is used for testing whether the fitting and connection between the components conform to the standards of BIM model design, and the standards are described by geometric information and geometric data. In the geometrical information, the fit and connection between one component and the adjacent component can be detected by visualization, and the visual fit and connection between the components can be calculated by geometrical data. That is, the correctness of the fit and connection between the members passing through the error range can be automatically judged by the threshold value or the threshold value range.

Disclosure of Invention

The technical scheme is that the coding formed by a spatial information system of a BIM model is utilized to sort the primitives of spatial positions, so that the management and application of the information and data of the primitives of the component are realized, and the judgment of the primitive relation between the component and the component can be realized. Detecting the relation between the component primitive and the adjacent primitive by using a geometrical coordinate set of a series of points of the BIM model primitive, traversing the coordinate relation between the component primitive and the adjacent primitive, finding out whether the adjacent primitive interferes or not, and judging the relation;

the invention provides an intelligent detection method of a BIM geometric model based on a GIS, which comprises the steps of utilizing the geometric coordinate space expression of the BIM model, taking the unique coordinate of a component primitive as a component code, representing the BIM model by a three-dimensional array, automatically selecting any group of coded detection primitives, setting the detection primitives as feasible regions and adjacent primitives as infeasible regions, and automatically selecting the primitives adjacent to the detection primitives according to the spatial positions to respectively establish a three-dimensional geometric coordinate set of the detection primitives and a three-dimensional geometric coordinate set of the adjacent component primitives. When the members are matched with each other, the allowed errors between the members are correct, so that when the primitive is detected, the detected primitive and the allowed errors form a feasible domain. And performing intersection detection when traversing the feasible region and the infeasible region, and generating three results, namely intersection, no intersection and unclear detection result. If the intersection is generated, performing intersection verification to determine whether the intersection situation of the geometric space is met, and if the intersection situation is not met, correcting and detecting again; if no intersection is generated, the distance of the coordinate points is reduced, detection is carried out again after the scale of the coordinate set is enlarged to prevent detection omission, but the detection cannot be repeated for multiple times so as to avoid enlarging the calculated amount and allow the detection in a reasonable error range; if the result is an unclear condition, the scale of the three-dimensional geometric coordinate set of the detection primitive and the adjacent primitives thereof needs to be enlarged, and then the feasible region and the infeasible region are traversed again for intelligent detection.

Further, the detection method is multi-thread detection.

And further, setting the detection primitive as a feasible region, setting adjacent primitives as an infeasible region in the viewing cone, when the feasible region and the infeasible region are irradiated by the rays of the viewing cone, respectively collecting a certain number of three-dimensional geometric coordinates of the feasible region and the infeasible region to form a set, and comparing and filtering the two sets to perform intersection detection.

And further, when the result is unclear, subdividing the primitive space by using an octave method to expand the primitive three-dimensional geometric coordinate set.

Further, before the step of traversing the feasible domain and the infeasible domain, threshold values are set based on BIM standards, and then intersection detection is carried out according to the feasible domain and the infeasible domain.

Further, detecting whether the non-feasible region primitive exists or not by using the detection body movement in the feasible region.

The invention provides an intelligent detection method of a GIS-based BIM geometric model, which comprises the steps of utilizing the geometric space coordinate expression of the BIM model, taking the unique coordinate of a component primitive as a component code, using a three-dimensional array to represent the BIM model, extracting a component according to the code, setting the component as a feasible region, setting a neighboring component primitive as an infeasible region, respectively establishing a feasible region coordinate set and an infeasible region coordinate set, traversing the feasible region and the infeasible region to perform intersection detection, generating intersection, no intersection and unclear results, and if the intersection verification is performed on the results by generating intersection, whether the intersection condition of the geometric space is met or not, and if the intersection is not met, performing the detection again; if no intersection exists, the distance of the coordinate point is reduced, and the scale of the coordinate set is enlarged and then the detection is carried out again; if the detection primitive and the adjacent primitive are unclear, the feasible region and the infeasible region are traversed again after the scale of the coordinate set of the detection primitive and the adjacent primitive is enlarged.

Further, after the unique coordinates of the component primitives are used as the component codes and the BIM is represented by a three-dimensional array, the subsystem is set as a feasible region according to the code extraction subsystem, and the adjacent component primitives are set as infeasible regions.

Further, the subsystem makes a pass recording the set of coordinates of the neighboring primitives with which it interferes.

Further, intersection detection is carried out on possible and impossible domains by adopting detection body motion in the possible domain of the subsystem.

The scheme achieves the following effects:

according to the unique code of the spatial position of the BIM component, the BIM can realize system sequencing management according to the spatial position, and can search and apply the coordinate system of the component primitive in real time.

According to the geometric relationship of the components, the geometric model of the BIM can be expressed by using the space coordinate set of the point set, and the unique code of the components is used as an index to realize the management and application of the geometric information and the geometric data of the components.

And setting a feasible region and an infeasible region for the picture element detected by the BIM and the adjacent picture element thereof, and selecting a part of point set for the detected picture element and the adjacent picture element thereof as a coordinate set, so that the detected picture element and the adjacent picture element thereof can traverse the coordinate.

Using the feasible and infeasible fields enables detection of intersection relationships between constructs and constructs, including intersections or disjointedness, as well as Notclear relationships that occur when a set of points may not be large enough.

The intersection can be used to determine the geometric relationship between a detection subsystem, which detects similar to the detection of a building block, and its neighboring building blocks. The subsystem detection is theoretically equivalent to batch detection, so the speed is relatively high, and the time consumption is relatively shortened.

When intersection results in Notclear. The coordinates of the primitives can be subdivided by applying an octree method, the point set coordinate set of the component model primitives is expanded, and the unclear geometric relationship is detected and judged more accurately.

The method can utilize simulation to realize visual operation, utilizes the movement of the detection body in the feasible region of the detection component to find out the interference of the infeasible region of the detection component and the adjacent component, directly checks the geometric relationship of errors and faults, and automatically records the geometric relationship.

Drawings

FIG. 1 is a block diagram of automatic detection;

FIG. 2 is a BIM automated review block diagram;

FIG. 3 is a flowchart of an embodiment of extracting the IfcRelationship subclass;

FIG. 4 is a diagram of a BIM detection system;

Detailed Description

The intelligent detection method of the GIS-based BIM geometric model of the invention is explained in detail below with reference to the accompanying drawings.

The intelligent geometric detection of the BIM model is mainly judged by utilizing the correlation between the models. The direct way is to set the model of the detecting member as a feasible domain in the visible BIM geometric model and set the model of the member adjacent to the feasible domain as the existence of the non-feasible domain to detect the BIM model.

Because the spatial coordinate relationship of the BIM model is a data expression of the BIM geometric model, the spatial coordinate relationship between the BIM component and its neighboring components is used to determine whether the relationship between the primitive of the component and its neighboring primitives is compliant, which is also the geometric detection compliance of the BIM model essentially. Therefore, the coordinate set of the detection component primitive and the coordinate set of the adjacent primitive are established, and the coordination or interference relationship between the coordinate sets is detected and judged by utilizing the mutual relationship between the coordinate sets.

And traversing the coordinates of the primitive of the detection member and the primitive of the adjacent member to obtain the intersection of the primitives and the adjacent member, and then automatically detecting and comparing the intersection to obtain the mutual relation between the detected primitive and the adjacent primitive. There are two ways to traverse the primitive, one is loop traversal. One is to set the feasible region of the detected primitive and the infeasible region of the neighboring primitive in the model, find out the intersection between the feasible region and the infeasible region, and then automatically detect whether the intersection meets the standard or not.

Automatic detection flow when a detected primitive/component is selected, its neighboring primitives/components are selected, and then the appropriate detected primitive/component and its neighboring primitive/component point sets are selected as their spatial coordinate sets, respectively, as shown in fig. 1. And finding out the intersection of the detected primitive and the adjacent primitive thereof by traversing the detected primitive and the adjacent primitive thereof or establishing a feasible region of the detected primitive and an infeasible region of the adjacent primitive, and comparing and judging the intersection to determine the geometric relationship between the detected primitive and the adjacent primitive thereof.

Specifically, first, the structureA primitive space coordinate system of the BIM system component is established, where BIMs are used to represent the primitive system of the BIM model. The graphic element of each component has a unique coordinate BIMsC (x) corresponding to the BIMs system_i，y_j，z_k). The coordinates i, j, k do not exceed the maximum of BIMs in the x, y, z direction, row, column, page. Using BIMsC (x)_i，y_j，z_k) Represents the building block and encodes the index as the building block. BIMsC (x)_i，y_j，z_k) And serves to link the member to all other geometric coordinates and geometric information of the member, denoted as B (i, j, k).

The coordinates in space of the three-dimensional model point set of the primitive for each member in the BIM may be expressed in a matrix of the BIM member in three-dimensional space. BIM models were expressed as BIMs (a1, a2, A3). A1, a2, A3 represent a three-dimensional set of a finite number of coordinates of BIM building blocks in the x, y, z directions. BIM consists of B (i, j, k) and BIMs, which constitute the search coordinate space system of BIM. This system is the basis of the BIM detection system. The detection system is denoted Bcheck ═ (B (i, j, k), BIMs).

A certain number of point-forming sets of the detecting member and point-forming sets of the adjacent members are respectively selected based on the index coordinates of the detecting member and the adjacent members. Or in the viewing cone, when the ray of the viewing cone irradiates the feasible region and the infeasible region, respectively collecting a certain number of coordinates of the feasible region and the infeasible region to form a set, comparing and filtering the two sets, judging whether an intersection relation exists or not, and judging whether the intersection relation is in compliance or not. The composition of the component primitive point set is detected. Since a primitive is made up of numerous points, automatically selecting the appropriate set of points is very important to detection efficiency. Since the geometric matching relationship between the members is judged, when the detection point is automatically selected, the point at the matching position between the members is required for judgment, and the coordinate thereof is the basis for judgment, so that a point set near the matching position should be selected when the point set is selected. And uniformly distributing and taking points on the space coordinate by utilizing the component primitives. If the obtained point set is not enough to judge the relation between the components, the octal subdivision primitive space is used, points are obtained at the octal subdivision space, and the coordinate set of the points is enlarged.

The intersection of a building block primitive with a neighboring primitive reflects the geometric relationship of the building block with the neighboring building block. If the intersection is zero, the member is not in contact with the adjacent member. If the intersection is not zero, the component and the adjacent component belong to adjacent mating or intersect with each other. At this time it is necessary to determine whether the fit or intersection is in compliance, and if so, Pass the test (Pass), and if not, generate an error output (Fail).

Another case is the case of Notclear, because both the building elements and the neighboring elements are three-dimensional spatial coordinates. There may be an intersection at one coordinate but no intersection at the other two coordinates. At this time, the relationship between the detected primitive and the adjacent primitive cannot be determined. When the (Notclear) condition occurs, the spatial coordinate value range needs to be further octadivision and then judged. The more the coordinates of the pixel point set of the BIM component are obtained, the more the three-dimensional model of the component pixel can be expressed, and whether the detected pixel interferes with the adjacent pixel can be more accurately judged. But at the same time, the denser the point coordinate positions are, the more the detection time is consumed.

In the BIM detection process, the feasible region and the infeasible region are automatically traversed, the adjacent component primitives are detected by taking the feasible region as a reference, and whether each component primitive of the surrounding infeasible region is correctly matched with the feasible region or not and meets the standard or not is judged.

The traversal can be set to be a feasible domain by setting one detection component primitive, and meanwhile, the detection component primitive can be set to be multi-thread detection so as to improve the detection efficiency. That is, component primitives that may identify multiple infeasible domains are detected simultaneously. In the feasible region, a plurality of detection objects may be provided to perform detection at the same time.

Since it is time consuming for the subsystem to detect primitives only from primitive to primitive. For example, the detection of whether the water supply pipeline system is unblocked or not is favorable when the detection of the standby system pixel group is favorable. For a component or subsystem, the same steps as above can be used to detect the component or subsystem by extracting the component or subsystem according to the encoding and setting the component or subsystem as a feasible region and the surrounding adjacent component primitives as an infeasible region. The detection is performed directly by using a geometric model of a BIM subsystem. One or a plurality of detection bodies are arranged according to needs, and the arrangement of a plurality of detection bodies can improve the detection efficiency of a large subsystem and shorten the detection time. And setting an infeasible domain adjacent to the primitive for the subsystem. The detection body moves in the feasible region of the detection component, the possible infeasible region in the feasible region is detected, and when the intersection of the feasible region and the infeasible region is detected, the intersection is judged according to the compliance. One possible case is that an empty set occurs in the intersection, which may be a special case of intersection. And comparing the empty set with the standard component, and outputting the result.

And when the detection is finished, outputting the traversal result, and carrying out correction and rechecking on the primitives of the wrong and faulty BIM model component. And after the error and the wrong graphic elements are corrected, detecting the corrected model until no error and mistake happen.

Example 1

The following description will take the example of the BIM model extracting the IfcRelationship subclass, the Ifcproduct sequence and the IfcObjectType tissue data according to the geometric semantic and geometric information and the IFC standard as an example.

As shown in fig. 3, the IFC relationship subclass starts to be traversed, and if the abstract information required for the check cannot be obtained, the IFC data can be refined. If the information required for the inspection is obtained, an IfcProduct sequence set is returned, and then data are organized according to IfcObjectType, the information and the data express the geometric relationship between the detection component element or the detection subsystem element and the adjacent component, the geometric relationship simultaneously expresses the intersection relationship between the BIM model components, and the intersections can be expressed by the geometric elements or the data set.

The invention firstly codes the graphic element of each single component in the space position of the large BIM model, and each component is provided with a unique identification coordinate BIMsC (x)_i，y_j，z_k). And representing the BIM model by a three-dimensional array. And taking a certain amount of points at the positions of the adjacent members for each primitive according to octal bifurcation to form a three-dimensional geometrical coordinate set of the primitive. These sets of all primitives therefore constitute the coordinate set BIM ═ of BIM (a1, a2, A3). Automatically selecting any mark as BIMsC (x)_i，y_j，z_k) Detecting the primitive. Because the primitives are sorted according to spatial positions, the detected primitives can be selected and the primitives adjacent to i, j and k can be automatically selected. As shown in fig. 4, the above steps form a coordinate set of the detection member primitive and a three-dimensional coordinate set of its neighboring primitives, respectively.

And setting the detection primitive as a feasible region, and setting the primitive adjacent to the detection primitive as an infeasible region. And traversing the three-dimensional coordinate sets of the detected primitive and the adjacent primitive, wherein the results of traversing the feasible region and the infeasible region can generate three results, namely intersection, non-intersection and unclear (unclear).

1. If the intersection is generated, the result needs to be subjected to intersection verification, whether the intersection meets the condition of intersection of the geometric space is judged, and if the intersection meets the condition, pass is verified; if not, the error and the non-corresponding place need to be corrected, and then the detection is carried out until the intersection meets the geometric intersection requirement.

2. If no intersection exists, the detected primitive and the adjacent primitive have two relations, and firstly, the detected primitive and the adjacent primitive do not have any intersection and intersection; secondly, detecting that the distance between the pixel and the adjacent pixel is too large to generate intersection, at this moment, adjusting the coordinate set of the BIM pixel to reduce the distance of the coordinate point to generate more point coordinates, namely enlarging the scale of the coordinate set. And then detecting the detected primitive and the adjacent primitives until the detected primitive and the adjacent primitives are satisfied.

3. A third situation is where the result is unclear, which may occur because a point is detected where some coordinate of the primitive intersects a neighboring primitive, but the other two coordinates do not have a point between the primitive and the neighboring primitive that can produce an intersection. At this time, the point set coordinate scales of the BIM detection primitive and the adjacent primitive need to be enlarged, and traversal is performed again.

In the automatic selection of the values of the set of points, since many points are not relevant for the relationship between the member and the member, for example, the middle part of the door member has no relation to the fit with the door frame. Therefore, when the automatic selection points are set, the value trend is that points of the edges of adjacent pieces are selected approximately to form a point set according to the direction of the extreme value of the adjacent member of the geometric data, which has great significance, so that the detection workload can be reduced, and the detection time can be shortened.

The filtering formed by the establishment of the feasible regions and the infeasible regions of the detection primitive and the adjacent primitives of the BIM model enables the intersection to be generated. The intersection results form the mutual relation for distinguishing the feasible region of the detected primitive from the infeasible region of the adjacent primitive, and the mutual relation is used for detecting whether the model is correct or not.

Whether the previous fitting relation of the components is correct or not is determined by the BIM standard, and the setting of the threshold value is also caused by the BIM relevant standard. The threshold may be a value or an error range, so that when the automatic filtering point value is traversed, if the point falls within a certain threshold range, the matching can be determined to be correct or not. If the point set is too few, a judgment error may be generated, and at this time, the range of the point set may be expanded appropriately.

In order to improve the detection efficiency, parallel detection may be adopted, that is, multiple primitives may be detected simultaneously, or one detected primitive may be used to detect the intersection relationship with multiple neighboring primitives simultaneously.

The detection efficiency can be improved by detecting the primitive of the subsystem model and the adjacent primitive. And setting the subsystem needing detection as a feasible region, and setting the adjacent component primitives as an infeasible region. When the subsystem is traversed, the set of coordinates of the neighboring building block primitives that interfere with it is recorded. And automatically compares the collection to a standard fit of the subsystem to adjacent building elements.

Geometrical detection of BIM detection of the coordinates of primitives and the detection of the relationship of the coordinates between primitives is essentially the same thing. Visual detection can thus be performed directly using the model of the primitive. The method comprises the following specific steps: and arranging detection bodies, wherein the detection bodies can be arranged differently according to the detection pixels. For example, the water pipe system can be provided with a sphere detection body, and the wall body can be provided with a cube detection body. The detection primitive is set to be a feasible region, and the adjacent primitive is set to be an infeasible region. The detection body moves in the feasible region, and if the infeasible region is met, the coordinates of the detection body are automatically recorded. These records form an intersection.

And filtering and traversing the intersection set to detect whether the intersection set meets the standard or not. The detection results are labeled as Pass, Fail and Notclear. And if Notclear occurs, subdividing the sizes of the detection component primitive, the adjacent primitive and the detection body, and traversing again.

According to the scheme, a space coordinate system of the GIS is used for reference, the geometric space coordinate expression of the BIM model is utilized, and geometric intelligent detection is carried out on the BIM model through geometric information and geometric data. Through the geometric relation of the primitives between the members mapped by the space coordinates, or the relation of the component primitive group and the member primitive, or the relation of the subdivided primitives between the members, a threshold value or a threshold value range is set for the connection relation between the components, and the geometric relation is automatically traversed and detected, so that the detection theory can be given, no matter the result is correct, error or wrong, or Notclear. Visualization may be used when inspection of the BIM subsystem (e.g., water supply system) may be faster than inspection of the components alone, as component-to-component inspection may be time consuming. Setting a feasible region of a detection component or assembly and an infeasible region of a neighboring component, and automatically judging by using the intersection of the automatic detection feasible region and the infeasible region.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and should be construed as being included therein.

Claims (10)

1. An intelligent detection method of a BIM geometric model based on a GIS is characterized in that the method comprises the steps of utilizing the geometric coordinate space expression of the BIM model, taking the unique coordinate of a component primitive as a component code, representing the BIM model by a three-dimensional array, automatically selecting any group of coded detection primitives, setting the detection primitive as a feasible region, setting an adjacent primitive as an infeasible region, and automatically selecting the primitives adjacent to the detection primitive according to the spatial position to respectively establish a three-dimensional geometric coordinate set of the detection primitive and a three-dimensional geometric coordinate set of the component primitive adjacent to the detection primitive. When the members are matched with each other, the allowed errors between the members are correct, so that when the primitive is detected, the detected primitive and the allowed errors form a feasible domain. And performing intersection detection when traversing the feasible region and the infeasible region, and generating three results, namely intersection, no intersection and unclear detection result. If the intersection is generated, performing intersection verification to determine whether the intersection situation of the geometric space is met, and if the intersection situation is not met, correcting and detecting again; if no intersection is generated, the distance of the coordinate points is reduced, detection is carried out again after the scale of the coordinate set is enlarged to prevent detection omission, but the detection cannot be repeated for multiple times so as to avoid enlarging the calculated amount and allow the detection in a reasonable error range; if the result is an unclear condition, the scale of the three-dimensional geometric coordinate set of the detection primitive and the adjacent primitives thereof needs to be enlarged, and then the feasible region and the infeasible region are traversed again for intelligent detection.

2. The intelligent detection method according to claim 1, wherein the detection method is multi-thread detection.

3. The intelligent detection method according to claim 1, wherein the detection primitive is set as a feasible region, the adjacent primitive is set as an infeasible region in the viewing cone, when the feasible region and the infeasible region are irradiated by the rays of the viewing cone, a certain number of three-dimensional geometric coordinates of the feasible region and the infeasible region are collected respectively to form a set, and intersection detection is performed on the two sets by comparing and filtering.

4. The intelligent detection method according to claim 1 or 3, wherein when the result is unclear, the primitive space is subdivided by using an octant method to enlarge the primitive three-dimensional geometric coordinate set.

5. The intelligent detection method according to claim 1, wherein the step of traversing the feasible domain and the infeasible domain is preceded by traversing the infeasible domain by the feasible domain for intersection detection after setting a threshold based on a BIM standard.

6. The intelligent detection method according to claim 1, wherein detecting whether there is an infeasible domain primitive is performed using detector movement within the feasible domain.

7. An intelligent detection method of a BIM geometric model based on a GIS is characterized by comprising the steps of expressing the geometric space coordinates of the BIM, using the unique coordinates of a component primitive as a component code, expressing the BIM model by using a three-dimensional array, extracting a component according to the code, setting the component as a feasible region and an adjacent component primitive as an infeasible region, respectively establishing a feasible region coordinate set and an infeasible region coordinate set, traversing the feasible region and the infeasible region to perform intersection detection, generating three results of intersection, no intersection and unclear clarity, and if the intersection verification is performed on the result by generating the intersection, whether the result accords with the intersection situation of the geometric space, and detecting again after the result is corrected; if no intersection exists, the distance of the coordinate point is reduced, and the scale of the coordinate set is enlarged and then the detection is carried out again; if the detection primitive and the adjacent primitive are unclear, the feasible region and the infeasible region are traversed again after the scale of the coordinate set of the detection primitive and the adjacent primitive is enlarged.

8. The intelligent detection method according to claim 7, wherein after the BIM model is represented by three-dimensional arrays by using the unique coordinates of the component primitives as the component codes, the subsystem is set as a feasible region according to the code extraction subsystem, and the adjacent component primitives are set as infeasible regions.

9. The intelligent detection method of claim 8, wherein the subsystem performs a traversal that records a set of coordinates of neighboring primitives that interfere therewith.

10. The intelligent detection method according to claim 8, wherein intersection detection of possible non-feasible regions is performed using detector motion within the subsystem feasible region.

Images