Engineering structure entity quality detection point intelligent planning method

Abstract

The invention belongs to the technical field of constructional engineering quality detection and intelligent planning, and discloses an engineering structure entity quality detection point intelligent planning method, which comprises the following steps: analyzing component geometry and position parameters based on a BIM model, and generating a detection point candidate set according to detection specification terms; screening redundant points through centroid calculation, normalization and symmetry analysis; a walking space constraint is constructed by combining the boundary dimension of the detection robot and the safety gap, and unreachable point locations are removed; establishing a spatial topological structure according to a communication relationship between a room and a door, identifying key door nodes by using betweenness indexes, and dividing a cover type and a public area through clustering; and the shortest passing distance between the suit type doors is calculated under the constraint of the configuration space, and a traveling salesman problem model is constructed to solve the sequence of the public areas. And executing shortest path search in the condom and sequentially connecting the gate nodes with the public areas to generate a global detection sequence and a task instruction sequence for guiding the detection robot to implement detection.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of building engineering quality detection and intelligent planning, and particularly relates to an intelligent planning method for engineering structure entity quality detection points. BACKGROUND

[0002] In the process of engineering construction, the quality detection of structure entities is an important link to ensure construction quality and use safety. According to the current detection process, the detection points are usually laid out by manual on-site experience or design drawings, and then the measurement is completed by using corresponding instruments. However, this manual point laying method has many limitations. First, the distribution of detection points often lacks direct correspondence with the items of the Building Engineering Construction Quality Acceptance Specification and the like, and the setting of points has randomness, which makes it difficult to ensure that the detection range and frequency of different components meet the specification requirements. Second, the building components have obvious geometric symmetry, and if only manual judgment is relied on, repeated point laying may occur, which not only increases the workload, but also increases the complexity of subsequent path planning. Third, the traffic conditions of detection robots or automated detection equipment are not considered in the point laying process, and some points may meet the specification but cannot be reached in space, resulting in a disconnection between the planning results and actual execution.

[0003] With the popularization of BIM technology in the field of architecture, some methods attempt to extract geometric information from the model to assist in detection point generation. However, these methods mostly stop at the parameter acquisition level, lack the combination with specification items, and cannot realize intelligent candidate point screening. At the same time, although the existing path planning research introduces the Traveling Salesman Problem model, it generally fails to consider the configuration constraints of robots in architectural space, and the calculated optimal path often lacks executability. In addition, the research on the topological relationship of architectural space is still insufficient, and the existing schemes often ignore the logical structure of connecting rooms through doors, making it difficult to effectively distinguish between internal spaces of suites and public areas, resulting in a lack of hierarchy and logic in global detection sequence generation.

[0004] Therefore, the existing detection methods have deficiencies in candidate point generation, redundant point screening, feasible domain constraints, and global sequence planning, and it is difficult to balance specification coverage and execution feasibility. Therefore, it is necessary to propose an intelligent detection point planning method that can combine BIM model information and quality detection specification items, reduce redundant points by analyzing geometric symmetry, and construct global sequence optimization by combining robot walkable space constraints and room-door spatial topological relationship. SUMMARY

[0005] The purpose of the present application is to provide an intelligent planning method for engineering structure entity quality detection points to solve the problems raised in the background.

[0006] In order to achieve the above object, the present application provides the following technical scheme: an engineering structure entity quality detection point intelligent planning method, the specific steps of which are as follows: Candidate detection point generation: extract the geometric and position parameters of walls and components from the building information model, and match the component parameters according to the requirements of the quality detection specification items to automatically generate a candidate set of detection points; Symmetry screening process: on the basis of the candidate set, use the Procrustes method for geometric symmetry analysis, identify symmetric points through centroid translation, normalization and rotation comparison, and only keep the necessary points on one side, so as to reduce redundant detection points and reduce the complexity of subsequent path calculation while ensuring specification coverage; Walkable space constraint: combine the screened detection points with the building space occupancy area, the detection robot shape size and the safety gap to establish the configuration space constraint of the robot that can be walked, and ensure that the detection point planning is within the reachable range of the robot; Space topology construction and classification: based on the door connectivity relationship between the room and the public space where the detection point is located, the space topology structure is constructed, the key door nodes are identified by using the betweenness index, and the space is divided into room type, living room type and public area by using the K-Means clustering method, so as to identify the suite boundary and public area; Public area sequence optimization: under the topological structure and configuration space constraint, the shortest path search method of configuration space constraint is used to calculate the passable distance between the suite doors, which is used as the edge weight of the traveling salesman problem to obtain the optimal detection sequence of the public area; Suite internal sequence optimization: the shortest path search method is also used in each suite to obtain the local detection sequence, and the local sequence is embedded in the main sequence of the public area with the suite door as the connection point; Global sequence generation and output: through the above embedded combination, the global optimal detection sequence covering all detection points is formed, and the task instruction sequence containing the detection point position identification, entry path, exit path and door traversal sequence is output, which is used to guide the detection robot to execute quality detection in the building structure.

[0007] Preferably, the specific steps of the candidate detection point generation are as follows: Obtain the IFC file based on the BIM model of the engineering structure; Extract the geometric size parameters and spatial position parameters of walls and other components through the IfcOpenShell analysis tool; According to the requirements of the building quality detection specification on component type, size and position, match the parameters with the specification items; Determine the detection points on the surface of the component that meet the specification requirements to form a candidate set of detection points; In the candidate detection point generation stage, first, the IFC file is acquired based on the BIM model of the engineering structure, and the geometric size parameters and spatial position parameters of the wall and other components are parsed by using the IfcOpenShell tool, then the extracted parameters are matched with the building quality detection specification items one by one, the detection points meeting the specification requirements are determined, and the candidate set of detection points is formed; ; In the formula: The coordinates of the first candidate detection point; The number of candidate detection points; The centroid of the point set; The coordinates of the point set after centroid translation; Through the above processing, the candidate detection point set with geometric and specification information is obtained, which provides a basis for subsequent geometric screening.

[0008] Preferably, the specific steps of the symmetry screening process are as follows: The centroid of the candidate detection point set is calculated, and the centroid translation of the point set is performed; The normalized processing is performed on the point set after translation; The normalized point set is rotated and aligned to determine whether the point set has symmetry; In the case of identifying symmetry, only one side of the detection point is retained to form the screened detection point set; Building structures generally have geometric symmetry, which will lead to excessive detection points and increase the subsequent calculation overhead if not processed, and the Procrustes symmetry analysis method is used to screen the point set in the embodiment; First, the root mean square scale radius is calculated: ; In the formula: The root mean square radius; The point coordinates after translation; The number of points; The Euclidean distance of the point to the origin; Then, normalization is performed: ; Wherein: The normalized coordinates; The point coordinates after translation; The root mean square radius;​ Then the symmetry difference measure is calculated: ; Where R is the rotation / reflection matrix for alignment; is the normalized point; D is the symmetry difference measure; n is the number of points; And the optimal matrix is obtained by minimizing the difference measure: ; Where R is the rotation / reflection matrix that minimizes the difference measure D; its solution is exactly the same as the meaning of R in the above formula; When D is less than the threshold value, it means that there is symmetry, and only one side of the detection point is retained to form the simplified point set; Unlike traditional manual point deletion, this method is based on mathematical quantitative judgment, which reduces redundancy and maintains detection coverage.

[0009] Preferably, the specific steps of the walkable space constraint are as follows: Obtain the occupied area of the building space based on the BIM model; Determine the size parameters of the detection robot and the required safety clearance; Envelope modeling is performed on the size parameters and safety clearance to obtain the safety envelope of the robot in space (representing the envelope range formed by the detection robot size and the safety clearance extended outside); Superimpose the safety envelope and the building space occupied area to obtain the configuration space of the robot that can be walked, which is used to constrain subsequent path planning; The walkable space is defined as: ; Where: The configuration space of the robot that can be walked; The pose of the robot; The occupied area of the building space; Pose Shape mapping in the BIM model; Safety envelope generated by the size of the detection robot and the safety clearance; Empty set, indicating disjoint; This ensures that the detection points are planned within the actual reachable area of the robot. Compared with simple geometric visual judgment, this method establishes a mathematical model of the walkable space, significantly improving the reliability of the planning.

[0010] Preferably, the specific steps of the spatial topology construction and classification are as follows: According to the connection relationship between rooms and doors in the building space, a spatial topology structure is established, taking rooms and doors as nodes and door connection as edges; In the spatial topology structure, the betweenness value of each door node is calculated to identify key door nodes that frequently appear in multiple paths; The betweenness value of each room node is taken as a clustering input, and a K-Means clustering method is used to classify the rooms; Rooms belonging to the same functional unit in the clustering result are classified as suites, and areas connected to multiple suites are divided into public areas; A building space is usually composed of multiple rooms and doors, and its connection relationship determines the feasibility of the detection path. The rooms and doors are abstracted as nodes, and the connection relationship of the doors is abstracted as edges to form a spatial topology graph; In the topology structure, the identification formula of the key door node is: ; In the formula: The betweenness of the node ; The total number of shortest paths from to , The number of in the shortest path from to ; The node in the topology graph; Taking the betweenness result as an input, a K-Means clustering method is used to classify the rooms, and rooms with similar functions are divided into suites, and areas connecting multiple suites are divided into public areas; Compared with traditional simple plane partitioning, this method can automatically identify public key channels and suite boundaries, making path planning more close to actual use scenarios.

[0011] Preferably, the specific steps of the public area sequence optimization are as follows: Under the spatial topology structure and configuration space constraints, the passable paths between the doors of each suite are determined; The shortest travel distance between the doors of each suite is calculated under the configuration space constraints; The shortest travel distance is taken as the edge weight to build a Traveling Salesman Problem model; The path sequence covering all suite doors is solved in the model to obtain the detection sequence of the public area; Under the constraints of topology and configuration space, the shortest path distance between the suit-type doors needs to be calculated, and Dijkstra algorithm is used for shortest path search, and the iteration relationship is: ; In the formula: The shortest path estimate value from the source point to the node ; The shortest path value from the source point to the node ; The weight of the edge , that is, the path distance; The adjacent node in the graph; After obtaining the shortest path distance between the doors, the traveling salesman problem model is constructed by taking the shortest path distance as the edge weight, and the detection order of the public area covering all suit-type doors is solved by minimizing the total path length as the goal; Compared with manual sequence setting, the method can automatically generate the optimal path and reduce the invalid movement of the robot in the public area.

[0012] Preferably, the specific steps of the suit-type internal sequence optimization are as follows: In each suit-type, the local topology structure is established by taking the detection point set and the suit-type door as the node; The shortest path search is performed in the local topology structure to obtain the local sequence covering all detection points in the suit-type; The local sequence is connected with the public area detection sequence at the position of the corresponding suit-type door; Thus, the global detection sequence of the nested combination of the public area main sequence and the local sequence of each suit-type is formed; In each suit-type, the sequence covering all detection points needs to be determined and connected with the public area sequence, and the optimization goal is: ; In the formula: The first detection point; The path distance between adjacent detection points; The local sequence is obtained by shortest path search, and is connected with the public area sequence at the suit-type door to realize nested combination. Compared with the traditional sequence scanning or point-to-point nearest neighbor, the method is more in line with the movement law of the robot in the complex space.

[0013] Preferably, the specific steps of the global sequence generation and output are as follows: ​The common area detection sequence and the local detection sequence in each set of type are nested and combined at the corresponding set of type door position to obtain a global detection sequence covering all detection points; A task instruction sequence is generated based on the global detection sequence, and the task instruction sequence comprises a detection point position identifier, an entry path, an exit path and a door crossing sequence; The task instruction sequence is used as an input instruction for a detection robot; Finally, the common area sequence and the sequence in each set of type are nested and combined to form a global detection sequence, and a task instruction sequence is generated based on the sequence, and the instruction content comprises: a detection point position identifier; an entry path; an exit path; a door crossing sequence; The task instruction sequence is directly issued to the detection robot to ensure that the detection robot completes the detection task in the engineering structure according to the optimal sequence, and the process not only solves the problem of how to set the points, but also solves the problem of how to detect the points, and realizes seamless connection of detection point planning and robot task scheduling.

[0014] The beneficial effects of the present application are as follows: 1. The present application automatically extracts the geometric dimensions and spatial positions of components such as walls, beams and columns by analyzing the IFC file in the BIM model, and generates a detection point candidate set according to the requirements of the clauses in the quality detection specification. This process makes the distribution of detection points directly correspond to the content of the specification, avoiding the omission and unevenness caused by experience differences when manually setting points. The candidate points are subjected to centroid calculation and normalization processing during the formation process, ensuring that the detection points of different components and different areas can be compared and subsequently processed in a unified coordinate system, thereby making the point setting process repeatable and consistent.

[0015] 2. The present application identifies the symmetry relationship of component geometry by using the symmetry analysis method, and only retains the necessary detection points on one side by centroid translation, scale normalization and rotation comparison, thereby reducing the number of redundant point setting. At the same time, the shape size and safety gap of the detection robot are combined to constrain the configuration of the building space, and the unreachable points are automatically excluded, ensuring that all generated points are within the range that the robot can enter and complete the detection. In this way, the complexity of subsequent path calculation is reduced, and the problem of inconsistency between planning and actual execution caused by unreachable points is avoided.

[0016] 3. This invention establishes a spatial topology based on the connectivity between rooms and doors after screening and constraining detection points. It identifies key door nodes using betweenness indices and classifies rooms using clustering methods, thereby distinguishing between the internal space of the suite and the public area. Based on this structure, shortest path search and sequence optimization are performed in the public area and inside the suite, respectively, and are connected through door nodes to form a global sequence covering all detection points. The final output task instruction sequence includes detection point identifiers, entry paths, exit paths, and door crossing sequences, which can be directly issued to the detection robot for execution, realizing a close connection between detection point planning and detection operations. Attached Figure Description

[0017] Fig. 1 This is a flowchart of the detection point generation and screening process of the present invention; Fig. 2 This is a flowchart illustrating the spatial topology construction and classification process of this invention. Fig. 3 This is a flowchart of the global detection sequence planning for this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figs. 1 to 3 As shown in the figure, this embodiment of the invention provides an intelligent planning method for quality inspection points of engineering structures. The specific steps of this method are as follows: Candidate inspection point generation: Extract the geometric and positional parameters of walls and components from the building information model, and match the component parameters according to the requirements of the quality inspection specifications to automatically generate a candidate set of inspection points; Symmetry sieving and reduction: Based on the candidate set, geometric symmetry analysis is performed using the Prouk method. Symmetrical points are identified by centroid translation, normalization and rotation comparison. Only necessary points on one side are retained, thereby reducing redundant detection points while ensuring standard coverage and reducing the complexity of subsequent path calculation. Walking space constraints: By combining the screened and reduced detection points with the building space occupied area, the size of the detection robot and the safety clearance, a configuration space constraint for the robot to walk is established to ensure that the detection points are planned within the robot's reach. Spatial topology construction and classification: Based on the door connectivity between the room where the detection point is located and the public space, the spatial topology is constructed. The betweenness index is used to identify key door nodes, and the K-Means clustering method is used to divide the space into room class, living room class and public area, thereby identifying the apartment boundary and public area. Public area order optimization: Under the constraints of topology and configuration space, the shortest path search method with configuration space constraints is used to calculate the passable distance between the nested doors, and the distance is used as the edge weight of the traveling salesman problem to obtain the optimal detection order of the public area; Optimization of the order within each apartment type: The shortest path search method is also used within each apartment type to obtain the local detection order, and the local order is embedded into the main order of the common area using the apartment door as the connection point; Global sequence generation and output: Through the above embedded combination, a globally optimal detection sequence covering all detection points is formed, and a task instruction sequence containing detection point identifiers, entry paths, exit paths and door crossing sequences is output to guide the detection robot to perform quality inspection within the building structure.

[0020] The specific steps involved in generating candidate detection points are as follows: Obtain IFC files from BIM models of engineering structures; Extract the geometric dimensions and spatial location parameters of the walls and other components using the IfcOpenShell parsing tool; According to the requirements for component type, size and location in the building quality inspection specifications, the parameters are matched with the corresponding items in the specifications; Identify test points on the surface of the component that meet the specifications, and form a candidate set of test points; In the candidate test point generation stage, the IFC file is first obtained based on the BIM model of the engineering structure, and the geometric dimension parameters and spatial location parameters of the walls and other components are parsed using the IfcOpenShell tool. Then, the extracted parameters are matched with the items of the building quality inspection specification one by one to determine the test points that meet the specification requirements and form a test point candidate set. ; In the formula: No. The coordinates of the candidate detection points; The number of candidate detection points; The centroid of the point set; The coordinates of the point set after translation by its centroid; Through the above processing, a set of candidate detection points with geometric and normative information is obtained, which provides a basis for subsequent geometric screening.

[0021] The specific steps of the symmetry screening process are as follows: The centroid of the candidate detection point set is calculated, and the centroid translation of the point set is performed; The point set after translation is subjected to scale normalization processing; The normalized point set is subjected to rotation alignment, and it is determined whether the point set has symmetry; In the case where symmetry is identified, only one side of the detection point is retained to form a screened detection point set; Building structures generally have geometric symmetry, which can lead to excessive detection points and increase subsequent calculation overhead if not handled. The present embodiment uses the Procrustes symmetry analysis method to screen the point set; First, the root mean square scale radius is calculated: ; In the formula: The root mean square radius; The point coordinates after translation; The number of points; The Euclidean distance of the point to the origin; Then, normalization is performed: ; In the formula: The normalized coordinates; The point coordinates after translation; The root mean square radius; Then, the symmetry difference measure is calculated: ; In the formula, R is the rotation / reflection matrix used for alignment; is the normalized point; D is the symmetry difference measure; n is the number of points; And the optimal matrix is obtained by minimizing the difference measure: ; In the formula: In the formula, R is the rotation / reflection matrix that minimizes the difference measure D; its solution is exactly the same as the meaning of R in the above formula; When D is less than a threshold value, it indicates that there is symmetry, and only one side of the detection point is retained to form a simplified point set; Unlike traditional manual point deletion, this method is based on mathematical quantitative judgment, which reduces redundancy and maintains detection coverage.

[0022] The specific steps of the walkable space constraint are as follows: Obtaining the occupancy area of the building space based on the BIM model; Determining the size parameters of the detection robot and the required safety clearance; Envelope modeling the size parameters and the safety clearance to obtain the safety envelope of the robot in the space (representing the envelope range formed by the size of the detection robot and the safety clearance extended outward); Superimposing the safety envelope and the occupancy area of the building space to obtain the configuration space of the robot that can be walked, which is used to constrain subsequent path planning; The walkable space is defined as: ; In the formula: The configuration space of the robot that can be walked; The pose of the robot; The occupancy area of the building space; The pose The shape mapping in the BIM model; The envelope formed by the size of the robot and the safety clearance; Empty set, indicating disjoint; This ensures that the detection point is planned in the actual reachable area of the robot. Compared with simple geometric visibility judgment, this method establishes a mathematical model of the walkable space, significantly improving the reliability of the planning.

[0023] Specific steps of space topology construction and classification are as follows: According to the connection relationship between rooms and doors in the building space, a space topology structure is established with rooms and doors as nodes and door connectivity as edges; In the space topology structure, the betweenness value of each door node is calculated to identify key door nodes that frequently appear in multiple paths; The betweenness value of each room node is used as the clustering input, and the K-Means clustering method is used to classify the rooms; Rooms belonging to the same functional unit in the clustering result are classified as suites, and areas connected to multiple suites are divided into public areas; The building space is usually composed of multiple rooms and doors, and their connectivity determines the feasibility of the detection path. The rooms and doors are abstracted as nodes, and the connectivity of the doors is abstracted as edges to form a space topology graph; In the topology structure, the identification formula of the key door node is: ; In the formula: nodes betweenness centrality of the nodes; the total number of shortest paths from to , the number of shortest paths from to passing through ; nodes in the topological graph; Taking the betweenness centrality result as input, the K-Means clustering method is used to classify the rooms, and the rooms with similar functions are divided into suites, and the areas connecting multiple suites are divided into public areas; Compared with the traditional simple plane partition, this method can automatically identify the public key channel and the suite boundary, so that the path planning is closer to the actual use scene.

[0024] The specific steps of the public area sequence optimization are as follows: Under the constraints of spatial topology and configuration space, the passable path between the doors of each suite is determined; Under the constraint of configuration space, the shortest travel distance between the doors of each suite is calculated; The shortest travel distance is used as the edge weight to construct a traveling salesman problem model; The path sequence covering all suite doors is solved in the model, and the detection sequence of the public area is obtained; Under the constraints of topology and configuration space, the shortest travel distance between the suite doors needs to be calculated, and the Dijkstra algorithm is used for shortest path search, and the iteration relationship is: ; In the formula: the shortest path estimate value from the source point to node ; the shortest path value from the source point to node ; the weight of edge , that is, the travel distance; adjacent nodes in the graph; After obtaining the shortest travel distance between the doors, a traveling salesman problem model is constructed with the shortest travel distance as the edge weight, and the public area detection sequence covering all suite doors is solved to minimize the total path length; Compared with manual sequence setting, this method can automatically generate the optimal path and reduce the invalid movement of the robot in the public area.

[0025] The specific steps involved in optimizing the internal sequence of the apartment type are as follows: Within each housing type, a local topology is established using the set of detection points and housing doors as nodes; Perform a shortest path search within the local topology to obtain the local order of all detection points within the coverage area; The local sequence is connected with the public area inspection sequence at the corresponding door position; The local detection sequence obtained within each apartment type is connected to the aforementioned common area detection sequence at the apartment type gate node. In this way, the path in the common area serves as the backbone of the global path, and the paths within each apartment type are connected to the backbone path via gate nodes, thus forming the global detection sequence. Within each unit type, the order in which all detection points are covered needs to be determined and coordinated with the order of the common area. The optimization objective is as follows: ; In the formula: No. One testing point; The travel distance between adjacent detection points; The local order is obtained by searching the shortest path, and then connected with the common area order at the nested door to achieve nested combination. Compared with the traditional sequential scanning or point-by-point nearest neighbor, this method is more in line with the movement law of robots in complex spaces.

[0026] The specific steps involved in generating and outputting the global sequence are as follows: The detection sequence of the public area and the local detection sequence within each unit are nested and combined at the corresponding unit door positions to obtain a global detection sequence that covers all detection points; A task instruction sequence is generated based on the global detection order. The task instruction sequence includes detection point identifiers, entry paths, exit paths, and door crossing order. Use the sequence of task instructions as input instructions for the detection robot; Finally, the order of the common area is nested and combined with the internal order of each suite to form a global detection order. Based on this order, a sequence of task instructions is generated, the content of which includes: Inspection point markings; Enter the path; Exit path; Door passage sequence; The task instruction sequence is directly issued to the inspection robot to ensure that it completes the inspection task in the optimal order within the engineering structure. This process not only solves the problem of how to set the points, but also the problem of how to inspect the points, and achieves seamless connection between inspection point planning and robot task scheduling. The implementation logic of gate node connection: After the detection sequence of the public area and the detection sequence of each suite type are determined respectively, an executable connection relationship needs to be established between the two to form a complete global detection sequence, for this purpose, the present application defines each "door node" as a passage interface between the public area and the suite type in the spatial topology, the door node corresponds to the geometric center point of the building door in the BIM model, and its coordinate index, passage direction and adjacent room identification are recorded in the form of node number in the topology graph; In the path planning stage, the end point of the public area detection path and the start point of the suite internal detection path are both directed to the same door node to ensure the continuity of the two paths in the same coordinate system, and the system automatically performs path splicing operation according to the number and connection relationship of the door node when generating the global sequence: if the end node number of a path segment in the public area detection sequence is consistent with the start node number of the suite detection sequence, the two path segments are merged into a continuous path, and the corresponding passage distance and sequence index are updated; The splicing process does not involve re-searching the path, but realizes path index replacement in the same topological coordinate framework, thereby ensuring that the public area and the internal path of the suite type are not overlapped in space and have no breakpoints in sequence, and each detection point in the finally formed global detection sequence can be continuously reached through the door node path, and the robot does not need to reposition or coordinate transform during execution, the connection mechanism not only ensures the continuity of the path and the accessibility of the passage, but also provides complete path data support for generating subsequent task instruction sequences.

[0027] It should be noted that in this text, relationship terms such as first and second are only used to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "include", "contain" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0028] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent planning method for quality detection points of an engineering structure entity, characterized in that: The specific steps of the method are as follows: Candidate detection point generation: extract the geometric and position parameters of walls and components from the building information model, and match the component parameters according to the requirements of the quality detection specification, to automatically generate a candidate set of detection points; Symmetry screening process: on the basis of the candidate set, use the Procrustes method for geometric symmetry analysis, identify symmetric points through centroid translation, normalization and rotation comparison, and only keep the necessary points on one side, thereby reducing redundant detection points and reducing the complexity of subsequent path calculation while ensuring specification coverage; Walkable space constraint: combine the screened detection points with the building space occupancy area, the detection robot shape size and the safety gap to establish the configuration space constraint of the robot, and ensure that the detection point planning is within the robot's reach; Space topology construction and classification: based on the door connectivity relationship between the rooms and public spaces where the detection points are located, construct the space topology structure, identify key door nodes using the betweenness index, and divide the space into room type, living room type and public area using the K-Means clustering method, thereby identifying the suite boundary and public area; Public area sequence optimization: under the topological structure and configuration space constraint, use the shortest path search method of configuration space constraint to calculate the passable distance between suite doors, use it as the edge weight of the traveling salesman problem to obtain the optimal detection sequence of the public area; Suite internal sequence optimization: also use the shortest path search method to obtain the local detection sequence within each suite, and connect the local detection sequence of the suite internal with the aforementioned public area detection sequence at the corresponding suite door node to form a global detection sequence covering all detection points; Global sequence generation and output: combine the aforementioned public area detection sequence and the suite internal detection sequence at the corresponding door node to form a global detection sequence covering all detection points, and output a task instruction sequence containing detection point identification, entry path, exit path and door traversal sequence.

2. The intelligent planning method for quality detection points of an engineering structure entity according to claim 1, characterized in that: The specific steps of the candidate detection point generation are as follows: Obtain the IFC file based on the BIM model of the engineering structure; Extract the geometric size parameters and spatial position parameters of walls and other components through analysis tools; According to the requirements of the building quality detection specification for component type, size and position, match the parameters with the specification items; Determine the detection points on the component surface that meet the specification requirements to form a candidate set of detection points.

3. The intelligent planning method for quality detection points of an engineering structure entity according to claim 2, characterized in that: The specific steps of the symmetry screening process are as follows: Calculate the centroid of the candidate detection point set, and perform centroid translation on the point set; Perform scale normalization processing on the translated point set; Align the normalized point set by rotation, and determine whether the point set has symmetry; In the case of identifying symmetry, only keep the detection points on one side to form a screened detection point set.

4. The intelligent planning method for quality detection points of an engineering structure entity according to claim 3, characterized in that: The specific steps of the walkable space constraint are as follows: Obtain the occupancy area of the building space based on the BIM model; Determine the shape size parameters of the detection robot and the required safety gap; Envelope modeling is performed on the shape size parameters and safety gap to obtain the safety envelope of the robot in space; The safety envelope is superimposed with the building space occupancy area to obtain a configuration space in which the robot can walk, which is used to constrain subsequent path planning.

5. The intelligent planning method for quality detection points of an engineering structure entity according to claim 4, characterized in that: The specific steps of the space topology construction and classification are as follows: According to the connection relationship between rooms and doors in the building space, a space topology structure is established, taking rooms and doors as nodes and door connection as edges. In the space topology structure, the betweenness value of each door node is calculated to identify key door nodes that frequently appear in multiple paths. The betweenness value of each room node is taken as clustering input, and the K-Means clustering method is used to classify the rooms. Rooms belonging to the same functional unit in the clustering result are classified as suites, and areas connected to multiple suites are divided into public areas.

6. The intelligent planning method for quality detection points of an engineering structure entity according to claim 5, characterized in that: The specific steps of the public area sequential optimization are as follows: Under the constraints of the space topology structure and the configuration space, the passable paths between the doors of each suite are determined. The shortest passable distances between the doors of each suite are calculated under the constraint of the configuration space. The shortest passable distances are taken as edge weights to construct a Traveling Salesman Problem model. The path sequence covering all suite doors is solved in the model to obtain the detection sequence of the public area.

7. The intelligent planning method for quality detection points of an engineering structure entity according to claim 6, characterized in that: The specific steps of the suite internal sequential optimization are as follows: In each suite, a local topology structure is established, taking the set of detection points and the doors of the suite as nodes. In the local topology structure, a shortest path search is performed to obtain a local sequence covering all detection points in the suite. The local sequence is connected with the public area detection sequence at the position of the corresponding suite door. Thus, a global detection sequence is formed, which is a nested combination of the public area main sequence and the local sequences of each suite.

8. The intelligent planning method for quality detection points of an engineering structure entity according to claim 7, characterized in that: The specific steps of the global sequence generation and output are as follows: The public area detection sequence and the local detection sequence in each suite are nested and combined at the position of the corresponding suite door to obtain a global detection sequence covering all detection points. Based on the global detection sequence, a task instruction sequence is generated, which includes detection point position identification, entry path, exit path, and door traversal sequence. The task instruction sequence is taken as the input instruction for the detection robot to execute.

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