An intelligent planning method for quality detection points of an engineering structure entity

Abstract

The application belongs to the technical field of building 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 clauses; removing redundant points through centroid calculation, normalization and symmetry analysis; constructing a walkable space constraint in combination with the shape size and safety gap of a detection robot, and removing unreachable points; establishing a space topology structure according to the connectivity relationship between rooms and doors, identifying key door nodes by using an intermediate index, and dividing a suite type and a public area by clustering; calculating the shortest travel distance between suite doors under the configuration space constraint, constructing a traveling salesman problem model to solve the public area sequence. The shortest path search is performed in the suite, and the door nodes and the public area sequence are connected, so that a global detection sequence and a task instruction sequence are generated to guide the detection robot to implement detection.

Description

Technical Field

[0001] This invention belongs to the field of building engineering quality inspection and intelligent planning technology, specifically a method for intelligent planning of quality inspection points for engineering structures. Background Technology

[0002] In the construction process, the quality inspection of structural entities is a crucial step in ensuring construction quality and operational safety. According to current inspection procedures, inspection points are typically set up manually on-site based on experience or design drawings, followed by measurements using appropriate instruments. However, this manual method of point placement has several limitations. First, the distribution of inspection points often lacks a direct correspondence with regulations such as the "Code for Acceptance of Construction Quality of Building Engineering," leading to arbitrary point placement and difficulty in ensuring that the inspection range and frequency of different components meet the requirements of the code. Second, building components exhibit significant geometric symmetry; relying solely on manual judgment can easily result in duplicate point placement, leading to an excessive number of inspection points, increasing workload and the complexity of subsequent path planning. Third, the accessibility of inspection robots or automated inspection equipment is generally not considered during point placement; some points, while conforming to the code, may be spatially inaccessible, causing a disconnect between the planned results and actual implementation.

[0003] With the promotion of BIM technology in the construction field, some methods attempt to extract geometric information from the model to assist in the generation of detection points. However, most of these methods remain at the level of parameter acquisition and lack integration with standard items, making it impossible to achieve intelligent candidate point selection. At the same time, although existing path planning research has introduced the traveling salesman problem model, it generally fails to consider the configuration constraints of the robot in the building space. The calculated optimal path often lacks feasibility. In addition, research on the topological relationship of building space is still insufficient. Existing solutions often ignore the logical structure of rooms connected by doors, making it difficult to effectively distinguish between the internal space of the suite and the public area, resulting in a lack of hierarchy and logic in the generation of the global detection sequence.

[0004] It is evident that existing detection methods have shortcomings in candidate point generation, redundant point reduction, feasible region constraints, and global sequence planning, making it 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 inspection specification items, reduce redundant points using geometric symmetry analysis, and optimize the global sequence by combining robot traversable space constraints and room-door spatial topology relationships. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent planning method for quality inspection points of engineering structures to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent planning method for quality inspection points of engineering structures, the specific steps of which are as follows:

[0007] 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;

[0008] Symmetry sieving and reduction processing: 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.

[0009] 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.

[0010] 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.

[0011] Public area order optimization: Under the given topology and configuration space constraints, the shortest path search method with configuration space constraints is used to calculate the passable distance between the door frames, and this distance is used as the edge weight of the traveling salesman problem to obtain the optimal detection order of the public area;

[0012] Optimization of the internal order of each apartment type: The shortest path search method is also used to obtain the local detection order within each apartment type, and the local order is embedded into the main order of the common area using the apartment type door as the connection point.

[0013] 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.

[0014] Preferably, the specific steps for generating candidate detection points are as follows:

[0015] Obtain IFC files from BIM models of engineering structures;

[0016] Extract the geometric dimensions and spatial location parameters of the walls and other components using the IfcOpenShell parsing tool;

[0017] According to the requirements for component type, size and location in the building quality inspection specifications, the parameters are matched with the corresponding entries in the specifications;

[0018] Identify test points on the surface of the component that meet the specifications, and form a candidate set of test points;

[0019] 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.

[0020] ;

[0021] In the formula:

[0022] No. The coordinates of the candidate detection points;

[0023] The number of candidate detection points;

[0024] The centroid of a point set;

[0025] The coordinates of the point set after translation by its centroid;

[0026] 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.

[0027] Preferably, the specific steps of the symmetric sieving process are as follows:

[0028] Calculate the centroid of the candidate detection point set and translate the centroid of the point set;

[0029] The translated set of points is then scaled.

[0030] Rotate and align the normalized point set to determine if the point set has symmetry;

[0031] In cases where symmetry exists, only the detection points on one side are retained, forming a set of reduced detection points.

[0032] Building structures generally have geometric symmetry. If left untreated, this can lead to too many detection points, increasing subsequent computational costs. In this embodiment, the Prouk symmetry analysis method is used to filter and reduce the point set.

[0033] First, calculate the root mean square radius:

[0034] ;

[0035] In the formula:

[0036] Root mean square radius;

[0037] The coordinates of the translated point;

[0038] points;

[0039] The Euclidean distance from the point to the origin;

[0040] Then normalize:

[0041] ;

[0042] in: Normalized coordinates;

[0043] The coordinates of the translated point;

[0044] Root mean square radius;

[0045] Next, the symmetry difference measure is calculated:

[0046] ;

[0047] In the formula, R is the rotation / reflection matrix used for alignment; The points are normalized; D is the symmetry difference measure; n is the number of points.

[0048] The optimal matrix is ​​obtained by minimizing the difference metric:

[0049] ;

[0050] In the formula: R is the rotation / reflection matrix that minimizes the difference metric D; its solution is completely consistent with the meaning of R in the above formula;

[0051] When D is less than the threshold, it indicates that there is symmetry, and only one side of the detection points is retained to form a simplified point set;

[0052] Unlike traditional manual deletion, this method is based on mathematical quantification, which reduces redundancy while maintaining detection coverage.

[0053] Preferably, the specific steps of the walkable space constraint are as follows:

[0054] Obtain the occupied area of ​​building space based on BIM model;

[0055] Determine the external dimensions and required safety clearances of the inspection robot;

[0056] The outer dimensions and safety clearance are used to perform envelope modeling to obtain the robot's safety envelope in space (representing the envelope range formed by the outer dimensions of the detection robot and its outward safety clearance).

[0057] The safety envelope is superimposed on the building space area to obtain the configuration space that the robot can walk on, which is used to constrain subsequent path planning;

[0058] Walkable space is defined as:

[0059] ;

[0060] In the formula:

[0061] A spatial configuration in which robots can walk;

[0062] Robot pose;

[0063] Building space occupied area;

[0064] posture Shape mapping in BIM models;

[0065] This represents the safety envelope generated by the dimensions of the inspection robot and the safety clearance;

[0066] The empty set indicates that the sets are disjoint.

[0067] This ensures that the detection points are planned within the robot's actual reachable area. Compared to simple geometric visual judgment, this method establishes a mathematical model of the walkable space, which significantly improves the reliability of the planning.

[0068] Preferably, the specific steps of spatial topology construction and classification are as follows:

[0069] Based on the connection relationship between rooms and doors in the architectural space, establish a spatial topology with rooms and doors as nodes and door connections as edges.

[0070] In the spatial topology, the intermediate value of each gate node is calculated to identify key gate nodes that frequently appear in multiple paths;

[0071] The middle value of each room node is used as the clustering input, and the K-Means clustering method is used to classify the rooms;

[0072] Rooms belonging to the same functional unit in the clustering results are classified as suites, and areas connected to multiple suites are divided into common areas.

[0073] Architectural spaces typically consist of multiple rooms and doors, and their connectivity determines the feasibility of detection paths. Rooms and doors are abstracted as nodes, and the connectivity of doors is abstracted as edges, forming a spatial topology graph.

[0074] In the topology, the formula for identifying key gate nodes is:

[0075] ;

[0076] In the formula:

[0077] node betweenness;

[0078] from arrive The total number of shortest paths,

[0079] from arrive The shortest path passes through Quantity;

[0080] Nodes in a topology graph;

[0081] Using the betweenness result as input, the K-Means clustering method is used to classify the rooms, dividing rooms with similar functions into suites and areas connecting multiple suites into common areas;

[0082] Compared with traditional simple planar partitioning, this method can automatically identify common key passages and unit boundaries, making path planning more closely resemble actual use cases.

[0083] Preferably, the specific steps of optimizing the public area order are as follows:

[0084] Under the constraints of the spatial topology and configuration space, determine the passable paths between each type of door;

[0085] Calculate the shortest passage distance between each set of doors under configuration space constraints;

[0086] The shortest travel distance is used as the edge weight to construct a traveling salesman problem model;

[0087] The path sequence covering all the door types is solved in the model to obtain the detection sequence of the public area;

[0088] Under topological and configuration space constraints, the shortest passage distance between nested doors needs to be calculated. Dijkstra's algorithm is used for shortest path search, and its iterative relationship is as follows:

[0089] ;

[0090] In the formula:

[0091] Source to Node The shortest path estimate;

[0092] Source to Node The shortest path value;

[0093] side The weight, i.e., the travel distance;

[0094] Adjacent nodes in the diagram;

[0095] After obtaining the shortest passage distance between doors, we use it as the edge weight to construct a traveling salesman problem model. With the goal of minimizing the total path length, we solve for the detection order of the common area covering all types of doors.

[0096] Compared to manual sequence setting, this method can automatically generate the optimal path and reduce the robot's ineffective movement in public areas.

[0097] Preferably, the specific steps of optimizing the internal sequence of the housing type are as follows:

[0098] Within each housing type, a local topology is established using the set of detection points and housing doors as nodes;

[0099] Perform a shortest path search within the local topology to obtain the local order of all detection points within the coverage area;

[0100] The local sequence is connected with the public area detection sequence at the corresponding door position;

[0101] This results in a global detection sequence that is nested and combined with the main sequence of the public area and the local sequence of each unit type;

[0102] 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:

[0103] ;

[0104] In the formula:

[0105] No. One testing point;

[0106] The travel distance between adjacent detection points;

[0107] 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.

[0108] Preferably, the specific steps of generating and outputting the global sequence are as follows:

[0109] 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;

[0110] Based on the global detection order, a task instruction sequence is generated, which includes detection point identifiers, entry paths, exit paths, and door crossing order.

[0111] The sequence of task instructions is used as the input instructions for the detection robot.

[0112] Finally, the order of the common area is nested and combined with the internal order of each suite type to form a global detection order. Based on this order, a sequence of task instructions is generated, the content of which includes:

[0113] Inspection point markings;

[0114] Enter the path;

[0115] Exit path;

[0116] Door passage sequence;

[0117] 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.

[0118] The beneficial effects of this invention are as follows:

[0119] 1. This invention automatically extracts the geometric dimensions and spatial positions of components such as walls, beams, and columns by parsing the IFC file in the BIM model, and generates a candidate set of inspection points according to the requirements of the quality inspection specifications. This process ensures that the distribution of inspection points directly corresponds to the specifications, avoiding omissions and unevenness caused by experience differences during manual point placement. The candidate points undergo centroid calculation and normalization during the formation process, ensuring that inspection points of different components and areas can be compared and subsequently processed under a unified coordinate system, thus making the point placement process repeatable and consistent.

[0120] 2. This invention identifies the geometric symmetry of components using symmetry analysis. Through centroid translation, scale normalization, and rotation comparison, only necessary detection points on one side are retained, reducing the number of redundant points. At the same time, combined with the external dimensions and safety clearance of the detection robot, the architectural space is constrained to automatically eliminate unreachable points, 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.

[0121] 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

[0122] Figure 1 This is a flowchart of the detection point generation and screening process of the present invention;

[0123] Figure 2 This is a flowchart illustrating the spatial topology construction and classification process of this invention.

[0124] Figure 3 This is a flowchart of the global detection sequence planning for this invention. Detailed Implementation

[0125] 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.

[0126] like Figures 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:

[0127] 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;

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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;

[0132] 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;

[0133] 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.

[0134] The specific steps involved in generating candidate detection points are as follows:

[0135] Obtain IFC files from BIM models of engineering structures;

[0136] Extract the geometric dimensions and spatial location parameters of the walls and other components using the IfcOpenShell parsing tool;

[0137] 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;

[0138] Identify test points on the surface of the component that meet the specifications, and form a candidate set of test points;

[0139] 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.

[0140] ;

[0141] In the formula:

[0142] No. The coordinates of the candidate detection points;

[0143] The number of candidate detection points;

[0144] The centroid of a point set;

[0145] The coordinates of the point set after translation by its centroid;

[0146] 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.

[0147] The specific steps of the symmetrical sieving process are as follows:

[0148] Calculate the centroid of the candidate detection point set and translate the centroid of the point set;

[0149] The translated set of points is then scaled.

[0150] Rotate and align the normalized point set to determine if the point set has symmetry;

[0151] In cases where symmetry exists, only the detection points on one side are retained, forming a set of reduced detection points.

[0152] Building structures generally have geometric symmetry. If left untreated, this can lead to too many detection points, increasing subsequent computational costs. In this embodiment, the Prouk symmetry analysis method is used to filter and reduce the point set.

[0153] First, calculate the root mean square radius:

[0154] ;

[0155] In the formula:

[0156] Root mean square radius;

[0157] The coordinates of the translated point;

[0158] points;

[0159] The Euclidean distance from the point to the origin;

[0160] Then normalize:

[0161] ;

[0162] In the formula:

[0163] Normalized coordinates;

[0164] The coordinates of the translated point;

[0165] Root mean square radius;

[0166] Next, the symmetry difference measure is calculated:

[0167] ;

[0168] In the formula, R is the rotation / reflection matrix used for alignment; The points are normalized; D is the symmetry difference measure; n is the number of points.

[0169] The optimal matrix is ​​obtained by minimizing the difference metric:

[0170] ;

[0171] In the formula: R is the rotation / reflection matrix that minimizes the difference metric D; its solution is completely consistent with the meaning of R in the above formula;

[0172] When D is less than the threshold, it indicates that there is symmetry, and only one side of the detection points is retained to form a simplified point set;

[0173] Unlike traditional manual deletion, this method is based on mathematical quantification, which reduces redundancy while maintaining detection coverage.

[0174] The specific aspects of walkable space constraints are as follows:

[0175] Obtain the occupied area of ​​building space based on BIM model;

[0176] Determine the external dimensions and required safety clearances of the inspection robot;

[0177] The outer dimensions and safety clearance are used to create an envelope model, which yields the robot's safety envelope in space (representing the envelope range formed by the robot's outer dimensions and its outward safety clearance).

[0178] The safety envelope is superimposed on the building space occupied area to obtain the configuration space that the robot can walk on, which is used to constrain subsequent path planning;

[0179] Walkable space is defined as:

[0180] ;

[0181] In the formula:

[0182] A spatial configuration in which robots can walk;

[0183] Robot pose;

[0184] Building space occupied area;

[0185] posture Shape mapping in BIM models;

[0186] The envelope formed by the robot's external dimensions and safety clearances;

[0187] The empty set indicates that the sets are disjoint.

[0188] This ensures that the detection points are planned within the robot's actual reachable area. Compared to simple geometric visual judgment, this method establishes a mathematical model of the walkable space, which significantly improves the reliability of the planning.

[0189] The specific steps involved in spatial topology construction and classification are as follows:

[0190] Based on the connection relationship between rooms and doors in the architectural space, establish a spatial topology with rooms and doors as nodes and door connections as edges.

[0191] In the spatial topology, the betweenness value of each gate node is calculated to identify key gate nodes that frequently appear in multiple paths;

[0192] The middle value of each room node is used as the clustering input, and the K-Means clustering method is used to classify the rooms;

[0193] Rooms belonging to the same functional unit in the clustering results are classified as suites, and areas connected to multiple suites are divided into common areas.

[0194] Architectural spaces typically consist of multiple rooms and doors, and their connectivity determines the feasibility of detection paths. Rooms and doors are abstracted as nodes, and the connectivity of doors is abstracted as edges, forming a spatial topology graph.

[0195] In the topology, the formula for identifying key gate nodes is:

[0196] ;

[0197] In the formula:

[0198] node betweenness;

[0199] from arrive The total number of shortest paths,

[0200] from arrive The shortest path passes through Quantity;

[0201] Nodes in a topology graph;

[0202] Using the betweenness result as input, the K-Means clustering method is used to classify the rooms, dividing rooms with similar functions into suites and areas connecting multiple suites into common areas;

[0203] Compared with traditional simple planar partitioning, this method can automatically identify common key passages and unit boundaries, making path planning more closely resemble actual use cases.

[0204] The specific steps involved in optimizing the order of public areas are as follows:

[0205] Under the constraints of spatial topology and configuration space, determine the passable paths between each type of door;

[0206] Calculate the shortest passage distance between each set of doors under configuration space constraints;

[0207] The shortest travel distance is used as the edge weight to construct a model for the traveling salesman problem;

[0208] The path sequence covering all the door types is solved in the model to obtain the detection sequence of the public area;

[0209] Under topological and configuration space constraints, the shortest passage distance between nested doors needs to be calculated. Dijkstra's algorithm is used for shortest path search, and its iterative relationship is as follows:

[0210] ;

[0211] In the formula:

[0212] Source to Node The shortest path estimate;

[0213] Source to Node The shortest path value;

[0214] side The weight, i.e., the travel distance;

[0215] Adjacent nodes in the diagram;

[0216] After obtaining the shortest passage distance between doors, we use it as the edge weight to construct a traveling salesman problem model. With the goal of minimizing the total path length, we solve for the detection order of the common area covering all types of doors.

[0217] Compared to manual sequence setting, this method can automatically generate the optimal path and reduce the robot's ineffective movement in public areas.

[0218] The specific steps involved in optimizing the internal sequence of the apartment type are as follows:

[0219] Within each housing type, a local topology is established using the set of detection points and housing doors as nodes;

[0220] Perform a shortest path search within the local topology to obtain the local order of all detection points within the coverage area;

[0221] The local sequence is aligned with the public area inspection sequence at the corresponding door position;

[0222] 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.

[0223] 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:

[0224] ;

[0225] In the formula:

[0226] No. One testing point;

[0227] The travel distance between adjacent detection points;

[0228] 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.

[0229] The specific steps involved in generating and outputting the global sequence are as follows:

[0230] 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;

[0231] 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.

[0232] Use the sequence of task instructions as input instructions for the detection robot;

[0233] Finally, the order of the common area is nested and combined with the internal order of each suite type to form a global detection order. Based on this order, a sequence of task instructions is generated, the content of which includes:

[0234] Inspection point markings;

[0235] Enter the path;

[0236] Exit path;

[0237] Door passage sequence;

[0238] 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.

[0239] Implementation logic for gate node connection:

[0240] After the inspection sequence of the public area and the inspection sequence of each unit are determined, an executable connection relationship needs to be established between the two to form a complete global inspection sequence. To this end, this invention defines each "door node" in the spatial topology as the passage interface between the public area and the unit. The door node corresponds to the geometric center point of the building doorway in the BIM model, and its coordinate index, passage direction and adjacent room identification are recorded in the topology diagram by node number.

[0241] During the path planning phase, the endpoint of the public area detection path and the starting point of the internal detection path of the apartment type are both directed to the same gate node to ensure that the two paths are continuous in the same coordinate system. When generating the global sequence, the system automatically performs path splicing operation based on the gate node number and connectivity: if the endpoint node number of a certain path segment in the public area detection sequence is the same as the starting point number of the apartment type detection sequence, the two paths are merged into a continuous path, and the corresponding travel distance and sequence index are updated.

[0242] This splicing process does not involve re-searching the path. Instead, it is achieved by replacing the path index within the same topological coordinate framework. This ensures that the paths within the common area and the inner loop are not spatially overlapping and are not discontinuous in sequence. In the final global detection sequence, each detection point can be reached continuously through the gate node path. The robot does not need to reposition or transform its coordinates during execution. This connection mechanism not only ensures the continuity and accessibility of the path, but also provides complete path data support for generating subsequent task instruction sequences.

[0243] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0244] Although embodiments of the invention 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 to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent planning of quality inspection points for engineering structures, characterized in that: 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 processing: 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 external dimensions 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 given topology and configuration space constraints, the shortest path search method with configuration space constraints is used to calculate the passable distance between the nested doors, and this 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 internal order of each unit: The shortest path search method is also used to obtain the local detection order within each unit. The unit door is used as the connection point to connect the local detection order within the unit with the aforementioned public area detection order at the corresponding unit door node, forming a global detection order that covers all detection points. Global sequence generation and output: The aforementioned public area detection sequence and the internal detection sequence of each type are connected and combined at the corresponding gate nodes to form a global detection sequence covering all detection points, and the task instruction sequence containing detection point identifiers, entry paths, exit paths and gate crossing sequences is output.

2. The intelligent planning method for quality inspection points of engineering structures according to claim 1, characterized in that: The specific steps for 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 analytical tools; According to the requirements for component type, size and location in the building quality inspection specifications, the parameters are matched with the corresponding entries in the specifications; Identify test points on the surface of the component that meet the specifications, and form a candidate set of test points.

3. The intelligent planning method for quality inspection points of engineering structures according to claim 2, characterized in that: The specific steps of the symmetrical sieving process are as follows: Calculate the centroid of the candidate detection point set and translate the centroid of the point set; The translated set of points is then scaled. Rotate and align the normalized point set to determine if the point set has symmetry; In cases where symmetry exists, only the detection points on one side are retained, forming a set of reduced detection points.

4. The intelligent planning method for quality inspection points of engineering structures according to claim 3, characterized in that: The specific steps of the walkable space constraint are as follows: Obtain the occupied area of ​​building space based on BIM model; Determine the external dimensions and required safety clearances of the inspection robot; The robot's safety envelope in space is obtained by performing an envelope modeling of the aforementioned dimensional parameters and safety clearance. The safety envelope is superimposed on the building space area to obtain the configuration space that the robot can walk on, which is used to constrain subsequent path planning.

5. The intelligent planning method for quality inspection points of engineering structures according to claim 4, characterized in that: The specific steps of spatial topology construction and classification are as follows: Based on the connection relationship between rooms and doors in the architectural space, establish a spatial topology with rooms and doors as nodes and door connections as edges. In the spatial topology, the intermediate value of each gate node is calculated to identify key gate nodes that frequently appear in multiple paths; The middle 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 results are classified as suites, and areas connected to multiple suites are divided into common areas.

6. The intelligent planning method for quality inspection points of engineering structures according to claim 5, characterized in that: The specific steps involved in optimizing the order of the public areas are as follows: Under the constraints of the spatial topology and configuration space, determine the passable paths between each type of door; Calculate the shortest passage distance between each set of doors under configuration space constraints; The shortest travel distance is used as the edge weight to construct a traveling salesman problem model; The path sequence covering all the door types is solved in the model to obtain the detection sequence of the public area.

7. The intelligent planning method for quality inspection points of engineering structures according to claim 6, characterized in that: The specific steps for optimizing the internal sequence of the housing 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 detection sequence at the corresponding door position; This results in a global detection sequence that is a nested combination of the main sequence of the public area and the local sequence of each unit type.

8. The intelligent planning method for quality inspection points of engineering structures according to claim 7, characterized in that: The specific steps for 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; Based on the global detection order, a task instruction sequence is generated, which includes detection point identifiers, entry paths, exit paths, and door crossing order. The sequence of task instructions is used as input instructions for the detection robot.

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