A traditional garden wall monitoring and diagnosing system and method fused with a digital model

Abstract

The application discloses a traditional garden wall monitoring and diagnosing system and method fusing digital models, relates to the technical field of garden wall monitoring, and acquires three-dimensional point cloud image data of a target garden wall, and constructs a three-dimensional simulation model; three-dimensional point cloud mapping coordinate data of the corresponding garden wall is acquired based on the three-dimensional simulation model, historical point cloud coordinate data is called, the position change degree of the point cloud is analyzed, abnormal point cloud data is determined, and the abnormal offset index of the corresponding abnormal point cloud is analyzed; an abnormal point cloud fusion model is constructed to compare and analyze the abnormal point cloud data, adjacent associated abnormal points are acquired, and an abnormal risk area is constructed; the abnormal disease index of the abnormal risk area is analyzed, and an abnormal disease area is output according to the analysis result; the application realizes all-around point cloud data collection of the target garden wall, captures the risk of wall structure change, judges and positions the disease area, and realizes risk evaluation.

Description

Technical Field

[0001] This invention relates to the field of garden wall monitoring, specifically a traditional garden wall monitoring and diagnostic system and method that integrates digital models. Background Technology

[0002] As a core component of the spatial layout of traditional gardens, the walls of traditional gardens serve multiple functions, including spatial division, landscape creation, and cultural preservation. Therefore, the stability of their structure is directly related to the protection and inheritance of garden heritage. Currently, most garden walls are affected by natural environmental factors or human factors due to their long construction time, resulting in common problems such as wall cracking, efflorescence, bulging, and settlement. Currently, the mainstream monitoring method for traditional garden walls is still mainly manual inspection. This method relies on the experience and judgment of technical personnel, which leads to problems such as strong subjective judgment, low monitoring frequency and poor data continuity. As a result, this method cannot capture the risk of changes in the internal structure of the wall. While some sensors are used for auxiliary monitoring, they can obtain local data to a certain extent, but they lack the connection with the three-dimensional spatial structure of the wall, making it difficult to accurately locate the disease and assess the risk. Summary of the Invention

[0003] The purpose of this invention is to provide a traditional garden wall monitoring and diagnosis system and method that integrates digital models to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A traditional garden wall monitoring and diagnosis method integrating digital models: This method includes the following steps: Acquire 3D point cloud image data of the target garden wall and construct a 3D simulation model; Based on the 3D simulation model, obtain the 3D point cloud mapping coordinate data of the corresponding garden wall, retrieve historical point cloud coordinate data to analyze the degree of point cloud position change, identify abnormal point cloud data, and analyze the abnormal offset index of the corresponding abnormal point cloud. An abnormal point cloud fusion model is constructed to compare and analyze abnormal point cloud data, obtain adjacent and related abnormal points, and construct abnormal risk areas; Analyze the abnormal disease index of the abnormal risk area and output the abnormal disease area based on the analysis results.

[0005] Furthermore, a 3D scanning platform was built to collect several 3D point cloud images of the target garden wall, and the location of the point cloud data was determined based on the corresponding 3D point cloud images. The 3D scanning platform was constructed using devices such as drones, radar, and sensors, and non-contact laser ranging and other scanning technologies were used to extract the point cloud images of the target garden wall. Model simulation is performed based on several 3D point cloud image data of the target garden wall to construct a 3D simulation model of the target garden wall and determine the point cloud position data in the corresponding 3D simulation model. Point cloud localization techniques such as target localization or feature localization are used to stitch multiple 3D point cloud images of the target garden wall to construct a 3D model of the corresponding garden wall and record the point cloud position information in the corresponding model.

[0006] Furthermore, the point cloud location data includes point cloud ID labels, acquisition timestamps, and coordinate data; The position data of each point cloud in the 3D simulation model are periodically recorded and stored in the database as historical point cloud coordinate data. The point cloud refers to the discrete points in the point cloud image. Periodic recording means recording the position data of each point cloud in the 3D simulation model within one period until the position data of the corresponding point cloud is reacquired in the next period, and then storing it in the database as historical data. The period refers to the acquisition period of the corresponding point cloud position data.

[0007] Furthermore, based on the coordinate data of each point cloud in the 3D simulation model within the current period, and retrieving the coordinate data of consecutive periods from the historical point cloud coordinate data of the corresponding number, the continuous period position offset change path value of the point cloud with the corresponding number up to the current period is analyzed and denoted as L. n Where n is the point cloud number label; the calculation of the point cloud position offset distance value for consecutive periods is obtained by calculating and summing the distances between the point cloud coordinates in consecutive periods in the historical period; By comparing the distance value Ln of the continuous periodic position offset change of each point cloud within the current period with the safety offset threshold s; if L n If the value is greater than or equal to s, then the corresponding point cloud position is judged to be an abnormal change within the current period; otherwise, the corresponding point cloud position is judged to be a normal change within the current period.

[0008] Furthermore, when it is determined that the position of a point cloud changes abnormally within the current period, it is listed as an abnormal point cloud and the current period point cloud position data and historical point cloud position data of the corresponding point cloud are marked as abnormal change data. The abnormal point clouds and corresponding abnormal change data existing in the 3D simulation model within the current period are extracted. Distance analysis is performed between the current period position coordinates and the initial period position coordinates of each abnormal point cloud to obtain the linear displacement value of the corresponding abnormal point cloud in the current period, denoted as c. m Where m corresponds to the number label of the abnormal point cloud; Based on the continuous periodic positional offset path value L of the corresponding anomalous point cloud m and offset linear displacement value c mBy combining the period length required for the positional shift change within a continuous period of the corresponding anomalous point cloud, an anomalous offset index analysis is performed on the anomalous point cloud, denoted as Abf. m The specific calculations for the anomaly offset index analysis of each anomalous point cloud are as follows: ; Among them, Abf m L is the anomaly offset index of the anomaly point cloud labeled m; m c represents the path length of the continuous periodic positional offset change of the anomalous point cloud labeled m; m T represents the linear displacement value of the abnormal point cloud labeled m; T is the total data monitoring cycle length of the target garden wall by the 3D simulation model; T m The length of the continuous change period from the start of the position change of the abnormal point cloud corresponding to the label m to the current period; In the above calculations, by analyzing the ratio of the offset path of the anomalous point cloud to its straight-line position within the continuous period of its positional displacement, the complexity of the path change of the anomalous point cloud under the condition of offset straight-line displacement is determined. Similarly, on the time scale, the complexity of the positional displacement of the anomalous point cloud over the time period is determined. Based on this, the anomalous offset index of the anomalous point cloud is calculated by combining path displacement analysis and time period analysis.

[0009] Furthermore, based on the coordinate data of each anomalous point cloud in the 3D simulation model, the plane where the corresponding anomalous point cloud is located is determined, and by comprehensively summarizing the coordinate data of anomalous point clouds on the same plane, a set of anomalous point clouds on the same plane is constructed. Each set of anomalous point clouds in the same plane is retrieved, and the planar point cloud positions are mapped using a 3D simulation model to obtain the planar distribution map of the corresponding anomalous point clouds in the same plane. An anomaly point cloud fusion model is constructed to perform position comparison analysis on the planar distribution map of anomaly point clouds on the same plane, identify adjacent and related anomaly points, and construct anomaly risk areas.

[0010] Furthermore, the specific steps for analyzing the planar distribution map of anomaly point clouds on the same plane using the anomaly point cloud fusion model are as follows: T1. Obtain the coordinate data of abnormal point clouds existing in the same plane; T2. Take any abnormal point in the plane as the base point, construct an abnormal correlation circle with the base point as the center and its offset linear displacement value as the diameter, and construct a retrieval correlation circle with the continuous periodic position offset change path value of the base point as the diameter; wherein the abnormal correlation circle and the retrieval correlation circle are concentric circles. T3. Search for other abnormal point clouds within the search association circle where the base point is located; if no other abnormal point clouds are found, the abnormal association circle of the base point is taken as the abnormal risk area; if other abnormal point clouds are found, proceed to step T4. T4. If other anomalous point clouds are found within the search association circle of the base point, the retrieved anomalous point cloud is used as the new base point, and an anomalous association circle and a search association circle are constructed respectively. If only one other anomalous point cloud exists, the anomalous association circle of the new base point is merged with the anomalous association circle corresponding to the original base point, and the merged anomalous association circle is obtained as the anomalous risk area. If two or more anomalous point clouds exist, the original base point and each new base point are merged with their respective anomalous association circles, and the merged anomalous association circle with the largest area is selected as the anomalous risk area. The process of fusing abnormally associated circles is as follows: connect the original base point with the new base point, take the midpoint of the connecting line as the new center of the circle, obtain the connecting line segment of the diameter of the abnormally associated circles of the two base points by the extended straight line of the connecting line, and construct the fused abnormally associated circle based on the connecting line segment of the diameter of the two abnormally associated circles as the new diameter, and use it as the abnormally risk area; for abnormal point clouds that are not selected for fusing abnormally associated circles with the original base point, repeat step T3 to search for new base points, and choose to end or execute step T4 based on the search results; Based on the abnormal risk areas existing on the same plane output by the abnormal point cloud fusion model, and combining the area data of the corresponding abnormal risk areas, the distance values ​​between the base points, and the abnormal offset index of the corresponding base points, an abnormal disease index analysis is performed on each abnormal risk area; specifically, the abnormal disease index analysis is as follows:

[0011] Among them, Adx i,j,p Abf is the abnormal disease index of the abnormal risk area corresponding to base points i and j on plane p; i,p and Abf j,p This is the anomalous offset index corresponding to base points i and j on plane p; Abf m,p S is the anomalous offset index of the base point with number m on the plane with number p; i,j,p S represents the area of ​​the abnormal risk region corresponding to base points i and j on plane p; p h represents the total area of ​​the abnormal risk regions existing on plane p; i,j,p The height difference between base points i and j on plane p; max(h p () represents the maximum height difference between two base points on plane p that are within the same abnormal risk area; where base points i and j are within the same abnormal risk area on the same plane. Based on the analysis results, a comparison threshold is set, and abnormal risk areas corresponding to those exceeding the comparison threshold are identified as abnormal disease areas. Based on the corresponding abnormal disease index, they are sorted from largest to smallest and assigned priority. A maintenance priority form is constructed, and maintenance sequence instructions are output based on the form. Conversely, areas with abnormal risks that are below or equal to the comparison threshold are identified as areas with potential abnormal diseases and are subject to continuous monitoring.

[0012] A traditional garden wall monitoring and diagnostic system that integrates digital models: The system includes a model simulation module, an abnormal point cloud determination module, an abnormal region fusion module, and a maintenance output module. The model simulation module includes an information acquisition unit and a simulation model construction unit; The abnormal point cloud determination module includes an abnormal point cloud determination unit and an abnormal offset index analysis unit; The abnormal region fusion module includes a point cloud planar distribution acquisition unit and a region fusion model analysis unit; The maintenance output module includes a disease area analysis unit and an instruction execution unit.

[0013] Furthermore, the information acquisition unit builds a three-dimensional scanning platform to collect several three-dimensional point cloud images of the target garden wall, and determines the location of the point cloud data based on the corresponding three-dimensional point cloud images; The simulation model construction unit performs model simulation based on several three-dimensional point cloud image data of the target garden wall, constructs a three-dimensional simulation model of the target garden wall, and determines the point cloud position data in the corresponding three-dimensional simulation model. The abnormal point cloud determination unit analyzes the path value of the continuous period position offset change of each point cloud in the current period, and compares the analysis results with the safety offset threshold to determine the abnormal point cloud. When the abnormal offset index analysis unit determines that a point cloud is an abnormal point cloud, it extracts the corresponding abnormal change data and performs distance analysis on the current period position coordinates and the initial period position coordinates of the abnormal point cloud to obtain the offset linear displacement value of the abnormal point cloud in the current period. Based on the continuous period position offset change path value and offset linear displacement value of the corresponding abnormal point cloud, and combined with the period length required for the position offset change within the continuous period of the corresponding abnormal point cloud, the abnormal offset index analysis is performed on the abnormal point cloud.

[0014] Furthermore, the point cloud planar distribution acquisition unit determines the plane where the corresponding abnormal point cloud is located based on the coordinate data of each abnormal point cloud in the three-dimensional simulation model, and constructs a set of abnormal point clouds in the same plane by comprehensively summarizing the coordinate data of abnormal point clouds in the same plane; it retrieves the set of abnormal point clouds in the same plane respectively, and performs planar point cloud position mapping through the three-dimensional simulation model to obtain the planar distribution map of the corresponding abnormal point clouds in the same plane. The regional fusion model analysis unit constructs an anomaly point cloud fusion model to perform position comparison analysis on the planar distribution map of anomaly point clouds on the same plane, determine adjacent and related anomaly points, and construct anomaly risk areas. The disease area analysis unit analyzes the abnormal disease index of each abnormal risk area based on the abnormal risk areas on the same plane output by the abnormal point cloud fusion model, combined with the area data of the corresponding abnormal risk area, the distance between the base points, and the abnormal offset index of the corresponding base points. The instruction execution unit takes a comparison threshold based on the analysis results, judges the identified abnormal disease areas based on the comparison results, generates a maintenance priority form, and outputs maintenance sequence instructions based on the form; otherwise, it continuously monitors the identified abnormal disease potential areas.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention combines model building to achieve 3D simulation of traditional garden walls, extracts point cloud data of the walls, and analyzes the abnormal state of the point cloud based on the continuous changes in the point cloud position data; it analyzes the abnormal point cloud by performing abnormal offset index analysis; it constructs a fusion model to perform impact area fusion analysis on abnormal point clouds on the same plane to obtain abnormal risk areas, and finally determines the existence of abnormal disease areas and generates maintenance instructions by correlation analysis of corresponding abnormal disease indices; this invention achieves all-round point cloud data acquisition of target garden walls by constructing a 3D spatial model, so as to capture the risk of wall structure changes, judge and locate disease areas, and realize risk assessment and maintenance logic generation; it improves the dependence on manual judgment and the limitations of local data analysis in traditional methods. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of a traditional garden wall monitoring and diagnosis system that integrates digital models according to the present invention; Figure 2 This is a flowchart illustrating a traditional garden wall monitoring and diagnosis method that integrates digital models, according to the present invention. Detailed Implementation

[0017] 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.

[0018] Example: Figure 1 As shown, the present invention provides a technical solution: A traditional garden wall monitoring and diagnostic system that integrates digital models: The system includes a model simulation module, an anomaly point cloud determination module, an anomaly region fusion module, and a maintenance output module. Furthermore, the model simulation module acquires the three-dimensional point cloud image data of the target garden wall and constructs a three-dimensional simulation model; The abnormal point cloud determination module obtains the 3D point cloud mapping coordinate data of the corresponding garden wall based on the 3D simulation model, and retrieves historical point cloud coordinate data to analyze the degree of point cloud position change, determine abnormal point cloud data, and analyze the abnormal offset index of the corresponding abnormal point cloud. The abnormal region fusion module constructs an abnormal point cloud fusion model to compare and analyze abnormal point cloud data, obtain adjacent and related abnormal points, and construct abnormal risk regions; The maintenance output module analyzes the abnormal disease index of abnormal risk areas and outputs the abnormal disease areas based on the analysis results; Furthermore, the model simulation module includes an information acquisition unit and a simulation model construction unit; The information acquisition unit collects several three-dimensional point cloud images of the target garden wall by building a three-dimensional scanning platform, and determines the location of the point cloud data based on the corresponding three-dimensional point cloud images. The simulation model construction unit performs model simulation based on several three-dimensional point cloud image data of the target garden wall, constructs a three-dimensional simulation model of the target garden wall, and determines the point cloud position data in the corresponding three-dimensional simulation model. The point cloud location data includes point cloud number labels, collection timestamps, and coordinate data; The point cloud position data in the 3D simulation model is periodically recorded and stored in the database as historical point cloud coordinate data. Furthermore, the abnormal point cloud determination module includes an abnormal point cloud determination unit and an abnormal offset index analysis unit; The abnormal point cloud determination unit is based on the coordinate data of each point cloud in the 3D simulation model within the current period, and retrieves the coordinate data of consecutive periods from the historical point cloud coordinate data of the corresponding number. It then analyzes the continuous period position offset change path value of the point cloud with the corresponding number up to the current period, denoted as L. nWhere n is the point cloud number label; By comparing the distance value Ln of the continuous periodic position offset change of each point cloud within the current period with the safety offset threshold s; if L n If the value is greater than or equal to s, then the corresponding point cloud position is judged to be an abnormal change within the current period; otherwise, the corresponding point cloud position is judged to be a normal change within the current period. When the abnormal offset index analysis unit determines that the position of a point cloud is abnormally changed in the current period, it will list it as an abnormal point cloud and mark the current period point cloud position data and historical point cloud position data of the corresponding point cloud as abnormal change data. The abnormal point clouds and corresponding abnormal change data existing in the 3D simulation model within the current period are extracted. Distance analysis is performed between the current period position coordinates and the initial period position coordinates of each abnormal point cloud to obtain the linear displacement value of the corresponding abnormal point cloud in the current period, denoted as c. m Where m corresponds to the number label of the abnormal point cloud; Based on the continuous periodic positional offset path value L of the corresponding anomalous point cloud m and offset linear displacement value c m By combining the period length required for the positional shift change within a continuous period of the corresponding anomalous point cloud, an anomalous offset index analysis is performed on the anomalous point cloud, denoted as Abf. m ; Furthermore, the abnormal region fusion module includes a point cloud planar distribution acquisition unit and a region fusion model analysis unit; The point cloud planar distribution acquisition unit determines the plane where the corresponding abnormal point cloud is located based on the coordinate data of each abnormal point cloud in the 3D simulation model, and constructs a set of abnormal point clouds in the same plane by comprehensively summarizing the coordinate data of abnormal point clouds on the same plane. Each set of anomalous point clouds in the same plane is retrieved, and the planar point cloud positions are mapped using a 3D simulation model to obtain the planar distribution map of the corresponding anomalous point clouds in the same plane. The regional fusion model analysis unit constructs an anomaly point cloud fusion model to perform position comparison analysis on the planar distribution map of anomaly point clouds on the same plane, identify adjacent and related anomaly points, and construct anomaly risk areas; Furthermore, the maintenance output module includes a disease area analysis unit and an instruction execution unit; The disease area analysis unit analyzes the abnormal disease index of each abnormal risk area based on the abnormal risk areas on the same plane output by the abnormal point cloud fusion model, combined with the area data of the corresponding abnormal risk areas, the distance between the base points, and the abnormal offset index of the corresponding base points. The instruction execution unit determines a comparison threshold based on the analysis results. Areas with abnormal risks exceeding the threshold are identified as abnormal disease areas. These areas are then sorted and prioritized according to their corresponding abnormal disease indices from largest to smallest, and a maintenance priority table is constructed. Maintenance sequence instructions are output based on this table. Conversely, areas with abnormal risks below or equal to the comparison threshold are identified as areas with potential abnormal diseases and are subject to continuous monitoring. like Figure 2 As shown, the present invention provides another technical solution: A traditional garden wall monitoring and diagnosis method integrating digital models: This method includes the following steps: Acquire the 3D point cloud data of the target garden wall and construct a 3D simulation model; Based on the 3D simulation model, obtain the 3D point cloud mapping coordinate data of the corresponding garden wall, retrieve historical point cloud coordinate data to analyze the degree of point cloud position change, identify abnormal point cloud data, and analyze the abnormal offset index of the corresponding abnormal point cloud. An abnormal point cloud fusion model is constructed to compare and analyze abnormal point cloud data, obtain adjacent and related abnormal points, and construct abnormal risk areas; Analyze the abnormal disease index in the abnormal risk area and output the abnormal disease area based on the analysis results.

[0019] Furthermore, a 3D scanning platform was built to collect several 3D point cloud images of the target garden wall, and the location of the point cloud data was determined based on the corresponding 3D point cloud images. The 3D scanning platform was constructed using devices such as drones, radar, and sensors, and non-contact laser ranging and other scanning technologies were used to extract the point cloud images of the target garden wall. Model simulation is performed based on several 3D point cloud image data of the target garden wall to construct a 3D simulation model of the target garden wall and determine the point cloud position data in the corresponding 3D simulation model; point cloud localization techniques such as target localization or feature localization are used to stitch multiple 3D point cloud images of the target garden wall to construct a 3D model of the corresponding garden wall and record the point cloud position information in the corresponding model. In this embodiment, since the point cloud data obtained from multiple stations are independent of each other, they need to be integrated into a unified coordinate system through a stitching algorithm. This is achieved by arranging multiple targets with obvious features in the scanning area. The scanner will acquire the point cloud information of the targets when scanning at different stations, and then the point cloud data stitching is completed according to the spatial position relationship of the targets.

[0020] Furthermore, the point cloud location data includes point cloud ID labels, collection timestamps, and coordinate data; The position data of each point cloud in the 3D simulation model is periodically recorded and stored in the database as historical point cloud coordinate data. The point cloud refers to the discrete points in the point cloud image. Periodic recording means recording the position data of each point cloud in the 3D simulation model within one period until the position data of the corresponding point cloud is reacquired in the next period, and then storing it in the database as historical data. The period is the acquisition period of the corresponding point cloud position data.

[0021] Furthermore, based on the coordinate data of each point cloud in the 3D simulation model within the current period, and retrieving the coordinate data of consecutive periods from the historical point cloud coordinate data of the corresponding number, the continuous period position offset change path value of the point cloud with the corresponding number up to the current period is analyzed and denoted as L. n Where n is the point cloud number label; the calculation of the point cloud position offset distance value for consecutive periods is obtained by calculating and summing the distances between the point cloud coordinates in consecutive periods in the historical period; By comparing the distance value Ln of the continuous periodic position offset change of each point cloud within the current period with the safety offset threshold s; if L n If the value is greater than or equal to s, then the corresponding point cloud position is judged to be an abnormal change within the current period; otherwise, the corresponding point cloud position is judged to be a normal change within the current period.

[0022] Furthermore, when it is determined that the position of a point cloud changes abnormally within the current period, it is listed as an abnormal point cloud and the current period point cloud position data and historical point cloud position data of the corresponding point cloud are marked as abnormal change data. The abnormal point clouds and corresponding abnormal change data existing in the 3D simulation model within the current period are extracted. Distance analysis is performed between the current period position coordinates and the initial period position coordinates of each abnormal point cloud to obtain the linear displacement value of the corresponding abnormal point cloud in the current period, denoted as c. m Where m corresponds to the number label of the abnormal point cloud; Based on the continuous periodic positional offset path value L of the corresponding anomalous point cloud m and offset linear displacement value c m By combining the period length required for the positional shift change within a continuous period of the corresponding anomalous point cloud, an anomalous offset index analysis is performed on the anomalous point cloud, denoted as Abf. m The specific calculations for the anomaly offset index analysis of each anomalous point cloud are as follows: ; Among them, Abf m L is the anomaly offset index of the anomaly point cloud labeled m; m c represents the path length of the continuous periodic positional offset change of the anomalous point cloud labeled m; mT represents the linear displacement value of the abnormal point cloud labeled m; T is the total data monitoring cycle length of the target garden wall by the 3D simulation model; T m The length of the continuous change period from the start of the position change of the abnormal point cloud corresponding to the label m to the current period; In the above calculations, by analyzing the ratio of the offset path of the anomalous point cloud to its straight-line position within the continuous period of its positional displacement, the complexity of the path change of the anomalous point cloud under the condition of offset straight-line displacement is determined. Similarly, on the time scale, the complexity of the positional displacement of the anomalous point cloud over the time period is determined. Based on this, the anomalous offset index of the anomalous point cloud is calculated by combining path displacement analysis and time period analysis.

[0023] Furthermore, based on the coordinate data of each anomalous point cloud in the 3D simulation model, the plane where the corresponding anomalous point cloud is located is determined, and by comprehensively summarizing the coordinate data of anomalous point clouds on the same plane, a set of anomalous point clouds on the same plane is constructed. Each set of anomalous point clouds in the same plane is retrieved, and the planar point cloud positions are mapped using a 3D simulation model to obtain the planar distribution map of the corresponding anomalous point clouds in the same plane. An anomaly point cloud fusion model is constructed to perform position comparison analysis on the planar distribution map of anomaly point clouds on the same plane, identify adjacent and related anomaly points, and construct anomaly risk areas.

[0024] Furthermore, the specific steps for analyzing the planar distribution map of anomaly point clouds on the same plane using the anomaly point cloud fusion model are as follows: T1. Obtain the coordinate data of abnormal point clouds existing in the same plane; T2. Take any abnormal point in the plane as the base point, construct an abnormal correlation circle with the base point as the center and its offset linear displacement value as the diameter, and construct a retrieval correlation circle with the continuous periodic position offset change path value of the base point as the diameter; wherein the abnormal correlation circle and the retrieval correlation circle are concentric circles. T3. Search for other abnormal point clouds within the search association circle where the base point is located; if no other abnormal point clouds are found, the abnormal association circle of the base point is taken as the abnormal risk area; if other abnormal point clouds are found, proceed to step T4. T4. If other anomalous point clouds are found within the search association circle of the base point, the retrieved anomalous point cloud is used as the new base point, and an anomalous association circle and a search association circle are constructed respectively. If only one other anomalous point cloud exists, the anomalous association circle of the new base point is merged with the anomalous association circle corresponding to the original base point, and the merged anomalous association circle is obtained as the anomalous risk area. If two or more anomalous point clouds exist, the original base point and each new base point are merged with their respective anomalous association circles, and the merged anomalous association circle with the largest area is selected as the anomalous risk area. The process of fusing abnormally associated circles is as follows: connect the original base point with the new base point, take the midpoint of the connecting line as the new center of the circle, obtain the connecting line segment of the diameter of the abnormally associated circles of the two base points by the extended straight line of the connecting line, and construct the fused abnormally associated circle based on the connecting line segment of the diameter of the two abnormally associated circles as the new diameter, and use it as the abnormally risk area; for abnormal point clouds that are not selected for fusing abnormally associated circles with the original base point, repeat step T3 to search for new base points, and choose to end or execute step T4 based on the search results; Based on the abnormal risk areas existing on the same plane output by the abnormal point cloud fusion model, and combining the area data of the corresponding abnormal risk areas, the distance values ​​between the base points, and the abnormal offset index of the corresponding base points, an abnormal disease index analysis is performed on each abnormal risk area; specifically, the abnormal disease index analysis is as follows: ; Among them, Adx i,j,p Abf is the abnormal disease index of the abnormal risk area corresponding to base points i and j on plane p; i,p and Abf j,p This is the anomalous offset index corresponding to base points i and j on plane p; Abf m,p S is the anomalous offset index of the base point with number m on the plane with number p; i,j,p S represents the area of ​​the abnormal risk region corresponding to base points i and j on plane p; p h represents the total area of ​​the abnormal risk regions existing on plane p; i,j,p The height difference between base points i and j on plane p; max(h p ) represents the maximum height difference between two base points located within the same abnormal risk area on plane p; where base points i and j are located within the same abnormal risk area on the same plane; and, considering the accuracy error of actual equipment acquisition or interference factors from wall surface structure or acquisition environment, the value is taken to be greater than 0 when calculating the height difference between the two base points. Based on the analysis results, a comparison threshold is set, and abnormal risk areas corresponding to those exceeding the comparison threshold are identified as abnormal disease areas. Based on the corresponding abnormal disease index, they are sorted from largest to smallest and assigned priority. A maintenance priority form is constructed, and maintenance sequence instructions are output based on the form. Conversely, areas with abnormal risks that are below or equal to the comparison threshold are identified as areas with potential abnormal diseases and are subject to continuous monitoring.

[0025] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A traditional garden wall monitoring and diagnosis method integrating digital models, characterized in that: The method includes the following steps: Acquire 3D point cloud image data of the target garden wall and construct a 3D simulation model; Based on the 3D simulation model, obtain the 3D point cloud mapping coordinate data of the corresponding garden wall, retrieve historical point cloud coordinate data to analyze the degree of point cloud position change, identify abnormal point cloud data, and analyze the abnormal offset index of the corresponding abnormal point cloud. An abnormal point cloud fusion model is constructed to compare and analyze abnormal point cloud data, obtain adjacent and related abnormal points, and construct abnormal risk areas; The specific analysis steps of the abnormal point cloud fusion model are as follows: T1. Obtain the coordinate data of abnormal point clouds existing in the same plane; T2. Take any abnormal point in the plane as the base point, use it as the center, construct an abnormal correlation circle with its offset linear displacement value as the diameter, and construct a retrieval correlation circle with the continuous periodic position offset change path value of the base point as the diameter. T3. Search for other abnormal point clouds within the search association circle where the base point is located; if no other abnormal point clouds are found, the abnormal association circle of the base point is taken as the abnormal risk area; if other abnormal point clouds are found, proceed to step T4. T4. If other anomalous point clouds are found within the search association circle of the base point, the retrieved anomalous point cloud is used as the new base point, and an anomalous association circle and a search association circle are constructed respectively. If there is only one other anomalous point cloud, the anomalous association circle of the new base point is merged with the anomalous association circle corresponding to the original base point to obtain the merged anomalous association circle as the anomalous risk area. If there are two or more, the original base point and each new base point are merged with the anomalous association circle, and the merged anomalous association circle with the largest area is selected as the anomalous risk area based on the processing result. Analyze the abnormal disease index of the abnormal risk area and output the abnormal disease area based on the analysis results.

2. The traditional garden wall monitoring and diagnosis method integrating digital models according to claim 1, characterized in that: A 3D scanning platform was built to collect several 3D point cloud images of the target garden wall, and the location of the point cloud data was determined based on the corresponding 3D point cloud images. Model simulation is performed based on several 3D point cloud image data of the target garden wall to construct a 3D simulation model of the target garden wall and determine the point cloud position data in the corresponding 3D simulation model.

3. The traditional garden wall monitoring and diagnosis method integrating digital models according to claim 2, characterized in that: The point cloud location data includes point cloud ID labels, acquisition timestamps, and coordinate data; The location data of each point cloud in the 3D simulation model are periodically recorded and stored in the database as historical point cloud coordinate data; where point cloud refers to discrete points in the point cloud image.

4. The traditional garden wall monitoring and diagnosis method integrating digital models according to claim 3, characterized in that: Based on the current cycle of three-dimensional simulation model of each point cloud coordinate data, and call the corresponding number of historical point cloud coordinate data in the continuous cycle of coordinate data, and analyze the corresponding number of point cloud of the current cycle of continuous cycle position offset change distance value, recorded as L n ; wherein n is the point cloud number label; By comparing the distance value Ln of the continuous periodic position offset change of each point cloud within the current period with the safety offset threshold s; if L n If the value is greater than or equal to s, then the corresponding point cloud position is judged to be an abnormal change within the current period; otherwise, the corresponding point cloud position is judged to be a normal change within the current period.

5. The traditional garden wall monitoring and diagnosis method integrating digital models according to claim 4, characterized in that: When a point cloud is determined to have an abnormal position change within the current period, it is listed as an abnormal point cloud and the current period point cloud position data and historical point cloud position data of the corresponding point cloud are marked as abnormal change data. The abnormal point clouds and corresponding abnormal change data existing in the 3D simulation model within the current period are extracted. Distance analysis is performed between the current period position coordinates and the initial period position coordinates of each abnormal point cloud to obtain the linear displacement value of the corresponding abnormal point cloud in the current period, denoted as c. m Where m corresponds to the number label of the abnormal point cloud; Based on the continuous periodic positional offset path value L of the corresponding anomalous point cloud m and offset linear displacement value c m By combining the period length required for the positional shift change within a continuous period of the corresponding anomalous point cloud, an anomalous offset index analysis is performed on the anomalous point cloud, denoted as Abf. m .

6. The traditional garden wall monitoring and diagnosis method integrating digital models according to claim 5, characterized in that: Based on the coordinate data of each abnormal point cloud in the 3D simulation model, the plane where the corresponding abnormal point cloud is located is determined, and a set of abnormal point clouds on the same plane is constructed by comprehensively summarizing the coordinate data of abnormal point clouds on the same plane. Each set of anomalous point clouds in the same plane is retrieved, and the planar point cloud positions are mapped using a 3D simulation model to obtain the planar distribution map of the corresponding anomalous point clouds in the same plane. An anomaly point cloud fusion model is constructed to perform position comparison analysis on the planar distribution map of anomaly point clouds on the same plane, identify adjacent and related anomaly points, and construct anomaly risk areas.

7. The traditional garden wall monitoring and diagnosis method integrating digital models according to claim 6, characterized in that: Based on the abnormal risk areas on the same plane output by the abnormal point cloud fusion model, abnormal disease index analysis is performed on each abnormal risk area by combining the area data of the corresponding abnormal risk area, the distance between the base points, and the abnormal offset index of the corresponding base points. Based on the analysis results, a comparison threshold is set, and abnormal risk areas corresponding to those exceeding the comparison threshold are identified as abnormal disease areas. Based on the corresponding abnormal disease index, they are sorted from largest to smallest and assigned priority. A maintenance priority form is constructed, and maintenance sequence instructions are output based on the form. Conversely, areas with abnormal risks that are below or equal to the comparison threshold are identified as areas with potential abnormal diseases and are subject to continuous monitoring.

8. A monitoring and diagnostic system for implementing the traditional garden wall monitoring and diagnostic method that integrates digital models according to any one of claims 1-7, characterized in that: The system includes a model simulation module, an abnormal point cloud determination module, an abnormal region fusion module, and a maintenance output module. The model simulation module includes an information acquisition unit and a simulation model construction unit; The abnormal point cloud determination module includes an abnormal point cloud determination unit and an abnormal offset index analysis unit; The abnormal region fusion module includes a point cloud planar distribution acquisition unit and a region fusion model analysis unit; The maintenance output module includes a disease area analysis unit and an instruction execution unit.

9. A traditional garden wall monitoring and diagnostic system integrating a digital model as described in claim 8, characterized in that: The information acquisition unit builds a three-dimensional scanning platform to collect several three-dimensional point cloud images of the target garden wall, and determines the location of the point cloud data based on the corresponding three-dimensional point cloud images. The simulation model construction unit performs model simulation based on several three-dimensional point cloud image data of the target garden wall, constructs a three-dimensional simulation model of the target garden wall, and determines the point cloud position data in the corresponding three-dimensional simulation model. The abnormal point cloud determination unit analyzes the path value of the continuous period position offset change of each point cloud in the current period, and compares the analysis results with the safety offset threshold to determine the abnormal point cloud. When the abnormal offset index analysis unit determines that a point cloud is an abnormal point cloud, it extracts the corresponding abnormal change data and performs distance analysis on the current period position coordinates and the initial period position coordinates of the abnormal point cloud to obtain the offset linear displacement value of the abnormal point cloud in the current period. Based on the continuous periodic positional offset change path value and offset linear displacement value of the corresponding abnormal point cloud, and combined with the period length required for the positional offset change within the continuous period of the corresponding abnormal point cloud, an abnormal offset index analysis is performed on the abnormal point cloud.

10. A traditional garden wall monitoring and diagnostic system integrating a digital model according to claim 9, characterized in that: The point cloud planar distribution acquisition unit determines the plane where the corresponding abnormal point cloud is located based on the coordinate data of each abnormal point cloud in the three-dimensional simulation model, and constructs a set of abnormal point clouds on the same plane by comprehensively summarizing the coordinate data of abnormal point clouds on the same plane. Each set of anomalous point clouds in the same plane is retrieved, and the planar point cloud positions are mapped using a 3D simulation model to obtain the planar distribution map of the corresponding anomalous point clouds in the same plane. The regional fusion model analysis unit constructs an anomaly point cloud fusion model to perform position comparison analysis on the planar distribution map of anomaly point clouds on the same plane, determine adjacent and related anomaly points, and construct anomaly risk areas. The disease area analysis unit analyzes the abnormal disease index of each abnormal risk area based on the abnormal risk areas on the same plane output by the abnormal point cloud fusion model, combined with the area data of the corresponding abnormal risk area, the distance between the base points, and the abnormal offset index of the corresponding base points. The instruction execution unit takes a comparison threshold based on the analysis results, judges the identified abnormal disease areas based on the comparison results, generates a maintenance priority form, and outputs maintenance sequence instructions based on the form; otherwise, it continuously monitors the identified abnormal disease potential areas.

Images