Method for stress-deformation co-monitoring of bottom structure based on multi-source data

Abstract

The application discloses a bottom structure stress-deformation cooperative monitoring method based on multi-source data and relates to the technical field of structure safety monitoring.The method comprises the following steps: collecting internal stress data of an anchor rod of a bottom structure and surface three-dimensional point cloud data and performing multi-source monitoring time sequence calibration to generate multi-source monitoring time record; positioning and calibrating the center of a target according to the surface three-dimensional point cloud data in the multi-source monitoring time record, restoring the internal stress monitoring position according to the installation direction in the anchor rod layout record and the anchor rod sensor depth, projecting each monitoring position to the surface of the bottom structure, and generating stress deformation mapping record; calibrating multi-period point cloud based on the stress deformation mapping record, tracing each cycle boundary displacement along the deformation discontinuous boundary and connecting the displacement into a front migration chain, constraining the step-by-step transmission position of the anchor rod stress increment according to the advancing direction of the front migration chain and the cross-period step length, and generating stress traction chain record. The application improves the accuracy of bottom structure stress deformation evolution positioning and the reliability of cooperative monitoring grading.

Description

Technical Field

[0001] This invention relates to the field of structural safety monitoring technology, and in particular to a method for coordinated monitoring of stress and deformation of bottom structures based on multi-source data. Background Technology

[0002] With the development of monitoring methods for the service status of underground engineering, slope engineering, tunnel foundation engineering, and large load-bearing structures, the existing bottom structure safety monitoring usually uses anchor stress sensors to obtain internal stress changes, and uses three-dimensional laser scanning, photogrammetry, or point cloud reconstruction to obtain surface displacement and deformation information. Then, through unified time stamping, measurement point coordinate transformation, and comparison of multi-period data, local stress concentration, surface deformation expansion, and structural stability are identified. This multi-source data fusion method has gradually become an important technical path for stress-deformation collaborative monitoring of bottom structures.

[0003] Existing methods often statistically analyze the internal stress sequence and surface point cloud shape variables separately, and then apply thresholds based on the same monitoring area or similar sampling times. In scenarios where the deformation front migrates across periods along the discontinuous boundary and stress disturbances are transmitted step by step along the anchor bolt layout direction, it is easy to encounter the problem that the stress mutation location and the location reached by the deformation front are only statically matched, making it difficult to confirm the true bearing relationship. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for coordinated monitoring of stress and deformation of bottom structure based on multi-source data, which solves the problem of difficult spatiotemporal coordinated tracking of stress propagation and surface deformation evolution of bottom structure.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for coordinated monitoring of stress and deformation of bottom structures based on multi-source data, comprising: acquiring internal stress data of anchor bolts and surface three-dimensional point cloud data of the bottom structure and performing multi-source monitoring time sequence calibration to generate a multi-source monitoring time record; locating and calibrating the target center based on the surface three-dimensional point cloud data in the multi-source monitoring time record, and reconstructing the internal stress monitoring position according to the installation direction and anchor bolt sensor depth in the anchor bolt layout record, projecting each monitoring position onto the bottom structure surface to generate a stress-deformation mapping record; calibrating the multi-period point cloud based on the stress-deformation mapping record, and tracking the boundary position of each period along the deformation discontinuity boundary. The process involves connecting the moving and paralleling chains to form a leading migration chain. The direction of advancement of the leading migration chain and the span of time constrain the stepwise transmission position of the anchor stress increment, generating a stress traction chain record. Each segment of the stress traction chain record is paired with the stress mutation moment and the arrival time of the deformation front, and the continuity of the link is verified in reverse using the time difference between adjacent segments. The chain is truncated at points where the direction of advancement deviates, generating a stress deformation evolution record. Based on the stress deformation evolution record, the stress propagation state and surface deformation state of each monitoring area are collaboratively classified, and the subsequent monitoring range and monitoring cycle are determined based on the classification state, generating a collaborative monitoring report for the bottom structure.

[0008] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the specific steps for collecting internal stress data and surface three-dimensional point cloud data of the bottom structure anchor bolts and performing multi-source monitoring time sequence calibration to generate multi-source monitoring time records are as follows:

[0009] The stress acquisition time corresponding to the internal stress data of the anchor bolt and the scanning time corresponding to the surface three-dimensional point cloud data are unified to the same monitoring time axis. The monitoring cycle is divided according to the scanning time period of the surface three-dimensional point cloud data in each period, and the internal stress data of the anchor bolt is assigned to the corresponding monitoring cycle to generate a monitoring cycle alignment record.

[0010] The stress acquisition time and point cloud scanning time in the monitoring cycle alignment record are checked for time sequence continuity to obtain monitoring time sequence alignment data. The monitoring time sequence alignment data is then organized according to the monitoring cycle to generate multi-source monitoring time synchronization records.

[0011] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the steps of locating and calibrating the target center based on the surface three-dimensional point cloud data in the multi-source monitoring time record, restoring the internal stress monitoring position according to the installation direction and anchor sensor depth in the anchor bolt layout record, and projecting each monitoring position onto the bottom structure surface are as follows:

[0012] Based on the surface three-dimensional point cloud data in the multi-source monitoring time record, the target point cloud is separated according to the reflection characteristics and contour range of the calibration target and the center of the calibration target is determined. Based on the center of the calibration target, the spatial position of the surface three-dimensional point cloud data in each period is unified, and the continuous surface point cloud is connected to generate surface reference data.

[0013] The anchor bolt construction layout information and anchor bolt sensor installation information in the surface reference data are converted to a unified spatial location, and the entry hole position, installation direction and anchor bolt sensor depth are organized according to the anchor bolt number to generate an anchor bolt layout record;

[0014] Based on the anchor bolt installation record, the anchor bolt sensor is extended from the anchor bolt entry hole position along the corresponding installation direction into the bottom structure according to the anchor bolt sensor depth to determine the embedment position of each anchor bolt sensor in a unified spatial location and generate stress monitoring position data.

[0015] Based on the stress monitoring location data and surface reference data, the local continuous surface corresponding to each internal stress monitoring location is determined, and the internal stress monitoring location is projected onto the bottom structure surface along the normal direction of the local continuous surface to generate surface monitoring projection data.

[0016] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the stress-deformation mapping record is obtained by associating the internal stress monitoring position, the surface projection position, and the corresponding bottom structure surface area based on surface monitoring projection data according to the anchor bolt number and monitoring cycle.

[0017] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the specific steps of calibrating multi-period point clouds based on stress-deformation mapping records, tracking the boundary displacement of each period along the discontinuous deformation boundary, and concatenating them into a leading-edge migration chain are as follows:

[0018] Based on stress-deformation mapping records, the position of multi-period point clouds is calibrated according to the corresponding bottom structure surface area and monitoring cycle, and the spatial deviation between adjacent monitoring cycles is corrected by the cross-period correspondence of stable surface points to generate periodic point cloud calibration records.

[0019] Based on the periodic point cloud calibration record, the inter-period displacement change of each surface point along the local normal direction is checked, and the positions with opposite displacement directions between adjacent surface regions are connected as deformation discontinuous boundaries to generate a periodic boundary candidate sequence.

[0020] Based on the candidate sequence of periodic boundaries, bidirectional correspondence verification is performed on discontinuous boundaries that are adjacent in location and have the same boundary extension order within adjacent monitoring periods, and correspondences that involve boundary crossing and reversed extension order are excluded, generating boundary cross-period correspondence records.

[0021] Based on the boundary cross-period corresponding records, the corresponding positions of the same deformation discontinuous boundary in adjacent monitoring cycles are connected to determine the advancement direction and cross-period advancement distance of each boundary segment, and the corresponding boundaries that are continuously connected by the advancement direction are sequentially connected to generate the frontier migration chain.

[0022] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the stress traction chain record is generated by constraining the progressive transmission position of the stress increment of the anchor bolt based on the advancing direction of the leading migration chain and the inter-period step length. The specific steps are as follows:

[0023] Based on the front migration chain, the projected position of each anchor bolt surface is checked along the corresponding front advancement direction to obtain the front advancement inter-period advancement distance. The allowable interval between the anchor bolt and the corresponding migration segment is limited by the front advancement inter-period advancement distance. Anchor bolts located on the front advancement side are screened out to generate stress reconnection candidate records.

[0024] Based on the stress reconnection candidate record, the stress traction elements of adjacent candidate anchors are checked segment by segment along the leading edge migration chain. Anchors that are consistent with the leading edge advancement direction and whose stress response is continuously transmitted are connected step by step to generate a stress traction chain record.

[0025] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the specific steps of recording the stress abrupt change time and the arrival time of the deformation front along the stress traction chain segment by segment are as follows:

[0026] The continuous stress changes of each anchor are checked along the connection sequence of the anchors in the stress traction chain record. The first sampling moment when the stress change continues to exceed the corresponding stable range is determined as the stress change moment, and a stress change location record is generated.

[0027] Based on the stress mutation location record, the projected position of each anchor bolt surface is mapped to the front migration chain, and the arrival time of the deformation front is determined according to the order in which the deformation front crosses the corresponding projected position, thus generating a stress front time pairing record.

[0028] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the specific steps of verifying the link continuity by reverse verification of the time difference between adjacent segments, truncating at the point of deviation in the advancing direction, and generating stress-deformation evolution records are as follows:

[0029] Based on the paired records of stress front moment, the response time difference between the arrival time of the deformation front and the stress change moment of the same anchor is calculated, and the response time difference of adjacent anchors and the corresponding front advancement direction are associated according to the connection order of the stress traction chain to generate a traction spatiotemporal continuity record.

[0030] Based on the traction time-space continuity record, the response time difference changes and the bearing status of the advancing direction of adjacent anchor rods are checked segment by segment from the end of each stress traction chain to the starting point. The traction segment with continuous time difference and consistent advancing direction is determined as the effective bearing segment, and a link reverse buckle verification record is generated.

[0031] Based on the link reverse verification record, the effective connection segment is continuously tracked along the connection sequence of each stress traction chain, and the corresponding link is cut off before the first occurrence of response time difference mismatch or deviation of the propulsion direction of the traction segment, generating an evolution link cut-off record.

[0032] Based on the evolution link truncation record, the anchor bolts that were continuously connected before the truncation and the corresponding stress change time, deformation front arrival time and response time difference change are classified into the same evolution segment to generate a stress deformation evolution record.

[0033] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the specific steps of collaboratively classifying the stress propagation state and surface deformation state of each monitoring area according to the stress-deformation evolution record, and determining the subsequent monitoring range and monitoring cycle based on the classification state, are as follows:

[0034] Based on the stress-deformation evolution record, the monitoring area is divided according to the coverage of the deformation discontinuous boundary, and the stress propagation location, surface deformation location, propagation direction and cooperative state change in each monitoring area are sorted out according to the monitoring cycle to generate regional evolution collection data;

[0035] Based on the regional evolution data, the co-evolution characteristics of stress propagation and surface deformation are verified, and the data is organized according to the monitoring area to generate co-evolutionary hierarchical characteristic data.

[0036] Based on the collaborative grading characteristic data, the stress propagation state and surface deformation state of each monitoring area are collaboratively verified, and each monitoring area is divided into strong collaborative area, medium collaborative area and weak collaborative area according to the degree of collaboration, and regional collaborative grading records are generated.

[0037] Based on the regional coordination classification record, the monitoring range of the strong coordination region is expanded along the direction of deformation discontinuity boundary, the current monitoring range of the medium coordination region is maintained, and the monitoring range of the weak coordination region is converged to the surface projection region and the stable surface control region to generate subsequent monitoring range records;

[0038] Based on subsequent monitoring range records and regional collaborative hierarchical records, the monitoring interval for strong collaborative areas is shortened, the monitoring interval for medium collaborative areas is maintained, and the monitoring interval for weak collaborative areas is extended. The subsequent collection order is organized according to the monitoring area, and a monitoring cycle arrangement record is generated.

[0039] As a preferred embodiment of the bottom structure stress-deformation collaborative monitoring method based on multi-source data described in this invention, the bottom structure collaborative monitoring report is obtained by organizing the stress propagation range and subsequent acquisition sequence according to the monitoring area based on the regional collaborative hierarchical record, subsequent monitoring range record, and monitoring cycle arrangement record.

[0040] The beneficial effects of this invention are as follows: by tracking the boundary displacement of each monitoring cycle along the discontinuous deformation boundary and connecting them to form a leading edge migration chain, and then defining the stepwise transmission position of the anchor stress increment by the leading edge advancement direction and the span step length, the spatial co-chain association between the internal stress propagation path and the surface deformation leading edge is completed. In addition, by combining the stress mutation moment and the arrival moment of the deformation leading edge to verify the continuity of the connection segment by segment, the stress deformation collaborative monitoring can identify the real traction segment, cut off the segment of deviation in the advancement direction and restore the cross-cycle evolution sequence, thereby improving the accuracy of the bottom structure stress deformation evolution positioning and the reliability of the collaborative monitoring classification. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a bottom structure stress-deformation collaborative monitoring method based on multi-source data.

[0043] Figure 2 This is a flowchart of the time-stress-deformation mapping for multi-source monitoring.

[0044] Figure 3 This is a flowchart for verifying the spatiotemporal evolution of the frontier migration chain and stress traction chain.

[0045] Figure 4 A flowchart for the coordinated hierarchical management of monitoring areas and subsequent monitoring arrangements. Detailed Implementation

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for coordinated monitoring of stress-deformation in bottom structures based on multi-source data, including the following steps:

[0050] S1. Collect internal stress data and surface three-dimensional point cloud data of the bottom structure anchor rods and perform multi-source monitoring time sequence calibration to generate multi-source monitoring time synchronization records.

[0051] The stress acquisition time corresponding to the internal stress data of the anchor bolt and the scanning time corresponding to the surface three-dimensional point cloud data are unified to the same monitoring time axis. The monitoring cycle is divided according to the scanning time period of the surface three-dimensional point cloud data in each period, and the internal stress data of the anchor bolt is assigned to the corresponding monitoring cycle to generate a monitoring cycle alignment record.

[0052] It should be noted that, based on the monitoring alignment data, the timing start point, time format, and time accuracy of the stress acquisition time and scanning time are checked according to a unified clock reference. The stress acquisition time and scanning time are then converted to the same monitoring time axis to generate monitoring time alignment data. Based on the monitoring time alignment data, the start and end times of scanning of the surface three-dimensional point cloud data for each period are determined, and continuous monitoring cycles are divided according to the successive positions of adjacent scanning periods to generate monitoring cycle division data. Based on the monitoring cycle division data, the time range of each monitoring cycle is checked against the stress acquisition time. The internal stress data of the anchor bolts falling within the corresponding time range is assigned to the corresponding monitoring cycle, and the internal stress data of the anchor bolts located at the boundary of the monitoring cycle is assigned to the monitoring cycle with the shortest time interval to generate cycle assignment data. Based on the cycle assignment data, the monitoring alignment data is associated with the monitoring cycle to generate monitoring cycle alignment records.

[0053] The stress acquisition time and point cloud scanning time in the monitoring cycle alignment record are checked for time sequence continuity to obtain monitoring time sequence alignment data. The monitoring time sequence alignment data is then organized according to the monitoring cycle to generate multi-source monitoring time synchronization records.

[0054] It should be noted that, based on the monitoring cycle alignment records, stress acquisition times and point cloud scanning times are arranged in chronological order within each monitoring cycle. Each stress acquisition time is checked to ensure it falls within the corresponding point cloud scanning time period. The sequence of adjacent stress acquisition times and adjacent point cloud scanning times is compared, and positions of time inversion, cycle out-of-bounds, and abnormal intervals are marked to generate time sequence continuity verification data. Based on this verification data, inverted time positions are reordered according to the monitoring time axis, cycle out-of-bounds positions are assigned to the corresponding monitoring cycles within the time range, and time position corrections are performed on abnormal intervals based on adjacent point cloud scanning times to generate time sequence deviation correction data. Based on this correction data, a time correspondence between stress acquisition times and corresponding point cloud scanning times is established, retaining the corresponding monitoring cycles and sequence of events to obtain monitoring time sequence alignment data. The time sequence alignment data is continuously arranged according to the monitoring cycle, and the time continuity between adjacent monitoring cycles is verified to generate multi-source monitoring time synchronization records.

[0055] S2. Locate and calibrate the target center based on the surface three-dimensional point cloud data in the multi-source monitoring time record, and restore the internal stress monitoring position according to the installation direction and anchor sensor depth in the anchor bolt layout record. Project each monitoring position onto the bottom structure surface to generate a stress deformation mapping record.

[0056] Based on the surface three-dimensional point cloud data recorded by multi-source monitoring, the target point cloud is separated according to the reflection characteristics and contour range of the calibration target, and the center of the calibration target is determined. The spatial position of the surface three-dimensional point cloud data in each period is unified according to the center of the calibration target, and the continuous surface point cloud is connected to generate surface reference data.

[0057] It should be noted that, based on the surface three-dimensional point cloud data recorded by multi-source monitoring, the reflection intensity of the point cloud is compared with the reflection intensity of the surrounding surface points point by point. Point clouds with a reflection intensity continuously higher than the surrounding surface points and located within the calibrated target contour range are retained, while discrete points outside the contour range and reflection anomalies within the contour range are excluded, thus generating target point cloud separation data.

[0058] The target contour is fitted and calibrated based on the target point cloud separation data, and the geometric center of the contour is calculated. The spatial coordinates corresponding to the geometric center of the contour are determined as the calibration target center, generating target center positioning data. Based on the target center positioning data, the spatial correspondence between the calibration target center and the surface 3D point cloud data of each period is established. The position offset and attitude deviation of the surface 3D point cloud data of each period relative to the benchmark monitoring cycle are calculated, and translation and rotation corrections are performed according to the position offset and attitude deviation to generate point cloud spatial calibration data. Based on the point cloud spatial calibration data, the continuous surface point cloud is connected segment by segment according to the spatial distance between adjacent surface points and the continuity of the surface orientation. The connection is stopped at the position where the spatial distance increases abruptly or the surface orientation breaks, generating continuous surface connection data. Based on the continuous surface connection data, the bottom structure surface continuously supported under a unified spatial position is retained to generate surface benchmark data.

[0059] It should also be noted that the reflection features are used to distinguish the point cloud of the calibration target from the point cloud of the bottom structure surface. By comparing the reflection intensity and the continuity of the reflection changes of the point cloud, point clouds that match the material response of the calibration target are selected, reducing the interference of ordinary surface points and discrete reflection points on the identification of the calibration target, and improving the accuracy of target point cloud separation and calibration target center positioning.

[0060] The contour range is used to define the spatial boundary of the calibration target point cloud. By checking whether the candidate point cloud falls within the boundary range corresponding to the shape of the calibration target, high reflectivity points outside the boundary and abnormal discrete points inside the boundary are excluded, the shape of the target point cloud is kept intact, and a stable basis is provided for the fitting of the calibration target contour and the determination of the calibration target center.

[0061] The anchor bolt construction layout information and anchor bolt sensor installation information in the surface reference data are converted to a unified spatial location, and the entry hole position, installation direction and anchor bolt sensor depth are organized according to the anchor bolt number to generate an anchor bolt layout record.

[0062] It should be explained that, based on the surface reference data, the coordinate origin, coordinate axis direction, and length scale of the unified spatial location are determined. The positioning coordinates in the anchor bolt construction layout information are then converted to the unified spatial location to generate construction location conversion data. Based on the construction location conversion data, the direction reference and depth reference in the anchor bolt sensor installation information are converted according to the same coordinate axis direction and length scale to generate installation location conversion data. According to the construction location conversion data and the installation location conversion data, the correspondence is checked one by one according to the anchor bolt number, eliminating duplicate anchor bolt numbers, missing positions, and inconsistent directions. The verified layout information is then written into the corresponding anchor bolt number to generate an anchor bolt layout record.

[0063] It should also be noted that the anchor bolt construction layout information mainly records the anchor bolt number, insertion position, construction positioning coordinates, and layout position on the bottom structural surface. This information is derived from on-site measurement and layout records, construction positioning result tables, and anchor bolt construction acceptance records. The purpose of this information is to determine the actual layout benchmark of each anchor bolt on the bottom structural surface, ensuring that the monitoring position inside the anchor bolt corresponds to its position in the three-dimensional point cloud space of the surface. This improves the accuracy of anchor bolt position reconstruction and subsequent stress-deformation mapping.

[0064] The anchor bolt sensor installation information mainly records the anchor bolt number, sensor installation direction, burial depth, sensor installation location, and corresponding detection channel. This information is derived from anchor bolt sensor installation records, burial records, commissioning records, and on-site acceptance data. The purpose of this information is to reconstruct the actual burial location of the sensor inside the anchor bolt, enabling internal stress data to correspond to specific monitoring points within the bottom structure. This ensures more reliable determination of stress propagation location and correlation with surface projection.

[0065] Based on the anchor bolt installation records, the installation location of each anchor bolt sensor is determined by extending from the anchor bolt entry point along the corresponding installation direction into the bottom structure according to the anchor bolt sensor depth, thus generating stress monitoring location data.

[0066] It should be noted that, based on the anchor bolt installation record, the corresponding anchor bolt insertion position is determined one by one according to the anchor bolt number, and the insertion position of each anchor bolt is converted into the starting coordinates in a unified spatial position to generate insertion positioning data.

[0067] Based on the manhole positioning data, the extension direction of each anchor rod from the bottom structure surface to the interior of the bottom structure is determined according to the corresponding installation direction. The installation direction is then normalized to generate extension direction data. Based on the extension direction data, the anchor rod is advanced segment by segment along the extension direction from the corresponding anchor rod manhole position according to the anchor rod sensor depth. The endpoint of the advancement is determined as the candidate embedment position of the anchor rod sensor, generating candidate embedment position data. Based on the candidate embedment position data, it is verified whether the candidate embedment position is located inside the bottom structure and is consistent with the corresponding anchor rod number. Positions with reverse extension direction and anchor rod sensor depth exceeding the deployment range are excluded. The verified candidate embedment positions are determined as the embedment positions of each anchor rod sensor in a unified spatial location, generating stress monitoring position data.

[0068] Based on the stress monitoring location data and surface reference data, the local continuous surface corresponding to each internal stress monitoring location is determined, and the internal stress monitoring location is projected onto the bottom structure surface along the normal direction of the local continuous surface to generate surface monitoring projection data.

[0069] It should be noted that, based on stress monitoring location data and surface reference data, the spatial search range is gradually expanded with each internal stress monitoring location as the center, and surface points that are closest to the internal stress monitoring locations and have continuous surface orientation are selected. The expansion stops at the locations where the surface spacing suddenly increases or the surface orientation breaks, thus generating local surface screening data.

[0070] Based on local surface screening data, surface fitting is performed on continuous surface points, and discrete surface points deviating from the fitted surface are excluded to determine the local continuous surface corresponding to each internal stress monitoring position, generating local continuous surface positioning data. According to the local continuous surface positioning data, the normal direction of the local continuous surface is calculated, and extended bidirectionally from the internal stress monitoring position along the normal direction. The direction that first intersects with the local continuous surface is determined as the surface projection direction, generating normal projection path data. Based on the normal projection path data, the internal stress monitoring position is moved along the surface projection direction to the intersection position of the local continuous surface, and it is checked whether the intersection position is within the effective range of the local continuous surface. The checked intersection position is determined as the corresponding position of the bottom structure surface, generating surface monitoring projection data.

[0071] Based on surface monitoring projection data, stress deformation mapping records are generated by associating internal stress monitoring locations, surface projection locations, and corresponding bottom structure surface areas according to anchor bolt numbers and monitoring cycles.

[0072] It should be noted that, based on the surface monitoring projection data, the correspondence between the internal stress monitoring position and the surface projection position is checked one by one according to the anchor bolt number. Positions with the same number and continuous projection path are paired and associated to generate position correspondence data.

[0073] Based on the location-corresponding data, the data is expanded outwards along the continuous surface boundary, centered on the surface projection location. Expansion stops at locations where the surface orientation changes abruptly or where there is a spatial break. The expanded range is then defined as the corresponding bottom structure surface area, generating area attribution data. Based on the area attribution data, the location-corresponding data for the same anchor bolt number is associated with the corresponding bottom structure surface area according to the monitoring cycle. The spatial continuity of the mapping relationship within adjacent monitoring cycles is verified, generating periodic mapping data. Based on the periodic mapping data, the mapping association data is continuously recorded according to the anchor bolt number and the monitoring cycle, generating stress-deformation mapping records.

[0074] S3. Based on the stress-deformation mapping record, calibrate the multi-period point cloud, track the boundary displacement of each period along the deformation discontinuous boundary and connect them into a leading migration chain. Use the advancing direction of the leading migration chain and the cross-period step size to constrain the step-by-step transmission position of the anchor stress increment, and generate a stress traction chain record.

[0075] Based on stress-deformation mapping records, the position of multi-period point clouds is calibrated according to the corresponding bottom structure surface area and monitoring cycle, and the spatial deviation between adjacent monitoring cycles is corrected by the cross-period correspondence of stable surface points to generate periodic point cloud calibration records.

[0076] It should be noted that, based on the stress-deformation mapping record, the point cloud of multiple periods is divided into partitions according to the corresponding bottom structure surface area and monitoring period, and the point cloud position of the first monitoring period is selected as the calibration benchmark. The point clouds of subsequent monitoring periods are superimposed according to the surface area boundary to generate partitioned point cloud alignment data.

[0077] Based on the alignment data of the partitioned point cloud, stable surface points with stable displacements and consistent local surface orientations are selected in adjacent monitoring cycles. Inter-period correspondences of these stable surface points are established according to spatial proximity, generating stable point correspondence data. Based on this data, translational and rotational deviations between adjacent monitoring cycles are calculated. The point cloud positions of subsequent monitoring cycles are corrected period by period according to the direction and magnitude of the deviations, generating spatial deviation correction data. The inter-period overlap of stable surface points is rechecked based on the spatial deviation correction data. For positions that do not continuously overlap, inter-period correspondence and spatial deviation correction are repeated until the stable surface points maintain continuous inter-period overlap. The calibrated multi-period point clouds are recorded according to the corresponding bottom structure surface area and monitoring cycle, generating a periodic point cloud calibration record.

[0078] The expressions for calculating the translational and rotational deviations between adjacent monitoring cycles are as follows:

[0079] ;

[0080] in, This refers to the rotational deviation between adjacent monitoring cycles; This refers to the translational deviation between adjacent monitoring periods; For the first The first monitoring cycle The three-dimensional spatial position of a stable surface point; For the first During the monitoring cycle, and the first The three-dimensional spatial position corresponding to a stable surface point; The corresponding index of the stable surface point; This refers to the cycle number of the previous monitoring period; The translational deviation vector required to move the stable surface point of the next monitoring cycle to the corresponding position of the previous monitoring cycle; This is the rotation correction matrix between point clouds in adjacent monitoring periods.

[0081] Based on the periodic point cloud calibration record, the inter-period displacement change of each surface point along the local normal direction is checked, and the positions with opposite displacement directions between adjacent surface regions are connected as deformation discontinuous boundaries to generate a periodic boundary candidate sequence.

[0082] It should be noted that, based on the periodic point cloud calibration records, the spatial correspondence between surface points of adjacent monitoring periods is established point by point according to the monitoring period, and the local tangent plane is fitted with the continuous surface around each surface point. The direction perpendicular to the local tangent plane is determined as the local normal direction, and normal positioning data is generated.

[0083] Based on the normal positioning data, the displacement of the corresponding surface point from the position of the previous monitoring cycle to the position of the next monitoring cycle is calculated, and the displacement is projected onto the local normal direction to determine the inter-period displacement direction and inter-period displacement distance of each surface point, generating normal displacement data. According to the normal displacement data, the inter-period displacement direction of adjacent surface areas is compared point by point along the continuous direction of the surface. The position where one side moves outward along the local normal direction and the other side moves inward in the opposite direction is marked as the direction reversal position, generating displacement reversal positioning data. Based on the displacement reversal positioning data, the continuous direction reversal positions are connected according to the spatial proximity relationship and the surface extension order, and the connection is stopped at the position where the displacement direction is consistent or the surface space is broken. The continuous connection boundary formed by each monitoring cycle is arranged in chronological order to generate a candidate sequence of cycle boundaries.

[0084] Based on the candidate sequence of periodic boundaries, a two-way correspondence verification is performed on discontinuous boundaries that are adjacent in location and have the same boundary extension order within adjacent monitoring periods, and correspondences that involve boundary crossing or reversed extension order are excluded, generating boundary cross-period correspondence records.

[0085] It should be noted that, based on the candidate sequence of periodic boundaries, discontinuous boundaries within adjacent monitoring periods are extracted in chronological order of monitoring periods, and the starting point, turning point and ending point of each discontinuous boundary are marked sequentially along the extension direction of each discontinuous boundary to generate boundary sequence positioning data.

[0086] Based on the boundary sequence location data, the system searches segment by segment from the previous monitoring period to the next, identifying the discontinuous boundaries with the smallest spatial distance and consistent boundary extension order. Then, it searches in reverse from the next monitoring period to the previous monitoring period for the corresponding discontinuous boundaries. Relationships where both forward and reverse searches point to the same discontinuous boundary are defined as bidirectional correspondences, generating bidirectional boundary verification data. Based on this bidirectional boundary verification data, the system connects the corresponding discontinuous boundaries in adjacent monitoring periods, verifying whether the connecting paths intersect and whether the order of boundary start, boundary turning point, and boundary end is reversed, generating boundary connection verification data. Based on this boundary connection verification data, it excludes correspondences with intersecting connecting paths or reversed boundary extension order, retaining correspondences with adjacent locations, consistent extension order, and valid bidirectional verification, generating cross-period boundary correspondence records.

[0087] Based on the boundary cross-period corresponding records, the corresponding positions of the same deformation discontinuous boundary in adjacent monitoring cycles are connected to determine the advancement direction and cross-period advancement distance of each boundary segment, and the corresponding boundaries that are continuously connected by the advancement direction are sequentially connected to generate the frontier migration chain.

[0088] It should be noted that, based on the corresponding records of the boundary spanning periods, and in accordance with the extension order of the same deformation discontinuous boundary, the corresponding sequential positions of the boundary start point, boundary turning point and boundary end point are selected segment by segment in adjacent monitoring periods, and the positions of the previous monitoring period are connected with the positions of the next monitoring period to generate boundary position connection data.

[0089] Based on boundary location connection data, the spatial direction from the position of the previous monitoring cycle to the position of the next monitoring cycle is determined as the advancement direction of each boundary segment, and the spatial distance between corresponding positions is calculated as the inter-period advancement distance to generate boundary advancement data. According to the boundary advancement data, the advancement direction of adjacent corresponding boundaries is compared segment by segment in chronological order of the monitoring cycles. Corresponding boundaries with consistent directions and continuous connection between preceding and following positions are established to form a continuation relationship to generate boundary advancement connection data. Based on the boundary advancement connection data, the position of the next monitoring cycle of the previous corresponding boundary segment is used as the connection starting point of the next corresponding boundary segment. Corresponding boundaries that maintain connection in the advancement direction are continuously connected according to the monitoring cycle, and the connection is stopped where the advancement direction is reversed or the spatial position is broken to generate the frontier migration chain.

[0090] Based on the front migration chain, the projected position of each anchor bolt surface is checked along the corresponding front advancement direction to obtain the front advancement distance. The allowable interval between the anchor bolt and the corresponding migration segment is limited by the front advancement distance. Anchor bolts located on the front advancement side are selected to generate stress reconnection candidate records.

[0091] It should be noted that, based on the leading edge migration chain, the corresponding leading edge advancement direction is determined segment by segment according to the monitoring cycle and migration segment. The surface projection position of each anchor bolt is projected onto the position axis formed by the corresponding leading edge advancement direction. The relationship between the surface projection position of each anchor bolt and the start and end positions of the migration segment is compared to generate anchor bolt leading edge position verification data. Based on the anchor bolt leading edge position verification data, the displacement length of the same migration segment along the leading edge advancement direction between adjacent monitoring cycles is calculated. The displacement length is determined as the leading edge inter-period advancement distance to generate leading edge advancement scale data. Based on the leading edge advancement scale data, the leading edge inter-period advancement distance is used as the upper limit of the allowable interval. The shortest spatial distance from the surface projection position of each anchor bolt to the corresponding migration segment is calculated. Positions whose shortest spatial distance does not exceed the upper limit of the allowable interval are retained to generate anchor bolt migration adjacency data. Based on the anchor bolt migration adjacency data, it is checked whether the surface projection position of each anchor bolt is located on the leading edge advancement side pointed to by the position of the migration segment in the next monitoring cycle. Anchor bolts located on the leading edge retreat side, outside the lateral range of the migration segment, and outside the allowable interval are excluded to generate stress reconnection candidate records.

[0092] Based on the stress reconnection candidate record, the stress traction elements of adjacent candidate anchors are checked segment by segment along the leading edge migration chain. Anchors that are consistent with the leading edge advancement direction and whose stress response is continuously transmitted are connected step by step to generate a stress traction chain record.

[0093] It should be noted that, based on the stress reconnection candidate record, each candidate anchor bolt is arranged according to the migration segment order and forward advancement direction of the leading edge migration chain. Candidate anchor bolts with adjacent spatial positions are then used for traction verification to generate candidate anchor bolt continuity data. Based on the candidate anchor bolt continuity data, the stress traction elements of adjacent candidate anchor bolts are compared segment by segment according to the monitoring cycle. This verifies whether the stress change direction is consistent with the forward advancement direction and whether the stress response of the subsequent candidate anchor bolt follows the stress response of the previous candidate anchor bolt, generating stress traction verification data. Based on the stress traction verification data, candidate anchor bolts with consistent stress change direction, continuous stress response order, and sequential spatial connection along the forward advancement direction are retained, while candidate anchor bolts with reversed stress change, interrupted stress response, or inverted spatial position are excluded, generating traction connection data. Based on the traction connection data, starting from the first candidate anchor bolt corresponding to the initial migration segment of the leading edge migration chain, continuously connected candidate anchor bolts are linked level by level along the forward advancement direction, stopping the connection at the first point where stress response interruption or deviation from the forward advancement direction occurs, generating a stress traction chain record.

[0094] S4. Pair the stress mutation time and the arrival time of the deformation front in each segment of the stress traction chain record, and verify the link connection relationship in reverse by the continuity of the time difference between adjacent segments. Cut off at the point where the direction of advancement deviates to generate stress deformation evolution record.

[0095] The continuous stress changes of each anchor are checked along the connection sequence of the anchor bolts in the stress traction chain record. The first sampling moment when the stress change continues to exceed the corresponding stable range is determined as the stress change moment, and a stress change location record is generated.

[0096] It should be noted that, following the anchor bolt connection sequence in the stress traction chain record, the continuous stress changes of each anchor bolt are arranged according to the sampling time sequence. From the continuous stress changes, continuous sampling intervals with slope changes less than a stable slope threshold and adjacent sampling differences less than a stable fluctuation threshold are extracted as stress stability intervals. The mean and standard deviation of stress values ​​within these intervals are calculated. Using the mean as the center, the mean plus a preset multiple of the standard deviation is determined as the upper stability limit, and the mean minus a preset multiple of the standard deviation is determined as the lower stability limit. The preset multiple is determined based on the noise fluctuation level and anchor bolt sensor accuracy during the undisturbed monitoring phase, generating stress stability benchmark data. Based on the stress stability benchmark data, the continuous stress changes are compared with the stable stress changes at each sampling time. The relationship between the upper and lower limits of stability is defined. Sampling times that are higher than the upper limit or lower than the lower limit are marked as out-of-limit sampling times. Out-of-limit sampling times that are adjacent in time and have the same direction of change are continuously connected to generate stress out-of-limit segment data. Based on the stress out-of-limit segment data, instantaneous fluctuations that recover to the stable range after a single sampling time exceeds the limit are excluded. Stress out-of-limit segments with a continuous sampling number reaching the continuous judgment number and a consistent stress change direction are retained to generate continuous stress change data. Based on the continuous stress change data, the first sampling time corresponding to the starting position of each continuous stress change is determined as the stress mutation time. The stress mutation times of each anchor are associated according to the anchor connection order to generate stress mutation location records.

[0097] Based on the stress mutation location record, the projected position of each anchor bolt surface is mapped to the front migration chain, and the arrival time of the deformation front is determined according to the order in which the deformation front crosses the corresponding projected position, thus generating a stress front time pairing record.

[0098] It should be noted that, based on the stress change location record, the stress change time and anchor surface projection position of each anchor are determined according to the anchor connection order. The shortest distance from the anchor surface projection position to each migration segment in the leading migration chain is calculated. The anchor surface projection position with the smallest distance and located within the extension range of the migration segment is mapped to the leading migration chain to generate projection position back-connection data.

[0099] Based on the projection position feedback data, the spatial relationship between the deformation front and the corresponding projection position is checked according to the chronological order of the monitoring cycle. The adjacent monitoring cycle in which the deformation front moves from the front of the propulsion side to the rear of the propulsion side is determined as the front crossing cycle, and front crossing positioning data is generated. According to the front crossing positioning data, the distance between the deformation front and the corresponding projection position in the two periods before and after the front crossing cycle is compared along the front propulsion direction. The time position of the deformation front crossing the corresponding projection position is determined according to the distance change ratio within the front crossing cycle. The time position is determined as the arrival time of the deformation front, and front arrival positioning data is generated. Based on the front arrival positioning data, the time pairing relationship is recorded according to the stress change time corresponding to the same anchor and the arrival time of the deformation front, and the time pairing relationship is recorded along the anchor connection order, and stress front time pairing records are generated.

[0100] Based on the paired records of stress front moment, the response time difference between the arrival time of the deformation front and the stress change moment of the same anchor is calculated, and the response time difference of adjacent anchors and the corresponding front advancement direction are associated according to the connection order of the stress traction chain to generate a traction spatiotemporal continuity record.

[0101] It should be noted that, based on the paired records of stress front moment, the time interval between the arrival time of the deformation front and the stress change moment is calculated one by one according to the corresponding relationship of the same anchor rod, and the time sequence is retained. The time interval is determined as the response time difference, and the response time difference calculation data is generated.

[0102] Based on the response time difference calculation data, adjacent anchor bolts are paired sequentially according to the connection order of the stress traction chain. The increase or decrease of the response time difference of adjacent anchor bolts is compared with the time sequence to generate adjacent time difference continuity data. Based on the adjacent time difference continuity data, the spatial connection direction of adjacent anchor bolts in the leading edge migration chain is compared segment by segment with the corresponding leading edge advancement direction. Connection positions with consistent direction, deviated direction, and reversed time sequence are marked to generate spatiotemporal direction verification data. Based on the spatiotemporal direction verification data, the relationship between adjacent anchor bolts with continuous response time difference and spatial connection direction consistent with the corresponding leading edge advancement direction is retained. The response time difference and corresponding leading edge advancement direction of each segment are continuously recorded according to the connection order of the stress traction chain to generate traction spatiotemporal continuity records.

[0103] Based on the traction time-space continuity record, the response time difference changes and the bearing status of the advancing direction of adjacent anchor rods are checked segment by segment from the end of each stress traction chain to the starting point. The traction segment with continuous time difference and consistent advancing direction is determined as the effective bearing segment, and a link reverse buckling verification record is generated.

[0104] It should be noted that, based on the traction spatiotemporal continuity record, the link end is determined according to the connection order of each stress traction chain, and the traction segments corresponding to adjacent anchor rods are arranged segment by segment from the link end to the link start point to generate reverse verification sequence data.

[0105] Based on the reverse verification sequence data, the response time difference changes of the subsequent traction segment and the preceding traction segment are compared segment by segment. It is verified whether the response time difference increases or decreases continuously according to the link connection order, and the positions of sudden increases, decreases, and reversals of time difference are marked to generate time difference reverse deduction data. According to the time difference reverse deduction data, the corresponding leading-edge advancement directions of adjacent traction segments are compared segment by segment. It is verified whether the advancement direction of the preceding traction segment is continuously reversed by the following traction segment, and the positions of advancement direction reversal and deviation are marked to generate direction acceptance verification data. Based on the direction acceptance verification data, traction segments with continuous response time difference and consistent advancement direction are retained. Traction segments with mismatched response time difference or deviation of advancement direction are marked as acceptance interruption segments. Valid acceptance segments and acceptance interruption segments are recorded according to the reverse verification order to generate link reverse deduction verification records.

[0106] Based on the link reverse verification record, the effective connecting segments are continuously tracked along the connection sequence of each stress traction chain. Before the first occurrence of a traction segment with response time mismatch or deviation in propulsion direction, the corresponding link is cut off, generating an evolution link cut-off record.

[0107] It should be noted that, based on the link reverse verification record, effective connecting segments and connecting interruption segments are read segment by segment from the link start point to the link end according to the connection order of each stress traction chain. Continuous effective connecting segments are then connected sequentially to generate effective segment tracking data. Based on the effective segment tracking data, the response time difference and propulsion direction connecting status are checked segment by segment. The traction segment in the connection order where the response time difference mismatch or propulsion direction deviation first occurs is identified as the first mismatch traction segment, generating first mismatch location data. Based on the first mismatch location data, the last effective connecting segment before the first mismatch traction segment is identified as the link truncation position, and the connection between the link truncation position and the first mismatch traction segment is disconnected, generating link truncation location data. Based on the link truncation location data, the continuously connected traction segments from the link start point to the link truncation position are retained, the first mismatch traction segment and subsequent traction segments are excluded, and the link before truncation, the link truncation position, and the link after truncation are recorded according to the connection order of each stress traction chain, generating an evolution link truncation record.

[0108] Based on the evolution link truncation record, the anchor bolts that were continuously connected before the truncation and the corresponding stress change time, deformation front arrival time and response time difference change are classified into the same evolution segment to generate a stress deformation evolution record.

[0109] It should be noted that, based on the evolution link truncation record, the continuous bearing range is defined according to the connection starting point, link truncation position and anchor bolt connection sequence of each link before truncation. The anchor bolts continuously bearing the link from the connection starting point to the link truncation position are grouped into the same range to generate evolution segment positioning data.

[0110] Based on the evolutionary segment location data, the stress mutation time and deformation front arrival time are associated one by one according to the anchor bolt connection sequence, and the increase or decrease of response time difference between adjacent anchor bolts is calculated to generate segment time-series association data. According to the segment time-series association data, it is verified whether the stress mutation time, deformation front arrival time, and response time difference changes are continuously carried out along the anchor bolt connection sequence. The continuously carried anchor bolts and their corresponding time-series information are determined as the same evolutionary segment, and segment evolution verification data is generated. Based on the segment evolution verification data, according to the continuously carried anchor bolts before the evolutionary segment record is cut off and their corresponding stress mutation time, deformation front arrival time, and response time difference changes, stress deformation evolution records are generated.

[0111] S5. Based on the stress-deformation evolution record, the stress propagation state and surface deformation state of each monitoring area are classified in a coordinated manner, and the subsequent monitoring range and monitoring cycle are determined according to the classification state, generating a bottom structure coordinated monitoring report.

[0112] Based on the stress-deformation evolution records, the monitoring areas are divided according to the coverage of the discontinuous deformation boundary. The stress propagation location, surface deformation location, propagation direction and co-occurrence changes in each monitoring area are collected according to the monitoring cycle to generate regional evolution aggregation data.

[0113] It should be noted that, based on the stress-deformation evolution record, the start and end positions and extension range of each deformation discontinuity boundary are extracted according to the monitoring cycle. The continuous surface on both sides of the deformation discontinuity boundary is extended to the position of the adjacent deformation discontinuity boundary or stable surface. The continuous coverage range formed by the extension is divided into monitoring areas, and monitoring area division data is generated.

[0114] Based on the monitoring area division data, each evolution segment is assigned to its corresponding monitoring area according to its spatial location. The regional evolution elements are determined along the anchor connection sequence and the progression sequence of deformation discontinuity boundaries, generating regional evolution positioning data. Based on the regional evolution positioning data, the regional evolution elements that appear consecutively within the same monitoring area are connected according to the chronological order of the monitoring cycle. The position migration, directional continuity, and coordinated state transformation between adjacent monitoring cycles are verified to generate regional inter-period continuity data. Based on the regional inter-period continuity data, regional evolution elements are continuously collected according to the monitoring area and monitoring cycle to generate regional evolution collection data.

[0115] Based on the regional evolution data, the co-evolution characteristics of stress propagation and surface deformation are verified, and the data is organized according to the monitoring area to generate co-level characteristic data.

[0116] It should be noted that, based on the regional evolution data, the stress propagation state and surface deformation state in each monitoring area are arranged in chronological order according to the monitoring cycle. The degree of spatial overlap between the stress propagation location and the surface deformation location, the degree of consistency between the stress propagation direction and the surface deformation advancement direction, and the temporal succession relationship between the two types of changes are checked on a cycle-by-cycle basis to generate co-evolution check data.

[0117] Based on the co-evolution verification data, connect the co-evolutionary changes that occur continuously across monitoring cycles within the same monitoring area, and verify whether stress propagation and surface deformation maintain synchronous advancement, directional continuity, and range coverage, generating inter-period co-evolutionary continuity data; based on the inter-period co-evolutionary continuity data, mark the co-evolutionary features that are spatially coincident and continuous, have consistent advancement directions, stable temporal continuity, and complete range coverage, and mark the co-evolutionary features that are spatially misaligned, directionally deviated, temporally discontinuous, and range-detached, generating co-evolutionary feature labeling data; based on the co-evolutionary feature labeling data, collect the corresponding co-evolutionary features according to the monitoring area to generate co-evolutionary hierarchical feature data.

[0118] It should also be noted that the co-evolution characteristics are used to characterize the matching status of stress propagation and surface deformation in the same monitoring area in terms of spatial location, direction of advancement, temporal continuity, and range coverage. By identifying whether the two types of changes are synchronously inherited and continuously correspond, it provides a basis for subsequent co-evolution classification, thereby improving the accuracy of co-evolution status identification and the reliability of classification in the monitoring area.

[0119] Based on the collaborative grading characteristic data, the stress propagation state and surface deformation state of each monitoring area are collaboratively verified, and each monitoring area is divided into strong collaborative area, medium collaborative area and weak collaborative area according to the degree of collaboration, generating regional collaborative grading records.

[0120] It should be noted that, based on the collaborative grading characteristic data, the matching of stress propagation state and surface deformation state in terms of spatial overlap, propagation direction, temporal continuity, and range coverage is checked item by item according to the monitoring area. The spatial overlap rate between stress propagation location and surface deformation location, the directional deviation angle between stress propagation direction and surface deformation propagation direction, the temporal continuity deviation between the stress abrupt change moment and the arrival moment of the deformation front, and the range coverage rate between stress propagation range and surface deformation coverage range are calculated to generate collaborative quantitative verification data. Specifically, when the spatial overlap rate is not less than 80%, it is marked as spatially consistent; when the spatial overlap rate is not less than 50% and less than 80%, it is marked as partially spatially consistent; and when the spatial overlap rate is less than 50%, it is marked as spatially inconsistent. Similarly, when the directional deviation angle is not greater than 5 degrees, it is marked as directionally consistent; when the directional deviation angle is greater than 5 degrees and not greater than 15 degrees, it is marked as partially directionally consistent; and when the directional deviation angle is greater than 15 degrees, it is marked as directionally inconsistent. Furthermore, when the temporal continuity deviation is not greater than one sampling period, it is marked as temporally consistent; when the temporal continuity deviation is greater than one sampling period and not greater than three sampling periods, it is marked as partially temporally consistent; and when the temporal continuity deviation is greater than three... During each sampling period, areas are marked as inconsistent in time; when the coverage rate is not less than 80%, it is marked as consistent in range; when the coverage rate is not less than 50% but less than 80%, it is marked as partially consistent in range; when the coverage rate is less than 50%, it is marked as inconsistent in range, generating regional collaborative verification data. Based on the regional collaborative verification data, the continuity status of spatial consistency, directional consistency, temporal consistency, and range consistency is continuously checked according to the monitoring cycle, distinguishing between continuous matching, intermittent matching, and continuous mismatch, generating collaborative degree judgment data. Based on the collaborative degree judgment data, monitoring areas with consistent spatial, directional, temporal, and range characteristics over multiple consecutive monitoring cycles are classified as strong collaborative areas; monitoring areas with at least two consistent collaborative characteristics and no continuous inconsistency in other collaborative characteristics are classified as medium collaborative areas; monitoring areas with continuous inconsistency in two or more collaborative characteristics, or with spatial misalignment, directional deviation, temporal discontinuity, and range separation over multiple consecutive monitoring cycles are classified as weak collaborative areas, generating regional collaborative grading data. Based on the regional collaborative grading data, regional collaborative grading records are generated according to the corresponding collaborative degree of monitoring area records, quantitative judgment criteria, and area category.

[0121] Based on the regional coordination classification records, the monitoring range of the strong coordination region is expanded along the direction of deformation discontinuity boundary, the current monitoring range of the medium coordination region is maintained, and the monitoring range of the weak coordination region is converged to the surface projection region and the stable surface control region to generate subsequent monitoring range records.

[0122] It should be noted that, based on the regional collaborative classification records, the current boundary range is determined according to the strong collaborative region, medium collaborative region and weak collaborative region, and the direction of deformation discontinuous boundary advancement, surface projection area and stable surface reference area are marked accordingly to generate classification range positioning data.

[0123] Based on the hierarchical range positioning data, the strong coordination region is progressively expanded segment by segment towards the deformation discontinuous boundary to the coverage position of the front-line intertemporal advancement distance, and the expansion stops when it reaches the boundary of the adjacent monitoring area, generating strong coordination range expansion data. Based on the strong coordination range expansion data, the current boundary range of the medium coordination region is kept unchanged, generating medium coordination range maintenance data. Based on the medium coordination range maintenance data, the boundary of the weak coordination region is contracted inward, retaining the surface projection area and the stable surface control area adjacent to the surface projection area, and excluding discontinuous monitoring positions outside the range, generating weak coordination range convergence data. Based on the strong coordination range expansion data, medium coordination range maintenance data, and weak coordination range convergence data, the subsequent monitoring boundaries and coverage ranges are recorded according to the monitoring area, generating subsequent monitoring range records.

[0124] Based on subsequent monitoring range records and regional collaborative hierarchical records, the monitoring interval for strong collaborative areas is shortened, the monitoring interval for medium collaborative areas is maintained, and the monitoring interval for weak collaborative areas is extended. The subsequent collection order is organized according to the monitoring area, and a monitoring cycle arrangement record is generated.

[0125] It should be noted that, based on subsequent monitoring range records and regional collaborative grading records, the corresponding subsequent monitoring range, collaborative level, and current monitoring interval are determined according to the monitoring area. The basic monitoring cycle in the current monitoring plan is then read, and this basic monitoring cycle is defined as one acquisition position. The basic monitoring cycle is determined jointly based on the completion time of a single-period scan of the surface 3D point cloud, the effective sampling window for anchor bolt internal stress data, the data transmission and processing time, and the minimum allowable interval for on-site safety monitoring, generating regional cycle benchmark data. Based on the regional cycle benchmark data, the current monitoring interval for strongly collaborative areas is subtracted by one basic monitoring cycle, the current monitoring interval for medium collaborative areas remains unchanged, and the current monitoring interval for weakly collaborative areas is increased by one basic monitoring cycle. A basic monitoring cycle is established, and the adjusted monitoring interval is verified to meet the equipment's data acquisition capabilities and on-site safety monitoring requirements, generating hierarchical monitoring cycle data. Based on the hierarchical monitoring cycle data, each monitoring area is arranged in order of coordination level: strong coordination area, medium coordination area, and weak coordination area. Within the same coordination level, the subsequent acquisition order is determined along the spatial connection order of the subsequent monitoring range, generating regional acquisition sorting data. According to the regional acquisition sorting data, the monitoring interval of each monitoring area is matched with the subsequent acquisition order item by item. It is verified whether adjacent acquisition times overlap, and the subsequent acquisition time that overlaps is postponed by one basic monitoring cycle until adjacent acquisition times are staggered, generating a monitoring cycle arrangement record.

[0126] Based on the regional collaborative hierarchical records, subsequent monitoring range records, and monitoring cycle schedule records, the stress propagation range and subsequent acquisition sequence are organized according to the monitoring area to generate a bottom structure collaborative monitoring report.

[0127] It should be explained that, based on the regional collaborative grading records, subsequent monitoring range records, and monitoring cycle schedule records, regional monitoring corresponding data is generated according to the collaborative level, subsequent monitoring boundary, and subsequent acquisition sequence for each monitoring area. Based on the regional monitoring corresponding data, the stress propagation positions continuously received within each monitoring area are connected along the propagation direction, and the stress propagation range is determined by the outermost propagation position, generating regional propagation range data. Based on the regional propagation range data, the acquisition sequence and monitoring interval of each monitoring area are sequentially written according to the monitoring cycle schedule records, generating regional acquisition arrangement data. Based on the regional acquisition arrangement data, each item is checked to see if the stress propagation range falls within the corresponding subsequent monitoring boundary, and the subsequent acquisition sequence of adjacent monitoring areas is checked to see if it overlaps. Positions exceeding the subsequent monitoring boundary are reclaimed, and positions with overlapping acquisition sequences are extended according to the collaborative level order, generating regional monitoring verification data. Based on the regional monitoring verification data, a bottom structure collaborative monitoring report is generated according to the collaborative level, stress propagation range, subsequent monitoring boundary, monitoring interval, and subsequent acquisition sequence continuously recorded for each monitoring area.

[0128] In summary, this invention achieves spatial co-linkage between the internal stress propagation path and the surface deformation front by: tracking the boundary displacement of each monitoring cycle along the discontinuous deformation boundary and connecting them to form a front migration chain; defining the stepwise transmission position of the anchor stress increment by the front advancement direction and the span step length; and verifying the continuity of the connection by pairing and verifying the stress change moment and the arrival moment of the deformation front segment by segment. This enables stress-deformation collaborative monitoring to identify the real traction segment, cut off the segment of deviation in the advancement direction, and restore the cross-cycle evolution sequence, thereby improving the accuracy of the bottom structure stress-deformation evolution positioning and the reliability of collaborative monitoring grading.

[0129] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated monitoring of stress and deformation of bottom structures based on multi-source data, characterized in that, include: Collect internal stress data and surface three-dimensional point cloud data of the bottom structure anchor bolts and perform multi-source monitoring time sequence calibration to generate multi-source monitoring time records; The target center is located and calibrated based on the surface three-dimensional point cloud data in the multi-source monitoring time record, and the internal stress monitoring position is restored according to the installation direction and anchor sensor depth in the anchor bolt layout record. Each monitoring position is projected onto the bottom structure surface to generate a stress deformation mapping record. Based on the stress-deformation mapping record, calibrate the multi-period point cloud, track the boundary displacement of each period along the deformation discontinuous boundary and connect them into a leading migration chain. Use the advancing direction of the leading migration chain and the cross-period step size to constrain the stepwise transmission position of the anchor stress increment, and generate the stress traction chain record. The stress abrupt change time and the arrival time of the deformation front are paired segment by segment along the stress traction chain. The link connection relationship is verified in reverse by the continuity of the time difference between adjacent segments. The link is cut off at the point where the direction of advancement deviates, and a stress deformation evolution record is generated. Based on the stress-deformation evolution record, the stress propagation state and surface deformation state of each monitoring area are classified in a coordinated manner, and the subsequent monitoring range and monitoring cycle are determined according to the classification state, generating a coordinated monitoring report of the bottom structure.

2. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 1, characterized in that, The process of collecting internal stress data and surface three-dimensional point cloud data of the bottom structure anchor bolts and performing multi-source monitoring time sequence calibration to generate multi-source monitoring time records is as follows: The stress acquisition time corresponding to the internal stress data of the anchor bolt and the scanning time corresponding to the surface three-dimensional point cloud data are unified to the same monitoring time axis. The monitoring cycle is divided according to the scanning time period of the surface three-dimensional point cloud data in each period, and the internal stress data of the anchor bolt is assigned to the corresponding monitoring cycle to generate a monitoring cycle alignment record. The stress acquisition time and point cloud scanning time in the monitoring cycle alignment record are checked for time sequence continuity to obtain monitoring time sequence alignment data. The monitoring time sequence alignment data is then organized according to the monitoring cycle to generate multi-source monitoring time synchronization records.

3. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 2, characterized in that, The target center is located and calibrated based on the surface three-dimensional point cloud data in the multi-source monitoring time record, and the internal stress monitoring position is restored according to the installation direction and anchor sensor depth in the anchor bolt layout record. The monitoring positions are then projected onto the bottom structure surface. The specific steps are as follows: Based on the surface three-dimensional point cloud data in the multi-source monitoring time record, the target point cloud is separated according to the reflection characteristics and contour range of the calibration target and the center of the calibration target is determined. Based on the center of the calibration target, the spatial position of the surface three-dimensional point cloud data in each period is unified, and the continuous surface point cloud is connected to generate surface reference data. The anchor bolt construction layout information and anchor bolt sensor installation information in the surface reference data are converted to a unified spatial location, and the entry hole position, installation direction and anchor bolt sensor depth are organized according to the anchor bolt number to generate an anchor bolt layout record; Based on the anchor bolt installation record, the anchor bolt sensor is extended from the anchor bolt entry hole position along the corresponding installation direction into the bottom structure according to the anchor bolt sensor depth to determine the embedment position of each anchor bolt sensor in a unified spatial location and generate stress monitoring position data. Based on the stress monitoring location data and surface reference data, the local continuous surface corresponding to each internal stress monitoring location is determined, and the internal stress monitoring location is projected onto the bottom structure surface along the normal direction of the local continuous surface to generate surface monitoring projection data.

4. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 3, characterized in that, The stress-deformation mapping record is obtained by associating the internal stress monitoring location, surface projection location, and corresponding bottom structure surface area based on surface monitoring projection data, according to anchor bolt number and monitoring cycle.

5. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 4, characterized in that, The specific steps for calibrating multi-period point clouds based on stress-deformation mapping records, tracing the boundary displacements of each period along the discontinuous deformation boundary, and concatenating them into a leading-edge migration chain are as follows: Based on stress-deformation mapping records, the position of multi-period point clouds is calibrated according to the corresponding bottom structure surface area and monitoring cycle, and the spatial deviation between adjacent monitoring cycles is corrected by the cross-period correspondence of stable surface points to generate periodic point cloud calibration records. Based on the periodic point cloud calibration record, the inter-period displacement change of each surface point along the local normal direction is checked, and the positions with opposite displacement directions between adjacent surface regions are connected as deformation discontinuous boundaries to generate a periodic boundary candidate sequence. Based on the candidate sequence of periodic boundaries, bidirectional correspondence verification is performed on discontinuous boundaries that are adjacent in location and have the same boundary extension order within adjacent monitoring periods, and correspondences that involve boundary crossing and reversed extension order are excluded, generating boundary cross-period correspondence records. Based on the corresponding records of boundary periods, the corresponding positions of the same deformation discontinuous boundary in adjacent monitoring periods are connected to determine the advancing direction and inter-period advancing distance of each boundary segment. The corresponding boundaries that are continuously connected by the advancing direction are then connected in sequence to generate the frontier migration chain.

6. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 5, characterized in that, The stress traction chain record is generated by using the advancing direction of the aforementioned front migration chain and the stepwise transmission position of the anchor stress increment constrained by the inter-period step length. The specific steps are as follows: Based on the front migration chain, the projected position of each anchor bolt surface is checked along the corresponding front advancement direction to obtain the front advancement inter-period advancement distance. The allowable interval between the anchor bolt and the corresponding migration segment is limited by the front advancement inter-period advancement distance. Anchor bolts located on the front advancement side are screened out to generate stress reconnection candidate records. Based on the stress reconnection candidate record, the stress traction elements of adjacent candidate anchors are checked segment by segment along the leading edge migration chain. Anchors that are consistent with the leading edge advancement direction and whose stress response is continuously transmitted are connected step by step to generate a stress traction chain record.

7. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 6, characterized in that, The specific steps for recording the stress abrupt change time and the arrival time of the deformation front in each segment of the stress traction chain along the stress traction chain are as follows: According to the anchor connection sequence in the stress traction chain record, the continuous stress change of each anchor is checked, and the first sampling time when the stress change continues to exceed the corresponding stable range is determined as the stress change moment, and a stress change location record is generated. Based on the stress mutation location record, the projected position of each anchor bolt surface is mapped to the front migration chain, and the arrival time of the deformation front is determined according to the order in which the deformation front crosses the corresponding projected position, thus generating a stress front time pairing record.

8. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 7, characterized in that, The link continuity is verified by reverse verification of the time difference between adjacent segments, and the link is truncated at the point of deviation in the direction of advancement to generate a stress deformation evolution record. The specific steps are as follows: Based on the paired records of stress front moment, the response time difference between the arrival time of the deformation front and the stress change moment of the same anchor is calculated, and the response time difference of adjacent anchors and the corresponding front advancement direction are associated according to the connection order of the stress traction chain to generate a traction spatiotemporal continuity record. Based on the traction time-space continuity record, the response time difference changes and the bearing status of the advancing direction of adjacent anchor rods are checked segment by segment from the end of each stress traction chain to the starting point. The traction segment with continuous time difference and consistent advancing direction is determined as the effective bearing segment, and a link reverse buckle verification record is generated. Based on the link reverse verification record, the effective connection segment is continuously tracked along the connection sequence of each stress traction chain, and the corresponding link is cut off before the first occurrence of response time difference mismatch or deviation of the propulsion direction of the traction segment, generating an evolution link cut-off record. Based on the evolution link truncation record, the anchor bolts that were continuously connected before the truncation and the corresponding stress change time, deformation front arrival time and response time difference change are classified into the same evolution segment to generate a stress deformation evolution record.

9. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 8, characterized in that, The stress propagation state and surface deformation state of each monitoring area are classified in a coordinated manner based on the stress deformation evolution record, and the subsequent monitoring range and monitoring cycle are determined according to the classification state. The specific steps are as follows: Based on the stress-deformation evolution record, the monitoring area is divided according to the coverage of the deformation discontinuous boundary, and the stress propagation location, surface deformation location, propagation direction and cooperative state change in each monitoring area are sorted out according to the monitoring cycle to generate regional evolution collection data; Based on the regional evolution data, the co-evolution characteristics of stress propagation and surface deformation are verified, and the data is organized according to the monitoring area to generate co-evolutionary hierarchical characteristic data. Based on the collaborative grading characteristic data, the stress propagation state and surface deformation state of each monitoring area are collaboratively verified, and each monitoring area is divided into strong collaborative area, medium collaborative area and weak collaborative area according to the degree of collaboration, and regional collaborative grading records are generated. Based on the regional coordination classification record, the monitoring range of the strong coordination region is expanded along the direction of deformation discontinuity boundary, the current monitoring range of the medium coordination region is maintained, and the monitoring range of the weak coordination region is converged to the surface projection region and the stable surface control region to generate subsequent monitoring range records; Based on subsequent monitoring range records and regional collaborative hierarchical records, the monitoring interval for strong collaborative areas is shortened, the monitoring interval for medium collaborative areas is maintained, and the monitoring interval for weak collaborative areas is extended. The subsequent collection order is organized according to the monitoring area, and a monitoring cycle arrangement record is generated.

10. The bottom structure stress-deformation collaborative monitoring method based on multi-source data as described in claim 9, characterized in that, The bottom structure collaborative monitoring report is obtained by organizing the stress propagation range and subsequent collection sequence according to the regional collaborative hierarchical record, subsequent monitoring range record, and monitoring cycle arrangement record.

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