Surrounding rock stress field visualization method and system based on digital twinning

Abstract

The present application relates to the technical field of digital information processing and engineering surrounding rock safety monitoring, and specifically provides a surrounding rock stress field visualization method and system based on digital twinning, a working condition event module, which is used for analyzing working condition events from dynamic working condition data and generating twinning update instructions based on preset trigger rules; a digital twinning state synchronous update module, which updates the surrounding rock digital twinning model and outputs the updated twinning state version corresponding to the update time window; a stress field generation module, which is used for generating surrounding rock stress field data through a simulation algorithm based on the updated twinning state version, boundary conditions and load change parameters; and a visualization data generation and output module, which is used for converting the surrounding rock stress field data into visualization layer data associated with the update time window and outputting to a display terminal, thereby improving the accuracy of engineering surrounding rock evaluation and the level of construction safety management.

Description

Technical Field

[0001] This invention relates to the field of digital information processing and engineering surrounding rock safety monitoring technology, specifically providing a method and system for visualizing the surrounding rock stress field based on digital twins. Background Technology

[0002] In the stability assessment of surrounding rock in underground engineering projects such as tunnels, chamber complexes, and slopes, existing technologies typically combine on-site monitoring with numerical analysis to obtain information on the stress state and distribution of the surrounding rock, providing a basis for construction process control and risk assessment. On one hand, stress gauges and strain gauges are used to collect response data of the surrounding rock; on the other hand, based on the initial rock mechanics parameters obtained during the geological exploration stage, a three-dimensional geological and structural model of the underground engineering project is established. Numerical analysis methods such as the finite element method and finite difference method are used to simulate and calculate the full-field stress distribution of the surrounding rock. The numerical calculation results are then visualized using two-dimensional cross-sectional cloud maps and three-dimensional models.

[0003] However, the above-mentioned technical solutions have shortcomings when facing the complex working conditions and dynamic monitoring needs of underground engineering: the simulated boundary conditions deviate from the actual working conditions, and due to the lack of automatic linkage updates, the digital twin state is difficult to evolve synchronously with the changes in working conditions in real time, so the stress field visualization results often remain in a static or lagging state; it cannot meet the decision support needs of real-time presentation and advanced prediction of the dynamic redistribution of surrounding rock stress during construction. Summary of the Invention

[0004] This application provides a method and system for visualizing the stress field of surrounding rock based on digital twins, in order to solve the problem of static or lagging visualization of the stress field of surrounding rock.

[0005] This application provides a digital twin-based visualization system for the stress field of surrounding rock, the system comprising:

[0006] The data access and spatiotemporal alignment module is used to acquire multi-source data of the surrounding rock in the project, including basic data, monitoring data, and dynamic working condition data; to unify the spatial coordinates and align the time base of the monitoring data and dynamic working condition data to generate a spatiotemporally aligned dataset with timestamps; and to construct a digital twin model of the surrounding rock based on the basic data.

[0007] The working condition event module is used to parse working condition events from the dynamic working condition data and generate twin update instructions based on preset triggering rules; the twin update instructions include an update time window, a target spatial range, and boundary conditions and load change parameters corresponding to the working condition event;

[0008] The digital twin state synchronization update module is used to receive the spatiotemporal aligned dataset and the twin update instruction, update the surrounding rock digital twin model, and output the updated twin state version corresponding to the update time window.

[0009] The stress field generation module is used to generate surrounding rock stress field data based on the updated twin state version, the boundary conditions, and load change parameters through a simulation algorithm.

[0010] The visualization data generation and output module is used to convert the surrounding rock stress field data into visualization layer data associated with the update time window and output it to the display terminal.

[0011] In some embodiments, the digital twin state synchronization update module includes:

[0012] The mapping relationship submodule and the online calibration update submodule;

[0013] The mapping relationship submodule is used to establish and store the mapping relationship between the measurement point location of the monitoring data and the twin model calculation unit;

[0014] The online calibration and update submodule is used to perform online calibration and update of the twin state parameters of the twin model based on the mapping relationship and the spatiotemporal alignment dataset as an observation constraint, and output the updated twin state version corresponding to the update time window.

[0015] In some embodiments, the stress field generation module is used to generate surrounding rock stress field data based on the updated twin state version, the boundary conditions, and load change parameters using a simulation algorithm, including:

[0016] The tunnel construction boundary and the application location, magnitude, and timing of blasting loads are obtained from the boundary conditions and load variation parameters; the surrounding rock type and support structure are obtained from the updated twin state version.

[0017] Using the tunnel construction boundary as a constraint, a finite element numerical simulation algorithm is used to calculate the stress field in real time. During the calculation process, based on the application location, magnitude, and timing of the blasting load, combined with the surrounding rock type and support structure, surrounding rock stress field data corresponding to the update time window is generated.

[0018] The method and system for visualizing the surrounding rock stress field based on digital twins provided in this application aligns the surrounding rock monitoring data and working condition data using a unified coordinate system and time reference. Combined with a working condition event triggering mechanism, the aligned monitoring data is introduced as an observation constraint into the surrounding rock digital twin model, and the twin state parameters are calibrated and updated online. This method overcomes the lag of traditional post-processing modes, enabling the surrounding rock stress field to be dynamically and synchronously updated as construction conditions change. This improves the consistency and timeliness between the calculated surrounding rock stress field and the actual engineering state. Through the dynamic output of the stress field visualization layer, the method achieves an intuitive presentation and interactive analysis of the surrounding rock stress distribution, facilitating risk identification and decision support during construction, and enhancing the intelligence level and accuracy of surrounding rock stability assessment and construction safety management in underground engineering. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the method and system for visualizing the surrounding rock stress field based on digital twins provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The following is combined Figure 1 The illustrated embodiments describe the technical solution of the present invention:

[0023] This application provides an embodiment of a method and system for visualizing the stress field of surrounding rock based on digital twins, referring to... Figure 1 As shown, the method and system for visualizing the surrounding rock stress field based on digital twins provided in this embodiment includes the following steps:

[0024] S110: Data Access and Spatiotemporal Alignment Module: Used to acquire multi-source data of the surrounding rock of the project, including basic data, monitoring data and dynamic working condition data; to unify the spatial coordinates and align the time base of the monitoring data and dynamic working condition data, and generate a spatiotemporally aligned dataset with timestamps.

[0025] In some embodiments, the implementation of step S110 (data access and spatiotemporal alignment module: used to acquire multi-source data of the surrounding rock of the engineering project, including basic data, monitoring data and dynamic working condition data; to unify the spatial coordinates and align the time base of the monitoring data and dynamic working condition data to generate a spatiotemporally aligned dataset with timestamps) may include:

[0026] It should be noted that in underground tunnel engineering, both monitoring data and operational data come from real-time monitoring of on-site equipment. Data acquisition is based on existing tunnel construction monitoring systems, and the collected data is interfaced through a spatiotemporal alignment module.

[0027] It should be noted that the data collected includes geological parameters, engineering geometry, surrounding rock and support monitoring data, and construction dynamic data. Stress sensors installed inside the tunnel have monitoring points calibrated by an underground measurement system. The monitoring system collects data once per second to ensure real-time reflection of the dynamic process of stress changes within the tunnel, with system time synchronization accuracy at the second level. For example, monitoring point A is located at (1200.0, 5800.0, 350.0) meters. Strain gauges and other point sensors are embedded deep in the borehole or on the surface of the surrounding rock to monitor changes in surrounding rock pressure and the stress state of the support structure. Multiple displacement gauges are deployed across the tunnel cross-section for displacement monitoring, measuring deformations such as crown settlement and perimeter convergence.

[0028] It should be noted that static basic data includes geological exploration data and engineering geometric information, used to construct a three-dimensional geological model, clarify lithological distribution and initial in-situ stress field, and invert the real-time surrounding rock stress field based on monitoring data. Pre-analysis of initial in-situ stress and surrounding rock mechanical state is performed using numerical calculation methods to generate an initial twin state version, including the initial stress field, displacement field, and support stress state.

[0029] Acquire field monitoring data of the surrounding rock of underground engineering. The monitoring data includes surrounding rock stress data, strain data, displacement deformation data, and rock mass microseismic signals, etc. Each data point carries the measuring point identifier and its spatial coordinates in the original spatial coordinate system.

[0030] Dynamic working condition data includes construction progress, excavation advance, excavation sequence, support parameters, blasting parameters, and other construction condition information.

[0031] It should be noted that the on-site monitoring data is used to provide real-time sensing information for the digital twin, reflecting the actual stress and deformation state of the surrounding rock. Dynamic construction process data is used to characterize the dynamic disturbances of construction activities to the surrounding rock.

[0032] It should be noted that the monitoring data all carry the measuring point identification and the original spatial coordinates of the measuring point. The spatial reference and sampling frequency of data from different sources are different. It is necessary to perform spatiotemporal collaborative alignment processing on multi-source data to provide a standardized data foundation for subsequent high-precision mapping of the surrounding rock stress field.

[0033] Specifically, the original coordinates of the measuring points corresponding to the monitoring data are converted into three-dimensional coordinates in the twin coordinate system. Spatial benchmark alignment is achieved through a spatial dynamic benchmark transformation algorithm, and the spatial positioning parameters of the monitoring data and dynamic working condition data are uniformly converted to the same digital twin coordinate system.

[0034] For example, the monitoring data shows that the field stress sensor has passed the calibration test with an accuracy of ±0.5MPa, and the surrounding rock stress value at the measuring point is 10MPa.

[0035] Timestamp: The time synchronization accuracy of the on-site automated monitoring system is at the second level, and the timestamp is 12:00:00 on January 20, 2026.

[0036] It should be noted that all monitoring data have undergone quality control verification and meet the accuracy requirement of ±0.5MPa. The sensor calibration is performed monthly by the underground measurement system.

[0037] Operating data: Operating event: The blasting operation record sheet records a blasting tunneling event, which occurred on January 20, 2026 at 12:05:00.

[0038] After the blasting load is applied instantaneously, its change over time is calculated using an exponential decay model, and the load value will decay rapidly within 10 seconds.

[0039] Target location: The event occurred at the tunnel location (1200.0, 5800.0, 350.0) meters.

[0040] Applying load: The instantaneous load generated by the blast is 50 kN / m², and the effect of the load will continuously change within a 30-meter radius around the tunnel, decaying over time. The load data comes from the output of the blasting equipment and the engineering analysis conducted in the early stages of construction.

[0041] It should be noted that the instantaneous load from the blast will decay rapidly within about 10 seconds after the blast, and its subsequent impact will gradually decrease.

[0042] For example, the original coordinates of the monitoring points corresponding to the monitoring data, such as the site coordinate system and the measurement coordinate system, as well as the construction coordinates, mileage coordinates, and cross-section positioning coordinates corresponding to the dynamic working condition data, are obtained. The transformation parameters between the original coordinate system and the digital twin coordinate system include translation parameters, rotation parameters, and scaling parameters. Based on the transformation parameters, the original spatial coordinates of the monitoring data and dynamic working condition data are uniformly mapped to three-dimensional coordinates under the digital twin coordinate system through spatial coordinate transformation formulas. According to the dynamic information such as the excavation progress, cross-section changes, and construction disturbances of the project, the coordinate transformation relationship is dynamically corrected to ensure that the spatial positions of multi-source data in the digital twin model are always accurately aligned, thus completing the spatial reference alignment.

[0043] Adaptive time interpolation and resampling algorithms are used to align the time base, unifying the monitoring data and dynamic operating condition data to the same time base, and resampling is performed according to a preset sampling step size to generate a time-stamped spatiotemporally aligned dataset; the preset sampling step size is set to 5 seconds.

[0044] For example, the system monitors the changing characteristics of the data stream in real time. When it detects drastic changes in stress and strain data or receives a trigger signal for a working event (such as blasting), the system automatically increases the sampling frequency and uses cubic spline interpolation to ensure data smoothness. During periods of stable data, the system reduces the sampling frequency and uses linear interpolation to save computing resources.

[0045] It should be noted that a digital twin model is built based on static basic data, and combined with real-time monitoring data and dynamic construction process data to achieve real-time mapping, updating and visualization of the surrounding rock stress field.

[0046] Basic data includes geological exploration data, engineering geometric information, surrounding rock mechanical parameters, and initial geostress field;

[0047] Specifically, based on geological exploration data and engineering geometric information, a three-dimensional geometric model of the surrounding rock and support structure is established; the mechanical parameters of the surrounding rock and the initial geostress field are imported into the corresponding region of the three-dimensional geometric model, and the interaction relationship between the surrounding rock and the support structure is set to establish a digital twin model of the surrounding rock that includes the surrounding rock and the support structure.

[0048] It should be noted that during the construction of the left tunnel of a certain highway tunnel, the construction mileage advanced to the section between mileage 246 and mileage 248 of the left tunnel. Based on the geological forecast results and the on-site exposure, and according to the surrounding rock classification standard adopted for this project, it was determined to be moderately fractured surrounding rock with poor integrity and well-developed joints and fissures. The construction adopted was the bench method combined with drill and blasting techniques.

[0049] It should be noted that the left tunnel mileage is the distance along the tunnel axis from the starting point of the left tunnel entrance, used to mark the construction and monitoring positions. The construction site records the operation data for each cycle through the construction management system. The data comes from the blasting design sheet, charge record sheet, and on-site detonation record, and is immediately uploaded to the work event module after blasting is completed.

[0050] For example, the source of blasting tunneling condition data:

[0051] At 09:42:10 on July 3, 2024, the second blasting cycle of the day was completed in this section. The corresponding blasting operation record is as follows: Operation mileage: Based on on-site measurement data and record verification, the mileage of the left tunnel is confirmed to be 246.3;

[0052] Single-cycle advance: Based on the conventional design advance under medium-fractured surrounding rock conditions in the project, the single-cycle advance is 1.2m;

[0053] Number of detonation holes: The blasting hole layout diagram for the cycle shows that the number of detonation holes is 38;

[0054] Maximum single-stage charge: According to the charge record sheet, the maximum single-stage charge is 3.0 kg;

[0055] Explosive consumption per unit: Based on the total amount of explosives used in this cycle and the actual excavation volume, the explosive consumption per unit is calculated to be 1.08 kg / m³.

[0056] It should be noted that by automatically generating twin update instructions, the digital twin model is updated synchronously in real time, and calibration is triggered by monitoring data deviations, thereby improving the monitoring and handling capabilities of surrounding rock stress response during tunnel construction.

[0057] S120: Operating Condition Event Module: Used to parse operating condition events from operating condition data and generate twin update instructions based on preset triggering rules. The twin update instructions include the update time window, target spatial range, and boundary conditions and load change parameters corresponding to the operating condition event.

[0058] In some embodiments, the implementation of step S120 (the working condition event module: used to parse working condition events from working condition data and generate twin update instructions based on preset triggering rules, the twin update instructions including update time window, target spatial range, and boundary conditions and load change parameters corresponding to the working condition event) may include:

[0059] It should be noted that after receiving the above monitoring data, the working condition event module matches and judges the operation type field and the blasting parameter field, confirms that the data meets the identification rules of blasting tunneling events, parses it as a blasting tunneling working condition event, and records the time of occurrence and the location of the operation.

[0060] The working events parsed by the working event module include: excavation sequence events, blasting and tunneling events, and support construction events.

[0061] Specifically, the preset triggering rules include event triggering rules, periodic triggering rules, and threshold triggering rules;

[0062] The event triggering rule is as follows: when a new operating condition event is parsed, a twin update instruction is immediately triggered; the periodic triggering rule is as follows: within the update time window, a twin update instruction is periodically triggered according to the preset update cycle; the threshold triggering rule is as follows: when the absolute value of the deviation between the actual value of the monitoring indicator associated with the operating condition event in the spatiotemporal aligned dataset and the predicted value of the previous twin state version reaches the preset threshold, a twin update instruction is triggered.

[0063] The update time window is determined by the occurrence time of the work condition event and the preset duration. The target spatial range for the twin model update is a spatial area centered on the work position corresponding to the work condition event and limited by a preset influence radius. This is used to clarify the update range of the twin model and ensure the efficiency and accuracy of the model update.

[0064] It should be noted that the preset update cycle is set to 10 minutes, the preset duration is set to 2 hours, the preset influence radius is taken as 3 times the tunnel diameter (approximately 15 meters) based on the surrounding rock grade (Class III), and the preset threshold is set to 5 mm. For example, the preset displacement threshold is ±1 mm to ±10 mm, and the preset stress threshold is ±0.1 MPa to ±2 MPa. These settings can be flexibly adjusted according to the scale of the underground project, the geological conditions of the surrounding rock, and construction requirements to adapt to different engineering scenarios.

[0065] For example, the time window value is determined based on the following: In tunnel engineering, the surrounding rock stress monitoring data shows that, through statistical analysis of the surrounding rock stress monitoring data corresponding to no less than 6 blasting cycles in the early stage of the project, it was found that the stress change of moderately fractured surrounding rock is most significant within 10-20 minutes after blasting, and the stress change tends to level off after 20 minutes.

[0066] Based on the above monitoring and statistical results, in this embodiment, the update time window length corresponding to this blasting and tunneling event is set to 20 minutes, and the update time window is determined to be 09:42:10-10:02:10, with the blasting completion time of 09:42:10 as the starting point. The update time window covers the main response stage of the surrounding rock blasting disturbance.

[0067] It should be noted that the target spatial range is determined based on the following: According to the engineering construction organization design and the construction experience of similar tunnels, under the condition of moderately fractured surrounding rock, the range of significant disturbance to the surrounding rock caused by a single blast is usually concentrated in the 10-20m range before and after the blasting face.

[0068] For example, based on the level of blasting charge and the density of monitoring points in this project, and according to the engineering experience of similar tunnels and the fractured rock of the section, 15m is set as the blasting influence radius.

[0069] Therefore, taking the blasting operation mileage of left tunnel mileage 246.3 as the center, the target spatial range is determined to be: left tunnel mileage 231.3 to left tunnel mileage 261.3, covering the direct blasting disturbance zone and the stress concentration change zone.

[0070] It should be noted that the load variation parameters are determined based on the following: for this blasting cycle, the working condition event module quantifies the blasting disturbance intensity according to the explosive consumption per unit and the maximum single-stage charge.

[0071] Specifically, the equivalent surrounding rock unloading ratio, determined by the preliminary numerical simulation results of this project, is 0.32, corresponding to the median of the instantaneous unloading level of the surrounding rock when the explosive consumption is approximately 1.0-1.1 kg / m³. The numerical simulation results are based on the same surrounding rock grade, explosive type, blasting parameters as in this embodiment, and the initial stress state of the surrounding rock obtained through on-site monitoring.

[0072] The blasting disturbance correction factor is 1.12: The blasting disturbance correction factor is used to reflect the degree of short-term deterioration of the surrounding rock mechanical parameters caused by blasting, and the average amplification ratio of the measured value of the surrounding rock stress after the section blasting compared with the static working condition.

[0073] The above parameters, along with the update time window and target spatial range, are written into this twin update instruction.

[0074] It should be noted that the actual basis for the monitoring deviation trigger data is: within the update time window, a rock stress gauge located at the left tunnel mileage 250.1 continuously collects stress data.

[0075] For example, at 09:55:00, the principal stress value recorded by the measuring point is 11.6 MPa. Compared with the predicted principal stress value of 10.4 MPa at the same location and time in the previous twin state version, the previous twin state version is the latest twin state version before the start of this blasting cycle, and the current version is the updated result after the previous round of blasting.

[0076] The relative deviation between the two is calculated as follows:

[0077] It should be noted that the allowable error range determined in the early model calibration phase of this project is 10%. The relative deviation between the two exceeds the trigger threshold preset by the system. Based on this, the operating condition event module determines that the current twin state needs to be supplemented and updated, and generates a new twin update instruction.

[0078] It should be noted that, in summary, by analyzing blasting operation data and surrounding rock monitoring data, the digital twin update command is automatically generated, covering key data such as update time window, target spatial range, and load change parameters, ensuring real-time monitoring and calibration of surrounding rock stress changes, and improving the accuracy and timeliness of the digital twin model's response to construction disturbances.

[0079] S130: Digital Twin State Synchronization Update Module: Used to receive spatiotemporal aligned datasets and twin update instructions, update the surrounding rock digital twin model, and output the updated twin state version corresponding to the update time window.

[0080] In some embodiments, the implementation of step S130 (digital twin state synchronization update module: used to receive spatiotemporally aligned dataset and twin update instructions, update the surrounding rock digital twin model, and output the updated twin state version corresponding to the update time window) may include:

[0081] It should be noted that by combining real-time monitoring data with twin models, the calculation of surrounding rock stress and model updates in tunnel engineering are optimized to improve the accuracy and reliability of stress prediction during construction.

[0082] It should be noted that after the digital twin model of the surrounding rock is constructed, the model is dynamically updated through the data access and spatiotemporal alignment module, the working condition event module, and the digital twin status synchronization update module.

[0083] The digital twin state synchronization update module receives the aligned dataset and the twin update instruction. Based on the mapping relationship between the measurement point location of the monitoring data and the twin model calculation unit, it uses the aligned dataset as an observation constraint to calibrate the twin state parameters online and outputs the updated twin state version corresponding to the update time window.

[0084] Specifically, the mapping relationship is performed according to the measurement point-computation unit mapping table, which is used to indicate the twin model computation unit identifier corresponding to each measurement point identifier;

[0085] The measurement point-computation unit mapping table works as follows: The measurement point location is determined to include the cell space range of the twin model mesh, identifying the target computation unit that includes the measurement point location. If the measurement point location falls within the cell space range of a computation unit in the twin model, that computation unit is selected as the target computation unit. If the measurement point location does not fall within the cell space range of any computation unit in the twin model, the computation unit with the smallest distance from the measurement point location and a distance not exceeding the tolerance threshold is selected as the target computation unit. This target computation unit is used to use the monitoring value corresponding to the monitoring point as an observation constraint for subsequent online calibration updates.

[0086] It should be noted that, based on the dimensions used in the digital twin model of the surrounding rock, the monitoring points are located using a total station (error ±0.02m), therefore the tolerance threshold is set to 0.2m.

[0087] Twin state parameters include the equivalent elastic modulus of the surrounding rock, Poisson's ratio, initial in-situ stress parameters, surrounding rock zoning parameters, and support stiffness parameters.

[0088] For example, the twin model mesh: the computational cell mesh size of the twin model is 10m×10m×10m; the initial state of the twin model is set based on the geotechnical exploration data before construction: the elastic modulus of the surrounding rock is 30.0GPa, the Poisson's ratio is 0.3, and the initial ground stress is 8.5MPa.

[0089] It should be noted that the resolution of the model mesh is dynamically adjusted according to requirements. In important areas or places with high stress changes, a finer mesh is used to improve calculation accuracy.

[0090] The spatial range of calculation unit A1 is: X=1195.0m to X=1210.0m, Y=5795.0m to Y=5810.0m, Z=345.0m to Z=355.0m.

[0091] It should be noted that the location of the measuring point is mapped to the calculation unit: the monitoring point A (1200.0, 5800.0, 350.0)m falls within the spatial range of the calculation unit A1. Therefore, the monitoring point and the calculation unit A1 are perfectly matched, and the data is directly used for online calibration.

[0092] It should be noted that since the location of measurement point A is directly within computational unit A1, the monitoring data will be directly mapped to computational unit A1. Next, the monitoring data will be used as constraints in the computational units of the twin model, participating in subsequent state updates.

[0093] The specific process of online calibration and update is as follows: Establish an observation equation to map the twin state parameters to predicted quantities with the same dimensions as the monitoring data; construct an objective function based on the residuals between the monitored values ​​and predicted quantities in the spatiotemporally aligned dataset, and use the weighted least squares method to iteratively update the twin state parameters; wherein, the weights of the weighted least squares method are jointly determined by the sensor type corresponding to the monitoring data, the data quality identifier, and the sampling timeliness.

[0094] For example, the establishment of the observation equation and the construction of the objective function:

[0095] Based on the twin model calculations described above, the predicted value is 9.5 MPa. The predicted value is based on the model estimation before construction and preliminary geotechnical survey data.

[0096] Residual calculation: The difference between the monitoring data and the predicted value is:

[0097] Residual value = 10MPa - 9.5MPa = 0.5MPa

[0098] It should be noted that, considering the sensor's accuracy is ±0.5MPa, the residual error of 0.5MPa is within acceptable limits. The goal of the model update is to minimize this error.

[0099] It should be noted that the objective function performs parameter calibration by minimizing the sum of squared residuals between observed and predicted values. The optimization objective of the objective function is to reduce the residuals to below 0.2 MPa to improve the accuracy and reliability of the model.

[0100] For example, weighted least squares method is used for iterative updates:

[0101] Weight Calculation: The monitoring data comes from a high-precision sensor, and the data quality is rated at 0.95. The weight is calculated to be 1 due to the high precision of the sensor.

[0102] It should be noted that the weighted least squares method was used to iteratively update the state parameters in the twin model. After several iterations, the updated surrounding rock parameters were finally obtained: the equivalent elastic modulus of the surrounding rock was updated to 30.2 GPa, from an initial value of 30.0 GPa; Poisson's ratio was updated from 0.3 to 0.31; the initial in-situ stress was updated to 8.5 MPa; and the support stiffness was updated to 12 kN / m².

[0103] The weighted least squares method converged after 5 iterations, with the termination condition being that the residual change was less than 0.05 MPa. Furthermore, each update during the iteration process employed a stepwise weighting method.

[0104] The updated twin version is identified as V1.1, with a timestamp of January 20, 2026, at 12:30:00, and the event status is identified as a blasting tunneling event.

[0105] It should be noted that the updated twin state output includes updated parameters for the surrounding rock and the latest working condition information. This updated twin state data can be used for subsequent stress field calculations and visualization output.

[0106] It should be noted that the updated twin version includes a version identifier, a corresponding timestamp, and a corresponding operational event identifier.

[0107] S140: Stress field generation module: Used to generate surrounding rock stress field data through simulation algorithms based on the updated twin state version, boundary conditions, and load variation parameters.

[0108] In some embodiments, the implementation of step S140 (stress field generation module: used to generate surrounding rock stress field data through simulation algorithm based on the updated twin state version, boundary conditions and load change parameters) may include:

[0109] It should be noted that, in conjunction with the blasting load, the system calculates and generates an updated surrounding rock stress field. The stress field generation module receives the updated twin state version from the digital twin state synchronization update module, as well as the boundary conditions and load change parameters and blasting load from the working condition event module, and calculates and generates the surrounding rock stress field data at the corresponding time.

[0110] It should be noted that the stress field calculation is based on the finite element method, comprehensively considering various factors such as the location and magnitude of the blasting load, the tunnel support structure, the surrounding rock type, and mechanical parameters. The boundary conditions for the calculation are set as fixed constraints at the tunnel wall.

[0111] Specifically, firstly, the surrounding rock type, mechanical parameters, and support structure information are obtained from the updated twin version, and finite element meshes are generated for the surrounding rock and support structure models to establish a finite element mesh model. Secondly, the location, magnitude, and timing of tunnel construction boundaries and blasting loads are obtained from boundary conditions and load variation parameters. During calculation, the outer boundary of the model is set as a fixed constraint to simulate the original rock stress, while the tunnel wall and excavation face are set as free boundaries. Based on the timing of the blasting loads, the corresponding impact pressures are converted into equivalent nodal forces and applied to the nodes at the corresponding locations. Subsequently, based on the elastoplastic constitutive relationship of the surrounding rock, the overall stiffness matrix is ​​established and the nodal displacements are solved. Then, the stress of each element is calculated through geometric and physical equations, and finally, the updated surrounding rock stress field data is output. This data is represented as a stress tensor field distributed on the mesh nodes of the twin model corresponding to the update time window.

[0112] For example, the calculated stress value for a certain area is 12 MPa. During the stress field calculation process, the stress field of the surrounding rock underwent a change from an initial uniform distribution to a gradual intensification. Updated stress field data show that the stress value at a certain monitoring location in the surrounding rock has increased from the predicted value of 9.5 MPa to 12 MPa, indicating a significant stress concentration phenomenon near the blasting area.

[0113] S150: Visualization Data Generation and Output Module: Used to convert surrounding rock stress field data into visualization layer data associated with the update time window and output it to the display terminal.

[0114] In some embodiments, step S150 (visualization data generation and output module: used to convert surrounding rock stress field data into visualization layer data associated with an update time window and output it to a display terminal) includes:

[0115] It should be noted that, through the above steps, the embodiment achieves dynamic updating and optimization of the surrounding rock stress field. The weighted least squares method is used to iteratively correct parameters such as the elastic modulus, Poisson's ratio, and initial ground stress of the surrounding rock in the twin model, improving the model's prediction accuracy. The generated stress field reveals stress concentration phenomena within the blasting area and achieves real-time monitoring and interactive display through visualization, enabling users to make decisions based on real-time data and enhancing the safety and reliability of tunnel construction.

[0116] It should be noted that the visualization data generation and output module is used to convert the surrounding rock stress field data into visualization layer data and output it to the display terminal for interactive presentation. The visualization layer data includes cross-sectional slice data, isosurface data and cloud map mapping parameters, and is associated with the update time window to realize the synchronous update of the corresponding stress field visualization dynamic presentation as the twin state is triggered by the working condition event.

[0117] Specifically, firstly, spatial interpolation and mesh mapping are performed on the surrounding rock stress field data to generate geometric data compatible with the display terminal rendering engine; this ensures the smoothness and accuracy of the rendering display.

[0118] Secondly, based on the preset stress threshold range, the numerical values ​​of the surrounding rock stress field data are mapped to RGB color values ​​using a color lookup table, generating cloud map mapping parameters for stress cloud map rendering. Simultaneously, based on user interaction commands, the transformed surrounding rock stress field data undergoes cross-sectional extraction and triangulation to generate cross-sectional slice data.

[0119] Using isosurface extraction algorithms such as the moving cube method, isosurface data is generated by extracting isosurface points with the same stress value from the surrounding rock stress field data and constructing triangular patches.

[0120] Finally, the cloud map mapping parameters, section slice data, and isosurface data are packaged together with the update time window to generate visualization layer data and output to the display terminal; this timestamp-associated packaging ensures that the visualization data and the real-time state of the twin model remain synchronized.

[0121] For example, sectioning slice data: Based on the input sectioning parameters of the display terminal, a stress distribution map along the plane is generated, showing the spatial distribution of stress.

[0122] Isosurface data: Extract isosurface data of the stress tensor field, specifically extract isosurfaces at 10MPa and 12MPa, and display them in the visualization interface.

[0123] Cloud map mapping parameters: Set the color mapping range to 0-15MPa, and use color changes to represent the stress magnitude of different areas; red represents areas with higher stress, and blue represents areas with lower stress.

[0124] It should be noted that the preset stress threshold range is set according to the surrounding rock grade and actual engineering conditions, generally ranging from 0 MPa to 20 MPa. The color mapping range is dynamically adjusted according to changes in the stress field; if the stress exceeds the original range of 0-15 MPa, the mapping range will be automatically expanded. The display terminal provides an interactive interface, allowing users to view changes in the stress field at different depths and locations through zoom and rotation functions. Ultimately, the above data will be displayed in real time on the display terminal, enabling users to view changes in the stress field through interactive operations and make decisions based on the real-time data.

[0125] It should be noted that the section slice data is obtained by sectioning the surrounding rock stress field data according to the sectioning plane parameters input by the display terminal. The isosurface data is obtained by extracting the stress tensor field as the isosurface object based on the preset isosurface threshold. The cloud map mapping parameters include the upper limit of the color mapping range, the lower limit of the color mapping range, and the isosurface grading interval. The section slice data, isosurface data, and cloud map mapping parameters are associated with the version identifier and timestamp of the updated twin state version and output.

[0126] It should be noted that the system visualizes stress cloud maps, displacement fields, plastic zones, and the spatiotemporal evolution of structural stress. The 3D stress field cloud map uses different colors to render the stress distribution of the surrounding rock in real time on a 3D geological model. For example, a gradient color spectrum from blue to red visually represents the transition from low-stress areas to high-stress concentration areas, with potentially hazardous areas represented by red high-stress zones. It also predicts and visualizes displacement trends over the next 24-72 hours based on real-time displacement data from monitoring points. Real-time parameters are visualized, including the current surrounding rock grade, stress at key points, whether deformation exceeds limits, and the safety factor of the support structure.

[0127] Based on the same inventive concept, embodiments of this application also provide a method for visualizing the surrounding rock stress field based on digital twins, including:

[0128] S1. Acquire multi-source monitoring data of the surrounding rock of underground engineering and working condition data corresponding to the construction progress. Perform coordinate system alignment and time reference alignment processing on the monitoring data and working condition data to form an aligned dataset with timestamps under a unified spatiotemporal reference.

[0129] S2. Parse the working condition events, including construction procedures such as excavation and support, from the working condition data, and generate twin update instructions based on preset trigger rules. The twin update instructions include the update time window, the target spatial range, and the boundary conditions and load change parameters corresponding to the current working condition event.

[0130] S3. Upon receiving the alignment dataset and the twin update instruction, based on the mapping relationship between the measurement point location corresponding to the monitoring data and the twin model calculation unit, the alignment dataset is used as an observation constraint to perform online calibration and update of the twin state parameters, and the updated twin state version corresponding to the update time window is output.

[0131] S4. Based on the updated twin state version, and the corresponding boundary conditions and load change parameters, generate surrounding rock stress field data through forward solving. The surrounding rock stress field data includes the stress tensor field on the twin model mesh.

[0132] S5. Convert the surrounding rock stress field data into visualization layer data and output it to the display terminal for interactive presentation. The visualization layer data includes cross-sectional slice data, isosurface data and cloud map mapping parameters, and is associated with the update time window to achieve dynamic visualization of the surrounding rock stress field that is triggered by working conditions and updated synchronously with the twin state.

[0133] It should be noted that by performing spatial and temporal consistency processing on multi-source monitoring data and establishing a trigger update mechanism based on operating conditions and deviation thresholds, the model can perform targeted calculations and online calibration within a limited impact range when key disturbances occur. This reduces systematic errors caused by coordinate and temporal inconsistencies and static parameter mismatches. Furthermore, by leveraging data quality weighting, the fusion stability under noise, packet loss, or delay conditions is improved. At the same time, with the stress tensor field as the core output and version management and visualization linkage display, the interpretability, traceability, and engineering decision support capabilities of the results are enhanced.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A visualization system for the stress field of surrounding rock based on digital twins, characterized in that, include: The data access and spatiotemporal alignment module is used to acquire multi-source data of the surrounding rock of the project, including basic data, monitoring data and dynamic working condition data. The monitoring data and dynamic operating condition data are unified in spatial coordinates and aligned with time references to generate a spatiotemporally aligned dataset with timestamps. A digital twin model of the surrounding rock is constructed based on the aforementioned basic data; The operating condition event module is used to parse operating condition events from the dynamic operating condition data and generate twin update instructions based on preset triggering rules, including: The preset triggering rules include event triggering rules, periodic triggering rules, and threshold triggering rules; The event triggering rule is as follows: when a new working condition event is parsed, a twin update instruction is immediately generated. The periodic triggering rule is as follows: within the update time window, twin update instructions are periodically generated according to a preset update cycle; The threshold triggering rule is as follows: when the absolute value of the actual value of the monitoring indicator associated with the working condition event in the spatiotemporal aligned dataset deviates from the predicted value of the previous twin state version, a twin update instruction is triggered. The twin update command includes an update time window, a target spatial range, and boundary conditions and load change parameters corresponding to the operating condition event; The update time window is determined by the occurrence time of the working condition event and the preset duration; the target spatial range is a spatial area centered on the work position corresponding to the working condition event and limited by a preset influence radius. The digital twin status synchronization and update module includes: The mapping relationship submodule and the online calibration update submodule; The mapping relationship submodule is used to establish and store the mapping relationship between the measurement point location of the monitoring data and the twin model calculation unit; The online calibration and update submodule is used to perform online calibration and update of the twin state parameters of the surrounding rock digital twin model based on the mapping relationship and the spatiotemporal alignment dataset as an observation constraint, and output the updated twin state version corresponding to the update time window. The stress field generation module is used to generate surrounding rock stress field data based on the updated twin state version, the boundary conditions, and load change parameters through a simulation algorithm. The visualization data generation and output module is used to convert the surrounding rock stress field data into visualization layer data associated with the update time window and output it to the display terminal.

2. The surrounding rock stress field visualization system based on digital twin as described in claim 1, characterized in that, The step of unifying the spatial coordinates and aligning the time base of the monitoring data and dynamic operating condition data to generate a time-stamped spatiotemporally aligned dataset specifically includes: A spatial dynamic reference transformation algorithm is adopted to uniformly transform the spatial positioning parameters of the monitoring data and dynamic working condition data to the same digital twin coordinate system to achieve spatial coordinate unification. An adaptive time interpolation and resampling algorithm is used to unify the monitoring data and dynamic operating condition data to the same time reference, and resampling is performed according to a preset sampling step size to achieve time reference alignment.

3. The surrounding rock stress field visualization system based on digital twin as described in claim 1, characterized in that, The construction of the surrounding rock digital twin model based on the aforementioned basic data includes: The basic data includes geological exploration data, engineering geometric information, surrounding rock mechanical parameters, and initial geostress field; Based on the geological exploration data and engineering geometric information, a three-dimensional geometric model of the surrounding rock and support structure is established; the mechanical parameters of the surrounding rock and the initial geostress field are imported into the corresponding region of the three-dimensional geometric model, and the interaction relationship between the surrounding rock and the support structure is set to establish a digital twin model of the surrounding rock including the surrounding rock and the support structure.

4. The surrounding rock stress field visualization system based on digital twin according to claim 1, characterized in that, The mapping relationship submodule specifically includes: The mapping relationship is recorded using a measurement point-computation unit mapping table, which contains the correspondence between each measurement point identifier and the computation unit identifier of the twin model. The method for determining the correspondence is as follows: determine whether the position of the measuring point falls within the unit space range of a certain calculation unit of the twin model; if so, the calculation unit is taken as the target calculation unit; if not, the calculation unit with the smallest distance from the measuring point position and the distance does not exceed the tolerance threshold is selected as the target calculation unit. The target calculation unit is used to take the monitoring value corresponding to the measuring point as the observation constraint and provide the observation constraint to the online calibration update submodule.

5. The surrounding rock stress field visualization system based on digital twin according to claim 1, characterized in that, The online calibration update submodule specifically includes: An observation equation is established to map the twin state parameters of the twin model into predicted quantities with the same dimensions as the monitoring data; The objective function is constructed using the residuals between the monitored values ​​and the predicted values ​​in the spatiotemporally aligned dataset. The twin state parameters are iteratively updated using a weighted least squares method, wherein the weights of the weighted least squares method are jointly determined by the sensor type, data quality identifier, and sampling timeliness of the monitoring data.

6. The surrounding rock stress field visualization system based on digital twin according to claim 1, characterized in that, The stress field generation module is used to generate surrounding rock stress field data based on the updated twin state version, the boundary conditions, and load change parameters through a simulation algorithm, including: The tunnel construction boundary and the application location, magnitude, and timing of blasting loads are obtained from the boundary conditions and load variation parameters; the surrounding rock type and support structure are obtained from the updated twin state version. Using the tunnel construction boundary as a constraint, a finite element numerical simulation algorithm is used to calculate the stress field in real time. During the calculation process, based on the application location, magnitude, and timing of the blasting load, combined with the surrounding rock type and support structure, surrounding rock stress field data corresponding to the update time window is generated.

7. The surrounding rock stress field visualization system based on digital twin according to claim 1, characterized in that, The visualization data generation and output module is used to convert the surrounding rock stress field data into visualization layer data associated with the update time window and output it to the display terminal, including: Spatial interpolation and mesh mapping are performed on the surrounding rock stress field data to generate geometric data compatible with the display terminal rendering engine; Based on the preset stress threshold range, the numerical values ​​of the surrounding rock stress field data are mapped to RGB color values ​​through a color lookup table to generate cloud map mapping parameters; Based on user interaction commands, the stress field data of the surrounding rock is subjected to cross-sectional extraction and triangulation to generate cross-sectional slice data; An isosurface extraction algorithm is used to extract isosurface points with the same stress value from the surrounding rock stress field data and construct triangular patches to generate isosurface data. The cloud map mapping parameters, section slice data, and isosurface data are packaged into visualization layer data associated with the update time window and output to the display terminal.

8. A method for visualizing the surrounding rock stress field based on digital twins, used to implement the surrounding rock stress field visualization system based on digital twins as described in any one of claims 1-7, characterized in that, include: S1. Acquire multi-source data of the surrounding rock of the project, including basic data, monitoring data and dynamic working condition data; The monitoring data and dynamic operating condition data are unified in spatial coordinates and aligned with time references to generate a spatiotemporally aligned dataset with timestamps. A digital twin model of the surrounding rock is constructed based on the aforementioned basic data; S2. Parse the operating events from the dynamic operating data and generate twin update instructions based on preset triggering rules; the twin update instructions include an update time window, a target spatial range, and boundary conditions and load change parameters corresponding to the operating events; S3. After receiving the spatiotemporal aligned dataset and the twin update instruction, update the surrounding rock digital twin model and output the updated twin state version corresponding to the update time window; S4. Based on the updated twin state version, the boundary conditions, and the load change parameters, generate surrounding rock stress field data through a simulation algorithm; S5. Convert the surrounding rock stress field data into visualization layer data associated with the update time window, and output it to the display terminal.

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