Pilot hole excavation sequence optimization control method and device, electronic equipment and storage medium

Abstract

The application provides a pilot hole excavation sequence optimization control method and device, electronic equipment and a storage medium, and belongs to the technical field of underground construction of rail transit. The method comprises the following steps: obtaining engineering parameters of a region to be constructed; obtaining measured values of a plurality of monitoring dimensions of a current pilot hole region at a current collection time; for each monitoring dimension, predicting a priori estimation value at the current collection time according to the measured value at the current collection time and a posteriori estimation value at a previous collection time, determining a risk level at the current collection time, and further determining a comprehensive risk level; if the comprehensive risk level meets a preset triggering condition, obtaining risk characteristic quantities of the plurality of monitoring dimensions corresponding to all unfinished pilot holes respectively, and dynamically adjusting the excavation sequence of each pilot hole region in combination with the engineering parameters corresponding to all unfinished pilot holes respectively. The application can realize optimization control of the pilot hole excavation sequence.

Description

Technical Field

[0001] This application belongs to the field of underground construction technology for rail transit, and more specifically, it relates to a method and device for optimizing and controlling the excavation sequence of a pilot tunnel, electronic equipment, and storage medium. Background Technology

[0002] In the construction of transfer nodes for urban rail transit stations using the cut-and-cover method, due to the large cross-section, shallow burial depth, and complex surrounding environment, the multi-pilot tunnel excavation technology under the mining method or shallow-buried cut-and-cover method is commonly adopted. This type of technology divides the large cross-section transfer node into multiple small cross-section pilot tunnels, and completes the excavation and initial support in stages according to the experienced sequence of "first the sides, then the middle" and "first the top, then the bottom", and maintains structural stability through measures such as temporary central partition walls and temporary invert arches.

[0003] Currently, the excavation sequence of pilot tunnels is typically determined statically during the specialized construction planning stage based on engineering experience and design documents. During construction, safety monitoring is conducted using monitoring points such as crown settlement, surrounding rock convergence, and deformation of existing structures. Existing condition monitoring methods generally involve deploying sensors at fixed measuring points to periodically collect data on surrounding rock convergence, support stress, and groundwater levels. The measured values ​​are then compared with preset warning thresholds. When data exceeds limits, a manual assessment of the risk is conducted, and passive measures such as suspending construction or localized reinforcement are implemented. In this method, the measured values ​​are primarily used for post-construction analysis or exceeding-limit alarms, and cannot achieve early intervention during pilot tunnel excavation. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, electronic equipment, and storage medium for optimizing and controlling the excavation sequence of pilot tunnels, so as to realize the dynamic adjustment of the excavation sequence of pilot tunnels based on real-time collected measured values, thereby solving the technical problem that the prior art cannot achieve early intervention in the excavation of pilot tunnels.

[0005] A first aspect of this application provides a method for optimizing and controlling the excavation sequence of a pilot tunnel, comprising: Obtain the engineering parameters of the area to be constructed; the area to be constructed includes multiple pre-set pilot tunnel areas, and the engineering parameters include the initial excavation sequence of multiple pilot tunnel areas, the initial support parameters corresponding to each pilot tunnel area, and the temporary structure support parameters. The measured values ​​of multiple monitoring dimensions in the current construction pilot tunnel area are obtained at the current acquisition time. These multiple monitoring dimensions include the surrounding rock convergence dimension, the existing structure deformation dimension, the initial support stress deformation dimension, the groundwater level dimension, and the arch settlement dimension. For each monitoring dimension, the posterior estimate at the current acquisition time is predicted based on the measured value of that monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time. The risk characteristic of this monitoring dimension at the current acquisition time is determined based on the posterior estimate at the current acquisition time. Based on this risk characteristic, the risk level of this monitoring dimension at the current data collection time is determined; The overall risk level at the current data collection time is determined based on the risk level of each monitoring dimension; If the combined risk level and the risk characteristics of multiple monitoring dimensions meet the preset triggering conditions, then the risk characteristics of multiple monitoring dimensions corresponding to each of the currently unfinished pilot tunnel areas are obtained. Based on the risk characteristics of multiple monitoring dimensions corresponding to each of the currently unfinished pilot tunnel areas, the initial support parameters, and the temporary structure support parameters, the excavation sequence of each pilot tunnel area is dynamically adjusted to achieve optimized control of the pilot tunnel excavation sequence. The currently unfinished pilot tunnel areas include the currently under construction pilot tunnel area.

[0006] A second aspect of this application provides a pilot tunnel excavation sequence optimization control device, comprising: The data acquisition module is used to acquire engineering parameters of the area to be constructed. The area to be constructed includes multiple preset pilot tunnel areas. The engineering parameters include the initial excavation sequence of the multiple pilot tunnel areas, the initial support parameters corresponding to each pilot tunnel area, and the temporary structure support parameters. The multiple pilot tunnel areas are preset areas in the area to be constructed. The pilot tunnel division module is used to determine the current construction pilot tunnel area according to the initial excavation sequence, and to obtain the measured values ​​of multiple monitoring dimensions of the current construction pilot tunnel area at the current acquisition time. The multiple monitoring dimensions include the surrounding rock convergence dimension, the existing structure deformation dimension, the initial support stress deformation dimension, the groundwater level dimension, and the arch settlement dimension. The estimation prediction module is used to predict the posterior estimate at the current acquisition time for each monitoring dimension based on the measured value of that monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time. The risk feature quantity determination module is used to determine the risk feature quantity of the monitoring dimension at the current collection time based on the posterior estimate at the current collection time. The risk level determination module is used to determine the risk level of the monitoring dimension at the current data collection time based on the risk characteristic quantity. The comprehensive risk determination module is used to determine the comprehensive risk level at the current data collection time based on the risk levels of each monitoring dimension. The sequence optimization module is used to obtain the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas if the comprehensive risk level and the risk characteristics of multiple monitoring dimensions meet the preset trigger conditions. Based on the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas, the initial support parameters, and the temporary structure support parameters, the module dynamically adjusts the excavation sequence of each pilot tunnel area to achieve optimized control of the pilot tunnel excavation sequence. The current unfinished pilot tunnel areas include the current construction pilot tunnel area.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described pilot tunnel excavation sequence optimization control method.

[0008] In a fourth aspect of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described method for optimizing and controlling the excavation sequence of the pilot tunnel.

[0009] The beneficial effects of the pilot tunnel excavation sequence optimization control method, device, electronic equipment, and storage medium provided in this application embodiment are as follows: This application embodiment overcomes the one-sidedness of single-index evaluation by integrating and judging multi-dimensional risk levels, and can more comprehensively reflect the overall status of pilot tunnel construction. Furthermore, by collecting multi-dimensional measured values ​​in real time, and using the measured values ​​at the current collection time and the prior estimates at the current collection time to predict the status at the current collection time, the risk level of each monitoring dimension and the comprehensive risk level are dynamically evaluated. When the comprehensive risk level meets the triggering conditions, the excavation sequence of all unfinished pilot tunnel areas is automatically adjusted, thereby transforming passive post-event response into proactive pre-control optimization. This allows for early adjustment of construction strategies before risks exceed limits, improving construction safety. Therefore, this embodiment continuously assesses risks and dynamically optimizes the construction sequence of subsequent pilot tunnels based on measured values ​​and predicted estimates, shortening the construction period delays caused by frequent alarms or reinforcement, and improving construction efficiency. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating a method for optimizing and controlling the excavation sequence of a pilot tunnel according to an embodiment of this application; Figure 2 This is a schematic diagram of the pilot tunnel area for a transfer node of a city rail transit underground station, provided in an embodiment of this application. Figure 3 This is a structural block diagram of a pilot tunnel excavation sequence optimization control device provided in an embodiment of this application; Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0014] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for optimizing and controlling the excavation sequence of a pilot tunnel according to an embodiment of this application. The method can be executed by an electronic device and includes: S101: Obtain the engineering parameters of the area to be constructed.

[0015] In this embodiment, the area to be constructed includes multiple pre-defined pilot tunnel areas. The engineering parameters include the initial excavation sequence of the multiple pilot tunnel areas, the initial support parameters corresponding to each pilot tunnel area, and the temporary structural support parameters. The area to be constructed can be the tunneling area of ​​an urban rail transit transfer node. Based on the structural dimensions, cross-sectional shape, geological conditions, and surrounding environment (especially the location of existing lines) of the tunneling area of ​​the transfer node, multiple pilot tunnel areas of the transfer node can be determined, and excavation operations can be carried out sequentially.

[0016] For example, consider a transfer node at a tunnel-excavated station in an urban rail transit system. The tunnel section is approximately 23 meters long, 26 meters wide, and 10.16 meters high. The area to be constructed is divided into multiple pilot tunnel units along the longitudinal and vertical axes of the station. Figure 2 The image shown is a schematic diagram of the pilot tunnel area for a transfer node in a city rail transit underground station, provided in an embodiment of this application. Figure 2 In this project, the excavation area of ​​the transfer node was divided into eight pilot tunnel areas: the upper left pilot tunnel area (numbered 1-1), the upper right pilot tunnel area (1-3), the lower left pilot tunnel area (1-2), the lower right pilot tunnel area (1-4), the middle left pilot tunnel area (2-1, 2-2), and the middle right pilot tunnel area (3-1, 3-2). Each pilot tunnel area was constructed longitudinally in steps, with a single excavation step distance controlled at 0.5 meters. Furthermore, the different pilot tunnel areas were spatially staggered to avoid simultaneous disturbance of a large area of ​​surrounding rock.

[0017] In this embodiment, the initial excavation sequence of multiple pilot tunnel areas is determined based on the engineering experience principles of "excavating the sides first, then the middle," "excavating from top to bottom first," and "staggered excavation" to quickly form a stable arch effect, control surrounding rock deformation, and obtain corresponding surrounding rock convergence data. The initial excavation sequence of the eight pilot tunnel areas in this embodiment can be: Phase 1: Simultaneous excavation of the upper left pilot tunnel area and the upper right pilot tunnel area (1-1, 1-3) to form a preliminary load-bearing arch; Second stage: After the initial support of a certain length (e.g., 3 meters) of the upper pilot tunnel area is completed, the lower pilot tunnel area on the left and the lower pilot tunnel area on the right are excavated in a staggered sequence (1-2, 1-4). Phase 3: Excavate the left-side pilot tunnel area from top to bottom (2-1, 2-2); Phase 4: Finally, excavate the pilot tunnel area on the right side of the middle section from top to bottom (3-1, 3-2).

[0018] In this embodiment, the initial support parameters refer to the parameters of the initial support that need to be implemented simultaneously after the excavation of each pilot tunnel area. Supporting the pilot tunnel area based on the above initial support parameters can ensure the stability of the surrounding rock and the safety of the structure.

[0019] In this embodiment, the initial support adopts a composite support form of shotcrete and steel arch frame. The support parameters of this composite support form include steel arch frame, shotcrete, and anchor pipes. The setting method of each support parameter can be as follows: The steel arch frame uses I28a I-beams with a longitudinal spacing of 0.5 meters (i.e., one frame is erected for every 0.5 meters of excavation). The parameters of this steel arch frame can be determined based on the cross-sectional dimensions, the surrounding rock grade (e.g., Grade IV or V), and engineering analogy to provide sufficient load-bearing capacity and bending stiffness. The shotcrete strength grade is C25, and the shotcrete thickness is 0.35 meters. In this embodiment, the shotcrete of this thickness and strength can wrap the steel arch frame and fit tightly against the surrounding rock, forming an effective load-bearing layer. Anchor pipes can be used... Millimeter-thick steel pipes, 3 meters long, are installed at the arch feet of each steel frame to prevent the arch frame from sinking or the arch feet from receding when bearing the upper load.

[0020] In this embodiment, it is also necessary to obtain the temporary structure support parameters that need to be set during and after the construction of each pilot tunnel area. The temporary structures include temporary central partition walls, temporary invert arches, horizontal braces, or vertical braces.

[0021] In this embodiment, a temporary central partition wall is set at the horizontal interface between the upper and lower steps in each pilot tunnel area. It is erected one by one simultaneously with the excavation of the upper step. It is used to temporarily close the section and prevent the arch foot from spreading outward and the steel frame from sinking before the steel arch frame has been fully closed into a ring. The temporary invert arch is set at the bottom of the lower step of the pilot tunnel, that is, at the closure part of the initial support at the bottom after the full section of the pilot tunnel has been excavated to the design position. It is constructed after the full section of the pilot tunnel has been excavated and is used to close the bottom of the support structure.

[0022] S102: Obtain the measured values ​​of multiple monitoring dimensions in the current construction pilot tunnel area at the current acquisition time.

[0023] In this embodiment, multiple monitoring dimensions include the surrounding rock convergence dimension, the existing structure deformation dimension, the initial support stress deformation dimension, the groundwater level dimension, and the crown settlement dimension. Specifically, the crown settlement dimension is used to monitor the vertical downward displacement of the crown in the pilot tunnel area, reflecting the degree of vertical deformation of the surrounding rock after excavation. In the crown settlement dimension, sensors can be static levels or total stations with reflective patches, set at the crown centerline of each pilot tunnel area, with a monitoring section set every 5 to 10 meters longitudinally, and the measurement unit is millimeters (mm). The surrounding rock convergence dimension is used to monitor the reduction in the horizontal distance between the two sidewalls of the pilot tunnel area, reflecting the degree of horizontal compression deformation of the surrounding rock. In this surrounding rock convergence dimension, sensors can be convergence meters or laser rangefinders, set at the waist height of each pilot tunnel area (usually 1 meter above the arch riser line), arranged longitudinally on the same section as the crown settlement measuring points, and the measurement unit is millimeters (mm). The existing structure deformation dimension is used to monitor settlement, horizontal displacement, or differential deformation of existing line structures (e.g., operational subway stations, tunnels, or surrounding buildings) within the impact area of ​​the cut-and-cover construction. In this dimension, sensors can be electronic levels, crack gauges, or displacement gauges, installed at key locations on the existing structure (e.g., sidewalls, base slabs, or columns adjacent to the new transfer node), with measurements measured in millimeters (mm). The initial support stress-deformation dimension is used to monitor the stress or strain generated within the initial support structure (shotcrete, steel arch frame), reflecting the stress state of the support structure. In this dimension, sensors can be vibrating wire strain gauges or steel stress gauges, welded or tied to the flanges or webs of the steel arch frame, or embedded within the shotcrete layer, with measurements measured in megapascals (MPa). The groundwater level dimension is used to monitor the dynamic changes in the groundwater level around the pilot tunnel area to assess the impact of construction dewatering or seepage on the stability of the surrounding rock. In this groundwater level dimension, the sensor can be a vibrating wire level gauge or a pressure level gauge, which is installed in the observation holes around the pilot tunnel. The depth of the observation holes is generally 2 to 3 meters below the bottom plate of the pilot tunnel, and the measurement unit is meters (m).

[0024] In this embodiment, the current construction pilot tunnel area refers to a pilot tunnel that has been started but has not yet completed its entire designed length (e.g., has not yet reached the designed cross-section or has not completed full cross-section closure). During the initial excavation stage, the current construction pilot tunnel area is determined by the initial excavation sequence and is dynamically adjusted based on the subsequent construction progress. When multiple pilot tunnel areas are excavated simultaneously, the current construction pilot tunnel area may contain multiple pilot tunnel areas. In this case, corresponding measured values ​​can be obtained for each current construction pilot tunnel area, and subsequent risk level monitoring can be performed.

[0025] In this embodiment, the current acquisition time refers to the time point at which data acquisition is performed. In this embodiment, the current acquisition time is t. Data acquisition tasks are periodically triggered according to a preset sampling period Δt. For each monitoring dimension i (i=1,...,n, where n is the total number of sensors), its measured value is obtained. For example, for a measuring point in the dimension of arch settlement, the measured value... This represents the measured value monitored by the sensor at the t-th acquisition time, corresponding to the dimension of the arch subsidence, in millimeters.

[0026] As can be seen from the above, this embodiment solves the problem of the disconnect between measured values ​​and construction status in the prior art by binding the acquisition action with the current construction tunnel area and collecting the measured values ​​corresponding to each monitoring dimension.

[0027] In order to achieve refined perception and data association of the construction status of the pilot tunnel, the acquisition process of the measured values ​​in this embodiment is also associated with the excavation cycle information.

[0028] Specifically, let the current construction pilot tunnel area be numbered g, where g∈{1,2,…,8}. For any pilot tunnel area number g, its excavation process is numbered according to the construction cycle, and the current excavation cycle number is recorded as k. In this embodiment, the preset sampling period is Δt, which can be dynamically adjusted according to the actual monitoring mode: for example, when using automated monitoring, Δt is set to 2 hours / time, and the sensor accuracy is 0.1mm; when using manual monitoring, Δt is set to 1 time / day; if abnormal working conditions are encountered, the sampling period can be automatically increased to 1 time / hour.

[0029] In this embodiment, the monitoring sensor set is S={s1,s2,…,s5}, including dimensions of crown settlement, surrounding rock convergence, initial support stress deformation, existing structure deformation, and groundwater level. Each sensor synchronously uploads data at the end of each sampling cycle via a wireless sensor network. In the k-th construction cycle, real-time data from each monitoring sensor is collected at the current acquisition time t, constructing a multi-source raw data vector: X(g,k,t)={x1(t),x2(t),…,x5(t)}, where k represents the current excavation cycle number, serving as an index key at the organizational level to mark which excavation cycle of which pilot tunnel area the currently collected data belongs to, playing a segmented archiving role and not participating in subsequent rate calculations; x i (t) represents the monitoring value of the i-th monitoring parameter (i.e., the i-th sensor) at the current acquisition time t.

[0030] In this embodiment, after obtaining the estimated values ​​of each monitoring dimension at the current acquisition time, in order to support subsequent risk feature extraction, risk level determination, and dynamic optimization of excavation sequence, the estimated value corresponding to each sensor is stored in the time series dataset in a four-element key-value structure. Specifically, the four-element key value is set to (g,k,t,s). i ), where s i This represents the sensor number, i=1,2,…,5.

[0031] In this embodiment, each quad key value corresponds to a data record, which contains the following fields: The estimated value at the current acquisition time, the error covariance at the current acquisition time, the cumulative change, and the instantaneous rate of change.

[0032] In this embodiment, all data records organized using quaternion keys constitute a time-series dataset D(g,k), where D(g,k) represents the set of all sensor data collected in the pilot tunnel area g during the kth construction cycle. This dataset is arranged in ascending order according to physical timestamps and can be quickly retrieved and statistically analyzed by pilot tunnel area number, excavation cycle number, time interval, or sensor number.

[0033] As can be seen from the above, this embodiment obtains quaternary key values ​​and realizes refined management of measured values ​​in four dimensions (tunnel number, excavation cycle number, timestamp, and sensor).

[0034] S103: For each monitoring dimension, predict the posterior estimate at the current acquisition time based on the measured value of that monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time.

[0035] In one embodiment of this application, the posterior estimate of the monitoring dimension at the current acquisition time can be predicted using Kalman filtering based on the measured value of the monitoring dimension at the current acquisition time and the prior estimate of the monitoring dimension at the current acquisition time. In this embodiment, the prior estimate of the monitoring dimension at the current acquisition time is predicted based on the posterior estimate of the previous acquisition time.

[0036] In this embodiment, the current acquisition time is t, and the posterior estimate of the previous acquisition time is t. The posterior covariance at the previous data collection time is... .

[0037] In this embodiment, since the changes in surrounding rock convergence data (surrounding rock deformation data) or support force can be approximated as uniform motion within adjacent sampling intervals, the state transition matrix A is set to 1. The prior estimate value at the current acquisition time in this embodiment is: The prediction covariance at the current data acquisition time: ,in, This represents the posterior covariance at the previous data collection time. This represents the process noise covariance, which reflects the degree of difference between uniform motion and the actual situation. The larger the value, the lower the confidence level of the prediction, and the more the filter will rely on the current measured value. The specific values ​​can be set manually based on the physical characteristics of the monitored object: for example, for physical quantities such as the crown subsidence dimension and the surrounding rock convergence dimension, which change relatively gently, The value range can be 0.1~0.5 (the unit is consistent with the square of the physical quantity, e.g., mm²); for the initial support stress deformation dimension, because it is sensitive to construction disturbance and fluctuates greatly, The value range can be 0.5~1.5 (MPa²); for the groundwater level dimension, The value can range from 0.2 to 0.8 (m²).

[0038] In this embodiment, the formula for calculating the Kalman gain at the current acquisition time can be: ,in, , The observation noise covariance can be determined based on the sensor's measurement accuracy. For example, if the calibration accuracy of a hydrostatic level is ±0.1 mm, the corresponding variance can be taken as 0.01 mm². If precise calibration data is lacking, the variance can be estimated as 1% to 2% of the sensor's range. In this embodiment, if... A value close to 1 indicates very low observation noise and high reliability of the measured value; if If the value is close to 0, the noise in the characterization process is very small, and the reliability of the predicted covariance is high.

[0039] In this embodiment, based on the sensor's measured value at the current acquisition time t Compare it with the prior estimate at the current acquisition time. Perform weighted fusion: This allows us to obtain the posterior estimate at the current acquisition time.

[0040] In this embodiment, the covariance is updated, and the posterior covariance at the current acquisition time is: The reliability of the estimation results is evaluated based on the updated covariance.

[0041] As can be seen from the above, this embodiment uses Kalman filtering to predict all monitoring dimensions within the current construction tunnel area. It weights and corrects the prior estimate and the measured value at the current acquisition time, which not only preserves the true trend of the measured value with the construction progress, but also suppresses high-frequency noise interference. This solves the problem of risk misjudgment caused by data fluctuations in the prior art, and realizes smooth tracking and early prediction of the stress state of the surrounding rock and support structure.

[0042] S104: Determine the risk characteristic of this monitoring dimension at the current acquisition time based on the posterior estimate at the current acquisition time.

[0043] In this embodiment, the risk characteristic quantities include the cumulative change and the instantaneous rate of change.

[0044] In one embodiment of this application, determining the risk characteristic of the monitoring dimension at the current acquisition time based on the posterior estimate at the current acquisition time includes: Obtain the preset reference time and the corresponding reference estimate value; The cumulative change is determined based on the baseline estimate and the posterior estimate at the current acquisition time. The instantaneous rate of change is determined by comparing the posterior estimate at the current acquisition time with the posterior estimate at the previous acquisition time.

[0045] In this embodiment, the cumulative change refers to the total change in this monitoring dimension from the reference time to the current acquisition time. The instantaneous rate of change refers to the change in this monitoring dimension per unit time.

[0046] In this embodiment, a reference time t0 is pre-set for each monitoring dimension (each sensor). This reference time can be determined by selecting several continuously collected data sets as initial stable references before the formal commencement of the pilot tunnel excavation. Specifically, after the sensors are set up, a preset time (e.g., 1-2 hours) is waited for the sensor readings to stop drifting. Starting from the reference time, multiple (e.g., 3) sets of measured data are continuously and independently collected, with each collection interval equal to the preset sampling period Δt. The arithmetic mean of the estimated values ​​obtained after Kalman filtering of each measured value is used as the reference estimate. For example, if the subsidence measurement point of the arch obtains filtered estimated values ​​of 0.02mm, 0.03mm, and 0.02mm at three consecutive sampling times, then the baseline estimated value is taken as 0.023mm.

[0047] In this embodiment, the determination of the reference time must be completed before the current construction pilot tunnel area is excavated, or in the early stage before the pilot tunnel has undergone significant deformation, so as to ensure that the reference estimate truly reflects the initial state.

[0048] In this embodiment, the cumulative change is calculated based on the baseline estimate and the posterior estimate at the current acquisition time, using the cumulative change formula. The cumulative change formula can be: ,in, This represents the cumulative change in the i-th monitoring dimension. Let represent the posterior estimate of the i-th monitoring dimension at the current data collection time. This represents the baseline estimate for the i-th monitoring dimension.

[0049] In this embodiment, the instantaneous rate of change is calculated based on the posterior estimate at the current acquisition time and the posterior estimate at the previous acquisition time, using the instantaneous rate of change formula. The instantaneous rate of change formula can be... ,in, This represents the instantaneous rate of change of the i-th monitoring dimension. This represents the posterior estimate of the i-th monitoring dimension at the previous data collection time.

[0050] As can be seen from the above, this embodiment generates two risk characteristic quantities for each monitoring dimension at the current acquisition time: cumulative change and instantaneous change rate. The cumulative change reflects the overall magnitude of deformation or stress; if it exceeds a certain limit, it indicates that the structure has undergone significant displacement. The instantaneous change rate reflects the current rate of deterioration; if the rate remains consistently high, it indicates that the situation is still developing, and attention should be paid even if the cumulative change has not yet reached the limit. The "dual control" mechanism formed by the combination of cumulative change and instantaneous change rate overcomes the shortcomings of relying solely on a single indicator, which can easily lead to missed or false alarms.

[0051] S105: Determine the risk level of this monitoring dimension at the current data collection time based on this risk characteristic quantity.

[0052] In one embodiment of this application, determining the risk level of the monitoring dimension at the current data collection time based on the risk characteristic quantity includes: Obtain a first cumulative threshold, a second cumulative threshold, and a third cumulative threshold, wherein the severity of the first cumulative threshold, the second cumulative threshold, and the third cumulative threshold decreases sequentially; A first rate threshold, a second rate threshold, and a third rate threshold are obtained, wherein the severity of the first rate threshold, the second rate threshold, and the third rate threshold decreases sequentially. If the cumulative change and the instantaneous change rate meet the first preset condition, then the risk level of the monitoring dimension at the next collection time is determined to be the first warning level; If the cumulative change and the instantaneous change rate do not meet the first preset condition, but meet the second preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the second warning level. If the cumulative change and the instantaneous change rate do not meet the first and second preset conditions, but meet the third preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the third warning level. If neither the cumulative change nor the instantaneous change rate meets the first, second, and third preset conditions, then the risk level of the monitoring dimension at the current data collection time is determined to be normal. Among them, the first warning level, the second warning level, and the third warning level decrease in that order; The first preset condition is: the cumulative change is greater than or equal to the first cumulative threshold, and the instantaneous change rate is greater than or equal to the first rate threshold. The second preset condition is: the cumulative change is greater than or equal to the second cumulative threshold, and the instantaneous change rate is greater than or equal to the second rate threshold; or, the cumulative change is greater than or equal to the first cumulative threshold, or the instantaneous change rate is greater than or equal to the first rate threshold. The third preset condition is: the cumulative change is greater than or equal to the third cumulative threshold, and the instantaneous change rate is greater than or equal to the third rate threshold; or, the cumulative change is greater than or equal to the second cumulative threshold, or the instantaneous change rate is greater than or equal to the second rate threshold.

[0053] In this embodiment, the cumulative thresholds and rate thresholds can be determined according to the design documents of a specific project. The design documents provide the "control value" (i.e., the maximum allowable cumulative change) and "rate control value" (i.e., the maximum allowable daily rate of change) for each monitoring item. In this embodiment, the control value is used as the first cumulative threshold T. 1,d 85% of the control value is used as the second cumulative threshold T.2,d 70% of the control value is used as the third cumulative threshold T. 3,d The rate control value is used as the first rate threshold T. 1,v 85% of the rate control value is used as the second rate threshold T. 2,v 70% of the rate control value is used as the third rate threshold T. 3,v For example, taking the arch settlement dimension as an example, if the control value is 4mm, then T 1,d =4mm, T 2,d =3.4mm, T 3,d =2.8mm; the rate control value is taken as 2mm / d, then T 1,v =2mm / d,T 2,v =1.7mm / d, T 3,v =1.4mm / d.

[0054] This embodiment will obtain the cumulative change amount and instantaneous rate of change The risk level is compared step-by-step with its corresponding threshold, starting from the highest risk level (first warning level). If the threshold is not met, the assessment proceeds downwards until the final level is determined. The specific assessment rules are as follows: (1) Determine the first warning level If the cumulative change ≥T 1,d and instantaneous rate of change ≥T 1,v (First preset condition) If the risk level of the monitoring dimension at the current acquisition time is determined to be the first warning level (the highest risk level), then the risk level of the monitoring dimension is determined to be the first warning level (the highest risk level). In this embodiment, the first warning level corresponds to the "red warning" in the specification, which indicates that the measured value corresponding to the monitoring dimension has reached or exceeded the corresponding control value, and the current rate of change is still too high, the structure is in a dangerous state, and the relevant construction must be stopped immediately and a special assessment must be organized.

[0055] (2) Determine the second warning level If the cumulative change With instantaneous rate of change If the first preset condition is not met, the system continues to determine whether the second preset condition is met. In this embodiment, the second preset condition includes any one of the following: Cumulative change ≥T 2,d and instantaneous rate of change ≥T 2,v ; Cumulative change ≥T 1,d or instantaneous rate of change ≥T 1,v . If any of the above conditions are met, the risk level of this monitoring dimension is determined to be the second warning level.

[0056] It should be noted that in this embodiment, "cumulative change" refers to... ≥T 1,d or instantaneous rate of change ≥T 1,v "The conditions cover situations where a single indicator has reached a red alert level (even if another indicator has not met the standard). For example, the cumulative change has exceeded 4 mm but the instantaneous change rate is only 1.5 mm / d (less than 2 mm / d), or the cumulative change is only 3.5 mm but the rate has reached 2.2 mm / d. In this embodiment, the second warning level corresponds to the "orange alarm" in the specification, indicating a high risk, requiring intensified monitoring, inspection of construction processes, and approval from the design and construction units before construction can continue."

[0057] (3) Determine the third warning level If neither the first nor the second preset condition is met, then the process continues to determine whether the third preset condition is met. The third preset condition includes any one of the following: Cumulative change ≥T 3,d and instantaneous rate of change ≥T 3,v ; Cumulative change ≥T 2,d or instantaneous rate of change ≥T 2,v .

[0058] If any of the above conditions are met, the risk level of this monitoring dimension is determined to be the third warning level. The third warning level corresponds to the "yellow warning" in the specification, indicating that the measured value of this monitoring dimension has exceeded the yellow threshold, requiring attention, appropriate increase in monitoring frequency, and on-site technical personnel to check for any anomalies.

[0059] (4) Determine the normal level If the cumulative change With instantaneous rate of change None of the first, second, and third preset conditions are met; that is, all of the following conditions are met simultaneously: Cumulative change <T 3,d and instantaneous rate of change <T 3,v If neither the second nor the first preset condition is met, the risk level of this monitoring dimension is determined to be normal, indicating that the surrounding rock and support structure are within a stable and controllable range under the current construction conditions, and construction can continue according to the established plan.

[0060] As can be seen from the above, this embodiment solves the problem of alarms based solely on a single threshold (such as exceeding the cumulative limit) and lack of rate coordination criteria by determining the risk level of a single monitoring dimension at the current collection time, thus achieving a refined hierarchical assessment of risk status.

[0061] S106: Determine the overall risk level at the current data collection time based on the risk level of each monitoring dimension.

[0062] In this embodiment, a high risk in any monitoring dimension within the current construction pilot tunnel area indicates a potential safety hazard in the local structure, which could trigger a chain reaction. Therefore, the highest risk level among the risk levels corresponding to each monitoring dimension is taken as the comprehensive risk level at the current data acquisition moment. Specifically, the comprehensive risk level can be determined as follows: ,in, Indicates the overall risk level. This represents the risk level corresponding to the i-th monitoring dimension at the current data collection time.

[0063] S107: If the comprehensive risk level and the risk characteristics of multiple monitoring dimensions meet the preset triggering conditions, then obtain the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnels, and dynamically adjust the excavation sequence of each pilot tunnel area according to the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnels, the initial support parameters, and the temporary structure support parameters.

[0064] In this embodiment, the following steps can be used to determine the preset triggering conditions: Get the instantaneous rate of change of the arch subsidence dimension of the current construction pilot tunnel area over the most recent N historical acquisition times, or the instantaneous rate of change of the surrounding rock convergence dimension of the current construction pilot tunnel area over the most recent N historical acquisition times; where N is a natural number and N≥3, and the N historical acquisition times include the current acquisition time and multiple acquisition times before the current acquisition time; Obtain the cumulative change in the deformation dimension of the existing structure in the current construction pilot tunnel area at the current acquisition time, and the instantaneous change rate of the deformation dimension of the existing structure in the current construction pilot tunnel area over the most recent N historical acquisition times; Obtain the sequence of changes in the groundwater level dimension of the current construction pilot tunnel area over the most recent N historical acquisition times, and the sequence of increments in the initial support stress deformation dimension of the current construction pilot tunnel area over the most recent N historical acquisition times.

[0065] Based on this, the preset triggering conditions may include: The overall risk level is either the first or second warning level; or, The instantaneous rate of change of the arch subsidence dimension at the most recent N historical acquisition times is greater than or equal to the second rate threshold, and the instantaneous rate of change of the arch subsidence dimension at the most recent N historical acquisition times increases monotonically; or, The instantaneous rate of change of the surrounding rock convergence dimension at the most recent N historical acquisition times is greater than or equal to the second rate threshold, and the instantaneous rate of change of the surrounding rock convergence dimension at the most recent N historical acquisition times monotonically increases; or, The cumulative change in the deformation dimension of the existing structure at the current acquisition time is greater than or equal to a preset proportion of the first cumulative threshold, and the instantaneous rate of change of the deformation dimension of the existing structure at the most recent N historical acquisition times is monotonically increasing; or, The Pearson correlation coefficient between the change series and the increment series is greater than or equal to the preset coefficient threshold.

[0066] In this embodiment, the comprehensive risk level and the risk characteristics of multiple monitoring dimensions meet the preset triggering conditions, including any one of the following two situations: First, the comprehensive risk level itself has reached the first warning level or the second warning level; Second, although the comprehensive risk level has not reached the above level, it meets any one of the single-dimensional triggering conditions (e.g., the first triggering condition, the second triggering condition, or the third triggering condition).

[0067] Specifically, if the overall risk level is the first or second warning level, the preset triggering conditions are met; if the overall risk level is the third or normal level, it is determined whether the single-dimensional triggering conditions are met. If any one of the single-dimensional triggering conditions is met, the preset triggering conditions are met.

[0068] In this embodiment, the instantaneous rate of change of the crown subsidence dimension for the most recent N consecutive historical acquisition times (including the current acquisition time) can be read from the time series dataset D(g,k), or the instantaneous rate of change of the surrounding rock convergence dimension for the most recent N consecutive historical acquisition times (including the current acquisition time) can be read from the time series dataset D(g,k), where N≥3. In this embodiment, N is 3.

[0069] In this embodiment, the first triggering condition is determined to be satisfied if the following conditions are met simultaneously: Condition A1: The instantaneous rate of change of the crown subsidence dimension at N historical acquisition times is greater than or equal to the second rate threshold; or, the instantaneous rate of change of the surrounding rock convergence dimension at N historical acquisition times is greater than or equal to the second rate threshold. Condition A2: The instantaneous rate of change of the arch subsidence dimension at N historical acquisition times shows a monotonically increasing trend, or the instantaneous rate of change of the surrounding rock convergence dimension at N historical acquisition times shows a monotonically increasing trend. Taking N=3 as an example, the determination rule for monotonically increasing trend in this embodiment can be: That is, the instantaneous rate of change at the next acquisition time is not less than the instantaneous rate of change at the previous acquisition time.

[0070] In this embodiment, if the above two conditions are met at the same time, it is determined that the first triggering condition is met, indicating that the deformation rate of the surrounding rock has exceeded the orange warning value for multiple consecutive cycles and is still accelerating, indicating that the deformation is in an accelerated development stage. Even if the current cumulative deformation has not yet reached the red limit, the excavation sequence must still be actively adjusted to control the disturbance of the surrounding rock.

[0071] In this embodiment, the instantaneous rate of change corresponding to the deformation dimension of the existing structure refers to the instantaneous rate of change calculated based on the settlement or displacement monitoring points of the existing structure (e.g., an operational subway station); the cumulative amount of change corresponding to the deformation dimension of the existing structure refers to the total amount of deformation of the existing structure from the reference time to the current acquisition time, in mm.

[0072] In this embodiment, the second triggering condition is determined to be satisfied if the following conditions are met simultaneously: Condition B1: The cumulative change in the deformation dimension of the existing structure at the current acquisition time. Greater than or equal to the first cumulative threshold T 1,d The preset ratio. In this embodiment, the preset ratio is taken as 80% based on engineering experience; Condition B2: The instantaneous rate of change of the deformation dimension of the existing structure at the current acquisition moment has not decreased.

[0073] If all of the above conditions are met ( If the second trigger condition is met, it indicates that the cumulative deformation of the existing structure has approached the maximum allowable value and the deformation rate is still accelerating. This means that the impact of construction on the existing structure has not been controlled and the impact must be reduced by adjusting the excavation sequence of subsequent pilot tunnels (e.g., suspending the construction of pilot tunnels adjacent to existing structures).

[0074] In this embodiment, the change in groundwater level dimension at any acquisition time refers to the change in water level (in meters) at that acquisition time relative to the previous acquisition time. The sequence of changes in groundwater level dimension over the most recent N historical acquisition times can reflect the water level fluctuation. The increment of initial support stress deformation dimension at any acquisition time refers to the increment of initial support stress (in MPa) at that acquisition time relative to the previous acquisition time. The sequence of increments of initial support stress deformation dimension over the most recent N historical acquisition times can reflect the increase in stress on the support structure.

[0075] In this embodiment, the groundwater level change sequence for the most recent Z sampling periods (Z is a preset number of sampling periods, which is 10 in this embodiment) can be extracted from the time series dataset D(g,k). and support stress increment sequence .

[0076] In this embodiment, the Pearson correlation coefficient between the two sequences is calculated. r The calculation formula can be: ,in, This represents the c-th change in groundwater level, where c = 1, 2, ..., Z. This represents the c-th support stress increment; This represents the arithmetic mean of the changes in groundwater level at 10 sampling times. This represents the arithmetic mean of the support stress increments over 10 sampling times; This indicates that groundwater has directly caused the stress and synergy of the support system to deteriorate, requiring early intervention.

[0077] In this embodiment, if r If the value is ≥0.7, the third trigger condition is met, indicating that the fluctuation of the groundwater level is highly synchronized with the increase of the stress on the initial support. This indicates that the action of groundwater is the main factor leading to the deterioration of the support stress. At this time, relying solely on local reinforcement has limited effect, and it is necessary to adjust the excavation sequence (e.g., prioritize the construction of the secondary lining in the affected area or apply waterproofing in advance) to cut off the path of groundwater influence.

[0078] In this embodiment, all currently unfinished pilot tunnels include the pilot tunnel currently under construction. When the overall risk level is detected to meet the preset triggering conditions, the excavation sequence of each pilot tunnel area can be dynamically adjusted to achieve optimized control of the pilot tunnel excavation sequence.

[0079] As can be seen from the above, this embodiment solves the problem in the prior art that it only relies on single-point over-limit alarms and lacks the ability to identify risk development trends and multi-factor collaborative anomalies by setting the above triggering conditions. Specifically, the first triggering condition in this embodiment can identify potential risks that are "not yet exceeding the limit but are deteriorating" by judging the rate of continuous monotonous increase; the second triggering condition can issue an early warning before the deformation of the existing structure reaches the dangerous critical point by judging the criterion of approaching the limit and the rate not decreasing; the third triggering condition can identify hidden water-induced risk factors by analyzing the correlation between groundwater level and support stress. This embodiment combines the three triggering conditions with the comprehensive risk level, realizing the improvement from passive response to active prediction, and improving the timeliness and pertinence of the optimized control of the pilot tunnel excavation sequence.

[0080] In one embodiment of this application, the excavation sequence of each pilot tunnel area is dynamically adjusted based on the risk characteristics, initial support parameters, and temporary structure support parameters of each of the multiple monitoring dimensions corresponding to all unfinished pilot tunnel areas, including: The static priority of each unfinished pilot tunnel area is determined based on the initial excavation sequence; For each unfinished pilot tunnel area, determine the first ratio of the instantaneous change rate of each monitoring dimension of the unfinished pilot tunnel area to the corresponding first rate threshold, and the second ratio of the cumulative change of each monitoring dimension of the unfinished pilot tunnel area to the corresponding first cumulative threshold; determine the support capacity coefficient of the unfinished pilot tunnel area based on the initial support parameters and temporary structure support parameters of the unfinished pilot tunnel area; determine the dynamic construction priority index of the unfinished pilot tunnel area based on the static priority, each first ratio, each second ratio, and the support capacity coefficient of the unfinished pilot tunnel area. The excavation sequence of each pilot tunnel area is dynamically adjusted according to the order of the dynamic construction priority index from high to low.

[0081] In this embodiment, the ranking sequence of each pilot tunnel area can be determined according to the initial excavation order. For two or more pilot tunnel areas excavated simultaneously, they can be sorted according to their numbers. For example, if the upper left pilot tunnel area (numbered 1-1) and the upper right pilot tunnel area (numbered 1-3) are excavated simultaneously in the first stage, then the pilot tunnel area numbered 1-1 will be placed before the pilot tunnel area numbered 1-3. Based on this, the ranking sequence of each pilot tunnel area can be converted into a static priority value. The specific conversion rule is: the earlier the ranking, the higher the static priority. For example, if there are a total of 8 pilot tunnels, the static priority of the first (earliest constructed) pilot tunnel in the initial sequence is 8, the second is 7, and so on, with the last one being 1.

[0082] In this embodiment, for each monitoring dimension of each incomplete pilot tunnel area, the same method as in the above embodiment can be used to calculate the instantaneous rate of change and the cumulative amount of change of the pilot tunnel area. The ratio of the instantaneous rate of change of the pilot tunnel area to a first rate threshold is used as the first ratio, and the ratio of the cumulative amount of change to a first cumulative threshold is used as the second ratio.

[0083] Select the largest first ratio from the first ratios corresponding to each monitoring dimension, and select the largest second ratio from the second ratios corresponding to each monitoring dimension.

[0084] This embodiment determines the support capacity coefficient based on the initial support parameters and temporary structure support parameters corresponding to each incomplete pilot tunnel area. Cs The support capacity coefficient is a dimensionless value between 0 and 1, used to reflect the ability of the support system that has been completed and put into use in the pilot tunnel to resist deformation. The larger the support capacity coefficient, the stronger the support capacity, and the more suitable it is for priority construction; the smaller the support capacity coefficient, the weaker the support capacity, and construction should be postponed or strengthened before construction.

[0085] In this embodiment, the support capacity coefficient can be specifically determined as follows: The evaluation is based on a comprehensive assessment of the steel arch frame type, spacing, and shotcrete thickness. For example, the baseline configuration is I28a steel, 0.5m spacing, and 0.35m C25 concrete thickness, corresponding to a coefficient of 0.5. If the steel type is larger (e.g., I32a) or the spacing is denser (0.4m), the coefficient is adjusted upwards (maximum 0.7); if the type is smaller or the spacing is larger, the coefficient is adjusted downwards (minimum 0.3).

[0086] Bonus points are awarded based on the installation of temporary central partition walls and temporary inverts. If the pilot tunnel has completed the construction of temporary central partition walls and temporary inverts, the coefficient increases by 0.2 points; if only one of them is completed, 0.1 points are added; if neither is completed, 0 points are added.

[0087] Support capacity coefficient Cs = Initial support foundation coefficient + temporary structure bonus, where... Cs The value range is [0.2, 1.0].

[0088] In this embodiment, the obtained static priority and the largest first ratio (denoted as...) will be used. Rv ) and the largest second ratio (denoted as Rd ), support capacity coefficient Cs A weighted summation is performed to obtain the dynamic construction priority index Q. This embodiment uses a linear weighted summation method, and the calculation formula is as follows: ,in, Let each represent a weighting coefficient, satisfying the following conditions: The specific values ​​of each weighting coefficient can be set based on engineering experience. For example, static priority reflects the ideal design order, and the corresponding weighting coefficient can be 0.3; the largest first ratio... Rv The second largest ratio Rd The coefficient for reflecting current risk can be 0.25; the coefficient for support capacity Cs reflects the stability of the structure itself, and the corresponding coefficient for support capacity Cs can be 0.2.

[0089] In the formula and The physical meaning of the part is: Rv and Rd The higher the value, the higher the risk, and thus... and The smaller the value, the more likely it is to cause Q A lower risk value results in a lower construction priority for that pilot tunnel area. Conversely, a lower risk pilot tunnel area corresponds to a lower construction priority. Q The higher the value, the higher the priority for construction. In this embodiment, static priority is used. Ps It is a positive integer (ranging from 1 to the total number of tunnels). To match it with other items between 0 and 1, we can first... PsNormalize to the interval 0 to 1. The normalization method is: Ps,norm=(Ps Pmin) / (Pmax Pmin), where Pmax represents the value corresponding to the highest priority in the initial sequence (i.e., the total number of pilot tunnel areas), and Pmin represents the value corresponding to the lowest priority (i.e., 1).

[0090] This embodiment applies a dynamic construction priority index to all incomplete pilot tunnel areas. Q Sort from highest to lowest Q The higher the value, the higher the priority for construction of the pilot tunnel. If two pilot tunnels are required... Q If the values ​​are equal, the one that appears earlier in the initial excavation sequence will be prioritized (i.e., the one with higher static priority). After sorting, a new excavation sequence will be generated.

[0091] In one embodiment of this application, after a new excavation sequence is generated, one or more of the following optimization measures can be automatically executed based on the current comprehensive early warning level, specifically: If the current construction pilot tunnel area is the central pilot tunnel area (e.g., 2-1, 2-2, 3-1, 3-2), and the comprehensive warning level reaches the third warning level or above, the continued excavation of the central pilot tunnel will be automatically suspended, and the construction of the sealing wall of the already excavated pilot tunnel will be completed first. For the pilot tunnel area where the initial support has been completed and the deformation has tended to stabilize, the secondary lining structure should be implemented in advance to form a permanent load-bearing system as soon as possible. The number of extended excavation cycles for the temporary central diaphragm, temporary invert arch, and horizontal and vertical bracing is determined based on the comprehensive early warning level. Specifically, if the comprehensive early warning level is the third level, the excavation cycle is extended by 1 to 2 cycles; if it is the second level, the excavation cycle is extended by 2 to 3 cycles; if it is the first level, construction is immediately stopped and a special assessment is organized. The minimum step distance requirement for adjacent pilot tunnel areas is directly determined by the comprehensive early warning level. The higher the comprehensive early warning level, the larger the required step distance and the stricter the spatial constraints on adjacent pilot tunnels.

[0092] In this embodiment, the optimization measures and the dynamic construction priority index work together to form a multi-level proactive control system that ranges from "sequential rearrangement" to "local measure adjustment".

[0093] As can be seen from the above, this embodiment directly quantifies the measured values ​​into risk ratios and introduces a priority index, realizing "data-driven" construction decisions and solving the problem of the disconnect between measured values ​​and construction decisions. By weightedly integrating static priority, risk ratios, and support capacity coefficients, it avoids the construction chaos that may result from simply ranking based on risk level (for example, delaying all high-risk pilot tunnels may lead to prolonged exposure of certain areas and greater risks). By introducing the support capacity coefficient, pilot tunnels with stronger support capabilities can be appropriately prioritized for construction, forming a virtuous cycle of "strong support first," which is beneficial to the stability of the overall construction area.

[0094] In one embodiment of this application, for a pilot tunnel area that has already been constructed, the pilot tunnel excavation sequence optimization and control method further includes: Identify the pilot tunnel areas to be demolished within the already constructed pilot tunnel areas; The instantaneous rate of change of multiple monitoring dimensions of the pilot tunnel area to be demolished and its adjacent pilot tunnel areas at the most recent M historical acquisition times, axial force or bending moment data of temporary support, and force increment of adjacent undemolished temporary structures are obtained; where M is a natural number and M≥3; If the instantaneous change rate of multiple monitoring dimensions of the pilot tunnel area to be demolished and its adjacent pilot tunnel areas is lower than the third rate threshold in the most recent M historical acquisition times, the axial force or bending moment data of the temporary support drops to the preset bearing conversion threshold, and the force increment of the adjacent undemolished temporary structure is less than or equal to the preset allowable increment, then the pilot tunnel area to be demolished is determined to meet the demolition conditions.

[0095] In this embodiment, the area to be demolished is determined from the area of ​​completed pilot tunnels, and the instantaneous change rate of the area to be demolished and its adjacent pilot tunnel areas (pilot tunnels that are adjacent to each other on the left and right or vertically) is obtained in multiple consecutive sampling periods.

[0096] If the instantaneous rate of change of the pilot tunnel area to be demolished and its adjacent pilot tunnel areas is lower than the third rate threshold for M consecutive sampling periods (e.g., 3), then the first dynamic index is deemed to be satisfied. This first dynamic index is used to ensure that the surrounding rock deformation has stabilized and that excessive deformation will not occur due to stress release after the temporary support is removed.

[0097] In this embodiment, axial force or bending moment data of the temporary support are acquired. Axial force refers to the pressure exerted on the temporary central partition or temporary cross brace along its axial direction, measured in kilonewtons (kN); bending moment refers to the bending moment exerted on the temporary structure, measured in kN·m. Axial force or bending moment data can be obtained using vibrating wire axial force gauges or strain gauges pre-installed on the temporary structure.

[0098] In this embodiment, the load transfer threshold refers to the target value to which the temporary support should be unloaded when the permanent structure (i.e., the secondary lining) reaches its design strength and begins to bear load. This load transfer threshold can be taken as a certain percentage of the historical maximum axial force (or maximum bending moment) of the temporary support, for example, taking 60% of the historical maximum axial force (or maximum bending moment) as the load transfer threshold.

[0099] In this embodiment, it is determined whether the axial force or bending moment data of the temporary support has decreased to a preset load-bearing conversion threshold. If the measured axial force or bending moment data is less than or equal to the preset load-bearing conversion threshold, it is determined that the second dynamic index is met. In this embodiment, the preset load-bearing conversion threshold refers to the target value to which the temporary support should be unloaded when the permanent structure (i.e., the secondary lining) reaches its design strength and begins to bear load. This preset load-bearing conversion threshold can be determined based on a certain proportion of the historical maximum axial force (or maximum bending moment) measured or calculated during the construction of the temporary support. The specific proportion can be determined comprehensively based on factors such as engineering geological conditions, support design, and permanent structure form. For example, this proportion can be taken as 50% to 70%, and in this embodiment, it is taken as 60%.

[0100] In this embodiment, adjacent unremoved temporary structures refer to temporary partition walls or temporary cross braces that have not yet been removed in other pilot tunnel areas adjacent to the pilot tunnel area to be removed. The force increment refers to the increase in axial force or bending moment of adjacent temporary structures within the current sampling period relative to the previous sampling period, expressed in kilonewtons.

[0101] In this embodiment, the preset allowable increment can be determined based on the design bearing capacity of the adjacent temporary structure. For example, 10% of the maximum design bearing capacity of the adjacent temporary structure (i.e., the maximum allowable axial force or bending moment) can be taken as the allowable increment.

[0102] This embodiment obtains the force increment of adjacent unremoved temporary structures and determines whether the force increment is less than or equal to a preset allowable increment. If the force increment of adjacent unremoved temporary structures is less than or equal to the preset allowable increment, it is determined that the third dynamic index is satisfied.

[0103] In this embodiment, if the first dynamic index, the second dynamic index, and the third dynamic index are all satisfied at the same time, then combined with the strength condition (i.e., the secondary lining has reached more than 80% of the design strength), it is determined that the temporary support meets the conditions for removal.

[0104] Once the demolition conditions are met, the demolition mode is automatically selected based on the current comprehensive risk level: If the comprehensive risk level is normal, a "sequential demolition" method is used, meaning temporary supports are removed one by one from one end to the other. If the comprehensive risk level is the third warning level, a "skip-one demolition" method is used, meaning temporary supports are removed in stages, while some temporary structures are retained to maintain a certain structural rigidity. If the comprehensive risk level is the second warning level or higher, demolition is temporarily suspended until the risk level decreases and is reassessed.

[0105] As can be seen from the above, this embodiment solves the problem in the prior art of temporarily removing supports based solely on concrete age or strength, neglecting the actual stress state and deformation stability of the structure. Specifically, in this embodiment, the first dynamic indicator continuously monitors the deformation rate to ensure that the surrounding rock has stabilized and converged; the second dynamic indicator monitors the decrease in axial force or bending moment to confirm that the permanent structure has begun to bear load; and the third dynamic indicator monitors the stress increment of adjacent structures to avoid stress surges caused by removal. Through these three indicators, the temporary support is upgraded from "timed removal" to "state-based removal," avoiding the risk of sudden structural stress changes or collapse due to improper timing of support removal.

[0106] Corresponding to the pilot tunnel excavation sequence optimization control method in the above embodiment, Figure 3 This is a structural block diagram of a pilot tunnel excavation sequence optimization control device provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 3 The pilot tunnel excavation sequence optimization control device 20 includes: a data acquisition module 21, a pilot tunnel division module 22, an estimated value prediction module 23, a risk characteristic quantity determination module 24, a risk level determination module 25, a comprehensive risk determination module 26, and a sequence optimization module 27.

[0107] The data acquisition module 21 is used to acquire engineering parameters of the area to be constructed. The area to be constructed includes multiple preset pilot tunnel areas. The engineering parameters include the initial excavation sequence of the multiple pilot tunnel areas, the initial support parameters corresponding to each pilot tunnel area, and the temporary structure support parameters. The multiple pilot tunnel areas are preset areas in the area to be constructed. The pilot tunnel division module 22 is used to determine the current construction pilot tunnel area according to the initial excavation sequence and obtain the measured values ​​of multiple monitoring dimensions of the current construction pilot tunnel area at the current acquisition time. The multiple monitoring dimensions include the surrounding rock convergence dimension, the existing structure deformation dimension, the initial support stress deformation dimension, the groundwater level dimension, and the arch settlement dimension. The estimation prediction module 23 is used to predict the posterior estimate at the current acquisition time for each monitoring dimension based on the measured value of the monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time. The risk feature quantity determination module 24 is used to determine the risk feature quantity of the monitoring dimension at the current collection time based on the posterior estimate at the current collection time. Risk level determination module 25 is used to determine the risk level of the monitoring dimension at the current data collection time based on the risk characteristic quantity; The comprehensive risk determination module 26 is used to determine the comprehensive risk level at the current data collection time based on the risk levels of each monitoring dimension. The sequence optimization module 27 is used to obtain the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas if the comprehensive risk level and the risk characteristics of multiple monitoring dimensions meet the preset trigger conditions. Based on the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas, the initial support parameters, and the temporary structure support parameters, the excavation sequence of each pilot tunnel area is dynamically adjusted to achieve optimized control of the pilot tunnel excavation sequence. The current unfinished pilot tunnel areas include the current construction pilot tunnel area.

[0108] In one embodiment of this application, the estimation module 23, when predicting the posterior estimate at the current acquisition time based on the measured value of the monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time, is specifically used for: Based on the measured value of the monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time, the posterior estimate at the current acquisition time is predicted by Kalman filtering.

[0109] In one embodiment of this application, the risk characteristic quantity includes cumulative change and instantaneous change rate. The risk characteristic quantity determination module 24, when determining the risk characteristic quantity of the monitoring dimension at the current acquisition time based on the posterior estimate at the current acquisition time, is specifically used for: Obtain the preset reference time and the corresponding reference estimate value; The cumulative change is determined based on the baseline estimate and the posterior estimate at the current acquisition time. The instantaneous rate of change is determined by comparing the posterior estimate at the current acquisition time with the prior estimate at the previous acquisition time.

[0110] In one embodiment of this application, the risk level determination module 25, when determining the risk level of the monitoring dimension at the current data collection time based on the risk characteristic quantity, is specifically used for: Obtain a first cumulative threshold, a second cumulative threshold, and a third cumulative threshold, wherein the severity of the first cumulative threshold, the second cumulative threshold, and the third cumulative threshold decreases sequentially; A first rate threshold, a second rate threshold, and a third rate threshold are obtained, wherein the severity of the first rate threshold, the second rate threshold, and the third rate threshold decreases sequentially. If the cumulative change and the instantaneous change rate meet the first preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the first warning level; If the cumulative change and the instantaneous change rate do not meet the first preset condition, but meet the second preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the second warning level. If the cumulative change and the instantaneous change rate do not meet the first and second preset conditions, but meet the third preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the third warning level. If neither the cumulative change nor the instantaneous change rate meets the first, second, and third preset conditions, then the risk level of the monitoring dimension at the current data collection time is determined to be normal. Among them, the first warning level, the second warning level, and the third warning level decrease in that order; The first preset condition is: the cumulative change is greater than or equal to the first cumulative threshold, and the instantaneous change rate is greater than or equal to the first rate threshold. The second preset condition is: the cumulative change is greater than or equal to the second cumulative threshold, and the instantaneous change rate is greater than or equal to the second rate threshold; or, the cumulative change is greater than or equal to the first cumulative threshold, or the instantaneous change rate is greater than or equal to the first rate threshold. The third preset condition is: the cumulative change is greater than or equal to the third cumulative threshold, and the instantaneous change rate is greater than or equal to the third rate threshold; or, the cumulative change is greater than or equal to the second cumulative threshold, or the instantaneous change rate is greater than or equal to the second rate threshold.

[0111] In one embodiment of this application, the sequence optimization module 27 is specifically used for: Obtain the instantaneous rate of change of the arch subsidence dimension of the current construction pilot tunnel area over the most recent N historical acquisition times, or the instantaneous rate of change of the surrounding rock convergence dimension of the current construction pilot tunnel area over the most recent N historical acquisition times; where N is a natural number and N≥3; Obtain the cumulative change in the deformation dimension of the existing structure in the current construction pilot tunnel area at the current acquisition time, and the instantaneous change rate of the deformation dimension of the existing structure in the current construction pilot tunnel area over the most recent N historical acquisition times; Obtain the sequence of changes in the groundwater level dimension of the current construction pilot tunnel area over the most recent N historical acquisition times, and the sequence of increments in the initial support stress deformation dimension of the current construction pilot tunnel area over the most recent N historical acquisition times. Among them, the sequence optimization module 27, when determining that the comprehensive risk level meets the preset triggering conditions, is specifically used for: The overall risk level is either the first or second warning level; or, The instantaneous rate of change of the arch subsidence dimension at the most recent N historical acquisition times is greater than or equal to the second rate threshold, and the instantaneous rate of change of the arch subsidence dimension at the most recent N historical acquisition times increases monotonically; or, The instantaneous rate of change of the surrounding rock convergence dimension at the most recent N historical acquisition times is greater than or equal to the second rate threshold, and the instantaneous rate of change of the surrounding rock convergence dimension at the most recent N historical acquisition times monotonically increases; or, The cumulative change in the deformation dimension of the existing structure at the current acquisition time is greater than or equal to a preset proportion of the first cumulative threshold, and the instantaneous rate of change of the deformation dimension of the existing structure at the most recent N historical acquisition times is monotonically increasing; or, The Pearson correlation coefficient between the change series and the increment series is greater than or equal to the preset coefficient threshold.

[0112] In one embodiment of this application, the sequence optimization module 27, when dynamically adjusting the excavation sequence of each pilot tunnel area based on the risk characteristics, initial support parameters, and temporary structure support parameters corresponding to multiple monitoring dimensions of all unfinished pilot tunnel areas, is specifically used for: The static priority of each unfinished pilot tunnel area is determined based on the initial excavation sequence; For each unfinished pilot tunnel area, determine the first ratio of the instantaneous change rate of each monitoring dimension of the unfinished pilot tunnel area to the corresponding first rate threshold, and the second ratio of the cumulative change of each monitoring dimension of the unfinished pilot tunnel area to the corresponding first cumulative threshold; determine the support capacity coefficient of the unfinished pilot tunnel area based on the initial support parameters and temporary structure support parameters of the unfinished pilot tunnel area; determine the dynamic construction priority index of the unfinished pilot tunnel area based on the static priority, each first ratio, each second ratio, and the support capacity coefficient of the unfinished pilot tunnel area. The excavation sequence of each pilot tunnel area is dynamically adjusted according to the order of the dynamic construction priority index from high to low.

[0113] In one embodiment of this application, the pilot tunnel excavation sequence optimization control device 20 further includes a support removal module. For the pilot tunnel area that has already been constructed, the support removal module is specifically used for: Identify the pilot tunnel areas to be demolished within the already constructed pilot tunnel areas; The instantaneous rate of change of multiple monitoring dimensions of the pilot tunnel area to be demolished and its adjacent pilot tunnel areas at the most recent M historical acquisition times, axial force or bending moment data of temporary support, and force increment of adjacent undemolished temporary structures are obtained; where M is a natural number and M≥3; If the instantaneous change rate of multiple monitoring dimensions of the pilot tunnel area to be demolished and its adjacent pilot tunnel areas is lower than the third rate threshold in the most recent M historical acquisition times, the axial force or bending moment data of the temporary support drops to the preset bearing conversion threshold, and the force increment of the adjacent undemolished temporary structure is less than or equal to the preset allowable increment, then the pilot tunnel area to be demolished is determined to meet the demolition conditions.

[0114] See Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 4 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 3 The functions of the data acquisition module 21, the tunnel division module 22, the estimated value prediction module 23, the risk characteristic quantity determination module 24, the risk level determination module 25, the comprehensive risk determination module 26, and the sequence optimization module 27 are shown.

[0115] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0116] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0117] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store information such as the measured values ​​corresponding to each monitoring dimension, the comprehensive risk level, and the excavation sequence of each pilot tunnel area.

[0118] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the pilot tunnel excavation sequence optimization control method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0119] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0120] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces or units, or they may be electrical, mechanical, or other forms of connection.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated modules described above can be implemented in hardware or as software functional modules.

[0126] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing and controlling the excavation sequence of a pilot tunnel, characterized in that, include: Obtain engineering parameters for the area to be constructed; the area to be constructed includes multiple preset pilot tunnel areas, and the engineering parameters include the initial excavation sequence of the multiple pilot tunnel areas, the initial support parameters corresponding to each pilot tunnel area, and the temporary structure support parameters. The measured values ​​of multiple monitoring dimensions in the current construction pilot tunnel area are obtained at the current acquisition time. The multiple monitoring dimensions include the surrounding rock convergence dimension, the existing structure deformation dimension, the initial support stress deformation dimension, the groundwater level dimension, and the arch settlement dimension. For each monitoring dimension, the posterior estimate at the current acquisition time is predicted based on the measured value of that monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time. The posterior estimate is determined by using Kalman filtering based on the prior estimate at the current acquisition time. The prior estimate is predicted based on the posterior estimate at the previous acquisition time. The risk characteristic of this monitoring dimension at the current acquisition time is determined based on the posterior estimate at the current acquisition time. The risk characteristic quantities include cumulative changes and instantaneous rates of change; Based on this risk characteristic, the risk level of this monitoring dimension at the current data collection time is determined; The overall risk level at the current data collection time is determined based on the risk level of each monitoring dimension; If the comprehensive risk level and the risk characteristics of multiple monitoring dimensions meet the preset triggering conditions, then the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas are obtained. Based on the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas, the initial support parameters, and the temporary structure support parameters, the excavation sequence of each pilot tunnel area is dynamically adjusted to achieve optimized control of the pilot tunnel excavation sequence. The currently unfinished pilot tunnel areas include the currently under-construction pilot tunnel areas; The method also includes: Obtain the instantaneous rate of change of the arch subsidence dimension of the current construction pilot tunnel area over the most recent N historical acquisition times, or the instantaneous rate of change of the surrounding rock convergence dimension of the current construction pilot tunnel area over the most recent N historical acquisition times; where N is a natural number and N≥3; Obtain the cumulative change in the deformation dimension of the existing structure in the current construction pilot tunnel area at the current acquisition time, and the instantaneous change rate of the deformation dimension of the existing structure in the current construction pilot tunnel area over the most recent N historical acquisition times; Obtain the sequence of changes in the groundwater level dimension of the current construction pilot tunnel area over the most recent N historical acquisition times, and the sequence of increments in the initial support stress deformation dimension of the current construction pilot tunnel area over the most recent N historical acquisition times. The preset triggering condition is any one of the following: The overall risk level is either the first warning level or the second warning level; or, The instantaneous rate of change of the arch subsidence dimension at the most recent N historical acquisition times is greater than or equal to the second rate threshold, and the instantaneous rate of change of the arch subsidence dimension at the most recent N historical acquisition times increases monotonically; or, The instantaneous rate of change of the surrounding rock convergence dimension at the most recent N historical acquisition times is greater than or equal to the second rate threshold, and the instantaneous rate of change of the surrounding rock convergence dimension at the most recent N historical acquisition times increases monotonically; or, The cumulative change in the deformation dimension of the existing structure at the current acquisition time is greater than or equal to a preset proportion of the first cumulative threshold, and the instantaneous rate of change of the deformation dimension of the existing structure at the most recent N historical acquisition times increases monotonically; or, The Pearson correlation coefficient between the change sequence and the increment sequence is greater than or equal to a preset coefficient threshold. Specifically, based on the risk characteristics, initial support parameters, and temporary structure support parameters of each of the various monitoring dimensions corresponding to all incomplete pilot tunnel areas, the excavation sequence of each pilot tunnel area is dynamically adjusted, including: The static priority of each unfinished pilot tunnel area is determined based on the initial excavation sequence. For each unfinished pilot tunnel area, a first ratio is determined between the instantaneous change rate of each monitoring dimension of the unfinished pilot tunnel area and the corresponding first rate threshold, and a second ratio is determined between the cumulative change of each monitoring dimension of the unfinished pilot tunnel area and the corresponding first cumulative threshold; a support capacity coefficient for the unfinished pilot tunnel area is determined based on the initial support parameters and temporary structure support parameters of the unfinished pilot tunnel area; and a dynamic construction priority index for the unfinished pilot tunnel area is determined based on the static priority of the unfinished pilot tunnel area, each of the first ratios, each of the second ratios, and the support capacity coefficient. The excavation sequence of each pilot tunnel area is dynamically adjusted according to the dynamic construction priority index from high to low.

2. The method for optimizing and controlling the excavation sequence of the pilot tunnel as described in claim 1, characterized in that, Determining the risk characteristic quantity of the monitoring dimension at the current acquisition time based on the posterior estimate at the current acquisition time includes: Obtain the preset reference time and the corresponding reference estimate value; The cumulative change is determined based on the baseline estimate and the posterior estimate at the current acquisition time. The instantaneous rate of change is determined based on the posterior estimate at the current acquisition time and the posterior estimate at the previous acquisition time.

3. The method for optimizing and controlling the excavation sequence of the pilot tunnel as described in claim 2, characterized in that, The process of determining the risk level of the monitoring dimension at the current data collection time based on the risk characteristic includes: A first cumulative threshold, a second cumulative threshold, and a third cumulative threshold are obtained, wherein the severity of the first cumulative threshold, the second cumulative threshold, and the third cumulative threshold decreases sequentially. A first rate threshold, a second rate threshold, and a third rate threshold are obtained, wherein the severity of the first rate threshold, the second rate threshold, and the third rate threshold decreases sequentially. If the cumulative change and the instantaneous change rate meet the first preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the first warning level; If the cumulative change and the instantaneous change rate do not meet the first preset condition, but meet the second preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the second warning level. If the cumulative change and the instantaneous change rate do not meet the first preset condition and the second preset condition, but meet the third preset condition, then the risk level of the monitoring dimension at the current collection time is determined to be the third warning level. If neither the cumulative change nor the instantaneous change rate satisfies the first preset condition, the second preset condition, nor the third preset condition, then the risk level of the monitoring dimension at the current acquisition time is determined to be normal. Among them, the first warning level, the second warning level, and the third warning level decrease sequentially; The first preset condition is: the cumulative change is greater than or equal to the first cumulative threshold, and the instantaneous change rate is greater than or equal to the first rate threshold; The second preset condition is: the cumulative change is greater than or equal to the second cumulative threshold, and the instantaneous change rate is greater than or equal to the second rate threshold; or, the cumulative change is greater than or equal to the first cumulative threshold, or the instantaneous change rate is greater than or equal to the first rate threshold. The third preset condition is: the cumulative change is greater than or equal to the third cumulative threshold, and the instantaneous change rate is greater than or equal to the third rate threshold; or, the cumulative change is greater than or equal to the second cumulative threshold, or the instantaneous change rate is greater than or equal to the second rate threshold.

4. The method for optimizing and controlling the excavation sequence of the pilot tunnel as described in claim 1, characterized in that, The step of predicting the posterior estimate at the current acquisition time based on the measured value of the monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time includes: Based on the measured value of the monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time, the posterior estimate at the current acquisition time is predicted by Kalman filtering.

5. The method for optimizing and controlling the excavation sequence of the pilot tunnel as described in claim 3, characterized in that, For pilot tunnel areas that have already been constructed, the pilot tunnel excavation sequence optimization and control method further includes: Identify the pilot tunnel area to be demolished within the already constructed pilot tunnel area; The instantaneous rate of change of multiple monitoring dimensions of the pilot tunnel area to be demolished and its adjacent pilot tunnel areas at the most recent M historical acquisition times, axial force or bending moment data of temporary support, and force increment of adjacent undemolished temporary structures are obtained; where M is a natural number and M≥3; If the instantaneous change rate of multiple monitoring dimensions of the pilot tunnel area to be demolished and its adjacent pilot tunnel areas is lower than the third rate threshold in the most recent M historical acquisition times, the axial force or bending moment data of the temporary support drops to the preset bearing conversion threshold, and the force increment of the adjacent undemolished temporary structure is less than or equal to the preset allowable increment, then the pilot tunnel area to be demolished is determined to meet the demolition conditions.

6. A pilot tunnel excavation sequence optimization and control device according to any one of claims 1-5, characterized in that, include: The data acquisition module is used to acquire engineering parameters of the area to be constructed; the area to be constructed includes multiple preset pilot tunnel areas, and the engineering parameters include the initial excavation sequence of the multiple pilot tunnel areas, the initial support parameters corresponding to each pilot tunnel area, and the temporary structure support parameters; the multiple pilot tunnel areas are preset areas in the area to be constructed. The pilot tunnel division module is used to obtain the measured values ​​of multiple monitoring dimensions of the current construction pilot tunnel area at the current acquisition time; the multiple monitoring dimensions include the surrounding rock convergence dimension, the existing structure deformation dimension, the initial support stress deformation dimension, the groundwater level dimension, and the arch settlement dimension. The estimation prediction module is used to predict the posterior estimate at the current acquisition time for each monitoring dimension based on the measured value of that monitoring dimension at the current acquisition time and the prior estimate at the current acquisition time. The risk feature quantity determination module is used to determine the risk feature quantity of the monitoring dimension at the current acquisition time based on the posterior estimate value at the current acquisition time. The risk level determination module is used to determine the risk level of the monitoring dimension at the current data collection time based on the risk characteristic quantity. The comprehensive risk determination module is used to determine the comprehensive risk level at the current data collection time based on the risk levels of each monitoring dimension. The sequence optimization module is used to obtain the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas if the comprehensive risk level and the risk characteristics of multiple monitoring dimensions meet the preset trigger conditions. Based on the risk characteristics of multiple monitoring dimensions corresponding to each of the current unfinished pilot tunnel areas, the initial support parameters, and the temporary structure support parameters, the module dynamically adjusts the excavation sequence of each pilot tunnel area to achieve optimized control of the pilot tunnel excavation sequence. The current unfinished pilot tunnel areas include the current construction pilot tunnel area.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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