A tunnel deep foundation pit deformation monitoring data wireless acquisition terminal and an acquisition method

Abstract

The present application relates to the technical field of construction engineering data processing, in particular to a tunnel deep foundation pit deformation monitoring data wireless acquisition terminal and acquisition method, comprising: obtaining the deformation data sequence and the vibration data sequence of the tunnel deep foundation pit at the current time; according to the difference distribution between the fluctuation of the vibration data of each time before and after, combining the vibration data of each time after the construction starts to obtain the construction influence index; according to the data difference between the deformation data sequence and the smooth deformation sequence, analyzing the change of the data difference after the construction stops to obtain the stable decay degree; according to the construction influence index, adjusting the preset deformation data acquisition frequency range to obtain the acquisition frequency base, using the stable decay degree and the acquisition frequency base to determine the data acquisition frequency, which is used for the wireless acquisition operation of the deformation monitoring data. The present application can adaptively adjust the data acquisition frequency and effectively monitor the settlement displacement data.

Description

Technical Field

[0001] This invention relates to the field of building engineering data processing technology, specifically to a wireless data acquisition terminal and method for monitoring deformation in deep foundation pits of tunnels. Background Technology

[0002] During the excavation and support of deep tunnel foundation pits, stress redistribution and rheological effects in the surrounding soil can occur. Insufficient monitoring can easily lead to risks such as excessive surface settlement, instability of the support structure, and damage to surrounding buildings or underground pipelines. Therefore, deformation monitoring is essential. By collecting and analyzing key indicators such as horizontal displacement, vertical settlement, and internal force changes in the foundation pit in real time, potential anomalies can be detected in a timely manner, providing early warnings and guidance for construction adjustments. This ensures construction safety, protects the surrounding environment and facilities, and provides reliable data support for the later operation and maintenance of the project.

[0003] During the deformation monitoring of deep foundation pits in tunnels, the soil undergoes slow and continuous plastic deformation under long-term loads and disturbances, resulting in soil rheology. This phenomenon is not only related to the soil's own properties (such as water content, structure, and compressibility) but also affected by the unloading during foundation pit excavation and construction disturbances. It manifests as the gradual settlement and horizontal displacement of the soil around and at the bottom of the foundation pit over time. Often, even after excavation, even if the external load no longer increases, the soil will continue to deform due to creep. Therefore, it is essential to pay attention to the soil rheological effect during monitoring in order to more accurately assess the deformation development trend and formulate effective support and control measures.

[0004] While existing deep foundation pit monitoring technologies can collect key data such as displacement, settlement, and internal forces, they often focus on monitoring short-term or instantaneous changes, and are insufficient in identifying the slow, long-term deformation response caused by soil rheology. Because rheological effects are nonlinear, gradual, and time-varying, conventional fixed-frequency sampling cannot capture their evolution patterns in a timely manner, which can easily lead to underestimation or misjudgment of potential risks, thereby affecting the accurate assessment of foundation pit stability and construction safety decisions. Summary of the Invention

[0005] To address the technical problem that existing methods for collecting deformation monitoring data in deep tunnel foundation pits use fixed frequencies and struggle to capture data variation patterns, this invention aims to provide a wireless data acquisition terminal and method for monitoring deformation in deep tunnel foundation pits. The specific technical solution adopted is as follows:

[0006] In a first aspect, the present invention provides a wireless data acquisition method for monitoring deformation in deep foundation pits of tunnels, comprising:

[0007] Obtain the deformation data sequence and vibration data sequence of the deep foundation pit of the tunnel at the current moment. The vibration data sequence consists of the vibration data of the deformation monitoring device at the current moment and each previous moment.

[0008] Based on the differences in the vibration data fluctuations before each moment and the vibration data fluctuations after each moment, combined with the distribution of vibration data at each moment after the start of construction, the construction-affected index at the current moment is obtained.

[0009] Based on the data difference between the deformation data sequence and the smoothed deformation sequence, the change of this data difference after construction stops is analyzed to obtain the stable attenuation level at the current moment; the smoothed deformation sequence is obtained by smoothing the deformation data sequence.

[0010] The preset deformation data acquisition frequency range is adjusted based on the construction impact index at the current moment to obtain the acquisition frequency base at the current moment. The data acquisition frequency at the current moment is determined using the stability attenuation degree and the acquisition frequency base. The data acquisition frequency is used for the wireless acquisition operation of deformation monitoring data.

[0011] Preferably, the step of obtaining the construction-affected index at the current moment based on the difference distribution between the vibration data fluctuations at each moment before each moment and the vibration data fluctuations at each moment after each moment, combined with the distribution of vibration data at each moment after the start of construction, specifically includes:

[0012] Based on the difference distribution between the vibration data fluctuations before each moment and the vibration data fluctuations after each moment, the probability of each moment being the start of construction is analyzed, and each moment is screened to determine the suspected construction moment.

[0013] Based on the fluctuations in vibration data between each suspected construction time and the current time, and combined with the vibration data at each suspected construction time, the degree of construction impact at each suspected construction time is obtained.

[0014] By utilizing the difference in probability between each suspected construction time and the previous adjacent suspected construction time, the construction impact of each suspected construction time is weighted and summed to obtain the construction impact index at the current time.

[0015] Preferably, the step of analyzing the probability that each moment is the start of construction based on the difference distribution between the vibration data fluctuations before each moment and the vibration data fluctuations after each moment, and screening each moment to determine the suspected construction moment, specifically includes:

[0016] The variance of vibration data at all times within a preset time period before the target time is used as the first fluctuation level at the target time; the variance of vibration data at all times within a preset time period after the target time is used as the second fluctuation level at the target time; where the target time is any time before the current time.

[0017] Based on the difference between the second and first fluctuation levels, the probability of the target time being the start time of construction is determined.

[0018] For each time point before the current time, the time point corresponding to the maximum value of the probability level is taken as the suspected construction time.

[0019] Preferably, the step of determining the degree of construction impact at each suspected construction time based on the fluctuations of vibration data between each suspected construction time and the current time, combined with the vibration data at each suspected construction time, specifically includes:

[0020] The minimum value of the second fluctuation degree at each time between the first and second time points is obtained as the duration coefficient. The vibration data at each second time point are weighted and summed using the duration coefficient to determine the degree of construction impact at the first time point.

[0021] The first time point is any suspected construction time point, and the second time point is any time point between the first time point and the current time point.

[0022] Preferably, the step of analyzing the change in the data difference between the deformation data sequence and the smooth deformation sequence after construction has stopped, and obtaining the stable attenuation level at the current moment, specifically includes:

[0023] The difference between the deformed data sequence and the smoothed deformed sequence at corresponding time points is used as the fluctuation detection data for each time point;

[0024] Based on the fluctuation of the fluctuation detection data within the time neighborhood at each time moment, and the changing trend of the fluctuation detection data at each time moment and adjacent time moments, the probability index for each time moment is obtained.

[0025] The moment corresponding to the maximum probability index among all moments when the probability index is greater than or equal to the preset probability threshold is taken as the construction stop moment.

[0026] Based on the fluctuation detection data at the moment construction stopped and the fluctuation detection data at the current moment, the degree of stability decay at the current moment is obtained.

[0027] Preferably, the step of obtaining the probability index for each moment based on the fluctuation of the fluctuation detection data within the time neighborhood at each moment, and the changing trend of the fluctuation detection data between each moment and adjacent moments, specifically includes:

[0028] The variance of all fluctuation detection data within the time neighborhood at the target time is obtained as the neighborhood fluctuation coefficient at the target time;

[0029] The difference between the neighborhood fluctuation coefficients between the target time and the adjacent previous time is taken as the degree of state change at the target time.

[0030] Based on the changing trend of the deformation data between each two adjacent time points between the target time and the current time, the trend sign coefficient of the target time is determined.

[0031] The normalized result of the product between the degree of state change and the trend sign coefficient is used as the probability index for the target time.

[0032] Preferably, the step of obtaining the stability attenuation level at the current moment based on the neighborhood fluctuations corresponding to the fluctuation detection data at the construction stop time and the neighborhood fluctuations corresponding to the fluctuation detection data at the current moment specifically includes:

[0033] The difference between the deformation data at the current moment and the deformation data at the adjacent previous moment is taken as the displacement change at the current moment. The stability attenuation at the current moment is determined based on the ratio between the displacement change at the current moment and the displacement at the moment construction stopped. The stability attenuation is a normalized value, and the ratio is negatively correlated with the stability attenuation.

[0034] Preferably, adjusting the preset deformation data acquisition frequency range based on the construction impact index at the current moment to obtain the acquisition frequency base at the current moment specifically includes:

[0035] The numerical length of the preset deformation data acquisition frequency range is adjusted using the construction impact index at the current moment to obtain the degree of numerical adjustment. The sum of the minimum acquisition frequency in the acquisition frequency range and the degree of numerical adjustment is determined as the acquisition frequency base at the current moment.

[0036] Preferably, determining the data acquisition frequency at the current moment using the stable attenuation level and the acquisition frequency base specifically includes:

[0037]

[0038] Where S represents the data acquisition frequency at the current moment, R' represents the base value of the acquisition frequency at the current moment, and R min This represents the minimum acquisition frequency within the preset range of deformation data acquisition frequencies, and Q represents the degree of stable attenuation at the current moment.

[0039] Secondly, the present invention provides a wireless acquisition terminal for monitoring deformation data of deep foundation pits in tunnels, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the computer program is executed by the processor, it implements the steps of a method for wirelessly acquiring data for monitoring deformation of deep foundation pits in tunnels.

[0040] The embodiments of the present invention have at least the following beneficial effects:

[0041] This invention first collects deformation and vibration data during the deep foundation pit deformation monitoring process. By analyzing the fluctuation differences in vibration data before and after each moment, the degree to which each moment matches the characteristics at the start of construction is analyzed. Combined with the distribution of vibration data, the intensity of the impact of construction on conditions prior to the current moment is comprehensively assessed. Furthermore, as stress is gradually released and the soil consolidates, the rate of creep gradually decreases, entering a long-term slow creep stage. The changes in this data difference after construction stops are analyzed to assess the degree of stable decay due to conditions prior to construction cessation. Finally, the acquisition frequency of the laser rangefinder is adaptively adjusted based on the degree of impact of construction on soil rheology and the attenuation of the acquisition frequency, achieving effective monitoring of settlement and displacement data. Attached Figure Description

[0042] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of the steps of a wireless data acquisition terminal and acquisition method for monitoring deformation in deep foundation pits in tunnels, provided by the present invention.

[0044] Figure 2 This is a flowchart of the steps for obtaining the construction impact index at the current moment provided by the present invention;

[0045] Figure 3 This is a flowchart of the steps of the method for obtaining the stable attenuation level at the current moment provided by the present invention. Detailed Implementation

[0046] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a wireless data acquisition terminal and method for monitoring deformation in deep foundation pits in tunnels according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] The following description, in conjunction with the accompanying drawings, details the specific scheme of the wireless data acquisition terminal and acquisition method for monitoring deformation in deep foundation pits in tunnels provided by this invention.

[0049] Please see Figure 1 The diagram illustrates a flowchart of a wireless data acquisition terminal and method for monitoring deformation in deep foundation pits in tunnels, according to an embodiment of the present invention. The method includes the following steps:

[0050] Step S100: Obtain the deformation data sequence and vibration data sequence of the deep foundation pit of the tunnel at the current moment. The vibration data sequence consists of the vibration data of the deformation monitoring device at the current moment and each previous moment.

[0051] In the process of monitoring soil rheological phenomena, laser rangefinders and differential pressure hydrostatic levels are commonly used. This embodiment mainly uses a laser rangefinder as an example to adjust the acquisition frequency of the laser rangefinder.

[0052] Laser rangefinders are used to collect ranging data from deep foundation pits in tunnels, enabling subsequent analysis of the pit's deformation. Generally, the placement of the laser rangefinder needs to be determined by relevant personnel based on the specific implementation scenario; for example, the inner side of the retaining wall facing a reflective target, or the top of the support piles or anchor bolts for target measurement.

[0053] Vibration sensors are used to collect vibration data around the deformation monitoring location to facilitate subsequent analysis of vibrations generated during construction and their impact on distance measurement data acquisition. Generally, vibration sensors are mounted on rigid bases around the laser rangefinder, such as on the retaining structure or top of support piles near the rangefinder, with a sampling frequency of 200Hz. Since the vibration data is only used to determine the construction status, only data from the past day is retained, thus reducing the burden on data storage.

[0054] In this embodiment, the ranging data from the current moment and all previous moments are used as deformation data to form a deformation data sequence, and the vibration data from the deformation monitoring device at the current moment and all previous moments are used to form a vibration data sequence, wherein the deformation monitoring device is a laser rangefinder. The time length corresponding to the vibration data sequence is 24 hours.

[0055] Step S200: Based on the difference distribution between the vibration data fluctuations before each moment and the vibration data fluctuations after each moment, and combined with the distribution of vibration data at each moment after the start of construction, the construction-affected index at the current moment is obtained.

[0056] This step mainly analyzes the impact of construction activities prior to the current moment on soil rheology and quantifies the degree to which the deformation monitoring process is affected by construction.

[0057] Construction processes such as excavation, pile driving, blasting, and compaction disrupt the original stress balance of the soil, accompanied by vibration, unloading, and changes in pore water pressure. These factors can cause soil particles to rearrange, pore structure to be damaged, or water-soil coupling to intensify, resulting in faster plastic deformation and stress relaxation of the soil under the same external load. This manifests as an acceleration of the soil rheological process. Therefore, by analyzing the fluctuation of vibration data during the monitoring period, we can assess the characteristics of vibration data under possible construction conditions and quantify the degree of impact that may affect the deformation monitoring process.

[0058] In this regard, such as Figure 2 As shown, the method for obtaining the construction impact index at the current moment can be implemented by steps S201 to S203.

[0059] Step S201: Based on the difference distribution between the vibration data fluctuations before each moment and the vibration data fluctuations after each moment, analyze the probability that each moment is the start of construction, screen each moment, and determine the suspected construction moment.

[0060] Construction activities will generate significant mechanical disturbances around the foundation pit, which will manifest as continuous fluctuations in vibration data. Therefore, the degree of fluctuation in vibration data can be used to determine whether construction is underway at certain times.

[0061] Specifically, the first step is to obtain the variance of vibration data for all moments within a preset time period before the target moment as the first degree of fluctuation at the target moment; and to obtain the variance of vibration data for all moments within a preset time period after the target moment as the second degree of fluctuation at the target moment.

[0062] The target time is any time before the current time. The first fluctuation level is used to characterize the fluctuation of vibration data before the target time, reflecting the degree of construction suspension at the target time; the second fluctuation level is used to characterize the fluctuation of vibration data after the target time, reflecting the degree of construction continuation after the target time.

[0063] More specifically, in this embodiment, the time window length for monitoring vibration data is set to 5 minutes. This means that vibration data from the 5 minutes before the target time is acquired for feature analysis, and vibration data from the 5 minutes after the target time is acquired for feature analysis. It should be understood that feature analysis is not performed on moments when it is impossible to acquire complete 5-minute intervals of vibration data before and after the target time.

[0064] Because vibration data exhibits drastic fluctuations after construction begins, and shows relatively small fluctuations and tends to stabilize before construction begins, there will be significant differences in the fluctuations of vibration data before and after construction begins.

[0065] Therefore, by comparing the vibration data fluctuations before and after the target time, we can analyze whether the target time is a point in time when the vibration data fluctuation state changes.

[0066] The second step is to determine the probability of the target time being the start time of construction based on the difference between the second and first fluctuation levels.

[0067] The difference between the second and first fluctuation levels corresponding to the target time is normalized to obtain the probability that the target time is the start time of construction. The normalization process can be performed using the minimax normalization method, which will not be elaborated upon here.

[0068] The greater the difference between the second and first fluctuation levels, the greater the data fluctuation in the vibration data during the period after the target time. Furthermore, the fluctuation characteristics of the vibration data after the target time are greater than those of the vibration data between the target times. In this case, the data fluctuation at the target time is more consistent with the data characteristics at the start of construction, which indicates that the target time is more likely to be the start of construction. Therefore, the corresponding probability value is greater.

[0069] The smaller the difference between the second and first fluctuation levels, the less significant the data fluctuation state of the vibration data has changed in the time period after the target time. In this case, the data fluctuation state at the target time does not conform to the data characteristics at the start of construction, which means that the target time is less likely to be the start of construction, and the corresponding probability value is smaller.

[0070] The third step is to determine the probability of each time point before the current time, and then take the time point corresponding to the maximum probability value as the suspected construction time.

[0071] For each time point preceding the current moment, by filtering local extrema, we determine the time nodes most likely to be the start time of construction, providing a data foundation for the subsequent characteristic analysis of the construction impact. It should be noted that the method for obtaining the maxima is a well-known technique and will not be elaborated upon further here.

[0072] Step S202: Based on the fluctuation of vibration data between each suspected construction time and the current time, and combined with the vibration data of each suspected construction time, obtain the degree of construction impact at each suspected construction time.

[0073] Construction activities often introduce continuous dynamic disturbances into the soil, manifested in vibration data as increased values ​​and prolonged fluctuations. When large and persistent vibration values ​​are detected, it indicates ongoing or high-intensity construction. This dynamic disturbance disrupts the soil structure, alters pore water pressure distribution, and accelerates soil rheological processes. Based on this characteristic, by analyzing the numerical distribution and persistence of vibration data at each suspected construction moment, the impact of construction activities on soil rheology is assessed, thereby quantifying the influence of construction activities on deformation monitoring operations.

[0074] Specifically, the minimum value of the second fluctuation degree at each time point between the first and second time points is obtained as the persistence coefficient. The vibration data at each second time point are then weighted and summed using the persistence coefficient to determine the degree of construction impact at the first time point. Here, the first time point is any suspected construction time point, and the second time points are each time point between the first time point and the current time point.

[0075] As a concrete example, taking the c-th suspected construction time as the first time, and the time between the first time and the current time containing multiple corresponding second times, taking the b-th second time as an example, the method for obtaining the degree of construction impact at the first time can be expressed by the formula:

[0076]

[0077] Among them, D c This represents the degree of construction impact at the c-th suspected construction time, where c represents the c-th suspected construction time, also known as the first time. CZ' c,b Z represents the minimum value of the second fluctuation degree between the c-th suspected construction time and the corresponding b-th second time, which is also the persistence coefficient; c,bThis represents the vibration data at the b-th second moment corresponding to the c-th suspected construction moment; M c This represents the total number of second moments corresponding to the c-th suspected construction moment, and Norm is the normalization function. For example, the minimization normalization method can be selected.

[0078] Duration coefficient CZ' c,b The larger the value of the continuity coefficient, the more likely the construction is still ongoing at the b-th second time point after the first time point. In other words, during the time interval from the first time point to the b-th second time point, construction is uninterrupted and the soil is continuously disturbed, which needs to be taken into account as a key factor. The smaller the value of the continuity coefficient, the more likely the construction is interrupted, and the lower the corresponding impact weight.

[0079] Vibration data Z c,b It indicates the magnitude of vibration generated during construction at a corresponding moment, directly reflecting the intensity of construction disturbance. The larger the vibration data, the more drastic the changes in soil particle arrangement and pore water pressure, and the more obvious the acceleration of soil rheology.

[0080] The degree of construction impact at the first moment comprehensively reflects the total disturbance intensity of construction continuity and vibration intensity on soil rheology after the suspected construction moment has started.

[0081] Step S203: Using the difference in probability between each suspected construction time and the previous adjacent suspected construction time, the construction impact degree of each suspected construction time is weighted and summed to obtain the construction impact index at the current time.

[0082] The current construction impact index is a core indicator that quantifies the overall degree of soil impact from construction activities by comprehensively considering the interference intensity of multiple suspected construction moments. It provides a crucial basis for subsequently adaptively determining the data acquisition frequency. A higher degree of construction impact indicates more significant stress disturbance and plastic deformation in the soil due to construction, requiring a higher initial acquisition frequency to capture deformation risks. Conversely, a lower degree of construction impact allows for a lower acquisition frequency, balancing monitoring accuracy with equipment energy consumption and data storage pressure.

[0083] Furthermore, considering the spatial nature of the impact of construction activities on soil rheology, construction disturbances occurring closer to the site are generally more significant than those occurring further away. Simultaneously, the reliability of construction initiation and the intensity of disturbance differ at different suspected construction times. Therefore, the calculation of the construction impact index achieves accurate quantification of the total impact of current construction by screening valid times, assigning reasonable weights, and accumulating the total impact.

[0084] As a concrete example, the method for obtaining the construction impact index at the current moment can be expressed by the formula:

[0085]

[0086] Where F represents the construction impact index at the current moment, and G c G represents the probability of the c-th suspected construction time. c-1 D represents the probability of the (c-1)th suspected construction time. c The c-th suspected construction time represents the degree of construction impact, N0 represents the number of suspected construction times before the current time, ReLU is the activation function, and softmax is the normalization exponential function.

[0087] ReLU(G c -G c-1 ) represents max(0,G c -G c-1 ), where max represents the function that takes the maximum value, when G c -G c-1 When ≤0, ReLU(G) c -G c-1 If G = 0, it indicates that the credibility of the construction start time at the c-th suspected construction time is low, and it may be an invalid time node in the distant future or a misjudged time node. c -G c-1 When >0, ReLU(G) c -G c-1 ) = G c -G c-1 This indicates that the credibility of the construction start at the cth suspected construction moment is higher, and the degree of influence on the current soil condition is more credible.

[0088] The difference in the probability of two adjacent suspected construction moments is processed using the ReLU activation function, retaining only results with positive differences and discarding those with negative differences. This filters out suspected construction moments with low credibility and poor timeliness, preventing their corresponding construction impact from interfering with the overall index calculation results.

[0089] By weighting the impact of suspected construction times based on their probability, a higher-confidence suspected construction time contributes to the total impact of the current time. The final weighted summation achieves a coupling between timeliness and interference intensity.

[0090] Step S300: Based on the data difference between the deformation data sequence and the smoothed deformation sequence, analyze the change of the data difference after construction stops to obtain the stable attenuation level at the current moment; the smoothed deformation sequence is obtained by smoothing the deformation data sequence.

[0091] In deep foundation pit deformation monitoring in tunnels, the adaptive adjustment of the laser rangefinder's acquisition frequency needs to simultaneously match the monitoring requirements of both the construction disturbance phase and the post-construction stable creep phase. During construction, due to severe soil disturbance, a high acquisition frequency baseline needs to be set based on the construction impact index to capture instantaneous deformation risks. After construction, the soil layer enters a long-term slow creep phase as stress is released and consolidated. Maintaining a high acquisition frequency at this time not only wastes equipment energy and increases data storage pressure but also increases the complexity of subsequent deformation analysis. However, determining when and by how much to reduce the acquisition frequency requires precise judgment of the degree to which the soil layer has entered the stable creep phase, i.e., the degree of stable creep decay at the current moment. This indicator directly reflects the decay of the current soil deformation rate relative to the time of construction completion. It is crucial to reasonably reduce the acquisition frequency while ensuring that no slow creep deformation data is missed, thus achieving a balance between monitoring accuracy and resource utilization efficiency.

[0092] In this regard, such as Figure 3 As shown, the method for obtaining the stable decay level at the current moment can be implemented by steps S301 to S304.

[0093] Step S301: The data difference between the deformed data sequence and the smoothed deformed sequence at the corresponding time is used as the fluctuation detection data at each time.

[0094] The higher the construction-affected index at the current moment, the more frequent the sampling should be after construction to effectively monitor settlement and displacement. However, as the long-term slow creep phase begins, the monitoring focus shifts from capturing instantaneous changes to tracking slow, gradual displacement accumulation. At this point, the sampling frequency can be appropriately reduced to save energy, reduce data storage pressure, and decrease the complexity of subsequent deep tunnel foundation pit deformation analysis. Therefore, the construction-affected index at the current moment determines the initial sampling frequency after construction, which is the upper limit of the subsequent long-term slow creep phase. The sampling frequency needs to be reduced based on the rate of change of the distance measurement time series.

[0095] During construction, due to sudden stress changes and soil structural disturbances, the settlement displacement rate often increases rapidly, resulting in violent fluctuations. After construction, as stress is gradually released and the soil consolidates, the settlement displacement rate gradually decreases, entering a long-term, slow creep stage, characterized by a near-stable, low-rate continuous deformation. Therefore, fluctuation detection data is a crucial basis for determining whether construction is complete and whether the soil layer has entered a stable creep stage, directly providing data support for subsequent data calculations.

[0096] To address this, we first conduct a volatility analysis by examining the historical deformation monitoring processes prior to the current moment to assess whether the current moment is in a long-term, slow creep phase.

[0097] In this embodiment, a simple moving average method is used to smooth the deformation data sequence, resulting in a smoothed deformation sequence. It should be understood that the smoothed deformation sequence filters out short-term fluctuations and reflects the true trend of soil deformation. By calculating the difference between the deformation data sequence and the smoothed deformation sequence at the same time point, the trend can be removed while retaining the data volatility, yielding the volatility detection data for each time point. During construction, the volatility detection data values ​​are larger, while after construction, the values ​​are smaller.

[0098] Step S302: Based on the fluctuation of the fluctuation detection data within the time neighborhood at each time point, and the changing trend of the fluctuation detection data at each time point and adjacent time points, obtain the probability index for each time point.

[0099] In this embodiment, a time window of a preset time length is used as the time neighborhood range of each time point, with each time point as the center. The preset time length can be 11 time points, and there is no limitation here. The implementer can set an appropriate length for feature analysis according to the specific implementation scenario.

[0100] In the data processing of deformation monitoring for deep foundation pits in tunnels, the probability index of each time point before the current time represents the probability of construction completion at that corresponding moment. It is a core indicator for quantifying the reliability of that moment as the completion point of construction and can be used to identify the actual completion time of the most recent construction. The two core characteristics of construction completion are: first, the disappearance of construction interference, leading to a sharp drop in the intensity of fluctuations in the detected data; and second, the soil layer transitioning from disturbed, unstable deformation to slow, stable creep, which is reflected in a monotonic trend in the deformation data. Therefore, these two characteristics are used to analyze and measure the probability that each time point before the current time belongs to the construction completion point.

[0101] The first step is to obtain the variance of all fluctuation detection data within the time neighborhood of the target time as the neighborhood fluctuation coefficient of the target time, and to take the difference between the neighborhood fluctuation coefficients of the target time and the previous adjacent time as the degree of state change of the target time.

[0102] First, the target time represents any time before the current time. The neighborhood fluctuation coefficient reflects the degree of data volatility at that time, representing the degree of construction completion at that time. The degree of state change at the target time represents the abrupt change in the degree of construction completion. If construction ends at the target time, the volatility of the fluctuation detection data will decrease, the degree of construction completion (i.e., the neighborhood fluctuation coefficient) will drop sharply, and the degree of state change will increase significantly, more directly indicating that the target time may be the end point of construction.

[0103] The second step is to determine the trend sign coefficient of the target time based on the changing trend of the deformation data between every two adjacent time points between the target time and the current time; and to use the normalized result of the product between the degree of state change and the trend sign coefficient as the probability index of the target time.

[0104] As a concrete example, if we take the t-th time as the target time, the probability index of the target time can be obtained by the following formula:

[0105]

[0106] Among them, H t K represents the probability index at time t, where t represents time t, which is also the target time; t M represents the degree of state change at time t. t,d-1 M represents the deformed data at time d-1 between time t and the current time. t,d L represents the deformed data at time d between time t and the current time. t This represents the total number of times between the t-th time and the current time. Norm is the normalization function, which can be achieved using the minimax normalization method.

[0107] This represents the sum of deformation data differences between all adjacent time points after the target time. If the soil layer enters the stable creep stage, the deformation exhibits a slow and monotonic trend. If it continues to be submerged, the deformation data difference continues to decrease, and the deformation data difference between adjacent time points is M. t,d-1 -M t,d The signs will remain consistent, for example, all positive. The absolute value of the sum will be large. If there is still construction interference, the difference M will be... t,d-1 -M t,d The sign will change randomly, and the absolute value of the sum will be smaller. It is the sum of the absolute values ​​of the corresponding differences, representing the total magnitude of deformation during the period from the target time to the current time.

[0108] Trend sign coefficient It characterizes the degree to which the deformation data exhibits a monotonic trend after the target time. The larger the value, the more stable and unfluctuating the monotonic trend is after the target time, and thus the greater the possibility of being in a long-term slow creep stage.

[0109] Step S303: Among all the times when the probability index is greater than or equal to the preset probability threshold, the time corresponding to the maximum value of the probability index is taken as the construction stop time.

[0110] The degree of state change is analyzed by examining the differences in the degree of construction completion between adjacent time points, reflecting the abrupt change in the degree of construction completion. The trend sign coefficient is calculated by examining the changes in fluctuation detection data between adjacent time points, reflecting the deformation rate and trend continuity. When the results of both analyses are large, the corresponding time point is more likely to be the time when construction is completed.

[0111] In this embodiment, the probability threshold is set to 0.7, which can be adjusted by the implementer according to the specific implementation scenario. The threshold is used to filter out moments with a higher probability, and the moment with the highest probability, i.e., the maximum probability index, is taken as the construction stop moment, indicating that construction is complete.

[0112] If there were no instances where the probability index was greater than or equal to the probability threshold in all previous timeframes, it means that there were no construction stop times before the current time, and that the construction is still ongoing at the current time.

[0113] Step S304: Based on the neighborhood fluctuation situation corresponding to the fluctuation detection data at the construction stop time and the neighborhood fluctuation situation corresponding to the fluctuation detection data at the current time, obtain the stability attenuation level at the current time.

[0114] In the initial stage after construction, the soil layer is still affected by the previous disturbance, and the deformation rate is relatively fast, so a high acquisition frequency needs to be maintained. As the soil layer stress is gradually released and consolidation is completed, the deformation rate will gradually decrease and enter a long-term slow creep stage. At this time, the attenuation range can be judged by the degree of stable creep attenuation, and the acquisition frequency can be reasonably reduced. While ensuring the capture of slow creep data, the energy consumption of equipment, the pressure of data storage and the complexity of subsequent analysis are reduced.

[0115] The degree of stable attenuation is used to quantify the extent to which the soil layer transitions from a rapid deformation state to a long-term slow creep state after construction, providing a quantitative basis for reducing the sampling frequency after construction.

[0116] Specifically, the difference between the deformation data at the current moment and the deformation data at the adjacent previous moment is taken as the displacement change at the current moment. The stability attenuation at the current moment is determined based on the ratio between the displacement change at the current moment and the displacement at the moment construction stops. The stability attenuation is a normalized value, and the ratio is negatively correlated with the stability attenuation.

[0117] As a concrete example, the method for obtaining the degree of stable decay at the current moment can be expressed by the formula:

[0118]

[0119] Where Q represents the degree of stable decay at the current moment, P represents the degree of displacement change at the current moment, P' represents the degree of displacement change at the moment construction stops, Norm is the normalization function, and min is the minimization function. When the ratio... When it is greater than 1, it passes through the minimum value function. The ratio, with a value no greater than 1, represents the deviation of the displacement change between the current moment and the moment construction stopped. A larger value indicates a greater degree of change in deformation data at the current moment, and a lower probability that the current moment is in a slow, stable creep stage; therefore, the corresponding value for the stable decay degree is smaller. Conversely, a smaller ratio indicates that the deformation data at the current moment is closer to the deformation data at the moment construction stopped, and a higher probability that the current moment is in a slow, stable creep stage; therefore, the corresponding value for the stable decay degree is smaller. The stable decay degree at the current moment represents the probability that the soil layer has entered a stable creep stage.

[0120] The degree of displacement change at the current moment represents the actual deformation rate of the soil layer, directly reflecting the current level of creep activity. The larger the value, the more intense the deformation and the further away from the stable creep state. The degree of displacement change at the moment construction stops represents the benchmark value for judging the attenuation of the deformation rate. It represents the initial deformation rate of the soil layer immediately after construction ends and serves as a reference standard for subsequently measuring the degree of attenuation.

[0121] It should be noted that the calculation method for the degree of displacement change at the moment construction stops is the same as that for the degree of displacement change at the current moment. That is, the difference between the deformation data at the moment construction stops and the adjacent previous moment is taken as the degree of displacement change at the moment construction stops. It should be understood that deformation data refers to the data in the deformation data sequence, with each moment corresponding to one deformation data point.

[0122] It should also be noted that this embodiment only uses a single deep tunnel foundation pit as an example. When multiple foundation pits exist, in order to avoid inconsistencies in data measurement results, it is necessary to... Normalization is performed, and the normalization method can be either minimization or other methods. There are no restrictions here, and the implementer can choose according to the specific implementation scenario.

[0123] Step S400: Adjust the preset deformation data acquisition frequency range according to the construction impact index at the current moment to obtain the acquisition frequency base at the current moment. Use the stability attenuation degree and the acquisition frequency base to determine the data acquisition frequency at the current moment. The data acquisition frequency is used for the wireless acquisition operation of deformation monitoring data.

[0124] In the adaptive acquisition system for monitoring deformation in deep foundation pits of tunnels, the acquisition frequency is set to match the actual state of whether the construction activity has started or ended. This is mainly achieved by analyzing the characteristics of the current moment's construction impact index and the degree of stability attenuation, combined with the minimum and maximum acquisition frequencies of the laser rangefinder.

[0125] In this regard, when analyzing the various characteristics prior to the current moment, there are generally three scenarios: no construction start time, no construction stop time, and both construction start time and construction stop time exist simultaneously. The following is an introduction to the schemes for adaptively setting the corresponding acquisition frequency for the three different scenarios.

[0126] Firstly, the absence of a construction start point means that no construction activities that would disturb the soil (such as excavation, pile driving, or blasting) have been carried out in the foundation pit. The soil layer is in a natural initial state (without stress redistribution or accelerated plastic deformation caused by construction), and only extremely slow natural creep is possible. The deformation rate is stable and minimal, so high-frequency data acquisition is not required.

[0127] Therefore, if there is no construction start time before the current time, the data acquisition frequency is set to the minimum acquisition frequency.

[0128] It should be noted that the preset range of deformation data collection frequency can be set by relevant staff based on experience, or it can be obtained from the historical collection process in the database. For example, the minimum collection frequency is 1 hour / time, and the maximum collection frequency is 1Hz.

[0129] In this scenario, the main idea behind setting the data acquisition frequency is that in the initial stage without construction disturbance, low-frequency acquisition can meet the needs of natural creep monitoring, while minimizing equipment energy consumption and data storage pressure, and avoiding resource waste.

[0130] Secondly, the existence of a construction start point but no construction stop point indicates that the foundation pit is still in the continuous construction phase. Construction activities (such as continuous excavation and pile driving) are constantly disrupting the soil stress balance, leading to accelerated soil rheology, large deformation rate and violent fluctuations. High-frequency data acquisition is required to capture instantaneous deformation risks (such as instability of the support structure and sudden settlement).

[0131] At this point, we can analyze the construction impact index corresponding to the construction start time before the current moment. Then, based on the degree of construction impact, we can adjust the preset deformation data acquisition frequency range to obtain the acquisition frequency base for the current moment. Since there is no construction stop time, we do not perform a stability attenuation analysis; that is, we do not need to further adjust the acquisition frequency base, and directly use the acquisition frequency base as the data acquisition frequency for data acquisition.

[0132] In this scenario, the system adaptively determines whether to use a high-frequency or low-frequency sampling frequency based on the intensity of the current impact from construction. When the impact intensity is high, high-frequency sampling is maintained to ensure that no instantaneous deformation during construction is missed. The continuous construction phase is the period with the highest deformation risk; high-frequency sampling can capture sudden displacement changes caused by construction disturbances in real time, providing data support for safety warnings (such as support adjustments and construction pauses), and avoiding misjudgments of risk due to excessively long sampling intervals.

[0133] It should be noted that the method for obtaining the sampling frequency base will be described in detail in the subsequent feature analysis process (section 3), and will not be elaborated on here.

[0134] Thirdly, there are both the start and stop times of construction. The open foundation pit has been completed and has entered the post-construction stage. That is, the soil layer gradually transitions from rapid deformation caused by construction disturbance to long-term slow creep. The deformation rate continues to decrease with stress release and soil consolidation. The sampling frequency needs to be dynamically adjusted in combination with the construction impact index and the degree of attenuation stability at the current moment.

[0135] In this scenario, the initial high-frequency foundation after construction is first determined by the construction impact index. Then, the sampling frequency is adjusted by a certain amount based on the stability attenuation level to achieve a balance between accuracy and efficiency. That is, the higher the construction impact index, the stronger the disturbance to the soil, and more obvious rapid settlement and displacement may occur in the initial stage after construction. Therefore, a high sampling frequency needs to be set as the starting point. However, as the soil layer gradually transitions to a long-term slow creep stage, the settlement and displacement rate will gradually decrease. At this time, the sampling frequency can be dynamically reduced based on the rate change of deformation data collected by the laser rangefinder, so that the sampling frequency gradually falls from the initial high value to a reasonable lower limit, thereby taking into account both monitoring accuracy and resource utilization efficiency.

[0136] The first step is to adjust the preset deformation data collection frequency range based on the construction impact index at the current moment to obtain the current collection frequency base.

[0137] Specifically, the numerical length of the preset deformation data acquisition frequency range is adjusted using the construction impact index at the current moment to obtain the degree of numerical adjustment. The sum of the minimum acquisition frequency in the acquisition frequency range and the degree of numerical adjustment is determined as the acquisition frequency base at the current moment.

[0138] As a concrete example, the method for obtaining the current sampling frequency base can be expressed by the formula:

[0139] R′=R min +(R max -R min )×F

[0140] In the formula, R' represents the base number of the sampling frequency at the current moment; Rmin R represents the minimum acquisition frequency within the preset range of deformation data acquisition frequencies; max The maximum sampling frequency represents the preset range of deformation data sampling frequency; F is the construction-affected index at the current moment.

[0141] The sampling frequency range is linearly allocated based on the degree of construction impact. The higher the construction impact index, the closer the sampling frequency base is to the maximum sampling frequency, matching the need to capture instantaneous deformation under strong construction disturbances. The lower the construction impact index, the closer the sampling frequency base is to the minimum sampling frequency, adapting to scenarios with weak or no construction disturbances.

[0142] The data acquisition frequency base characterizes the degree of disturbance to soil rheology caused by quantified construction, determines the basic monitoring frequency in the initial stage or continuous construction stage after construction, and can adaptively determine the data acquisition frequency based on the intensity of construction disturbances experienced before the current moment, ensuring that no severe deformations caused by construction are missed.

[0143] The second step is to determine the data acquisition frequency at the current moment using the stability attenuation level and the acquisition frequency base.

[0144] As a concrete example, the method for obtaining the data acquisition frequency at the current moment can be expressed by the formula:

[0145]

[0146] Where S represents the data acquisition frequency at the current moment, R' represents the base value of the acquisition frequency at the current moment, and R min This represents the minimum acquisition frequency within the preset range of deformation data acquisition frequencies, and Q represents the degree of stable attenuation at the current moment.

[0147] The data acquisition frequency at the current moment is mainly adjusted based on the stable creep decay level at the current moment to target the acquisition frequency during the long-term slow creep phase after construction is completed. Specifically, the acquisition frequency is reduced based on the baseline acquisition frequency.

[0148] Through R'-R min The adjustable range of the acquisition frequency is determined. This range represents the redundancy of the initial acquisition frequency reference R' beyond the minimum monitoring requirements. The adjustable range is linearly allocated with the degree of stable attenuation as the weight.

[0149] A larger value for the stability attenuation level Q corresponds to a greater reduction in frequency, resulting in a data acquisition frequency closer to low frequencies, thus meeting the monitoring needs after soil stabilization. Conversely, a smaller value for Q corresponds to a smaller reduction in frequency, resulting in a data acquisition frequency closer to high frequencies, maintaining the monitoring intensity for unstabilized soil layers. This allows for adaptive adjustment of the acquisition frequency, enabling effective monitoring of settlement and displacement data.

[0150] In summary, this embodiment of the invention acquires the start time of construction and quantifies the impact of construction on soil rheology based on the vibration amplitude and duration of the construction stage represented by the start time. As stress is gradually released and the soil consolidates, the rate gradually decreases, entering a long-term slow creep stage. The construction stop time is acquired, and the soil rheology rate after the construction stop time is compared with the soil rheology rate at the current time to obtain the attenuation of the current acquisition frequency. Then, the acquisition frequency of the laser rangefinder is adaptively adjusted based on the degree of impact of construction on soil rheology and the attenuation of the acquisition frequency, thereby achieving effective monitoring of settlement displacement data.

[0151] This invention also provides a wireless data acquisition terminal for monitoring deformation in deep tunnel foundation pits, including a memory, a processor, and a computer program stored in the memory and running on the processor. When executed by the processor, the computer program implements the steps of a method for wirelessly acquiring data for monitoring deformation in deep tunnel foundation pits. Since an embodiment of a method for wirelessly acquiring data for monitoring deformation in deep tunnel foundation pits has already been described in detail, further elaboration will not be repeated here.

[0152] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for wirelessly acquiring deformation monitoring data of deep foundation pits in tunnels, characterized in that, The method includes the following steps: Obtain the deformation data sequence and vibration data sequence of the deep foundation pit of the tunnel at the current moment. The vibration data sequence consists of the vibration data of the deformation monitoring device at the current moment and each previous moment. Based on the differences in the vibration data fluctuations before each moment and the vibration data fluctuations after each moment, combined with the distribution of vibration data at each moment after the start of construction, the construction-affected index at the current moment is obtained. Based on the data difference between the deformation data sequence and the smoothed deformation sequence, the change of this data difference after construction stops is analyzed to obtain the stable attenuation level at the current moment; the smoothed deformation sequence is obtained by smoothing the deformation data sequence. The preset deformation data acquisition frequency range is adjusted based on the construction impact index at the current moment to obtain the acquisition frequency base at the current moment. The data acquisition frequency at the current moment is determined using the stability attenuation degree and the acquisition frequency base. The data acquisition frequency is used for wireless acquisition of deformation monitoring data. The method for obtaining the construction impact index at the current moment includes: Based on the difference distribution between the vibration data fluctuations before each moment and the vibration data fluctuations after each moment, the probability of each moment being the start of construction is analyzed, and each moment is screened to determine the suspected construction moment. Based on the fluctuations in vibration data between each suspected construction time and the current time, and combined with the vibration data at each suspected construction time, the degree of construction impact at each suspected construction time is obtained. By utilizing the difference in probability between each suspected construction time and the previous adjacent suspected construction time, the construction impact of each suspected construction time is weighted and summed to obtain the construction impact index at the current time.

2. The wireless acquisition method for monitoring deformation of deep foundation pits in tunnels according to claim 1, characterized in that, The method involves analyzing the probability of each moment being the start of construction based on the difference distribution between the vibration data fluctuations before and after each moment, and then screening each moment to determine the suspected construction moment. Specifically, this includes: The variance of vibration data at all times within a preset time period before the target time is used as the first fluctuation level at the target time; the variance of vibration data at all times within a preset time period after the target time is used as the second fluctuation level at the target time; where the target time is any time before the current time. Based on the difference between the second and first fluctuation levels, the probability of the target time being the start time of construction is determined. For each time point before the current time, the time point corresponding to the maximum value of the probability level is taken as the suspected construction time.

3. The wireless acquisition method for monitoring deformation of deep foundation pits in tunnels according to claim 2, characterized in that, The method involves analyzing the vibration data fluctuations between each suspected construction moment and the current moment, and combining this data with the vibration data from each suspected construction moment to determine the degree of construction impact at each suspected construction moment. Specifically, this includes: The minimum value of the second fluctuation degree at each time between the first and second time points is obtained as the duration coefficient. The vibration data at each second time point are weighted and summed using the duration coefficient to determine the degree of construction impact at the first time point. The first time point is any suspected construction time point, and the second time point is any time point between the first time point and the current time point.

4. The wireless acquisition method for monitoring deformation of deep foundation pits in tunnels according to claim 2, characterized in that, The step of analyzing the changes in the data difference between the deformation data sequence and the smooth deformation sequence after construction has stopped, and obtaining the degree of stable attenuation at the current moment, specifically includes: The difference between the deformed data sequence and the smoothed deformed sequence at corresponding time points is used as the fluctuation detection data for each time point; Based on the fluctuation of the fluctuation detection data within the time neighborhood at each time moment, and the changing trend of the fluctuation detection data at each time moment and adjacent time moments, the probability index for each time moment is obtained. The moment corresponding to the maximum probability index among all moments when the probability index is greater than or equal to the preset probability threshold is taken as the construction stop moment. Based on the fluctuation detection data at the moment construction stopped and the fluctuation detection data at the current moment, the degree of stability decay at the current moment is obtained.

5. The wireless acquisition method for monitoring deformation of deep foundation pits in tunnels according to claim 4, characterized in that, The process of obtaining a probability index for each moment based on the fluctuation of the fluctuation detection data within the time neighborhood at each moment, and the changing trend of the fluctuation detection data between each moment and adjacent moments, specifically includes: The variance of all fluctuation detection data within the time neighborhood at the target time is obtained as the neighborhood fluctuation coefficient at the target time; The difference between the neighborhood fluctuation coefficients between the target time and the adjacent previous time is taken as the degree of state change at the target time. Based on the changing trend of the deformation data between each two adjacent time points between the target time and the current time, the trend sign coefficient of the target time is determined. The normalized result of the product between the degree of state change and the trend sign coefficient is used as the probability index for the target time.

6. The wireless acquisition method for monitoring deformation of deep foundation pits in tunnels according to claim 5, characterized in that, The process of obtaining the stability attenuation level at the current moment based on the neighborhood fluctuations corresponding to the fluctuation detection data at the construction stop time and the neighborhood fluctuations corresponding to the fluctuation detection data at the current moment specifically includes: The difference between the deformation data at the current moment and the deformation data at the adjacent previous moment is taken as the displacement change at the current moment. The stability attenuation at the current moment is determined based on the ratio between the displacement change at the current moment and the displacement at the moment construction stopped. The stability attenuation is a normalized value, and the ratio is negatively correlated with the stability attenuation.

7. The wireless acquisition method for monitoring deformation of deep foundation pits in tunnels according to claim 1, characterized in that, The step of adjusting the preset deformation data acquisition frequency range based on the construction impact index at the current moment to obtain the acquisition frequency base at the current moment specifically includes: The numerical length of the preset deformation data acquisition frequency range is adjusted using the construction impact index at the current moment to obtain the degree of numerical adjustment. The sum of the minimum acquisition frequency in the acquisition frequency range and the degree of numerical adjustment is determined as the acquisition frequency base at the current moment.

8. The wireless acquisition method for monitoring deformation of deep foundation pits in tunnels according to claim 1, characterized in that, The process of determining the data acquisition frequency at the current moment using the stable attenuation level and the acquisition frequency base specifically includes: in, This indicates the data collection frequency at the current moment. This indicates the base frequency of the current sampling point. This indicates the minimum sampling frequency within the preset range of deformation data sampling frequencies. This indicates the degree of stable decay at the current moment.

9. A wireless data acquisition terminal for monitoring deformation in deep foundation pits of tunnels, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the wireless acquisition method for monitoring deformation data of deep foundation pits in tunnels as described in any one of claims 1-8.

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