A method for dynamically optimizing the control network for tunnel construction in complex terrain

Abstract

This invention relates to the field of data measurement technology, specifically to a method for dynamically optimizing the control network for tunnel construction in complex terrain. The method involves obtaining basic noise parameters based on the discrete distribution characteristics of residuals from a preset nearest-neighbor time period; deriving initial normal boundary thresholds and initial gross error boundary thresholds for robust estimation based on the basic noise parameters; obtaining a first threshold adjustment factor based on the weighting characteristics of the observed data, the distribution deviation characteristics of the residuals, and the difference in residual distribution between the preset nearest-neighbor time period and other historical time periods; and obtaining a second threshold adjustment factor based on the continuity and fluctuation characteristics of vibration data, and the variation characteristics of temperature and humidity data. This invention adjusts the initial normal boundary thresholds and initial gross error boundary thresholds based on the first and second threshold adjustment factors, and performs robust estimation and adjustment of the observed data based on adaptive normal boundary thresholds and adaptive gross error boundary thresholds, thereby improving adjustment accuracy and robustness.

Description

Technical Field

[0001] This invention relates to the field of data measurement technology, and specifically to a method for dynamically optimizing the control network for tunnel construction in complex terrain. Background Technology

[0002] With the increasing number of tunnel construction projects in complex and special terrains, the difficulty and precision requirements of construction have significantly increased. The tunnel construction control network, as a benchmark system for spatial positioning and attitude control, directly affects the tunnel excavation direction, breakthrough error, and structural safety, making it a core element for ensuring efficient and safe progress throughout the entire construction process. In traditional tunnel construction control network adjustment, robust estimation, as a key technology to resist the influence of gross errors in observations, focuses on iteratively reducing the impact of abnormal observations on the adjustment results, thereby improving the reliability of parameter estimates. Traditional robust estimation uses fixed thresholds to assign weights to observations. However, the tunnel construction environment may change dynamically, with significant differences in observation noise levels at different construction stages, such as blasting and support. If the fixed threshold is too small, normal observations will be misjudged as gross errors and discarded, resulting in the loss of effective data and affecting adjustment accuracy. If the threshold is too large, gross errors will be mixed into the calculation, leading to parameter estimation deviations and failing to accurately reflect the true state of the tunnel construction control network. Therefore, the fixed threshold in existing robust estimation methods is insufficient to address the accuracy and adaptability issues of gross error identification in complex construction scenarios, affecting adjustment accuracy. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a method for dynamically optimizing the control network for tunnel construction in complex terrain. The specific technical solution adopted is as follows: Acquire observation data and residuals at different times during the control network adjustment process, as well as vibration data, temperature data, and humidity data at different times; The basic noise parameters are obtained based on the discrete distribution characteristics of the residuals of the preset nearest neighbor time period at the current time; the basic noise parameters are used to make initial adjustments to the preset normal basic boundary and preset gross error basic boundary in the differential estimation to obtain the initial normal boundary threshold and the initial gross error boundary threshold. A first threshold adjustment factor is obtained based on the weighting characteristics of the observation data in the preset neighboring time period, the distribution deviation characteristics of the residuals, and the difference characteristics of the residual distribution between the preset neighboring time period and other historical time periods; a second threshold adjustment factor is obtained based on the continuity and fluctuation characteristics of the vibration data, and the change characteristics of the temperature data and the humidity data. The initial normal boundary threshold is adjusted according to the first threshold adjustment factor and the second threshold adjustment factor to obtain an adaptive normal boundary threshold; the initial gross error boundary threshold is adjusted according to the first threshold adjustment factor and the second threshold adjustment factor to obtain an adaptive gross error boundary threshold; robust estimation and adjustment of the observed data are performed according to the adaptive normal boundary threshold and the adaptive gross error boundary threshold.

[0004] Furthermore, the step of obtaining the basic noise parameters based on the discrete distribution characteristics of the residuals of the preset nearest neighbor time periods at the current time includes: In the formula, R represents the basic noise parameter. Normalization is indicated by M, which represents the normalized value of the median absolute deviation of the residuals within the preset nearest neighbor time period; Q represents the normalized value of the interquartile range of the residuals within the preset nearest neighbor time period; the difference between the residuals and the root mean square error of the residuals within the preset nearest neighbor time period is calculated to obtain the standardized residuals, and the median of the absolute value of the standardized residuals is obtained, where Q represents the normalized value of the median of the absolute value.

[0005] Further, the step of initially adjusting the preset normal baseline boundary and preset gross margin boundary in the adversarial estimation based on the basic noise parameters to obtain the initial normal boundary threshold and the initial gross margin threshold includes: , In the formula This represents the initial normal boundary threshold. This represents the preset normal baseline boundary, and R represents the baseline noise parameter. This indicates the preset first adjustment range. This represents the initial gross boundary threshold. Indicates the preset gross error baseline boundary. This indicates the preset second adjustment range.

[0006] Further, the step of obtaining the first adjustment factor of the threshold based on the weight characteristics of the observed data in the preset nearest neighbor time period, the distribution deviation characteristics of the residuals, and the difference characteristics of the residual distribution between the preset nearest neighbor time period and other historical time periods includes: In the formula, B represents the first adjustment factor for the threshold. Let Z represent normalization, H represent the number of observations within the preset nearest neighbor time period, H represent the number of observations whose weight in robust estimation is greater than the preset gross margin, and W represent the standard deviation of the residuals within the preset nearest neighbor time period. This indicates the number of time periods in history that are randomly selected and have the same duration as the preset nearest neighbor time period. Let F represent the standard deviation of the residuals in the j-th time period, F represent the kurtosis of the residuals in the preset nearest neighbor time period, and P represent the skewness of the residuals in the preset nearest neighbor time period.

[0007] Further, the step of obtaining the threshold second adjustment factor based on the continuity and fluctuation characteristics of the vibration data, and the variation characteristics of the temperature data and the humidity data includes: In the formula, E represents the second adjustment factor for the threshold. This represents the maximum vibration data within the next minute immediately preceding the current moment. T represents the minimum vibration data within the adjacent minute of the current moment. and The time interval is defined as follows: F represents the number of peak points of vibration data within the adjacent minute of the current moment, G represents the range of temperature data within the preset long-term period, and S represents the range of humidity data within the preset long-term period.

[0008] Further, the step of adjusting the initial normal boundary threshold according to the first threshold adjustment factor and the second threshold adjustment factor to obtain the adaptive normal boundary threshold includes: In the formula, L represents the adaptive normal boundary threshold. B represents the initial normal boundary threshold, E represents the first threshold adjustment factor, and E represents the second threshold adjustment factor.

[0009] Further, the step of adjusting the initial gross boundary threshold according to the first threshold adjustment factor and the second threshold adjustment factor to obtain an adaptive gross boundary threshold includes: In the formula, U represents the adaptive gross error boundary threshold. denoted as the initial gross error boundary threshold, B represents the first threshold adjustment factor, and E represents the second threshold adjustment factor.

[0010] The present invention has the following beneficial effects: In this invention, basic noise parameters are obtained based on the discrete distribution characteristics of the residuals in the current preset neighboring time period. These parameters reflect the amplitude of the noise, thereby obtaining initial normal boundary thresholds and gross error boundary thresholds. Initial adjustments are made to the preset normal and gross error basic boundaries in robust estimation based on the basic noise parameters, resulting in initial normal boundary thresholds and initial gross error boundary thresholds. This allows for the preliminary determination of a suitable dual threshold for robust estimation. A first threshold adjustment factor is obtained based on the weighting characteristics of the observed data in the preset neighboring time period, the distribution deviation characteristics of the residuals, and the difference in residual distribution between the preset neighboring time period and other historical time periods. This factor reflects the intrinsic quality of the data in the preset neighboring time period, thus obtaining more accurate dual thresholds. A second threshold adjustment factor is obtained based on the continuity and fluctuation characteristics of vibration data, and the variation characteristics of temperature and humidity data. This factor reflects the impact of the construction environment on noise level estimation, thus enabling more accurate adaptive adjustment of the thresholds. Obtaining adaptive normal boundary thresholds and adaptive gross boundary thresholds can further improve the accuracy of robust estimation. Performing robust estimation and adjustment on the observation data based on the adaptive normal boundary thresholds and adaptive gross boundary thresholds can ultimately improve the accuracy and robustness of the adjustment, which is beneficial to the high precision requirements of tunnel construction. Attached Figure Description

[0011] 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.

[0012] Figure 1 The flowchart illustrates a method for dynamically optimizing the control network for tunnel construction in complex terrain, as provided in one embodiment of the present invention. Detailed Implementation

[0013] 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 dynamic optimization setting method for a construction control network in complex terrain tunnels proposed 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.

[0014] 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.

[0015] The following description, in conjunction with the accompanying drawings, details the specific scheme of the dynamic optimization setting method for construction control network of tunnels in complex terrain provided by the present invention.

[0016] Please see Figure 1 The diagram illustrates a flowchart of a method for dynamically optimizing the control network for tunnel construction in complex terrain, according to an embodiment of the present invention. The method includes the following steps: Step S1: Obtain observation data and residuals at different times during the control network adjustment process, as well as vibration data, temperature data, and humidity data at different times.

[0017] Adjustment of the control network is a core technical step in ensuring high-precision tunnel breakthrough. Adjustment optimizes measurement data with observational errors using mathematical methods to achieve the best estimated value. In the high-precision requirements of tunnel construction, adjustment not only eliminates closure errors but also plays a crucial role in suppressing the influence of gross errors and improving the robustness of the results. Robust estimation in control network adjustment is a mathematical processing method that improves the reliability and robustness of the adjustment results by identifying and mitigating the influence of outlier observations such as gross errors. In the iterative calculation of robust estimation, the weight function divides the residuals of the observation data into three categories based on their magnitude: normal observations, suspicious observations (reduced weighting zone), and gross errors (rejection zone). Traditional fixed threshold methods use pre-set boundaries C0 and C1. For example, residuals less than C0 are considered normal observations and given full weight; residuals between C0 and C1 are reduced in weight; and residuals greater than C1 are considered gross errors and directly rejected. By categorizing observation data into three types—normal observations (fully weighted), questionable observations (reduced weighting), and gross errors (removed)—the weighting function achieves differentiated data processing: normal observations that conform to model expectations are given maximum trust to ensure information utilization; questionable observations that fall between normal and abnormal are reduced in weighting rather than directly removed; and gross errors that significantly deviate from the model are completely prevented from contaminating parameter estimates, ultimately resulting in stable and reliable parameter estimates. Due to the complex tunnel construction environment, the fixed threshold in robust estimation is insufficient to address the accuracy and adaptability issues of gross error identification. Therefore, improvements to the fixed threshold are needed to enhance the robustness of the adjustment.

[0018] First, observation data and residuals, vibration data, temperature data, and humidity data at different times during the control network adjustment process are acquired. Vibration sensors are installed in locations within the tunnel susceptible to blasting and mechanical operations to capture vibration characteristics. Temperature and humidity sensors are installed at multiple locations within the tunnel. In this embodiment, the acquisition frequency for observation data, temperature data, and humidity data is once per minute, and for vibration data, it is 10 times per second. The implementer can determine the frequency based on the implementation scenario. The residual is the difference between the observation data and the fitted data during the adjustment process. The residual is a key data point that directly reflects the degree of fit between the observation data and the model during the adjustment process, and it is also the basis for gross error detection and robustness estimation. The types of observation data during tunnel construction include horizontal angle, vertical angle, distance, and elevation difference, and the subsequent processing methods for each type of observation data are the same.

[0019] Step S2: Obtain the basic noise parameters based on the discrete distribution characteristics of the residuals of the preset nearest neighbor time period at the current time; make initial adjustments to the preset normal basic boundary and preset gross error basic boundary in the adversarial estimation based on the basic noise parameters to obtain the initial normal boundary threshold and the initial gross error boundary threshold.

[0020] In the robust estimation of tunnel construction control networks, traditional methods are easily affected by gross errors, leading to distorted noise level estimation. Therefore, it is necessary to construct basic noise parameters that are highly resistant to gross errors. These basic noise parameters are obtained based on the discrete distribution characteristics of the residuals of the preset nearest neighbor time periods at the current moment. Preferably, in this embodiment of the invention, the steps for obtaining the basic noise parameters include: In the formula, R represents the basic noise parameter. The normalization is represented by M, which represents the normalized value of the median absolute deviation of the residuals within the preset nearest neighbor time period. The normalized value refers to the value after normalizing the calculation result. Q represents the normalized value of the interquartile range of the residuals within the preset nearest neighbor time period. The difference between the residuals and the root mean square error of the residuals within the preset nearest neighbor time period is calculated to obtain the standardized residuals. The median of the absolute value of the standardized residuals is then obtained, and Q represents the normalized value of the median of the absolute value. In this embodiment of the invention, the preset nearest neighbor time period is 10 minutes adjacent to the current time, which can be determined by the implementer according to the implementation scenario. The median absolute deviation of the residuals refers to: calculating the difference between each residual and the median of the residuals as the absolute deviation of the residuals, and using the median of the absolute deviations as the median absolute deviation. The larger the median absolute deviation, the larger the distribution range of the observed data within that time period, and the more discrete the residuals. The interquartile range (IQR) represents the difference between the third quartile and the first quartile of the residuals. A larger IQR means that after removing the extreme values ​​at both ends, the range of the middle residual data is larger. This indicates that even if the middle residuals appear normal, the overall background noise is greater due to their wide range. D reflects the degree to which the residuals are generally large when considering the prior accuracy of the observed data, such as the nominal accuracy of the measuring instruments. A larger D means that even after deducting the theoretically allowable error range, the actual residuals are still larger, suggesting a systematic problem in the model fitting and that the model itself may be biased.

[0021] Therefore, the basic noise parameters capture noise characteristics from three levels: the residuals of the recent construction period, their core distribution range, and the weighted residuals, to obtain an actual accuracy level that can be represented by the control network observations. The basic noise parameters reflect the amplitude of the noise. A larger basic noise parameter means that the fluctuation amplitude of the entire residual dataset is generally larger, and the current observation data is more strongly affected by gross errors, systematic errors, or random errors. All observation data may become discrete as a whole due to environmental disturbances and other factors. If a fixed narrow threshold is still used at this time, it is easy to misjudge the larger residuals caused by increased noise as gross errors, resulting in the loss of effective information. Therefore, it is necessary to relax the threshold according to the magnitude of the basic noise parameters so that the threshold matches the actual noise level, retaining more effective observations for adjustment during periods of higher noise, and avoiding excessive rejection. Therefore, the basic noise parameters are used to initially adjust the preset normal basic boundary and preset gross error basic boundary in the adversarial estimation to obtain the initial normal boundary threshold and the initial gross error boundary threshold. Preferably, in this embodiment of the invention, the steps of obtaining the initial normal boundary threshold and the initial gross error boundary threshold include: In the formula, This represents the initial normal boundary threshold. This represents the preset normal baseline boundary, and R represents the baseline noise parameter. This indicates the preset first adjustment range. This represents the initial gross boundary threshold. Indicates the preset gross error baseline boundary. This indicates a preset second adjustment range. In this embodiment of the invention, Taking 1 as the basic normal judgment boundary can preliminarily define the normal observation range. Taking 1.5 as the basic gross error judgment boundary can initially distinguish between normal fluctuations and gross errors, providing a reasonable benchmark for robust estimation; and In this embodiment of the invention, the threshold adjustment range is indicated. Take 1, Take 1.5; it should be noted that each parameter can be determined by the implementer according to the implementation scenario to ensure that the robust estimation can reasonably adapt to data changes in the control network adjustment, thereby improving the adjustment accuracy and robustness. and This means that the greater the basic noise, the stronger the impact of gross errors, systematic errors, or random errors on the data, and the more necessary it is to relax the threshold to avoid excessive rejection of normal observations.

[0022] Step S3: Obtain the first threshold adjustment factor based on the weight characteristics of the observation data in the preset neighboring time period, the distribution deviation characteristics of the residuals, and the difference characteristics of the residual distribution between the preset neighboring time period and other historical time periods; obtain the second threshold adjustment factor based on the continuous and fluctuating characteristics of the vibration data, and the changing characteristics of the temperature and humidity data.

[0023] Since the basic adaptive threshold is obtained only based on the static noise level, while the tunnel construction environment is dynamically changing and data quality and observation conditions vary in time and space, a two-dimensional adjustment factor is needed to dynamically correct the threshold in order to further improve the accuracy of robust estimation and adjustment. To obtain the first adjustment factor based on the intrinsic quality of the data, it is necessary to analyze multiple features reflecting the degree of data anomalies, and then obtain the first threshold adjustment factor based on the weight characteristics of the observed data in the preset nearest time period, the distribution deviation characteristics of the residuals, and the difference characteristics of the residual distribution between the preset nearest time period and other historical time periods. Preferably, in this embodiment of the invention, the step of obtaining the first threshold adjustment factor includes: In the formula, B represents the first adjustment factor for the threshold. Z represents the normalization, Z represents the number of observations within the preset nearest neighbor time period, and H represents the number of observations whose weights in robust estimation are greater than the preset gross margin. The larger the residuals of the observations, the faster the weight decays. The smaller the value, the lower the proportion of recent valid observations, the worse the recent data quality, and the higher the probability of data anomalies. W represents the standard deviation of the residuals within the preset nearest neighbor time period. This indicates the number of time periods in history that are randomly selected and have the same duration as a preset neighboring time period; in this embodiment of the invention, five time periods with the same duration are randomly selected from history. This represents the standard deviation of the residuals in the j-th time period. The larger the value, the greater the recent residual variation characteristics compared to historical random periods, the greater the likelihood of noise in the preset nearest neighbor period, and the more likely the observed data will exhibit anomalies. F represents the kurtosis of the residuals in the preset nearest neighbor period. The kurtosis can characterize the difference between the residual distribution and the normal distribution. A larger kurtosis indicates a higher probability of heavy-tailed distribution in recent time periods, and thus a greater likelihood of outliers and gross errors. P represents the skewness of the residuals in the preset nearest time period. A larger skewness indicates a higher probability of systematic errors or one-sided outliers in the preset nearest time period. Therefore, a larger threshold adjustment factor indicates poorer data quality, more outliers, and a residual data distribution pattern that deviates from normal statistical patterns.

[0024] Furthermore, in the robust estimation of tunnel construction observation data, after obtaining the first adjustment factor of the threshold based on the intrinsic quality characteristics of the data, the impact of the complex and variable tunnel construction environment on observation noise is also significant. Therefore, it is also necessary to obtain a second adjustment factor of the threshold based on the construction environment to improve the accuracy of the threshold adjustment. Thus, the second adjustment factor of the threshold is obtained based on the continuous and fluctuating characteristics of vibration data, and the changing characteristics of temperature and humidity data. Preferably, in this embodiment of the invention, the step of obtaining the second adjustment factor of the threshold includes: In the formula, E represents the second adjustment factor for the threshold. This represents the maximum vibration data within the next minute immediately preceding the current moment. T represents the minimum vibration data within the adjacent minute of the current moment. and The time interval is defined as follows: F represents the number of peak points in the vibration data within the adjacent minute of the current moment; G represents the range of temperature data within a preset long-term period; and S represents the range of humidity data within a preset long-term period. The preset long-term period is one hour adjacent to the current moment. The range of temperature and humidity data is calculated by taking the difference between the mean values ​​of the two data collection times. It should be noted that... F After calculation, each value is normalized, and the final calculation is based on its normalized value. Vibration data reflects the impact of vibrations caused by blasting and mechanical operations within tunnels on the stability of the instrument and prism. The more pronounced the vibration characteristics, the greater the possibility of measurement errors. Temperature and humidity data characterize the impact of changes in atmospheric refractive index caused by temperature and humidity variations on the accuracy of distance and angle measurements. The greater the change, the greater the possibility of errors in total station distance measurement. The larger the value of F, the greater the probability of significant fluctuations within the current minute, potentially influenced by explosive events; a larger F value also indicates a greater number of peak points, a higher probability of recent sustained mechanical vibrations, and consequently, a greater likelihood of errors. Therefore... The larger the value, the greater the likelihood of a blast or continuous mechanical operation occurring within one minute, and the greater the possibility of errors in the currently collected observation data. The larger the value, the stronger the recent atmospheric disturbance, and the greater the possibility of errors in recent data. Therefore, the second adjustment factor of the threshold can reflect the impact of the construction environment on noise level estimation; the more severe the construction environment, the larger the value.

[0025] Step S4: Adjust the initial normal boundary threshold according to the first threshold adjustment factor and the second threshold adjustment factor to obtain the adaptive normal boundary threshold; adjust the initial gross boundary threshold according to the first threshold adjustment factor and the second threshold adjustment factor to obtain the adaptive gross boundary threshold; perform robust estimation and adjustment on the observed data according to the adaptive normal boundary threshold and the adaptive gross boundary threshold.

[0026] The initial normal boundary threshold and the initial gross error boundary threshold provide an initial scale that is insensitive to gross errors. The first threshold adjustment factor can make initial corrections to these two based on the distribution of outliers and the degree of systematic errors. The second threshold adjustment factor, combined with the disturbance characteristics of the construction environment, further adjusts them to match the actual observation conditions. The first threshold adjustment factor reflects the abnormal shape of the data distribution, i.e., the "proportion of data contamination." It can measure whether outliers deviating from the main distribution are mixed into the observed data through indicators such as kurtosis, skewness, and effective observation ratio. When this value is large, it means that the quality of the current observed data is poor, there are many outliers, the data distribution shape deviates from the normal statistical law, and there are many gross errors or systematic biases in the data. That is, the decline in data quality is due to the mixing of outliers rather than the amplification of overall noise. If the threshold is too loose at this time, it is easy to miss the actual gross errors and mix them into the adjustment calculation, contaminating the parameter estimation. Therefore, it is necessary to narrow the threshold and enhance the sensitivity to outliers, placing them in the adjustment calculation when real gross errors are mixed in. When the second threshold adjustment factor is large, it indicates a harsh construction environment and significant external disturbances. In this case, the observed data itself carries considerable environmental noise, and even normal observations without gross errors may have excessively large residuals. If the threshold is set too narrowly, these abnormally large residuals caused by environmental disturbances may be mistakenly identified as gross errors and removed. Therefore, the threshold needs to be widened according to its value to accommodate additional fluctuations caused by external environmental factors, avoiding the accidental deletion of valid data during high-noise periods. Furthermore, the initial normal boundary threshold can be adjusted based on the first and second threshold adjustment factors to obtain an adaptive normal boundary threshold; similarly, the initial gross error boundary threshold can be adjusted based on the first and second threshold adjustment factors to obtain an adaptive gross error boundary threshold.

[0027] Preferably, in this embodiment of the invention, the steps of obtaining the adaptive normal boundary threshold and the adaptive gross boundary threshold include: In the formula, L represents the adaptive normal boundary threshold. Let B represent the initial normal boundary threshold, B represent the first threshold adjustment factor, and E represent the second threshold adjustment factor. U represents the adaptive gross error boundary threshold. This represents the initial gross error boundary threshold. The larger B is, the narrower the normal boundary threshold and the gross error boundary threshold should be to avoid missing gross errors; the larger E is, the larger the normal boundary threshold and the gross error boundary threshold should be to avoid misjudging gross errors. It should be noted that in this embodiment of the invention, the adaptive normal boundary threshold ranges from 1 to 2, and the adaptive gross error boundary threshold ranges from 1.5 to 3. Limiting the value range can prevent the calculation results from being too extreme, ensuring the stability and reliability of robust estimation, and enhancing the accuracy and robustness of adjustment. When the calculation result exceeds this range, the boundary value of this set range is taken; the implementer can determine this according to the implementation scenario. After obtaining the adaptive normal boundary threshold and the adaptive gross error boundary threshold for robust estimation, robust estimation and adjustment of the observation data can be performed based on the adaptive normal boundary threshold and the adaptive gross error boundary threshold. It should be noted that robust estimation and adjustment are existing technologies, and the specific steps will not be elaborated further. Using the adaptive normal boundary threshold and the adaptive gross error boundary threshold for robust estimation and adjustment can improve the accuracy and robustness of control network adjustment, thus benefiting the high-precision requirements of tunnel construction.

[0028] In summary, this invention provides a method for dynamically optimizing the control network for tunnel construction in complex terrain. It obtains basic noise parameters based on the discrete distribution characteristics of residuals from preset neighboring time periods; derives initial normal boundary thresholds and initial gross error boundary thresholds for robust estimation based on the basic noise parameters; obtains a first threshold adjustment factor based on the weighting characteristics of the observed data, the distribution deviation characteristics of the residuals, and the difference characteristics of the residual distribution between the preset neighboring time periods and other historical time periods; and obtains a second threshold adjustment factor based on the continuity and fluctuation characteristics of vibration data, and the variation characteristics of temperature and humidity data. This invention adjusts the initial normal boundary thresholds and initial gross error boundary thresholds based on the first and second threshold adjustment factors, and performs robust estimation and adjustment of the observed data based on adaptive normal boundary thresholds and adaptive gross error boundary thresholds, thereby improving adjustment accuracy and robustness.

[0029] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0030] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for dynamically optimizing the control network for tunnel construction in complex terrain, characterized in that, The method includes the following steps: Acquire observation data and residuals at different times during the control network adjustment process, as well as vibration data, temperature data, and humidity data at different times; The basic noise parameters are obtained based on the discrete distribution characteristics of the residuals of the preset nearest neighbor time period at the current time; the basic noise parameters are used to make initial adjustments to the preset normal basic boundary and preset gross error basic boundary in the differential estimation to obtain the initial normal boundary threshold and the initial gross error boundary threshold. A first threshold adjustment factor is obtained based on the weighting characteristics of the observation data in the preset neighboring time period, the distribution deviation characteristics of the residuals, and the difference characteristics of the residual distribution between the preset neighboring time period and other historical time periods; a second threshold adjustment factor is obtained based on the continuity and fluctuation characteristics of the vibration data, and the change characteristics of the temperature data and the humidity data. The initial normal boundary threshold is adjusted according to the first threshold adjustment factor and the second threshold adjustment factor to obtain an adaptive normal boundary threshold; the initial gross error boundary threshold is adjusted according to the first threshold adjustment factor and the second threshold adjustment factor to obtain an adaptive gross error boundary threshold; robust estimation and adjustment of the observed data are performed according to the adaptive normal boundary threshold and the adaptive gross error boundary threshold.

2. The method for dynamically optimizing the control network for tunnel construction in complex terrain according to claim 1, characterized in that, The step of obtaining the basic noise parameters based on the discrete distribution characteristics of the residuals of the preset nearest neighbor time periods at the current time includes: In the formula, R represents the basic noise parameter. Normalization is indicated by M, which represents the normalized value of the median absolute deviation of the residuals within the preset nearest neighbor time period; Q represents the normalized value of the interquartile range of the residuals within the preset nearest neighbor time period; the difference between the residuals and the root mean square error of the residuals within the preset nearest neighbor time period is calculated to obtain the standardized residuals, and the median of the absolute value of the standardized residuals is obtained, where Q represents the normalized value of the median of the absolute value.

3. The method for dynamically optimizing the control network for tunnel construction in complex terrain according to claim 1, characterized in that, The step of initially adjusting the preset normal boundary and preset gross boundary in the adversarial estimation based on the basic noise parameters to obtain the initial normal boundary threshold and the initial gross boundary threshold includes: , In the formula This represents the initial normal boundary threshold. This represents the preset normal baseline boundary, and R represents the baseline noise parameter. This indicates the preset first adjustment range. This represents the initial gross boundary threshold. Indicates the preset gross error baseline boundary. This indicates the preset second adjustment range.

4. The method for dynamically optimizing the control network for tunnel construction in complex terrain according to claim 1, characterized in that, The step of obtaining the first adjustment factor of the threshold based on the weight characteristics of the observation data in the preset nearest neighbor time period, the distribution deviation characteristics of the residuals, and the difference characteristics of the residual distribution between the preset nearest neighbor time period and other historical time periods includes: In the formula, B represents the first adjustment factor for the threshold. Let Z represent normalization, Z represent the number of observations within the preset nearest neighbor time period, H represent the number of observations whose weight in robust estimation is greater than the preset gross margin, and W represent the standard deviation of the residuals within the preset nearest neighbor time period. This indicates the number of time periods in history that are randomly selected and have the same duration as the preset nearest neighbor time period. Let F represent the standard deviation of the residuals in the j-th time period, F represent the kurtosis of the residuals in the preset nearest neighbor time period, and P represent the skewness of the residuals in the preset nearest neighbor time period.

5. The method for dynamically optimizing the control network for tunnel construction in complex terrain according to claim 1, characterized in that, The step of obtaining the threshold second adjustment factor based on the continuity and fluctuation characteristics of the vibration data, and the variation characteristics of the temperature and humidity data includes: In the formula, E represents the second adjustment factor for the threshold. This represents the maximum vibration data within the next minute immediately preceding the current moment. T represents the minimum vibration data within the adjacent minute of the current moment. and The time interval is defined as follows: F represents the number of peak points of vibration data within the adjacent minute of the current moment, G represents the range of temperature data within the preset long-term period, and S represents the range of humidity data within the preset long-term period.

6. The method for dynamically optimizing the control network for tunnel construction in complex terrain according to claim 1, characterized in that, The step of adjusting the initial normal boundary threshold according to the first threshold adjustment factor and the second threshold adjustment factor to obtain the adaptive normal boundary threshold includes: In the formula, L represents the adaptive normal boundary threshold. B represents the initial normal boundary threshold, E represents the first threshold adjustment factor, and E represents the second threshold adjustment factor.

7. The method for dynamically optimizing the control network for tunnel construction in complex terrain according to claim 1, characterized in that, The step of adjusting the initial gross boundary threshold according to the first threshold adjustment factor and the second threshold adjustment factor to obtain an adaptive gross boundary threshold includes: In the formula, U represents the adaptive gross error boundary threshold. denoted as the initial gross error boundary threshold, B represents the first threshold adjustment factor, and E represents the second threshold adjustment factor.

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