Array displacement meter deep hole installation quality self-evaluation method

Abstract

This invention proposes a self-assessment method for the installation quality of an array-type displacement gauge in deep holes, comprising the following steps: Step 1, acquiring the acceleration of each sensor measurement unit of the array-type displacement gauge, calculating the three-dimensional attitude angle of each measurement unit, and preprocessing it; Step 2, calculating the three-dimensional coordinates of each measurement unit based on the preprocessed three-dimensional attitude angles, and constructing an actual spatial distribution curve using the three-dimensional coordinates of all measurement units; Step 3, determining the reference spatial curve of the array-type displacement gauge; Step 4, matching the reference spatial curve with the actual spatial distribution curve point by point and calculating the evaluation index; Step 5, comparing the evaluation index with preset multi-level thresholds and performing self-assessment and alarm based on the comparison results. This invention achieves real-time, automatic, and intelligent assessment of installation quality, and is applicable to deep deformation monitoring scenarios such as dams, slopes, and tunnels.

Description

Technical Field

[0001] This invention relates to a method for self-evaluation of the installation quality of displacement gauges, and more particularly to a method for self-evaluation of the deep-hole installation quality of array-type displacement gauges. Background Technology

[0002] Array-type displacement gauges are core equipment for monitoring deformation of soil and rock masses or deep tunnels in water conservancy and hydropower projects. They achieve distributed deformation sensing through multi-sensor measurement units deployed in series, and the installation status of the equipment directly determines the accuracy of the monitoring data. Ideally, the displacement gauge should be in close contact with the borehole or tunnel wall, without any gaps or twists. However, in actual construction, due to factors such as grouting process, cable sag, and irregular borehole walls, problems often occur such as incomplete grouting, cable sag due to its own weight, and local gaps or twists in the sensors.

[0003] Currently, the industry's assessment of the installation quality of array displacement gauges relies on manual construction records and sampling inspections, which suffers from problems such as assessment lag, subjective results, and incomplete coverage. Meanwhile, while the MEMS accelerometers built into array displacement gauges can collect multi-dimensional sensing data, existing technologies only use this data for "soil and rock deformation calculations," failing to explore its application value in "equipment installation quality assessment," thus limiting the functionality of the sensing data. Summary of the Invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide a self-evaluation method for the deep hole installation quality of array displacement gauges, which addresses the shortcomings of the existing technology.

[0005] To address the aforementioned technical problems, this invention discloses a self-assessment method for the deep hole installation quality of an array-type displacement gauge, comprising the following steps:

[0006] Step 1: Obtain the acceleration of each sensor measurement unit of the array displacement meter, calculate the three-dimensional attitude angle of each measurement unit and preprocess it;

[0007] Step 2: Calculate the three-dimensional coordinates of each measurement unit based on the three-dimensional attitude angles of each measurement unit after preprocessing, and construct the actual spatial distribution curve using the three-dimensional coordinates of all measurement units.

[0008] Step 3: Determine the reference space curve of the array displacement gauge;

[0009] Step 4: Match the baseline spatial curve with the actual spatial distribution curve point by point and calculate the evaluation index;

[0010] Step 5: Compare the evaluation indicators with the preset multi-level thresholds and install quality self-evaluation and alarm based on the comparison results.

[0011] Furthermore, the calculation and preprocessing of the three-dimensional attitude angles of each measurement unit described in step 1 includes:

[0012] Step 1-1: Obtain the raw output values ​​of the MEMS accelerometers of each sensor measurement unit in the array displacement meter, including the acceleration in the three axial directions of the measurement unit, i.e., the acceleration in the X-axis direction. Y-axis acceleration and acceleration in the Z-axis direction ;

[0013] Steps 1-2: Based on the acceleration in three directions, calculate the three-dimensional attitude angles of each measurement unit, including the angles between the three axes and the gravity vector. , and The specific calculation method is as follows:

[0014]

[0015]

[0016]

[0017] Steps 1-3: Measure the three-dimensional attitude angles of each measurement unit. , and Kalman filtering preprocessing is then performed.

[0018] Furthermore, the Kalman filtering preprocessing described in steps 1-3 includes:

[0019] Step 1-3-1, based on acceleration in three directions and gravitational acceleration. Calculate the deviation between the acceleration modulus and the gravitational acceleration. The intensity of vibration disturbance is represented as follows:

[0020]

[0021] Step 1-3-2: Use a sliding window to statistically analyze multiple deviations. mean and standard deviation According to 3 Criteria determine operating condition identification thresholds , means as follows:

[0022]

[0023] Step 1-3-3: Update the mean and standard deviation in real time using a sliding window, and dynamically refresh the threshold. ;

[0024] Steps 1-3-4: Establish the covariance between vibration intensity and Kalman process noise. The piecewise continuous mapping relationship, for process noise covariance The adjustments are as follows:

[0025]

[0026] in, For static reference process noise covariance, For the maximum process noise covariance of strong vibration, This is the upper limit threshold for vibration intensity;

[0027] Steps 1-3-5, based on the adjusted process noise covariance Kalman filtering is applied to the three-dimensional attitude angle data.

[0028] Furthermore, step 2, which involves calculating the three-dimensional coordinates of each measurement unit based on the preprocessed three-dimensional attitude angles of each measurement unit—that is, recursively calculating the three-dimensional coordinates of other units using the first sensor measurement unit as the coordinate origin—includes:

[0029] when That is, when the first sensor measurement unit is used, its three-dimensional coordinates are calculated. The details are as follows:

[0030]

[0031]

[0032]

[0033] in, The effective length of the first sensor measurement unit. , and The attitude angle of the first sensor measurement unit;

[0034] when At that time, that is, the first When measuring a single sensor unit, calculate its three-dimensional coordinates. The details are as follows:

[0035]

[0036]

[0037]

[0038] in, , and The first The coordinate values ​​of each sensor measurement unit For the first The effective length of each sensor measurement unit , and For the first The sensor measures the attitude angle of each unit.

[0039] Furthermore, the determination of the reference space curve of the array displacement gauge in step 3 includes:

[0040] Step 3-1: Obtain engineering design parameters such as the design axis of the borehole or tunnel, the theoretical length of the displacement gauge unit, and the initial installation design tilt and azimuth angle, and perform preprocessing.

[0041] Step 3-2: Using the method in Step 2, calculate the three-dimensional coordinates of each sensor measurement unit under standard conditions based on the data preprocessed in Step 3-1;

[0042] Step 3-3: Connect the three-dimensional coordinates of each sensor measurement unit in the standard state in sequence to form the theoretical space curve of the displacement gauge under the ideal installation state along the design axis, that is, the reference space curve.

[0043] Furthermore, step 4, which involves point-by-point matching of the baseline spatial curve with the actual spatial distribution curve and calculation of evaluation indicators, includes:

[0044] Step 4-1: Match the reference spatial curve with the actual spatial distribution curve point by point using the sensor measurement unit. After matching, the points on the reference spatial curve... Points on the actual spatial distribution curve correspond;

[0045] Step 4-2: Calculate three types of evaluation indicators: overall morphological offset, local morphological anomaly degree, and contact state index.

[0046] Furthermore, the overall morphological offset mentioned in step 4-2 is calculated based on the root mean square value of the normal deviation of each point on the actual spatial distribution curve relative to the corresponding point on the reference spatial curve, specifically including:

[0047] Calculate each point in the actual spatial distribution curve normal deviation , i.e., point Relative to point The normal distance is used to obtain the sequence. ,in The number of points matched;

[0048] Calculate all normal deviations root mean square value , means as follows:

[0049]

[0050] in, This represents the overall shape offset.

[0051] Furthermore, the local morphological anomaly degree mentioned in step 4-2 is calculated using the three-point curvature method, specifically including:

[0052] With the current point Centered on the actual spatial distribution curve, select two consecutive points that are adjacent to each other. and Forming a three-point group ;

[0053] Calculate the curvature of the current point by fitting an arc through three points. Then calculate the current curvature relative to the reference curvature. relative rate of change , means as follows:

[0054]

[0055] in, This represents the degree of local morphological anomaly.

[0056] Furthermore, the contact state index mentioned in step 4-2 is calculated using the following method:

[0057] With point Centered on, select a preset length of Let the set of points within the sliding window be denoted as . In this sliding window, the calculation point The contact state index is calculated by sliding the slider point by point along the actual spatial distribution curve and based on the curvature of each point within the sliding window. , means as follows:

[0058]

[0059] in, For the first in the sliding window The curvature of each point.

[0060] Furthermore, step 5, which involves comparing the evaluation indicators with preset multi-level thresholds and installing quality self-assessment and alarms based on the comparison results, includes:

[0061] When all evaluation indicators are within the normal threshold range, it is judged as normal;

[0062] If a preset number of evaluation indicators exceed the normal threshold but do not reach the alarm threshold, an early warning will be issued.

[0063] An alarm will be triggered if the number of preset evaluation indicators exceeds the alarm threshold.

[0064] Beneficial effects:

[0065] 1. The technical solution proposed in this invention has strong real-time performance, enabling online real-time monitoring of installation quality and timely detection of installation defects without the need for manual on-site inspection.

[0066] 2. The technical solution proposed in this invention has high precision. Based on a high-precision MEMS accelerometer, it employs a precise spatial coordinate recursion algorithm and Kalman filter preprocessing, and multiple deviation calculation methods to ensure reliable results.

[0067] 3. The technical solution proposed in this invention has a high degree of intelligence, can automatically evaluate without human intervention, has intelligent alarms, promptly notifies relevant personnel, and supports remote monitoring and management.

[0068] 4. The technical solution proposed in this invention is the first to use the attitude data of an array displacement meter for installation quality assessment, thus expanding the application scope of attitude data. Attached Figure Description

[0069] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0070] Figure 1 This is a schematic diagram of the recursive calculation process for space curves.

[0071] Figure 2 This is a schematic diagram of the curve comparison and evaluation index calculation process.

[0072] Figure 3 This is a schematic diagram of the overall process of the self-evaluation method of the present invention.

[0073] Figure 4 This is a schematic diagram of the three-axis angles of each sensor measurement unit. Detailed Implementation

[0074] This invention proposes a self-assessment method for deep-hole installation quality based on data sensed by the built-in sensors of an array-type displacement gauge. This method is applicable to the installation quality assessment of array-type displacement gauges in deep rock and soil environments, tunnels, and other similar scenarios. The technical solution of this invention is as follows: Figure 3 As shown, it includes the following steps:

[0075] Step S1: Obtain the raw output values ​​of the MEMS accelerometers of each sensor measurement unit in the array displacement meter, calculate the three-dimensional attitude angle of each measurement unit, and preprocess it using Kalman filtering. For example... Figure 4 The three-dimensional attitude angles shown include the angles θ, ψ, and f between the three axes of each measurement unit and the gravity vector. ;

[0076] In step S1, the original output value of the acceleration sensor of each measurement unit is assumed to be the acceleration in the X-axis direction. Y-axis acceleration and acceleration in the Z-axis direction Then the three-dimensional attitude angle The formula for calculating ) is:

[0077]

[0078]

[0079]

[0080] The step of performing Kalman filtering preprocessing on the calculated attitude angle data to eliminate noise effects such as drilling vibration and electromagnetic interference includes:

[0081] First, calculate the deviation between the magnitude of acceleration and the acceleration due to gravity. :

[0082]

[0083] Under static conditions, the acceleration modulus approaches the gravitational acceleration g; however, when drilling vibrations or external disturbances occur, the modulus deviates from g. Characterizes the intensity of vibration disturbance.

[0084] Next, a sliding window method was used to count multiple frames. mean and standard deviation According to 3 Criteria determine operating condition identification thresholds :

[0085]

[0086] The threshold is dynamically refreshed by updating the mean and standard deviation in real time through a sliding window. .

[0087] Establish a piecewise continuous mapping relationship between vibration intensity and the Kalman process noise covariance Q:

[0088]

[0089] in: For static reference process noise covariance, For the maximum process noise covariance of strong vibration, This is the upper limit threshold for vibration intensity.

[0090] Finally, based on the adjusted process noise covariance Kalman filtering is applied to the attitude angle data to reduce noise and output stable attitude data, which is then used for subsequent 3D coordinate extrapolation and installation quality assessment.

[0091] Step S2: Define the fitting length of each sensor measurement unit, and recursively calculate the three-dimensional coordinates of each unit with the first sensor measurement unit as the coordinate origin to construct the actual spatial distribution curve. The principle of recursive calculation of the spatial curve is as follows: Figure 1 As shown, the specific calculation method is as follows;

[0092] Among them, the coordinates of the end point of each sensor measurement unit The calculation formula is:

[0093] when When the first sensor measurement unit is used, its three-dimensional coordinates at the end point are calculated. :

[0094]

[0095]

[0096]

[0097] in, The effective length of the first sensor measurement unit. , and This is the attitude angle of the first sensor measurement unit.

[0098] when hour:

[0099]

[0100]

[0101]

[0102] in, , and The first The coordinate values ​​of each sensor measurement unit For the first The effective length of each sensor measurement unit , , For the first The sensor measures the attitude angle of each unit.

[0103] Step S3: Determine the reference space curve of the ideal installation state of the array displacement gauge, and match and compare it with the actual space distribution curve point by point according to the sensor number;

[0104] The reference space curve is the theoretical space curve under ideal installation conditions of the array displacement gauge, and it is determined in the following way:

[0105] Based on the design axis of the borehole or tunnel, the theoretical length of the displacement gauge unit, the design tilt angle and azimuth angle of the initial installation, and other engineering design and equipment factory-existing parameters, the theoretical space curve of the displacement gauge under the ideal installation state along the design axis is generated by the recursive algorithm in step S2.

[0106] Step S4: As Figure 2 As shown, three evaluation indicators are calculated based on the curve comparison results: overall morphological offset, local morphological anomaly degree, and contact state index.

[0107] The method for calculating the overall shape offset is as follows:

[0108] Calculate the root mean square value of the normal deviation of each point on the actual spatial distribution curve from the corresponding point on the reference spatial curve.

[0109] That is, to calculate the normal deviation of each point on the actual curve. : Practical Relative to the corresponding point on the reference curve The normal distance is used to obtain the sequence. ;

[0110] Calculate the normal deviation root mean square value :

[0111]

[0112] The larger the value, the greater the overall deviation.

[0113] The method for calculating the degree of local morphological anomaly is as follows:

[0114] Curvature calculation using the three-point method, with the current calculation point... Centered on the sample, select two consecutive sampling points that are adjacent to each other in the sampling sequence. , Forming a three-point group

[0115] Calculate the curvature of the current point by fitting an arc through three points. Then calculate the current curvature relative to the reference curvature. The relative rate of change, as the degree of local morphological anomaly. :

[0116]

[0117] If the baseline curve is a straight line, the corresponding points In this case, the absolute rate of change is used. Characterizes the degree of local morphological anomaly.

[0118] when When the threshold for a sudden change is exceeded, the point is determined to be a curvature change point, corresponding to a local slab or local compression defect in the sensor unit.

[0119] The contact condition index is calculated as follows:

[0120] At the current calculation point Centered on a sliding window of a preset length (window length is 2), select a sliding window of that length. +1 consecutive sampling points, wherein the window length is preset according to the sensor sampling frequency, the probe layout length and the defect identification accuracy, and is not less than the minimum identification length of the target defect), and the set of sampling points within the window is: This window follows the calculation point. Slide along the sampling sequence point by point, based on the curvature of each sampling point within the window. Calculate the contact state index :

[0121]

[0122] in, The reference curvature on the reference space curve corresponding to the current window position; when When using the mean curvature within the window As a contact state index; For the first in the window The curvature of each sampling point; The smaller the value, the higher the degree of curve flattening, and the greater the risk of the sensor unit being suspended.

[0123] Step S5: Compare various evaluation indicators with preset multi-level thresholds to achieve automatic judgment and graded alarm of installation quality.

[0124] In step S5, the installation quality assessment includes:

[0125] Normal: All assessment indicators are within the normal threshold range;

[0126] Warning: If one or more indicators exceed the normal threshold but do not reach the alarm threshold, it is judged as "minor abnormality" or "caution";

[0127] Alarm: If one or more indicators exceed the alarm threshold, it is determined as "abnormal installation quality". The system will trigger a high-level alarm and accurately locate the abnormal location.

[0128] Example:

[0129] To illustrate the specific process of the above self-assessment method using a practical example, the self-assessment method for the deep hole installation quality of the array displacement gauge in this embodiment includes the following steps:

[0130] Step S1: Acquire attitude angle data. Acquire the raw output values ​​of the MEMS accelerometers of each sensor measurement unit of the array displacement meter, calculate the three-dimensional attitude angles (θ, ψ, φ) of each measurement unit, and perform Kalman filtering preprocessing to eliminate noise effects.

[0131] Step S2: Calculate the spatial curve. Define the fitting length Lᵢ for each sensor measurement unit. Using the beginning of the first sensor measurement unit as the origin, calculate the three-dimensional coordinates of the end points of each unit using a recursive algorithm. Connect the coordinates to form the actual spatial distribution curve.

[0132] Step S3: Curve Comparison. Determine the reference space curve (the curve under ideal installation conditions), and compare the current space curve with the reference space curve point by point according to the sensor number.

[0133] Step S4: Calculate the evaluation indicators. Based on the curve comparison results, calculate three types of evaluation indicators: overall morphological offset (root mean square deviation), local morphological anomaly (curvature change rate), and contact state index (straightening degree).

[0134] Step S5: Quality Assessment and Alarm. Various evaluation indicators are compared with preset multi-level thresholds to achieve automatic assessment and graded alarms for installation quality.

[0135] like Figure 1 As shown, the recursive calculation process of the spatial curve in this embodiment is as follows:

[0136] Taking the beginning of the first sensor measurement unit as the origin O(0,0,0), calculate the coordinates (x1,y1,z1) of its end point based on the attitude angle (θ1, ψ1, φ1) and effective length L1 of the unit.

[0137] Starting from the end point of the first unit, calculate the coordinates of the end point of the second unit based on the attitude angle and effective length of the second unit;

[0138] The process is repeated until the coordinates of the endpoints of all units are calculated, and then the points are connected to form a space curve.

[0139] This embodiment uses a 20m deep drainage hole on the left bank high slope of a small to medium-sized reservoir as an engineering scenario. This drainage hole is a shared hole for shallow slope drainage and monitoring, with a diameter of 110mm. The installation quality self-assessment is carried out using a mainstream 19-section unit array displacement meter (flexible inclinometer) to verify the practical application effect of the method of the present invention.

[0140] The specific technical parameters of this embodiment are shown in Table 1:

[0141] Table 1 Technical Specifications

[0142]

[0143] S1 collects raw triaxial acceleration data from 19 measurement units, calculates the three-dimensional attitude angles (θ, ψ, φ) of each unit, and uses Kalman filtering to remove environmental vibration noise, outputting stable attitude data.

[0144] Partial unit attitude data:

[0145] Unit 1: θ1=0.08°, ψ1=0.05°, φ1=0.03°

[0146] Unit 2: θ²=0.09°, ψ²=0.04°, φ²=0.02°

[0147] Unit 3: θ3=0.07°, ψ3=0.06°, φ3=0.02°

[0148] S2 takes the beginning of the first measurement unit as the origin O(0,0,0), calculates the three-dimensional coordinates of the end points of each unit in sequence, and connects all nodes to form the actual spatial distribution curve of the displacement gauge.

[0149] Coordinates of some key nodes:

[0150] End points of Unit 1: (0.0014m, 0.0009m, 0.99999m)

[0151] End point of Unit 6: (0.0032m, 0.0021m, 5.9998m)

[0152] End point of Unit 12: (0.0025m, 0.0018m, 11.9997m)

[0153] S3 uses the standard space curve under the ideal installation posture, obtained through a recursive algorithm based on inherent design parameters such as the borehole design axis, the theoretical length of the displacement gauge unit, the design inclination angle, and the azimuth angle, as the reference space curve. The root mean square value of the overall shape offset of this reference curve is... =3.2mm, which is a typical value for defect-free installation. The actual spatial curve is matched point by point with the reference curve according to the unit number to obtain the sequence D of normal deviation at each point.

[0154] S4 Overall Shape Offset: Calculate the root mean square value of the normal deviation between each point on the actual curve and the corresponding point on the reference curve. =8.7mm, which is less than the preset normal threshold of 10mm, and there is no significant offset in the overall installation.

[0155] Local morphological anomaly: Taking measurement unit 6 as the current point, the curvature is calculated by taking the three adjacent points 5 and 7 before and after it. The actual curvature is... =0.012rad / m, reference curvature =0.005rad / m, the local morphological anomaly degree is calculated as follows:

[0156]

[0157] The value exceeded the preset alarm threshold of 0.5, indicating that there was a local compression defect at the location of Unit 6 (subsequent manual verification showed that the defect was caused by excessive pressure from the backfilled fine sand at that location).

[0158] Contact state index: A sliding window with a length of 5 points is taken, centered on measurement unit number 12. =2, window length 2 +1=5, covering a length of 4m), the unit numbers within the window are 10, 11, 12, 13, and 14, and the average curvature of each point is 0.0028 rad / m. The contact state index is calculated as follows:

[0159]

[0160] The value was lower than the preset alarm threshold of 0.7, indicating that there was a continuous overhead defect in unit segments 10-14 (subsequent manual verification showed that this was caused by a backfilling omission in this segment).

[0161] S5's overall offset is normal; Unit 6's local morphological anomaly exceeds the standard, triggering a local anomaly alarm; Units 10-14's contact state index exceeds the standard, triggering a continuous overhead alarm; the system automatically locates the defect location and outputs an acceptance evaluation report.

[0162] In this embodiment, the threshold value is set based on engineering experience. The threshold value can be set in various ways, such as:

[0163] Finite element simulation: Establish a finite element model of an array displacement gauge in a borehole, simulate defects of different degrees such as suspension and torsion, and establish the correspondence between the degree of defects and the index values;

[0164] Experimental calibration: Known and quantified installation defects are artificially created in the laboratory, data are collected and various indicators are calculated to establish calibration curves;

[0165] Historical data statistics: Collect a large amount of on-site data on confirmed good installation quality and existing problems, and conduct statistical analysis to determine thresholds;

[0166] Machine learning optimization: As the amount of accumulated case data increases, machine learning algorithms can be used to optimize the threshold.

[0167] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention content of the self-assessment method for deep hole installation quality of an array-type displacement gauge, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0168] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0169] This invention provides a concept and method for self-evaluation of the installation quality of array-type displacement gauges in deep holes. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for self-evaluation of deep hole installation quality of an array displacement meter, characterized in that, Includes the following steps: Step 1: Obtain the acceleration of each sensor measurement unit of the array displacement meter, calculate the three-dimensional attitude angle of each measurement unit and preprocess it; Step 2: Calculate the three-dimensional coordinates of each measurement unit based on the three-dimensional attitude angles of each measurement unit after preprocessing, and construct the actual spatial distribution curve using the three-dimensional coordinates of all measurement units. Step 3: Determine the reference space curve of the array displacement gauge; Step 4: Match the baseline spatial curve with the actual spatial distribution curve point by point and calculate the evaluation index; Step 5: Compare the evaluation indicators with the preset multi-level thresholds and install quality self-evaluation and alarm based on the comparison results.

2. The method according to claim 1, wherein, Step 1, which involves calculating and preprocessing the three-dimensional attitude angles of each measurement unit, includes: Step 1-1, obtaining the original output value of the MEMS acceleration sensor of each sensor measurement unit of the array displacement meter, including the acceleration in the X-axis direction, the Y-axis direction and the Z-axis direction ;​​ Steps 1-2: Based on the acceleration in three directions, calculate the three-dimensional attitude angles of each measurement unit, including the angles between the three axes and the gravity vector. , and ; Steps 1-3: Measure the three-dimensional attitude angles of each measurement unit. , and Kalman filtering preprocessing is then performed.

3. The method for self-evaluation of the deep hole installation quality of an array-type displacement gauge according to claim 2, characterized in that, The Kalman filtering preprocessing described in steps 1-3 includes: Step 1-3-1, based on acceleration in three directions and gravitational acceleration. Calculate the deviation between the acceleration modulus and the gravitational acceleration. Used to characterize the intensity of vibration disturbance; Step 1-3-2: Use a sliding window to statistically analyze multiple deviations. mean and standard deviation According to 3 Criteria determine operating condition identification thresholds ; Step 1-3-3: Update the mean and standard deviation in real time using a sliding window, and dynamically refresh the threshold. ; Steps 1-3-4: Establish the covariance between vibration intensity and Kalman process noise. The piecewise continuous mapping relationship, for process noise covariance The adjustments are as follows: ； in, For static reference process noise covariance, For the maximum process noise covariance of strong vibration, This is the upper limit threshold for vibration intensity; Steps 1-3-5, based on the adjusted process noise covariance Kalman filtering is applied to the three-dimensional attitude angle data.

4. The method for self-evaluation of the deep hole installation quality of an array-type displacement gauge according to claim 3, characterized in that, Step 2, which involves calculating the three-dimensional coordinates of each measurement unit based on the preprocessed three-dimensional attitude angles of each measurement unit (i.e., recursively calculating the three-dimensional coordinates of other units using the first sensor measurement unit as the coordinate origin), includes: when That is, when the first sensor measurement unit is used, its three-dimensional coordinates are calculated. The details are as follows: ； ； ； in, The effective length of the first sensor measurement unit. , and The attitude angle of the first sensor measurement unit; when At that time, that is, the first When measuring a single sensor unit, calculate its three-dimensional coordinates. The details are as follows: ； ； ； in, , and The first The coordinate values ​​of each sensor measurement unit For the first The effective length of each sensor measurement unit , and For the first The sensor measures the attitude angle of each unit.

5. The method for self-evaluation of the deep hole installation quality of an array-type displacement gauge according to claim 4, characterized in that, Step 3, which involves determining the reference space curve of the array displacement gauge, includes: Step 3-1: Obtain the engineering design parameters for deep hole installation of the array displacement gauge; Step 3-2: Using the method in Step 2, based on the engineering design parameters obtained in Step 3-1, calculate the three-dimensional coordinates of each sensor measurement unit under standard conditions; Step 3-3: Connect the three-dimensional coordinates of each sensor measurement unit in the standard state in sequence to form the theoretical space curve of the displacement gauge under the ideal installation state along the design axis, that is, the reference space curve.

6. The method for self-evaluation of the deep hole installation quality of an array-type displacement gauge according to claim 5, characterized in that, Step 4, which involves matching the baseline spatial curve with the actual spatial distribution curve point by point and calculating the evaluation index, includes: Step 4-1: Match the reference spatial curve with the actual spatial distribution curve point by point using the sensor measurement unit. After matching, the points on the reference spatial curve... Points on the actual spatial distribution curve correspond; Step 4-2: Calculate three types of evaluation indicators: overall morphological offset, local morphological anomaly degree, and contact state index.

7. The method for self-evaluation of the deep hole installation quality of an array-type displacement gauge according to claim 6, characterized in that, The overall morphological offset mentioned in step 4-2 is calculated based on the root mean square value of the normal deviation of each point on the actual spatial distribution curve relative to the corresponding point on the reference spatial curve, specifically including: Calculate each point in the actual spatial distribution curve normal deviation , i.e., point Relative to point The normal distance is used to obtain the sequence. ,in The number of points matched; Calculate all normal deviations root mean square value .

8. The method for self-evaluation of the deep hole installation quality of an array displacement gauge according to claim 7, characterized in that, The local morphological anomaly degree mentioned in step 4-2 is calculated using the three-point curvature method, specifically including: With the current point Centered on the actual spatial distribution curve, select two consecutive points that are adjacent to each other. and Forming a three-point group ; Calculate the curvature of the current point by fitting a circular arc using three points. Then calculate the current curvature relative to the reference curvature. relative rate of change , means as follows: ； in, This represents the degree of local morphological anomaly.

9. A method for self-evaluation of the deep hole installation quality of an array-type displacement gauge according to claim 8, characterized in that, The contact state index mentioned in step 4-2 is calculated using the following methods: With point Centered on, select a preset length of Let the set of points within the sliding window be denoted as . In this sliding window, the calculation point The contact state index is calculated by sliding the slider point by point along the actual spatial distribution curve and based on the curvature of each point within the sliding window. .

10. A method for self-evaluation of the deep hole installation quality of an array-type displacement gauge according to claim 9, characterized in that, Step 5, which involves comparing the evaluation indicators with preset multi-level thresholds and installing quality self-assessment and alarms based on the comparison results, includes: When all evaluation indicators are within the normal threshold range, it is judged as normal; If a preset number of evaluation indicators exceed the normal threshold but do not reach the alarm threshold, an early warning will be issued. An alarm will be triggered if the number of preset evaluation indicators exceeds the alarm threshold.

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