A dual-source strong earthquake signal identification method and device for high-rise building monitoring

Abstract

The present invention relates to the field of vibration detection technology, specifically a dual-source strong earthquake signal identification method and device for high-rise building monitoring, the method comprising the following steps: S1 preparation and installation of a dual-source strong earthquake instrument: MEMS accelerometer and quartz accelerometer are vertically upward on the Z axis and horizontally distributed on the X and Y axes; S2 real-time synchronous acquisition of vibration signal components of the two accelerometers in three axial directions and consistency judgment; S3 strong earthquake instrument dual-source signal feature extraction: starting from T0, data vectors of a set time length are recorded for the three components of the MEMS accelerometer and the quartz accelerometer to form an input matrix for an identification model; S4 strong earthquake instrument dual-source signal type identification: forming a sample feature data set, optimizing a one-dimensional convolutional neural network, and constructing a dual-source signal identification model. The present invention has the advantages of integrating multiple types of sensors to identify vibration signals, thereby improving identification accuracy, reliability, and anti-interference ability.

Description

Technical Field

[0001] The present invention relates to the field of vibration detection technology, and in particular to a dual-source strong earthquake signal recognition method and device for high-rise building monitoring. Background Art

[0002] The increasing frequency and intensity of global seismic activity are placing higher demands on the accuracy and reliability of earthquake monitoring systems. Currently, earthquake monitoring primarily relies on a single type of accelerometer, such as quartz accelerometers or MEMS accelerometers (also known as microelectromechanical system accelerometers). These single sensors have limitations, such as slow response time and sensitivity to noise. This makes it difficult to accurately distinguish seismic signals from external interference in complex environments, resulting in significant errors in detecting strong and minor earthquakes.

[0003] Quartz accelerometers are widely used for precision detection due to their high accuracy and low noise, but their long response time limits their performance in high-frequency dynamic detection. MEMS accelerometers, on the other hand, are widely used in earthquake detection equipment due to their compactness, affordability, and fast response. However, MEMS sensors lack the sensitivity and anti-interference capabilities of quartz sensors and are susceptible to environmental noise and human-induced vibration, increasing the risk of false alarms.

[0004] Therefore, developing more accurate earthquake monitoring equipment by combining the high precision of quartz accelerometers with the fast response of MEMS accelerometers is key to solving the problems of existing earthquake monitoring systems. In particular, in strong earthquake monitoring, distinguishing true earthquake signals from interference from ambient noise or man-made vibrations and improving the reliability of data analysis are pressing technical challenges in the earthquake monitoring field.

[0005] The shortcomings of existing earthquake monitoring systems include reliance on single accelerometers, such as quartz or MEMS. The limitations of these sensors make it difficult to accurately distinguish seismic signals from external interference in complex environments, resulting in significant errors in detecting strong and minor earthquakes. Quartz accelerometers have a long response time, which affects high-frequency dynamic detection. While MEMS accelerometers offer fast response times, they lack sensitivity and interference resistance, making them susceptible to environmental noise and human-induced vibrations, resulting in a high false alarm rate. Distinguishing true seismic signals from interference from environmental noise or human-induced vibrations, and improving the reliability of data analysis, are key technical challenges facing the earthquake monitoring field.

[0006] Therefore, there is an urgent need to design a device that can well combine quartz accelerometers and MEMS accelerometers, integrate the detection characteristics and advantages of both, and improve the accuracy of vibration data detection and the anti-interference ability of the system. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a dual-source strong earthquake signal identification method and device for high-rise building monitoring, which is used to combine high-precision quartz accelerometers and high-response speed MEMS accelerometers to overcome unnecessary interference data generated by the different working properties of the sensors, and use intelligent algorithms to analyze the vibration data differences between the two in real time, effectively distinguish between earthquake signals and environmental noise or human interference, integrate the detection characteristics and advantages of the two, and improve the accuracy of vibration data detection and the anti-interference ability of the system.

[0008] In order to achieve the above objectives, a dual-source strong earthquake signal identification method for high-rise building monitoring is designed, which includes the following steps: S1. Preparation and installation of dual-source strong earthquake instrument: Install the MEMS accelerometer box on a flat surface, ensure that the Z axis of the MEMS accelerometer is vertically upward, and the X and Y axes are horizontally distributed; install the quartz accelerometer box on the flat exterior wall of the building in the same area, ensure that the Z axis of the quartz accelerometer is vertically upward, and the X and Y axes are horizontally distributed; S2. Dual-source signal acquisition and consistency analysis: Real-time synchronous acquisition of the three-axis vibration signal components of the MEMS accelerometer and the quartz accelerometer. When the amplitude of the vertical component of the Z axis of one of the accelerometers exceeds the set threshold, the moment is marked as the trigger moment T0, and the dual-source signal consistency judgment step is entered. If the consistency is judged, step S3 is entered. If not, it is determined to be artificial interference or instrument failure. The dual-source signal consistency judgment step is as follows: S21. Starting from the trigger threshold moment T0, the data of the set time of the MEMS accelerometer and the quartz accelerometer are recorded; S22. The difference characteristic values ​​between the sub-components of the X, Y and Z axis components of the above two accelerometers are calculated; S23. A dual-source signal consistency evaluation model is established and input The difference feature value is used to obtain the feature judgment value; S24. After the feature judgment value is substituted into the decision tree model, the consistency of the dual-source signal is judged according to the specified output label value; S3. Feature extraction of the strong earthquake instrument dual-source signal: starting from T0, the data vector of the set time length is recorded for the three components of the MEMS accelerometer and the quartz accelerometer to form the recognition model input matrix. The signal feature extraction method is as follows: S31. X, Y, Z axis positive envelope area: normalize the original signal of the X, Y, Z axis vertical direction, calculate the positive envelope area, and use the serial number as the horizontal axis and the X, Y, Z acceleration value as the vertical axis. Axis, draw a plane scatter plot, connect the adjacent positive points together in sequence, and the entire range enclosed by these curves projected onto the coordinate axis is the area to be calculated; S32. Calculate the negative envelope area of ​​the X, Y, and Z axes; S4. Identify the dual-source signal type of the strong earthquake instrument, as follows: S41. Obtain the known seismic wave type label value according to the characteristic value extracted in step S3 to form a sample feature data set; S42. Use a decomposition-based multi-objective evolutionary algorithm to optimize the one-dimensional convolutional neural network, adjust the convolution kernel size, pooling method, and regularization strategy to build a dual-source signal recognition model.

[0009] Preferably, the method of the present invention also includes: step S1 also includes a MEMS accelerometer box installation verification method: observe whether the free ball on one side of the MEMS accelerometer box shell straightens the vertical rope and completely covers the spherical pattern on the back of the cavity where the ball is located. If it is not completely covered, it means that the MEMS accelerometer is not placed correctly, and a leveling system is used to place it on its base and adjust it until the free ball completely overlaps with the spherical pattern behind it.

[0010] Preferably, the method of the present invention also includes: in step S1, the quartz accelerometer box is installed on a flat external wall of a building more than 1 meter from the ground, and the quartz accelerometer box installation verification method is as follows: observe whether the free ball on one side of the quartz accelerometer box shell straightens the vertical rope and completely covers the spherical pattern on the back of the cavity where the ball is located. If it is not completely covered, it means that the quartz accelerometer is not placed correctly and needs to be adjusted until the free ball completely coincides with the vertical line and the spherical pattern and long scale line behind it.

[0011] Preferably, the method of the present invention further includes: step S1 further includes: without destroying the condition that the Z axis of the aforementioned dual-source accelerometer is in an absolutely vertical direction, determining whether the shell surfaces of the quartz accelerometer box and the MEMS accelerometer box are parallel through the laser ranging units at the four corners of the bottom of the shell, thereby determining whether the X, Y directions of the three-axis quartz accelerometer and the X, Y component directions of the three-axis MEMS accelerometer are parallel in a one-to-one correspondence.

[0012] Preferably, the method of the present invention further comprises: the step S2 is specifically as follows: the difference feature values ​​include: rank correlation coefficient, cosine similarity, KL divergence and JS divergence, and the calculation method of the difference feature is as follows:

[0013]

[0014] Where A represents any one of the three axes X, Y, and Z. i It represents the point value of the i-th data point on the A-axis component within 2s from the trigger threshold time T0 of the MEMS accelerometer. It represents the average value of the point value on the A-axis component of the MEMS accelerometer within 2s from the trigger threshold time T0, i It represents the point value of the ith point on the A-axis component of the quartz accelerometer within 2s from the trigger threshold time T0. It represents the average value of the point value on the A-axis component of the quartz accelerometer within 2s from the trigger threshold time T0, CC A It represents the rank correlation coefficient of the two accelerometers on the A-axis component, CS A Indicates the cosine similarity of the two accelerometers on the A-axis component, KL A represents the KL divergence of the two accelerometers on the A-axis component, JS A represents the JS divergence of the two accelerometers on the A-axis component, i represents the data point sequence number of each accelerometer signal involved in the calculation, i = 1, 2, 3, ..., N, where N is the accelerometer data length; the calculation method of the dual-source signal consistency evaluation model is as follows:

[0015]

[0016] Where E(Q,M,A) represents the average characteristic value of the two accelerometer signals in the A-axis direction, Q represents the quartz accelerometer, M represents the MEMS accelerometer, A represents any one of the three axes X, Y, and Z, CC A It represents the rank correlation coefficient of the two accelerometers on the A-axis component, CS A Indicates the cosine similarity of the two accelerometers on the A-axis component, KL A represents the KL divergence of the two accelerometers on the A-axis component, JS A Represent the JS divergence of the two accelerometers on the A-axis component; obtain the average eigenvalues ​​of the three groups of two accelerometers on the X, Y, and Z axes respectively, and form the corresponding feature matrix; form the calculation results of the dual-source signal consistency evaluation model into a one-dimensional vector, and group and label the formed one-dimensional vector to form a label vector; substitute the feature matrix and label vector into the CART decision tree algorithm to establish a dual-source signal consistency evaluation model.

[0017] Preferably, the method of the present invention further comprises: the step S3 is specifically as follows: the forward envelope area is calculated as follows:

[0018]

[0019] in, Represents the closed irregular envelope area formed by the envelope formed by the positive data points on the A-axis of the quartz accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; Represents the closed irregular envelope area formed by the envelope formed by the positive data points of the MEMS accelerometer's A-axis starting from the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; represents the arithmetic mean of the areas of the two aforementioned envelope regions; Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i+1th positive data point on the A axis of the quartz accelerometer. Represents the absolute value of the amplitude of the i-th positive data point on the A-axis of the MEMS accelerometer, represents the absolute value of the amplitude of the i+1th positive data point on the A-axis of the MEMS accelerometer, N1 represents the length of the new data vector consisting of the positive value of the quartz accelerometer signal starting from the trigger threshold time T0 and lasting for 10 seconds, N2 represents the length of the new data vector consisting of the positive value of the MEMS accelerometer signal starting from the trigger threshold time T0 and lasting for 10 seconds, and i represents the data point sequence number; the negative envelope area is calculated as follows:

[0020]

[0021] in, Represents the closed irregular envelope area formed by the envelope formed by the negative data points on the A-axis of the quartz accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; Represents the closed irregular envelope area formed by the envelope formed by the negative A-axis data points of the MEMS accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; represents the arithmetic mean of the areas of the two aforementioned envelope regions; Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i+1th negative data point on the A axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the MEMS accelerometer. represents the absolute value of the amplitude of the i+1th negative data point on the MEMS accelerometer A-axis. N3 represents the length of the new data vector consisting of the negative values ​​of the quartz accelerometer signal, which lasts for 10 seconds starting at the trigger threshold time T0. N4 represents the length of the new data vector consisting of the negative values ​​of the MEMS accelerometer signal, which lasts for 10 seconds starting at the trigger threshold time T0. i represents the serial number.

[0022] The present invention also provides a dual-source strong earthquake signal identification device for high-rise building monitoring, comprising: several accelerometer boxes, one side of which is provided with a groove; a free ball is provided in the groove; a vertical groove and a ball groove are provided on one side of the cavity of the groove; a MEMS accelerometer or a quartz accelerometer is provided in the accelerometer box to form a MEMS accelerometer box or a quartz accelerometer box, the MEMS accelerometer box and the quartz accelerometer box are placed in the same area, the MEMS accelerometer box and the quartz accelerometer box are signal-connected to a data processing module, and the data processing module includes any one of the above-mentioned dual-source strong earthquake signal identification methods for high-rise building monitoring.

[0023] Preferably, the device of the present invention also includes: the free ball includes a vertical rope and a swinging ball, the upper end of the vertical rope is connected to the upper top surface of the groove cavity, and the lower end is connected to the swinging ball, and the swinging ball is suspended in the groove cavity by the vertical rope and swings freely; the vertical groove is arranged in the same shape as the vertical rope, and the ball groove is arranged in the same shape as the swinging ball; so that when the accelerometer box is in a horizontal state, the area projection of the free ball in the horizontal direction fully overlaps with the ball groove and the vertical groove.

[0024] Preferably, the device of the present invention further comprises: a plurality of laser ranging units symmetrically arranged on the bottom of the accelerometer housing, for determining whether the axes of the accelerometers in the plurality of accelerometer housings are parallel.

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] Compared with the vibration recognition model that only uses a single type of sensor, the present invention improves the recognition accuracy by integrating multiple types of sensors to identify vibration signals, and at the same time specifically solves the problems of high false trigger rate and inaccurate seismic wave recognition in the multi-sensor, multi-signal source vibration recognition model. First, the shell structure of the accelerometer sensor is optimized to ensure the accuracy of multiple types of sensors during the signal acquisition process. Then, a signal consistency judgment method is proposed to ensure that the strong earthquake monitoring system can still work normally under environmental interference and internal faults. In addition, a data set containing multiple positive and negative half-envelope areas is constructed, and the parameters of the one-dimensional convolutional neural network are optimized using a decomposition-based multi-objective evolutionary algorithm. Finally, we combine the optimized one-dimensional convolutional neural network with the data set to form an accurate and reliable dual-source signal recognition model, thereby improving the accuracy, reliability and anti-interference ability of vibration signal recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 , is a flow chart of dual-source signal identification in the method of the present invention;

[0028] Figure 2 , is a leveling flow chart of the dual-source strong motion seismograph system in the method of the present invention;

[0029] Figure 3 , is a schematic diagram of the structure of the device of the present invention;

[0030] Figure 4 , is a logical diagram of the CART decision tree algorithm when predicting the new characteristic value of the dual-source signal in the method of the present invention;

[0031] In the figure: 1 accelerometer box, 2 groove, 3 vertical rope, 4 swing ball, 5 vertical groove, 6 ball groove, 7 laser ranging unit. DETAILED DESCRIPTION

[0032] In order to make the purpose, principle and structure of the present invention more clear, it is further described below with reference to the accompanying drawings and specific embodiments.

[0033] Example 1:

[0034] This embodiment provides a dual-source strong earthquake signal recognition method for high-rise building monitoring, the specific contents of which are as follows:

[0035] Step 1: Preparation and installation of dual-source strong motion seismometer.

[0036] The dual-source strong motion instrument includes a MEMS accelerometer housing, a quartz accelerometer housing, and a data processing module.

[0037] See also Figure 2 First, place the MEMS accelerometer box on a flat surface in the same area, ensuring that its Z component is in the true vertical direction and that its X and Y components are in the horizontal plane. The inspection and adjustment method is to observe whether the vertical rope 3 of the free ball on one side of the MEMS accelerometer box shell is straightened, and observe whether the projection of the free ball's vertical rope 3 and the swinging ball 4 completely cover the pattern of the vertical groove 5 and the ball groove 6 of the groove 2 of the accelerometer box 1. If not, it means that the MEMS accelerometer is not placed correctly and needs to be placed on its base with the help of a third-party leveling system and fine-tuned until the free ball completely overlaps with the pattern of the vertical groove 5 and the ball groove 6 behind it.

[0038] Then, the quartz accelerometer box in the dual-source strong motion seismometer is fitted and fixed on the flat wall of the building in the same area (the box should be more than 1 meter from the ground), ensuring that its Z component is the true vertical direction and X and Y are distributed in the horizontal plane. The inspection and adjustment method is: observe whether the free ball on one side of the quartz accelerometer box shell straightens the vertical rope, and observe whether the vertical rope 3 of the free ball, the projection of the swinging ball 4, and the pattern of the vertical groove 5 and the ball groove 6 of the groove 2 of the accelerometer box 1 are completely covered. If not completely covered, it means that the quartz accelerometer is not placed correctly and needs to be fine-tuned until the vertical rope 3 of the free ball, the projection of the swinging ball 4, and the pattern of the vertical groove 5 and the ball groove 6 of the groove 2 of the accelerometer box 1 are completely covered.

[0039] Finally, without destroying the Z-axis of the dual-source accelerometer composed of the aforementioned MEMS accelerometer and quartz accelerometer and maintaining it in an absolutely vertical direction, the laser ranging units on the four corners of the bottom of the accelerometer housing are used to determine whether the X and Y directions of the three-axis quartz accelerometer and the X and Y component directions of the three-axis MEMS accelerometer are parallel one to one (that is, the bottom edges of the housing remain parallel one to one), that is, the X-axis is parallel to the X-axis, and the Y-axis is parallel to the Y-axis. The specific operation method is as follows: (1) keep the accelerometer box arranged on the wall of the building still, and slightly rotate the accelerometer box set on the flat ground so that the side with the XYZ mark is basically flush with the outside of the accelerometer box set on the wall of the building with the XYZ mark; (2) operate the computer to make the laser measuring units 7 at the two corners of the bottom of the accelerometer box set on the flat ground simultaneously emit laser signals to the laser measuring units 7 at the two corners of the bottom of the accelerometer box set on the wall of the building, so as to measure whether the multi-point distances of the bottom edge lines of the two grid accelerometer box outer shell surfaces are equal (or whether the distance measurement difference is 0). If they are equal, it means that the two outer shell surfaces are flush. Otherwise, the two surfaces need to be manually adjusted according to the measurement value.

[0040] Step 2: Dual-source signal acquisition and consistency analysis

[0041] See also Figure 1 .

[0042] (1) Real-time synchronous acquisition of a total of 6 ground vibration signal components of the two accelerometers mentioned above ( q represents a quartz accelerometer, m represents a MEMS accelerometer, x, y, and z represent three axes, and a represents a signal component. When the vertical (also known as the Z-axis) component amplitude of one of the accelerometers is or When the threshold a is exceeded (the threshold triggering moment is recorded as T0), the dual-source signal consistency judgment step is entered. If they are consistent, the dual-source signal type identification step is entered. Otherwise, it is considered that there is a dual-source signal inconsistency problem caused by artificial interference or instrument failure.

[0043] (2) First, taking the vertical Z component as an example, data with a time length of 2s is recorded from the trigger threshold time T0 (the sampling rate is set to 2000Hz, and can also be other values ​​between 200-4000Hz); since the accelerometer boxes of the two accelerometers are not installed in the same place, the instrument output signals will be different, but the signal difference will show a similar waveform fluctuation development trend. The similar waveform fluctuation development trend only differs in amplitude and local minimum value. The difference eigenvalues ​​between the three subcomponents of the vertical Z component of the two accelerometers are calculated. The difference between the three-component outputs of the two accelerometers is described by four eigenvalues: Spearman Coefficient of Rank Correlation (CC), Cosine Similarity (CS), Kullback-Leibler Divergence (KL), and Jensen-Shannon Divergence (JS). They represent the waveform shape similarity, angle, statistical error, and distribution difference between the two sets of vectors, respectively. Their values ​​range from [-1, 1], [-1, 1], [0, +∞], and [0, 1]. The eigenvalue calculation formula is as follows:

[0044]

[0045] Where A represents any one of the three axes X, Y, and Z. i It represents the point value of the i-th data point on the A-axis component within 2s from the trigger threshold time T0 of the MEMS accelerometer. It represents the average value of the point value on the A-axis component of the MEMS accelerometer within 2s from the trigger threshold time T0, i It represents the point value of the ith point on the A-axis component of the quartz accelerometer within 2s from the trigger threshold time T0. It represents the average value of the point value on the A-axis component of the quartz accelerometer within 2s from the trigger threshold time T0, CC A It represents the rank correlation coefficient of the two accelerometers on the A-axis component, CS A Indicates the cosine similarity of the two accelerometers on the A-axis component, KL A represents the KL divergence of the two accelerometers on the A-axis component, JS A represents the JS divergence of the two accelerometers on the A-axis component, i represents the data point sequence number of each accelerometer signal involved in the calculation, i = 1, 2, 3, ..., N, where N is the accelerometer data length;

[0046] Substituting Z into A, we can obtain the characteristic values ​​of the two accelerometers on the Z-axis component: CC Z , CS Z , KL Z JS Z Similarly, the characteristic values ​​of the two accelerometers of the X and Y axis components can be obtained: CC X , CS X , KL X JS X 、CC Y , CS Y , KL Y JS Y .

[0047] (3) Using the eigenvalues ​​of the above components, a dual-source signal consistency evaluation model is established:

[0048]

[0049] Where E(Q,M,A) represents the average characteristic value of the two accelerometer signals in the A-axis direction, Q represents the quartz accelerometer, M represents the MEMS accelerometer, A represents any one of the three axes X, Y, and Z, CC A It represents the rank correlation coefficient of the two accelerometers on the A-axis component, CS A Indicates the cosine similarity of the two accelerometers on the A-axis component, KL A represents the KL divergence of the two accelerometers on the A-axis component, JS A It represents the JS divergence of the two accelerometers on the A-axis component. After substituting it into the calculation, we can get E(Q,M,X), E(Q,M,Y), and E(Q,M,Z).

[0050] After substituting E(Q,M,X), E(Q,M,Y), and E(Q,M,Z) into the decision tree model, the consistency of the dual-source signals is judged according to the specified output label value.

[0051] See also Figure 4The specific process is as follows: 50 natural earthquake event signals, 50 artificial blasting event signals, and 50 accelerometer installation anomaly or sensor failure signals (which can be obtained through installation misalignment and hammering of buildings) are collected. A total of 150 sets of E(Q,M,X), E(Q,M,Y), and E(Q,M,Z) eigenvalues ​​are calculated to form a 150×3 feature matrix. A one-dimensional cell vector consisting of 150 rows of label values ​​is designed, with the labels for rows 1-100 set to "Y" and rows 101-150 set to "N". The set of eigenvalues ​​and the one-dimensional vector with label values ​​are substituted into the CART decision tree algorithm to establish a trained model. When predicting the new eigenvalue of the dual-source signal, if it is "Y", the signal output consistency of the current dual-source system is considered high. If it is "N", it is considered that the current system layout is abnormal or the sensor is faulty, and the signal will not enter the subsequent strong motion seismic wave identification stage. The CART decision tree algorithm, taking the eigenvalue set judgment method on the Z-axis component as an example, can be set as follows: in the first comparison link, when E(Q,M,Z)<4.75, output "Y", otherwise enter the next comparison link; when E(Q,M,Z)≥5.05, output "N", otherwise enter the next comparison link; when E(Q,M,X) is greater than or equal to 6.5, output "Y", otherwise enter the next comparison link; when E(Q,M,Y)<3.1, output "N", otherwise output "Y".

[0052] Step 3: Strong motion detector dual-source signal feature extraction process

[0053] When step 2 determines that the signal output consistency of the dual-source system is high, the strong vibration signal recognition link is entered to identify strong vibration signals, artificial blasting signals, and other event signals (mechanical construction, human activities, etc.).

[0054] Specifically, the process of extracting signal features is as follows:

[0055] (1) Positive envelope area of ​​X, Y, and Z axes: Normalize the original signal of a certain axis and calculate the positive envelope area. That is, use the serial number as the horizontal axis and the axial acceleration value of the axis as the vertical axis to draw a plane scatter plot. Connect adjacent positive points in sequence. The entire range enclosed by these curves projected onto the coordinate axis is the area to be calculated.

[0056] The forward envelope area is calculated as follows:

[0057]

[0058] in, Represents the closed irregular envelope area formed by the envelope consisting of the positive data points on the A-axis of the quartz accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is composed of the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis.

[0059] Represents the closed irregular envelope area formed by the envelope consisting of the positive data points on the A-axis of the MEMS accelerometer starting from the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of the two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is composed of the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis.

[0060] It represents the arithmetic mean of the areas of the two aforementioned envelope regions.

[0061] Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i+1th positive data point on the A axis of the quartz accelerometer. Represents the absolute value of the amplitude of the i-th positive data point on the A-axis of the MEMS accelerometer, represents the absolute value of the amplitude of the i+1th positive data point on the A-axis of the MEMS accelerometer. N1 represents the length of the new data vector consisting of the positive values ​​of the quartz accelerometer signal starting at the trigger threshold time T0 and lasting for 10 seconds. N2 represents the length of the new data vector consisting of the positive values ​​of the MEMS accelerometer signal starting at the trigger threshold time T0 and lasting for 10 seconds. i represents the data point sequence number.

[0062] (2) Negative envelope area of ​​X, Y, and Z axes:

[0063] The negative envelope area is calculated as follows:

[0064]

[0065] in, Represents the closed irregular envelope area formed by the envelope consisting of the negative data points on the A-axis of the quartz accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is composed of the amplitude of the point on the vertical axis and the serial number of the point on the horizontal axis.

[0066] Represents the closed irregular envelope area formed by the envelope consisting of the negative data points on the A-axis of the MEMS accelerometer starting from the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is composed of the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis.

[0067] It represents the arithmetic mean of the areas of the two aforementioned envelope regions.

[0068] Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i+1th negative data point on the A axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the MEMS accelerometer. represents the absolute value of the amplitude of the i+1th negative data point on the MEMS accelerometer A-axis. N3 represents the length of the new data vector consisting of the negative values ​​of the quartz accelerometer signal, which lasts for 10 seconds starting at the trigger threshold time T0. N4 represents the length of the new data vector consisting of the negative values ​​of the MEMS accelerometer signal, which lasts for 10 seconds starting at the trigger threshold time T0. i represents the serial number.

[0069] Similarly, calculate the positive envelope area of ​​the X-axis, the negative envelope area of ​​the X-axis, the positive envelope area of ​​the Y-axis, and the negative envelope area of ​​the Y-axis.

[0070] Step 4: Strong motion detector dual-source signal type identification process

[0071] (1) Calculate the above-mentioned characteristic parameters to form a 6-dimensional × 1000-row sample feature data set with 1000 known seismic wave type label values ​​(including 600 rows of natural earthquake event signals, 300 artificial blasting event signals, and 100 environmental noise signals or artificial construction signals), and randomly split it into a training set, a validation set, and a test set in a ratio of 7:1:2;

[0072] (2) Then, based on the decomposition multi-objective evolutionary algorithm (MOEA / D), the existing one-dimensional convolutional neural network (1D-CNN) parameters such as the convolution kernel size, pooling operation method (maximum pooling or average pooling), and regularization method (L1 or L2) are optimized for hyperparameters, in the hope of obtaining the best parameters to replace the original default parameters or default methods in the model, forming a new prediction model - MOEA / D-ID-CNN, which then identifies the type of unknown signals.

[0073] (3) The core of multi-objective optimization is to construct multiple objective functions. Here we construct an objective function with the training set as the sample, the three hyperparameters (convolution kernel size, pooling operation method, and regularization method) as independent variables, and the prediction effect as the dependent variable. The prediction effect is evaluated by the AUC value (area under the ROC curve), mean absolute error (MAE), mean bias error (MBE), and determination coefficient (R2).

[0074] Since optimization is all about finding the minimum value of the objective function, the above evaluation indicators need to be transformed. The final form of the solution model for the MOEA / D multi-objective optimization problem is:

[0075] minF = {f1,f2,f3,f4};

[0076]

[0077] In the above formula, f1 is the mean bias error (MBE), which can determine whether the prediction model has positive or negative bias; f2 is the mean absolute error (MAE); f3 is the calculation formula of 1 minus the coefficient of determination (R2 score), which is used to evaluate the degree of explanation of the independent variable to the dependent variable in the regression analysis; f4 is the calculation formula of 1 minus the AUC value; y i The predicted value identified by the model; is the actual value; is the average value of the predicted value set; n is the total number of samples involved in the regression evaluation calculation; M is the number of points on the ROC curve.

[0078] Example 2:

[0079] This embodiment provides a dual-source strong earthquake signal recognition device for high-rise building monitoring. Figure 3 . It includes: several accelerometer boxes 1, one side of the accelerometer box 1 is provided with a groove 2; a free ball is provided in the groove 2; a vertical groove 5 and a ball groove 6 are provided on one side of the cavity of the groove 2; a MEMS accelerometer or a quartz accelerometer is provided in the accelerometer box 1 to form a MEMS accelerometer box or a quartz accelerometer box, the MEMS accelerometer box and the quartz accelerometer box are placed in the same area, the MEMS accelerometer box and the quartz accelerometer box are signal-connected to a data processing module, and the data processing module is used for the dual-source strong earthquake signal identification method for high-rise building monitoring as described in Example 1.

[0080] The free ball comprises a vertical rope 3 and a swinging ball 4. The upper end of the vertical rope 3 is connected to the upper surface of the groove 2 cavity, and the lower end is connected to the swinging ball 4. The swinging ball 4 is suspended within the groove 2 cavity by the vertical rope 3 and freely swings. The vertical groove 5 is configured to align with the vertical rope 3, and the ball groove 6 is configured to align with the swinging ball 4. When the accelerometer case 1 is horizontal, the horizontal projection of the free ball fully overlaps with the ball groove 6 and the vertical groove 5. Several laser ranging units 7 are symmetrically arranged on the bottom of the accelerometer case 1 to determine whether the axes of the accelerometers in the accelerometer cases 1 are parallel.

[0081] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent replacement or change made by any technician familiar with the technical field within the technical scope disclosed by the present invention based on the technical solution and novel concept of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A dual-source strong earthquake signal identification method for high-rise building monitoring, characterized in that: The steps include: S1. Preparation and Installation of the Dual-Source Strong Motion Seismometer: Install the MEMS accelerometer enclosure on a flat surface, ensuring that the Z axis of the MEMS accelerometer is pointing vertically upward and the X and Y axes are horizontally aligned. Install the quartz accelerometer enclosure on a flat exterior building wall in the same area, ensuring that the Z axis of the quartz accelerometer is pointing vertically upward and the X and Y axes are horizontally aligned. S2. Dual-source signal acquisition and consistency analysis: The three-axis vibration signal components of the MEMS accelerometer and the quartz accelerometer are collected synchronously in real time. When the amplitude of the vertical component of the Z axis of one of the accelerometers exceeds the set threshold, this moment is marked as trigger time T0, and the dual-source signal consistency judgment step is entered. If the consistency is judged, the process proceeds to step S3. If not, it is determined to be human interference or instrument failure. The steps for determining the consistency of the dual-source signals are as follows: S21. Starting from the trigger threshold time T0, record the MEMS accelerometer and quartz accelerometer set time data; S22. Calculate the difference eigenvalues ​​between the subcomponents of the X, Y, and Z axis components of the two accelerometers; S23. Establish a dual-source signal consistency evaluation model, input the difference feature value to obtain the feature judgment value; S24. Substituting the feature criterion value into the decision tree model, and judging the consistency of the dual-source signal according to the specified output label value; S3. Strong motion detector dual-source signal feature extraction: Starting from T0, data vectors of the three components of the MEMS accelerometer and the quartz accelerometer are recorded for a set time length to form the recognition model input matrix. The signal feature extraction method is as follows: S31. X, Y, and Z Axis Positive Envelope Area: Normalize the original vertical signals of the X, Y, and Z axes and calculate the positive envelope area. Draw a scatter plot with the serial number as the horizontal axis and the X, Y, and Z acceleration values ​​as the vertical axis. Connect adjacent positive points in sequence. The entire area enclosed by these curves projected onto the coordinate axes is the area to be calculated. S32. Calculate the negative envelope area of ​​the X, Y, and Z axes; S4. Identification of the type of strong motion detector dual-source signal, as follows: S41. Obtain known seismic wave type label values ​​according to the feature values ​​extracted in step S3 to form a sample feature data set; S42. A decomposition-based multi-objective evolutionary algorithm is used to optimize the one-dimensional convolutional neural network, adjusting the convolution kernel size, pooling method, and regularization strategy to construct a dual-source signal recognition model.

2. A dual-source strong earthquake signal identification method for high-rise building monitoring according to claim 1, characterized in that: The step S1 also includes a MEMS accelerometer box installation verification method: observing whether the free ball on one side of the MEMS accelerometer box shell straightens the vertical rope and completely covers the spherical pattern on the back of the cavity where the ball is located. If it is not completely covered, it means that the MEMS accelerometer is not placed correctly. A leveling system is used to place it on its base and adjust it until the free ball completely overlaps with the spherical pattern behind it.

3. The dual-source strong earthquake signal identification method for high-rise building monitoring according to claim 1, characterized in that: In step S1, the quartz accelerometer box is installed on a flat exterior wall of a building at least 1 meter above the ground. The quartz accelerometer box installation verification method is as follows: observe whether the free ball on one side of the quartz accelerometer box shell straightens the vertical rope and completely covers the spherical pattern on the back of the cavity where the ball is located. If it is not completely covered, it means that the quartz accelerometer is not placed correctly and needs to be adjusted until the free ball completely coincides with the vertical line, the spherical pattern behind it, and the long scale line.

4. The dual-source strong earthquake signal identification method for high-rise building monitoring according to claim 1, characterized in that: The step S1 further includes: without destroying the condition that the Z axis of the aforementioned dual-source strong motion seismometer is in an absolutely vertical direction, determining whether the shell surfaces of the quartz accelerometer box and the MEMS accelerometer box are parallel through the laser ranging units at the four corners of the bottom of the shell, thereby determining whether the X and Y directions of the three-axis quartz accelerometer and the X and Y component directions of the three-axis MEMS accelerometer are parallel in a one-to-one correspondence.

5. The dual-source strong earthquake signal identification method for high-rise building monitoring according to claim 1, characterized in that: The step S2 is specifically as follows: The difference feature values ​​include: rank correlation coefficient, cosine similarity, KL divergence and JS divergence. The calculation method of the difference feature is as follows: Where A represents any one of the three axes X, Y, and Z. i It represents the point value of the i-th data point on the A-axis component within 2s from the trigger threshold time T0 of the MEMS accelerometer. It represents the average value of the point value on the A-axis component of the MEMS accelerometer within 2s from the trigger threshold time T0, i It represents the point value of the ith point on the A-axis component of the quartz accelerometer within 2s from the trigger threshold time T0. It represents the average value of the point value on the A-axis component of the quartz accelerometer within 2s from the trigger threshold time T0, CC A It represents the rank correlation coefficient of the two accelerometers on the A-axis component, CS A Indicates the cosine similarity of the two accelerometers on the A-axis component, KL A represents the KL divergence of the two accelerometers on the A-axis component, JS A represents the JS divergence of the two accelerometers on the A-axis component, i represents the data point sequence number of each accelerometer signal involved in the calculation, i = 1, 2, 3, ..., N, where N is the accelerometer data length; The calculation method of the dual-source signal consistency evaluation model is as follows: Where E(Q,M,A) represents the average characteristic value of the two accelerometer signals in the A-axis direction, Q represents the quartz accelerometer, M represents the MEMS accelerometer, A represents any one of the three axes X, Y, and Z, CC A It represents the rank correlation coefficient of the two accelerometers on the A-axis component, CS A Indicates the cosine similarity of the two accelerometers on the A-axis component, KL A represents the KL divergence of the two accelerometers on the A-axis component, JS A It represents the JS divergence of the two accelerometers on the A-axis component; Obtain the average eigenvalues ​​of the three groups of two accelerometers on the X, Y, and Z axes, and form corresponding characteristic matrices; form a one-dimensional vector by calculating the obtained dual-source signal consistency evaluation model, and group and label the formed one-dimensional vector to form a label vector; Substitute the feature matrix and label vector into the CART decision tree algorithm to establish a dual-source signal consistency evaluation model.

6. The dual-source strong earthquake signal identification method for high-rise building monitoring according to claim 1, characterized in that: The step S3 is specifically as follows: The forward envelope area is calculated as follows: in, Represents the closed irregular envelope area formed by the envelope formed by the positive data points on the A-axis of the quartz accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; Represents the closed irregular envelope area formed by the envelope formed by the positive data points of the MEMS accelerometer's A-axis starting from the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; represents the arithmetic mean of the areas of the two aforementioned envelope regions; Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i+1th positive data point on the A axis of the quartz accelerometer. Represents the absolute value of the amplitude of the i-th positive data point on the A-axis of the MEMS accelerometer, represents the absolute value of the amplitude of the i+1th positive data point on the A-axis of the MEMS accelerometer. N1 represents the length of the new data vector consisting of the positive values ​​of the quartz accelerometer signal starting at the trigger threshold time T0 and lasting for 10 seconds. N2 represents the length of the new data vector consisting of the positive values ​​of the MEMS accelerometer signal starting at the trigger threshold time T0 and lasting for 10 seconds. i represents the data point sequence number. The negative envelope area is calculated as follows: in, Represents the closed irregular envelope area formed by the envelope formed by the negative data points on the A-axis of the quartz accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; Represents the closed irregular envelope area formed by the envelope formed by the negative A-axis data points of the MEMS accelerometer starting at the trigger threshold time T0 and lasting for 10 seconds, projected onto the horizontal axis of a two-dimensional plane, where the A-axis is any one of the X, Y, and Z axes, and the two-dimensional plane is constructed with the amplitude of the point as the vertical axis and the serial number of the point as the horizontal axis; represents the arithmetic mean of the areas of the two aforementioned envelope regions; Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i+1th negative data point on the A axis of the quartz accelerometer. Indicates the absolute value of the amplitude of the i-th negative data point on the A-axis of the MEMS accelerometer. represents the absolute value of the amplitude of the i+1th negative data point on the MEMS accelerometer A-axis. N3 represents the length of the new data vector consisting of the negative values ​​of the quartz accelerometer signal, which lasts for 10 seconds starting at the trigger threshold time T0. N4 represents the length of the new data vector consisting of the negative values ​​of the MEMS accelerometer signal, which lasts for 10 seconds starting at the trigger threshold time T0. i represents the serial number.

7. A dual-source strong earthquake signal identification device for high-rise building monitoring, characterized in that: include: A plurality of accelerometer boxes, each having a groove formed on one side thereof; A free ball is provided in the groove; a vertical groove and a ball groove are provided on one side of the cavity of the groove; A MEMS accelerometer or a quartz accelerometer is provided in the accelerometer box to form a MEMS accelerometer box or a quartz accelerometer box. The MEMS accelerometer box and the quartz accelerometer box are placed in the same area. The MEMS accelerometer box and the quartz accelerometer box are signal-connected to a data processing module. The data processing module includes a dual-source strong earthquake signal identification method for high-rise building monitoring as described in any one of claims 1-6.

8. The dual-source strong earthquake signal identification device for high-rise building monitoring according to claim 7, characterized in that: Also includes: The free ball includes a vertical rope and a swinging ball. The upper end of the vertical rope is connected to the upper top surface of the groove cavity, and the lower end is connected to the swinging ball. The swinging ball is suspended in the groove cavity by the vertical rope and swings freely. The vertical groove is arranged in the shape of the vertical rope, and the ball groove is arranged in the shape of the swinging ball; When the accelerometer box is in a horizontal state, the area projection of the free ball in the horizontal direction fully overlaps with the ball groove and the vertical groove.

9. The dual-source strong earthquake signal identification device for high-rise building monitoring according to claim 7, characterized in that: A plurality of laser distance measuring units are symmetrically arranged on the bottom of the accelerometer box, and are used to determine whether the axes of the accelerometers in the accelerometer boxes are parallel.

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