Falling weight deflectometer impact point identification method and system

Abstract

The invention relates to the technical field of road surface detection, in particular to a method and a system for identifying impact points of a drop weight deflectometer. The method comprises the following steps: acquiring impact data of the drop weight deflectometer in a calibration process, and setting an initial boundary of a target frequency band according to the impact data; calculating a frequency spectrum according to the initial boundary of the target frequency band, and completing signal reconstruction by using a frequency spectrum calculation result; performing normalization processing on the reconstructed signal, and obtaining a forward differential sequence through a normalization processing result; constructing an acceleration simulation model, preliminarily identifying an impact point based on the forward difference sequence, and adjusting the initial boundary of the target frequency band in combination with the acceleration simulation model and the preliminary identification result of the impact point to obtain an optimal target frequency band boundary; and identifying the impact point of the drop weight deflectometer by using the optimal target frequency band boundary. By combining frequency domain filtering and gradient optimization, the problem of impact point misrecognition caused by high interference noise when the drop weight deflectometer is calibrated is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of road surface detection, and in particular to a method and system for identifying an impact point of a falling weight deflectometer. Background Art

[0002] Falling Weight Deflectometer (FWD) is a non-destructive testing device widely used in road engineering, mainly used to evaluate the bearing capacity of pavement structures. Its working principle is to simulate the impact of vehicle load on the road surface, thereby causing the road surface to sink, and then indirectly evaluate the road surface quality and structural performance. Specifically, the FWD device applies a rated impact force to the road surface by lifting and releasing a heavy hammer of a certain mass, and uses sensors to record the road surface response.

[0003] The accuracy of FWD measurement results has always been a key concern in the field of highway engineering. In order to ensure the accuracy of FWD measurement results, an accelerometer is usually used as a reference, and the accelerometer and the calibrated seismic detector are coaxially arranged. The reference device collects the impact signal and identifies the impact point by the amplitude and standard deviation of the acceleration. However, in actual operation, FWD equipment is easily disturbed by mechanical vibration during calibration, which can lead to incorrect identification of impact events and misjudge high-frequency vibration interference as impact signals. In actual calibration tests, the structural design, lack of maintenance and other factors of the equipment under test will cause large mechanical vibration interference, and the current traditional impact point detection method judges the impact event through a single threshold condition, which cannot effectively eliminate noise interference.

[0004] In the calibration test of FWD, the accelerometer collects acceleration signals in real time, and identifies the occurrence of drop hammer impact events through signal processing procedures. In the traditional algorithm, when searching for impact points, the impact event trigger point is first preliminarily screened with a threshold of 0.2g. Further checking forward from the trigger point, it is generally believed that when the standard deviation of 25 consecutive sampling points does not exceed 0.02g and the absolute value of acceleration does not exceed 0.03g, the sampling point is considered to be an impact point. Although this method is simple, its effect is significantly reduced in experimental environments with large interference or noise. The most obvious problem in the traditional algorithm is that the fluctuation of the 0.2g acceleration signal is used as the trigger condition for determining an impact event. When the interference signal exceeds the threshold, the subsequent algorithm process will be triggered. For example Figure 1 As shown in Figure 1, when the interference signal amplitude exceeds the threshold, the trigger point and impact point are identified according to the traditional algorithm. The sudden change of the signal here is caused by the vibration of the mechanical structure when the FWD lifts the heavy hammer, and the actual impact occurs after 0.3s. Summary of the invention

[0005] In view of the shortcomings of the existing methods and the needs of practical applications, in order to improve the recognition accuracy of the impact point during the calibration process of the drop-weight deflectometer, the problem of misidentification of the impact point caused by high interference noise is solved. On the one hand, the present invention provides a method for identifying the impact point of the drop-weight deflectometer, comprising the following steps: obtaining the impact data of the drop-weight deflectometer during the calibration process, setting the initial boundary of the target frequency band according to the impact data; calculating the spectrum according to the initial boundary of the target frequency band, and completing signal reconstruction using the spectrum calculation result; normalizing the reconstructed signal, and obtaining a forward differential sequence through the normalization result; constructing an acceleration simulation model, preliminarily identifying the impact point based on the forward differential sequence, combining the acceleration simulation model and the preliminary identification result of the impact point, adjusting the initial boundary of the target frequency band, and obtaining the optimal target frequency band boundary; using the optimal target frequency band boundary, identifying the impact point of the drop-weight deflectometer. The present invention preliminarily divides the initial boundary of the target frequency band from the original impact data and then reconstructs the signal according to this boundary, and then constructs the acceleration simulation model and performs gradient optimization based on the reconstructed signal, which solves the problem of misidentification of the impact point caused by high interference noise during the calibration of the drop-weight deflectometer.

[0006] Optionally, setting the target frequency band initial boundary according to the impact data comprises the following steps: The impact data is subjected to fast Fourier analysis to calculate the average amplitude of each frequency point; the frequency band width is set, and the initial boundary of the target frequency band is set according to the frequency band width and the average amplitude. The present invention sets the initial boundary of the target frequency band by performing fast Fourier analysis on the impact data, which is conducive to ensuring that the component peak of the target frequency band is completely consistent with the actual impact event in terms of time scale, thereby improving the accuracy of the impact point identification of the present invention.

[0007] Optionally, calculating the spectrum according to the initial boundary of the target frequency band and completing signal reconstruction using the spectrum calculation result comprises the following steps: Based on the initial boundary of the target frequency band, the target frequency band data is obtained; the spectrum of the target frequency band data is calculated, the remaining positive frequency is set to zero, and the negative frequency spectrum is supplemented by conjugate symmetry to obtain the spectrum calculation result; based on the spectrum calculation result, the signal is reconstructed by inverse Fourier transform. The present invention removes interference components in the signal through signal reconstruction, which is conducive to extracting useful information.

[0008] Optionally, the frequency spectrum of the target frequency band data is calculated to satisfy the following formula: in, represents the spectrum of the target frequency band, represents the index of the frequency point, Indicates the number of sampling points, Represents the sequence of signals to be processed, represents the imaginary unit, Represents the index of the upper frequency point, Indicates the index of the upper frequency point.

[0009] Optionally, the remaining part of the positive frequency is set to zero, satisfying the following formula: in, Indicates that the positive frequency part corresponds to the spectrum, represents the spectrum of the target frequency band, represents the index of the frequency point, Represents the index of the upper frequency point, Represents the index of the upper frequency point, Indicates the number of sampling points.

[0010] Optionally, the signal reconstruction is completed by inverse Fourier transform based on the spectrum calculation result, satisfying the following formula: in, represents the reconstructed signal, Indicates the number of sampling points, represents the index of the frequency point, Indicates that the positive frequency part corresponds to the spectrum, Represents an imaginary unit.

[0011] Optionally, the construction of the acceleration simulation model comprises the following steps: The road surface is simplified into an underdamped second-order system; the heavy hammer impact is simplified into a pulse input and then Laplace transform is performed; the output result of the underdamped second-order system is obtained based on the Laplace transform result; the impact signal is simulated according to the output result, high-frequency mechanical vibration noise and high-frequency background noise are constructed, and the acceleration simulation model is obtained by combining the simulated impact signal, the high-frequency mechanical vibration noise and the high-frequency background noise. By constructing the acceleration simulation model, the present invention is conducive to quickly adjusting the initial boundary of the target frequency band, obtaining the optimal target frequency band boundary, and further improving the recognition efficiency of the present invention.

[0012] Optionally, the simulating impact signal according to the output result satisfies the following formula: in, Indicates the output result, represents the scaling factor, represents the undamped natural frequency, represents the damping ratio, represents the damped natural frequency.

[0013] Optionally, the simulation impact signal, the high-frequency mechanical vibration noise and the high-frequency background noise are combined to obtain an acceleration simulation model that satisfies the following formula: in, represents the simulated signal, represents high-frequency background noise, Indicates high-frequency mechanical noise moving backward 0.1s on the time axis. Indicates the output result of moving backward 0.32s on the time axis.

[0014] In the second aspect, in order to be able to efficiently execute the drop-weight deflectometer impact point identification method provided by the present invention, the present invention also provides a drop-weight deflectometer impact point identification system, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the drop-weight deflectometer impact point identification method as described in the first aspect of the present invention. The drop-weight deflectometer impact point identification system of the present invention has a compact structure and stable performance, and can stably execute the drop-weight deflectometer impact point identification method provided by the present invention, further improving the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A comparison chart of the trigger point and impact point identified by the traditional algorithm when the interference signal amplitude exceeds the threshold; Figure 2 A flow chart of a method for identifying an impact point of a falling weight deflectometer provided in an embodiment of the present invention; Figure 3 An average amplitude-frequency diagram provided by an embodiment of the present invention; Figure 4 A diagram showing the results of a typical signal analysis using a short-time Fourier transform according to an embodiment of the present invention; Figure 5 A framework diagram of an impact point identification system for a falling weight deflectometer provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0017] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0018] See also Figure 2 In order to improve the recognition accuracy of the impact point during the calibration of the drop weight deflectometer and solve the problem of misidentification of the impact point caused by high interference noise. The present invention provides a method for identifying the impact point of a drop weight deflectometer, such as Figure 2 As shown, in one embodiment, the method includes the following steps: S1. Obtaining impact data of a drop weight deflectometer during calibration, and setting an initial boundary of a target frequency band according to the impact data.

[0019] In an embodiment, 500 impact signal data of the falling weight deflectometer during the calibration process are obtained through a relevant management database.

[0020] Furthermore, the step S1 of setting the initial boundary of the target frequency band according to the impact data comprises the following steps: S11, performing fast Fourier analysis on the impact data to calculate the average amplitude of each frequency point.

[0021] Specifically, the 500 impact signal data were subjected to fast Fourier transform for amplitude-frequency analysis, the average amplitude of each frequency point was calculated, and then the average amplitude-frequency graph was drawn.

[0022] S12: Setting the frequency band width, and setting the target frequency band initial boundary according to the frequency band width and the average amplitude.

[0023] In order to ensure that the phase shift effect is as small as possible, the frequency bandwidth is set to 30 Hz. In other embodiments, the frequency bandwidth can also be adjusted according to actual conditions.

[0024] Furthermore, typical data are analyzed by short-time Fourier transform, and the target frequency band boundary is set according to the statistical results.

[0025] In this embodiment, Figure 3As shown, from the results of the average amplitude-frequency diagram, it can be seen that the maximum amplitude of the signal appears in the frequency band from 10 Hz to 40 Hz, so the initial boundaries of the target frequency band are 10 Hz and 40 Hz.

[0026] Further, the typical signal is analyzed using short-time Fourier transform. The analysis results are as follows: Figure 4 As shown in the figure, the corresponding frequency components are checked by setting a vertical plane. The vertical plane in the figure is 40Hz in frequency, which proves that the peak value of the frequency component from 10Hz to 40Hz is completely consistent with the actual impact event in terms of time scale, confirming that the signal frequency within this frequency band responds significantly to the FWD drop hammer impact.

[0027] S2. Calculate a spectrum according to the initial boundary of the target frequency band, and use the spectrum calculation result to complete signal reconstruction.

[0028] In an embodiment, the step of calculating a spectrum according to the initial boundary of the target frequency band and completing signal reconstruction using the spectrum calculation result comprises the following steps: S21. Obtain target frequency band data based on the target frequency band initial boundary.

[0029] Specifically, data of the starting frequency and the ending frequency represented by the initial boundary of the target frequency band are intercepted as the target frequency band data.

[0030] S22, calculating a spectrum for the target frequency band data, setting the remaining positive frequency parts to zero, and supplementing the negative frequency spectrum through conjugate symmetry to obtain a spectrum calculation result.

[0031] Specifically, for an analog signal that varies with time t , which is at the sampling frequency The discrete signal expression obtained is: in, represents a discrete time series, represents the discrete signal index, represents the sampling interval, Indicates the number of sampling points.

[0032] Furthermore, the spectrum calculation formula for the signal is as follows: in, is the index of the frequency point, It's a signal In frequency The amplitude and phase of It is a frequency sequence, the index of the lower frequency point of the frequency band , the calculation formula is as follows: The index of the upper frequency point of the frequency band , the calculation formula is as follows: Furthermore, the spectrum of the target frequency band data is calculated to satisfy the following formula: in, represents the spectrum of the target frequency band, represents the index of the frequency point, Indicates the number of sampling points, Represents the sequence of signals to be processed, represents the imaginary unit, Represents the index of the upper frequency point, Indicates the index of the upper frequency point.

[0033] Furthermore, the remaining part of the positive frequency is set to zero, satisfying the following formula: in, Indicates that the positive frequency part corresponds to the spectrum, represents the spectrum of the target frequency band, represents the index of the frequency point, Represents the index of the upper frequency point, Represents the index of the upper frequency point, Indicates the number of sampling points.

[0034] In the embodiment, in order to obtain a real number reconstructed signal, the positive and negative frequencies of the signal in the frequency domain need to satisfy conjugate symmetry, so the frequency domain representation of the signal to be reconstructed is: in, represents the spectrum of the reconstructed signal, Indicates that the positive frequency part corresponds to the spectrum, The operator representing the conjugate complex number, represents the index of the frequency point, Indicates the number of sampling points.

[0035] S23. Based on the spectrum calculation result, the signal is reconstructed by inverse Fourier transform.

[0036] In an embodiment, the signal reconstruction is completed by inverse Fourier transform based on the spectrum calculation result, satisfying the following formula: in, represents the reconstructed signal, Indicates the number of sampling points, represents the index of the frequency point, Indicates that the positive frequency part corresponds to the spectrum, Represents an imaginary unit.

[0037] S3. Perform normalization processing on the reconstructed signal, and obtain a forward difference sequence through the normalization processing result.

[0038] Specifically, the reconstructed signal is normalized to satisfy the following formula: in, represents the result of normalizing the reconstructed signal. represents the mean value of the signal, Represents the standard deviation of the signal.

[0039] Furthermore, the forward difference sequence is obtained by normalizing the result, which satisfies the following formula: in, express The forward difference sequence of .

[0040] S4, constructing an acceleration simulation model, preliminarily identifying an impact point based on the forward differential sequence, adjusting the initial boundary of the target frequency band in combination with the acceleration simulation model and the preliminary identification result of the impact point, and obtaining an optimal target frequency band boundary.

[0041] In the embodiment, the construction of the acceleration simulation model in step S4 includes the following steps: S41. Simplify the road surface into an underdamped second-order system.

[0042] Specifically, the road surface is simplified to an underdamped second-order system, and its differential equation is: in, Indicates the output result, represents the input signal, represents the undamped natural frequency, Represents the damping ratio.

[0043] S42. Simplify the hammer impact into a pulse input and then perform Laplace transform.

[0044] Specifically, the hammer impact is simplified to a pulse input and then Laplace transform is performed to satisfy: Here, s represents a complex variable.

[0045] S43. Obtain an output result of the underdamped second-order system based on a Laplace transform result.

[0046] Substitute the differential equation to obtain the transfer function and system output results.

[0047] The resulting transfer function expression is: The output of the system is: Furthermore, the system output result is subjected to an inverse Laplace transform to obtain a time domain response, and is multiplied by a scaling factor to obtain a simulated impact signal.

[0048] Specifically, the impact signal is simulated according to the output result, satisfying the following formula: in, Indicates the output result, represents the scaling factor, represents the undamped natural frequency, represents the damping ratio, represents the damped natural frequency, In the embodiment, in order to simulate the actual signal, The value is 0.021. Take the value 50π, The value is 0.2.

[0049] S44, simulating an impact signal according to the output result, constructing high-frequency mechanical vibration noise and high-frequency background noise, and combining the simulated impact signal, the high-frequency mechanical vibration noise and the high-frequency background noise to obtain an acceleration simulation model.

[0050] In the embodiment, two types of interference noise need to be added to the simulation signal. One type simulates the high-frequency mechanical vibration generated when the FWD lifts the heavy hammer, which has a large amplitude and is ahead of the hammer impact in timing; the other type simulates the high-frequency background noise that always exists. Two types of interference noise: in: and They are high-frequency mechanical vibration noise and high-frequency background noise, and is the signal amplitude, which takes values ​​of 1.5g and 0.5g respectively, and are signal frequencies, which are 100 Hz and 200 Hz respectively.

[0051] Furthermore, the simulation impact signal, the high-frequency mechanical vibration noise and the high-frequency background noise are combined to obtain an acceleration simulation model, which satisfies the following formula: Among them, represents the simulation signal, represents the high-frequency background noise, represents the high-frequency mechanical noise shifted backward by 0.1 s on the time axis, represents the output result shifted backward by 0.32 s on the time axis.

[0052] Furthermore, based on the forward difference sequence, initially identifying the impact points includes the following steps: Set a threshold to retrieve the gradient sequence to obtain the trigger point index, which is set to 0.02 according to engineering experience.

[0053] (1) Obtain the length L of the data to be processed; (2) Set the sampling point index i = 1; (3) Check whether the amplitude of the gradient sequence index i is greater than the gradient threshold 0.02. If it is, jump to (4); if not, jump to (6); (4) Output the index i; (5) i = i + 2*f s -1 (the index skips the sampling data within 2 s); (6) i < L - 1 (judge whether the index exceeds the limit). If it is, end; if not, jump to (7); (7) i = i + 1, and jump to (3).

[0054] Even further, combining the acceleration simulation model and the preliminary identification result of the impact points, adjusting the initial boundary of the target frequency band to obtain the optimal target frequency band boundary includes: Using the simulation signal of the acceleration simulation model, obtaining the corresponding forward difference sequence for impact point identification simulation, and satisfying the following conditions: Condition 1: The maximum value of the normalized reconstructed signal is greater than the set gradient threshold; Condition 2: The identified impact point is less than or equal to 0.32 s on the time axis, and the difference is not greater than 0.05 s.

[0055] If any of the above conditions is not satisfied, the gradient threshold and the initial boundary of the target frequency band need to be adjusted accordingly and to achieve the best identification effect and obtain the optimal target frequency band boundary and the optimal gradient threshold.

[0056] S5. Using the optimal target frequency band boundary, identify the impact points of the falling weight deflectometer.

[0057] The optimal target frequency band boundary and the optimal gradient threshold are introduced to obtain the optimal forward difference sequence and then identify the impact point of the falling weight deflectometer.

[0058] The present invention performs amplitude-frequency analysis on multiple impact data through fast Fourier transform and analyzes typical data through short-time Fourier transform. The frequency band bandwidth is set at 30 Hz in the invention, which provides a specific and executable method for determining the upper and lower limit parameters of the frequency band and solves the problem that the traditional method relies on engineering experience.

[0059] In the signal reconstruction process, the amount of calculation is reduced to the greatest extent and the calculation efficiency is improved by accurately calculating the spectrum of the signal within the target frequency band and directly setting the frequency components outside the target frequency band to zero.

[0060] After the signal is reconstructed, the normalization operation is used to achieve the unification of the impact recognition thresholds at different amplitude levels, and the problem of difficult amplitude estimation in the later stage of signal frequency domain filtering is solved through gradient detection, which greatly increases the universality of the invention.

[0061] At the same time, the adjustment of amplitude and frequency can be applied to test simulations of all similar impact types, and the best impact point identification effect can be achieved by adjusting the upper and lower limits of the frequency band and the gradient trigger threshold parameters in the present invention.

[0062] See also Figure 5 In an embodiment, in order to be able to efficiently execute a drop-weight deflectometer impact point identification method provided by the present invention, the present invention also provides a drop-weight deflectometer impact point identification system, including: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory contains program instructions, and the program instructions are used for the steps of the drop-weight deflectometer impact point identification method. The drop-weight deflectometer impact point identification system of the present invention has a compact structure and stable performance, and can stably execute a drop-weight deflectometer impact point identification method of the present invention, further improving the overall applicability and practical application capabilities of the present invention.

[0063] In an embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. An input device may be used to obtain data information. An output device may be used to output the results obtained by storing program instructions contained in a computer program in a memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0064] In one possible implementation, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0065] The embodiment further provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for identifying the impact point of a falling weight deflectometer are implemented.

[0066] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0067] In summary, the present invention preliminarily divides the initial boundary of the target frequency band from the original impact data and reconstructs the signal according to this boundary, then constructs an acceleration simulation model and performs gradient optimization based on the reconstructed signal, thereby solving the problem of misidentification of the impact point caused by high interference noise during the calibration of the drop weight deflectometer.

[0068] Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope recorded in the present invention.

Claims

1. A method for identifying the impact point of a falling weight deflectometer, characterized in that: The drop weight deflectometer impact point identification method comprises the following steps: Acquiring impact data of the drop weight deflectometer during calibration, and setting an initial boundary of the target frequency band according to the impact data; Calculate the frequency spectrum according to the initial boundary of the target frequency band, and complete signal reconstruction using the frequency spectrum calculation result; Perform normalization processing on the reconstructed signal, and obtain a forward difference sequence through the normalization processing result; Constructing an acceleration simulation model, preliminarily identifying an impact point based on the forward differential sequence, and adjusting the initial boundary of the target frequency band in combination with the acceleration simulation model and the preliminary identification result of the impact point to obtain an optimal target frequency band boundary; The optimal target frequency band boundary is used to identify the impact point of the drop weight deflectometer.

2. The method for identifying the impact point of a falling weight deflectometer according to claim 1, characterized in that: The step of setting the target frequency band initial boundary according to the impact data comprises the following steps: Performing fast Fourier analysis on the impact data to calculate the average amplitude of each frequency point; The frequency band bandwidth is set, and the initial boundary of the target frequency band is set according to the frequency band bandwidth and the average amplitude.

3. The method for identifying the impact point of a falling weight deflectometer according to claim 1, characterized in that: The step of calculating a spectrum according to the initial boundary of the target frequency band and completing signal reconstruction using the spectrum calculation result comprises the following steps: Based on the target frequency band initial boundary, obtaining target frequency band data; Calculating the spectrum of the target frequency band data, setting the remaining positive frequency parts to zero, and supplementing the negative frequency spectrum through conjugate symmetry to obtain a spectrum calculation result; Based on the spectrum calculation results, signal reconstruction is completed through inverse Fourier transform.

4. The method for identifying the impact point of a falling weight deflectometer according to claim 3, characterized in that: The spectrum of the target frequency band data is calculated to satisfy the following formula: in, represents the spectrum of the target frequency band, represents the index of the frequency point, Indicates the number of sampling points, Represents the sequence of signals to be processed, represents the imaginary unit, Represents the index of the upper frequency point, Indicates the index of the upper frequency point.

5. The method for identifying the impact point of a falling weight deflectometer according to claim 3, characterized in that: The remaining positive frequencies are set to zero to satisfy the following formula: in, Indicates that the positive frequency part corresponds to the spectrum, represents the spectrum of the target frequency band, represents the index of the frequency point, Represents the index of the upper frequency point, Represents the index of the upper frequency point, Indicates the number of sampling points.

6. The method for identifying the impact point of a falling weight deflectometer according to claim 3, characterized in that: The signal reconstruction is completed by inverse Fourier transform based on the spectrum calculation result, satisfying the following formula: in, represents the reconstructed signal, Indicates the number of sampling points, represents the index of the frequency point, Indicates that the positive frequency part corresponds to the spectrum, Represents an imaginary unit.

7. The method for identifying the impact point of a falling weight deflectometer according to claim 1, characterized in that: The construction of the acceleration simulation model comprises the following steps: The road surface is simplified as an underdamped second-order system; Simplify the hammer impact into pulse input and then perform Laplace transform; Obtaining an output result of the underdamped second-order system based on a Laplace transform result; According to the output result, an impact signal is simulated to construct high-frequency mechanical vibration noise and high-frequency background noise, and an acceleration simulation model is obtained by combining the simulated impact signal, the high-frequency mechanical vibration noise and the high-frequency background noise.

8. The method for identifying the impact point of a falling weight deflectometer according to claim 7, characterized in that: The simulated impact signal according to the output result satisfies the following formula: in, Indicates the output result, represents the scaling factor, represents the undamped natural frequency, represents the damping ratio, represents the damped natural frequency.

9. The method for identifying the impact point of a falling weight deflectometer according to claim 7, characterized in that: The acceleration simulation model is obtained by combining the simulated impact signal, the high-frequency mechanical vibration noise and the high-frequency background noise, and satisfies the following formula: in, represents the simulated signal, represents high-frequency background noise, Indicates high-frequency mechanical noise moving backward 0.1s on the time axis. Indicates the output result of moving backward 0.32s on the time axis.

10. A falling weight deflectometer impact point identification system, characterized in that: The drop-weight deflectometer impact point identification system comprises: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory comprises program instructions, and the program instructions are used to execute the drop-weight deflectometer impact point identification method as described in any one of claims 1 to 9.

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