A method for detecting internal defects of standing trees in the wild

Abstract

A method for detecting internal defects of outdoor living trees belongs to the technical field of tree pest detection. In order to improve the detection accuracy of tree pests and diseases, the present invention includes assembling a detection platform around the target living tree, connecting cables to complete the construction of an imaging device for detecting internal defects of outdoor living trees; detecting the target living trees surrounded by the detection platform, first setting the initial positions of the transmitting antenna and the receiving antenna, setting initialization parameters, setting initial estimation parameters according to the detected living trees, and then fixing the transmitting antenna and moving the receiving antenna to perform electromagnetic wave non-destructive testing, collect time domain waveform data, and transmit it to the main control device; the main control device processes and transforms the time domain waveform data to obtain transformed frequency domain waveform data; using a subspace optimization algorithm to perform electromagnetic wave imaging of internal defects of the target living trees; and identifying internal defects of the target living trees based on electromagnetic wave imaging. The present invention improves the accuracy and resolution of imaging.

Description

Technical Field

[0001] The invention belongs to the technical field of tree disease and insect pest detection, and in particular relates to a method for detecting internal defects of outdoor living standing trees. Background Art

[0002] In recent years, the loss of forest resources caused by forest borers has increased year by year. There are many types of stem borers in my country, such as longhorn beetles, bark beetles, and jewel beetles. Stem borers will eat the xylem and phloem of trees, not only forming irreversible defects inside the trees, seriously affecting the health of the trees, but also affecting the mechanical properties of the trees, limiting the utilization rate of wood. Electromagnetic wave nondestructive testing is a fast and high-precision technology that has been widely used in medicine, military industry, ground detection and other fields. Electromagnetic wave detection equipment is lighter and less expensive, so it is suitable for use in field environments. In addition, electromagnetic wave detection has better penetration, which means that it can effectively detect internal defects of living trees of various sizes. Due to the different growth environment, health status and forest age of living trees in the field, the corresponding trunk size, moisture content and electromagnetic parameters are different, and due to the lack of prior information, the traditional electromagnetic detection device is difficult to detect living trees in the field, and the detection precision and accuracy are difficult to meet the requirements. Summary of the invention

[0003] The problem to be solved by the present invention is to improve the detection accuracy of tree diseases and insect pests, and to provide a method for detecting internal defects of standing trees in the field.

[0004] To achieve the above object, the present invention is implemented through the following technical solutions:

[0005] A detection method of an imaging device for detecting internal defects of outdoor living trees is implemented by using an imaging device for detecting internal defects of outdoor living trees, and comprises the following steps:

[0006] S1. Assemble the detection platform around the target standing tree, connect the cables to complete the construction of an imaging device for detecting internal defects of standing trees in the field;

[0007] S2. Detect the target standing trees surrounded by the detection platform. First, set the initial positions of the transmitting antenna and the receiving antenna, set the initialization parameters, set the initial estimation parameters of the background medium, the target dielectric constant, and the detection depth in the main control system according to the detected standing trees, then fix the transmitting antenna, move the receiving antenna, perform electromagnetic wave nondestructive testing, measure the scattering data of 8 evenly distributed detection points, move the transmitting antenna to the next detection position to repeat the test, collect 8×8 groups of time domain waveform data, and transmit them to the main control device;

[0008] S3. The main control device processes and transforms the 8×8 groups of time domain waveform data obtained in step S2 to obtain 8×8 groups of transformed frequency domain waveform data;

[0009] S4. Based on the converted frequency domain waveform data obtained in step S3, a subspace optimization algorithm is used to perform electromagnetic wave imaging of internal defects of the target living trees;

[0010] S5. Identify internal defects of the target standing trees based on the electromagnetic wave imaging obtained in step S4.

[0011] Furthermore, the imaging device for detecting internal defects of standing trees in the wild includes a detection platform, a main control device, a marking channel, a transmitting antenna, a splicing opening, a receiving antenna, a signal generator, and a signal receiver;

[0012] The detection platform is composed of two semicircular ring platforms connected by a splicing opening, and a scratching track is arranged on the upper top surface of the detection platform, and the scratching track is a ring-shaped scratching track;

[0013] A transmitting antenna and a receiving antenna are placed on the lane;

[0014] A signal generator and a signal receiver are installed on the side of the detection platform, and the main control device is connected to the signal generator and the signal receiver through a cable.

[0015] Furthermore, the transmitting antenna and the receiving antenna are flexible microstrip antenna arrays.

[0016] Furthermore, the model of the signal generator is DSG3000B signal transmitter, and the model of the signal receiver is E5061B vector network analyzer.

[0017] Furthermore, the specific implementation method of step S2 includes the following steps:

[0018] S2.1. Set initialization parameters, including setting the dielectric constant of the internal defect model of the target standing tree to 5-20, the magnetic permeability to 1, the background medium and the tree internal burrows to air, the dielectric constant to 1, the detection radius, the electromagnetic wave frequency, the gain, the sampling interval, and the time window;

[0019] S2.2. Conduct electromagnetic wave nondestructive testing to establish an internal defect model of the target standing tree;

[0020] S2.2.1. In order to simplify the influence of the internal defect model of the target standing tree on the detection accuracy, the equivalent principle is used for the Maxwell equations. The equivalent Maxwell equations are as follows:

[0021]

[0022] Among them, J is the equivalent current source, K is the equivalent magnetic current source, E is the electric field strength, i is the current, ω is the angular frequency, μ0 is the air permeability, H is the magnetic field strength, ε0 is the absolute dielectric constant of the medium, ρ e is the equivalent conductivity, ρ m is the equivalent magnetic permeability;

[0023] S2.2.2. Based on the internal defect model of standing trees in step S2.1, the magnetic permeability is 1, the magnetic scattering effect of scatterers is ignored, and K and ρ are ignored. m ;

[0024] S2.2.3. Place the target scatterer in the region of interest. The signal emitted by the signal transmitter is the incident field, and the data measured by the receiver is the total field, denoted as E t , the scattered field is equal to the total field minus the incident field. The total field is modified to the Helmholtz form, and the expression is:

[0025]

[0026] Among them, E t is the total field, k0 is the background wave number of the homogeneous medium;

[0027] Introducing the two-dimensional Green's function g(r,r'), the expression is:

[0028]

[0029] Among them, r is the observation point, the location of the electromagnetic field is solved, r' is the source point, that is, the source location of the electromagnetic field, is the zero-order Hankel function of the first kind;

[0030] S2.2.4. Construct the internal defect model of the target standing tree, expressed as:

[0031]

[0032] Among them, E i is the incident field, k0 represents the background wave number of the uniform medium, and I is the contrast current density;

[0033] S2.2.5. Set Ricker wavelet as the incident wave to detect the internal defect model of living standing trees. The expression is:

[0034]

[0035] Where E(t) is the incident wave function, f is the center frequency, and t is time.

[0036] Furthermore, the specific implementation method of step S3 includes the following steps:

[0037] S3.1. The 8×8 groups of time domain waveform data obtained in step S2 are screened, filtered, and denoised using a low-pass filter. The expression is:

[0038]

[0039] Where y[n] is the filtered signal, x[n] is the input signal waveform data, and N is the filter window size, which represents the number of data points used to calculate the average value.

[0040] S3.2. Convert the data processed in step S3.1 into units. The expression is:

[0041]

[0042]

[0043] Where P is the power of the received signal, G is the gain of the incident wave, Z is the impedance in free space, Es is the transformed scattered field data, and P dB The power expressed in decibels is, P0 is the reference power;

[0044] S3.3. The transformed scattered field data obtained in step S3.2 is converted into frequency domain electromagnetic wave data by time domain waveform data, and the expression is:

[0045]

[0046] Where X(m) is the amplitude and phase of the complex component with frequency m, x(n) is the discrete time domain signal of the input sequence, N is the length of the input sequence, and [cos(2πmn / N)-j·sin(2πmn / N)] is the complex exponential of the rotation factor expanded by Euler's formula, which represents the rotation of the signal at frequency m.

[0047] Furthermore, the specific implementation method of step S4 includes the following steps:

[0048] S4.1. Use the BP algorithm to calculate the target electromagnetic parameter distribution as the prior information of the subspace as the input of the SOM algorithm. The expression of the BP algorithm is:

[0049]

[0050] Among them, χ is the contrast distribution of the target estimated by BP, N i is the total number of line sources, χ(r) is the contrast, p is the number of incidents, J p (r) is the induced current at the pth incidence, r is the cost function of the quadratic error of the defined scattered field;

[0051] S4.2. Set the induced current of the subspace optimization algorithm to be decomposed into two parts, including deterministic induced current and fuzzy induced current, expressed as:

[0052]

[0053] in, is the induced current at the current moment, and the first term of the equation is the deterministic part of the induced current, calculated by formula (15), and the latter term is the fuzzy part of the induced current. represents the jth left singular vector Conjugate transpose, represents the scattered field, σ j represents the jth singular value, Represents a right singular matrix The orthogonal right singular vectors of ;

[0054] S4.3. Take the contrast and deterministic induced current obtained in step S4.1 and step S4.2 as the initial estimate, and The parameters identified as independent are updated alternately with the contrast, and the expression of the updated contrast is obtained as follows:

[0055]

[0056] in, Determine the partial Euclidean norm for the induced current. is the scattered field matrix after discretization;

[0057] S4.4. Iterate based on steps S4.1 to S4.3, output the contrast result after the iteration, and then convert it into a dielectric constant and perform imaging.

[0058] Beneficial effects of the present invention:

[0059] The method for detecting internal defects of standing trees in the field described in the present invention can collect full-aperture scattering field data of standing trees, and use the result of a backpropagation algorithm (BP) as the initial estimation of an imaging algorithm to solve the problem of insufficient prior information, thereby greatly improving the accuracy and resolution of imaging.

[0060] The present invention discloses a method for detecting internal defects of standing trees in the field. The detection platform is designed as a circular ring, which surrounds the standing trees and realizes all-round scanning of internal defects of the standing trees. Full-aperture information collection is adopted to retain more detailed and comprehensive standing tree information, thereby improving the accuracy of defect imaging.

[0061] The invention discloses a method for detecting internal defects of standing trees in the wild, which reduces the cost of equipment construction and operation. A simple and portable electromagnetic wave detection device is designed, and electromagnetic detection technology is applied to the detection of standing trees in the wild, which greatly increases the imaging resolution and accuracy of internal defects of standing trees.

[0062] The invention discloses a method for detecting internal defects of standing trees in the wild. The method adopts electromagnetic wave nondestructive testing technology and designs a band-selectable transceiver antenna system on the basis of meeting the imaging accuracy requirements, so as to realize fast and flexible switching of electromagnetic waves in the frequency band of 10 MHz to 100 GHz, so as to meet the requirements of accurate detection of internal defects of trees of various sizes.

[0063] The present invention discloses a method for detecting internal defects of outdoor living trees. The SOM algorithm is embedded in the imaging device for detecting internal defects of outdoor living trees. The prediction accuracy of the target parameter distribution and the resolution of inversion imaging are improved by pre-estimating the target parameters and the initial estimation of the BP inversion algorithm as prior information. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a structural schematic diagram of an imaging device for detecting internal defects of standing trees in the wild according to the present invention;

[0065] Figure 2 It is a flow chart of a method for detecting internal defects of outdoor standing trees according to the present invention;

[0066] Figure 3 This is a model diagram of the single worm defect inversion of the present invention;

[0067] Figure 4 This is a diagram of the inversion result of the single worm defect simulation experiment of the present invention;

[0068] Figure 5 This is a photo of a sample of water-containing standing wood of the present invention;

[0069] Figure 6 This is a diagram of the inversion results of the physical platform detection data of the water-containing living standing tree samples of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0071] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0072] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 6 The detailed instructions are as follows: Specific implementation method one:

[0074] A detection method of an imaging device for detecting internal defects of outdoor living trees is implemented by using an imaging device for detecting internal defects of outdoor living trees, and comprises the following steps:

[0075] S1. Assemble the detection platform around the target standing tree, connect the cables to complete the construction of an imaging device for detecting internal defects of standing trees in the field;

[0076] S2. Detect the target standing trees surrounded by the detection platform. First, set the initial positions of the transmitting antenna and the receiving antenna, set the initialization parameters, set the initial estimation parameters of the background medium, the target dielectric constant, and the detection depth in the main control system according to the detected standing trees, then fix the transmitting antenna, move the receiving antenna, perform electromagnetic wave nondestructive testing, measure the scattering data of 8 evenly distributed detection points, move the transmitting antenna to the next detection position to repeat the test, collect 8×8 groups of time domain waveform data, and transmit them to the main control device;

[0077] Furthermore, electromagnetic wave detection has a high penetration ability and can penetrate non-metallic materials with a large thickness to detect internal structures. Secondly, electromagnetic waves have high resolution and sensitivity and can detect small changes and defects, such as cracks and holes, which helps to improve the detection accuracy and efficiency of internal defects in standing timber. The basic principle of electromagnetic wave nondestructive testing technology is based on the analysis and derivation of Maxwell's principle. In order to simplify the impact of the complex situation of the standing timber itself on the detection accuracy, the equivalent principle is used for the Maxwell equations;

[0078] Furthermore, the imaging device for detecting internal defects of standing trees in the wild includes a detection platform 1, a main control device 2, a marking channel 3, a transmitting antenna 4, a splicing opening 5, a receiving antenna 6, a signal generator 7, and a signal receiver 8;

[0079] The detection platform 1 is composed of two semicircular ring platforms connected by a splicing opening 5. A scratching track 3 is arranged on the upper top surface of the detection platform 1, and the scratching track 3 is a ring-shaped scratching track;

[0080] A transmitting antenna 4 and a receiving antenna 6 are placed on the lane 3;

[0081] A signal generator 7 and a signal receiver 8 are installed on the side of the detection platform 1, and the main control device 2 is connected to the signal generator 7 and the signal receiver 8 via a cable.

[0082] Furthermore, the transmitting antenna 4 and the receiving antenna 6 are flexible microstrip antenna arrays.

[0083] Furthermore, the model of the signal generator 7 is a DSG3000B signal transmitter, and the model of the signal receiver 8 is an E5061B vector network analyzer.

[0084] Furthermore, the upper surface of the detection platform has a circular track for moving the receiving and transmitting antennas to different detection positions. The scattering field information is obtained by moving the transmitting and receiving antennas around the tree, and the full-aperture scattering data is recorded to retain more comprehensive information, thereby increasing the accuracy of inversion imaging.

[0085] Furthermore, the signal generator and signal receiver are embedded in the detection platform and connected to the main control device via cables. The designed antenna system has the function of discrete frequency adjustment to meet the detection of internal defects of living trees of different sizes, and the frequency range that can be covered is 10MHz~100GHz.

[0086] Furthermore, the functions of the main control device include prior parameter setting, data display, data storage, and embedded data processing and inversion imaging algorithms. The main control device flexibly adjusts the detection parameters through prior parameter setting to adapt to different types and sizes of standing trees. The data display function provides real-time display of detection results, which is convenient for operators to monitor and analyze the detection process. At the same time, the main control device also has a data storage function, which can convert the detection data through an analog-to-digital module and transmit it to the main control device for storage and data analysis. A subspace optimization algorithm is proposed for internal defect detection imaging algorithm of standing trees in the field. The rough estimate of the back-propagation algorithm is used as prior information, and the objective function is iteratively minimized to approximate the true solution, which greatly improves the detection accuracy of tree defects. Overall, the detection system has a reasonable structure and complete functions, which can meet the needs of efficient and accurate defect detection of standing trees in the field.

[0087] Furthermore, the specific implementation method of step S2 includes the following steps:

[0088] S2.1. Set initialization parameters, including setting the dielectric constant of the internal defect model of the target standing tree to 5-20, the magnetic permeability to 1, the background medium and the tree internal burrows to air, the dielectric constant to 1, the detection radius, the electromagnetic wave frequency, the gain, the sampling interval, and the time window;

[0089] S2.2. Conduct electromagnetic wave nondestructive testing to establish an internal defect model of the target standing tree;

[0090] S2.2.1. In order to simplify the influence of the internal defect model of the target standing tree on the detection accuracy, the equivalent principle is used for the Maxwell equations. The equivalent Maxwell equations are as follows:

[0091]

[0092] Among them, J is the equivalent current source, K is the equivalent magnetic current source, E is the electric field strength, i is the current, ω is the angular frequency, μ0 is the air permeability, H is the magnetic field strength, ε0 is the absolute dielectric constant of the medium, ρ e is the equivalent conductivity, ρ m is the equivalent magnetic permeability;

[0093] S2.2.2. Based on the internal defect model of standing trees in step S2.1, the magnetic permeability is 1, the magnetic scattering effect of scatterers is ignored, and K and ρ are ignored. m ;

[0094] S2.2.3. Place the target scatterer in the region of interest. The signal emitted by the signal transmitter is the incident field, and the data measured by the receiver is the total field, denoted as E t , the scattered field is equal to the total field minus the incident field. The total field is modified to the Helmholtz form, and the expression is:

[0095]

[0096] Among them, E t is the total field, k0 is the background wave number of the homogeneous medium;

[0097] Introducing the two-dimensional Green's function g(r,r'), the expression is:

[0098]

[0099] Among them, r is the observation point, the location of the electromagnetic field is solved, r' is the source point, that is, the source location of the electromagnetic field, is the zero-order Hankel function of the first kind;

[0100] S2.2.4. Construct the internal defect model of the target standing tree, expressed as:

[0101]

[0102] Among them, E i is the incident field, k0 represents the background wave number of the uniform medium, and I is the contrast current density;

[0103] Furthermore, the parameter setting and solution algorithm of the Lippmann-Schwinger equation are the core basis for the design of non-destructive testing devices and inversion imaging algorithms. By fitting mathematical models and physical models with experimental data, the target geometry and size, propagation environment, prior electromagnetic parameters, noise level and other parameter settings are adjusted to achieve higher detection accuracy.

[0104] S2.2.5. Set Ricker wavelet as the incident wave to detect the internal defect model of living standing trees. The expression is:

[0105]

[0106] Where E(t) is the incident wave function, f is the center frequency, and t is time.

[0107] Furthermore, the signal receiver includes a receiving antenna and an analog-to-digital conversion module (ADC), and the ADC module transmits the digital signal to the main control device through a cable for subsequent data extraction and processing.

[0108] S3. The main control device processes and transforms the 8×8 groups of time domain waveform data obtained in step S2 to obtain 8×8 groups of transformed frequency domain waveform data;

[0109] Furthermore, the specific implementation method of step S3 includes the following steps:

[0110] S3.1. The 8×8 groups of time domain waveform data obtained in step S2 are screened, filtered, and denoised using a low-pass filter. The expression is:

[0111]

[0112] Where y[n] is the filtered signal, x[n] is the input signal waveform data, and N is the filter window size, which represents the number of data points used to calculate the average value.

[0113] Furthermore, in field detection situations with less metal interference, the sampling low-pass filtering method helps to remove high-frequency noise while retaining low-frequency signals, which can effectively smooth the signal and reduce the impact of noise, thereby improving imaging quality and accuracy.

[0114] S3.2. Convert the data processed in step S3.1 into units. The expression is:

[0115]

[0116] Where P is the power of the received signal, G is the gain of the incident wave of the receiving antenna, Z is the impedance in free space, Es is the transformed scattered field data, and P dB is the power expressed in decibels, P0 is the reference power;

[0117] S3.3. The transformed scattered field data obtained in step S3.2 is converted into frequency domain electromagnetic wave data by time domain waveform data, and the expression is:

[0118]

[0119] Where X(m) is the amplitude and phase of the complex component with frequency m, x(n) is the discrete time domain signal of the input sequence, N is the length of the input sequence, and [cos(2πmn / N)-j·sin(2πmn / N)] is the complex exponential of the rotation factor expanded by Euler's formula, which represents the rotation of the signal at frequency m;

[0120] S4. Based on the converted frequency domain waveform data obtained in step S3, a subspace optimization algorithm is used to perform electromagnetic wave imaging of internal defects of the target living trees;

[0121] The electromagnetic inverse scattering imaging problem is to solve the scattering equation based on the incident waveform emitted by the known electromagnetic wave detection system and the scattered field waveform collected by the receiving device, usually solving the scattering equation or optimization problem. This involves numerical simulation or analytical solution of the propagation and scattering process of the electromagnetic field.

[0122] The collection, processing and inversion imaging of scattered field data are achieved through the imaging algorithm pre-embedded in the main control device and the basic prior information set on the detection equipment before the detection begins, including environmental electromagnetic parameters, electromagnetic wave frequency and gain information.

[0123] The subspace optimization algorithm (SOM) is an improvement of the contrast source method. It uses the conjugate gradient method and the source integral equation. The SOM method introduces subspaces in the variable reconstruction process. In order to reduce the solution dimension and effectively improve the solution speed and success probability, the solution is decomposed into a linear combination of different spatial bases, and its signal and noise subspaces are iteratively updated to approach the correct form.

[0124] Furthermore, the specific implementation method of step S4 includes the following steps:

[0125] S4.1. Use the BP algorithm to calculate the target electromagnetic parameter distribution as the prior information of the subspace as the input of the SOM algorithm. The expression of the BP algorithm is:

[0126]

[0127] Among them, χ is the contrast distribution of the target estimated by BP, N i is the total number of line sources, χ(r) is the contrast, p is the number of incidents, J p (r) is the induced current at the pth incidence, r is the cost function of the quadratic error of the defined scattered field;

[0128] S4.2. Set the induced current of the subspace optimization algorithm to be decomposed into two parts, including deterministic induced current and fuzzy induced current, expressed as:

[0129]

[0130]

[0131] in, is the induced current at the current moment, and the first term of the equation is the deterministic part of the induced current, calculated by formula (15), and the latter term is the fuzzy part of the induced current. represents the jth left singular vector Conjugate transpose, represents the scattered field, σ j represents the jth singular value, Represents a right singular matrix The orthogonal right singular vectors of ;

[0132] S4.3. Take the contrast and deterministic induced current obtained in step S4.1 and step S4.2 as the initial estimate, and The parameters identified as independent are updated alternately with the contrast, and the expression of the updated contrast is obtained as follows:

[0133]

[0134] in, Determine the partial Euclidean norm for the induced current. is the scattered field matrix after discretization;

[0135] S4.4. Iterate based on step S4.1-step S4.3, output the contrast result after iteration, and then convert it into a dielectric constant and perform imaging;

[0136] S5. Identify internal defects of the target standing trees based on the electromagnetic wave imaging obtained in step S4.

[0137] Further, according to the non-destructive detection device for internal defects of living trees, defects and the occurrence of stem borers can be discovered in time. Here are some simulation cases and physical detection cases of the results, such as Figure 3-Figure 6 As shown, Figure 3It is an electromagnetic detection model for defects in a single standing tree. Figure 3 The scattering data is inverted and imaged, and the results are as follows Figure 4 As shown, Figure 5 The four holes on the water-containing living tree samples represent the defects of insects and the borers. The tree samples were tested and the data were processed, analyzed and inverted using the detection platform. The results are as follows: Figure 6 As shown;

[0138] The SOM algorithm can basically reflect the outline shape of the tree model (not an ideal circle), and has a certain ability to distinguish bark tissue, and can identify the size and position information of the insect body and the borer inside the trunk. The IOU value of the imaging result obtained by the tree radar equipment exceeds 0.9. This shows that the imaging device for detecting internal defects of standing trees in the wild proposed in this embodiment can realize the discovery and location of internal defects of standing trees. It provides important theoretical basis and technical support for the precise control of insect pests in standing trees.

[0139] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0140] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for detecting internal defects of standing trees in the wild, implemented by an imaging device for detecting internal defects of standing trees in the wild, characterized in that: The steps include: S1. Assemble the detection platform around the target standing tree, connect the cables to complete the construction of an imaging device for detecting internal defects of standing trees in the field; The imaging device for detecting internal defects of standing trees in the wild comprises a detection platform (1), a main control device (2), a marking track (3), a transmitting antenna (4), a splicing opening (5), a receiving antenna (6), a signal generator (7), and a signal receiver (8); The detection platform (1) is composed of two semicircular ring-shaped platforms connected by a splicing opening (5); a scratching track (3) is arranged on the upper top surface of the detection platform (1); and the scratching track (3) is a ring-shaped scratching track; A transmitting antenna (4) and a receiving antenna (6) are placed on the lane (3); A signal generator (7) and a signal receiver (8) are installed on the side of the detection platform (1), and the main control device (2) is connected to the signal generator (7) and the signal receiver (8) via a cable; The transmitting antenna (4) and the receiving antenna (6) are flexible microstrip antenna arrays; S2. Detect the target standing trees surrounded by the detection platform. First, set the initial positions of the transmitting antenna and the receiving antenna, set the initialization parameters, set the initial estimation parameters of the background medium, the target dielectric constant, and the detection depth in the main control system according to the detected standing trees, then fix the transmitting antenna, move the receiving antenna, perform electromagnetic wave nondestructive testing, measure the scattering data of 8 evenly distributed detection points, move the transmitting antenna to the next detection position to repeat the test, collect 8×8 groups of time domain waveform data, and transmit them to the main control device; The specific implementation method of step S2 includes the following steps: S2.

1. Set initialization parameters, including setting the dielectric constant of the internal defect model of the target standing tree to 5-20, the magnetic permeability to 1, the background medium and the tree internal burrows to air, the dielectric constant to 1, the detection radius, the electromagnetic wave frequency, the gain, the sampling interval, and the time window; S2.

2. Conduct electromagnetic wave nondestructive testing to establish an internal defect model of the target standing tree; S2.2.

1. In order to simplify the influence of the internal defect model of the target standing tree on the detection accuracy, the equivalent principle is used for the Maxwell equations. The equivalent Maxwell equations are as follows: Among them, J is the equivalent current source, K is the equivalent magnetic current source, E is the electric field strength, i is the current, ω is the angular frequency, μ0 is the air magnetic permeability, H is the magnetic field strength, ε0 is the absolute dielectric constant of the medium, ρ e is the equivalent conductivity, ρ m is the equivalent magnetic permeability; S2.2.

2. Based on the internal defect model of standing trees in step S2.1, the magnetic permeability is 1, the magnetic scattering effect of scatterers is ignored, and K and ρ are ignored. m ; S2.2.

3. Place the target scatterer in the region of interest. The signal emitted by the signal transmitter is the incident field, and the data measured by the receiver is the total field, denoted as E t , the scattered field is equal to the total field minus the incident field, and the total field is modified to the Helmholtz form, the expression is: Among them, E t is the total field, k0 is the background wave number of the homogeneous medium; Introducing the two-dimensional Green's function g(r,r′), the expression is: Among them, r is the observation point, the location of the electromagnetic field is solved, r′ is the source point, that is, the location of the electromagnetic field, H 1 0 is the zero-order Hankel function of the first kind; S2.2.

4. Construct the internal defect model of the target standing tree, expressed as: Among them, E i is the incident field, k0 represents the background wave number of the uniform medium, and I is the contrast current density; S2.2.

5. Set Ricker wavelet as the incident wave to detect the internal defect model of standing trees. The expression is: Where E(t) is the incident wave function, f is the center frequency, and t is time; S3. The main control device processes and transforms the 8×8 groups of time domain waveform data obtained in step S2 to obtain 8×8 groups of transformed frequency domain waveform data; The specific implementation method of step S3 includes the following steps: S3.

1. The 8×8 groups of time domain waveform data obtained in step S2 are screened, filtered, and denoised using a low-pass filter. The expression is: Where y[n] is the filtered signal, x[n] is the input signal waveform data, and N' is the filter window size, which indicates the number of data points used to calculate the average value. S3.

2. Convert the data processed in step S3.1 into units. The expression is: Where P is the power of the received signal, G is the gain of the incident wave, Z is the impedance in free space, Es is the transformed scattered field data, and P dB is the power expressed in decibels, P0 is the reference power; S3.

3. The transformed scattered field data obtained in step S3.2 is converted into frequency domain electromagnetic wave data by time domain waveform data, and the expression is: Where X(m) is the amplitude and phase of the complex component with frequency m, x(n) is the discrete time domain signal of the input sequence, N is the length of the input sequence, and [cos(2πmn / N)-j·sin(2πmn / N)] is the complex exponential of the rotation factor expanded by Euler's formula, which represents the rotation of the signal at frequency m; S4. Based on the converted frequency domain waveform data obtained in step S3, a subspace optimization algorithm is used to perform electromagnetic wave imaging of internal defects of the target living trees; The specific implementation method of step S4 includes the following steps: S4.

1. Use the BP algorithm to calculate the target electromagnetic parameter distribution as the prior information of the subspace as the input of the SOM algorithm. The expression of the BP algorithm is: Among them, χ is the contrast distribution of the target estimated by BP, N i is the total number of line sources, X(r) is the contrast, p is the number of incidents, J p (r) is the induced current at the pth incident time; S4.

2. Set the induced current of the subspace optimization algorithm to be decomposed into two parts, including deterministic induced current and fuzzy induced current, expressed as: in, is the induced current at the current moment, and the first term of the equation is the deterministic part of the induced current, calculated by formula (15), and the latter term is the fuzzy part of the induced current, represents the jth left singular vector Conjugate transpose, represents the scattered field, σ j represents the jth singular value, Represents a right singular matrix The orthogonal right singular vectors of ; S4.

3. Take the contrast and deterministic induced current obtained in step S4.1 and step S4.2 as the initial estimate, and The parameters identified as independent are updated alternately with the contrast, and the expression of the updated contrast is obtained as follows: in, The Euclidean norm of the induced current determination part is, is the scattered field matrix after discretization; S4.

4. Iterate based on step S4.1-step S4.3, output the contrast result after iteration, and then convert it into dielectric constant and perform imaging; S5. Identify internal defects of the target standing trees based on the electromagnetic wave imaging obtained in step S4.

2. A method for detecting internal defects of outdoor standing timber according to claim 1, characterized in that: The model of the signal generator (7) is a DSG3000B signal transmitter, and the model of the signal receiver (8) is an E5061B vector network analyzer.

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