A forest stress wave fault imaging method based on intersection fitting

Abstract

The application discloses a kind of forest stress wave fault imaging methods based on intersection fitting, belong to timber nondestructive testing technical field.The method is first to the original stress wave velocity data is corrected and based on the normalization processing of deviation rate. Then, according to the data after processing, the imaging area is grid division.The method according to the propagation characteristics of stress wave in the cross section of forest, the velocity of ray intersection is fitted, so as to determine the velocity function of each ray, until all rays are fitted processing.According to the propagation velocity of stress wave, the state of tree is classified as healthy, rotten or hollow, and different colors are used to represent different states.This process generates an improved stress wave propagation ray diagram, significantly improving the accuracy of grid cell velocity.The velocity of grid cell with ray passing through is calculated by weighted summation method, and the velocity of grid cell without ray passing through is estimated by nearest neighbor interpolation method to achieve high-precision imaging of tree trunk cross section.

Description

Technical Field

[0001] This invention relates to a forest tree stress wave tomography method based on intersection fitting, belonging to the field of non-destructive testing technology for wood. Background Technology

[0002] Timber, widely used in building structures, is favored for its ease of access and excellent mechanical properties. Non-destructive testing (NDT) techniques can provide a deeper understanding of the quality characteristics of timber resources, which is crucial for achieving efficient timber production and utilization. Stress wave testing instruments, as a NDT tool, offer advantages over other NDT techniques (such as ultrasonic testing, impedance testing, and microwave testing) including safety, portability, and interference resistance. Stress wave tomography effectively identifies timber defects based on propagation speed and displays the results as visual images, making it suitable for timber property assessment, defect identification, resource utilization, and the protection of ancient trees.

[0003] Numerous studies, both domestic and international, have explored the relationship between stress wave velocity and wood defects. However, existing foreign stress wave tomography techniques have limitations in detecting internal tree trunk defects, particularly in accurately mapping actual cavities. Traditional stress wave imaging techniques suffer from insufficient accuracy and susceptibility to misinterpretation, thus failing to obtain accurate stress wave velocity distributions. Consequently, they cannot improve the accuracy of grid cell velocities and achieve high-precision stress wave tomography. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a forest stress wave tomography method based on intersection fitting. This method effectively solves the problems of insufficient accuracy and easy misjudgment in traditional stress wave imaging by employing an intersection fitting algorithm, obtaining a more accurate stress wave velocity distribution, thereby improving the accuracy of grid cell velocity and successfully achieving high-precision stress wave tomography.

[0005] In a first aspect, the present invention provides a forest stress wave tomography method based on intersection point fitting, comprising:

[0006] Step S1: Distribute several sensors evenly along the cross-section of the tree being measured to collect stress wave velocity data, correct the collected stress wave velocity data, and perform normalization processing based on the deviation rate on the corrected velocity data.

[0007] Step S2: Divide the imaging area into multiple grid units, refit the velocity of each stress wave ray according to the intersection velocity of each stress wave ray, the velocity function of the ray is shown in Equation (1), and draw the corresponding stress wave propagation ray diagram according to the fitted ray velocity.

[0008] S ij (x)=f(x;P) Equation (1)

[0009] Among them, S ij Let f(.) be the stress wave propagation velocity function between sensor i and sensor j, x be the distance from a point on the ray to sensor i, P be the set of intersection points of other rays with the ray, and f(.) be the table fitting function.

[0010] Step S3: Calculate the velocity of each grid cell within the imaging area, use an interpolation algorithm to complete the grid cells without data, determine their state based on the grid cell velocity, and use image processing methods to reconstruct the image of the internal defects of the tree.

[0011] In one embodiment of the present invention, step S1 specifically includes the following steps:

[0012] Step S11: Stress wave velocity data is obtained by a stress wave detector, which uses multiple sensors placed at different locations on the tree trunk to capture and analyze stress waves propagating through the tree.

[0013] Step S12: According to equation (2), use the normalization method based on the deviation rate to select appropriate percentiles to calculate the maximum, minimum, and deviation rates of the stress wave velocity data:

[0014]

[0015] Where s is the deviation rate, data max The velocity value at the 85th quantile in the stress wave velocity data. min This represents the velocity value at the 15th quantile of the stress wave velocity data, and mean() is the average value function.

[0016] Step S13: The magnitude of the deviation rate reflects the dispersion of the stress wave velocity data. The smaller the deviation rate, the more concentrated the data, which means that the probability of the tree being in a healthy state is greater. Reduce the minimum value of the stress wave velocity data interval to place the initial data within the healthy threshold range and image it correctly. Calculate the maximum and minimum values ​​of the more reasonable stress wave velocity data according to equation (3), and normalize the collected stress wave velocity data.

[0017] v max =data max +s*(data max -data min ),

[0018] v min =data min +(1-s)*(data max -data min Equation (3)

[0019] Among them, v maxv represents the updated maximum speed. min This represents the minimum speed after the update.

[0020] In one embodiment of the present invention, step S2 specifically includes the following steps:

[0021] Step S21: Treat the tree trunk as an ideal cylinder, and the imaging cross-section is a standard circle;

[0022] Step S22: Fit any ray, and fit the velocity function of the ray according to the velocity at the intersection point. Repeat this process until all rays have undergone the above fitting process to generate an improved stress wave propagation ray diagram. The stress wave velocity propagation function in the log is the sum of polynomials with a minimum degree of 2 and a maximum degree of 4, as shown in equation (4).

[0023] f(x) = a4x 4 +a3x 3 +a2x 2 +a1x+b (4)

[0024] Where f() is a quartic function with undetermined parameters, and a1, a2, a3, a4 and b are undetermined parameters;

[0025] Step S23: Based on the magnitude of the stress wave velocity value, the health status of the trees is divided into three states: healthy, decayed, and hollow, and different colors are presented according to the health status.

[0026] In one embodiment of the present invention, step S3 specifically includes the following steps:

[0027] Step S31: When a stress wave ray passes through a mesh element, the velocity estimate of that mesh element is calculated using a weighted method according to equation (5), where the weight depends on the proportion of the number of rays:

[0028] V xy =w1v G +w2v Y +w3v R Equation (5)

[0029] Among them, V xy v is the velocity estimate of the grid cell at coordinates (x, y). G v Y v R These are the average values ​​of three different colored rays passing through the grid cell, with w1, w2, and w3 being the weight values ​​of the corresponding velocities.

[0030] Step S32: Use the nearest neighbor interpolation method to interpolate the mesh cells through which no rays pass;

[0031] Step S33: Perform image average pooling on the abnormal grid cells to reduce the impact of abnormal data and obtain the defect map of the measured forest cross section.

[0032] In one embodiment of the present invention, the acquisition of stress wave data in step S1 includes:

[0033] Several sensors are evenly distributed along the cross-section of the tree being measured. Each sensor emits and receives stress waves. A pulse hammer is used to strike any sensor, and the stress wave propagates along the cross-section. The remaining sensors act as receivers to record the propagation time. The propagation time of the stress wave within the tree cross-section is measured to collect stress wave velocity data.

[0034] In one embodiment of the present invention, the correction of the acquired stress wave velocity in step S1 includes:

[0035] The collected stress wave velocity data are corrected according to equation (6):

[0036] v(β)≈(1-0.2β 2 )v r Equation (6)

[0037] Where v(β) is the propagation speed of the stress wave along the chord direction, v r Let β be the propagation velocity of the stress wave along the diameter direction, and β be the angle between the chord direction and the radial direction.

[0038] In one embodiment of the present invention, step S3 further includes: determining whether each grid cell is located within the imaging region according to the following formula (7):

[0039]

[0040] Where r is the radius of the cross-section of the tree being detected, and x and y are the coordinates of the grid cell on the horizontal and vertical axes, respectively; if g(x,y)=1, it means that the grid is within the imaging area; if g(x,y)=0, it means that the grid is not within the imaging area.

[0041] In one embodiment of the present invention, the image of the internal defects of the tree in step S3 includes information on the location, size, and shape of the internal defects of the tree.

[0042] In a second aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the forest stress wave tomography method based on intersection fitting.

[0043] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the forest stress wave tomography method based on intersection fitting.

[0044] The beneficial effects of this invention are as follows:

[0045] This invention provides a forest tree stress wave tomographic imaging method based on intersection fitting, which achieves accurate detection of cross-sectional defects in forest trees. This method normalizes and corrects the original stress wave velocity data using a deviation rate to adapt to the heterogeneous and anisotropic characteristics within the tree. Based on the corrected data, the algorithm divides the imaging region into grid cells and plots a stress wave propagation ray map. According to the propagation law of stress waves in the trunk, the algorithm fits the intersection velocities to determine the velocity function of the rays until all rays are fitted. This process generates an improved stress wave propagation ray map, enhancing the accuracy of grid cell velocities and achieving high-precision imaging of the trunk cross-section. Using this method for tomographic imaging of defective logs can accurately detect the location and size of defects, contributing to the efficient utilization of timber and providing a theoretical basis for the later protection of ancient and famous trees. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This invention provides an experimental platform for non-destructive testing in the forest tree stress wave tomography method.

[0048] Figure 2 This is a schematic diagram illustrating the intersection point fitting effect in the forest stress wave tomography method provided by the present invention; wherein, Figure 2 In this context, 'a' represents a defective sample. Figure 2 In the diagram, b represents the corrected ray diagram. Figure 2 In the diagram, c represents the ray diagram after fitting the intersection points.

[0049] Figure 3 This is a tomographic image of the forest stress wave tomographic imaging method provided by the present invention; wherein, Figure 3 In this context, 'a' represents the *Cotinus coggygria* sample. Figure 3 In the image, b represents the imaging result obtained using the FAKOPP method. Figure 3 In the figure, c represents the imaging result obtained using the forest stress wave tomography method provided by this invention. Detailed Implementation

[0050] The invention is described in detail below. In the following paragraphs, different aspects of the embodiments are defined in more detail. The aspects so defined may be combined with any of the remaining aspects or aspects unless expressly stated otherwise. In particular, any feature considered preferred or advantageous may be combined with one or more other features considered preferred or advantageous.

[0051] This invention provides a forest stress wave tomography method based on intersection fitting. This forest stress wave tomography method is mainly based on the stress wave principle and uses the intersection fitting method to accurately detect defects inside the forest, including their location, size and shape.

[0052] This forest stress wave tomography method uses a stress wave detector to collect stress wave propagation time data within trees. Several sensors are uniformly placed around the tree under examination, and the tree is struck to measure the propagation time of stress waves within its cross-section. Based on the heterogeneous and anisotropic characteristics of the tree's interior, the stress wave velocity data is corrected according to a specific formula and then normalized based on the deviation rate. Finally, these data are used to construct stress wave propagation ray maps to assess the structural condition of the tree's interior.

[0053] This forest stress wave tomography method relies on studying the propagation velocity of stress waves across log cross sections, establishing a comprehensive theoretical mathematical model, and refining the processing of each stress wave ray. The velocity of each ray is refitted based on the velocity at the intersection point to address the accuracy loss caused by using the average velocity of the entire ray to represent the velocity within a cell. This process is repeated until all rays have undergone the aforementioned fitting process, generating an improved stress wave propagation ray map. When a stress wave ray passes through a grid cell, a weighted method is used to calculate the estimated velocity of that cell.

[0054] In practice, due to the insufficient number of stress wave rays and the large number of grid cells, grid cells often lack coverage by the rays, meaning some cells lack reference velocity values. Therefore, the nearest neighbor interpolation method is used to complete these missing data grid cells, ensuring the integrity and accuracy of the overall data. Finally, by analyzing the velocity data of the grid cells, their condition can be determined, and healthy, decayed, and hollow areas in the tree cross-section can be visualized.

[0055] Specifically, the present invention provides a forest stress wave tomography method based on intersection point fitting, comprising the following steps:

[0056] Step S1: Distribute several sensors evenly along the cross-section of the tree being measured to collect stress wave velocity data, correct the collected stress wave velocity data, and perform normalization processing based on the deviation rate on the corrected velocity data.

[0057] Step S2: Divide the imaging area into multiple grid units, refit the velocity of each stress wave ray according to the intersection velocity of each stress wave ray, the velocity function of the ray is shown in Equation (1), and draw the corresponding stress wave propagation ray diagram according to the fitted ray velocity.

[0058] S ij (x)=f(x;P) Equation (1)

[0059] Among them, S ij Let f(.) be the stress wave propagation velocity function between sensor i and sensor j, x be the distance from a point on the ray to sensor i, P be the set of intersection points of other rays with the ray, and f(.) be the table fitting function.

[0060] Step S3: Calculate the velocity of each grid cell within the imaging area, use an interpolation algorithm to complete the grid cells without data, determine their state based on the grid cell velocity, and use image processing methods to reconstruct the image of the internal defects of the tree.

[0061] Optionally, step S1 specifically includes the following steps:

[0062] Step S11: Stress wave velocity data is obtained by a stress wave detector, which uses multiple sensors placed at different locations on the tree trunk to capture and analyze stress waves propagating through the tree.

[0063] Step S12: Since directly normalizing the corrected stress wave data will lead to the inability to identify healthy trees, the deviation rate-based normalization method is used according to Equation (2) to select appropriate percentiles to calculate the maximum value, minimum value and deviation rate.

[0064]

[0065] Where s is the deviation rate, data max The velocity value at the 85th quantile in the stress wave velocity data. min This represents the velocity value at the 15th quantile of the stress wave velocity data, and mean() is the average value function.

[0066] Step S13: The magnitude of the deviation rate reflects the dispersion of the stress wave velocity data. The smaller the deviation rate, the more concentrated the data, that is, the greater the probability that the tree is in a healthy state. The minimum value of the stress wave velocity data range should be appropriately reduced so that the initial data is placed within the healthy threshold range and correctly imaged. The stress wave velocity data range is updated according to equation (3), and the collected stress wave velocity data is normalized.

[0067] v max =data max+s*(data max -data min ),

[0068] v min =data min +(1-s)*(data max -data min Equation (3)

[0069] Among them, v max v represents the updated maximum speed. min This represents the minimum speed after the update.

[0070] Optionally, step S2 specifically includes the following steps:

[0071] Step S21: Treat the tree trunk as an ideal cylinder, and the imaging cross-section is a standard circle;

[0072] Step S22: By studying the propagation velocity law of stress waves in the cross section of logs, a comprehensive theoretical mathematical model is established; any ray is fitted, and the velocity function of the ray is fitted according to the velocity of the intersection point, and this process is repeated until all rays have undergone the above fitting process to generate an improved stress wave propagation ray diagram; wherein, the velocity propagation function of stress waves in logs is the sum of polynomials with a minimum degree of 2 and a maximum degree of 4, as shown in equation (4):

[0073] f(x) = a4x 4 +a3x 3 +a2x 2 +a1x+b (4)

[0074] Where f() is a quartic function with undetermined parameters, and a1, a2, a3, a4 and b are undetermined parameters;

[0075] Step S23: Based on the magnitude of the stress wave velocity, the health status of the trees is divided into three states: healthy, decayed, and hollow. Different colors are used to represent the health status, with green for healthy, yellow for decayed, and red for hollow.

[0076] Optionally, step S3 specifically includes the following steps:

[0077] Step S31: When a stress wave ray passes through a mesh element, the velocity estimate of that element can be calculated using a weighted method according to equation (5), where the weight depends on the proportion of rays:

[0078] V xy =w1v G +w2v Y +w3v R Equation (5)

[0079] Among them, V xy v is the velocity estimate of the grid cell at coordinates (x, y). G v Y v R These are the average values ​​of the green, yellow, and red rays passing through the grid cell, respectively, with w1, w2, and w3 being the weight values ​​of the corresponding velocities.

[0080] Step S32: Use the nearest neighbor interpolation method, which has a faster processing speed, to interpolate the grid cells that have no rays passing through them;

[0081] Step S33: Perform image average pooling on the abnormal grid cells to reduce the impact of abnormal data and obtain the defect map of the measured forest cross section.

[0082] Specifically, the present invention provides a forest tree stress wave tomography method based on intersection fitting, which employs stress wave nondestructive testing technology for trees to identify internal defects in trees by analyzing the propagation process of stress waves within the trees. For example... Figure 1 As shown, the tree trunk is considered an ideal cylinder with a standard circular cross-section. Multiple sensors are evenly arranged along the cross-section, each both emitting and receiving stress waves. When any sensor is struck with a pulse hammer, the stress wave propagates along the cross-section, while the remaining sensors act as receivers, recording the propagation time. When a stress wave propagates through the tree, its path is typically considered a straight line. However, if it encounters a defect area, the stress wave will bypass the defect, resulting in a longer propagation time. Therefore, for the same propagation distance, a longer propagation time indicates that the stress wave may have passed through a defect area inside the tree, thus indicating the presence of a defect. After all sensors have been struck once, the imaging plane displays multiple propagation paths. Subsequently, by analyzing the velocity differences recorded by the sensors, the location, size, and shape of the defect can be analyzed in detail.

[0083] The internal heterogeneity and anisotropy of trees cause stress wave propagation velocities to be non-uniform within them. Since the velocity at the tree edges is usually lower than the radial propagation velocity, and stress wave tree detection technology identifies internal defects by analyzing stress wave velocities, this can easily lead to misjudgments of tree edges. Therefore, the velocity data is corrected according to equation (6), and the resulting diagram is shown below. Figure 2 As shown in b in the diagram, the different colored rays represent different propagation speeds: green indicates a faster speed, representing a healthy tree cross-section; yellow indicates a slower speed, representing mild decay; and red indicates an extremely slow speed, representing severe decay or cavities.

[0084] v(β)≈(1-0.2β 2 )v r Equation (6)

[0085] Where v(β) is the propagation speed of the stress wave along the chord direction, v r Let β be the propagation velocity of the stress wave along the diameter direction, and β be the angle between the chord direction and the radial direction.

[0086] Direct normalization may lead to the inability to identify healthy trees. Therefore, this invention proposes a normalization algorithm based on deviation rate. Specifically, the maximum and minimum values ​​of stress wave velocity data are first calculated, and then the deviation rate of the data is calculated using equation (2). The smaller the deviation rate, the more concentrated the data, i.e., the greater the probability that the tree is healthy, and vice versa. In order to improve the robustness of the algorithm and mitigate the impact of some potentially abnormal data on the algorithm, this invention selects data from quantile 15 to quantile 85 to calculate the maximum, minimum, and deviation rate. Finally, the collected data is normalized using equation (3) to calculate a more reasonable maximum and minimum stress wave velocity.

[0087] Then, appropriate thresholds are selected to classify the health of the wood. Because different tree species have different wood properties such as density and moisture content, the propagation speed of stress waves varies within different tree species, thus requiring different thresholds. Let the thresholds be θ1 and θ2. Then, the interval [0, θ1] represents a severely decayed area, the interval [θ1, θ2] represents a slightly decayed area, and the interval [θ2, 1] represents a healthy area.

[0088] like Figure 2 As shown in b, although the yellow and red rays can indicate the presence of decay, their location cannot be accurately determined and they may appear anywhere along the ray path. When calculating the velocity of each grid cell within the imaging area, only the average velocity of the entire ray is used to represent the velocity within the cell, which may lead to a loss of accuracy. To solve this problem, this invention proposes a tree stress wave tomography imaging algorithm based on intersection fitting. This algorithm uses the intersection points between rays to fit the velocity function of each ray, thereby achieving more refined stress wave tomography imaging. By studying the propagation velocity law of stress waves in log cross sections and establishing a comprehensive theoretical mathematical model, it is shown that the velocity propagation function of stress waves in logs is the sum of polynomials with a minimum degree of 2 and a maximum degree of 4, as shown in equation (4). Fit any ray and fit the velocity function of the ray based on the intersection velocity. Repeat this operation until all rays have undergone the above fitting process. This process produces an improved stress wave propagation ray diagram, as shown in equation (4). Figure 2 As shown in c in the figure.

[0089] Then, the velocity of each grid cell within the imaging region is estimated, and equation (7) is used to determine whether a specific point is located within the imaging region:

[0090]

[0091] Where r is the radius of the cross section of the tree being detected, and x and y are the coordinates of the grid cell on the horizontal and vertical axes, respectively; if g(x,y)=1, it means that the grid is within the imaging area; if g(x,y)=0, it means that the grid is not within the imaging area; when the stress wave ray passes through a grid cell, the velocity estimate of the cell can be calculated by weighted method using equation (5), and its weight depends on the proportion of the number of rays.

[0092] In practice, due to the insufficient number of stress wave rays and the large number of grid cells, grid cells often lack coverage by the rays, meaning some cells lack reference velocity values. Therefore, the nearest neighbor interpolation method is used to complete these missing data grid cells, ensuring the integrity and accuracy of the overall data. Finally, by analyzing the velocity data of the grid cells, their condition can be determined, and healthy, decayed, and hollow areas in the tree cross-section can be visualized.

[0093] To verify the detection effect of the forest stress wave tomography method based on intersection fitting provided by this invention, the following comparison is made between the commonly used FAKOPP imaging method and the forest stress wave tomography method provided by this invention:

[0094] like Figure 3 As shown, Figure 3 Figure 'a' shows a *Caragana korshinskii* sample with an artificial defect, roughly located in the middle of the trunk. Imaging results were obtained using both the FAKOPP imaging method and the forest stress wave tomography method provided in this invention. Figure 3 b in Figure 3 It is shown in c in the middle.

[0095] When analyzing the imaging results of these two methods, some significant differences can be observed. Due to the influence of cracks, the size of the cavity region in the tomographic image generated by the FAKOPP software imaging method is significantly magnified, exceeding the actual range. Conversely, the tomographic image generated by the forest stress wave tomographic imaging method provided in this invention is closer to the actual location and size of the cavity, demonstrating higher accuracy and reliability. This situation stems from some inherent limitations of the FAKOPP method in handling image boundaries, leading to misjudgment and overestimation of the actual defect size.

[0096] This invention provides a forest stress wave tomographic imaging method based on intersection fitting. First, the original stress wave velocity data is corrected and normalized based on the deviation rate. Next, the imaging area is gridded according to the processed data. This method fits the velocity at the intersection points of rays based on the propagation characteristics of stress waves in the cross-section of the tree, thereby determining the velocity function of each ray until all rays have been fitted. Based on the propagation velocity of the stress waves, the tree state is classified as healthy, decayed, or hollow, and different colors represent different states. This process generates an improved stress wave propagation ray map, significantly improving the accuracy of grid cell velocities. The velocity of grid cells with rays passing through is calculated using a weighted summation method, while the velocity of grid cells without rays passing through is estimated using the nearest neighbor interpolation method, achieving high-precision imaging of the tree trunk cross-section. This invention contributes to the efficient utilization of timber and provides a theoretical basis for the later protection of ancient and famous trees.

[0097] In this invention, a normalization algorithm based on deviation rate is used to recalculate the velocity range, and a method based on intersection point fitting is used to fit the velocity distribution of the entire ray by incorporating the intersection point velocity, which is more accurate than the average value method.

[0098] Furthermore, the present invention provides a computer device that may include a processor, memory, network interface, and database connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it causes the processor to perform the steps of a forest stress wave tomography method based on intersection fitting as described in any of the above embodiments.

[0099] The working process, working details, and technical effects of the computer equipment provided in this embodiment can be found in the embodiment above regarding a forest stress wave tomography method based on intersection fitting, and will not be repeated here.

[0100] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a forest stress wave tomography method based on intersection fitting as described in any of the above embodiments. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks, etc. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0101] The working process, working details and technical effects of the computer-readable storage medium provided in this embodiment can be found in the embodiment of a forest stress wave tomography method based on intersection fitting mentioned above, and will not be repeated here.

[0102] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0103] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A forest stress wave tomography method based on intersection fitting, characterized in that, include: Step S1: Distribute several sensors evenly along the cross-section of the tree being measured to collect stress wave velocity data, correct the collected stress wave velocity data, and perform normalization processing based on the deviation rate on the corrected velocity data. Step S2: Divide the imaging area into multiple grid units, refit the velocity of each stress wave ray according to the intersection velocity of each stress wave ray, the velocity function of the ray is shown in Equation (1), and draw the corresponding stress wave propagation ray diagram according to the fitted ray velocity. Equation (1) in, S ij For the first i Sensor No. and No. j The stress wave propagation velocity function between the sensors x For a point on that ray to the th i The distance of the sensor, P Let be the set of points where other rays intersect with this ray. f(.) This is the fitted function; Step S3: Calculate the velocity of each grid cell within the imaging area, use an interpolation algorithm to complete the grid cells without data, determine their state based on the grid cell velocity, and use image processing methods to reconstruct the image of the internal defects of the tree. Step S1 specifically includes the following steps: Step S11: Stress wave velocity data is obtained by a stress wave detector, which uses multiple sensors placed at different locations on the tree trunk to capture and analyze stress waves propagating through the tree. Step S12: Using the normalization method based on the deviation rate according to equation (2), calculate the maximum value, minimum value, and deviation rate of the stress wave velocity data: Equation (2) in, s The deviation rate, data max This is the velocity value at the 85th quantile of the stress wave velocity data. data min This is the velocity value at the 15th quantile of the stress wave velocity data. mean ( ) represents the average value function; Step S13: The magnitude of the deviation rate reflects the dispersion of the stress wave velocity data. The smaller the deviation rate, the more concentrated the data, which means that the probability of the tree being in a healthy state is greater. Reduce the minimum value of the stress wave velocity data interval to place the initial data within the healthy threshold range and image it correctly. Update the stress wave velocity data range according to equation (3) and normalize the collected stress wave velocity data. Equation (3) in, v max This is the updated maximum speed. v min This represents the minimum speed after the update.

2. The forest stress wave tomography method based on intersection fitting according to claim 1, characterized in that, Step S2 specifically includes the following steps: Step S21: Treat the tree trunk as an ideal cylinder, and the imaging cross-section is a standard circle; Step S22: Fit any ray, and fit the velocity function of the ray according to the velocity at the intersection point. Repeat this process until all rays have undergone the above fitting process to generate an improved stress wave propagation ray diagram. The velocity propagation function of the stress wave in the log is a sum of polynomials, as shown in equation (4): Equation (4) in, f( ) It is a quartic function with undetermined parameters. a 1. a 2. a 3. a 4 and b These are parameters to be determined. Step S23: Based on the magnitude of the stress wave velocity value, the health status of the trees is divided into three states: healthy, decayed, and hollow, and different colors are presented according to the health status.

3. The forest stress wave tomography method based on intersection fitting according to claim 2, characterized in that, Step S3 specifically includes the following steps: Step S31: When a stress wave ray passes through a mesh element, the velocity estimate of that mesh element is calculated using a weighted method according to equation (5), where the weight depends on the proportion of the number of rays: Equation (5) in, V xy coordinates ( x,y The velocity estimate of the grid cell at () v G 、v Y 、v R These represent the average values ​​of three different colored rays passing through this grid cell. w 1 、w 2 、w 3 represents the weight value for the corresponding speed; Step S32: Use the nearest neighbor interpolation method to interpolate the mesh cells through which no rays pass; Step S33: Perform image average pooling on the abnormal grid cells to reduce the impact of abnormal data and obtain the defect map of the measured forest cross section.

4. The forest stress wave tomography method based on intersection fitting according to claim 1, characterized in that, The stress wave velocity data collected in step S1 includes: Several sensors are evenly distributed along the cross-section of the tree being measured. Each sensor emits and receives stress waves. A pulse hammer is used to strike any sensor, and the stress wave propagates along the cross-section. The remaining sensors act as receivers to record the propagation time. The propagation time of the stress wave within the tree cross-section is measured to collect stress wave velocity data.

5. The forest stress wave tomography method based on intersection fitting according to claim 4, characterized in that, The correction of the collected stress wave velocity data in step S1 includes: The collected stress wave velocity data are corrected according to equation (6): Equation (6) in, v(β) Let be the propagation velocity of the stress wave along the chord direction. v r Let be the propagation velocity of the stress wave along the diameter direction. β It is the angle between the chord direction and the radial direction.

6. The forest stress wave tomography method based on intersection fitting according to claim 3, characterized in that, Step S3 further includes: Determine whether each grid cell is located within the imaging region according to the following formula (7): Equation (7) in r Let be the radius of the cross-section of the tree being tested. x and y These are the coordinates of the grid cells on the horizontal and vertical axes, respectively; if g(x,y) =1 indicates that the grid is within the imaging region; if g(x,y) =0 indicates that the grid is not within the imaging area.

7. The forest stress wave tomography method based on intersection fitting according to claim 1, characterized in that, The image of the tree's internal defects in step S3 includes information on the location, size, and shape of the defects.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the forest stress wave tomography method based on intersection fitting as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the forest stress wave tomography method based on intersection fitting as described in any one of claims 1-7.

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