A tree detection method, device and tree detection equipment

Abstract

The application discloses a kind of tree detection method, device and tree detection equipment, belong to tree protection technical field, first the N patch type ultrasonic wave module in tree detection equipment is evenly surrounded and adhered to the four around of the tree to be measured, the ultrasonic wave propagation velocity between N patch type ultrasonic wave module is obtained, and the propagation velocity matrix V between patch type ultrasonic wave module is formed, since when there is defect inside tree, the propagation velocity of ultrasonic wave is influenced, therefore can generate the internal defect image of the tree to be measured according to the propagation velocity matrix V between patch type ultrasonic wave module, it can be seen that by this way, the internal situation of tree can be measured more accurately, and the cost of patch type ultrasonic wave module is lower, simultaneously also will not cause damage to tree itself.

Description

Technical Field

[0001] This invention relates to the field of tree protection technology, and in particular to a tree detection method, apparatus, and tree detection equipment. Background Technology

[0002] As one of the four basic materials, wood has a wide range of uses in people's daily lives and has high economic value. At the same time, trees, as cultural carriers, also have ornamental and cultural value. Among them, precious trees have special historical commemorative significance or scientific research value. Therefore, it is very necessary to detect the defect rate of wood. The defect rate refers to the degree of defects in the wood. By detecting the defect rate of wood, we can understand the current condition of the wood.

[0003] Common methods for detecting internal defects in trees in existing technologies include electromagnetic methods, nuclear methods, and mechanical methods. Electromagnetic methods utilize the principle that electromagnetic waves generate reflected waves of different amplitudes when passing through media with different dielectric constants. The differences in the amplitude of the detected reflected waves are used to detect internal defects in the tree. However, this method requires expensive equipment, resulting in high detection costs and making it unsuitable for widespread application. Nuclear methods offer various ways to detect internal defects in wood. One method uses neutron scanning imaging to measure changes in tree density, offering high detection accuracy. However, the equipment is also expensive and inconvenient to carry, hindering widespread application. Mechanical methods involve drilling holes in the wood. Based on the principle that wood of different densities generates different resistances during drilling, the internal condition of the wood is obtained by comparing these resistances. However, this method has low measurement accuracy and can damage the tree itself. Summary of the Invention

[0004] The purpose of this invention is to provide a tree detection method, apparatus, and equipment that can accurately measure the internal condition of a tree without damaging the tree itself, and at a low cost.

[0005] To address the aforementioned technical problems, this invention provides a tree detection method, applied to a processor in a tree detection device. The tree detection device further includes N patch-type ultrasonic modules, which are uniformly attached around the tree to be tested for emitting and receiving ultrasonic waves. N is an integer not less than 2. The tree detection method includes:

[0006] Obtain the ultrasonic propagation velocity v between each pair of the N patch-type ultrasonic modules. i,j v i,j The speed of ultrasonic propagation between the i-th patch ultrasonic module and the j-th patch ultrasonic module is represented, where i and j are both positive integers not greater than N.

[0007] According to all v i,j The propagation velocity matrix V between the patch-type ultrasonic modules is obtained;

[0008] The internal defect image of the tree under test is obtained based on the propagation speed matrix V.

[0009] Preferably, the ultrasonic propagation velocity v between each pair of the N patch-type ultrasonic modules is obtained. i,j According to all v i,j The propagation velocity matrix V between the patch-type ultrasonic modules is obtained by:

[0010] Obtain the perimeter γ of the tree to be tested;

[0011] The straight-line distance d between the i-th patch-type ultrasonic module and the j-th patch-type ultrasonic module is calculated based on the perimeter γ. i,j ;

[0012] According to all d i,j The distance matrix D between the patch-type ultrasonic modules;

[0013] At preset time intervals, the i-th patch-type ultrasonic module emits ultrasonic waves, and the time t when the j-th patch-type ultrasonic module receives ultrasonic waves is recorded. i,j This constitutes the propagation time matrix T between the patch-type ultrasonic modules;

[0014] According to the d i,j and the t i,j The ultrasonic propagation speed v between the i-th patch ultrasonic module and the j-th patch ultrasonic module is calculated. i,j According to all v i,j The propagation velocity matrix V constitutes the area between the patch-type ultrasonic modules.

[0015] Preferably, after forming the propagation velocity matrix V between the patch-type ultrasonic modules, the method further includes:

[0016] Obtain the time error o of the location of each of the N patch-type ultrasonic modules. i o i The time error represents the location of the i-th patch-type ultrasonic module and forms a time error matrix O;

[0017] The propagation velocity matrix V is processed to eliminate time error based on the time error matrix O, resulting in the propagation velocity matrix V' after eliminating time error.

[0018] The internal defect image of the tree under test is obtained based on the propagation velocity matrix V, including:

[0019] The internal defect image of the tree under test is obtained based on the propagation velocity matrix V' after eliminating time error.

[0020] Preferably, the time error o of each of the N patch-type ultrasonic modules is obtained. i o i The time error at the i-th patch-type ultrasonic module is represented and constitutes a time error matrix O, including:

[0021] Obtain N of the adjacent patch-type ultrasonic modules. i,j ;

[0022] The above-mentioned v i,j The corresponding d i,j and the t i,j This data was identified as healthy ultrasound data.

[0023] Substitute all the health ultrasound data into the ultrasound tangential velocity relationship to form a superlinear equation system, and use the least squares method to solve the superlinear equation system to obtain the time error matrix O.

[0024] The ultrasonic tangential velocity relationship is as follows: Where, d i，j d is the distance between the i-th patch ultrasonic module and the j-th patch ultrasonic module; r,s The distance between the r-th patch ultrasonic module and the s-th patch ultrasonic module; Where, θ i,j Let be the tangential arc between the i-th patch ultrasonic module and the j-th patch ultrasonic module, and k be the velocity offset coefficient. Where, θ r,s The tangential radius between the r-th patch ultrasonic module and the s-th patch ultrasonic module is given, where r and s are both no greater than N, and k is the velocity offset coefficient. i The time error at the i-th patch-type ultrasonic module; o j The time error at the j-th patch-type ultrasonic module; o r The time error at the r-th patch-type ultrasonic module; o s The time error at the s-th patch-type ultrasonic module is denoted as .

[0025] Preferably, N of the adjacent patch-type ultrasonic modules are obtained. i,j Following that, it also includes:

[0026] Each v in the propagation velocity matrix Vi,j Sort by size from largest to smallest and get the first N v. i,j .

[0027] Preferably, after obtaining the internal defect image of the tree under test based on the propagation velocity matrix V', the method further includes:

[0028] The defect rate of the tree under test is determined based on the internal defect image;

[0029] Obtain the location information of the tree to be tested;

[0030] The location information and defect rate of the trees to be tested are uploaded to the network map system, and the defect rate of the trees to be tested is marked and displayed at the corresponding location on the map.

[0031] Preferably, after uploading the location information and defect rate of the tree to be tested to the network map system, and marking and displaying the defect rate of the tree to be tested at the corresponding location on the map, the method further includes:

[0032] The system detects in real time whether the defect rate of the tree under test is lower than a threshold. If so, the tree under test with a defect rate lower than the threshold is marked on the map.

[0033] Preferably, obtaining the internal defect image of the tree under test based on the propagation velocity matrix V includes:

[0034] The cross-section of the tree to be tested is divided into a predetermined number of grids;

[0035] According to each v in the propagation speed matrix V i,j The distance from the corresponding two patch-type ultrasonic modules to the pith of the tree and the magnitude of their velocity determine an influence area within the cross-section of the tree under test. i,j The speed of the speed is negatively correlated with the area of ​​influence.

[0036] Each v i,j The grid corresponding to the area of ​​influence is assigned the value v. i,j The velocity magnitude value, when the grid is divided by multiple v i,j When the determined area of ​​influence is covered, the grid is assigned multiple v values. i,j The average value;

[0037] The grid with a value lower than the preset value is set as a defect grid, and an internal defect image of the tree under test is generated.

[0038] To address the aforementioned technical problems, the present invention also provides a tree detection device, comprising:

[0039] Memory, used to store computer programs and calibration coefficients;

[0040] A processor for executing the computer program to implement the steps of the tree detection method described above.

[0041] To address the aforementioned technical problems, the present invention also provides a tree detection device, characterized in that it includes N patch-type ultrasonic modules and the tree detection device as described above.

[0042] This invention provides a tree detection method, apparatus, and equipment. The processor in the tree detection equipment uniformly surrounds N patch-type ultrasonic modules around the tree to be tested. The ultrasonic propagation speed between each pair of the N patch-type ultrasonic modules is acquired, forming a propagation speed matrix V between the patch-type ultrasonic modules. Since the propagation speed of ultrasonic waves is affected when there are defects inside the tree, an image of the internal defects of the tree can be generated based on the propagation speed matrix V between the patch-type ultrasonic modules. Therefore, this method can accurately measure the internal condition of the tree, and the patch-type ultrasonic modules are low-cost and do not damage the tree itself. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the prior art and 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 based on these drawings without creative effort.

[0044] Figure 1 A flowchart of a tree detection method provided by the present invention;

[0045] Figure 2 A schematic diagram of ultrasonic velocity distribution provided by the present invention;

[0046] Figure 3 This is a schematic diagram of the structure of a tree detection device provided by the present invention. Detailed Implementation

[0047] The core of this invention is to provide a tree detection method, apparatus, and equipment that can accurately measure the internal condition of a tree without damaging the tree itself, and at a low cost.

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please refer to Figure 1 , Figure 1 The flowchart of a tree detection method provided by the present invention includes a processor applied in a tree detection device. The tree detection device further includes N patch-type ultrasonic modules, which are uniformly attached around the tree to be tested for emitting and receiving ultrasonic waves. N is an integer not less than 2. The tree detection method includes:

[0050] S11: Obtain the ultrasonic propagation velocity v between each pair of N patch-type ultrasonic modules. i,j v i,j This represents the ultrasonic propagation speed between the i-th patch ultrasonic module and the j-th patch ultrasonic module, where i and j are both positive integers not greater than N.

[0051] S12: Based on all v i,j The propagation velocity matrix V between the patch-type ultrasonic modules is obtained;

[0052] S13: Obtain the internal defect image of the tree under test based on the propagation velocity matrix V.

[0053] Considering that most existing methods for detecting the internal condition of trees suffer from problems such as high instrument costs, difficulty in portability, and deployment difficulties, and that some methods can even damage the trees themselves, there is a need for a tree detection method that is low-cost, portable, widely applicable, highly accurate, and does not damage the trees.

[0054] To address this issue, this embodiment utilizes ultrasound for non-destructive testing of trees. Specifically, N patch-type ultrasonic modules are attached and arranged around the tree. Each patch-type ultrasonic module can emit and receive ultrasonic waves. When defects exist inside the tree, the ultrasonic waves diffract when they encounter these defects, increasing the propagation time. The increase in propagation time is affected by the size of the defects and the ultrasonic wave propagation angle. Therefore, this solution obtains the ultrasonic wave propagation velocity v between each pair of the N patch-type ultrasonic modules. i,j And according to all v i,j We obtain the propagation velocity matrix V between the patch-type ultrasonic modules, where v i,jThis represents the ultrasonic propagation speed between the i-th patch ultrasonic module and the j-th patch ultrasonic module, including the speed from v. 1，1 to v N，N The propagation speed matrix V, representing all ultrasonic wave propagation speeds, is defined as follows:

[0055]

[0056] Then, based on the propagation angle between each pair of patch-type ultrasonic modules and the corresponding ultrasonic propagation speed, images of defects inside the tree under test can be obtained.

[0057] In addition, tree detection equipment typically includes 6, 8, 10, or 12 patch ultrasonic modules. The more patch ultrasonic modules there are, the more accurate the tree detection results will be. This application does not impose a specific limit on the number of patch ultrasonic modules, which can be set according to the actual situation.

[0058] In addition, in one specific embodiment, the ultrasonic module is model AJ-SR04K with an accuracy of 10μs, and the ultrasonic probe used is model JS1640F2-W with an operating frequency of 40kHz.

[0059] In summary, this invention provides a tree detection method applied to a processor in a tree detection device. N patch-type ultrasonic modules are uniformly attached around the tree to be tested. The ultrasonic propagation speed between each pair of the N patch-type ultrasonic modules is obtained, forming a propagation speed matrix V between the patch-type ultrasonic modules. Since the propagation speed of ultrasonic waves is affected when there are defects inside the tree, an image of the internal defects of the tree can be generated based on the propagation speed matrix V between the patch-type ultrasonic modules. Therefore, this method can accurately measure the internal condition of the tree, and the patch-type ultrasonic modules are low-cost and do not damage the tree itself.

[0060] Based on the above embodiments:

[0061] As a preferred embodiment, the ultrasonic propagation velocity v between each pair of N patch-type ultrasonic modules is obtained. i,j According to all v i,j The propagation velocity matrix V between the patch-type ultrasonic modules is obtained, including:

[0062] Obtain the perimeter γ of the tree to be tested;

[0063] The straight-line distance d between the i-th patch ultrasonic module and the j-th patch ultrasonic module is calculated based on the perimeter γ. i,j ;

[0064] According to all d i,jThe distance matrix D between the patch-type ultrasonic modules;

[0065] At preset time intervals, the i-th patch ultrasonic module emits ultrasonic waves and the time t when the j-th patch ultrasonic module receives ultrasonic waves is recorded. i,j This constitutes the propagation time matrix T between the patch-type ultrasonic modules;

[0066] According to d i,j and t i,j The ultrasonic propagation speed v between the i-th patch ultrasonic module and the j-th patch ultrasonic module was calculated. i,j According to all v i,j The propagation velocity matrix V between the patch-type ultrasonic modules.

[0067] In this embodiment, considering that the ultrasonic propagation speed between any two patch-type ultrasonic modules can be calculated based on the distance between them and the ultrasonic propagation time, the perimeter γ of the tree to be tested is first obtained. Since the patch-type ultrasonic modules are uniformly attached and distributed around the tree, the distance between any two patch-type ultrasonic modules can be calculated using the distance formula. Specifically, the distance formula is as follows: d i,j Let represent the straight-line distance from the i-th patch ultrasonic module to the j-th patch ultrasonic module, and according to all d... i,j The distance matrix D is obtained, and the definition of the distance matrix D is as follows:

[0068]

[0069] Then, the acquisition subsystem is used to obtain the time t required for ultrasonic waves to propagate between any two of the patch-type ultrasonic modules. i,j Specifically, considering that simultaneous data acquisition by multiple patch-type ultrasonic modules may generate significant data noise, this solution controls one patch-type ultrasonic module to emit ultrasonic waves at preset time intervals, while another patch-type ultrasonic module receives the ultrasonic waves and records the interval time, until the ultrasonic wave propagation time data between all pairs of patch-type ultrasonic modules has been recorded, thus accumulating all the t... i,j The propagation time matrix T is constructed, and the definition of the propagation time matrix T is as follows:

[0070]

[0071] The propagation velocity matrix V can be obtained from the distance matrix D and the propagation time matrix T. Specifically, the propagation velocity matrix V is defined as follows:

[0072]

[0073] As a preferred embodiment, after forming the propagation velocity matrix V between the patch-type ultrasonic modules, the method further includes:

[0074] Obtain the time error of the location of each of the N patch-type ultrasonic modules. i o i The time error represents the location of the i-th patch ultrasonic module and forms a time error matrix O;

[0075] Based on the time error matrix O, the propagation velocity matrix V is processed to eliminate the time error, resulting in the propagation velocity matrix V' after eliminating the time error.

[0076] The internal defect image of the tree under test is obtained based on the propagation velocity matrix V, including:

[0077] The internal defect image of the tree under test is obtained from the propagation velocity matrix V' after eliminating time error.

[0078] In this embodiment, to avoid damaging the tree itself, the commonly used invasive sensor in the prior art is replaced with a patch-type ultrasonic module. However, due to factors such as tree bark, paint, and cracks on the bark surface, the patch-type ultrasonic module cannot be tightly bonded to the tree under test, resulting in additional data noise and an increase in ultrasonic wave propagation time, thus reducing the accuracy of defect detection. Therefore, in practical use, the time error caused by the inability of the patch-type ultrasonic module to be tightly bonded to the bark should be eliminated to improve the accuracy of detecting the internal condition of the tree under test. Specifically, the time error of obtaining the position of each patch-type ultrasonic module should be considered. i When calculating the ultrasonic propagation speed between two patch ultrasonic modules, the ultrasonic propagation time between the two patch ultrasonic modules is subtracted from the time error of each ultrasonic module to obtain the ultrasonic propagation speed after eliminating the time error, thus obtaining the propagation speed matrix V' after eliminating the time error. Then, the internal defect image of the tree under test is obtained based on the propagation speed matrix V' after eliminating the time error, which improves the accuracy of the internal defect image.

[0079] As a preferred embodiment, the time error o of each of the N patch-type ultrasonic modules is obtained. i o i This represents the time error at the i-th patch-type ultrasonic module and forms a time error matrix O, including:

[0080] Obtain N v values ​​from adjacent patch-type ultrasonic modules i,j ;

[0081] The above v i,j The corresponding d i,j and ti,j This data was identified as healthy ultrasound data.

[0082] Substitute all health ultrasound data into the ultrasound tangential velocity relationship to form a superlinear equation system, and use the least squares method to solve the superlinear equation to obtain the time error matrix O.

[0083] The relationship between ultrasonic tangential velocity and other parameters is as follows: Where, d i,j d represents the distance between the i-th patch ultrasonic module and the j-th patch ultrasonic module. r,s Let be the distance between the r-th patch ultrasonic module and the s-th patch ultrasonic module; Where, θ i,j Let be the tangential arc between the i-th patch ultrasonic module and the j-th patch ultrasonic module, and k be the velocity offset coefficient. Where, θ r,s Let r be the tangential radius between the r-th patch ultrasonic module and the s-th patch ultrasonic module, where r and s are both no greater than N, and k is the velocity offset coefficient; i The time error at the i-th patch-type ultrasonic module; o j The time error at the j-th patch-type ultrasonic module; o r The time error at the r-th patch ultrasonic module; o s Let be the time error at the s-th patch-type ultrasonic module.

[0084] In this embodiment, as the tangential angle changes, the interference caused by the bark remains within a relatively small range. Therefore, we assume that the time error caused by the non-close contact between each patch-type ultrasonic module and the tree is fixed, based on the tangential velocity-radial velocity relationship V. T ≈V R (1-kθ 2 It can be seen that when there are no defects inside the tree, the tangential velocity V T With radial velocity V R The above relationship should be satisfied, where k is the velocity bias coefficient, which is determined by the characteristics of the tree itself and can be obtained through experimental measurement. Since the ultrasonic data selected in this embodiment are all healthy ultrasonic data, that is, ultrasonic data that are not affected by internal defects of the tree, these ultrasonic data should only be affected by the time error caused by not being tightly attached to the bark. Therefore, after eliminating the time error, the propagation speed of the new ultrasonic wave should satisfy the above relationship, from which the ultrasonic tangential velocity relationship can be obtained: Among them, o i o j o r o sLet be the time errors of the positions of the i-th, j-th, r-th, and s-th patch-type ultrasonic modules, respectively. After simplifying this relationship, we can obtain: d r, s c i,j o i +d r,s c i,j o j -d i,j c r,s o r -d i,j c r,s o s =d r,s c i,j t i,j -d i,j c r,s t r,s , Where k is the tree characteristic coefficient mentioned above, θ i,j Let be the tangential arc between the i-th patch ultrasound module and the j-th patch ultrasound module. Substituting the determined health ultrasound data into the simplified relation above, we obtain multiple sets of relations, which together form a superlinear equation:

[0085]

[0086] Solving the superlinear equations using the least squares method yields the time error of each patch-type ultrasonic module's position. Here, 'a' is calculated by superimposing 'd' and 'c'.

[0087] Specifically, a time-correction algorithm based on the least squares method can be used to solve superlinear equations. The time-correction algorithm based on the least squares method is as follows:

[0088] Input C, T, L, γ, n, ι, where C is the ratio of the ultrasonic velocity in the healthy tangential path to the radial path velocity;

[0089] Output T, D;

[0090] 1) Initialize A = 0 ι×n B=0 ι×1 D=0 n×n S = 0 l×(n+1) e = l 1,1 f = l 1,2 Where e and l are used to calculate the coefficient matrix, due to the formula Calculations need to be performed for every two paths. For ease of calculation, the first ultrasonic path is fixed here, where e and f are the subscripts of the first ultrasonic path.

[0091] 2) for i = 1 to n do

[0092] 3) for j = 1 to n do

[0093] 4)

[0094] 5) for i = 1 to ι do

[0095] 6) g = l i,1 ;

[0096] 7) h = l i,2 ;

[0097] 8)a i,e =a i,e +d g,h c e,f ;

[0098] 9)a i,f =a i,f +d g,h c e,f ;

[0099] 10)a i,g =a i,g -d e,f c g,h ;

[0100] 11)a i,h =a i,h -d e,f c g,h ;

[0101] 12)b i =d g,h c e,f t e,f -d e,f c g,h t g,h ;

[0102] 13) for i = 1 to ι do;

[0103] 14) forj = 1 to n do;

[0104] 15)s i,j =a i,j ;

[0105] 16)s i,13 =b i ;

[0106] 17) Solve the superlinear system of equations S using the least squares method and store the results in matrix O;

[0107] 18) for i = 1 to n do;

[0108] 19) forj = 1 to n do;

[0109] 20)t i,j =t i,j -o i -o j ;

[0110] 21) Return matrices T and D;

[0111] The generation process of the ultrasonic velocity ratio matrix A and B of the healthy tangential path to the radial path velocity is described in lines 5-12, and it is looped ι times. Lines 13-16 are looped ιn times, and the complexity of line 17 is O(ι²n). Lines 18-20 are looped n² times. Therefore, the complexity of the above is O(ι²n).

[0112] As a preferred embodiment, N v values ​​of adjacent patch-type ultrasonic modules are obtained. i,j Following that, it also includes:

[0113] In the propagation velocity matrix V, each v i,j Sort by size from largest to smallest and get the first N v. i,j .

[0114] In this embodiment, to improve the accuracy of the time error, it is necessary to increase the amount of health data. Therefore, in addition to defining the data between adjacent patch-type ultrasonic modules as health data, considering that the ultrasonic propagation speed will slow down when the ultrasonic propagation is affected by defects, theoretically, the fastest ultrasonic propagation speed data in the propagation speed matrix should not be affected by defects. Therefore, this solution also includes each v in the propagation speed matrix V. i,j Sort from largest to smallest, and select the first N v i,j It was also identified as health data, thereby increasing the amount of health data and further increasing the accuracy of time error.

[0115] As a preferred embodiment, after obtaining the internal defect image of the tree under test based on the propagation velocity matrix V', the method further includes:

[0116] Determine the defect rate of the tree under test based on internal defect images;

[0117] Obtain the location information of the tree to be tested;

[0118] The location information and defect rate of the trees to be tested are uploaded to the online map system, and the defect rate of the trees to be tested is marked and displayed at the corresponding location on the map.

[0119] In this embodiment, after acquiring the internal defect image of the tree to be tested, the result is uploaded to a subsystem implemented in the form of a website. The defect rate of the tree to be tested can be obtained based on the internal defect image. As a specific embodiment, the network map system here can be based on the Spring and Vue framework, using Baidu Map as the basis, and display the defect rate of the tree to be tested in real time on the map.

[0120] In addition, different icons can be used on the map to represent trees with different defect rates, allowing users to observe the defect status of each tree more intuitively.

[0121] As a preferred embodiment, after uploading the location information and defect rate of the trees to be tested to a network map system, and marking and displaying the defect rate of the trees to be tested at the corresponding locations on the map, the method further includes:

[0122] The system detects in real time whether the defect rate of the trees under test is lower than the threshold. If so, the trees under test with a defect rate lower than the threshold are marked on the map.

[0123] In this embodiment, considering that the purpose of defect detection on trees is to understand the internal condition of the trees so as to facilitate the implementation of protective measures for trees with defects, this solution will also detect in real time whether the defect rate of the trees to be tested is lower than a threshold after marking the defect rate of the trees on the map system. When it is lower than a certain threshold, the trees with defect rates lower than the threshold will be marked on the map to facilitate the implementation of subsequent protective measures.

[0124] As a preferred embodiment, obtaining an image of the internal defects of the tree under test based on the propagation velocity matrix V includes:

[0125] The cross-section of the tree to be tested is divided into a preset number of grids;

[0126] According to each v in the propagation speed matrix V i,j The distance from the corresponding two patch-type ultrasonic modules to the pith of the tree and the magnitude of their velocity are used to determine an area of ​​influence within the cross-section of the tree under test. i,j The speed of the event is negatively correlated with the area of ​​influence.

[0127] Each v i,j The grid corresponding to the area of ​​influence is assigned the value v. i,j The magnitude of the velocity, when there are multiple v in the grid. i,j When the defined area of ​​influence is covered, the grid is assigned multiple v values. i,j The average value;

[0128] Grids with values ​​lower than the preset value are set as defect grids, generating internal defect images of the tree under test.

[0129] In this embodiment, in order to obtain an image of the defects inside the tree under test, the cross-section of the tree is first gridded into a preset number of grids, and then the image is generated according to each v. i,j The speed value determines its influence range; please refer to [reference needed]. Figure 2 , Figure 2 This invention provides a schematic diagram of ultrasonic velocity distribution. An influence range is determined between every two patch-type ultrasonic modules based on the ultrasonic propagation velocity between these two modules. Figure 2 As shown, the area of ​​influence is larger on the side closer to the medullary core and smaller on the side farther from the medullary core. Specifically, when determining v... i,j When determining the range of influence, the width of the range is determined by the formula w + σ(δ - v) + ξα, where w is taken as one-eighth of the tree diameter. δ is the maximum value in the velocity matrix V. To determine the minimum value in the velocity matrix V, considering that the area closer to the core has a larger influence range than the area farther away, ξα is added, where ξ is a control coefficient and α is a Boolean value indicating whether it is near or far from the core. When near the core, α is 1, and when far from the core, α is -1. For example, when the k-th grid is located near the core within the influence range determined by the i-th and j-th patch ultrasound modules, α is 1. Then, it is determined whether the length of the k-th grid, the straight-line distance between the i-th and j-th patch ultrasound modules, is less than or equal to w + σ(δ - v) + ξα. If so, it is determined that the k-th grid is affected by the influence range determined by the i-th and j-th patch ultrasound modules, so the k-th grid is assigned the value v. i,j The magnitude of the velocity, similarly, when the k-th grid is subjected to multiple v i,j When the influence range is determined, the k-th grid is assigned multiple v values. i,j The average speed value is used to assign values ​​to all grids. After assigning values ​​to all grids in this way, grids with values ​​lower than the preset value are set as defect grids. Finally, the grids are convolved and transposed to improve the accuracy of the obtained defect image.

[0130] Input: T, D, U, n, m, w, σ, δ, ξ, where U is an n*n*m dimensional matrix, and the elements ui,j,k of U are the lengths intercepted by the straight line formed by the i-th and j-th sensors in the k-th cell;

[0131] Output: X; where X represents the grayscale image of the internal thermal map of the wood, and the larger the value, the healthier it is.

[0132] 1) Initialize X = 0 m×1 ;

[0133] 2) δ is the maximum value in matrix V;

[0134] 3) for k = 1 to m do;

[0135] 4) Create an empty list Y;

[0136] 5) for i = 1 to n do;

[0137] 6) for j = 1 to n do;

[0138] 7)

[0139] 8)

[0140] 9)

[0141] 10)

[0142] 11)

[0143] 12) Let This is the distance from the core to the k-th grid cell;

[0144] 13) if

[0145] 14) α = 1;

[0146] 15) else;

[0147] 16) α = -1;

[0148] 17) v = d i,j / t i,j ;

[0149] 18) Let Let be the length of the line connecting the i-th sensor and the j-th sensor at the k-th grid cell;

[0150] 19) if

[0151] 20) Add v to Y;

[0152] twenty one)

[0153] 22) Return matrix X;

[0154] In the algorithm above, lines 3-20 loop through n. 2 The complexity of line 12 is O(1) for m iterations. Therefore, the complexity of TPSI is O(n^m). 2 (m). Compared to TRRI, the above algorithm has a smaller time complexity.

[0155] The present invention also provides a tree detection device, please refer to... Figure 3 , Figure 3 This is a schematic diagram of a tree detection device provided by the present invention. The device includes:

[0156] Memory 31 is used to store computer programs and calibration coefficients;

[0157] Processor 32 is used to execute computer programs to implement the steps of the tree detection method described above.

[0158] For a description of the tree detection device provided by the present invention, please refer to the above method embodiments; the present invention will not be described again here.

[0159] The present invention also provides a tree detection device, comprising N patch-type ultrasonic modules, and the tree detection device described above.

[0160] For a description of the tree detection device provided by the present invention, please refer to the above method embodiments; the present invention will not be described again here.

[0161] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0162] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tree detection method characterized by, The application discloses a processor applied to a tree detection device, and a tree detection method. acquire ultrasonic wave propagation speeds between each two of the N patch ultrasonic wave modules , denotes an ultrasonic wave propagation speed between the i-th patch ultrasonic wave module and the j-th patch ultrasonic wave module, i and j are both positive integers not greater than N; According to all a propagation speed matrix V between said patch ultrasonic modules is obtained; An internal defect image of the tree to be detected is acquired according to the propagation velocity matrix V. The ultrasonic wave propagation speed between each two of the N patch ultrasonic wave modules is obtained According to all of The propagation speed matrix V between the patch ultrasonic wave modules is obtained, including: acquiring a circumference of the tree to be measured ; According to the circumference The linear distance from the i-th patch type ultrasonic wave module to the j-th patch type ultrasonic wave module is calculated ; According to all constitute a matrix of distances D between said patch ultrasonic modules; controlling the i-th patch type ultrasonic wave module to emit ultrasonic wave and recording the time when the j-th patch type ultrasonic wave module receives the ultrasonic wave every preset time , and a propagation time matrix T between the patch type ultrasonic wave modules is constituted. According to the above And the above The ultrasonic propagation speed between the i-th patch ultrasonic module and the j-th patch ultrasonic module is calculated , according to all The propagation speed matrix V between the patch ultrasonic modules is constituted After the propagation velocity matrix V is constructed, the following steps are further included: acquire time errors of positions of the N patch ultrasonic wave modules respectively , denotes a time error of a position of the i-th patch ultrasonic wave module, and constitutes a time error matrix O; The time error matrix O is acquired according to the healthy ultrasonic data, and a time error elimination process is performed on the propagation velocity matrix V to obtain a propagation velocity matrix V' after time error elimination. The internal defect image of the tree to be detected is acquired according to the propagation velocity matrix V, and the method includes the following steps: The internal defect image of the tree to be detected is acquired according to the propagation velocity matrix V' after time error elimination. wherein the time error of each of the N patch ultrasonic wave modules is obtained denotes the time error at the i-th patch ultrasonic wave module and constitutes a time error matrix O, comprising: acquiring N adjacent patch ultrasonic wave modules ; The above-described The above-described The above-described determined to be healthy ultrasound data; All the healthy ultrasonic data is substituted into an ultrasonic tangential velocity relation formula to form a super-linear equation group, and the super-linear equation is solved by using a least square method to obtain the time error matrix O. The ultrasonic wave tangential velocity relationship is: wherein, is a distance between the i-th patch ultrasonic wave module and the j-th patch ultrasonic wave module; is a distance between the r-th patch ultrasonic wave module and the s-th patch ultrasonic wave module; wherein, is a tangential arc radian between the i-th patch ultrasonic wave module and the j-th patch ultrasonic wave module, and k is a velocity bias coefficient; wherein, is a tangential arc radian between the r-th patch ultrasonic wave module and the s-th patch ultrasonic wave module, and r, s are all not greater than N, and k is a velocity bias coefficient; is a time error at the i-th patch ultrasonic wave module; is a time error at the j-th patch ultrasonic wave module; is a time error at the r-th patch ultrasonic wave module; is a time error at the s-th patch ultrasonic wave module.

2. The tree detection method of claim 1, wherein, acquiring N adjacent patch ultrasonic wave modules of the patch ultrasonic wave module After that, further comprising: Sort the values in the propagation speed matrix V in descending order and obtain the top N values Sort the values in the propagation speed matrix V in descending order and obtain the top N values .

3. The tree detection method of claim 1, wherein, After the internal defect image of the tree to be detected is acquired according to the propagation velocity matrix V', the following steps are further included: The defect rate of the tree to be detected is determined according to the internal defect image. Position information of the tree to be detected is acquired. The position information and the defect rate of the tree to be detected are uploaded to a network map system, and the defect rate of the tree to be detected is marked and displayed at a corresponding position in a map.

4. The tree detection method of claim 3, wherein, After the position information and the defect rate of the tree to be detected are uploaded to the network map system and the defect rate of the tree to be detected is marked and displayed at the corresponding position in the map, the following steps are further included: Whether the defect rate of the tree to be detected is lower than a threshold value is detected in real time, and if yes, the tree to be detected with the defect rate lower than the threshold value is marked in the map.

5. The tree detection method according to any one of claims 1 to 4, wherein, The internal defect image of the tree to be detected is acquired according to the propagation velocity matrix V, and the method includes the following steps: The cross section of the tree to be detected is divided into a preset number of grids. According to each of the propagation speed matrix V The distance and speed size of the straight line where the corresponding two patch ultrasonic modules are located from the tree pith determine an influence range area for the cross section of the tree to be measured, The speed and the influence range area are negatively correlated. The grid corresponding to the influence range area of each is assigned a speed magnitude value of , and when the grid is covered by multiple determined influence areas, the grid is assigned an average value of the speed magnitude values of the multiple . The grid with an assigned value lower than a preset value is set as a defect grid, and the internal defect image of the tree to be detected is generated.

6. A tree detection apparatus characterized by comprising: The tree detection device includes a memory for storing a computer program and a calibration coefficient, and a processor for executing the computer program to realize the steps of the tree detection method in any one of claims 1 to 5. The tree detection device includes N patch ultrasonic modules, and the tree detection device is as claimed in claim 6. The tree detection device includes N patch ultrasonic modules, and the tree detection device is as claimed in claim 6.

7. A tree detection apparatus characterized by comprising: ​

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