CN104950040B - Wood internal defect three-D imaging method based on Top k inverse distance-weighting - Google Patents

Abstract

The invention discloses a three-dimensional imaging method for internal defects of wood based on Top-k inverse distance weighting. According to the stress wave propagation velocity data, the velocity data set of known points in the neighborhood of spatially estimated points is obtained; the space is calculated according to the inverse distance weighting algorithm. Draw a three-dimensional spatial point distribution map based on the attribute values of the estimated points, and analyze the internal decay of the wood to be tested according to the three-dimensional spatial point distribution map; use this clear method to perform three-dimensional imaging of the internal defects of the wood, and expand the neighborhood relationship of the spatial estimated points In three-dimensional space, increase the search radius of the estimated point and introduce top-k query to find the k known points with the greatest influence in its neighborhood, calculate the attribute value of the estimated point and perform three-dimensional imaging, with high imaging accuracy ; Detect the internal defects of wood, and analyze the decay position and severity of decay. The technology is simple, fast and efficient, and can accurately and quickly know the internal decay of wood, which greatly improves the efficiency of internal decay detection of wood.

Description

Three-dimensional imaging method of wood internal defects based on Top-k inverse distance weighting

technical field

The invention relates to an imaging method for internal defects of wood, in particular to a three-dimensional imaging method for internal defects of wood based on Top-k inverse distance weighting.

Background technique

my country is a country that is seriously short of timber resources. With the continuous improvement of economic development and people's living standards, the demand for timber is also increasing year by year. Using wood non-destructive testing technology to carry out a large number of regular detection and timely remediation of living wood has become an important means to effectively improve the utilization rate of related forestry resources. Wood non-destructive testing technology is used to detect wood growth characteristics, physical properties of wood and its structure, internal defects of wood, mechanical properties of wood, etc. The use of wood non-destructive testing technology can improve the utilization rate of wood.

In the wood non-destructive testing technology, some imaging methods are usually used to make the internal defects of the wood visually displayed, so that researchers can more quickly and intuitively study and analyze the position of the internal defects of the wood. The existing technology provides a stress wave Tomographic imaging technology, this imaging technology mainly obtains two-dimensional tomographic images of wood through stress wave tomography technology, and then obtains information such as the size and shape of internal defects in wood through tomographic images, but this method requires multiple cross-sectional images of wood The acquisition of three-dimensional research by obtaining two-dimensional tomograms of wood has a long experimental cycle and low implementation efficiency; in addition, there is a method of obtaining three-dimensional conditions inside wood through CT scanning technology in the prior art. The three-dimensional situation inside the wood is realized, but the cost is relatively high and it is not easy to realize.

Contents of the invention

Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a three-dimensional imaging method for wood internal defects based on Top-k inverse distance weighting, which can accurately locate the position and decay of wood internal defects, greatly improving the detection efficiency.

Technical solution: In order to achieve the above object, a method for three-dimensional imaging of wood internal defects based on Top-k inverse distance weighting of the present invention comprises the following steps:

S1 fixes the sensors around the wood to be tested so that the sensors are evenly distributed around the wood to be tested and the sensors are fixed at different heights around the wood to be tested;

S2 taps the sensors one by one to make the stress wave propagate inside the material to be tested, record the propagation time of the stress wave inside the wood, and calculate the propagation speed of the stress wave between any two sensors;

S3 Obtain the known point velocity data set in the neighborhood of the spatially estimated point according to the stress wave propagation velocity data;

S4 calculates the attribute value of the spatial estimation point according to the inverse distance weighting algorithm through the known point velocity data set in the neighborhood of the spatial estimation point;

S5 draws a three-dimensional space point distribution map according to the attribute values of the space estimated points, and analyzes the internal decay of the wood to be tested according to the three-dimensional space point distribution map.

Further, before calculating the attribute value of the spatial estimation point, the known points in the field of the spatial estimation point are screened, which specifically includes the following steps:

S21 first extends the two-dimensional-based four-way search method in the azimuth search method to the three-dimensional space search method. Let the space estimation point be p _i ( _xi , y _i ), and the space estimation point p _i ( _xi ,y _i ) there are known points q _i ( _xi ,y _i ) in 8 domain spaces around it;

S22 Then use the search radius r to search for known points in each field space, where r _min <r<R, R is the radius of the wood, r _min refers to the number of known points in the field space searched by r _min must Reaching the preset threshold δ;

S23 Finally, the Top-k-based query technology is used to query the k known points in the domain space of the spatial estimation point p _i ( _xi , y _i ) that are most correlated with the spatial estimation point.

Further, the step S23 specifically includes the following steps:

Given a set T of M tuples, each tuple has m'=(u ₁ ,u ₂ ,...,u _m., ) attributes, store the set T as a set S of column files={S ₁ ,S ₂ ,...,S _m }, each column file is a binary combination S _i (rid,u _i ), where rid represents the identifier of the object, and u _i represents the attribute value of the object at the attribute, where each The storage method of the column file is a monotonous non-increasing sequence of the attribute values of each tuple, and F is defined as the scoring function of m' attributes. The formula of F is as follows:

(Formula 1)

In the formula, λ _i is the weight of the scoring function F on the attribute value u _i ;

Use the Top-k query technology to query the k subsets of each tuple in the query set T. By reading the column file S that has been arranged in descending order by the m columns, in the sequential read sequence, when the tuple rid appears, the random read method is Another m-1 column file obtains other attribute values, and then calculates their scoring values. If the scoring value is the largest k currently, use the priority queue to maintain k tuples and their related information. For each column sequence, set Its current read position u _i , set the threshold τ=F(u ₁ , u ₂ ,..., u _m' ), when the minimum value of k elements in the priority queue is not less than τ, the query ends.

Further, the step S4 includes the following steps:

Order S41 Indicates the weight of the spatial estimation point p _i ( _xi , y _i ) to the known points in its field, then Expressed as:

(Formula 2)

In the formula, Indicates the distance between the space estimation point p _i ( _xi , y _i ) and the known point q _i ( _xi , y _i ), m is a constant;

S42 The attribute value of the space estimation point p _i ( _xi , y _i ) is expressed as:

(Formula 3)

In the formula, is the attribute value of the spatially estimated point, is the attribute value of the i-th known point in the field of p _i ( _xi , y _i ), and δ is the number of known points in the neighborhood involved in the calculation.

Further, the value of m is 1.

Further, in the step S1, the maximum height difference between the sensors fixed on the wood to be measured ranges from 15 cm to 30 cm.

Beneficial effect: compared with the prior art, the present invention has the advantages of:

1. Use this method to image the internal defects of wood in 3D, extend the neighborhood relationship of estimated points to 3D space, increase the search radius of predicted points and introduce top-k query to find out the most influential in its neighborhood K known points, calculate the attribute value of the estimated point and perform three-dimensional imaging, with high imaging accuracy;

2. The method of the present invention is used to detect internal defects of wood and analyze the decay position and severity of decay. The technology is simple, fast and efficient, and the internal decay of wood can be accurately and quickly known, which greatly improves the efficiency of detection of internal decay of wood.

Description of drawings

Fig. 1 is a flowchart of the three-dimensional imaging method of wood internal defects based on Top-k inverse distance weighting in the present invention.

Fig. 2 is the figure of 5 kinds of experimental samples of the embodiment of the present invention.

Fig. 3 is an imaging effect diagram of wood internal defects obtained based on the algorithm of the present invention.

detailed description

The present invention will be further described below in conjunction with the accompanying drawings.

The three-dimensional imaging method of wood internal defects based on Top-k inverse distance weighting proposed by the present invention, with reference to Fig. 1, comprises the following steps:

Fix the sensors around the wood to be tested so that the sensors are evenly distributed around the wood to be tested and the sensors are fixed at different heights around the wood to be tested, wherein the maximum height difference between the sensors fixed on the wood to be tested ranges from 15cm to 30cm, tap the sensors one by one to make the stress wave propagate inside the material to be tested, record the propagation time of the stress wave inside the wood, and calculate the propagation speed of the stress wave between any two sensors;

According to the stress wave propagation velocity data, the known point velocity data set in the neighborhood of the spatially estimated point is obtained;

Through the known point velocity data set in the neighborhood of the spatial estimation point, the attribute value of the spatial estimation point is calculated according to the inverse distance weighted algorithm. Before calculating the attribute value of the spatial estimation point, it needs to be known in the field of the spatial estimation point Points to filter, specifically include the following steps:

Firstly, the 2D-based four-way search method in the azimuth search method is extended to the 3D-based space search method. Let the space estimation point be p _i ( _xi , y _i ), and the space estimation point p _i ( _xi , There are known points q _i ( _xi , y _i ) in 8 domain spaces around y _i );

Then use the search radius r to search for known points in each field space, where r _min <r<R, R is the radius of the wood, and r _min means that the number of known points in the field space searched by r _min must reach Pre-set threshold δ;

Finally, the Top-k query technology is used to query the k known points in the domain space of the spatial estimation point p _i ( _xi , y _i ) that are most correlated with the spatial estimation point: Given a set T of M tuples , each tuple has m'=(u ₁ ,u ₂ ,...,u _m., ) attributes, and the set T is stored as a set of column files S={S ₁ ,S ₂ ,...,S _m }, each column file is a binary combination S _i (rid,u _i ), where rid represents the identifier of the object, u _i represents the attribute value of the object at the attribute, and the storage method of each column file is each tuple The monotonous non-increasing sequence of attribute values of , define F as the scoring function of m' attributes, and the formula of F is as follows:

(Formula 1)

In the formula, λ _i is the weight of the scoring function F on the attribute value u _i ; F is a monotone function, namely If for all 1≤i≤m', a ₁ ·u ₁ ≤a ₂ ·u ₂ , then F(a ₁ )≤F(a ₂ ) _; use the Top-k query technology to query the tuples in the query set T K subsets, by reading the column file S that has m columns in descending order, sequentially read the sequence, when the tuple rid appears, randomly read in another m-1 column file to obtain other attribute values, and then calculate Their score values, if the score value is currently the largest k, use the priority queue to maintain k tuples and their related information, for each sequence, set its current reading position u _i , set the threshold τ=F(u ₁ ,u ₂ ,...,u _m' ), when the minimum value of k elements in the priority queue is not less than τ, the query ends;

Calculate the attribute value of the space estimation point according to the inverse distance weighting algorithm, including the following steps:

make Indicates the weight of the spatial estimation point p _i ( _xi , y _i ) to the known points in its field, then Expressed as:

(Formula 2)

In the formula, Indicates the distance between the estimated spatial point p _i ( _xi , y _i ) and the known point q _i ( _xi , y _i ), m is a constant, and the value of m is 1;

The attribute value of the spatial estimation point p _i ( _xi , y _i ) is expressed as:

(Formula 3)

In the formula, is the attribute value of the spatially estimated point, is the attribute value of the i-th known point in the field of p _i ( _xi , y _i ), and δ is the number of known points in the neighborhood involved in the calculation;

Because the value of m in the embodiment of the present invention is 1, the attribute value of the spatial estimation point p _i ( _xi , y _i ) is expressed as:

(Formula 4)

Draw a three-dimensional spatial point distribution map according to the attribute values of the spatial estimation points, and analyze the internal decay of the wood to be tested according to the three-dimensional spatial point distribution map. Specifically, assign the color of the spatial estimation points according to different attribute values, and perform three-dimensional visualization.

Embodiment of the invention:

Among the present invention, the wood to be measured is selected 5 kinds of experimental samples, and the sample tree species types are respectively hickory tree, paulownia tree and sycamore tree, as shown in Figure 2, sample serial number 1 and 3 are Paulownia tree samples among Fig. 2, sample serial number 2 and 5 is a hickory tree sample, sample number 4 is a sycamore tree sample, of which sample numbers 1 and 3 are artificially excavated holes to simulate natural decay, and the defects of sample numbers 2, 4 and 5 are natural decay, and the sample defect point ratio is used The product of the actual sample volume is calculated to obtain the defect volume of the sample, and the actual measured defect volume of sample 1 is 4019.2cm ³ , the actual measured defect volume of sample 2 is 2601.12cm ³ , the actual measured defect volume of sample 3 is 1074.84cm ³ , and the measured defect volume of sample 4 is is 2418.39cm ³ , and the measured defect volume of sample 5 is 2307.9cm ³ ;

By using the self-developed portable wood tomography equipment, 12 sensors were fixed at different heights around the selected wood samples for data collection, and the maximum height differences of the 6 samples were 20cm, 30cm, 15cm, 15cm, 15cm, 20cm, Knock 12 sensors in turn. After each sensor is knocked, extract the test data twice, and calculate a total of 24 sets of stress wave propagation velocity data for three-dimensional imaging analysis;

Use the algorithm that the present invention proposes to carry out experiment to above-mentioned 5 kinds of samples respectively, experimental result diagram obtains by MATLAB software simulation, finally obtains the experimental effect diagram of 5 kinds of samples, as shown in Figure 3, Fig. 3 reaction corresponds to the sample of different sample sequence numbers respectively Three-dimensional map, sample top view, imaging three-dimensional map based on Top-k inverse distance weighting algorithm, and imaging top view based on Top-k inverse distance weighting algorithm. It can be clearly seen in the figure that the area composed of black points is the actual decayed part of the sample. Area, the area composed of other light-colored points is the healthy area in the sample;

The present invention uses the relative error between the actual measured volume of the sample defect and the defect volume calculated based on the algorithm of the present invention to verify the correctness of the detection result, and the difference between the actual measured volume of the sample defect and the defect volume calculated based on the algorithm of the present invention The relative error of is calculated by formula 5:

Δt=|v ₁ -v|/v ₁ (Formula 5)

In the formula, v refers to the actual measured volume _of the sample defect, v1 refers to the defect volume calculated based on the algorithm of the present invention, and the detection results of the sample defect are shown in Table 1:

Table 1

It can be seen from Table 1 that the relative error Δt of sample 5 is 16.1%. The reason may be that the decay degree of the upper and lower ends of sample 5 is quite different. There is a large error between the calculated defect volume and the actual defect volume. For other samples, the defect position in the three-dimensional map obtained by the algorithm of the present invention is very close to the actual defect position, and the average detection accuracy rate reaches 83.9%.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those skilled in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications should also be It is regarded as the protection scope of the present invention.

Claims (4)

1. based on the Top-k inverse distance weighted wood internal defect three-dimensional imaging method, it is characterized in that: comprise the following steps: S1 fixes the sensors around the wood to be tested so that the sensors are evenly distributed around the wood to be tested and the sensors are fixed at different heights around the wood to be tested; S2 taps the sensors one by one to make the stress wave propagate inside the material to be tested, record the propagation time of the stress wave inside the wood, and calculate the propagation speed of the stress wave between any two sensors; S3 Obtain the known point velocity data set in the neighborhood of the spatially estimated point according to the stress wave propagation velocity data; S4 calculates the attribute value of the spatial estimation point according to the inverse distance weighting algorithm through the known point velocity data set in the neighborhood of the spatial estimation point; Before calculating the attribute value of the spatial estimation point, the known points in the field of the spatial estimation point are screened, which specifically includes the following steps: S21 first extends the two-dimensional-based four-way search method in the azimuth search method to the three-dimensional space search method. Let the space estimation point be p _i ( _xi , y _i ), and the space estimation point p _i ( _xi ,y _i ) there are known points q _i ( _xi ,y _i ) in 8 domain spaces around it; S22 Then use the search radius r to search for known points in each field space, where r _min <r<R, R is the radius of the wood, r _min refers to the number of known points in the field space searched by r _min must Reaching the preset threshold δ; S23 Finally, use the Top-k query technology to query the k known points in the field space of the spatial estimation point p _i ( _xi , y _i ) that have the greatest correlation with the spatial estimation point; The step S23 specifically includes the following steps: Given a set T of M tuples, each tuple has m'=(u ₁ ,u ₂ ,...,u _m .,) attributes, store the set T as a set S of column files={S ₁ ,S ₂ ,...,S _m }, each column file is a binary combination S _i (rid,u _i ), where rid represents the identifier of the object, and u _i represents the attribute value of the object at the attribute, where each The storage method of the column file is a monotonous non-increasing sequence of the attribute values of each tuple, and F is defined as the scoring function of m' attributes. The formula of F is as follows: In the formula, λ _i is the weight of the scoring function F on the attribute value u _i ; Use the Top-k query technology to query the k subsets of each tuple in the query set T. By reading the column file S that has been arranged in descending order by the m columns, in the sequential read sequence, when the tuple rid appears, the random read method is Another m-1 column file obtains other attribute values, and then calculates their scoring values. If the scoring value is the largest k currently, use the priority queue to maintain k tuples and their related information. For each column sequence, set Its current reading position u _i , set the threshold τ=F(u ₁ ,u ₂ ,...,u _m' ), when the minimum value of k elements in the priority queue is not less than τ, the query ends; S5 draws a three-dimensional space point distribution map according to the attribute values of the space estimated points, and analyzes the internal decay of the wood to be tested according to the three-dimensional space point distribution map. 2. The three-dimensional imaging method for wood internal defects based on Top-k inverse distance weighting according to claim 1, characterized in that: said step S4 comprises the following steps: Order S41 Indicates the weight of the spatial estimation point p _i ( _xi , y _i ) to the known points in its field, then Expressed as: In the formula, Indicates the distance between the space estimation point p _i ( _xi , y _i ) and the known point q _i ( _xi , y _i ), m is a constant; S42 The attribute value of the space estimation point p _i ( _xi , y _i ) is expressed as: In the formula, is the attribute value of the spatially estimated point, is the attribute value of the i-th known point in the field of p _i ( _xi , y _i ), and δ is the number of known points in the neighborhood involved in the calculation. 3. The method for three-dimensional imaging of wood internal defects based on Top-k inverse distance weighting according to claim 2, characterized in that: the value of m is 1. 4. The three-dimensional imaging method for wood internal defects based on Top-k inverse distance weighting according to claim 1, characterized in that: in the step S1, the maximum height difference range between the sensors fixed on the wood to be measured is 15cm ~ 30cm.