CN104090030A - Tree hole detection method - Google Patents
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- CN104090030A CN104090030A CN201310441500.4A CN201310441500A CN104090030A CN 104090030 A CN104090030 A CN 104090030A CN 201310441500 A CN201310441500 A CN 201310441500A CN 104090030 A CN104090030 A CN 104090030A
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Abstract
The invention discloses a tree hole detection method. The tree hole detection method comprises the following steps of detecting if the tree trunk having a large diameter has a hole by stress wave or not, further detecting a position of the hole by surface acoustic wave if the hole exists, and building a three-dimensional model of the tree trunk and the hole by computer software so that the hole in the tree trunk is shown accurately. The tree hole detection method can accurately detect the size of the hole in the tree trunk, determine the position of the hole in the tree trunk and acquire the three-dimensional model of the tree trunk and the hole in the tree trunk, and has the characteristics of fast detection rate and good economical efficiency.
Description
Technical field
The present invention relates to a kind of trees insect pest situation detection method, especially relate to a kind of position and big or small large tree hole inspection method that can accurately detect the hole in large tree.
Background technology
Trees pest control method mainly contains trunk whitewashing method, agricultural chemicals buriing method, trunk gluing method, trunk cure the wound method and medicine-injecting method.
Wherein, medicine-injecting method is injection into holes around trunk, makes Quan Shuti all have agriculture the effective elements of the medicine, no matter insect is got feed at what position, all can be poisoned to death.This method is easy and simple to handle, saving of labor, economizes medicine, contaminated air not, does not injure natural enemy, and prevention effect is good.
But common medicine-injecting method can not obtain the residing particular location of insect, in order to kill off the insect pests, need to inject the medicine of larger dose, but dose conference causes certain influence to the growth of trees.
If select wrong position to inject pesticide, both can cause damage to trees, the position of being damaged by worms, because the induction of pesticide easily produces pathology, not only can not eliminate destructive insects timely and effectively, has damaged on the contrary trees.
Chinese patent mandate publication number: CN102706963A, authorize open day on October 3rd, 2012, the inner rotten stress wave lossless detection device of a kind of ancient tree and historic building structure is disclosed, this device comprises a plurality of sensors, sensor fastening device, power hammer, micro-damage type pin type connector, data handling system and the inner rotten fault imaging software of timber, and described sensor, described data handling system and the computing machine that described imaging software is installed are connected by some wires.This invention exists cannot accurately determine the position of hole and the big or small deficiency of hole.
Summary of the invention
Goal of the invention of the present invention is cannot accurately determine the position of hole and the big or small deficiency of hole in order to overcome prospection stress wave detection method of the prior art, provides a kind of and can accurately detect the position of the hole in large tree and the large tree hole inspection method of size.
To achieve these goals, the present invention is by the following technical solutions:
A large tree hole inspection method, a kind of cavity detection device for large tree cavity detection comprises acoustic surface wave device, distance measuring sensor and shockwave sensor; On shockwave sensor, distance measuring sensor and acoustic surface wave device, be respectively equipped with the data-interface for being electrically connected to computing machine; Described acoustic surface wave device comprises counter, oscillator and SAW (Surface Acoustic Wave) resonator; Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, counter is electrically connected to oscillation circuit, counter is provided with the data-interface for being electrically connected to computing machine, and SAW (Surface Acoustic Wave) resonator is electrically connected to two electrodes by wire, and distance measuring sensor is located at the junction of an electrode and wire; Described detection method comprises the steps:
(1-1) in computing machine, be provided with in advance stochastic resonance system model and hole wall thickness forecast model;
In computing machine, set
n span and the absolute value A of standard signal to noise ratio (S/N ratio) valley
icorresponding relation, i=1, L, n; Specification error threshold value d, sets surface acoustic wave detection threshold S; Wherein,
the diameter of hole,
it is the large tree trunk diameter of hole position;
(1-2) top of choosing large tree trunk is for measuring initiating terminal, and large tree bottom is for measuring clearing end; Shockwave sensor and pulse hammer are separately fixed on the opposite side of trunk outer peripheral face of initiating terminal;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal one;
(1-3) by shockwave sensor and pulse hammer respectively along the outer peripheral face of trunk 30 to 90 degree fixing that turn clockwise;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal two; Computing machine calculates the average signal of stress wave signal one and stress wave signal two, obtains mean stress ripple signal;
(1-4) in mean stress ripple signal, extract 1 complete stress wave pulse signal I (t), the described stress wave pulse signal I (t) in 0 to 100ms is inputted in stochastic resonance system model, stochastic resonance system model is resonated; Computing machine utilizes snr computation formula to calculate output signal-to-noise ratio SNR;
Accidental resonance technology is shown up prominently in detection data feature values extraction field at present.This theory is proposed in 1981 by Italian physicist Benzi, in order to the phenomenon of explaining that the earth meteorological glacial epoch in time immemorial and cycle warm climate phase alternately occur.Accidental resonance has three elements: nonlinear system, weak signal and noise source.From signal process angle, consider, accidental resonance is in nonlinear properties transmitting procedure, by regulating intensity or other parameter of system of noise, make system output reach optimum value, in fact also can think the collaborative state of input signal, nonlinear system, noise.
Generally, input the signal that external force can be thought desirable electric nasus system in flip-flop-model, noise is the interchannel noise of introducing in testing process, and the input of bistable system (signal plus noise) is as the detection signal of electric nasus system reality.Under the excitation of excitation noise, system produces accidental resonance, and now output signal is greater than input signal, thereby has played the effect that signal amplifies.Meanwhile, accidental resonance is transformed into the noise energy in part detection signal in signal and goes, thereby has effectively suppressed the noisiness in detection signal.Therefore, stochastic resonance system is equivalent to improve the effect of output signal-noise ratio, and signal, excitation noise and bistable system can be regarded an efficient signal processor as.On above technical foundation, stochastic resonance system output signal-to-noise ratio analytical technology can be reacted the essential characteristic information of sample preferably.
What accidental resonance output signal-to-noise ratio characteristic information reflected is the essential information of sample, and this characteristic information does not change with the restriction of detection method or multiplicity, only relevant with the character of sample, is conducive to the demarcation of properties of samples, improves accuracy of detection.
Accidental resonance analytical approach favorable reproducibility, repeats 100 times and calculates, and the resultant error ratio of output is no more than 0.1%.And the error ratio of the frequency signal error rate that simple stress wave detects after than accidental resonance Analysis signal-to-noise ratio (SNR) exceeds several times.
(1-5) computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve, and using the absolute value of signal to noise ratio (S/N ratio) valley as signal to noise ratio (S/N ratio) eigenwert F;
(1-6) when
to make the hole in the trunk of large tree be A to computing machine
icorresponding
the judgement of span;
(1-7) when
and measuring position is during apart from 1 centimetre of clearing end >, pulse hammer and shockwave sensor are taken off from the outer peripheral face of trunk, pulse hammer and shockwave sensor are moved to clearing end gradually, and pulse hammer and shockwave sensor are fixed on the trunk outer peripheral face apart from 0.5 to 1 centimetre of measuring position last time, return to step (1-3);
When
and measuring position is during apart from clearing end≤1 centimetre, and pulse hammer quits work, and computing machine is made in large tree trunk does not have pertusate judgement, and will judge that information shows on computer screen;
When
proceed to step (1-8);
(1-8) measure the wall thickness of hole:
Step a, takes off pulse hammer and shockwave sensor from the outer peripheral face of trunk; Two electrodes of SAW (Surface Acoustic Wave) resonator are fixed on the trunk outer peripheral face of initiating terminal to 2 to 3 millimeters along the circumferencial direction interval of trunk outer peripheral face, two electrodes; Clearing end outer peripheral face is provided with for reflecting the annular reflex mirror of the measuring-signal of distance measuring sensor;
Step b, SAW (Surface Acoustic Wave) resonator work, counter detects surface acoustic wave response frequency Freq
sAW, surface acoustic wave response frequency Freq
sAWbe input in computing machine, computing machine utilizes hole wall thickness forecast model to calculate hole in large tree with respect to the wall thickness of electrode side;
Step c, two electrodes and the large fixing position of outer peripheral face of setting of along the circumferencial direction of trunk, moving SAW (Surface Acoustic Wave) resonator, repeating step b detects, and obtains several wall thickness d;
Steps d, the distance value between the electrode that distance measuring sensor is detected and clearing end and being stored in computing machine with corresponding each wall thickness d of distance value;
Step e, repeating step a to d carries out the detection of axial scan formula to large tree trunk;
(1-9) in advance the outer peripheral face of large tree trunk is measured, the coordinate of the point on the outer peripheral face of setting trunk, according to the distance value between the coordinate of the point of the outer peripheral face of trunk, electrode and clearing end and each wall thickness d corresponding with distance value, computing machine is set up the three-dimensional model of large tree trunk and trunk Hole.
Stress wave has the advantages that penetration capacity is strong, so the present invention adopts stress wave first to detect in the large tree trunk that diameter is large whether have hole;
While having hole, with surface acoustic wave, further detect the position of hole, and use computer software to set up three-dimensional model for trunk and hole, thereby the hole in trunk greatly is accurately presented.
Simple stress wave can only detect in trunk, whether there is hole, the detection method that adopts stress wave to combine with surface acoustic wave in the present invention, stress wave is mainly realized the size detection of trunk inner void, and surface acoustic wave Detection Techniques can precise positioning hole position, the two is in conjunction with the accurate judgement that can realize large tree trunk Hole size and position simultaneously, this is for damage by worms situation or find position accurately for trees and suit the remedy to the case significant of the large tree of judgement inside, can save in time the live body trees that damaged by worms, reduce economic loss.
As preferably, in described step b, hole wall thickness forecast model is
As preferably, described stochastic resonance system model is
Wherein, V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and A is input signal strength, and D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving.
As preferably, described snr computation formula is
wherein, ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be the noise intensity in signal frequency range.
As preferably, described surface acoustic wave detection threshold S is 0.5% to 2.5%.
As preferably, described error threshold d is 0.15 to 0.6.
As preferably, the centre frequency of SAW (Surface Acoustic Wave) resonator is 433.92MHZ.
Therefore, the present invention has following beneficial effect: (1) can accurately detect the size of the hole in large tree trunk; (2) can determine the position of large tree trunk Hole; (3) can access the three-dimensional model of the hole in large tree trunk and trunk; (4) detect quick, good economy performance.
Accompanying drawing explanation
Fig. 1 is a kind of process flow diagram of the present invention;
Fig. 2 is theory diagram of the present invention;
Fig. 3 is stress wave pulse signal figure of the present invention;
Fig. 4 is the output signal-to-noise ratio curve of stress wave detection signal of the present invention;
Fig. 5 is trunk cross-sectional view of the present invention and coordinate system figure;
Fig. 6 is a kind of structural representation of annular reflex mirror of the present invention.
In figure: acoustic surface wave device 1, distance measuring sensor 2, shockwave sensor 3, counter 4, oscillator 5, SAW (Surface Acoustic Wave) resonator 6, electrode 7, computing machine 8, trunk 9, hole 10, annular reflex mirror 11.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
Embodiment is as shown in Figure 2 a kind of large tree hole inspection method, for the large cavity detection device of setting cavity detection, comprises acoustic surface wave device 1, laser range sensor 2 and shockwave sensor 3; On shockwave sensor, laser range sensor and acoustic surface wave device, be respectively equipped with the data-interface for being electrically connected to computing machine 8;
Acoustic surface wave device comprises counter 4, oscillator 5 and SAW (Surface Acoustic Wave) resonator 6; Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, counter is electrically connected to oscillation circuit, counter is provided with the data-interface for being electrically connected to computing machine, and SAW (Surface Acoustic Wave) resonator is electrically connected to two electrodes 7 by wire, and distance measuring sensor is located at the junction of an electrode and wire;
In computing machine, set in advance stochastic resonance system model:
Wherein, V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and A is input signal strength, and D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving;
In computing machine, set hole wall thickness forecast model:
In computing machine, set the diameter of hole
trunk diameter with hole position
between ratio
4 spans and the absolute value A of standard signal to noise ratio (S/N ratio) valley
icorresponding relation, i=1, L, 4; Specification error threshold value d=0.2, sets surface acoustic wave detection threshold S=2%;
and A
icorresponding relation be to obtain by experiment: with stress wave to difference
the trunk of the hole of span detects, and obtains signal I (t), by frequency signal I (t) input stochastic resonance system model, stochastic resonance system model is resonated; Computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve;
To this hole duplicate detection 120 times, obtain the absolute value of 120 signal to noise ratio (S/N ratio) valleies, the absolute value of signal to noise ratio (S/N ratio) valley is averaged, and measures the size of hole and the trunk diameter size of detection position of this trunk simultaneously, thereby obtain
with A
ibetween corresponding relation.
In the present embodiment, when
be defined as the large tree of duck eye, corresponding A
1=88dB;
When
be defined as middle hole great Shu, corresponding A
2=85.7dB;
When
be defined as large hole great Shu, corresponding A
3=84.5dB;
When
be defined as rotten hollow of trunk, corresponding A
4=82dB.
Detection method as shown in Figure 1 comprises the steps:
Step 100, chooses the top of large tree trunk for measuring initiating terminal, and large tree bottom is for measuring clearing end; With fixing belt, shockwave sensor and pulse hammer are separately fixed on the opposite side of trunk outer peripheral face of initiating terminal;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal one;
Step 200, turn 90 degrees shockwave sensor and pulse hammer fix along the outer peripheral face dextrorotation of trunk respectively;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal two; Computing machine calculates the average signal of stress wave signal one and stress wave signal two, obtains mean stress ripple signal;
Step 300, in mean stress ripple signal, extract 1 complete stress wave pulse signal I (t) as shown in Figure 3, the described stress wave pulse signal I (t) in 0 to 100ms is inputted in stochastic resonance system model, stochastic resonance system model is resonated; Computing machine utilizes snr computation formula
calculate output signal-to-noise ratio SNR; Wherein, ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be the noise intensity in signal frequency range;
Step 400, computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve, and using the absolute value of signal to noise ratio (S/N ratio) valley as signal to noise ratio (S/N ratio) eigenwert F;
In the present embodiment, computing machine draws output signal-to-noise ratio curve as shown in Figure 4, and signal to noise ratio (S/N ratio) valley is-85.8dB that F is 85.8dB;
Step 500, when
to make the hole in the trunk of large tree be A to computing machine
icorresponding
the judgement of span;
In the present embodiment,
so the large tree in the present embodiment is middle hole great Shu.
Step 600, when
and measuring position is during apart from 1 centimetre of clearing end >, pulse hammer and shockwave sensor are taken off from the outer peripheral face of trunk, pulse hammer and shockwave sensor are moved to clearing end gradually, with fixing belt, shockwave sensor and pulse hammer are separately fixed on the opposite side of trunk outer peripheral face apart from 0.5 centimetre of measuring position last time;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal one;
Shockwave sensor and pulse hammer are turn 90 degrees and fixed along the outer peripheral face dextrorotation of trunk respectively;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal two;
Computing machine calculates the average signal of stress wave signal one and stress wave signal two, obtains mean stress ripple signal; Return to step 300;
When
and measuring position is during apart from clearing end≤1 centimetre, and pulse hammer quits work, and computing machine is made in large tree trunk does not have pertusate judgement, and will judge that information shows on computer screen;
When
proceed to step 700;
Step 700, the wall thickness of measurement hole:
Step 701, takes off pulse hammer and shockwave sensor from the outer peripheral face of trunk;
With fixing belt, two electrodes of SAW (Surface Acoustic Wave) resonator are fixed on the outer peripheral face of trunk of initiating terminal, two electrodes along the circumferencial direction of trunk at a distance of 2 millimeters; Clearing end outer peripheral face be provided with as shown in Figure 6 for reflecting the annular reflex mirror 11 of the measuring-signal of distance measuring sensor; The catoptron of right two curved annular of annular reflex mirror forms, and the catoptron of two curved annular can rotate and pass through snap lock relatively.
Step 702, SAW (Surface Acoustic Wave) resonator work, counter detects surface acoustic wave response frequency Freq
sAW, surface acoustic wave response frequency Freq
sAWbe input in computing machine, computing machine utilizes hole wall thickness forecast model
calculate hole in trunk with respect to the wall thickness of electrode side;
Hole wall thickness forecast model is to detect through the different holes to trunk, thereby obtains 12 wall thickness and the Freq corresponding with this wall thickness
sAW, by 12 wall thickness and the Freq corresponding with this wall thickness
sAWform point, 12 points are carried out to linear fit, obtain matched curve, thereby reach hole wall thickness forecast model.
Step 703, two electrodes and the large fixing position of outer peripheral face of setting of along the circumferencial direction of trunk, moving SAW (Surface Acoustic Wave) resonator, the fixed position of electrode is respectively trunk outer peripheral face right side, left side, front side and rear side; Repeating step 702 detects, and obtains 4 wall thickness d1, d2, d3, d4;
Step 704, electrode and the distance value between clearing end and 4 wall thickness d1 that distance measuring sensor is detected, d2, d3, d4 is stored in computing machine;
Step 705, is moved two electrodes of SAW (Surface Acoustic Wave) resonator gradually by initiating terminal to clearing end, repeating step 701 to 704 detects;
Step 800, trunk cross-sectional view and coordinate system as shown in Figure 5, in advance the outer peripheral face of trunk 9 is measured, obtain the coordinate of the point on the outer peripheral face of trunk, according to distance value and the d1 between the coordinate of the point of the outer peripheral face of trunk, electrode and large tree end, d2, d3, d4, computing machine is set up the three-dimensional model of trunk and trunk Hole 10.
In the present embodiment, suppose that trunk hollow space is a circle, according to the value of d1, d2, d3 and d4, the coordinate that computing machine calculates four points that obtain hole is: d1 (x1, y1), d2 (x2, y2), d3 (x3, y3), d4 (x4, y4), presetting round central coordinate of circle is (x0, y0);
First make the perpendicular bisector of d1 and d3 line segment:
In like manner, make the perpendicular bisector of d2 and d4 line segment:
y
s1and y
s2intersection point be required central coordinate of circle (x0, y0), (x0, y0) to d1, d2, d3, the distance of d4 equates, makes and take (x0, y0) as the center of circle, circumference is through putting a d1, d2, d3, the circle of d4.Make respectively the disalignment of electrode to the circle of measuring position, and in conjunction with the distance of the large tree bottom of distance of round, thereby in computing machine, make the three-dimensional model of trunk and hole.
Also can d1, d2, d3 and d4 be coupled together with smooth curve, form the hole sectional view of the large tree xsect of current detection, make respectively the disalignment of electrode to the hole sectional view of measuring position, in conjunction with the distance of the large tree of hole sectional view distance end, in computing machine, make the three-dimensional model of trunk and hole.
Should be understood that the present embodiment is only not used in and limits the scope of the invention for the present invention is described.In addition should be understood that those skilled in the art can make various changes or modifications the present invention after having read the content of the present invention's instruction, these equivalent form of values fall within the application's appended claims limited range equally.
Claims (7)
1. set greatly a hole inspection method, a kind of cavity detection device for large tree cavity detection comprises acoustic surface wave device (1), distance measuring sensor (2) and shockwave sensor (3); On shockwave sensor, distance measuring sensor and acoustic surface wave device, be respectively equipped with the data-interface for being electrically connected to computing machine; It is characterized in that, described acoustic surface wave device comprises counter (4), oscillator (5) and SAW (Surface Acoustic Wave) resonator (6); Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, counter is electrically connected to oscillation circuit, counter is provided with the data-interface for being electrically connected to computing machine (8), SAW (Surface Acoustic Wave) resonator is electrically connected to two electrodes (7) by wire, and distance measuring sensor is located at the junction of an electrode and wire; Described detection method comprises the steps:
(1-1) in computing machine, be provided with in advance stochastic resonance system model and hole wall thickness forecast model;
In computing machine, set
n span and the absolute value A of standard signal to noise ratio (S/N ratio) valley
icorresponding relation, i=1, L, n; Specification error threshold value d, sets surface acoustic wave detection threshold S; Wherein,
the diameter of hole,
it is the large tree trunk diameter of hole position;
(1-2) top of choosing large tree trunk is for measuring initiating terminal, and large tree bottom is for measuring clearing end; Shockwave sensor and pulse hammer are separately fixed on the opposite side of trunk outer peripheral face of initiating terminal;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal one;
(1-3) by shockwave sensor and pulse hammer respectively along the outer peripheral face of trunk 30 to 90 degree fixing that turn clockwise;
Pulse hammer is knocked trunk outer peripheral face, the inner stress wave that produces of trunk, and the stress wave signal that shockwave sensor detects is input in computing machine, obtains stress wave signal two; Computing machine calculates the average signal of stress wave signal one and stress wave signal two, obtains mean stress ripple signal;
(1-4) in mean stress ripple signal, extract 1 complete stress wave pulse signal I (t), the described stress wave pulse signal I (t) in 0 to 100ms is inputted in stochastic resonance system model, stochastic resonance system model is resonated; Computing machine utilizes snr computation formula to calculate output signal-to-noise ratio SNR;
(1-5) computing machine draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio (S/N ratio) valley of signal to noise ratio (S/N ratio) curve, and using the absolute value of signal to noise ratio (S/N ratio) valley as signal to noise ratio (S/N ratio) eigenwert F;
(1-6) when
to make the hole in the trunk of large tree be A to computing machine
icorresponding
the judgement of span;
(1-7) when
and measuring position is during apart from 1 centimetre of clearing end >, pulse hammer and shockwave sensor are taken off from the outer peripheral face of trunk, pulse hammer and shockwave sensor are moved to clearing end gradually, and pulse hammer and shockwave sensor are fixed on the trunk outer peripheral face apart from 0.5 to 1 centimetre of measuring position last time, return to step (1-3);
When
and measuring position is during apart from clearing end≤1 centimetre, and pulse hammer quits work, and computing machine is made in large tree trunk does not have pertusate judgement, and will judge that information shows on computer screen;
When
proceed to step (1-8);
(1-8) measure the wall thickness of hole:
Step a, takes off pulse hammer and shockwave sensor from the outer peripheral face of trunk; Two electrodes of SAW (Surface Acoustic Wave) resonator are fixed on the trunk outer peripheral face of initiating terminal to 2 to 3 millimeters along the circumferencial direction interval of trunk outer peripheral face, two electrodes; Clearing end outer peripheral face is provided with for reflecting the annular reflex mirror (11) of the measuring-signal of distance measuring sensor;
Step b, SAW (Surface Acoustic Wave) resonator work, counter detects surface acoustic wave response frequency Freq
sAW, surface acoustic wave response frequency Freq
sAWbe input in computing machine, computing machine utilizes hole wall thickness forecast model to calculate hole in large tree with respect to the wall thickness of electrode side;
Step c, two electrodes and the large fixing position of outer peripheral face of setting of along the circumferencial direction of trunk, moving SAW (Surface Acoustic Wave) resonator, repeating step b detects, and obtains several wall thickness d;
Steps d, the distance value between the electrode that distance measuring sensor is detected and clearing end and being stored in computing machine with corresponding each wall thickness d of distance value;
Step e, repeating step a to d carries out the detection of axial scan formula to large tree trunk;
(1-9) in advance the outer peripheral face of large tree trunk is measured, the coordinate of the point on the outer peripheral face of setting trunk, according to the distance value between the coordinate of the point of the outer peripheral face of trunk, electrode and clearing end and each wall thickness d corresponding with distance value, computing machine is set up the three-dimensional model of large tree trunk and trunk Hole.
2. large tree hole inspection method according to claim 1, is characterized in that, in described step b, hole wall thickness forecast model is
3. large tree hole inspection method according to claim 1, is characterized in that, described stochastic resonance system model is
Wherein, V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and A is input signal strength, and D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving.
4. large tree hole inspection method according to claim 1, is characterized in that, described snr computation formula is
wherein, ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be the noise intensity in signal frequency range.
5. large tree hole inspection method according to claim 1, is characterized in that, described surface acoustic wave detection threshold S is 0.5% to 2.5%.
6. according to the large tree hole inspection method described in claim 1 or 2 or 3 or 4 or 5, it is characterized in that, described error threshold d is 0.15 to 0.6.
7. according to the large tree hole inspection method described in claim 1 or 2 or 3 or 4 or 5, it is characterized in that, the centre frequency of SAW (Surface Acoustic Wave) resonator is 433.92MHZ.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106596854A (en) * | 2016-12-09 | 2017-04-26 | 重庆市黑土地白蚁防治有限公司 | Tree detection method |
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