CN111982905B - Wood quality intelligent detection system based on industrial big data image analysis - Google Patents

Wood quality intelligent detection system based on industrial big data image analysis Download PDF

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CN111982905B
CN111982905B CN202010870904.5A CN202010870904A CN111982905B CN 111982905 B CN111982905 B CN 111982905B CN 202010870904 A CN202010870904 A CN 202010870904A CN 111982905 B CN111982905 B CN 111982905B
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grain area
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CN111982905A (en
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郭含新
杨小毛
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Beixin International Wood Co.,Ltd.
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B15/00—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
    • G01N33/0063—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital using a threshold to release an alarm or displaying means
    • G01N33/0065—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital using a threshold to release an alarm or displaying means using more than one threshold
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N5/00—Analysing materials by weighing, e.g. weighing small particles separated from a gas or liquid
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/10—Different kinds of radiation or particles
    • G01N2223/101—Different kinds of radiation or particles electromagnetic radiation
    • G01N2223/1016—X-ray
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/60—Specific applications or type of materials
    • G01N2223/619—Specific applications or type of materials wood

Abstract

The invention discloses an intelligent wood quality detection system based on industrial big data image analysis, which comprises an image acquisition module, an image processing module, a wood grain area detection module, a wood grain area analysis module, a weight detection module, a weight analysis module, a gas detection module, a gas analysis module, an analysis server, a display terminal and a storage database, wherein the image acquisition module is used for acquiring an image of a wood grain area; the invention analyzes the types of all the woods through the image acquisition module and the image processing module, comprehensively analyzes the strength grade of each wood through the wood grain area detection module and the wood grain area analysis module in combination with the analysis server, marks the corresponding strength grade of each wood through related personnel, simultaneously detects the weight of each wood and the concentration value of leaked decay gas, calculates the comprehensive quality influence coefficient of each wood, compares and judges whether the quality of each wood is qualified or not, displays the wood number with unqualified quality, and processes waste products, thereby ensuring the quality of the wood.

Description

Wood quality intelligent detection system based on industrial big data image analysis
Technical Field
The invention relates to the field of industrial wood quality detection, in particular to an intelligent wood quality detection system based on industrial big data image analysis.
Background
The wood demand in China is large, the wood grain area, decay and water content all affect the quality of the wood, and then the use of the wood is affected, and in order to effectively utilize wood resources, the quality of the wood needs to be detected, so that the maximum economic benefit is generated.
However, the existing wood quality detection technology generally has some defects, the traditional wood quality detection is mainly carried out by means of manual visual inspection, the wood color is observed by manual visual inspection to judge the wood type, but the manual detection standards are different, so that the species misjudgment is increased to influence the wood quality, the strength of the wood cannot be accurately judged through manual experience, meanwhile, the dryness of the wood cannot be detected, and the volume of the wet wood becomes small after water loss, so that the wood is cracked and warped, thereby causing economic property loss, detecting the concentration of decay gas emitted by the wood through artificial olfaction, therefore, the intensity of manual operation is high, the body fatigue is easy to cause for a long time, the detection precision is influenced, and the manual detection efficiency is still very low, and in order to solve the problems, an intelligent wood quality detection system based on industrial big data image analysis is designed.
Disclosure of Invention
The invention aims to provide an intelligent wood quality detection system based on industrial big data image analysis, the invention analyzes the types of all woods through an image acquisition module and an image processing module, simultaneously comprehensively analyzes the strength grades of all the woods through a wood grain area detection module and a wood grain area analysis module in combination with an analysis server, marks the corresponding strength grades of all the woods through related personnel, simultaneously detects the weight of all the woods and the concentration value of leaked rotten gas, calculates the comprehensive quality influence coefficient of all the woods, contrasts and judges whether the quality of all the woods is qualified or not, displays the wood numbers with unqualified qualities, and processes waste products according to the display of the related personnel, thereby solving the problems in the background technology.
The purpose of the invention can be realized by the following technical scheme:
an intelligent wood quality detection system based on industrial big data image analysis comprises an image acquisition module, an image processing module, a wood grain area detection module, a wood grain area analysis module, a weight detection module, a weight analysis module, a gas detection module, a gas analysis module, an analysis server, a display terminal and a storage database;
the analysis server is respectively connected with the image processing module, the wood grain area analysis module, the weight analysis module, the gas analysis module, the storage database and the display terminal, the storage database is respectively connected with the image acquisition module, the image processing module, the weight analysis module and the gas analysis module, the image acquisition module is connected with the image processing module, the wood grain area detection module is respectively connected with the wood grain area analysis module and the weight analysis module, the weight detection module is connected with the weight analysis module, and the gas detection module is connected with the gas analysis module;
the image acquisition module comprises a high-definition camera and is used for acquiring images of the surface of each wood to be detected, acquiring surface images of each wood to be detected through the high-definition camera, sending the acquired surface images of each wood to the image processing module, numbering the acquired surface images of each wood in sequence, wherein the numbering is 1,2, a.
The image processing module is used for receiving the surface images of the woods sent by the image acquisition module, extracting the characteristics of the received surface images of the woods, extracting the color and texture tissues in the surface images of the woods, extracting the standard color and standard texture tissues in the surface images of the various woods stored in the storage database, comparing the color and texture tissues in the surface images of the woods with the corresponding standard color and standard texture tissues in the surface images of the various woods one by one, counting the similarity between the color and texture tissues in the surface images of the woods and the corresponding standard color and standard texture tissues in the surface images of the various woods, screening the surface images of the woods with the highest color and texture tissue similarity, and counting the types corresponding to the woods with the highest color and texture tissue similarity in the surface images, sending the species corresponding to each wood to an analysis server;
the wood grain area detection module comprises an x-ray detector which is used for detecting the wood grain area of each wood surface, acquiring the wood grain image of each wood surface through the x-ray detector, respectively acquiring each transverse wood grain area, each longitudinal wood grain area, the total board surface area and the total volume of each wood, sending each transverse wood grain area, each longitudinal wood grain area and the total board surface area of each wood to the wood grain area analysis module, and sending the total volume of each wood to the weight analysis module;
the wood grain area analysis module is used for receiving each transverse wood grain area, each longitudinal wood grain area and the total board surface area of each wood sent by the wood grain area detection module to form each transverse wood grain area set W of each woodnS1(w1s1a,w2s1a,...,wis1a,...,wns1a) Each longitudinal wood grain area set W of each woodnS2(w1s2b,w2s2b,...,wis2b,...,wns2b) And the total board area set W of each woodn(w1s1,w2s2,...,wisi,...,wnsn)，wis1a is expressed as the a-th transverse grain area of the ith wood, a is 1,2is2b is expressed as the b-th longitudinal grain area of the ith wood, b is 1,2isiRepresenting the total board surface area of the ith wood, and sending each transverse wood grain area set, each longitudinal wood grain area set and the total board surface area set of each wood to an analysis server;
the analysis server is used for receiving the types corresponding to the woods sent by the image processing module, receiving the transverse wood grain area sets, the longitudinal wood grain area sets and the overall board area sets of the woods sent by the wood grain area analysis module, extracting the standard wood strength of unit volumes of the types stored in the storage database, calculating the overall strength of the woods, extracting the strength corresponding to the strength grades of the types of the woods stored in the storage database, comparing the overall strength of the woods with the strength corresponding to the strength grades of the types of the corresponding woods, screening the strength grades corresponding to the overall strength of the woods, counting the strength grades of the woods, and sending the counted strength grades of the woods to the display terminal;
the weight detection module comprises a weight sensor for detecting the weight of each wood, detecting the weight of each wood through the weight sensor, counting the weight of each wood, and forming a weight set G of each woodn(g1,g2,...,gi,...,gn)，giSending the weight set of each wood to a weight analysis module, wherein the weight set is expressed as the weight of the ith wood;
the weight analysis module is used for receiving the total volume of each wood sent by the wood grain area detection module and simultaneously receiving the weight set of each wood sent by the weight detection module to form a total volume set V (V) of each wood1,V2,...,Vi,...,Vn)，ViExpressing the total volume of the ith wood, extracting the standard weight of the oven dry wood of each kind of unit volume stored in a storage database, comparing the weight of each wood with the standard weight of the oven dry wood of the corresponding volume, counting the weight comparison difference value of each wood, and forming a weight comparison difference value set delta G of each woodn(Δg1,Δg2,...,Δgi,...,Δgn)，ΔgiThe weight comparison difference value of the ith wood is expressed, and the weight comparison difference value of each wood is sent to an analysis server;
the gas detection module comprises an odor gas detector for detecting decay gas leaked by each wood, detecting a decay gas concentration value leaked by each wood through the odor gas detector and sending the decay gas concentration value leaked by each wood to the gas analysis module;
the gas analysis module is used for receiving decay gas concentration values of all wood leaks sent by the gas detection module, extracting decay gas concentration values corresponding to all decay grades stored in the storage database, comparing the received decay gas concentration values of all wood leaks with the stored decay gas concentration values corresponding to all decay grades, screening decay grades corresponding to the decay gas concentration values of all wood leaks, and sending decay grades corresponding to the screened decay gas concentration values of all wood leaks to the analysis server;
the analysis server is used for receiving the weight comparison difference value of each wood sent by the weight analysis module, receiving the decay grade of each wood sent by the gas analysis module, extracting the decay influence coefficient corresponding to each decay grade stored in the storage database, calculating the comprehensive quality influence coefficient of each wood, extracting the standard comprehensive quality influence coefficient of each wood stored in the storage database, comparing the comprehensive quality influence coefficient of each wood with the standard comprehensive quality influence coefficient of the corresponding wood, if the comprehensive quality influence coefficient of a certain wood is less than or equal to the standard comprehensive quality influence coefficient of the corresponding wood, indicating that the wood is qualified in quality, if the comprehensive quality influence coefficient of a certain wood is greater than the standard comprehensive quality influence coefficient of the corresponding wood, indicating that the wood is unqualified in quality, counting the number of each wood with unqualified quality, sending the wood numbers with unqualified quality to a display terminal;
the display terminal is used for receiving and displaying the strength grade and the wood number with unqualified quality of each wood sent by the analysis server, and related personnel label the corresponding strength grade of each wood and perform waste treatment on each wood with unqualified quality;
the storage database is used for receiving the surface image numbers of the woods sent by the image acquisition module, storing standard color and standard texture tissues in the surface images of the woods, storing standard wood strength and standard dead wood weight of each kind of unit volume, and simultaneously storing the strength corresponding to each strength grade of each kind of wood, and storing decay gas concentration values and decay influence coefficients corresponding to each decay grade, wherein each decay grade is respectively decay-free, slightly decay, moderately decay and severely decay, and the decay influence coefficients corresponding to each decay grade are respectively k1、k2、k3、k4，k1,k2,k3,k4Expressed as decay-free, slightly decay, moderately decay andstoring decay influence coefficients corresponding to the serious decay, storing the strength influence coefficients of transverse wood grains and longitudinal wood grains in various kinds of wood, and respectively recording the strength influence coefficients as lambda1And λ2And storing standard comprehensive quality influence coefficients of various types of wood;
further, the calculation formula of the total strength of each wood is
Figure GDA0002851481500000061
PiExpressed as the overall strength, w, of the ith woodisiExpressed as the total panel area, w, of the ith timberis2b is expressed as the b-th longitudinal grain area of the ith wood, b is 1, 2.., y,
Figure GDA0002851481500000062
standard strength of wood, λ, expressed as unit volume of species corresponding to the ith wood1 iExpressed as the coefficient of influence of the intensity, w, of the transverse grain in the category corresponding to the ith woodis1a is expressed as the a-th transverse grain area of the ith wood, a is 1,22 iExpressing the strength influence coefficient of the longitudinal wood grain in the category corresponding to the ith wood;
further, the calculation formula of the comprehensive quality influence coefficient of each wood is
Figure GDA0002851481500000063
ξiExpressed as the combined mass influence coefficient, w, of the ith woodis2b is expressed as the b-th longitudinal grain area of the ith wood, b is 1,2isiExpressed as the total panel area, w, of the ith timberis1a is expressed as the a-th transverse grain area of the i-th wood, a ═ 1,2,. once, x,
Figure GDA0002851481500000064
standard strength of wood expressed as unit volume of species corresponding to ith wood, PiExpressed as the overall strength, Δ g, of the ith woodiTo representThe weight of the ith wood is compared to the difference,
Figure GDA0002851481500000065
expressed as standard weight of oven-dried wood, V, per unit volume of species corresponding to the ith woodiExpressed as the total volume of the ith wood, e is expressed as a natural number, equal to 2.718, kiThe decay influence coefficient is expressed as the decay grade corresponding to the ith wood, and k is k1,k2,k3,k4。
Has the advantages that:
(1) according to the wood quality intelligent detection system based on the industrial big data image analysis, the types of all wood are analyzed through the image acquisition module and the image processing module, so that the detection standards can be unified, the type misjudgment rate is reduced, and the quality of the wood is ensured; meanwhile, the wood grain area detection module and the wood grain area analysis module are combined with the analysis server to comprehensively analyze the strength grade of each wood, and the corresponding strength grade mark is carried out on each wood by related personnel, so that a user can visually know the strength grade of the wood, the occurrence of safety accidents is reduced, and the weight of each wood is detected, thereby avoiding the water loss problem after the user uses the wet wood, preventing the wood from cracking and warping, detecting the concentration value of the leaked decay gas, analyzing the decay grade corresponding to the concentration value of the decay gas leaked from each wood, improving the detection precision and the detection efficiency, and providing reliable reference data for calculating the comprehensive quality influence coefficient of each wood in the later period.
(2) According to the invention, the comprehensive quality influence coefficient of each wood is comprehensively calculated by the analysis server, whether the quality of each wood is qualified or not is judged by comparison, the wood number with unqualified quality is counted, the wood number with unqualified quality is displayed, so that related personnel can accurately find the wood with unqualified quality, the waste treatment is carried out on the wood with unqualified quality, the secondary utilization rate of the wood is improved, and the economic and property loss of people is ensured.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an intelligent wood quality detection system based on industrial big data image analysis includes an image acquisition module, an image processing module, a wood grain area detection module, a wood grain area analysis module, a weight detection module, a weight analysis module, a gas detection module, a gas analysis module, an analysis server, a display terminal and a storage database;
the analysis server is respectively connected with the image processing module, the wood grain area analysis module, the weight analysis module, the gas analysis module, the storage database and the display terminal, the storage database is respectively connected with the image acquisition module, the image processing module, the weight analysis module and the gas analysis module, the image acquisition module is connected with the image processing module, the wood grain area detection module is respectively connected with the wood grain area analysis module and the weight analysis module, the weight detection module is connected with the weight analysis module, and the gas detection module is connected with the gas analysis module;
the image acquisition module comprises a high-definition camera and is used for acquiring images of the surface of each wood to be detected, acquiring surface images of each wood to be detected through the high-definition camera, sending the acquired surface images of each wood to the image processing module, numbering the acquired surface images of each wood in sequence, wherein the numbering is 1,2, a.
The image processing module is used for receiving the surface images of the woods sent by the image acquisition module, extracting the characteristics of the received surface images of the woods, extracting the color and texture tissues in the surface images of the woods, extracting the standard color and standard texture tissues in the surface images of the various woods stored in the storage database, comparing the color and texture tissues in the surface images of the woods with the corresponding standard color and standard texture tissues in the surface images of the various woods one by one, counting the similarity between the color and texture tissues in the surface images of the woods and the corresponding standard color and standard texture tissues in the surface images of the various woods, screening the surface images of the woods with the highest color and texture tissue similarity, and counting the types corresponding to the woods with the highest color and texture tissue similarity in the surface images, therefore, the detection standards can be unified, the type misjudgment rate is reduced, the quality of the wood is ensured, and the types corresponding to the wood are sent to the analysis server.
The wood grain area detection module comprises an x-ray detector which is used for detecting the wood grain area of each wood surface, acquiring the wood grain image of each wood surface through the x-ray detector, respectively acquiring each transverse wood grain area, each longitudinal wood grain area, the total board surface area and the total volume of each wood, sending each transverse wood grain area, each longitudinal wood grain area and the total board surface area of each wood to the wood grain area analysis module, and sending the total volume of each wood to the weight analysis module;
the wood grain area analysis module is used for receiving each transverse wood grain area, each longitudinal wood grain area and the total board surface area of each wood sent by the wood grain area detection module to form each transverse wood grain area set W of each woodnS1(w1s1a,w2s1a,...,wis1a,...,wns1a) Each longitudinal wood grain area set W of each woodnS2(w1s2b,w2s2b,...,wis2b,...,wns2b) And the total board area set W of each woodn(w1s1,w2s2,...,wisi,...,wnsn)，wis1a is expressed as the a-th transverse grain area of the ith wood, a is 1,2is2b is expressed as the b-th longitudinal grain area of the ith wood, b is 1,2isiRepresenting the total board surface area of the ith wood, and sending each transverse wood grain area set, each longitudinal wood grain area set and the total board surface area set of each wood to an analysis server;
the analysis server is used for receiving the types corresponding to the woods sent by the image processing module, receiving the transverse wood grain area sets, the longitudinal wood grain area sets and the overall board area sets of the woods sent by the wood grain area analysis module, extracting the standard wood strength of unit volumes of the types stored in the storage database, and calculating the overall strength of the woods, wherein the calculation formula of the overall strength of the woods is
Figure GDA0002851481500000091
PiExpressed as the overall strength, w, of the ith woodisiExpressed as the total panel area, w, of the ith timberis2b is expressed as the b-th longitudinal grain area of the ith wood, b is 1, 2.., y,
Figure GDA0002851481500000101
standard strength of wood, λ, expressed as unit volume of species corresponding to the ith wood1 iExpressed as the coefficient of influence of the intensity, w, of the transverse grain in the category corresponding to the ith woodis1a is expressed as the a-th transverse grain area of the ith wood, a is 1,22 iExpressing the intensity influence coefficient of longitudinal wood grain in the category corresponding to the ith wood, extracting the intensity corresponding to each intensity grade of each kind of wood stored in the storage database, and storing the intensity of each woodComparing the overall strength of the timber with the strength corresponding to each strength grade of the corresponding wood species, screening the strength grade corresponding to the overall strength of each wood species, counting the strength grade of each wood species, and sending the counted strength grade of each wood species to a display terminal;
the weight detection module comprises a weight sensor for detecting the weight of each wood, detecting the weight of each wood through the weight sensor, counting the weight of each wood, and forming a weight set G of each woodn(g1,g2,...,gi,...,gn)，giSending the weight set of each wood to a weight analysis module, wherein the weight set is expressed as the weight of the ith wood;
the weight analysis module is used for receiving the total volume of each wood sent by the wood grain area detection module and simultaneously receiving the weight set of each wood sent by the weight detection module to form a total volume set V (V) of each wood1,V2,...,Vi,...,Vn)，ViExpressing the total volume of the ith wood, extracting the standard weight of the oven dry wood with various unit volumes stored in the storage database, comparing the weight of each wood with the standard weight of the oven dry wood with the corresponding volume, and counting the weight comparison difference value of each wood, thereby avoiding the water loss problem after people use wet wood, preventing the wood from cracking and warping, and forming a weight comparison difference value set delta G of each woodn(Δg1,Δg2,...,Δgi,...,Δgn)，ΔgiThe weight comparison difference value of the ith wood is expressed, and the weight comparison difference value of each wood is sent to an analysis server;
the gas detection module comprises an odor gas detector for detecting decay gas leaked by each wood, detecting a decay gas concentration value leaked by each wood through the odor gas detector and sending the decay gas concentration value leaked by each wood to the gas analysis module;
the gas analysis module is used for receiving decay gas concentration values of all wood leaks sent by the gas detection module, extracting decay gas concentration values corresponding to all decay grades stored in the storage database, comparing the received decay gas concentration values of all wood leaks with the stored decay gas concentration values corresponding to all decay grades, screening decay grades corresponding to the decay gas concentration values of all wood leaks, so that the detection precision and the detection efficiency are improved, reliable reference data are provided for calculating comprehensive quality influence coefficients of all wood in the later period, and decay grades corresponding to the screened decay gas concentration values of all wood leaks are sent to the analysis server;
the analysis server is used for receiving the weight comparison difference value of each wood sent by the weight analysis module, receiving the decay grade of each wood sent by the gas analysis module, extracting the decay influence coefficient corresponding to each decay grade stored in the storage database, and calculating the comprehensive quality influence coefficient of each wood, wherein the calculation formula of the comprehensive quality influence coefficient of each wood is as follows
Figure GDA0002851481500000111
ξiExpressed as the combined mass influence coefficient, w, of the ith woodis2b is expressed as the b-th longitudinal grain area of the ith wood, b is 1,2isiExpressed as the total panel area, w, of the ith timberis1a is expressed as the a-th transverse grain area of the i-th wood, a ═ 1,2,. once, x,
Figure GDA0002851481500000112
standard strength of wood expressed as unit volume of species corresponding to ith wood, PiExpressed as the overall strength, Δ g, of the ith woodiExpressed as the weight of the ith wood versus difference,
Figure GDA0002851481500000113
expressed as standard weight of oven-dried wood, V, per unit volume of species corresponding to the ith woodiExpressed as the total volume of the ith wood, e is expressed as a natural number, equal to 2.718, kiThe decay influence coefficient is expressed as the decay grade corresponding to the ith wood, and k is k1,k2,k3,k4(ii) a Extract and accessStoring standard comprehensive quality influence coefficients of various types of woods stored in a database, comparing the comprehensive quality influence coefficients of the various types of woods with the standard comprehensive quality influence coefficients of the corresponding types of woods, if the comprehensive quality influence coefficients of certain woods are smaller than or equal to the standard comprehensive quality influence coefficients of the corresponding types of woods, indicating that the qualities of the woods are qualified, if the comprehensive quality influence coefficients of certain woods are larger than the standard comprehensive quality influence coefficients of the corresponding types of woods, indicating that the qualities of the woods are unqualified, counting the numbers of the various woods with unqualified qualities, and sending the numbers of the various woods with unqualified qualities to a display terminal;
the display terminal is used for receiving the strength grade of each timber and each unqualified timber serial number of quality that analysis server sent, and show, be convenient for relevant personnel can be accurate seek the unqualified timber of quality, relevant personnel carry out the strength grade mark that corresponds to each timber simultaneously, the person of facilitating the use can audio-visually know ligneous strength grade, the emergence of incident has been reduced, and carry out waste product treatment to each unqualified timber of quality, ligneous reutilization ratio has been improved, people's economic property loss has been ensured.
The storage database is used for receiving the surface image numbers of the woods sent by the image acquisition module, storing standard color and standard texture tissues in the surface images of the woods, storing standard wood strength and standard dead wood weight of each kind of unit volume, and simultaneously storing the strength corresponding to each strength grade of each kind of wood, and storing decay gas concentration values and decay influence coefficients corresponding to each decay grade, wherein each decay grade is respectively decay-free, slightly decay, moderately decay and severely decay, and the decay influence coefficients corresponding to each decay grade are respectively k1、k2、k3、k4，k1,k2,k3,k4Sequentially and respectively representing decay influence coefficients corresponding to decay-free, slight decay, moderate decay and severe decay, storing the strength influence coefficients of transverse wood grains and longitudinal wood grains in various kinds of wood, and respectively representing the strength influence coefficients as lambda1And λ2And stored standards of various kinds of woodAnd (4) integrating the quality influence coefficients.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.

Claims (1)

1. The utility model provides a timber quality intelligent detection system based on industry big data image analysis which characterized in that: the system comprises an image acquisition module, an image processing module, a wood grain area detection module, a wood grain area analysis module, a weight detection module, a weight analysis module, a gas detection module, a gas analysis module, an analysis server, a display terminal and a storage database;
the analysis server is respectively connected with the image processing module, the wood grain area analysis module, the weight analysis module, the gas analysis module, the storage database and the display terminal, the storage database is respectively connected with the image acquisition module, the image processing module, the weight analysis module and the gas analysis module, the image acquisition module is connected with the image processing module, the wood grain area detection module is respectively connected with the wood grain area analysis module and the weight analysis module, the weight detection module is connected with the weight analysis module, and the gas detection module is connected with the gas analysis module;
the image acquisition module comprises a high-definition camera and is used for acquiring images of the surface of each wood to be detected, acquiring surface images of each wood to be detected through the high-definition camera, sending the acquired surface images of each wood to the image processing module, numbering the acquired surface images of each wood in sequence, wherein the numbering is 1,2, a.
The image processing module is used for receiving the surface images of the woods sent by the image acquisition module, extracting the characteristics of the received surface images of the woods, extracting the color and texture tissues in the surface images of the woods, extracting the standard color and standard texture tissues in the surface images of the various woods stored in the storage database, comparing the color and texture tissues in the surface images of the woods with the corresponding standard color and standard texture tissues in the surface images of the various woods one by one, counting the similarity between the color and texture tissues in the surface images of the woods and the corresponding standard color and standard texture tissues in the surface images of the various woods, screening the surface images of the woods with the highest color and texture tissue similarity, and counting the types corresponding to the woods with the highest color and texture tissue similarity in the surface images, sending the species corresponding to each wood to an analysis server;
the wood grain area detection module comprises an x-ray detector which is used for detecting the wood grain area of each wood surface, acquiring the wood grain image of each wood surface through the x-ray detector, respectively acquiring each transverse wood grain area, each longitudinal wood grain area, the total board surface area and the total volume of each wood, sending each transverse wood grain area, each longitudinal wood grain area and the total board surface area of each wood to the wood grain area analysis module, and sending the total volume of each wood to the weight analysis module;
the wood grain area analysis module is used for receiving each transverse wood grain area, each longitudinal wood grain area and the total board surface area of each wood sent by the wood grain area detection module to form each transverse wood grain area set W of each woodnS1(w1s1 a,w2s1 a,...,wis1 a,...,wns1 a) Each longitudinal wood grain area set W of each woodnS2(w1s2 b,w2s2 b,...,wis2 b,...,wns2 b) And the total board area set W of each woodn(w1s1,w2s2,...,wisi,...,wnsn)，wis1 aThe ith transverse grain area, denoted as the ith wood, a 1,2is2 bThe ith longitudinal grain area, b 1,2isiRepresenting the total board surface area of the ith wood, and sending each transverse wood grain area set, each longitudinal wood grain area set and the total board surface area set of each wood to an analysis server;
the analysis server is used for receiving the types corresponding to the woods sent by the image processing module, receiving the transverse wood grain area sets, the longitudinal wood grain area sets and the overall board area sets of the woods sent by the wood grain area analysis module, extracting the standard wood strength of unit volumes of the types stored in the storage database, calculating the overall strength of the woods, extracting the strength corresponding to the strength grades of the types of the woods stored in the storage database, comparing the overall strength of the woods with the strength corresponding to the strength grades of the types of the corresponding woods, screening the strength grades corresponding to the overall strength of the woods, counting the strength grades of the woods, and sending the counted strength grades of the woods to the display terminal;
the weight detection module comprises a weight sensor for detecting the weight of each wood, detecting the weight of each wood through the weight sensor, counting the weight of each wood, and forming a weight set G of each woodn(g1,g2,...,gi,...,gn)，giSending the weight set of each wood to a weight analysis module, wherein the weight set is expressed as the weight of the ith wood;
the weight analysis module is used for receiving the total volume of each wood sent by the wood grain area detection module and simultaneously receiving the weight set of each wood sent by the weight detection module to form a total volume set V (V) of each wood1,V2,...,Vi,...,Vn)，ViExpressing the total volume of the ith wood, extracting the standard weight of the oven dry wood of each kind of unit volume stored in a storage database, comparing the weight of each wood with the standard weight of the oven dry wood of the corresponding volume, counting the weight comparison difference value of each wood, and forming a weight comparison difference value set delta G of each woodn(Δg1,Δg2,...,Δgi,...,Δgn)，ΔgiThe weight comparison difference value of the ith wood is expressed, and the weight comparison difference value of each wood is sent to an analysis server;
the gas detection module comprises an odor gas detector for detecting decay gas leaked by each wood, detecting a decay gas concentration value leaked by each wood through the odor gas detector and sending the decay gas concentration value leaked by each wood to the gas analysis module;
the gas analysis module is used for receiving decay gas concentration values of all wood leaks sent by the gas detection module, extracting decay gas concentration values corresponding to all decay grades stored in the storage database, comparing the received decay gas concentration values of all wood leaks with the stored decay gas concentration values corresponding to all decay grades, screening decay grades corresponding to the decay gas concentration values of all wood leaks, and sending decay grades corresponding to the screened decay gas concentration values of all wood leaks to the analysis server;
the analysis server is used for receiving the weight comparison difference value of each wood sent by the weight analysis module, receiving the decay grade of each wood sent by the gas analysis module, extracting the decay influence coefficient corresponding to each decay grade stored in the storage database, calculating the comprehensive quality influence coefficient of each wood, extracting the standard comprehensive quality influence coefficient of each wood stored in the storage database, comparing the comprehensive quality influence coefficient of each wood with the standard comprehensive quality influence coefficient of the corresponding wood, if the comprehensive quality influence coefficient of a certain wood is less than or equal to the standard comprehensive quality influence coefficient of the corresponding wood, indicating that the wood is qualified in quality, if the comprehensive quality influence coefficient of a certain wood is greater than the standard comprehensive quality influence coefficient of the corresponding wood, indicating that the wood is unqualified in quality, counting the number of each wood with unqualified quality, sending the wood numbers with unqualified quality to a display terminal;
the display terminal is used for receiving and displaying the strength grade and the wood number with unqualified quality of each wood sent by the analysis server, and related personnel label the corresponding strength grade of each wood and perform waste treatment on each wood with unqualified quality;
the storage database is used for receiving the surface image numbers of the woods sent by the image acquisition module, storing standard color and standard texture tissues in the surface images of the woods, storing standard wood strength and standard dead wood weight of each kind of unit volume, and simultaneously storing the strength corresponding to each strength grade of each kind of wood, and storing decay gas concentration values and decay influence coefficients corresponding to each decay grade, wherein each decay grade is respectively decay-free, slightly decay, moderately decay and severely decay, and the decay influence coefficients corresponding to each decay grade are respectively k1、k2、k3、k4，k1,k2,k3,k4Sequentially and respectively representing decay influence coefficients corresponding to decay-free, slight decay, moderate decay and severe decay, storing the strength influence coefficients of transverse wood grains and longitudinal wood grains in various kinds of wood, and respectively representing the strength influence coefficients as lambda1And λ2And storing standard comprehensive quality influence coefficients of various types of wood;
the total strength calculation formula of each wood is
Figure FDA0002851481490000041
PiExpressed as the overall strength, w, of the ith woodisiExpressed as the total panel area, w, of the ith timberis2 bExpressed as the b-th longitudinal grain area of the ith wood, b ═ 1,2, ·, y,
Figure FDA0002851481490000042
standard strength of wood, λ, expressed as unit volume of species corresponding to the ith wood1 iExpressed as the coefficient of influence of the intensity, w, of the transverse grain in the category corresponding to the ith woodis1 aThe ith transverse grain area, denoted as the ith wood, a 1,22 iExpressed as corresponding to the ith woodIntensity influence coefficient of longitudinal wood grain in the species;
the calculation formula of the comprehensive quality influence coefficient of each wood is
Figure FDA0002851481490000051
ξiExpressed as the combined mass influence coefficient, w, of the ith woodis2 bThe ith longitudinal grain area, b 1,2isiExpressed as the total panel area, w, of the ith timberis1 aThe transverse grain area, denoted as the ith wood, a 1,2,., x,
Figure FDA0002851481490000052
standard strength of wood expressed as unit volume of species corresponding to ith wood, PiExpressed as the overall strength, Δ g, of the ith woodiExpressed as the weight of the ith wood versus difference,
Figure FDA0002851481490000053
expressed as standard weight of oven-dried wood, V, per unit volume of species corresponding to the ith woodiExpressed as the total volume of the ith wood, e is expressed as a natural number, equal to 2.718, kiThe decay influence coefficient is expressed as the decay grade corresponding to the ith wood, and k is k1,k2,k3,k4。
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