CN113240446A - Data classification and processing method of artwork valuation system - Google Patents

Data classification and processing method of artwork valuation system Download PDF

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CN113240446A
CN113240446A CN202110318403.0A CN202110318403A CN113240446A CN 113240446 A CN113240446 A CN 113240446A CN 202110318403 A CN202110318403 A CN 202110318403A CN 113240446 A CN113240446 A CN 113240446A
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黄志�
万林
彭干
尹晖
范友振
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Shenzhen Yachang Art Net Co ltd
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Abstract

The invention provides a data classification and processing method of an artwork valuation system, which comprises the following steps: step S1: the first data processing module converts the data information; step S2: the second data processing module generates a first estimated transaction price and a first auction price estimation interval; step S3: the valuation module generates a second estimated transaction price and a second auction valuation interval; wherein the character conversion part stores the size, weight, color, age and material information of the work of art in a character database in a byte manner; the picture conversion part stores the picture of the artwork in a picture database in a pixel value mode; the byte discrimination part generates a discrimination byte through an external input mode, and the discrimination byte is stored in a discrimination database; the information combining part generates an artwork transformation matrix; and the second data processing module generates a first pre-estimated transaction price and a first auction price estimation interval according to the artwork transformation matrix.

Description

Data classification and processing method of artwork valuation system
Technical Field
The present invention relates generally to the field of valuation calculations, and more particularly to a data classification and processing method for an art valuation system.
Background
The artwork valuation refers to estimation and prediction of the market trading price of the artwork. Because the characteristics of the art are heterogeneous, that is, each piece of art is unique, and one piece of art may be very different from another piece of art, the traditional asset assessment method (such as the assessment of real estate, used cars and other assets) cannot be completely suitable for the assessment of the art. At present, the common artwork estimation method in the market comprises the artwork estimation performed by business personnel such as auction houses and galleries according to market trading experience, and the artwork estimation performed by artwork estimation experts and museum art staff according to the experience of artwork such as art value, historical value and academic value. The two common art estimation methods are estimation based on subjective judgment of people, are influenced by personal factors such as experience, learning, hobbies and the like of an estimator, and are difficult to establish a uniform method and a standardized flow.
In the field of Art evaluation using data, academic studies have been conducted on Art indices of Art as an invent and the integrity of the masterpieces, Mei, Jianping, with meijiaping and michael morse; moses, Michael, The American Economic Review; dec 2002; 92, 5; the paper of ProQuest pg.1656 basically confirms that a large idea of data evaluation is feasible, and proposes a 'repeated transaction model' to carry out price prediction. However, such calculations are particularly demanding for data that can be used with artwork that is subject to repeated transactions at the auction. Skate's Art Investment Handbook indicates another possible direction of computation, namely "feature pricing", which refers to price assessment using the features of the work of Art itself, such as size, subject matter, etc. However, the accuracy of such calculation cannot be guaranteed. To this end, the present invention provides a data classification and processing method for an art estimation system to at least partially solve the above problems.
Disclosure of Invention
In this summary, concepts in a simplified form are introduced that are further described in the detailed description section. This summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
To at least partially solve the above technical problem, the present invention provides a data classifying and processing method for an art evaluation system, including:
step S1: the first data processing module converts the data information;
step S2: the second data processing module generates a first estimated transaction price and a first auction price estimation interval;
step S3: the classification module performs hierarchical classification on the auction companies; the valuation module obtains a valuation calculation formula and data characteristics required by calculation, and generates a second estimated bargaining interval and a second auction valuation interval;
the first data processing module comprises a character conversion part, an image conversion part, a byte screening part and an information combination part; the character conversion part stores the size, weight, color, age and material information of the artwork in a character database in a byte mode; the picture conversion part stores the picture of the artwork in a picture database in a pixel value mode; the byte discrimination part generates a discrimination byte through an external input mode, and the discrimination byte is stored in a discrimination database; the information combining part calls the information in the character database, the picture database and the discrimination database to generate an artwork transformation matrix Tr; and the second data processing module generates a first pre-estimated transaction price and a first auction price estimation interval according to the artwork transformation matrix Tr.
Further, the method can be used for preparing a novel materialThe image conversion part comprises an image segmentation unit, an image identification comparison unit and an image marking unit; the image segmentation unit segments an image of an artwork, and generates a characteristic pixel matrix Q (Q) by segmenting the image1 q2 q3....qn) In matrix, q1、q2、q3Recursion to qnAre pixel values of the image;
the identification comparison unit acquires the artwork information from the Internet in real time and generates the existing artwork valuation matrix ScAnd the information file doc to be calledcSubscript c represents the number of the existing artwork, and the subscript c is 1, 2 and 3 and is increased in sequence till n; the existing work of art valuation matrix is Sc(Mc Nc) In matrix, McFor initial identification of the pixel matrix, NcFor the characteristic pixel matrix, the initial identification pixel matrix is Mc(mc1 mc2 mc3) In matrix, mc1、mc2、mc3Are all identifiable pixel values; pixel value mc1、mc2、mc3Are all set by human, and the characteristic pixel matrix is Nc(nc1 nc2 nc3....ncn) In matrix, nc1 nc2 nc3Recursion to ncnAre all characteristic pixel values; information file doc to be calledcThe method comprises the steps of including an original bargaining price and an original auction valuation interval of a corresponding artwork;
the image marking unit is used for marking a characteristic pixel matrix Q and an initial identification pixel matrix McIn comparison, when there is an initial identification pixel matrix MjThe subscript j is any one of 1, 2 and 3jValue m inj1 mj2 mj3When both exist in the characteristic pixel matrix Q, the image marking unit enables the characteristic pixel matrix Q and the characteristic pixel matrix NjComparing, the image marking unit generates an identification parameter k according to the comparison resultj。
Further, the image marking unit records the number of pixel values numQ in the characteristic pixel matrix Q, andthe image marking unit records a characteristic pixel matrix NjThe number numN of the same characteristic pixel values present in the characteristic pixel matrix Q, the identification parameter kj＝numN/numQ。
Further, the byte screening part comprises an external information input device, and the external information input device acquires the byte amount of the artwork needing to be supplemented; the byte screening part generates an artwork integrity evaluation value Inte, and the integrity evaluation value Inte is byte0/byte 1; in the formula, byte0 is the amount of bytes needed to be supplemented by the artwork, and byte1 is the amount of bytes existing in the artwork.
Further, the artwork transformation matrix is Tr (k)jInte), the second data processing module stores the original bargaining price and the original auction valuation interval of the artwork; first estimated bargain SAL1 ═ kjSAL0Inte; first auction valuation interval Δ SAL1 ═ kjΔSAL0(ii) a Wherein k isjTo identify parameters, SAL0Is the original bargain price, Δ SAL, of the artwork0The original auction valuation interval of the artwork; int is the integrity evaluation value.
Further, the classification module performs hierarchical classification according to the number of auction companies meeting the judgment standard, wherein the more the number of auction companies meeting the judgment standard, the higher the hierarchy; influencing factor FOIThe larger.
Further, the judgment criteria include:
the judgment standard of market value, when the total amount of the traded amount of the auction company is more than or equal to seven years of the total amount of the market, the auction company meets the judgment standard of the market value;
the method comprises the steps of collecting a capacity judgment standard of works, and setting the auction company to meet the capacity judgment standard of the collected works when the auction quantity of the auction company exceeds 50% of the same line in the same period;
a capability judgment standard package for converting into sales, the auction company satisfying a judgment standard for converting the collected auction into a capability of sales when the auction company's quantity of the auction exceeds 50% of the same year;
a judgment criterion of an average bargain price, wherein when the average bargain price of the auction company exceeds 50% of the same-period colleagues, the auction company meets the judgment criterion of the average bargain price;
and when the auction company meets the judgment standard of continuously gathering works and successful sale capability, and when the auction company has a transaction record in the last 5 years and has continuous auction records in the last 6 auction seasons, the auction company meets the judgment standard of continuously gathering works and successful sale capability.
Further, the estimation module calculates the weight value by using a Cauchy-type function, and the calculation formula is as follows:
Figure BDA0002992201490000051
in the formula, α, β, a, and b are parameters to be determined, c is an acceptable level value, d is a numerical value of the highest level, c is m, a numerical value of the lowest level is 1, f (1) is 0.01, f (d) is 1, f (m) is n, and the parameters α, β, a, b, m, and n are assigned to each other, so that a weight value of each level is calculated according to formula (1).
Further, the valuation module uses multiple regression analysis to establish a mathematical model quantitative relationship;
establishing a linear model: ln (p) ═ α + β1ln(x) (2)；
Wherein P is a dependent variable, x is an independent variable, alpha, beta1Is the undetermined coefficient. The independent variables include: transaction price rp, size m, subject f and auction company grade a of the similar work; establishing a linear model according to the transaction price rp, the size m, the subject f and the grade a of the auction company:
ln(P)＝α+β1lnrp+β2lnm+β3lnf+β4lna (3)；
in the formula, P is a dependent variable, alpha and beta1、β2、β3、β4Is the undetermined coefficient.
Further, the valuation module determines price influencing factors comprising a similar work transaction price rp and a size m; the estimation module sets the influence factorThe numbers are respectively i1、i2And obtaining a calculation formula (4):
Figure BDA0002992201490000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002992201490000062
is the average number of traded prices for similar works,
Figure BDA0002992201490000063
alpha is the undetermined coefficient for the average size number.
The second estimated bargain price is P ═ EXP [ ln (P)]+FOISAL1；
Second auction valuation interval PrThe calculation formula of (2) is as follows: pr＝P±σ+FOIΔ SAL1, where σ is the calculated float value at 90% confidence level, σ ═ 1-90% P, FOIAs an influencing factor, SAL1 is the first estimated bid, and Δ SAL1 is the first auction valuation interval.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a data classification and processing method of an artwork valuation system, which comprises the following steps: step S1: the first data processing module converts the data information; step S2: the second data processing module generates a first estimated transaction price and a first auction price estimation interval; step S3: the classification module performs hierarchical classification on the auction companies; the valuation module obtains a valuation calculation formula and data characteristics required by calculation, and generates a second estimated closing price and a second auction valuation interval; the first data processing module comprises a character conversion part, an image conversion part, a byte screening part and an information combination part; the character conversion part stores the size, weight, color, age and material information of the artwork in a character database in a byte mode; the picture conversion part stores the picture of the artwork in a picture database in a pixel value mode; the byte discrimination part generates a discrimination byte through an external input mode, and the discrimination byte is stored in a discrimination database; the information combining part calls the information in the character database, the picture database and the discrimination database to generate an artwork transformation matrix Tr; and the second data processing module generates a first pre-estimated transaction price and a first auction price estimation interval according to the artwork transformation matrix Tr. The work of art valuation can be carried out more accurately.
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In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
Fig. 1 is a schematic step diagram of a data classifying and processing method of an art evaluation system according to an embodiment of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that embodiments of the invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in detail so as not to obscure the embodiments of the invention.
In the following description, a detailed structure will be presented for a thorough understanding of embodiments of the invention. It is apparent that the implementation of the embodiments of the invention is not limited to the specific details familiar to those skilled in the art. The following detailed description of preferred embodiments of the invention, however, the invention is capable of other embodiments in addition to those detailed.
In the description of the present invention, the terms "inside", "outside", "longitudinal", "transverse", "upper", "lower", "top", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are for convenience only to describe the present invention without requiring the present invention to be necessarily constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1, the present invention provides a data classifying and processing method of an art evaluation system, which includes:
step S1: the first data processing module converts the data information;
step S2: the second data processing module generates a first estimated transaction price and a first auction price estimation interval;
step S3: the classification module performs hierarchical classification on the auction companies; the valuation module obtains a valuation calculation formula and data characteristics required by calculation, and generates a second estimated bargaining interval and a second auction valuation interval;
the first data processing module comprises a character conversion part, an image conversion part, a byte screening part and an information combination part; the character conversion part stores the size, weight, color, age and material information of the artwork in a character database in a byte mode; the picture conversion part stores the picture of the artwork in a picture database in a pixel value mode; the byte discrimination part generates a discrimination byte through an external input mode, and the discrimination byte is stored in a discrimination database; the information combining part calls the information in the character database, the picture database and the discrimination database to generate an artwork transformation matrix Tr; and the second data processing module generates a first pre-estimated transaction price and a first auction price estimation interval according to the artwork transformation matrix Tr.
Specifically, the image conversion part comprises an image segmentation unit, an image identification comparison unit and an image marking unit; the image segmentation unit segments an image of an artwork, and generates a characteristic pixel matrix Q (Q) by segmenting the image1 q2 q3....qn) In matrix, q1、q2、q3Recursion to qnAre pixel values of the image;
the identification comparison unit acquires the artwork information from the Internet in real time and generates the existing artwork valuation matrix ScAnd the information file doc to be calledcSubscript c represents the number of the existing artwork, and the subscript c is 1, 2 and 3 and is increased in sequence till n; the existing work of art valuation matrix is Sc(Mc Nc) In matrix, McFor initial identification of the pixel matrix, NcFor the characteristic pixel matrix, the initial identification pixel matrix is Mc(mc1 mc2 mc3) In matrix, mc1、mc2、mc3Are all identifiable pixel values; pixel value mc1、mc2、mc3Are all set by human, and the characteristic pixel matrix is Nc(nc1 nc2 nc3....ncn) In matrix, nc1 nc2 nc3Recursion to ncnAre all characteristic pixel values; information file doc to be calledcThe method comprises the steps of including an original bargaining price and an original auction valuation interval of a corresponding artwork;
the image marking unit is used for marking a characteristic pixel matrix Q and an initial identification pixel matrix McIn comparison, when there is an initial identification pixel matrix MjThe subscript j is any one of 1, 2 and 3jValue m inj1 mj2 mj3When both exist in the characteristic pixel matrix Q, the image marking unit enables the characteristic pixel matrix Q and the characteristic pixel matrix NjComparing, the image marking unit generates an identification parameter k according to the comparison resultj。
The image marking unit records the number of pixel values numQ in the characteristic pixel matrix Q, and the image marking unit records the characteristic pixel matrix NjThe number numN of the same characteristic pixel values present in the characteristic pixel matrix Q, the identification parameter kjnummn/numQ. The byte screening part comprises an external information input device, and the external information input device acquires the byte quantity required to be supplemented by the artwork; the byte screening part generates an artwork integrity evaluation value Inte, and the integrity evaluation value Inte is byte0/byte 1; in the formula, byte0 is the amount of bytes needed to be supplemented by the artwork, and byte1 is the amount of bytes existing in the artwork. Conversion matrix of artwork into Tr (k)j Inte) The second data processing module stores the original bargaining price and the original auction valuation interval of the artwork; first estimated bargain SAL1 ═ kjSAL0Inte; first auction valuation interval Δ SAL1 ═ kjΔSAL0(ii) a Wherein k isjTo identify parameters, SAL0Is the original bargain price, Δ SAL, of the artwork0The original auction valuation interval of the artwork; int is the integrity evaluation value.
Specifically, when the currency of the original bargaining price is non-RMB, the currency is converted into RMB price, and the intermediate exchange rate is the intermediate exchange rate of exchanging RMB with the currency in the month of the auction time; in dollars for example, the formula is Prmb＝EXt*PUSDWherein EXtObtaining the middle exchange rate of the dollars of the month of the auction time to exchange the Renminbi; and when the currency type of the auction evaluation interval is non-RMB, converting the currency type into RMB price, wherein the intermediate exchange rate is the intermediate exchange rate of exchanging RMB with the currency type in the month of the obtained auction time.
Specifically, the classification module performs hierarchical classification according to the number of auction companies meeting the judgment standard, wherein the more the number of auction companies meeting the judgment standard, the higher the hierarchy; influencing factor FOIThe larger.
Specifically, the judgment criteria include:
the judgment standard of market value, when the total amount of the traded amount of the auction company is more than or equal to seven years of the total amount of the market, the auction company meets the judgment standard of the market value;
the method comprises the steps of collecting a capacity judgment standard of works, and setting the auction company to meet the capacity judgment standard of the collected works when the auction quantity of the auction company exceeds 50% of the same line in the same period;
a capability judgment standard package for converting into sales, the auction company satisfying a judgment standard for converting the collected auction into a capability of sales when the auction company's quantity of the auction exceeds 50% of the same year;
a judgment criterion of an average bargain price, wherein when the average bargain price of the auction company exceeds 50% of the same-period colleagues, the auction company meets the judgment criterion of the average bargain price;
and when the auction company meets the judgment standard of continuously gathering works and successful sale capability, and when the auction company has a transaction record in the last 5 years and has continuous auction records in the last 6 auction seasons, the auction company meets the judgment standard of continuously gathering works and successful sale capability.
Specifically, in the present specification, the size is converted into a size area; the size area is here converted into square footage taking into account the habits of the painting and calligraphy category. The calculation formula is (length cm × width cm) × 0.0009 ═ square ruler.
Specifically, the estimation module calculates the weight value by using a cauchy function, and the calculation formula is as follows:
Figure BDA0002992201490000111
where α, β, a, b are the parameters to be determined, c are the acceptable level values, d is the value of the highest level,
setting c to m, the value of the lowest level to 1, f (1) to 0.01, f (d) to 1, and f (m) to n, assigning values to the parameters α, β, a, b, m, n to be determined, respectively, and calculating the weight value of each level according to the formula (1).
In some embodiments of the present invention, the auction companies are sequentially classified into seven-level auction companies a, B, C, D, E, F, G (7,6,5,4,3,2,1), wherein the class a auction company satisfies 5 judgment criteria, the class B auction company satisfies 4 judgment criteria, the class C auction company satisfies 3 judgment criteria, the class D auction company satisfies 2 judgment criteria, the class E auction company satisfies 1 judgment criteria, the class F auction company satisfies 0 judgment criteria, and the class G auction company is an auction company that does not have a medium auction or a provincial auction.
In some embodiments of the present invention, the highest-level value d is 7, m is 4, n is 0.7, the pending parameter α is 4.8003, β is 0.7788, a is 0.5372, and b is-0.04532, and f (x) is calculated by formula (1) as follows: A. b, C, D, E, F, G corresponds to equal (1, 0.92, 0.82, 0.70, 0.51, 0.24, 0.01).
The estimation module establishes a mathematical model quantity relational expression by using multivariate regression analysis;
establishing a linear model: ln (p) ═ α + β1ln(x) (2)；
Wherein P is a dependent variable, x is an independent variable, alpha, beta1Is the undetermined coefficient. The independent variables include: transaction price rp, size m, subject f and auction company grade a of the similar work; establishing a linear model according to the transaction price rp, the size m, the subject f and the grade a of the auction company:
ln(P)＝α+β1lnrp+β2lnm+β3lnf+β4lna (3)；
in the formula, P is a dependent variable, alpha and beta1、β2、β3、β4Is the undetermined coefficient.
Specifically, the valuation module determines price influencing factors comprising a similar work transaction price rp and a size m; the estimation module sets the influence factors to i respectively1、i2And obtaining a calculation formula (4):
Figure BDA0002992201490000131
in the formula (I), the compound is shown in the specification,
Figure BDA0002992201490000132
is the average number of traded prices for similar works,
Figure BDA0002992201490000133
alpha is the undetermined coefficient for the average size number.
The second estimated bargain price is P ═ EXP [ ln (P)]+FOISAL1；
Second auction valuation interval PrThe calculation formula of (2) is as follows: pr＝P±σ+FOIΔ SAL1, where σ is the calculated float value at 90% confidence level, σ ═ 1-90% P, FOIAs an influencing factor, SAL1 is the first estimated bid, and Δ SAL1 is the first auction valuation interval.
In the inventionIn some embodiments of (1), the original transaction data of an artwork lacks the screening field S1Supplementary related information is required. Setting S1Is the value n, by retrieving all matches S in the database1N and packaging these data into a suitable data set.
In some embodiments of the invention, a data set meeting conditions is screened out by taking four items of dimensions of size, subject, creation age and mounting as dimensions respectively, the estimated target size is 4.5 flat feet, the mountains and waters, the creation age is 1962, and the mounting is a vertical shaft; the specific screening steps are as follows:
1. screening the works with 3-6 flat feet, and reserving the works;
2. screening the landscape subject matter and reserving the landscape subject matter;
3. the screening years are reserved during 1960-;
4. screening and mounting to be vertical shaft, and reserving.
The filtered data is the appropriate data set that can be calculated.
In particular, after a suitable data set is found, the set should now include the so-calculable price influencing factor i1,i2And an artist constant a. E.g. an artist constant α -1.164, i1＝0.991,i20.857, the trade price average of the similar works is 10,000, the flat bar is 4.5, and the formula is substituted, wherein Ln (P) is-1.164 +0.991 ln (10,000) +0.857 ln (4.5) ═ 9.127+1.289-1.164 ═ 9.252;
the estimate of the artwork is EXP (9.252) ═ 10425.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Terms such as "component" and the like, when used herein, can refer to either a single part or a combination of parts. Terms such as "mounted," "disposed," and the like, as used herein, may refer to one component as being directly attached to another component or one component as being attached to another component through intervening components. Features described herein in one embodiment may be applied to another embodiment, either alone or in combination with other features, unless the feature is otherwise inapplicable or otherwise stated in the other embodiment.
The present invention has been described in terms of the above embodiments, but it should be understood that the above embodiments are for purposes of illustration and description only and are not intended to limit the invention to the scope of the described embodiments. It will be appreciated by those skilled in the art that many variations and modifications may be made to the teachings of the invention, which fall within the scope of the invention as claimed.

Claims (10)

1. A data classification and processing method of an artwork estimation system is characterized by comprising the following steps:
step S1: the first data processing module converts the data information;
step S2: the second data processing module generates a first estimated transaction price and a first auction price estimation interval;
step S3: the classification module performs hierarchical classification on the auction companies; the valuation module obtains a valuation calculation formula and data characteristics required by calculation, and generates a second estimated bargaining interval and a second auction valuation interval;
the first data processing module comprises a character conversion part, an image conversion part, a byte screening part and an information combination part; the character conversion part stores the size, weight, color, age and material information of the artwork in a character database in a byte mode; the picture conversion part stores the picture of the artwork in a picture database in a pixel value mode; the byte discrimination part generates a discrimination byte through an external input mode, and the discrimination byte is stored in a discrimination database; the information combining part calls the information in the character database, the picture database and the discrimination database to generate an artwork transformation matrix Tr; and the second data processing module generates a first pre-estimated transaction price and a first auction price estimation interval according to the artwork transformation matrix Tr.
2. The data classification and processing method of an art evaluation system according to claim 1, wherein the picture conversion part comprises an image segmentation unit, an image identification comparison unit and an image marking unit; the image segmentation unit segments an image of an artwork, and generates a characteristic pixel matrix Q (Q) by segmenting the image1 q2 q3 .... qn) In matrix, q1、q2、q3Recursion to qnAre pixel values of the image;
the identification comparison unit acquires the artwork information from the Internet in real time and generates the existing artwork valuation matrix ScAnd the information file doc to be calledcSubscript c represents the number of the existing artwork, and the subscript c is 1, 2 and 3 and is increased in sequence till n; the existing work of art valuation matrix is Sc(Mc Nc) In matrix, McFor initial identification of the pixel matrix, NcFor the characteristic pixel matrix, the initial identification pixel matrix is Mc(mc1 mc2 mc3) In matrix, mc1、mc2、mc3Are all identifiable pixel values; pixel value mc1、mc2、mc3Are all set by human, and the characteristic pixel matrix is Nc(nc1 nc2nc3 .... ncn) In matrix, nc1 nc2 nc3Recursion to ncnAre all characteristic pixel values; information file doc to be calledcThe method comprises the steps of including an original bargaining price and an original auction valuation interval of a corresponding artwork;
the image marking unit is used for marking a characteristic pixel matrix Q and an initial identification pixel matrix McIn comparison, when there is an initial identification pixel matrix MjThe subscript j is any one of 1, 2 and 3jValue m inj1 mj2 mj3When both exist in the characteristic pixel matrix Q, the image marking unit enables the characteristic pixel matrix Q and the characteristic pixel matrix NjBy comparison, the image marks the cell rootGenerating an identification parameter k according to the comparison resultj。
3. The method for classifying and processing data of an art estimation system according to claim 2, wherein the image marking unit records the number numQ of pixel values in the characteristic pixel matrix Q, and the image marking unit records the characteristic pixel matrix NjThe number numN of the same characteristic pixel values present in the characteristic pixel matrix Q, the identification parameter kj＝numN/numQ。
4. The data classification and processing method of an art evaluation system according to claim 3, wherein the byte discriminating section includes an external information input device that acquires the amount of bytes of the art that needs to be supplemented; the byte screening part generates an artwork integrity evaluation value Inte, and the integrity evaluation value Inte is byte0/byte 1; in the formula, byte0 is the amount of bytes needed to be supplemented by the artwork, and byte1 is the amount of bytes existing in the artwork.
5. The method of classifying and processing data of an art estimation system according to claim 4, wherein the art transformation matrix is Tr (k)jInte), the second data processing module stores the original bargaining price and the original auction valuation interval of the artwork; first estimated bargain SAL1 ═ kjSAL0Inte; first auction valuation interval Δ SAL1 ═ kjΔSAL0(ii) a Wherein k isjTo identify parameters, SAL0Is the original bargain price, Δ SAL, of the artwork0The original auction valuation interval of the artwork; int is the integrity evaluation value.
6. The data classifying and processing method of an art evaluation system according to claim 5, wherein the classifying module performs hierarchical classification according to the number of auction companies satisfying the judgment criteria, the larger the number of auction companies satisfying the judgment criteria, the higher the hierarchy; influencing factor FOIThe larger.
7. The method of claim 6, wherein the criteria comprises:
the judgment standard of market value, when the total amount of the traded amount of the auction company is more than or equal to seven years of the total amount of the market, the auction company meets the judgment standard of the market value;
the method comprises the steps of collecting a capacity judgment standard of works, and setting the auction company to meet the capacity judgment standard of the collected works when the auction quantity of the auction company exceeds 50% of the same line in the same period;
a capability judgment standard package for converting into sales, the auction company satisfying a judgment standard for converting the collected auction into a capability of sales when the auction company's quantity of the auction exceeds 50% of the same year;
a judgment criterion of an average bargain price, wherein when the average bargain price of the auction company exceeds 50% of the same-period colleagues, the auction company meets the judgment criterion of the average bargain price;
and when the auction company meets the judgment standard of continuously gathering works and successful sale capability, and when the auction company has a transaction record in the last 5 years and has continuous auction records in the last 6 auction seasons, the auction company meets the judgment standard of continuously gathering works and successful sale capability.
8. The method of claim 7, wherein the valuation module uses Cauchy-type functions to calculate the weight values according to the following formula:
Figure FDA0002992201480000041
in the formula, α, β, a, and b are parameters to be determined, c is an acceptable level value, d is a numerical value of the highest level, c is m, a numerical value of the lowest level is 1, f (1) is 0.01, f (d) is 1, f (m) is n, and the parameters α, β, a, b, m, and n are assigned to each other, so that a weight value of each level is calculated according to formula (1).
9. The method of data classification and processing of an art valuation system of claim 8 wherein said valuation module uses multiple regression analysis to build mathematical model quantitative relationships;
establishing a linear model: ln (p) ═ α + β1ln(x) (2)；
Wherein P is a dependent variable, x is an independent variable, alpha, beta1Is the undetermined coefficient. The independent variables include: transaction price rp, size m, subject f and auction company grade a of the similar work; establishing a linear model according to the transaction price rp, the size m, the subject f and the grade a of the auction company:
ln(P)＝α+β1ln rp+β2ln m+β3ln f+β4ln a (3)；
in the formula, P is a dependent variable, alpha and beta1、β2、β3、β4Is the undetermined coefficient.
10. The method of data classification and processing of artwork estimation system of claim 9, wherein said estimation module determines price influencing factors including transaction price rp and size m of similar works; the estimation module sets the influence factors to i respectively1、i2And obtaining a calculation formula (4):
Figure FDA0002992201480000051
in the formula (I), the compound is shown in the specification,
Figure FDA0002992201480000052
is the average number of traded prices for similar works,
Figure FDA0002992201480000053
alpha is the undetermined coefficient for the average size number.
The second estimated bargain price is P ═ EXP [ ln (P)]+FOISAL1；
Second auction valuation interval PrThe calculation formula of (2) is as follows: pr＝P±σ+FOIΔ SAL1, where σ is the calculated float value at 90% confidence level, σ ═ 1-90% P, FOIAs an influencing factor, SAL1 is the first estimated bid, and Δ SAL1 is the first auction valuation interval.
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