CN112685589A - Jadeite hierarchical valuation assessment method, system and application based on artificial intelligence - Google Patents
Jadeite hierarchical valuation assessment method, system and application based on artificial intelligence Download PDFInfo
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- 229910052640 jadeite Inorganic materials 0.000 title claims abstract description 86
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 title claims description 21
- 238000011156 evaluation Methods 0.000 claims abstract description 32
- 239000010977 jade Substances 0.000 claims abstract description 23
- 238000000611 regression analysis Methods 0.000 claims abstract description 21
- 241000579895 Chlorostilbon Species 0.000 claims description 18
- 239000010976 emerald Substances 0.000 claims description 18
- 229910052876 emerald Inorganic materials 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 13
- 238000013135 deep learning Methods 0.000 claims description 7
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- NNBFNNNWANBMTI-UHFFFAOYSA-M brilliant green Chemical compound OS([O-])(=O)=O.C1=CC(N(CC)CC)=CC=C1C(C=1C=CC=CC=1)=C1C=CC(=[N+](CC)CC)C=C1 NNBFNNNWANBMTI-UHFFFAOYSA-M 0.000 claims description 2
- 230000010365 information processing Effects 0.000 abstract description 2
- 241000894007 species Species 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
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Abstract
The invention belongs to the technical field of intelligent terminal information processing, and discloses a jadeite hierarchical estimated value evaluation method, a jadeite hierarchical estimated value evaluation system and application based on artificial intelligence, wherein statistical distribution of HSV color domains of sold jadeite pictures is determined, the statistical distribution is used as a feature vector of the sold jadeite pictures, and a jadeite hierarchical estimated value information database is established; determining the statistical distribution of HSV color domains of the jadeite picture to be estimated, calculating the similarity of the jadeite picture to be estimated and the pictures contained in the database, inquiring the database, and finding out the sold jadeite picture with the similarity smaller than 5; searching the database, and if a sold jadeite picture with a picture distance smaller than 1 exists, using the price represented by the sold jadeite picture as the estimation of the jadeite price to be estimated; and if not, selecting the characteristics of which the correlation accords with the set numerical value for the most similar historical picture to perform regression analysis, and predicting the price of the jade to be estimated. The invention improves the efficiency of offline authentication service.
Description
Technical Field
The invention belongs to the technical field of intelligent terminal information processing, and particularly relates to a jadeite hierarchical valuation evaluation method, a jadeite hierarchical valuation evaluation system, a jadeite hierarchical valuation evaluation medium, a jadeite hierarchical valuation evaluation terminal and jadeite hierarchical valuation equipment based on artificial intelligence.
Background
At present: the jade known as the king of jade has a long history in China and also has a huge jade jewelry consumer market. The trade of the jade market is closely related to the purchasing enthusiasm of consumers; in order to solve the problems of transaction in the emerald market and enthusiasm of consumers in purchasing, more than 100 detection organizations exist in the industry all over the country at present, and main services comprise true and false identification of emerald, certificate evaluation and the like. Meanwhile, a plurality of intelligent jade trading platforms exist, a safe and advanced online trading mode is mainly provided, and true and false authentication services are provided. Generally, an evaluation of the authenticity of jadeite takes approximately 10 minutes, while an evaluation of jadeite takes 1-2 weeks. In terms of efficiency, the intelligent jade trading platform provides offline authentication service, and can not meet the jade evaluation requirements of a large number of consumers; in terms of standards, jadeite does not have a uniform quality grading standard at present; from a price point of view, merchants have a market for the price of jadeite due to information and professional asymmetry.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the efficiency that present intelligent jadeite trading platform provided the authentication service of off-line is lower, can not satisfy a large amount of consumers' jadeite aassessment demand far away.
(2) At present, jadeite does not have a uniform quality grading standard, and information and profession are asymmetric, so that the price of the jadeite is greatly different from the actual value.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a jadeite hierarchical valuation evaluation method, a system, a medium and a terminal based on artificial intelligence.
The invention is realized in such a way that an artificial intelligence based jadeite hierarchical valuation evaluation method comprises the following steps:
step one, determining statistical distribution of HSV color domains of sold jadeite pictures, taking the statistical distribution as a feature vector of the sold jadeite pictures, and establishing a jadeite hierarchical estimation information database;
determining the statistical distribution of HSV color domains of the jadeite picture to be estimated, calculating the similarity of the jadeite picture to be estimated and the pictures contained in the database, inquiring the database, and finding out the sold jadeite picture with the similarity smaller than a set threshold (set as 5);
searching the database, and if a sold jadeite picture with a picture distance smaller than a set threshold (set to be 1) exists, using the price represented by the sold jadeite picture as the estimation of the jadeite price to be estimated; and if not, selecting the characteristics of which the correlation accords with the set numerical value for the most similar historical picture to perform regression analysis, and predicting the price of the jade to be estimated.
Further, the method for estimating graded evaluation of emerald green further includes a method for estimating a price interval, specifically:
in the step of predicting the divalent case, obtaining a predicted value through regression analysis, and adopting an interval estimation result with a confidence coefficient of 50% in an interval of the jade price to be estimated; if the coverage range of the interval is larger, the confidence coefficient is properly reduced, and a smaller interval is selected;
for the prices obtained by similarity weighted averaging, the interval estimate has a lower limit of estimate 0.7 and an upper limit of estimate 1.3.
Further, in the first step and the second step, before determining the statistical distribution, a semantic segmentation model in deep learning is used for performing background removal prediction and highlight removal prediction on the sold jadeite and jadeite pictures to be estimated of the historical transaction.
Further, the HSV color domain adopts three parameters of hue H, saturation S and brightness V.
Further, in the third step, the method for predicting the price of the jadeite to be estimated analyzes the inquired most similar sold jadeite picture by selecting the characteristic of which the correlation accords with the set numerical value to perform regression analysis, obtains the represented value information of the jadeite to be estimated, and estimates the jadeite to be estimated based on the value information;
selecting features with the relevance larger than a certain threshold value for regression analysis according to the relevance between the features in the value information and the price, and using the features when the relevance between the features and the price exceeds the preset threshold value; when none of the features shows obvious correlation with the price, the price estimation of the jadeite to be estimated is carried out by carrying out weighted average on the prices of the commodities with different degrees of similarity and then giving a predicted price result.
It is a further object of the invention to provide a computer arrangement comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of the above-mentioned method.
It is a further object of the invention to provide a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, causes the processor to carry out the above-mentioned method steps.
Another object of the present invention is to provide an information data processing terminal, which is used for implementing the above jadeite hierarchical estimation evaluation method based on artificial intelligence.
Another object of the present invention is to provide an artificial intelligence based hierarchical estimation evaluation system for jadeite, which implements the artificial intelligence based hierarchical estimation evaluation method for jadeite, wherein the artificial intelligence based hierarchical estimation evaluation system for jadeite comprises:
the evaluation database establishing module is used for carrying out background removal prediction and highlight removal prediction on the commodity pictures in the historical transactions by using a semantic segmentation model in deep learning; counting the statistical distribution of HSV color domains of the pictures subjected to background and highlight processing; taking the statistical distribution as a feature vector of the picture;
the similar picture query module is used for performing background removal and highlight removal processing on jade during value estimation, and counting the statistical distribution of HSV color domains of the processed picture; calculating the similarity degree of the picture and the full library picture;
the price prediction module is used for considering that the jadeite to be estimated is consistent with the quality of the jadeite in the commodity library if the data with the distance less than 1 exists in the whole commodity library, and taking the price of the commodity represented by the most similar picture as the price estimation of the jadeite to be estimated; otherwise, the inquired most similar picture is used for obtaining other information of the represented commodity, and the commodity to be estimated can be evaluated based on the other information;
and the price interval estimation module is used for obtaining a predicted value through regression analysis, and the commodity price interval adopts an interval estimation result with the confidence coefficient of 50%.
Another object of the present invention is to provide a hierarchical estimation apparatus for emerald which is provided with the hierarchical estimation system for emerald.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention solves the problems that the efficiency of the existing intelligent jade trading platform for providing offline authentication service is lower and the jade evaluation requirements of a large number of consumers can not be met; by adopting the technical scheme of the invention, the efficiency of offline identification service is improved, and the jade evaluation requirements of a large number of consumers are met.
The invention solves the problem that the price of the jadeite is greatly different from the actual value because the jadeite does not have a uniform quality grading standard and the information and the specialty are not symmetrical at present; by adopting the technical scheme of the invention, jadeite has a uniform quality grading standard, so that information and professions are symmetrical.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of an evaluation method for hierarchical estimation of emerald based on artificial intelligence according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an artificial intelligence based hierarchical estimation system for emerald;
in the figure: 1. a valuation database establishing module; 2. a similar picture query module; 3. a price prediction module; 4. and a price interval estimation module.
Fig. 3 is a flowchart illustrating an implementation of a method for evaluating a hierarchical estimation of emerald based on artificial intelligence according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a jadeite hierarchical valuation evaluation method, a system, a medium and a terminal based on artificial intelligence, and the invention is described in detail with reference to the attached drawings.
As shown in fig. 1, the method for evaluating a hierarchical estimation of jade based on artificial intelligence according to the present invention includes the following steps:
s101: performing background removal prediction and highlight removal prediction on sold jadeite pictures in historical transactions by using a semantic segmentation model in deep learning; and (4) counting the statistical distribution of the HSV color domain of the picture subjected to background and highlight treatment, and establishing a database by taking the statistical distribution as the characteristic vector of the picture.
S102: when value estimation is carried out, background removal and highlight removal processing are carried out on the jadeite to be estimated, and statistical distribution of HSV color domains of the picture is carried out after the statistical processing; and calculating the similarity between the jadeite picture to be estimated and the database picture.
S103: if data with the distance of less than 1 exists in the whole commodity library, the jadeite to be estimated is considered to be consistent with the quality of the jadeite in the commodity library, and the price of the commodity represented by the most similar picture is used as the price estimation of the jadeite to be estimated; otherwise, the inquired most similar picture obtains other information of the represented commodity, and the commodity to be estimated can be evaluated based on the other information.
S104: and obtaining a predicted value through regression analysis, wherein the commodity price interval adopts an interval estimation result with the confidence coefficient of 50%.
Those skilled in the art can also implement the method for evaluating the hierarchical estimation of emerald based on artificial intelligence, and the method for evaluating the hierarchical estimation of emerald based on artificial intelligence provided by the present invention shown in fig. 1 is only one specific embodiment.
As shown in fig. 2, the present invention provides an evaluation system for hierarchical estimation of emerald based on artificial intelligence, which comprises:
the evaluation database establishing module 1 is used for carrying out background removal prediction and highlight removal prediction on commodity pictures in historical transactions by using a semantic segmentation model in deep learning; counting the statistical distribution of HSV color domains of the pictures subjected to background and highlight processing; and taking the statistical distribution as a feature vector of the picture.
The similar picture query module 2 is used for performing background removal and highlight removal processing on jade during value estimation, and counting the statistical distribution of HSV color domains of the processed picture; and calculating the similarity degree of the picture and the full library picture.
The price prediction module 3 is configured to, if data with a distance of <1 exists in the entire commodity library, consider that the jadeite to be estimated is consistent with the quality of the article in the commodity library, and use the price of the commodity represented by the most similar picture as the price estimation of the jadeite to be estimated; otherwise, the inquired most similar picture obtains other information of the represented commodity, and the commodity to be estimated can be evaluated based on the other information.
And the price interval estimation module 4 is used for obtaining a predicted value through regression analysis, and the commodity price interval adopts an interval estimation result with the confidence coefficient of 50%.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
As shown in fig. 3, the method for evaluating a hierarchical estimation of jade based on artificial intelligence according to the present invention includes the following steps:
firstly, establishing an estimation database:
performing background removal prediction and highlight removal prediction on commodity pictures of historical transactions of the dealer platform by using a semantic segmentation model in deep learning; counting the statistical distribution of HSV color domains (Hue, Saturation, S and V) of the picture subjected to background and highlight processing; and when the subsequent picture is searched, taking the statistical distribution as the characteristic vector of the picture.
Secondly, inquiring pictures with similar pictures:
when value estimation is carried out on certain egg-side jade, the same background removal and highlight removal processing is carried out on the egg-side jade, and the statistical distribution of HSV color domains of the processed pictures is counted; calculating the similarity degree of the picture and the full library picture; the measurement method adopted for calculating the similarity is Wasserstein distance, and the picture with the most similar degree smaller than 5 threshold is found out.
Third, price prediction:
(1) if data with the distance of less than 1 exists in the whole commodity library, the jadeite to be estimated is considered to be consistent with the quality of the jadeite in the commodity library, and the price of the commodity represented by the most similar picture is used as the price estimation of the jadeite to be estimated; otherwise, the following estimation logic is followed.
(2) The inquired most similar pictures obtain other information of the represented commodity, such as size, species, water head and the like, and the estimation can be carried out on the commodity to be estimated based on the information; first, analyzing some factors which may be related to the price in such a small area, such as the size, the species, the water head and the like, and the price; then selecting the characteristic with the correlation larger than a certain threshold value to perform regression analysis, wherein the reasonable range of the Pearson correlation coefficient is between-1 and 1, and the more the absolute value of the finally calculated numerical value is close to 1, the stronger the linear correlation of the two attributes is. In practical applications, 0.6 or 0.7 and above are available, and this range is usually chosen, and this threshold is suitably increased if the actual data shows a stronger correlation. However, when the correlation value is less than 0.6, the regression analysis is not performed.
When the correlation of the characteristics is larger than a preset threshold value, performing regression analysis; when no feature shows correlation, a "weighted average" is made. Regression analysis aims to find a set of parameters (a, b, c, d …, constant) using the least squares method, so that the equation of print factor 1+ b factor 2+ c factor 3+ … + constant can fit the data as closely as possible. The weighted average is obtained by converting the similarity distance of the searched commodities into a weight and then multiplying the weight by the price of the searched commodities respectively.
The specific numerical values of the weights are calculated in real time, and assuming that three distances of a certain searched picture are 0.1, 0.2 and 0.5 respectively, the reciprocal 10, 5 and 2 of the picture are obtained first, then the picture is normalized to be 0.588, 0.294 and 0.118, and then the final price is obtained by multiplying the price of the picture by the price of the picture. When selecting the features, using the features when the correlation between the features and the prices exceeds a preset threshold; when none of these features show a significant correlation with price, the price of the good is estimated by taking a weighted average of the prices of the goods of different degrees of similarity and then giving a predicted price result.
In the embodiment of the present invention, the correlation between some information (such as size, species, water head) of the jade to be sold and the price is calculated by using the pearson correlation coefficient.
In the case that the correlation is greater than a certain threshold, the threshold is determined by the following method:
a reasonable range for the pearson correlation coefficient is between-1 and 1, with the absolute value of the last calculated value being closer to 1, indicating a stronger linear correlation between these two properties. In practical applications, 0.6 or 0.7 and above are available, and this range is usually chosen, and this threshold is suitably increased if the actual data shows a stronger correlation. However, when the correlation value is less than 0.6, it is not very significant to perform regression analysis.
When the correlation of the characteristics is larger than a preset threshold value, performing regression analysis; when no feature shows correlation, a "weighted average" is made. Regression analysis aims to find a set of parameters (a, b, c, d …, constant) using the least squares method, so that the equation of print factor 1+ b factor 2+ c factor 3+ … + constant can fit the data as closely as possible. The weighted average is obtained by converting the similarity distance of the searched commodities into a weight, and then multiplying the weight by the price of the searched commodities respectively. The specific numerical values of the weights are calculated in real time, and assuming that three distances of a certain searched picture are 0.1, 0.2 and 0.5 respectively, the reciprocal 10, 5 and 2 of the picture are obtained first, then the picture is normalized to be 0.588, 0.294 and 0.118, and then the picture is multiplied by the price of the picture to be used as the final price.
Fourth, price interval estimation:
in the third step of price prediction, a predicted value is obtained through regression analysis, and an interval estimation result with the confidence coefficient of 50% is adopted in a commodity price interval; if the coverage of the interval is larger, the confidence is properly reduced, and a smaller interval is selected. For the prices obtained by similarity weighted averaging, the interval estimate has a lower limit of estimate 0.7 and an upper limit of estimate 1.3. The confidence degree in the invention refers to the confidence degree in statistics, and the confidence degree is the reliability degree of interval estimation.
The quantization index with larger interval coverage is (upper confidence limit-lower confidence limit)/(upper confidence limit + lower confidence limit) when the value is larger than 0.5, i.e. to the extent that the coverage of the interval is unacceptable. The confidence level is usually adjusted to be lower than 0.5 by the confidence level. The confidence measure is the reliability of the estimation result when applying statistical methods to estimate the price. The lower the confidence, the less reliable the estimation result and the meaningless the result.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An artificial intelligence based jadeite hierarchical estimation evaluation method, characterized in that the artificial intelligence based jadeite hierarchical estimation evaluation method comprises:
step one, determining statistical distribution of HSV color domains of sold jadeite pictures, taking the statistical distribution as a feature vector of the sold jadeite pictures, and establishing a jadeite hierarchical estimation information database;
determining the statistical distribution of HSV color domains of the jadeite picture to be estimated, calculating the similarity of the jadeite picture to be estimated and pictures contained in the database, inquiring the database, and finding out sold jadeite pictures with the similarity smaller than a set threshold;
searching the database, and if a sold jadeite picture with a picture distance smaller than a set threshold exists, using the price represented by the sold jadeite picture as the estimation of the jadeite price to be estimated; and if not, selecting the characteristics of which the correlation accords with the set numerical value for the most similar historical picture to perform regression analysis, and predicting the price of the jade to be estimated.
2. The method for evaluating hierarchical estimation of emerald green based on artificial intelligence of claim 1, further comprising a price interval estimation method, specifically:
in the step of predicting the divalent case, obtaining a predicted value through regression analysis, and adopting an interval estimation result with a confidence coefficient of 50% in an interval of the jade price to be estimated; if the coverage range of the interval is larger, the confidence coefficient is properly reduced, and a smaller interval is selected;
for the prices obtained by similarity weighted averaging, the interval estimate has a lower limit of estimate 0.7 and an upper limit of estimate 1.3.
3. The method as claimed in claim 1, wherein in the first and second steps, a semantic segmentation model in deep learning is used to perform background removal prediction and highlight removal prediction before determining the statistical distribution for the sold jadeite and jadeite pictures to be estimated in the historical transaction.
4. The method as claimed in claim 1, wherein said HSV color gamut employs three parameters of hue H, saturation S, and value V;
selecting sold jadeite pictures with similarity less than 5;
and in the third step, searching the database, and selecting the sold emerald pictures with the picture distance less than 1.
5. The hierarchical estimation evaluation method for emerald based on artificial intelligence as claimed in claim 1, wherein in the third step, regression analysis is performed by selecting the features whose correlation matches the set value, the method for predicting the price of emerald analyzes the inquired most similar sold emerald picture to obtain the value information of the represented emerald to be estimated, and the emerald to be estimated is estimated based on the value information;
selecting features with the relevance larger than a certain threshold value for regression analysis according to the relevance between the features in the value information and the price, and using the features when the relevance between the features and the price exceeds the preset threshold value; when none of the features shows obvious correlation with the price, the price estimation of the jadeite to be estimated is carried out by carrying out weighted average on the prices of the commodities with different degrees of similarity and then giving a predicted price result.
6. A computer arrangement, characterized in that the computer arrangement comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of claim 1.
7. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the steps of claim 1.
8. An information data processing terminal, wherein the information data processing terminal is configured to implement the artificial intelligence based jadeite hierarchical estimation evaluation method according to any one of claims 1 to 4.
9. An artificial intelligence based hierarchical estimation evaluation system for jadeite based hierarchical estimation evaluation method according to any one of claims 1 to 6, wherein the artificial intelligence based hierarchical estimation evaluation system comprises:
the evaluation database establishing module is used for carrying out background removal prediction and highlight removal prediction on the commodity pictures in the historical transactions by using a semantic segmentation model in deep learning; counting the statistical distribution of HSV color domains of the pictures subjected to background and highlight processing; taking the statistical distribution as a feature vector of the picture;
the similar picture query module is used for performing background removal and highlight removal processing on jade during value estimation, and counting the statistical distribution of HSV color domains of the processed picture; calculating the similarity degree of the picture and the full library picture;
the price prediction module is used for considering that the jadeite to be estimated is consistent with the quality of the jadeite in the commodity library if the data with the distance less than 1 exists in the whole commodity library, and taking the price of the commodity represented by the most similar picture as the price estimation of the jadeite to be estimated; otherwise, the inquired most similar picture is used for obtaining other information of the represented commodity, and the commodity to be estimated can be evaluated based on the other information;
and the price interval estimation module is used for obtaining a predicted value through regression analysis, and the commodity price interval adopts an interval estimation result with the confidence coefficient of 50%.
10. An apparatus for estimating a hierarchical level of emerald provided with the system for estimating a hierarchical level of emerald according to claim 9.
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