A data optimization retrieval method in the process of automobile parts valuation and recycling

Abstract

The present invention relates to the field of big data storage technology, and more specifically, to a data optimization and retrieval method for the automotive parts valuation and recovery process. The method comprises the following steps: obtaining dimensional indicator data sets and transaction price data sequences for several types of automotive parts; obtaining the dimensional correlation between each dimensional indicator data sequence and the transaction price data sequence in the dimensional indicator data set for any type of automotive part; obtaining the degree of similarity in characteristic change trends between each type of automotive part and other types of automotive parts; and obtaining a compressed dimensional indicator data set for the automotive parts. The present invention improves the accuracy and efficiency of data retrieval during the automotive parts valuation and recovery process.

Description

Technical Field

[0001] The present invention relates to the technical field of big data storage, and in particular to a data optimization retrieval method in the process of automobile parts valuation and recovery. Background Art

[0002] During the valuation and recovery process of auto parts, a large amount of auto parts information big data is generated, and the storage and retrieval of the auto parts information big data incurs a large resource overhead. Therefore, the auto parts information big data should be compressed and stored to achieve data optimization and retrieval processing in the valuation and recovery process of auto parts. Through efficient retrieval and matching, the parts that need to be recycled can be quickly matched with potential buyers to improve the recycling efficiency.

[0003] Differential coding is used in the existing technology to compress big data of automobile parts information. However, in the process of compressing big data of automobile parts information using differential coding, multi-dimensional influencing factors such as the service life and maintenance status of automobile parts of the same type and model jointly affect the price of automobile parts. Moreover, multi-dimensional influencing factors often have different degrees of influence in the price change stages of different types of automobile parts. If differential coding compression is performed only considering the trend characteristics of automobile parts price changes, it is difficult to achieve efficient data retrieval in the process of automobile parts valuation and recovery. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a data optimization retrieval method in the process of automobile parts valuation and recycling, the method comprising:

[0005] Obtaining a dimensional indicator data set and a transaction price data sequence for several types of automobile parts, wherein the dimensional indicator data set includes several dimensional indicator data sequences;

[0006] Each dimension indicator data sequence in the transaction price data sequence and dimension indicator data set of each model of automobile parts is evenly divided into a number of dimension data segments and price data segments; based on the data difference between each dimension data segment of each dimension indicator data sequence and each price data segment of the transaction price data sequence, the similarity between each dimension data segment of each dimension indicator data sequence and each price data segment of the transaction price data sequence is obtained; based on the proportion of the similarity between all dimension data segments of each dimension indicator data sequence and each price data segment of the transaction price data sequence, the dimensional correlation between each dimension indicator data sequence and the transaction price data sequence is obtained;

[0007] Obtain the similarity of the characteristic change trends between any two models of auto parts based on the dimensional correlation between each dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any two models of auto parts;

[0008] According to the similarity of the feature change trends between any two models of automobile parts, a compressed dimensional indicator data set of automobile parts is obtained.

[0009] Preferably, the method of obtaining the similarity between each dimension data segment of each dimension indicator data sequence and each price data segment of the transaction price data sequence based on the data difference between each dimension data segment of each dimension indicator data sequence and each price data segment of the transaction price data sequence includes the following specific methods:

[0010] Obtain the index characteristic value of the i-th data in the j-th dimension data segment of the a-th dimension index data sequence; obtain the price characteristic value of the i-th data in the j-th price data segment of the transaction price data sequence; then the similarity calculation method between the j-th dimension data segment of the a-th dimension index data sequence and the j-th price data segment of the transaction price data sequence in the dimension index data set of any model of automobile parts is:

[0011]

[0012] Where D Qa,j represents the similarity between the jth dimension data segment of the ath dimension indicator data sequence and the jth price data segment of the transaction price data sequence in the dimension indicator data set of any model of automobile parts; c a,j,i Represents the index characteristic value of the i-th data in the j-th dimension data segment of the a-th dimension index data sequence in the dimension index data set of any model of automobile parts; m a,j,i It represents the price characteristic value of the i-th data in the j-th price data segment of the transaction price data sequence of any model of automobile parts; w is the preset parameter; exp() represents the exponential function with a natural constant as the base; | | represents taking the absolute value.

[0013] Preferably, the specific method for obtaining the index characteristic value of the i-th data in the j-th dimension data segment of the a-th dimension index data sequence is:

[0014] The absolute value of the difference between the i-th data and the i+1-th data in the j-th dimensional data segment of the a-th dimensional indicator data sequence is used as the indicator characteristic value of the i-th data in the j-th dimensional data segment of the a-th dimensional indicator data sequence.

[0015] Preferably, the specific method for obtaining the price characteristic value of the i-th data in the j-th price data segment of the transaction price data sequence is:

[0016] The absolute value of the difference between the i-th data and the i+1-th data in the j-th price data segment of the transaction price data sequence is used as the price feature value of the i-th data in the j-th price data segment of the transaction price data sequence.

[0017] Preferably, the specific formula for obtaining the dimensional correlation between each dimensional indicator data sequence and the transaction price data sequence based on the ratio of similarities between all dimensional data segments of each dimensional indicator data sequence and each price data segment of the transaction price data sequence is:

[0018]

[0019] Where WL a Represents the dimensional correlation between the ath dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any model of automobile parts; n a Represents the total number of all dimensional data segments of the ath dimensional indicator data sequence in the dimensional indicator data set of any model of automobile parts; DQ a,j Indicates the similarity between the jth dimension data segment of the ath dimension indicator data sequence and the jth price data segment of the transaction price data sequence in the dimension indicator data set of any model of automobile parts; DQ a,b Represents the similarity between the bth dimension data segment of the ath dimension indicator data sequence and the bth price data segment of the transaction price data sequence in the dimension indicator data set of any model of automobile parts.

[0020] Preferably, the method of obtaining the similarity of characteristic change trends between any two models of automobile parts based on the dimensional correlation between each dimensional indicator data series and the transaction price data series in the dimensional indicator data sets of any two models of automobile parts includes the following specific methods:

[0021] Obtain the target dimension indicator data sequence in the dimension indicator data set of the s-th type of automobile parts; obtain the feature similarity between the a-th dimension indicator data sequence in the dimension indicator data set of the s-th type of automobile parts and the z-th type of automobile parts; then the method for determining the similarity between the feature change trends of the s-th type of automobile parts and the z-th type of automobile parts is:

[0022]

[0023] Where, QL s,z represents the similarity between the characteristic change trends of the s-th type of automobile parts and the z-th type of automobile parts; K represents the total number of all dimensional indicator data sequences in the dimensional correlation sequence of any type of automobile parts; g s,zIndicates the degree of feature similarity between the target dimension index data sequence in the dimension index data set of the s-th type of automobile parts and the z-th type of automobile parts; g s,z,b WL represents the similarity between the feature of the bth dimension indicator data sequence in the dimension indicator data set of the sth model automobile parts and the zth model automobile parts; s WL represents the dimensional correlation between the target dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the s-th type of automobile parts; s,b WL represents the dimensional correlation between the bth dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the sth model of automobile parts; z WL represents the dimensional correlation between the target dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the z-th type of automobile parts; z,b It represents the dimensional correlation between the bth dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the zth model of automobile parts; exp() represents the exponential function with a natural constant as the base; | | represents taking the absolute value.

[0024] Preferably, the specific method of obtaining the target dimension indicator data sequence in the dimension indicator data set of the s-th type of automobile parts includes:

[0025] The dimensional indicator data sequence with the maximum value of the dimensional correlation between the dimensional indicator data sequence and the transaction price data sequence in the dimensional indicator data set of the s-th model of automobile parts is used as the target dimensional indicator data sequence in the dimensional indicator data set of the s-th model of automobile parts.

[0026] Preferably, the method of obtaining the feature similarity between the ath dimension indicator data sequence in the dimension indicator data set of the sth type of automobile parts and the zth type of automobile parts includes the following specific methods:

[0027] Obtain the dimensional correlation sequence of the s-th type of automobile parts and the dimensional correlation sequence of the z-th type of automobile parts; then the method for calculating the feature similarity between the a-th dimensional indicator data sequence in the dimensional indicator data set of the s-th type of automobile parts and the z-th type of automobile parts is:

[0028]

[0029] Where g s,z,a represents the degree of feature similarity between the ath dimension indicator data sequence in the dimension indicator data set of the sth model automobile parts and the zth model automobile parts; ρ s represents the dimensional correlation sequence of the s-th model of automobile parts; ρz represents the dimensional correlation sequence of the z-th type of automobile parts; COSS(ρ s ,ρ z ) represents the cosine similarity between the dimensional correlation sequence of the s-th type of automobile parts and the dimensional correlation sequence of the z-th type of automobile parts; WL s,a WL represents the dimensional correlation between the ath dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the sth model of automobile parts; z,a Represents the dimensional correlation between the ath dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the zth model automobile parts.

[0030] Preferably, the specific method for obtaining the dimensional correlation sequence of the s-th type of automobile parts and the dimensional correlation sequence of the z-th type of automobile parts is:

[0031] The set consisting of the dimensional correlations between all dimensional indicator data sequences and the transaction price data sequences in the dimensional indicator data set of the s-th model of automobile parts is recorded as the dimensional correlation sequence of the s-th model of automobile parts; the set consisting of the dimensional correlations between all dimensional indicator data sequences and the transaction price data sequences in the dimensional indicator data set of the z-th model of automobile parts is recorded as the dimensional correlation sequence of the z-th model of automobile parts.

[0032] Preferably, the method of obtaining the compressed dimensional index data set of automobile parts according to the similarity of the characteristic change trends between any two models of automobile parts includes the following specific methods:

[0033] Preset a threshold If the similarity between the feature change trends of the first model auto parts and the second model auto parts is greater than the threshold The first type of auto parts is used as the target type of auto parts, and the dimension index data set of the second type of auto parts is differentially encoded and compressed. If the similarity of the feature change trend between the first type of auto parts and the second type of auto parts is less than or equal to the threshold The second model of auto parts is used as the target model of auto parts, and the dimension index data set of the first model of auto parts is not compressed; if the similarity of the feature change trend between the target model of auto parts and the third model of auto parts is greater than the threshold The dimensional index data set of the third model of automobile parts is differentially encoded and compressed. If the similarity of the feature change trend between the target model automobile parts and the third model automobile parts is less than or equal to the threshold Then the third type of automobile parts is taken as the target type of automobile parts; and so on, until all types of automobile parts are traversed; thereby, the dimensional indicator data set of all types of automobile parts after compression is obtained, and recorded as the dimensional indicator data set of compressed automobile parts.

[0034] The beneficial effects of the technical solution of the present invention are as follows: the present invention obtains the degree of similarity in the characteristic change trends between any two models of automobile parts based on the dimensional correlation between each dimensional indicator data sequence and the transaction price data sequence in the dimensional indicator data set of any two models of automobile parts, thereby accurately reflecting the degree of influence of multi-dimensional influencing factors on the price change stages of different types of automobile parts; obtains the compressed dimensional indicator data set of automobile parts based on the degree of similarity in the characteristic change trends between any two models of automobile parts; thereby differentially coding and compressing model automobile parts with similar influence degrees, completing the optimization adjustment of the differential coding compression algorithm, and improving the accuracy and efficiency of data retrieval in the automobile parts valuation and recovery process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 The present invention is a flowchart of the steps of a data optimization retrieval method in the process of automobile parts valuation and recovery. DETAILED DESCRIPTION

[0037] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a data optimization retrieval method for automotive parts valuation and recycling, as proposed by the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0038] Unless defined otherwise, all 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.

[0039] The following describes in detail a specific scheme of a data optimization retrieval method in the process of automobile parts valuation and recycling provided by the present invention in conjunction with the accompanying drawings.

[0040] See also Figure 1 , which shows a flowchart of a data optimization retrieval method in an automobile parts valuation and recycling process provided by one embodiment of the present invention, the method comprising the following steps:

[0041] Step S001: Obtain dimension indicator data sets and transaction price data series of several types of automobile parts.

[0042] It should be noted that the price changes shown by the transaction price time series data of individual auto parts generally show a downward trend. The price changes of each transaction node are mainly affected by other dimensional indicator data, but the degree of influence of different dimensional indicator data varies at different stages of the price change curve. Therefore, the differential coding compression process is adjusted according to the changing characteristics of the influence degree to achieve data optimization retrieval processing in the subsequent auto parts valuation and recovery process.

[0043] Specifically, we first need to collect dimension indicator data sets and transaction price data series for several types of auto parts. The specific process is as follows:

[0044] For any model of auto parts, obtain the transaction price, service life, maintenance status, degree of wear and appearance integrity of the auto parts of this model at the corresponding transaction time, and record the time series data formed by the transaction price, service life, maintenance status, degree of wear and appearance integrity of all auto parts of this model as the transaction price data series, service life data series, maintenance status data series, degree of wear data series and appearance integrity data series of the auto parts of this model respectively; record the set consisting of the service life data series, maintenance status data series, degree of wear data series and appearance integrity data series of the auto parts of this model as the dimensional indicator data set of the auto parts of this model; the service life data series, maintenance status data series, degree of wear data series and appearance integrity data series of the auto parts of this model are collectively referred to as dimensional indicator data series.

[0045] At this point, the above method has obtained the dimensional indicator data set and transaction price data series of several types of automobile parts.

[0046] Step S002: Obtain the dimensional correlation between each dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any model of automobile parts.

[0047] It should be noted that since the obtained dimensional indicator data sets have different change characteristics, that is, the change trends of different dimensional indicator data sequences and their transaction price data sequences are inconsistent, it shows that the decisive factors affecting the transaction prices at the transaction nodes of the same model of auto parts have different influence proportions of different dimensional indicator data. However, in general, there is an obvious degree of consistency in the change rates of the corresponding data segments of different dimensional indicator data sequences and the different data segments of the transaction price data sequences. Therefore, the dimensional correlation between the single indicator dimensional data sequence and the transaction price data sequence can be obtained.

[0048] A parameter w is preset, wherein this embodiment is described by taking w=15 as an example, and this embodiment does not make any specific limitation, wherein w is determined according to specific implementation conditions.

[0049] Specifically, for any model of auto parts, the transaction price data sequence and the ath dimension indicator data sequence in the dimension indicator data set of the model of auto parts are evenly divided into a number of dimension data segments and price data segments of size w; the absolute value of the difference between the i-th data and the i+1-th data in the j-th dimension data segment of the a-th dimension indicator data sequence is used as the indicator feature value of the i-th data in the j-th dimension data segment of the a-th dimension indicator data sequence; the absolute value of the difference between the i-th data and the i+1-th data in the j-th price data segment of the transaction price data sequence is used as the price feature value of the i-th data in the j-th price data segment of the transaction price data sequence; then the similarity calculation method between the j-th dimension data segment of the a-th dimension indicator data sequence and the j-th price data segment of the transaction price data sequence in the dimension indicator data set of any model of auto parts is:

[0050]

[0051] Where DQ a,j represents the similarity between the jth dimension data segment of the ath dimension indicator data sequence and the jth price data segment of the transaction price data sequence in the dimension indicator data set of any model of automobile parts; c a,j,i Represents the index characteristic value of the i-th data in the j-th dimension data segment of the a-th dimension index data sequence in the dimension index data set of any model of automobile parts; m a,j,i It represents the price characteristic value of the i-th data in the j-th price data segment of the transaction price data sequence of any model of automobile parts; w is the preset parameter; exp() represents the exponential function with a natural constant as the base; | | represents taking the absolute value.

[0052] At this point, the similarity between each dimension data segment of each dimension index data sequence in the dimension index data set of any model of automobile parts and each price data segment of the transaction price data sequence is obtained.

[0053] It should be noted that the similarity between each dimensional data segment of the dimensional indicator data sequence and each price data segment of the transaction price data sequence reflects the degree of similarity in a single dimensional segment. Different indicator dimensional data have different significances of similarity in the same dimensional data segment. The significance of similarity of the indicator data of the same dimension within the local neighborhood of the dimensional data segment should tend to be consistent. Therefore, the dimensional correlation between the single dimensional indicator data and the transaction price data is obtained based on the significance distribution characteristics of the similarity between the dimensional data segment and the price data segment.

[0054] Specifically, the calculation method for the dimensional correlation between the ath dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any model of automobile parts is:

[0055]

[0056] Where WL a Represents the dimensional correlation between the ath dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any model of automobile parts; n a Represents the total number of all dimensional data segments of the ath dimensional indicator data sequence in the dimensional indicator data set of any model of automobile parts; DQ a,j Indicates the similarity between the jth dimension data segment of the ath dimension indicator data sequence and the jth price data segment of the transaction price data sequence in the dimension indicator data set of any model of automobile parts; DQ a,b Represents the similarity between the bth dimension data segment of the ath dimension indicator data sequence and the bth price data segment of the transaction price data sequence in the dimension indicator data set of any model of automobile parts.

[0057] At this point, the dimensional correlation between each dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any model of automobile parts is obtained through the above method.

[0058] Step S003: Obtain the similarity between the characteristic change trends of each model of automobile parts and other models of automobile parts.

[0059] It should be noted that the spatial distribution characteristics of the eigenvectors can represent the similarity measurement between the dimensional indicator data set of automobile parts and the transaction price data series; the cosine similarity is used to measure the similarity of the eigenvectors. Due to the changing trend of the transaction price data series of automobile parts, its eigenvectors show clustering characteristics, that is, the cosine similarities of the eigenvectors of some automobile parts are spatially similar, while there is a large difference with the cosine similarities of the remaining eigenvectors. However, when the cosine similarity measures the similarity characteristics of multidimensional vectors, it often ignores the influence of other indicator data and dimensional data. Therefore, the consistency relationship between the cosine similarity of the eigenvectors of the dimensional indicator data set and the dimensional correlation between the dimensional indicator data series and the transaction price data series is used to obtain the similarity degree of the characteristic change trend between each model of automobile parts and other models of automobile parts.

[0060] Specifically, the set consisting of the dimensional correlations between all dimensional indicator data sequences and the transaction price data sequences in the dimensional indicator data set of the s-th type of automobile parts is recorded as the dimensional correlation sequence of the s-th type of automobile parts; the set consisting of the dimensional correlations between all dimensional indicator data sequences and the transaction price data sequences in the dimensional indicator data set of the z-th type of automobile parts is recorded as the dimensional correlation sequence of the z-th type of automobile parts; the dimensional indicator data sequence with the maximum dimensional correlation between the dimensional indicator data sequences and the transaction price data sequences in the dimensional indicator data set of the s-th type of automobile parts is used as the target dimensional indicator data sequence in the dimensional indicator data set of the s-th type of automobile parts; then the method for determining the similarity between the characteristic change trends of the s-th type of automobile parts and the z-th type of automobile parts is as follows:

[0061]

[0062]

[0063] Where g s,z,a represents the degree of feature similarity between the ath dimension indicator data sequence in the dimension indicator data set of the sth model automobile parts and the zth model automobile parts; ρ s represents the dimensional correlation sequence of the s-th model of automobile parts; ρ z represents the dimensional correlation sequence of the z-th type of automobile parts; COSS(ρ s ,ρ z ) represents the cosine similarity between the dimensional correlation sequence of the s-th type of automobile parts and the dimensional correlation sequence of the z-th type of automobile parts; WL s,a WL represents the dimensional correlation between the ath dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the sth model of automobile parts;z,a represents the dimensional correlation between the ath dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the zth model automobile parts; QL s,z represents the similarity between the characteristic change trends of the s-th type of automobile parts and the z-th type of automobile parts; K represents the total number of all dimensional indicator data sequences in the dimensional correlation sequence of any type of automobile parts; g s,z Indicates the degree of feature similarity between the target dimension index data sequence in the dimension index data set of the s-th type of automobile parts and the z-th type of automobile parts; g s,z,b WL represents the similarity between the feature of the bth dimension indicator data sequence in the dimension indicator data set of the sth model automobile parts and the zth model automobile parts; s WL represents the dimensional correlation between the target dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the s-th type of automobile parts; s,b WL represents the dimensional correlation between the bth dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the sth model of automobile parts; z WL represents the dimensional correlation between the target dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the z-th type of automobile parts; z,b It represents the dimensional correlation between the bth dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the zth model of automobile parts; exp() represents the exponential function with a natural constant as the base; | | represents taking the absolute value.

[0064] At this point, the similarity of the characteristic change trends between each model of automobile parts and other models of automobile parts is obtained through the above method.

[0065] Step S004: Obtain the compressed dimensional index data set of automobile parts.

[0066] Preset a threshold In this embodiment, This example is described as an example, and this embodiment is not specifically limited. It depends on the specific implementation situation.

[0067] Specifically, if the similarity between the feature change trends of the first model auto parts and the second model auto parts is greater than the threshold The first type of auto parts is used as the target type of auto parts, and the dimension index data set of the second type of auto parts is differentially encoded and compressed. If the similarity of the feature change trend between the first type of auto parts and the second type of auto parts is less than or equal to the threshold The second model of auto parts is used as the target model of auto parts, and the dimension index data set of the first model of auto parts is not compressed; if the similarity of the feature change trend between the target model of auto parts and the third model of auto parts is greater than the threshold The dimensional index data set of the third model of automobile parts is differentially encoded and compressed. If the similarity of the feature change trend between the target model automobile parts and the third model automobile parts is less than or equal to the threshold The third model of auto parts is used as the target model of auto parts; if the similarity between the feature change trend of the target model of auto parts and the fourth model of auto parts is greater than the threshold The dimensional index data set of the fourth model of automobile parts is differentially encoded and compressed. If the similarity of the feature change trend between the target model automobile parts and the fourth model automobile parts is less than or equal to the threshold The fourth type of automobile parts is taken as the target type of automobile parts; and so on, until all types of automobile parts are traversed; and then the dimensional indicator data set of all types of automobile parts after compression is obtained, and recorded as the dimensional indicator data set of compressed automobile parts.

[0068] The compressed dimensional index data set of automobile parts is stored in a database, and the data in the database is retrieved.

[0069] The data stored in the database is a collection of dimension indicator data of automobile parts with similar feature change trends, which is compressed together to reduce the amount of data during retrieval and increase retrieval efficiency.

[0070] Among them, differential coding compression is an existing technology and will not be described in detail in this implementation.

[0071] At this point, this embodiment is completed.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data optimization retrieval method in the process of automobile parts valuation and recycling, characterized in that: The method comprises the following steps: Obtaining a dimensional indicator data set and a transaction price data sequence for several types of automobile parts, wherein the dimensional indicator data set includes several dimensional indicator data sequences; Each dimension indicator data sequence in the transaction price data sequence and dimension indicator data set of each model of automobile parts is evenly divided into a number of dimension data segments and price data segments; based on the data difference between each dimension data segment of each dimension indicator data sequence and each price data segment of the transaction price data sequence, the similarity between each dimension data segment of each dimension indicator data sequence and each price data segment of the transaction price data sequence is obtained, including the specific method as follows: Where, Represents the first dimension index data set of any type of automobile parts The first dimension indicator data series The first dimension data segment and the first dimension data segment of the transaction price data sequence The similarity between price data segments; Represents the first dimension index data set of any type of automobile parts The first dimension indicator data series The first dimension in the data segment The indicator characteristic value of each data; The first row of the transaction price data series of any type of auto parts The first price data segment The price characteristic value of each data; are preset parameters; Obtain the dimensional correlation between each dimensional indicator data sequence and the transaction price data sequence based on the proportion of similarities between all dimensional data segments of each dimensional indicator data sequence and each price data segment of the transaction price data sequence; Obtain the similarity of the characteristic change trends between any two models of auto parts based on the dimensional correlation between each dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any two models of auto parts; According to the similarity of the feature change trends between any two models of automobile parts, a compressed dimensional indicator data set of automobile parts is obtained.

2. The data optimization retrieval method in the process of automobile parts valuation and recycling according to claim 1 is characterized in that: The acquisition The first dimension indicator data series The first dimension in the data segment The specific method of the indicator characteristic value of each data is as follows: The first The first dimension indicator data series The first dimension in the data segment The data and The absolute value of the difference between the data is taken as the The first dimension indicator data series The first dimension in the data segment The indicator characteristic value of each data point.

3. The data optimization retrieval method in the process of automobile parts valuation and recycling according to claim 1 is characterized in that: The first The first price data segment The specific method for the price characteristic value of each data is: The transaction price data series The first price data segment The data and The absolute value of the difference between the data is used as the first The first price data segment The price feature value of each data point.

4. The data optimization retrieval method in the process of automobile parts valuation and recycling according to claim 1 is characterized in that: The specific formula for obtaining the dimensional correlation between each dimensional indicator data sequence and the transaction price data sequence based on the ratio of similarities between all dimensional data segments of each dimensional indicator data sequence and each price data segment of the transaction price data sequence is: Where, Represents the first dimension index data set of any type of automobile parts Dimensional correlation between the dimensional indicator data series and the transaction price data series; Represents the first dimension index data set of any type of automobile parts The total number of all dimension data segments of a dimension indicator data series; Represents the first dimension index data set of any type of automobile parts The first dimension indicator data series The first dimension data segment and the first dimension data segment of the transaction price data sequence The similarity between price data segments; Represents the first dimension index data set of any type of automobile parts The first dimension indicator data series The first dimension data segment and the first dimension data segment of the transaction price data sequence The similarity between price data segments.

5. The data optimization and retrieval method in the process of automobile parts valuation and recycling according to claim 1 is characterized in that: The specific method for obtaining the similarity of the characteristic change trends between any two models of automobile parts based on the dimensional correlation between each dimensional indicator data series and the transaction price data series in the dimensional indicator data set of any two models of automobile parts is as follows: Get the The target dimension indicator data sequence in the dimension indicator data set of the type of automobile parts; get the Type of auto parts and The dimensional index data set of the automobile parts model The degree of feature similarity between the dimension indicator data series; Type of auto parts and The method for determining the similarity of the characteristic change trends of automobile parts of different models is as follows: Where, Indicates the Type of auto parts and Similarity of the characteristic change trends of the auto parts of different models; Represents the total number of all dimension indicator data sequences in the dimension correlation sequence of any model of automobile parts; Indicates the Type of auto parts and The degree of feature similarity between target dimension indicator data sequences in the dimension indicator data set of various models of automobile parts; Indicates the Type of auto parts and The dimensional index data set of the automobile parts model The degree of feature similarity between the data series of the dimension indicators; Indicates the The dimensional correlation between the target dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the automobile parts of a certain model; Indicates the The dimensional index data set of the automobile parts model Dimensional correlation between the dimensional indicator data series and the transaction price data series; Indicates the The dimensional correlation between the target dimensional indicator data series and the transaction price data series in the dimensional indicator data set of the automobile parts of a certain model; Indicates the The dimensional index data set of the automobile parts model Dimensional correlation between the dimensional indicator data series and the transaction price data series; represents an exponential function with a natural constant as its base; Indicates taking the absolute value.

6. The data optimization retrieval method in the process of automobile parts valuation and recycling according to claim 5 is characterized in that: The acquisition The target dimension indicator data sequence in the dimension indicator data set of the automobile parts of a certain model includes the following specific methods: The first The dimension indicator data sequence with the maximum value of the dimension correlation between the dimension indicator data sequence and the transaction price data sequence in the dimension indicator data set of the model of automobile parts is taken as the first The target dimension indicator data sequence in the dimension indicator data set of various models of automobile parts.

7. The data optimization retrieval method in the process of automobile parts valuation and recycling according to claim 5 is characterized in that: The acquisition Type of auto parts and The dimensional index data set of the automobile parts model The degree of feature similarity between the dimension indicator data series, including the specific methods: Get the Dimensional correlation sequence of automobile parts of the first model The dimension correlation sequence of the automobile parts of the model; Type of auto parts and The dimensional index data set of the automobile parts model The calculation method of the feature similarity between the dimension indicator data series is: Where, Indicates the Type of auto parts and The dimensional index data set of the automobile parts model The degree of feature similarity between the data series of the dimension indicators; Indicates the Dimensional correlation sequence of various types of automobile parts; Indicates the Dimensional correlation sequence of various types of automobile parts; Indicates the The dimensional correlation sequence of the first model of automobile parts The cosine similarity between the dimensional correlation sequences of the various models of automobile parts; Indicates the The dimensional index data set of the automobile parts model Dimensional correlation between the dimensional indicator data series and the transaction price data series; Indicates the The dimension index data set of model automobile parts The dimensional correlation between the dimensional indicator data series and the transaction price data series.

8. The data optimization retrieval method in the process of automobile parts valuation and recycling according to claim 7 is characterized in that: The acquisition Dimensional correlation sequence of automobile parts of the first model The specific method for calculating the dimensional correlation sequence of a certain type of automobile parts is as follows: The first The set of dimensional correlations between all dimensional indicator data series and transaction price data series in the dimensional indicator data set of the type of automobile parts is recorded as Dimensional correlation sequence of automobile parts of a certain model; The set of dimensional correlations between all dimensional indicator data series and transaction price data series in the dimensional indicator data set of the type of automobile parts is recorded as Dimensional correlation sequence of various types of automobile parts.

9. The data optimization and retrieval method in the process of automobile parts valuation and recycling according to claim 1 is characterized in that: The method of obtaining a compressed dimensional index data set of automobile parts based on the similarity of the characteristic change trends between any two models of automobile parts includes the following specific methods: Preset a threshold If the similarity between the feature change trends of the first model auto parts and the second model auto parts is greater than the threshold , the first type of auto parts is used as the target type of auto parts, and the dimension index data set of the second type of auto parts is differentially encoded and compressed. If the similarity of the feature change trend between the first type of auto parts and the second type of auto parts is less than or equal to the threshold , the second model of auto parts is used as the target model of auto parts, and the dimension index data set of the first model of auto parts is not compressed; if the similarity of the feature change trend between the target model of auto parts and the third model of auto parts is greater than the threshold , then the dimensional index data set of the third model of automobile parts is differentially encoded and compressed. If the similarity of the feature change trend between the target model automobile parts and the third model automobile parts is less than or equal to the threshold , then the third type of automobile parts is taken as the target type of automobile parts; and so on, until all types of automobile parts are traversed; thereby, the dimensional indicator data set of all types of automobile parts after compression is obtained, and recorded as the dimensional indicator data set of compressed automobile parts.

Images