CN111652641A - Data processing method, device, equipment and computer readable storage medium - Google Patents
Data processing method, device, equipment and computer readable storage medium Download PDFInfo
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Abstract
The invention provides a data processing method, a device, equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring a data processing request sent by terminal equipment, wherein the data processing request comprises identification information of data to be processed; acquiring the data to be processed from a database according to the identification information of the data to be processed; calculating a target vector corresponding to the data to be processed; determining Euclidean distances between the target vector and each standard vector in a preset feature matrix; calculating target characteristic information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm; and sending the target pricing information to the terminal equipment for display, so that accurate target characteristic information corresponding to the data to be processed can be determined, the accuracy of the target characteristic information is improved, and a basis is provided for the use of subsequent target characteristic information.
Description
Technical Field
The present invention relates to the field of block chains, and in particular, to a data processing method, apparatus, device, and computer readable storage medium.
Background
With the rapid development of new-generation information technologies such as mobile internet, internet of things, cloud computing and the like, big data become new elements of social development, new engines of industrial development and new power for governing modernization. The data has great influence on the development of the whole industry, and great business opportunities can be obtained by reasonably utilizing the big data; data has become a necessary trend as a new asset. When data is traded and circulated as an asset, the following problems are mainly considered:
one is data rights. As a special digital resource, the data rights include ownership, usage rights, copyable rights, forgetting rights, and the like. Some data can be stored permanently while others are only suitable for short-term use, and establishment of data rights requires innovation in both legal architecture and business models.
The second is the data value. Data pricing is very complex due to differences in data type, real-time, reliability, quantity, quality, format, availability, and degree of cross-border. In the aspect of standard specification, a standardized pricing model and a standardized pricing strategy are unified, so that a reasonable data price range is formed; in the aspect of market mechanism, a neutral, credible and large-scale transaction platform can form a relatively reasonable data price system through game of the supply and demand parties.
And thirdly, the data quality. To some extent, compared with other industrial products, the data production threshold is lower, and unscientific, unreal, unreliable and unverified data cannot be traded at present. Data quality and credibility are one of the important factors for determining the future sustainable development of data circulation and transaction.
And fourthly, data security. In the big data era, information of everyone is at risk of possible leakage, and information security is a non-negligible problem of national security strategy. How to realize the protection of national security, citizen privacy and business privacy in the data circulation and transaction process is a hot topic which is widely concerned by the society at present.
Based on the above premise, how to more accurately and comprehensively obtain the target feature information of the enterprise becomes a technical problem to be solved urgently, wherein the target feature information may specifically include pricing information. For example, in practical applications, when a certain enterprise is broken, in order to be able to adopt a more appropriate processing manner for the enterprise, it is necessary to accurately determine the target characteristic information of the broken enterprise.
Disclosure of Invention
The invention provides a data processing method, a data processing device, data processing equipment and a computer readable storage medium, which are used for solving the technical problem that the prior art cannot accurately acquire target characteristic information of an enterprise.
A first aspect of the present invention provides a data processing method, including:
acquiring a data processing request sent by terminal equipment, wherein the data processing request comprises identification information of data to be processed;
acquiring the data to be processed from a database according to the identification information of the data to be processed;
calculating a target vector corresponding to the data to be processed;
determining Euclidean distances between the target vector and each standard vector in a preset feature matrix;
calculating target characteristic information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm;
sending the target pricing information to the terminal equipment for display;
the calculating the target characteristic information corresponding to the data to be processed according to the Euclidean distance, the preset reference price and the preset pricing algorithm comprises the following steps:
sorting the Euclidean distances of the standard vectors according to a preset sorting rule to obtain sorted Euclidean distances;
and calculating target characteristic information corresponding to the data to be processed according to the average value of the first K Euclidean distances in the sorted Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
Another aspect of the present invention provides a data processing apparatus comprising:
the request acquisition module is used for acquiring a data processing request sent by the terminal equipment, wherein the data processing request comprises identification information of data to be processed;
the acquisition module is used for acquiring the data to be processed from a database according to the identification information of the data to be processed;
the target vector calculation module is used for calculating a target vector corresponding to the data to be processed;
the Euclidean distance determining module is used for determining Euclidean distances between the target vector and each standard vector in a preset feature matrix;
the target characteristic information determining module is used for calculating target characteristic information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm;
the display module is used for sending the target pricing information to the terminal equipment for display;
the target characteristic information determination module includes:
the sorting unit is used for sorting the Euclidean distances of the standard vectors according to a preset sorting rule to obtain sorted Euclidean distances;
and the calculating unit is used for calculating the target characteristic information corresponding to the data to be processed according to the mean value of the first K Euclidean distances in the sorted Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
Yet another aspect of the present invention provides a data processing apparatus comprising: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to perform the data processing method of the first aspect by the processor.
Yet another aspect of the present invention is to provide a computer-readable storage medium having stored therein computer-executable instructions for implementing the data processing method according to the first aspect when the computer-executable instructions are executed by a processor.
According to the data processing method, the data processing device, the data processing equipment and the computer readable storage medium, data to be processed are obtained from a database according to a data processing request sent by terminal equipment, target vectors of the data to be processed are calculated, Euclidean distances between the target vectors and each standard vector in a preset feature matrix are calculated, and target feature information corresponding to the data to be processed is calculated according to the Euclidean distances, a preset reference price and a preset pricing algorithm. By carrying out vectorization on the data to be processed, determining the Euclidean distance between the target vector subjected to vectorization and the preset standard vector, and calculating the target characteristic information according to the preset reference price, the Euclidean distance and the preset pricing algorithm, the accurate target characteristic information corresponding to the data to be processed can be determined, and the accuracy of the obtained target characteristic information is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a schematic diagram of a network architecture on which the present invention is based;
fig. 2 is a schematic flowchart of a data processing method according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a data processing method according to a second embodiment of the present invention;
fig. 4 is a schematic flowchart of a data processing method according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a data processing apparatus according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of a data processing apparatus according to a fifth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other examples obtained based on the examples in the present invention are within the scope of the present invention.
In view of the above-mentioned technical problem that the prior art cannot accurately price assets of an enterprise, the present invention provides a data processing method, apparatus, device and computer-readable storage medium.
It should be noted that the data processing method, apparatus, device, and computer-readable storage medium provided in the present application may be applied in a scenario where characteristic information of any kind of enterprise target is determined.
Fig. 1 is a schematic diagram of a network architecture based on the present invention, and as shown in fig. 1, the network architecture based on the present invention at least includes: the system comprises a data processing device 1, a data server 2 and a terminal device 3, wherein the data processing device 1 is in communication connection with the data server 2 and the terminal device 3 respectively, and the data processing device 1 is written by adopting languages such as C/C + +, Java, Shell or Python; the terminal device 3 may be a desktop computer, a tablet computer, or the like. The data server 2 may be a cloud server or a server cluster, and a large amount of data is stored therein.
A further network architecture on which the invention is based comprises at least a data processing device and a blockchain. The block chain comprises a plurality of nodes, and each node can upload digital asset case information to the block chain, wherein the digital asset case information comprises but is not limited to enterprise bankruptcy recombination digital asset pricing experience sharing and management cases, related asset value information, related asset historical valuation information, ownership historical change information of related assets, debtor information, market pricing rules of related digital assets, market pricing reference standards of related digital assets, macroscopic economy and market conditions, enterprise cash flow conditions, enterprise other fixed asset conditions and other enterprise bankruptcy recombination digital asset pricing experience sharing and management update information to the block chain. Accordingly, the data processing device can acquire the digital asset case information from the blockchain, and can further determine the feature matrix according to the digital asset case information.
Fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present invention, and as shown in fig. 2, the data processing method includes:
102, acquiring data to be processed from a database according to the identification information of the data to be processed;
103, calculating a target vector corresponding to the data to be processed;
104, determining Euclidean distances between the target vector and each standard vector in a preset feature matrix;
and 105, calculating pricing information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm.
And 106, sending the target pricing information to the terminal equipment for display.
The execution subject of the present embodiment is a data processing apparatus. In order to determine the data to be processed of the data to be processed, the data to be processed needs to be acquired first. Specifically, a data processing request sent by the terminal device may be obtained, where the data processing request includes identification information of data to be processed, and the data to be processed is obtained from the database according to the identification information of the data to be processed. The data to be processed may be digitized asset information of a bankruptcy enterprise, or may be asset information of any other mechanism, which is not limited herein. And calculating a target vector corresponding to the data to be processed. Specifically, the type of the data to be processed may be determined first, and if the data to be processed is continuous data, discretization may be performed on the digitalized asset information first, and the data to be processed after discretization is labeled, so as to obtain a target vector corresponding to the data to be processed; if the data is discrete data, the data to be processed can be directly labeled to obtain a target vector corresponding to the data to be processed. After the target vector corresponding to the data to be processed is determined, the euclidean distance between the target vector and each standard vector in the preset feature matrix can be determined. Specifically, the feature matrix may include a plurality of standard vectors, and therefore, for each standard vector, the euclidean distance between the target vector and the standard vector may be calculated to obtain the euclidean distances corresponding to the number of the standard vectors. And further calculating target characteristic information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm. The preset reference price can be determined according to the market pricing standard of the related digital assets, and then the reference price is corrected according to the price of the similar assets. The target characteristic information may specifically be pricing information of the enterprise. Further, after the target characteristic information is obtained, the target characteristic information can be sent to the terminal device for displaying, so that the user can know the target characteristic information more intuitively.
According to the data processing method provided by the invention, the data to be processed is obtained, the target vector is calculated for the data to be processed, the Euclidean distance between the target vector and each standard vector in the preset feature matrix is calculated, and the target feature information corresponding to the data to be processed is calculated according to the Euclidean distance, the preset reference price and the preset pricing algorithm, so that the accurate target feature information corresponding to the data to be processed can be determined, and the accuracy of the target feature information is improved.
Further, on the basis of any of the above embodiments, the calculating target feature information corresponding to the to-be-processed data according to the euclidean distance, a preset reference price, and a preset pricing algorithm includes:
sorting the Euclidean distances of the standard vectors according to a preset sorting rule to obtain sorted Euclidean distances;
and calculating target characteristic information corresponding to the data to be processed according to the average value of the first K Euclidean distances in the sorted Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
In this embodiment, after determining the euclidean distances between the target vector and each standard vector in the preset feature matrix, since the number of the standard vectors in the feature matrix is multiple, multiple euclidean distances can be obtained. In order to further improve the accuracy of the target feature information, the euclidean distances of the standard vectors may be sorted according to a preset sorting rule to obtain sorted euclidean distances. The preset ordering rule may be an order from large to small, an order from small to large, or any other ordering rule, and the present invention is not limited herein. After the Euclidean distances are sequenced, the first K Euclidean distances in the sequenced Euclidean distances can be selected, the mean value of the first K Euclidean distances is calculated, and target characteristic information corresponding to the data to be processed is calculated according to the mean value, a preset reference price and a preset pricing algorithm, wherein K is not less than 2. Specifically, the pricing algorithm is shown in equation 1:
p＝w1*p1+w2*p2 (1)
wherein p is target feature information corresponding to the data to be processed, p1 is a preset reference price, p2 is an average value of the previous K distances, and w1 and w2 are preset weight parameters, respectively.
In the data processing method provided by this embodiment, the euclidean distances of the standard vectors are sorted according to a preset sorting rule to obtain sorted euclidean distances, and the target feature information corresponding to the data to be processed is calculated according to a mean value of the top K euclidean distances among the sorted euclidean distances, a preset reference price, and a preset pricing algorithm, so that the calculation accuracy of the target feature information can be further improved.
Fig. 3 is a schematic flow chart of a data processing method according to a second embodiment of the present invention, where on the basis of any one of the above embodiments, as shown in fig. 3, before determining euclidean distances between the target vector and each standard vector in a preset feature matrix, the method further includes:
Wherein, step 201 specifically includes: and acquiring prestored digital case information from the blockchain.
In this embodiment, in order to obtain pricing information corresponding to the digital assets to be processed, a feature matrix needs to be determined first. In particular, pre-stored digitized case information may be obtained from the blockchain. The block chain comprises a plurality of nodes, and each node can upload digital case information to the block chain, wherein the digital case information comprises but is not limited to enterprise bankruptcy recombination digital pricing experience sharing and management cases, related asset value information, related asset historical valuation information, ownership historical change information of related assets, debtor information, creditor information, market pricing rules of related digital assets, market pricing reference standards of related digital assets, macroscopic economy and market conditions, enterprise cash flow conditions, enterprise other fixed asset conditions and other enterprise bankruptcy recombination digital asset pricing experience sharing and management update information to the block chain. After the digitized case information is obtained, a feature matrix can be determined from the digitized case information.
In the data processing method provided by this embodiment, the digitized case information is acquired from the block chain, and the feature matrix is determined according to the digitized case information, so that a basis is provided for the calculation of the subsequent target feature information.
Fig. 4 is a schematic flow chart of a data processing method according to a third embodiment of the present invention, where on the basis of any of the above embodiments, as shown in fig. 4, the determining the feature matrix according to the digitized case information includes:
and 303, generating the feature matrix according to the feature vector corresponding to the at least one feature information.
In this embodiment, after acquiring the digitized case information from the blockchain, at least one feature information in the digitized case information may be extracted first. The characteristic information comprises at least one of asset value information, debtor business information, creditor information, enterprise asset condition, economic cycle, enterprise cash flow information and ownership historical change information. For each feature vector, it may be converted into a vector form for the convenience of subsequent calculations. Specifically, the feature information may be subjected to tagging processing, so as to obtain a feature vector corresponding to at least one piece of feature information. A feature matrix is generated from the at least one feature vector.
Specifically, on the basis of any one of the above embodiments, the performing tagging processing on the feature information to obtain a feature vector corresponding to the at least one piece of feature information includes:
determining the category of the feature information, wherein the feature information comprises continuous feature information and discrete feature information;
and performing labeling processing on the feature information according to the category of the feature information to obtain a feature vector corresponding to the at least one feature information.
In this embodiment, different feature information are different types, and therefore, in order to further improve the accuracy of the feature matrix, after at least one piece of feature information is acquired, the type of the feature information needs to be determined, where the feature information includes continuous feature information and discrete feature information. Correspondingly, after the type of the feature information is determined, according to the type, the feature information can be subjected to labeling processing by adopting a method corresponding to the type, and a feature vector corresponding to at least one feature information is obtained.
According to the data processing method provided by the embodiment, different processing methods are adopted for different types of feature information, so that the accuracy of the feature matrix can be further improved, and the calculation accuracy of the target feature information can be further improved.
Specifically, on the basis of any of the above embodiments, the performing tagging processing on the feature information according to the category of the feature information to obtain a feature vector corresponding to the at least one feature information includes:
if the feature information is continuous feature information, performing discretization operation on the continuous feature information, and performing labeling processing on the discretized continuous feature information to obtain a feature vector corresponding to the at least one feature information;
and if the characteristic information is discrete characteristic information, performing labeling processing on the characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information.
In this embodiment, the feature information may be continuous feature information, for example, the enterprise cash flow information is continuous feature information. For the continuous feature information, discretization operation can be performed on the continuous feature information to obtain discretized feature information, and then labeling processing can be directly performed on the discretized feature information to obtain at least one feature vector corresponding to the feature information. Correspondingly, the feature information may also be discretization feature information, and the discretization feature information may be directly subjected to labeling processing to obtain a feature vector corresponding to at least one feature information.
According to the data processing method provided by the embodiment, different processing methods are adopted for different types of feature information, so that the accuracy of the feature matrix can be further improved, and the calculation accuracy of pricing information can be further improved.
Fig. 5 is a schematic structural diagram of a data processing apparatus according to a fourth embodiment of the present invention, and as shown in fig. 5, the data processing apparatus includes:
a request obtaining module 41, configured to obtain a data processing request sent by a terminal device, where the data processing request includes identification information of data to be processed;
an obtaining module 42, configured to obtain the data to be processed from a database according to the identification information of the data to be processed;
a target vector calculation module 43, configured to calculate a target vector corresponding to the to-be-processed data;
the Euclidean distance determining module 44 is configured to determine Euclidean distances between the target vector and each standard vector in a preset feature matrix;
a target characteristic information determining module 45, configured to calculate target characteristic information corresponding to the to-be-processed data according to the euclidean distance, a preset reference price, and a preset pricing algorithm;
a display module 46, configured to send the target pricing information to the terminal device for display;
the target characteristic information determination module includes:
the sorting unit is used for sorting the Euclidean distances of the standard vectors according to a preset sorting rule to obtain sorted Euclidean distances;
and the calculating unit is used for calculating the target characteristic information corresponding to the data to be processed according to the mean value of the first K Euclidean distances in the sorted Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
The execution subject of the present embodiment is a data processing apparatus. In order to determine the data to be processed of the data to be processed, the data to be processed needs to be acquired first. Specifically, a data processing request sent by the terminal device may be obtained, where the data processing request includes identification information of data to be processed, and the data to be processed is obtained from the database according to the identification information of the data to be processed. The data to be processed may be digitized asset information of a bankruptcy enterprise, or may be asset information of any other mechanism, which is not limited herein. And calculating a target vector corresponding to the data to be processed. Specifically, the type of the data to be processed may be determined first, and if the data to be processed is continuous data, discretization may be performed on the digitalized asset information first, and the data to be processed after discretization is labeled, so as to obtain a target vector corresponding to the data to be processed; if the data is discrete data, the data to be processed can be directly labeled to obtain a target vector corresponding to the data to be processed. After the target vector corresponding to the data to be processed is determined, the euclidean distance between the target vector and each standard vector in the preset feature matrix can be determined. Specifically, the feature matrix may include a plurality of standard vectors, and therefore, for each standard vector, the euclidean distance between the target vector and the standard vector may be calculated to obtain the euclidean distances corresponding to the number of the standard vectors. And further calculating target characteristic information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm. The preset reference price can be determined according to the market pricing standard of the related digital assets, and then the reference price is corrected according to the price of the similar assets. The target characteristic information may specifically be pricing information of the enterprise. Further, after the target characteristic information is obtained, the target characteristic information can be sent to the terminal device for displaying, so that the user can know the target characteristic information more intuitively.
According to the data processing device provided by the invention, the data to be processed is obtained, the target vector is calculated for the data to be processed, the Euclidean distance between the target vector and each standard vector in the preset feature matrix is calculated, and the target feature information corresponding to the data to be processed is calculated according to the Euclidean distance, the preset reference price and the preset pricing algorithm, so that the accurate target feature information corresponding to the data to be processed can be determined.
Further, on the basis of any one of the above embodiments, the apparatus further includes:
the digital case information acquisition module is used for acquiring prestored digital case information;
and the characteristic matrix determining module is used for determining the characteristic matrix according to the digital case information.
Further, on the basis of any of the above embodiments, the digital case information obtaining module is specifically configured to obtain pre-stored digital case information from the blockchain.
Further, on the basis of any of the above embodiments, the feature matrix determination module includes:
the characteristic information extraction unit is used for extracting at least one piece of characteristic information in the digital case information;
the labeling processing unit is used for performing labeling processing on the feature information aiming at each feature information to obtain a feature vector corresponding to the at least one feature information;
and the generating unit is used for generating the characteristic matrix according to the characteristic vector corresponding to the at least one piece of characteristic information.
Further, on the basis of any of the above embodiments, the characteristic information includes at least one of asset value information, debtor business information, creditor information, enterprise asset status, economic cycle, enterprise cash flow information, and ownership history change information.
Further, on the basis of any of the above embodiments, the labeling processing unit is specifically configured to:
determining the category of the feature information, wherein the feature information comprises continuous feature information and discrete feature information;
and performing labeling processing on the feature information according to the category of the feature information to obtain a feature vector corresponding to the at least one feature information.
Further, on the basis of any of the above embodiments, the labeling processing unit is specifically configured to:
if the feature information is continuous feature information, performing discretization operation on the continuous feature information, and performing labeling processing on the discretized continuous feature information to obtain a feature vector corresponding to the at least one feature information;
and if the characteristic information is discrete characteristic information, performing labeling processing on the characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information.
Fig. 6 is a schematic structural diagram of a data processing apparatus according to a fifth embodiment of the present invention, and as shown in fig. 6, the data processing apparatus includes: a memory 51, a processor 52;
a memory 51; a memory 51 for storing instructions executable by the processor 52;
wherein the processor 52 is configured to execute the data processing method according to any of the above embodiments by the processor 52.
The invention further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium is used for implementing the data processing method according to any one of the above embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A data processing method, comprising:
acquiring a data processing request sent by terminal equipment, wherein the data processing request comprises identification information of data to be processed;
acquiring the data to be processed from a database according to the identification information of the data to be processed;
calculating a target vector corresponding to the data to be processed;
determining Euclidean distances between the target vector and each standard vector in a preset feature matrix;
calculating target characteristic information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm;
sending the target pricing information to the terminal equipment for display;
the calculating the target characteristic information corresponding to the data to be processed according to the Euclidean distance, the preset reference price and the preset pricing algorithm comprises the following steps:
sorting the Euclidean distances of the standard vectors according to a preset sorting rule to obtain sorted Euclidean distances;
and calculating target characteristic information corresponding to the data to be processed according to the average value of the first K Euclidean distances in the sorted Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
2. The method according to claim 1, wherein before determining the euclidean distance between the target vector and each standard vector in the preset feature matrix, the method further comprises:
acquiring prestored digital case information;
and determining the feature matrix according to the digital case information.
3. The method of claim 2, wherein the obtaining pre-stored digitized case information comprises:
and acquiring prestored digital case information from the blockchain.
4. The method of claim 2 or 3, wherein determining the feature matrix from the digitized case information comprises:
extracting at least one piece of characteristic information in the digital case information;
labeling the characteristic information aiming at each characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information;
and generating the feature matrix according to the feature vector corresponding to the at least one feature information.
5. The method of claim 4, wherein the characteristic information comprises at least one of asset value information, debtor business information, creditor information, enterprise asset status, economic cycles, enterprise cash flow information, and historical change of ownership information.
6. The method according to claim 4, wherein the labeling the feature information to obtain a feature vector corresponding to the at least one feature information includes:
determining the category of the feature information, wherein the feature information comprises continuous feature information and discrete feature information;
and performing labeling processing on the feature information according to the category of the feature information to obtain a feature vector corresponding to the at least one feature information.
7. The method according to claim 6, wherein the labeling the feature information according to the category of the feature information to obtain a feature vector corresponding to the at least one feature information includes:
if the feature information is continuous feature information, performing discretization operation on the continuous feature information, and performing labeling processing on the discretized continuous feature information to obtain a feature vector corresponding to the at least one feature information;
and if the characteristic information is discrete characteristic information, performing labeling processing on the characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information.
8. A data processing apparatus, comprising:
the request acquisition module is used for acquiring a data processing request sent by the terminal equipment, wherein the data processing request comprises identification information of data to be processed;
the acquisition module is used for acquiring the data to be processed from a database according to the identification information of the data to be processed;
the target vector calculation module is used for calculating a target vector corresponding to the data to be processed;
the Euclidean distance determining module is used for determining Euclidean distances between the target vector and each standard vector in a preset feature matrix;
the target characteristic information determining module is used for calculating target characteristic information corresponding to the data to be processed according to the Euclidean distance, a preset reference price and a preset pricing algorithm;
the display module is used for sending the target pricing information to the terminal equipment for display;
the target characteristic information determination module includes:
the sorting unit is used for sorting the Euclidean distances of the standard vectors according to a preset sorting rule to obtain sorted Euclidean distances;
and the calculating unit is used for calculating the target characteristic information corresponding to the data to be processed according to the mean value of the first K Euclidean distances in the sorted Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
9. A data processing apparatus, characterized by comprising: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to perform the data processing method of any one of claims 1-7 by the processor.
10. A computer-readable storage medium, having stored thereon computer-executable instructions for implementing the data processing method of any one of claims 1 to 7 when executed by a processor.
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| CN202010475938.4A CN111652641B (en) | 2020-05-29 | 2020-05-29 | Data processing method, device, equipment and computer readable storage medium |
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