CN111652641B - 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 distance 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 the 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 foundation is provided for the use of the subsequent target characteristic information.
Description
Technical Field
The present invention relates to the field of blockchain, 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 and cloud computing, big data has become a new element of social development, a new engine of industrial development and a new power for treating modernization. The data has great influence on the development of the whole industry, and a larger business opportunity can be obtained by reasonably utilizing the big data; data has become a necessary trend as a new asset. When data is transacted and circulated as an asset, the following problems are mainly considered:
first is data equity. As special digital resources, data rights include ownership, usage rights, replicable rights, forgetfulness rights, and the like. Some data may be permanently stored while others are suitable for short term use, and establishment of data rights requires innovation in both legal architecture and business model.
And secondly, 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-range. In the aspect of standard specification, unifying a standard pricing model and strategy, which is helpful for forming a reasonable data price range; in the aspect of market mechanism, a neutral, credible and large-scale transaction platform can form a relatively reasonable data price system through a supply and demand party game.
Third, data quality. To some extent, data production thresholds are low compared to other industrial products, and it is currently difficult to ensure that unscientific, unrealistic, unreliable, unverified data is not traded. Data quality and reliability are one of the important factors in determining the future sustainable development of data traffic and transactions.
Fourth, data security. In the big data age, everyone's information is at risk of possible leakage, and information security is a national security strategic problem that cannot be ignored. How to realize the protection of national security, citizen privacy and business secrets in the process of data circulation and transaction is a hot topic which is widely focused by society at present.
Based on the premise, how to obtain the target feature information of the enterprise more accurately and comprehensively becomes the technical problem to be solved, wherein the target feature information can specifically comprise pricing information. For example, when an enterprise breaks, in order to enable the enterprise to adopt a more appropriate processing manner, the target feature information of the broken enterprise needs to be accurately determined.
Disclosure of Invention
The invention provides a data processing method, a device, 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 enterprises.
A first aspect of the present invention provides 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 distance 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;
the target pricing information is sent to the terminal equipment for display;
the calculating the target feature 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 distance of each standard vector according to a preset sorting rule to obtain the sorted Euclidean distance;
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 ordered 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 the 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 the Euclidean distance 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 feature information determining module includes:
the sorting unit is used for sorting the Euclidean distance of each standard vector according to a preset sorting rule to obtain the sorted Euclidean distance;
and the calculating unit is used for calculating the target characteristic information corresponding to the data to be processed according to the average value of the first K Euclidean distances in the ordered Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
A further 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.
A further aspect of the invention provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, are adapted to carry out the data processing method according to the first aspect.
According to the data processing method, the device, the equipment and the computer readable storage medium, data to be processed is obtained from a database according to a data processing request sent by a terminal device, target vector calculation is carried out on the data to be processed, euclidean distance between the target vector and each standard vector in a preset feature matrix is calculated, and target feature information corresponding to the data to be processed is calculated according to the Euclidean distance, a preset reference price and a preset pricing algorithm. The vectorization processing is carried out on the data to be processed, the Euclidean distance between the vectorized target vector and the preset standard vector is determined, and the target characteristic information is calculated according to the preset reference price, the Euclidean distance and the preset pricing algorithm, so that 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 embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a schematic diagram of a network architecture on which the present invention is based;
FIG. 2 is a flow chart of a data processing method according to a first embodiment of the present invention;
FIG. 3 is a flow chart of a data processing method according to a second embodiment of the present invention;
fig. 4 is a flow chart of a data processing method according to a third embodiment of the present invention;
FIG. 5 is a schematic 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 device according to a fifth embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments obtained based on the embodiments in the present invention are within the scope of the protection of the present invention.
Aiming at the technical problem that the prior art cannot accurately price the assets of enterprises, the invention provides a data processing method, a data processing device, data processing equipment and a 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 of determining any kind of enterprise target characteristic information.
Fig. 1 is a schematic diagram of a network architecture according to the present invention, as shown in fig. 1, where the network architecture according to the present invention at least includes: the data processing device 1, the data server 2 and the terminal equipment 3, wherein the data processing device 1 is respectively connected with the data server 2 and the terminal equipment 3 in a communication way, and the data processing device 1 is written in languages such as C/C++, java, shell or Python; the terminal device 3 may be, for example, a desktop computer, a tablet computer, etc. The data server 2 may be a cloud server or a server cluster, in which a large amount of data is stored.
Yet another network architecture on which the present invention is based includes at least a data processing device and a blockchain. The blockchain includes a plurality of nodes, each of which can upload digital asset case information into the blockchain, wherein the digital asset case information includes, but is not limited to, enterprise bankruptcy recombinant digital asset pricing experience sharing and management cases, related asset value information, related asset history valuation information, ownership history change information of related assets, liability person information, creditor information, market pricing rules of related digital assets, market pricing reference standards of related digital assets, macro economy and market conditions, enterprise cash flow conditions, enterprise other fixed asset conditions, and other enterprise bankruptcy recombinant digital asset pricing experience sharing and management update information. Accordingly, the data processing apparatus may obtain the digitized asset case information from the blockchain, and may determine the feature matrix based on the digitized asset case information.
Fig. 2 is a flow chart of a data processing method according to a first embodiment of the present invention, as shown in fig. 2, where the data processing method includes:
step 101, acquiring a data processing request sent by a terminal device, wherein the data processing request comprises identification information of data to be processed;
102, acquiring the data to be processed from a database according to the identification information of the data to be processed;
step 103, calculating a target vector corresponding to the data to be processed;
104, determining Euclidean distance 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 step 106, the target pricing information is sent to the terminal equipment for display.
The execution body of the present embodiment is a data processing apparatus. In order to realize the determination 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 digital 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 can be determined first, if the data is continuous data, discretization operation can be performed on the data asset information first, and the data to be processed after the discretization operation is labeled 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, and a target vector corresponding to the data to be processed is obtained. 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, so that for each standard vector, the euclidean distance between the target vector and the standard vector may be calculated, to obtain the euclidean distance corresponding to the number of standard vectors. And further, target characteristic information corresponding to the data to be processed can be calculated according to the Euclidean distance, the preset reference price and the preset pricing algorithm. Wherein the preset reference price may be determined based on market pricing criteria for the associated digital asset and then corrected based on the price of the similar asset. The target characteristic information may specifically be pricing information of the enterprise. Further, after the target feature information is obtained, the target feature information can be sent to the terminal device for display, so that a user can more intuitively know the target feature information.
According to the data processing method, the data to be processed is obtained, the target vector is calculated on 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 one of the above embodiments, the calculating, according to the euclidean distance, a preset reference price, and a preset pricing algorithm, target feature information corresponding to the data to be processed includes:
sorting the Euclidean distance of each standard vector according to a preset sorting rule to obtain the sorted Euclidean distance;
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 ordered 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 distance between the target vector and each standard vector in the preset feature matrix, since the number of standard vectors in the feature matrix is plural, plural euclidean distances can be obtained. In order to further improve the accuracy of the target feature information, the euclidean distance of each standard vector can be subjected to sorting operation according to a preset sorting rule, and the sorted euclidean distance is obtained. The preset ordering rule may be a big-to-small ordering, a small-to-big ordering, or any other ordering rule, which is not limited herein. After the euclidean distances are ordered, the first K euclidean distances in the ordered euclidean distances can be selected, the average value of the first K euclidean distances is calculated, and target feature information corresponding to the data to be processed is calculated according to the average value, a preset reference price and a preset pricing algorithm, wherein K is not smaller than 2. Specifically, the pricing algorithm is as shown in equation 1:
p＝w1*p1+w2*p2 (1)
wherein, p is the target characteristic information corresponding to the data to be processed, p1 is a preset reference price, p2 is the average value of the first K distances, and w1 and w2 are preset weight parameters respectively.
According to the data processing method provided by the embodiment, the euclidean distance of each standard vector is sequenced according to the preset sequencing rule, the sequenced euclidean distance is obtained, and the target feature information corresponding to the data to be processed is calculated according to the average value of the first K euclidean distances in the sequenced euclidean distance, the preset reference price and the preset pricing algorithm, so that the calculation accuracy of the target feature information can be further improved.
Fig. 3 is a flow chart of a data processing method according to a second embodiment of the present invention, where, based on any of the foregoing embodiments, before determining the euclidean distance between the target vector and each standard vector in a preset feature matrix, as shown in fig. 3, the method further includes:
step 201, obtaining pre-stored digital case information;
step 202, determining the feature matrix according to the digitized case information.
The step 201 specifically includes: pre-stored digitized case information is obtained from the blockchain.
In this embodiment, in order to obtain pricing information corresponding to the digital asset to be processed, a feature matrix needs to be determined first. Specifically, pre-stored digitized case information may be obtained from a blockchain. The blockchain comprises a plurality of nodes, and each node can upload digital case information into the blockchain, 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 history valuation information, ownership history change information of related assets, debtor information, creditor information, market pricing rules of related digital assets, market pricing reference standards of related digital assets, macro 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. After the digitized case information is obtained, a feature matrix may be determined from the digitized case information.
According to the data processing method provided by the embodiment, the digitalized case information is obtained from the blockchain, and the feature matrix is determined according to the digitalized case information, so that a basis is provided for calculation of subsequent target feature information.
Fig. 4 is a flow chart of a data processing method according to a third embodiment of the present invention, where, based on any of the foregoing embodiments, as shown in fig. 4, the determining the feature matrix according to the digitized case information includes:
step 301, extracting at least one characteristic information in the digitized case information;
step 302, labeling the feature information according to each feature information to obtain a feature vector corresponding to the at least one feature information;
step 303, generating the feature matrix according to the feature vector corresponding to the at least one piece of feature information.
In this embodiment, after the digitized case information is obtained from the blockchain, at least one piece of characteristic information in the digitized case information may be extracted first. Wherein the characteristic information includes at least one of asset value information, debt business information, creditor information, business asset status, economic cycle, business cash flow information, ownership history change information. For each feature vector, it may be converted into a vector form for facilitating subsequent computation. Specifically, the feature information may be subjected to labeling processing, so as to obtain a feature vector corresponding to at least one feature information. A feature matrix is generated from the at least one feature vector.
Specifically, on the basis of any one of the foregoing embodiments, the labeling processing is performed on the feature information to obtain a feature vector corresponding to the at least one feature information, where the labeling processing includes:
determining the category of the characteristic information, wherein the characteristic information comprises continuous characteristic information and discrete characteristic information;
and labeling the characteristic information according to the category of the characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information.
In this embodiment, different feature information is of different types, so, in order to further improve the accuracy of the feature matrix, after at least one 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. Accordingly, after determining the type of the feature information, the feature information may be labeled according to the type by a method corresponding to the type, so as to obtain at least one feature vector corresponding to the feature information.
According to the data processing method provided by the embodiment, different processing methods are adopted for different types of characteristic information, so that the accuracy of the characteristic matrix can be further improved, and the calculation accuracy of target characteristic information can be further improved.
Specifically, on the basis of any one of the foregoing embodiments, the labeling processing is performed 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, where the labeling processing includes:
if the feature information is continuous feature information, discretizing the continuous feature information, and labeling the discretized continuous feature information to obtain a feature vector corresponding to the at least one feature information;
if the feature information is discrete feature information, labeling the feature information to obtain a feature vector corresponding to the at least one feature information.
In this embodiment, the feature information may be continuous feature information, for example, enterprise cash flow information is continuous feature information. For continuous characteristic information, discretization operation can be performed on the continuous characteristic information to obtain discretized characteristic information, and then label processing can be directly performed on the discretized characteristic information to obtain at least one characteristic vector corresponding to the characteristic information. Correspondingly, the feature information can also be discretized feature information, and the discretized feature information can be directly subjected to labeling processing to obtain at least one feature vector corresponding to the feature information.
According to the data processing method provided by the embodiment, different processing methods are adopted for different types of characteristic information, so that the accuracy of the characteristic 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, as shown in fig. 5, where the data processing apparatus includes:
a request acquisition module 41, configured to acquire 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 data to be processed;
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;
the target feature information determining module 45 is configured to calculate target feature information corresponding to the data to be processed 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 feature information determining module includes:
the sorting unit is used for sorting the Euclidean distance of each standard vector according to a preset sorting rule to obtain the sorted Euclidean distance;
and the calculating unit is used for calculating the target characteristic information corresponding to the data to be processed according to the average value of the first K Euclidean distances in the ordered Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2.
The execution body of the present embodiment is a data processing apparatus. In order to realize the determination 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 digital 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 can be determined first, if the data is continuous data, discretization operation can be performed on the data asset information first, and the data to be processed after the discretization operation is labeled 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, and a target vector corresponding to the data to be processed is obtained. 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, so that for each standard vector, the euclidean distance between the target vector and the standard vector may be calculated, to obtain the euclidean distance corresponding to the number of standard vectors. And further, target characteristic information corresponding to the data to be processed can be calculated according to the Euclidean distance, the preset reference price and the preset pricing algorithm. Wherein the preset reference price may be determined based on market pricing criteria for the associated digital asset and then corrected based on the price of the similar asset. The target characteristic information may specifically be pricing information of the enterprise. Further, after the target feature information is obtained, the target feature information can be sent to the terminal device for display, so that a user can more intuitively know the target feature information.
According to the data processing device, the data to be processed is obtained, the target vector is calculated on 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 foregoing embodiments, the apparatus further includes:
the digital case information acquisition module is used for acquiring prestored digital case information;
and the feature matrix determining module is used for determining the feature matrix according to the digitized case information.
Further, on the basis of any of the above embodiments, the digitized case information obtaining module is specifically configured to obtain pre-stored digitized case information from the blockchain.
Further, on the basis of any one of the foregoing embodiments, the feature matrix determining module includes:
a feature information extracting unit for extracting at least one feature information in the digitized case information;
the labeling processing unit is used for labeling the characteristic information aiming at each characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information;
and the generating unit is used for generating the feature matrix according to the feature vector corresponding to the at least one piece of feature information.
Further, on the basis of any one of the above embodiments, the characteristic information includes at least one of asset value information, debt and pedestrian information, creditor information, enterprise asset status, economic cycle, enterprise cash flow information, ownership history change information.
Further, on the basis of any one of the above embodiments, the labeling processing unit is specifically configured to:
determining the category of the characteristic information, wherein the characteristic information comprises continuous characteristic information and discrete characteristic information;
and labeling the characteristic information according to the category of the characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information.
Further, on the basis of any one of the above embodiments, the labeling processing unit is specifically configured to:
if the feature information is continuous feature information, discretizing the continuous feature information, and labeling the discretized continuous feature information to obtain a feature vector corresponding to the at least one feature information;
if the feature information is discrete feature information, labeling the feature information to obtain a feature vector corresponding to the at least one feature information.
Fig. 6 is a schematic structural diagram of a data processing device according to a fifth embodiment of the present invention, as shown in fig. 6, where the data processing device 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 perform the data processing method according to any of the embodiments described above by the processor 52.
Still another embodiment of the present invention provides a computer readable storage medium having stored therein computer executable instructions which when executed by a processor are configured to implement a data processing method as in any of the above embodiments.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the apparatus described above, which is not described herein again.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. A method of data processing, 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; the data to be processed is digital asset information of bankruptcy enterprises; the digital asset information comprises enterprise bankruptcy recombination digital asset pricing experience sharing and management cases, related asset value information, related asset history valuation information, ownership history change information of related assets, liability person information, creditor information, market pricing rules of related digital assets, market pricing reference standards of related digital assets, macro economy and market conditions, enterprise cash flow conditions and other fixed asset conditions of enterprises;
calculating a target vector corresponding to the data to be processed;
determining Euclidean distance 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; the preset reference price is determined according to market pricing standards of related digital assets, and is obtained by correcting the reference price according to the price of similar assets;
the target pricing information is sent to the terminal equipment for display;
the calculating the target feature 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 distance of each standard vector according to a preset sorting rule to obtain the sorted Euclidean distance;
calculating target characteristic information corresponding to the data to be processed according to the average value of the first K Euclidean distances in the ordered Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2;
the preset pricing algorithm formula is as follows: p=w1×p1+w2×p2; wherein, p is the target characteristic information corresponding to the data to be processed, p1 is a preset reference price, p2 is the average value of the first K Euclidean distances, and w1 and w2 are preset weight parameters respectively.
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, further comprises:
acquiring pre-stored digitized case information;
and determining the feature matrix according to the digitized case information.
3. The method of claim 2, wherein the obtaining pre-stored digitized case information comprises:
pre-stored digitized case information is obtained from the blockchain.
4. The method of claim 2 or 3, wherein the determining the feature matrix from the digitized case information comprises:
extracting at least one characteristic information in the digitized case information;
performing labeling processing on the feature information aiming at each piece of feature information to obtain a feature vector corresponding to at least one piece of feature information;
and generating the feature matrix according to the feature vector corresponding to the at least one piece of feature information.
5. The method of claim 4, wherein the characteristic information comprises at least one of asset value information, liability industry information, creditor information, business asset status, economic cycle, business cash flow information, ownership history change information.
6. The method of claim 4, wherein the labeling the feature information to obtain the feature vector corresponding to the at least one feature information includes:
determining the category of the characteristic information, wherein the characteristic information comprises continuous characteristic information and discrete characteristic information;
and labeling the characteristic information according to the category of the characteristic information to obtain a characteristic vector corresponding to the at least one characteristic information.
7. The method according to claim 6, wherein the labeling the feature information according to the category of the feature information to obtain the feature vector corresponding to the at least one feature information includes:
if the feature information is continuous feature information, discretizing the continuous feature information, and labeling the discretized continuous feature information to obtain a feature vector corresponding to the at least one feature information;
if the feature information is discrete feature information, labeling the feature information to obtain a feature vector corresponding to the at least one feature 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 the database according to the identification information of the data to be processed; the data to be processed is digital asset information of bankruptcy enterprises; the digital asset information comprises enterprise bankruptcy recombination digital asset pricing experience sharing and management cases, related asset value information, related asset history valuation information, ownership history change information of related assets, liability person information, creditor information, market pricing rules of related digital assets, market pricing reference standards of related digital assets, macro economy and market conditions, enterprise cash flow conditions and other fixed asset conditions of enterprises;
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 the Euclidean distance 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 feature information determining module includes:
the sorting unit is used for sorting the Euclidean distance of each standard vector according to a preset sorting rule to obtain the sorted Euclidean distance;
the computing unit is used for computing target characteristic information corresponding to the data to be processed according to the average value of the first K Euclidean distances in the ordered Euclidean distances, a preset reference price and a preset pricing algorithm, wherein K is not less than 2; the preset pricing algorithm formula is as follows: p=w1×p1+w2×p2; wherein, p is the target characteristic information corresponding to the data to be processed, p1 is a preset reference price, p2 is the average value of the first K Euclidean distances, and w1 and w2 are preset weight parameters respectively; the preset reference price is determined according to market pricing criteria of the associated digital asset and is corrected according to the price of the similar asset.
9. 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 any of claims 1-7 by the processor.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein computer executable instructions which, when executed by a processor, are adapted to implement the data processing method according to any of claims 1-7.
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| CN114463217B (en) * | 2022-02-08 | 2024-08-23 | 口碑(上海)信息技术有限公司 | Image processing method and device |
| CN119474194B (en) * | 2025-01-17 | 2025-04-08 | 卓望数码技术（深圳）有限公司 | Virtual data vectorization management method and device, electronic equipment and storage medium |
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