CN111160783A - Method and system for evaluating digital asset value and electronic equipment - Google Patents
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Abstract
The invention provides an evaluation method, a system and electronic equipment of digital asset value, relating to the technical field of asset evaluation and obtaining asset characteristic data and asset behavior data of digital assets to be evaluated and user data of endorsement users; calculating a combined feature vector of the digital assets to be evaluated according to the asset characteristic data, the asset behavior data and the user data; then inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the tag of the digital asset as output; and determining the value of the digital assets to be evaluated according to the scores of the tags of the digital assets to be evaluated. According to the method, the comprehensive characteristics of the assets are reflected more truly by analyzing the joint feature vector of the assets; and the value of the corresponding label of the asset is obtained by combining with the neural network model, so that the quantitative evaluation of the value of the digital asset can be realized, and the evaluation result is more accurate.
Description
Technical Field
The invention relates to the technical field of asset evaluation, in particular to a method and a system for evaluating digital asset value and electronic equipment.
Background
The essence of the data assets is that the data assets are used as economic resources to participate in the economic activities of the enterprises, the risks in the economic activities of the enterprises can be reduced and eliminated, reasonable bases are provided for the management control and scientific decision of the enterprises, and the economic benefits are expected to be brought to the enterprises. Only data which is subjected to asset management and has 'credibility' can form data assets, and value increment is realized through data use, recalculation, analysis models and the like. Therefore, the credibility is one of the important bases of the data asset value, and the quantification of the credibility directly influences the quantification of the data asset value.
Currently, there is no effective method to evaluate the value of various digital assets.
Disclosure of Invention
In view of this, the present invention provides a method, a system and an electronic device for evaluating a digital asset value, which can quantitatively evaluate the value of a digital asset and obtain a more accurate judgment on the asset value.
In a first aspect, an embodiment of the present invention provides a method for evaluating a digital asset value, where the method includes: acquiring asset characteristic data and asset behavior data of a digital asset to be evaluated, and user data of a user endorsing the digital asset to be evaluated; calculating a combined feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data; inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the label of the digital asset as output; and determining the value of the digital asset to be evaluated according to the score of the label of the digital asset to be evaluated.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the user data includes user characteristic data and user behavior data; the step of calculating the joint feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data includes: according to the asset characteristic data and the asset behavior data, calculating an asset characteristic vector of the digital asset to be evaluated; calculating a user characteristic vector of the user according to the user characteristic data and the user behavior data; and calculating the combined feature vector of the digital assets to be evaluated according to the asset feature vector and the user feature vector.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the step of calculating an asset feature vector of the digital asset to be evaluated according to the asset characteristic data and the asset behavior data includes: training a preset first initial neural network model by taking the asset characteristic data as input and the asset behavior data as output to obtain a trained first neural network model and first model parameters of the first neural network model; and determining the first model parameter as an asset feature vector of the digital asset to be evaluated.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the step of calculating the user feature vector of the user according to the user characteristic data and the user behavior data includes: training a preset second initial neural network model by taking the user characteristic data as input and the user behavior data as output to obtain a trained second neural network model and second model parameters of the second neural network model; and determining the second model parameter as the user feature vector of the user.
With reference to the third possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the step of training a preset second initial neural network model to obtain a trained second neural network model includes: and training a preset second initial neural network model by adopting a gradient descent method to obtain a trained second neural network model.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the dimensions of the asset feature vector and the user feature vector are the same, and the step of calculating the joint feature vector of the digital asset to be evaluated according to the asset feature vector and the user feature vector includes: and splicing the asset characteristic vector and the user characteristic vector according to a preset splicing mode to obtain a combined characteristic vector of the digital asset to be evaluated.
With reference to the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where there are a plurality of tags of the digital asset to be evaluated, and the step of determining the value of the digital asset to be evaluated according to the score of the tag of the digital asset to be evaluated includes: summing the top three scores of the tags of the digital assets to be evaluated; and determining the value obtained by summation as the value of the digital asset to be evaluated.
With reference to one of the first possible implementation manner to the fifth possible implementation manner of the first aspect, an embodiment of the present invention provides a seventh possible implementation manner of the first aspect, where the asset characteristic data includes: asset type, uplink date, content word number, picture number and network link number; the asset behavior data includes: the browsed number, endorsed number, reprinted number, searched number and cancelled endorsement number; the user characteristic data includes: gender, total number of assets, liveness, registration time, and number of personal tags; the user behavior data includes: browsing number, endorsement number, registered assets number, search times and endorsement cancellation number.
In a second aspect, an embodiment of the present invention further provides a system for evaluating a digital asset value, where the system includes: the data acquisition module is used for acquiring asset characteristic data and asset behavior data of the digital asset to be evaluated and user data of a user endorsing the digital asset to be evaluated; the combined feature vector calculation module is used for calculating the combined feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data; the label score calculation module is used for inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the label of the digital asset as output; and the asset value evaluation module is used for determining the value of the digital asset to be evaluated according to the score of the label of the digital asset to be evaluated.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes a processor and a memory, where the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the above-mentioned method for evaluating the value of a digital asset.
The embodiment of the invention has the following beneficial effects:
according to the method, the system and the electronic equipment for evaluating the digital asset value, provided by the embodiment of the invention, firstly, asset characteristic data and asset behavior data of a digital asset to be evaluated are obtained, and user data of a user endorsing the digital asset to be evaluated are obtained; then, calculating a combined feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data; then inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the label of the digital asset as output; and determining the value of the digital asset to be evaluated according to the score of the label of the digital asset to be evaluated. In the method, the asset characteristic data and the asset behavior data of the digital assets are combined with the user data of the user who endorses the assets to analyze the characteristic vector of the assets, so that the comprehensive characteristics of the assets can be reflected more truly; and in combination with deep learning of the neural network, association between the asset feature vector and the tag score is established, so that the quantitative score of the tag corresponding to the asset can be obtained, the value of the digital asset is quantitatively evaluated, and more accurate judgment on the asset value is obtained.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.
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 without creative efforts.
FIG. 1 is a schematic flow chart of a method for evaluating the value of a digital asset according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a learning training of a first neural network model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of learning and training of a second neural network model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a learning training of an asset evaluation model according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an application scenario of a digital asset value evaluation provided by an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a system for evaluating the value of a digital asset according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Icon: 61-a data acquisition module; 62-a joint feature vector calculation module; 63-a tag score calculation module; 64-asset value evaluation module; 71-a processor; 72-a memory; 73-bus; 74 — communication interface.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the advent of one wave and another wave of internet wave, digital asset management, i.e., the management of the authenticity and ownership of articles published on the network, is becoming increasingly important. People have urgent needs for the authenticity certification and ownership management of various digital assets, and the block chain technology which is established in recent years records the behavior records of users on the digital assets, so that the authenticity of the data can be well ensured, and the authenticity and the irreparable modification of the behaviors are determined due to the characteristics of the block chain.
The user usually has the actions of browsing, approving, commenting, sharing and the like of the digital assets, and the practical value of the digital assets can be accurately held through the heat analysis of the operation records of the user on each kind of the assets, the analysis of the user source and the analysis of the number of the assets and various operation liveness of the user, so that the author can conveniently recognize the publishing actions, clear and definite recognition can be provided for general readers, and reference is provided for various kinds of digital assets seen by the author.
In general, the analysis of digital assets relies on three aspects:
firstly, behaviors of browsing, approving, comment forwarding and the like occur in the assets;
secondly, analyzing the characteristics of the user;
and thirdly, all behaviors are recorded on the blockchain, and any behavior is traceable.
In view of the fact that no effective method can evaluate the value of various digital assets at present, the embodiment of the invention provides an evaluation method, a system and electronic equipment of the value of the digital assets. For the convenience of understanding the embodiment, a detailed description will be given to a method for evaluating the value of a digital asset disclosed in the embodiment of the present invention.
Referring to fig. 1, there is shown a schematic flow chart of a method for evaluating the value of a digital asset according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102: the method comprises the steps of obtaining asset characteristic data and asset behavior data of a digital asset to be evaluated, and obtaining user data of a user endorsing the digital asset to be evaluated.
Here, the digital assets may be various articles, pictures, videos, voices, and the like, which may be the above-mentioned resources published on the internet, or the above-mentioned resources uploaded to the blockchain network.
In order to evaluate digital assets more reasonably and accurately, it is necessary to accurately acquire actual data of the digital assets themselves. In this embodiment, the asset characteristic data and the asset behavior data of the digital asset, and the user data of the user who endorsed the digital asset to be evaluated are comprehensively considered.
In one possible implementation, the user data includes user characteristic data and user behavior data. Wherein the user characteristic data comprises: gender, total number of assets, liveness, registration time, and number of personal tags; the user behavior data includes: browsing number, endorsement number, registered assets number, search times and endorsement cancellation number. And, the asset characteristic data includes: asset type, uplink date, content word number, picture number and network link number; the asset behavior data includes: the number of browsed, endorsed, reprinted, searched and cancelled endorsements.
In other possible embodiments, the parameters corresponding to the user characteristic data, the user behavior data, the asset characteristic data and the asset behavior data may be further increased or deleted, and the selection of the parameters may be flexibly selected according to the actual requirements of the user, which is not limited herein.
Step S104: and calculating the joint feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data.
The combined feature vector is obtained by combining the asset characteristic data, the asset behavior data and the user data, the real characteristic of the digital asset to be evaluated can be reflected, and the error of evaluating the asset only by a single parameter can be effectively relieved.
In one possible implementation, the joint feature vector of the digital asset to be evaluated may be calculated by the following steps 21-23:
(21) and calculating the asset feature vector of the digital asset to be evaluated according to the asset characteristic data and the asset behavior data.
In at least one possible implementation, the association characteristics between the asset characteristic data and the asset behavior data may be obtained by training a neural network. Specifically, the asset characteristic data may be used as input, the asset behavior data may be used as output, and a preset first initial neural network model may be trained to obtain a trained first neural network model and a first model parameter of the first neural network model; then, the first model parameter is determined as the asset feature vector of the digital asset to be evaluated.
Referring to fig. 2, a schematic diagram illustrating a learning and training process of a first neural network model according to an embodiment of the present invention is shown, in the embodiment shown in fig. 2, the neural network model takes asset type, uplink date, number of content words, number of pictures and number of network links as input data; and, the first neural network model is trained using the browsed number, endorsed number, transferred number, searched number, and canceled endorsements as output data.
And when the training reaches a certain cycle number or training time, terminating the training to obtain a trained first neural network model and first model parameters of the first neural network model. Here, the first model parameter is determined as an asset feature vector of the digital asset to be evaluated, which is a 5-dimensional vector in the present embodiment.
(22) And calculating the user characteristic vector of the user according to the user characteristic data and the user behavior data.
Similarly, the correlation characteristics between the user characteristic data and the user behavior data can also be obtained by training the neural network. Specifically, the user characteristic data may be used as input, the user behavior data may be used as output, and a preset second initial neural network model is trained to obtain a trained second neural network model and second model parameters of the second neural network model; and determining the second model parameter as the user feature vector of the user.
Referring to fig. 3, a schematic diagram illustrating a learning and training process of a second neural network model according to an embodiment of the present invention is shown, in the implementation shown in fig. 3, the neural network model takes gender, total number of assets, liveness, registration time, and number of personal tags as inputs, and takes browsing number, endorsement number, registration asset number, search number, and endorsement cancellation number as outputs, and trains the second neural network model.
And when the training reaches a certain cycle number or training time, terminating the training to obtain a trained second neural network model and second model parameters of the second neural network model. Here, the second model parameter is determined as a user feature vector of the user, and in the present embodiment, the user feature vector is a 5-dimensional vector.
When the preset first initial neural network model and the preset second initial neural network model are trained, a gradient descent method can be adopted for training, wherein the gradient descent method is a method for finding the minimization of an objective function, and is a most common way for realizing the learning process of a machine learning algorithm, and particularly in a deep learning (neural network) model, the core of a Back Propagation (BP) method is to continuously optimize the weight parameters of each layer by using gradient descent.
Here, it should be noted that "first" and "second" of the above-mentioned "first neural network model", "second neural network model", "first model parameter", and "second model parameter" are used for descriptive purposes only, and are not to be construed as indicating or implying relative importance.
(23) And calculating the combined feature vector of the digital assets to be evaluated according to the asset feature vector and the user feature vector.
In one possible implementation manner, the asset feature vector and the user feature vector may be spliced according to a preset splicing manner to obtain a joint feature vector of the digital asset to be evaluated.
Taking the asset feature vector and the user feature vector respectively obtained in fig. 2 and fig. 3 as an example, the asset feature vector and the user feature vector have the same dimension, and are both five-dimensional vectors, so that the asset feature vector and the user feature vector can be naturally spliced in tandem, and the obtained combined feature vector is a ten-dimensional vector.
Step S106: inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset valuation model takes the feature vector of the digital asset as input and takes the score of the tag of the digital asset as output.
In actual practice, a large amount of historical data may be obtained in advance, including asset characteristic data and asset behavior data for a digital asset, user data for a user endorsing the asset, and a tag for the digital asset. Here, the tag of the digital asset may be a category to which it belongs, for example, taking an article as an example, and the tag thereof may be: science, history, military, sports, literature, and the like. In other possible scenarios, the tags may also be customized by the user, without limitation.
As shown in fig. 4, which is a schematic view of learning and training an asset evaluation model according to an embodiment of the present invention, in which a corresponding asset feature vector and a user feature vector are obtained in the neural network learning manner, so as to obtain a joint feature vector of a digital asset. And training to obtain a trained asset evaluation model by taking the feature vector of the digital asset as input and the score of the label of the digital asset as output in a mode of training a neural network.
For example, for a science and technology article, when the article is written into the block chain network for the first time, the score of the "science and technology" of the label is preset to be "1", when a user searches the science and technology article and clicks the article, the score of the article is adjusted accordingly, for example, the score of the "science and technology" of the label can be adjusted to be "1.5", and similarly, when the user endorses the article, the score of the article can be adjusted accordingly. In this way, the score of a property may be correlated with its property attribute data, property behavior data, and user data by user settings.
According to the mode, after a large amount of historical data, namely asset characteristic data, asset behavior data, user data and score data of each label of the digital assets are obtained, the asset evaluation model is trained by taking the feature vector of the digital assets as input and the score of the label of the digital assets as output. When the value of a certain digital asset needs to be evaluated, asset characteristic data, asset behavior data and user data of the digital asset can be acquired, asset characteristic vectors and user characteristic vectors are acquired through the first neural network model and the second neural network model respectively, then combined characteristic vectors of the digital asset are acquired, and the combined characteristic vectors are input into the asset evaluation model to obtain corresponding scores of all tags.
Step S108: and determining the value of the digital asset to be evaluated according to the score of the label of the digital asset to be evaluated.
In one possible implementation, the top three scores of the tags of the digital assets to be evaluated are summed; and then determining the value obtained by summation as the value of the digital asset to be evaluated.
For example, suppose the digital asset to be evaluated is an article, the asset evaluation model outputs 5 tags, and the scores are 12 for the a tag, 2 for the B tag, 30 for the C tag, 7 for the D tag, and 9 for the E tag. Thus, the first three scores with the highest score, namely the score of C label 30, the score of a label 12 and the score of E label 9, can be added to obtain the score of 51, which is the value of the digital asset to be evaluated.
Thus, for the same type or different types of digital assets, scores of the values thereof can be obtained in the above manner, so that the values of various digital assets can be quantitatively evaluated.
The method for evaluating the digital asset value, provided by the embodiment of the invention, comprises the steps of firstly obtaining asset characteristic data and asset behavior data of a digital asset to be evaluated, and carrying out endorsement on user data of the digital asset to be evaluated; then, calculating a combined feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data; then inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the label of the digital asset as output; and determining the value of the digital asset to be evaluated according to the score of the label of the digital asset to be evaluated. In the method, the asset characteristic data and the asset behavior data of the digital assets are combined with the user data of the user who endorses the assets to analyze the characteristic vector of the assets, so that the comprehensive characteristics of the assets can be reflected more truly; and in combination with deep learning of the neural network, association between the asset feature vector and the tag score is established, so that the quantitative score of the tag corresponding to the asset can be obtained, the value of the digital asset is quantitatively evaluated, and more accurate judgment on the asset value is obtained.
In order to better understand the evaluation method of the digital asset value, the embodiment is described by an example scenario.
Referring to fig. 5, an application scenario diagram of digital asset value evaluation is shown, in which a user a registers various types of digital assets under their own name on a digital asset management platform, and through an automatic uplink function of the digital asset management platform, the assets can be synchronized on distributed nodes in real time, so that the digital assets of the user can be safely stored in a private chain that cannot be changed by anyone, and good bases and proofs are established for subsequent user's subsequent use and sharing of copyright statement requirements.
After the user B and the user C see the assets of the user A, browsing and endorsement actions are carried out on the assets of the user A, new changes are brought to the assets behaviors by the actions, data are counted again by a background, and the tag value of the assets is analyzed again.
According to the flow, the background analyzes the label value of the asset of the user A again to obtain and update the label value. And the change of the tag value causes the value of the asset to change, and the background reckons the asset value again and records the asset value in the background.
In the embodiment, by using the distributed storage technology of the blockchain, the authenticity and the value of the underlying data of the value analysis can be ensured. And by considering the asset characteristics and the user characteristics, the obtained tag value can reflect the characteristics of the asset value more truly. Along with the change of the user behavior and the asset behavior, the tag value of the asset can also change, and the real-time property is better.
Corresponding to the above evaluation method of the digital asset value, an embodiment of the present invention further provides an evaluation system of the digital asset value, referring to fig. 6, which is a schematic structural diagram of the evaluation system of the digital asset value, as can be seen from fig. 6, the system includes a data acquisition module 61, a combined feature vector calculation module 62, a tag score calculation module 63, and an asset value evaluation module 64, which are connected in sequence, wherein the functions of each module are as follows:
the data acquisition module 61 is used for acquiring asset characteristic data and asset behavior data of the digital asset to be evaluated and user data of a user endorsing the digital asset to be evaluated;
a joint feature vector calculation module 62, configured to calculate a joint feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data, and the user data;
a tag score calculation module 63, configured to input the joint feature vector into a preset asset evaluation model, so as to obtain a score of a tag of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the label of the digital asset as output;
and the asset value evaluation module 64 is used for determining the value of the digital asset to be evaluated according to the score of the tag of the digital asset to be evaluated.
The evaluation system of the digital asset value provided by the embodiment of the invention comprises the steps of firstly, acquiring asset characteristic data and asset behavior data of a digital asset to be evaluated, and carrying out endorsement on the user data of the digital asset to be evaluated; then, calculating a combined feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data; then inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the label of the digital asset as output; and determining the value of the digital asset to be evaluated according to the score of the label of the digital asset to be evaluated. In the system, the asset characteristic data and the asset behavior data of the digital assets are combined with the user data of the user who endorses the assets to analyze the characteristic vector of the assets, so that the comprehensive characteristics of the assets can be reflected more truly; and in combination with deep learning of the neural network, association between the asset feature vector and the tag score is established, so that the quantitative score of the tag corresponding to the asset can be obtained, the value of the digital asset is quantitatively evaluated, and more accurate judgment on the asset value is obtained.
In one possible implementation, the user data includes user characteristic data and user behavior data, and the joint feature vector calculation module 62 is further configured to: according to the asset characteristic data and the asset behavior data, calculating an asset characteristic vector of the digital asset to be evaluated; calculating a user characteristic vector of the user according to the user characteristic data and the user behavior data; and calculating the combined feature vector of the digital assets to be evaluated according to the asset feature vector and the user feature vector.
In another possible implementation, the joint feature vector calculation module 62 is further configured to: training a preset first initial neural network model by taking the asset characteristic data as input and the asset behavior data as output to obtain a trained first neural network model and first model parameters of the first neural network model; and determining the first model parameter as an asset feature vector of the digital asset to be evaluated.
In another possible implementation, the joint feature vector calculation module 62 is further configured to: training a preset second initial neural network model by taking the user characteristic data as input and the user behavior data as output to obtain a trained second neural network model and second model parameters of the second neural network model; and determining the second model parameter as the user feature vector of the user.
In another possible implementation, the joint feature vector calculation module 62 is further configured to: and training a preset second initial neural network model by adopting a gradient descent method to obtain a trained second neural network model.
In another possible embodiment, the dimension of the asset feature vector is the same as the dimension of the user feature vector, and the joint feature vector calculation module 62 is further configured to: and splicing the asset characteristic vector and the user characteristic vector according to a preset splicing mode to obtain a combined characteristic vector of the digital asset to be evaluated.
In another possible embodiment, the digital asset to be evaluated has a plurality of tags, and the asset value evaluation module 64 is further configured to: summing the top three scores of the tags of the digital assets to be evaluated; and determining the value obtained by summation as the value of the digital asset to be evaluated.
In another possible embodiment, the asset characteristic data includes: asset type, uplink date, content word number, picture number and network link number; the asset behavior data includes: the browsed number, endorsed number, reprinted number, searched number and cancelled endorsement number; the user characteristic data includes: gender, total number of assets, liveness, registration time, and number of personal tags; the user behavior data includes: browsing number, endorsement number, registered assets number, search times and endorsement cancellation number.
The implementation principle and the generated technical effect of the evaluation system of the digital asset value provided by the embodiment of the invention are the same as those of the evaluation method embodiment of the digital asset value, and for the sake of brief description, corresponding contents in the evaluation method embodiment of the digital asset value can be referred to where the embodiment of the evaluation system of the digital asset value is not mentioned.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, which is a schematic structural diagram of the electronic device, where the electronic device includes a processor 71 and a memory 72, the memory 72 stores machine executable instructions that can be executed by the processor 71, and the processor 71 executes the machine executable instructions to implement the above-mentioned method for evaluating the digital asset value.
In the embodiment shown in fig. 7, the electronic device further comprises a bus 73 and a communication interface 74, wherein the processor 71, the communication interface 74 and the memory 72 are connected by the bus.
The Memory 72 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 74 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, etc. may be used. The bus may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 7, but this does not indicate only one bus or one type of bus.
The processor 71 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 71. The Processor 71 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and the processor 71 reads the information in the memory 72, and completes the steps of the evaluation method of the digital asset value of the foregoing embodiment in combination with the hardware thereof.
The embodiment of the present invention further provides a machine-readable storage medium, where the machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to implement the above-mentioned method for evaluating the value of the digital asset, and specific implementation may refer to the foregoing method embodiment, and is not described herein again.
The method for evaluating digital asset value, the system for evaluating digital asset value, and the computer program product of the electronic device according to the embodiments of the present invention include a computer-readable storage medium storing program codes, where instructions included in the program codes may be used to execute the method for evaluating digital asset value described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for evaluating the value of a digital asset, the method comprising:
acquiring asset characteristic data and asset behavior data of a digital asset to be evaluated, and user data of a user endorsing the digital asset to be evaluated;
calculating a combined feature vector of the digital assets to be evaluated according to the asset characteristic data, the asset behavior data and the user data;
inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the tag of the digital asset as output;
and determining the value of the digital assets to be evaluated according to the scores of the tags of the digital assets to be evaluated.
2. The method of claim 1, wherein the user data includes user characteristic data and user behavior data; the step of calculating the joint feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data comprises:
according to the asset characteristic data and the asset behavior data, calculating an asset characteristic vector of the digital asset to be evaluated;
calculating a user characteristic vector of the user according to the user characteristic data and the user behavior data;
and calculating the combined feature vector of the digital assets to be evaluated according to the asset feature vector and the user feature vector.
3. The method of claim 2, wherein the step of calculating the asset feature vector of the digital asset to be evaluated according to the asset characteristic data and the asset behavior data comprises:
training a preset first initial neural network model by taking the asset characteristic data as input and the asset behavior data as output to obtain a trained first neural network model and first model parameters of the first neural network model;
and determining the first model parameter as an asset feature vector of the digital asset to be evaluated.
4. The method of claim 2, wherein the step of calculating a user feature vector of the user based on the user characteristic data and the user behavior data comprises:
training a preset second initial neural network model by taking the user characteristic data as input and the user behavior data as output to obtain a trained second neural network model and second model parameters of the second neural network model;
determining the second model parameters as a user feature vector of the user.
5. The method for evaluating a digital asset value according to claim 4, wherein the step of training a preset second initial neural network model to obtain a trained second neural network model comprises:
and training a preset second initial neural network model by adopting a gradient descent method to obtain a trained second neural network model.
6. The method of claim 2, wherein the asset feature vector and the user feature vector have the same dimension, and the step of calculating the joint feature vector of the digital asset to be evaluated according to the asset feature vector and the user feature vector comprises:
and splicing the asset characteristic vector and the user characteristic vector according to a preset splicing mode to obtain a combined characteristic vector of the digital asset to be evaluated.
7. The method of claim 1, wherein there are a plurality of tags of the digital assets to be evaluated, and the step of determining the value of the digital assets to be evaluated according to the scores of the tags of the digital assets to be evaluated comprises:
summing the top three scores of the tags of the digital assets to be evaluated;
and determining the value obtained by summation as the value of the digital assets to be evaluated.
8. The method of valuing a digital asset according to any of claims 2 to 6, wherein said asset characteristic data includes: asset type, uplink date, content word number, picture number and network link number; the asset behavior data comprises: the browsed number, endorsed number, reprinted number, searched number and cancelled endorsement number;
the user characteristic data includes: gender, total number of assets, liveness, registration time, and number of personal tags; the user behavior data includes: browsing number, endorsement number, registered assets number, search times and endorsement cancellation number.
9. A system for evaluating the value of a digital asset, the system comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring asset characteristic data and asset behavior data of a digital asset to be evaluated and user data of a user endorsing the digital asset to be evaluated;
the joint feature vector calculation module is used for calculating a joint feature vector of the digital asset to be evaluated according to the asset characteristic data, the asset behavior data and the user data;
the label score calculation module is used for inputting the combined feature vector into a preset asset evaluation model to obtain the score of the label of the digital asset to be evaluated; the asset evaluation model takes the feature vector of the digital asset as input and takes the score of the tag of the digital asset as output;
and the asset value evaluation module is used for determining the value of the digital asset to be evaluated according to the score of the tag of the digital asset to be evaluated.
10. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of valuing a digital asset of any of claims 1 to 8.
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