A method for generating and quickly comparing semantic features of digital assets

Abstract

The present invention discloses a method for generating and quickly comparing semantic features of digital assets. The method for generating semantic features of the present invention comprises: 1) designating an identifying character in a digital asset Te as a mark of the digital asset Te; 2) dividing the digital asset Te into N data segments according to the mark; 3) calculating a digital feature value H_Te_i of each data segment Te_i as an index value of the data segment Te_i; 4) for each index value, selecting a number of characters in the index value in a predetermined pattern, combining the mark and the characters selected from each index value in a predetermined pattern to form ID_Te; 5) combining the index values ​​of each data segment into H_Combin, and performing Merkle tree operations with each index value as a leaf to obtain a Merkle root value Root_Te; 6) using ID_Te, H_Combin and Root_Te as the semantic features of the digital asset Te.

Description

Technical Field

[0001] The present invention relates to computer technology and the Internet field, and in particular to a method for generating and quickly comparing semantic features of digital assets in an Internet environment. Background Art

[0002] In modern society, various digital assets are released and traded on the Internet. After being released on the Internet, various digital assets will be at risk of being plagiarized, rewritten, and tampered with, causing losses to the owners and stakeholders of the digital assets. Rapid comparison of different digital assets to provide a basis for verification, evaluation, and assessment is the premise and guarantee for online release and trading.

[0003] Online comparison of digital assets is the basis of the business model for the operation of the digital world. Otherwise, there will be great risks in the spread, dissemination and trading of digital assets. Currently, the methods usually adopted to deal with such risks are to compare the signature of the entire digital asset or to compare the form of separation. However, this method is prone to cause misjudgment due to the protection particles being too large or too coarse, or to cause heavy communication or processing burdens, such as high resource consumption of full-text transmission and full-text processing in scenarios such as paper duplication checking. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the purpose of the present invention is to provide a method for rapid comparison of digital assets used for Internet digital asset confirmation or transaction, which is used for verification and evaluation of digital assets and is applicable to all application scenarios such as digital asset production, storage, publication, and transaction. It is a rapid comparison method for generating semantic features of digital assets used in public occasions, which can improve the convenience and non-controversy of computer Internet digital asset transactions.

[0005] In order to meet the needs of online evaluation and assessment of digital assets, feature setting in the digital asset generation stage is the prerequisite for protecting the storage, publication and transaction processes. At the same time, attention should be paid to the rapidity, convenience and non-third-party participation of the comparison process. Therefore, a rapid comparison method for digital assets was invented.

[0006] For the digital asset Te in the computer network world, after analyzing the characteristics of the digital asset Te or other constraints, select the identification character Flag contained in Te and divide the entire Te into several fragments, such as Figure 1 , the characteristic values ​​H_Te_i (i=1~N) are taken for the fragments in turn and combined into H_Combin and the Merk root value Root_Te is obtained, such as Figure 2 , select some characters from the H_Te_i string, record them as C_H_i, and combine C_H_i and Flag to form ID_Te, such as Figure 3, and then deposit H_Combin and ID_Te into the digital asset Te.

[0007] For a digital asset Te to be released or distributed, the semantic feature generation process of the entire digital asset Te is as follows: Figure 4 , the specific steps are as follows:

[0008] 1) One of the top N characters with the highest frequency of occurrence in the digital asset Te (for example, one of the top 5 characters with the highest frequency of occurrence) or an identifying character under business constraints is designated as the flag of the digital asset Te, wherein the digital asset Te can be in various formats and can be structured or unstructured.

[0009] 2) According to the flag Flag, Te will be divided into N data fragments, and the i-th fragment is Te_i (i=1~N), where the Flag can be punctuation marks, letter combinations and other symbols appearing in the digital asset content. The identifier Flag is set by the user according to the specific application scenario of the digital asset. It can be a specific character in the text state, or a character or character combination set by the user according to the business characteristics; if the digital asset is in binary state or other, it can be other characters. The user can count the identifier Flag into the fragment or not according to the specific application scenario.

[0010] 3) Obtain a digital feature value for the segment Te_i, and obtain H_Te_i as the index value of Te_i, wherein the digital feature value may be a hash value or others.

[0011] 4) For each H_Te_i, select the characters of H_Te_i as the character module C_H_i according to the agreed pattern, and combine the character modules of Flag and all fragments to form ID_Te, wherein the characters of C_H_i can be one or more, and for the convenience of subsequent applications, they shall not exceed the length of H_Te_i itself; the agreed pattern characters of the index value H_Te_i can be characters in continuous positions or characters in discontinuous positions in H_Te_i, which is set by the specific business scenario.

[0012] 5) Combine the index values ​​H_Te_i (i=1-N) of all the fragments into H_Combin, and perform Merkle tree operations with all H_Te_i values ​​as leaves according to the agreed order to obtain the Merkle root value Root_Te. The agreed order can be the order of the corresponding fragments in the asset body or set according to the specific business scenario.

[0013] 6) Store ID_Te, H_Combin and Root_Te as semantic features of digital assets into digital asset Te, or set the operation according to specific scenarios.

[0014] The rapid comparison process for digital assets Te generated and identified by the same standardized semantic features is as follows: Figure 5 As shown in the figure, different digital assets can come from different computers or all be remote network assets. The initiator and responder comply with the same feature generation specifications and parameters. The specific steps are as follows:

[0015] 1) In order to save comparison resources, the number of digits of the Mega Root value Root_Te and ID_Te of the digital asset Te can be compared. If the number of Root_Te digits is greater than the number of ID_Te digits, step 3) can be directly entered; otherwise, step 2) can be performed.

[0016] 2) Extract the Mega Root value Root_Te of the digital asset Te and compare it with the Mega Root value of another digital asset Te'. If the two Root_Te values ​​are the same, the two digital assets are considered to be the same, and the comparison result is given to avoid transmitting the entire digital asset and reduce consumption. If the two Root_Te values ​​are not the same, proceed to the next step 3).

[0017] 3) The ID_Te of the digital asset Te is extracted and compared with the other digital asset Te'. If the two ID_Te values ​​are the same, it is preliminarily considered that the two digital assets are the same, avoiding the transmission of digital assets and reducing consumption; if the two ID_Te values ​​are different, proceed to the next step 4).

[0018] 4) Take out H_Te_i in H_Combin of digital asset Te and compare it with the parameters of another digital asset Te' to obtain whether the comparison results of each Te_i are the same. If all H_Te_i are the same, the two digital assets are considered to be the same, otherwise the same parts of digital asset Te and digital asset Te' are given, thus giving the comparison result.

[0019] Compared with the prior art, the fast comparison method of digital assets based on semantic feature extraction for Internet digital asset confirmation or transaction of the present invention has the following beneficial effects:

[0020] The present invention can realize the evaluation of digital assets with fine granularity, and has low requirements for communication and processing resources, so that fast, convenient and fine comparison and inspection operations can be performed. The proposed rapid comparison of digital assets is suitable for the semantic evaluation of digital assets in public network environments, including blockchain, cloud computing and other environments, and is applied to the publication, transmission, and transaction of digital assets.

[0021] The parts not involved in the device are the same as those in the prior art or can be implemented by using the prior art. The present invention has a simple structure and is easy to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the semantic separation encoding of digital assets proposed in the present invention.

[0023] Figure 2 This is a schematic diagram of the separation encoding and root generation of digital assets proposed by the present invention.

[0024] Figure 3 A schematic diagram of the encoding reorganization of the digital assets proposed in the present invention.

[0025] Figure 4 This is a schematic diagram of the process of generating the semantic features of digital assets proposed in the present invention.

[0026] Figure 5 A schematic diagram of the comparison process of the semantic features of digital assets proposed in the present invention. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0028] The digital asset Te in the computer network world can be in various forms. This implementation case is the Chinese text state of structured data.

[0029] After analyzing the characteristics of digital assets, the Chinese period "." in Te is selected as the identification character Flag, and the entire Te is divided into several segments, such as Figure 1 , perform 256-bit hash operations on the fragments in turn to obtain the values ​​H_Te_i (i=1~N) and combine them into H_Combin and further calculate to obtain the Mek root value Root_Te, such as Figure 2 , select the 8 characters starting from the rightmost end of the H_Te_i string as the character modulus C_H_i, combine them from right to left in order of i, and connect the Flag hash value to the rightmost end to form ID_Te, such as Figure 3 , and then deposit H_Combin and ID_Te into the digital asset Te.

[0030] For a digital asset Te to be released or distributed, the entire digital asset semantic feature generation process is as follows: Figure 4 , the specific steps are as follows:

[0031] 1) The Chinese period “.” that appears frequently in digital asset content is used as the identification character Flag.

[0032] 2) With Flag as the segmentation point, Te will be divided into N data segments, each segment is Te_i (i=1~N), such as Figure 1 shown.

[0033] 3) Calculate the 256-bit hash value of each fragment Te_i, and the solved value is called H_Te_i.

[0034] 4) For all H_Te_i, select the 8 characters starting from the rightmost end of each H_Te_i string as the character module C_H_i, and combine Flag and all the character modules C_H_i from right to left in order of i to form ID_Te.

[0035] 5) All H_Te_i are combined into H_Combin indexes in the form of an array, and all H_Te_i values ​​are used as leaves and the Merkle tree operation is performed in the order of the positions of their corresponding fragments in the text to obtain the Merkle root value Root_Te.

[0036] 6) Deposit ID_Te, H_Combin and Root_Te into digital asset Te.

[0037] The verification process for a digital asset Te that has been identified is as follows: Figure 5 As shown, the specific steps are explained in two cases.

[0038] Case 1 is as follows:

[0039] 1) Compared with ID_Te, the Mega Root value Root_Te of digital asset Te has fewer digits.

[0040] 2) Extract the Mega Root value Root_Te of the digital asset Te and compare it with the Mega Root value of another digital asset. If the two Root_Te values ​​are the same, the two digital assets are considered to be the same and the comparison result is given.

[0041] Case 2 is as follows:

[0042] 1) Compare the Mega Root value Root_Te of the digital asset Te with ID_Te. If Root_Te has more digits, go directly to step 3).

[0043] 3) The ID_Te of the digital asset Te is extracted and compared with the other digital asset. If the two ID_Te values ​​are the same, the two digital assets are preliminarily considered to be the same; if the two ID_Te values ​​are different, proceed to the next step.

[0044] 4) Take out H_Te_i in H_Combin of digital asset Te and compare it with the parameters of another digital asset to check whether each Te_i is the same. If all H_Te_i are the same, the two digital assets are considered to be the same; if they are different, the same parts and proportions of digital asset Te and other assets are given; thus giving the comparison result.

[0045] Case 3 is as follows:

[0046] 1) Compared with ID_Te, the Mega Root value Root_Te of digital asset Te has fewer digits.

[0047] 2) Extract the Mega Root value Root_Te of the digital asset Te and compare it with the Mega Root value of another digital asset. If the two Root_Te values ​​are different, proceed to the next step.

[0048] 3) The ID_Te of the digital asset Te is extracted and compared with the other digital asset. If the two ID_Te values ​​are the same, the two digital assets are preliminarily considered to be the same; if the two ID_Te values ​​are different, proceed to the next step.

[0049] 4) Take out H_Te_i in H_Combin of digital asset Te and compare it with the parameters of another digital asset to check whether each Te_i is the same. If all H_Te_i are the same, the two digital assets are considered to be the same; if they are different, the same parts and proportions of digital asset Te and other assets are given; thus giving the comparison result.

[0050] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for rapid comparison of digital assets based on semantic features, comprising the following steps: 1) For two digital assets Te and Te' to be compared that are not local to the client, first obtain the semantic features of one of the digital assets Te. If the number of bits of the Mega Root value Root_Te in the semantic features of the digital asset Te is greater than the number of bits of the ID_Te of the digital asset Te, directly proceed to step 3); otherwise, proceed to step 2); 2) Compare the Mek root value Root_Te in the semantic feature of the digital asset Te with the Mek root value Root_Te in the semantic feature of the digital asset Te'. If the two are the same, it is determined that the two digital assets are the same, otherwise proceed to step 3); 3) Compare the ID_Te in the digital asset Te semantic feature with the corresponding ID_Te in the digital asset Te' semantic feature, If the two ID_Te values ​​are the same, it is preliminarily determined that the two digital assets are the same, otherwise proceed to step 4); 4) Compare the index value in the digital asset Te semantic feature with the corresponding index value in the digital asset Te' semantic feature. If the index values ​​are consistent, the two digital assets are considered to be the same. Otherwise, the two digital assets are considered to be different. Among them, the method for generating semantic features of digital assets is: 11) designating the identifying characters in the digital asset Te as the flag Flag of the digital asset Te; 12) dividing the digital asset Te into N data segments according to the flag Flag, wherein the i-th segment is Te_i, i=1~N; 13) calculating the digital feature value H_Te_i of each data segment Te_i as the index value of the data segment Te_i; 14) for each index value, selecting a number of characters in the index value according to an agreed pattern, combining the flag Flag and the characters selected from each of the index values ​​according to the agreed pattern to form ID_Te; 15) combining the index values ​​of each data segment into H_Combin, and performing Merkle tree operations with each of the index values ​​as leaves to obtain the Merkle root value Root_Te; 16) using ID_Te, H_Combin and Root_Te as the semantic features of the digital asset Te.

2. The method according to claim 1, characterized in that In step 4), when it is determined that the two digital assets are different, the comparison result of the digital asset Te and the digital asset Te' is given according to the comparison result of the index value.

3. The method according to claim 1, characterized in that The digital assets Te and Te' originate from different computers, or are both remote network assets.

4. The method according to claim 1, characterized in that The digital characteristic value is a hash value of the data segment.

5. The method according to claim 1, characterized in that One or more characters in the index value H_Te_i are selected as the character modulus C_H_i according to the agreed pattern.

6. The method according to claim 1 or 5, characterized in that The characters selected in the agreed mode are multiple characters in consecutive positions in H_Te_i.

7. The method according to claim 1, characterized in that The characters selected in the agreed mode are multiple characters at discontinuous positions in H_Te_i.

8. The method according to claim 1, characterized in that The flag Flag is a punctuation mark, letter combination or other set symbol appearing in the digital asset Te.

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