Digital Asset Data Processing Method and Related Devices Based on Relationship Graphs

Abstract

This application provides a method and related apparatus for processing digital asset data based on a relationship graph. The method includes: acquiring digital asset data of a target user, the digital asset data including multiple categories of digital asset data; clustering the digital asset data to obtain k asset data categories; extracting m target asset data categories from the k asset data categories based on the user characteristic information of the target user; extracting the digital asset data corresponding to the m target asset data categories to obtain a first digital asset data set; extracting key information from the first digital asset data in the first digital asset data set to obtain a first key information set; and constructing an asset data relationship graph based on the first key information set to obtain a target relationship graph, which can improve the accuracy of subsequent data analysis based on the target relationship graph.

Description

Technical Field

[0001] This application relates to the fields of big data and data analysis technology, specifically to a digital asset data processing method and related apparatus based on relational graphs. Background Technology

[0002] Existing methods for analyzing and processing digital asset data typically involve directly classifying the data and then analyzing each category separately. This approach results in a somewhat simplistic approach to data analysis, leading to lower accuracy. Summary of the Invention

[0003] This application provides a digital asset data processing method and related apparatus based on a relationship graph, which can extract a set of key information based on the user characteristic information of the target user and generate a target relationship graph, thereby improving the accuracy of subsequent data analysis based on the target relationship graph.

[0004] A first aspect of this application provides a method for processing digital asset data based on a relational graph, the method comprising:

[0005] Acquire digital asset data of the target user, which includes digital asset data of various categories;

[0006] The digital asset data is clustered to obtain k asset data categories;

[0007] Based on the user characteristic information of the target user, m target asset data categories are extracted from k asset data categories;

[0008] Extract digital asset data corresponding to m target asset data categories to obtain a first digital asset data set;

[0009] Extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set;

[0010] Based on the first set of key information, an asset data relationship graph is constructed to obtain the target relationship graph.

[0011] In this example, by acquiring the digital asset data of the target user, which includes multiple categories of digital asset data, clustering is performed on the digital asset data to obtain k asset data categories. Based on the user characteristic information of the target user, m target asset data categories are extracted from the k asset data categories. The digital asset data corresponding to the m target asset data categories is extracted to obtain a first digital asset data set. Key information is extracted from the first digital asset data in the first digital asset data set to obtain a first key information set. An asset data relationship graph is constructed based on the first key information set to obtain a target relationship graph. Therefore, a key information set can be extracted and a target relationship graph can be generated based on the user characteristic information of the target user, thus improving the accuracy of subsequent data analysis based on the target relationship graph.

[0012] A second aspect of this application provides a digital asset data processing apparatus based on a relational graph, the apparatus comprising:

[0013] The acquisition unit is used to acquire the digital asset data of the target user, which includes asset data of various categories.

[0014] Clustering unit, used to perform clustering processing on the digital asset data to obtain k asset data categories;

[0015] The first extraction unit is used to extract m target asset data categories from k asset data categories based on the user characteristic information of the target user.

[0016] The second extraction unit is used to extract digital asset data corresponding to m target asset data categories to obtain a first digital asset data set.

[0017] The third extraction unit is used to extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set.

[0018] The construction unit is used to construct an asset data relationship graph based on the first set of key information to obtain the target relationship graph.

[0019] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0021] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This application provides a flowchart illustrating a digital asset data processing method based on a relational graph.

[0024] Figure 2 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0025] Figure 3 This application provides a schematic diagram of the structure of a digital asset data processing device based on a relational graph. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0027] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0028] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0029] To better understand the digital asset data processing method based on relationship graphs provided in this application, a brief introduction to existing methods for analyzing and processing digital asset data is given below. Existing methods typically involve directly classifying digital asset data and then analyzing each category separately. This approach is relatively simplistic and fails to consider the relationships between different categories, resulting in low accuracy in data analysis.

[0030] To address the aforementioned issues, this application provides a digital asset data processing method based on a relationship graph. This method can extract a set of key information based on the user characteristics of the target user and generate a target relationship graph, thereby improving the accuracy of subsequent data analysis based on the target relationship graph.

[0031] Please see Figure 1 , Figure 1 This application provides a flowchart illustrating a method for processing digital asset data based on a relationship graph. Figure 1 As shown, the method includes:

[0032] 101. Real-time acquisition of digital asset data of target users, including digital asset data of various categories.

[0033] The target users can be enterprises that need to process digital asset data. Digital asset data includes customer data, sales data, transaction data, market data, supply chain data, human resources data, operational data, product data, social media data, user behavior data, marketing campaign data, customer satisfaction data, and equipment data.

[0034] Specifically, customer data includes basic customer information, purchase history, preferences, etc.; sales data, such as sales amount, order quantity, sales channels, etc.; transaction data, which is data generated when customers make purchases; market data, such as market trends and competitor analysis; supply chain data, such as supplier information and logistics data; human resources data, such as employee information and performance data; operational data, such as production efficiency and quality control; product data, such as product features and usage; social media data, such as brand reputation and customer feedback; user behavior data, such as website or application browsing, clicks, and interactions; marketing campaign data, such as advertising effectiveness and conversion rates; customer satisfaction data, such as customer satisfaction ratings for products or services; and equipment data, such as the operating status and maintenance records of the company's equipment.

[0035] The aforementioned digital asset data of customers can be analyzed and processed to obtain the data needed by target users. For example, the purchasing preferences of target users can be obtained.

[0036] 102. Cluster the digital asset data to obtain k asset data categories.

[0037] Since the acquired digital asset data may be unclassified, it is necessary to cluster the data to obtain k asset data categories. These categories can be one of the asset data categories shown in the previous example, or other categories; this is merely an example and not a specific limitation.

[0038] A general clustering method can be used to cluster digital asset data to obtain k asset data categories.

[0039] 103. Extract m target asset data categories from the k asset data categories based on the user characteristic information of the target user.

[0040] User characteristic information may include enterprise type and business focus. Enterprise type can be, for example, joint venture, sole proprietorship, state-owned, private, state-owned, collectively owned, joint-stock, limited liability, etc. Business focus can be understood as the type of business the enterprise currently operates.

[0041] Based on user characteristics, the asset data categories that the target user needs to focus on can be determined, thus establishing that asset data category as the target asset data category. The type of asset data that the target user needs to focus on can be determined based on the enterprise type and its business direction. For example, different enterprise types have their own preferred asset data categories, and asset data categories need to be associated with the enterprise's business direction.

[0042] 104. Extract the digital asset data corresponding to m target asset data categories to obtain the first digital asset data set.

[0043] Based on the aforementioned embodiments, when clustering asset data categories, data extraction can be performed on the digitized asset data in each asset data category to obtain the first digitized asset data set.

[0044] 105. Extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set.

[0045] A method for extracting key information from the first set of digital asset data can be as follows: First set of digital asset data in each target asset data category is clustered to obtain a set of reference key information corresponding to each target asset data category. Then, a key information extraction parameter matrix is ​​constructed by combining the target user's core business information and confidential business information. Based on the constructed key information extraction parameter matrix, a membership function is built. Finally, the membership degree corresponding to the reference key information is calculated based on the membership function, and the first set of key information is determined based on this membership degree. Therefore, the first set of key information can be determined through matrix operations and the construction of membership functions, improving the accuracy of determining the first set of key information.

[0046] 106. Construct an asset data relationship graph based on the first set of key information to obtain the target relationship graph.

[0047] A method for constructing an asset data relationship graph based on a set of primary key information can be as follows: Extract the asset data category for each primary key information in the primary key information set; construct a graph baseline point based on this asset data category; and construct the asset data relationship graph based on the relationships between each primary key information point and the baseline point. Arrange the primary key information points within each category near the baseline point in a circular distribution, with the baseline point as the center. Then, determine the arrangement distance between each baseline point based on the relationships between primary key information points near other baseline points (the stronger the relationship, the shorter the arrangement distance; the weaker the relationship, the longer the arrangement distance). After determining the arrangement distance, connect the circular areas based on the arrangement distance and the baseline point to form the asset data relationship graph.

[0048] After constructing the asset data relationship graph, target users can use it for subsequent data analysis and processing, thereby improving their efficiency in this process.

[0049] In this example, by acquiring the digital asset data of the target user, which includes multiple categories of digital asset data, clustering is performed on the digital asset data to obtain k asset data categories. Based on the user characteristic information of the target user, m target asset data categories are extracted from the k asset data categories. The digital asset data corresponding to the m target asset data categories is extracted to obtain a first digital asset data set. Key information is extracted from the first digital asset data in the first digital asset data set to obtain a first key information set. An asset data relationship graph is constructed based on the first key information set to obtain a target relationship graph. Therefore, a key information set can be extracted and a target relationship graph can be generated based on the user characteristic information of the target user, thus improving the accuracy of subsequent data analysis based on the target relationship graph.

[0050] In one possible implementation, a method for extracting key information from the first digital asset data in the first digital asset data set to obtain a first key information set includes:

[0051] A1. Perform key information clustering processing on the first digital asset data set in the corresponding target asset data category to obtain a reference key information set corresponding to each target asset data category.

[0052] A2. Obtain the target user's core business information and confidential business information;

[0053] A3. Determine the first key information extraction parameter vector based on the core business information;

[0054] A4. Determine the second key information extraction parameter vector based on the aforementioned business confidentiality information;

[0055] A5. Concatenate the first key information extraction parameter vector and the second key information extraction parameter vector to obtain the key information extraction parameter matrix.

[0056] A6. Using the key information to extract the parameter matrix, construct the membership function to obtain the target membership function;

[0057] A7. Calculate the membership degree of the reference key information in the reference key information set corresponding to each target asset data category according to the target membership function, and obtain the membership degree value of each reference key information.

[0058] A8. Based on the membership value of each reference key information and the preset membership threshold, determine the first key information from the reference key information set corresponding to each target asset data category to obtain the first key information set.

[0059] Specifically, a general key information clustering processing method can be used to perform key information clustering processing on the first digital asset data set in the corresponding target asset data category to obtain a reference key information set.

[0060] Core business information and confidential business information can be obtained through pre-input by the target user. This business information and confidential information can be understood as data that the target user needs to process securely. Then, a first key information extraction parameter vector can be determined based on the core business information, and a second key information extraction parameter vector can be determined based on the confidential business information. The first and second key information extraction parameter vectors have the same vector size. If the core business information includes L core businesses, then the first key information extraction parameter vector can be (W1, W2, W3, ..., WL), where W1 is the business description information of the first core business. The confidential business information includes L businesses that need to be processed confidentially, and the second key information extraction parameter vector is (M1, M2, M3, ..., ML). M1 is the business description information of the first business that needs to be processed confidentially.

[0061] The first and second key information extraction parameter vectors can be vertically concatenated to obtain a key information extraction parameter matrix. Weights can then be assigned to this matrix to obtain a key information extraction parameter weight matrix. Based on this weight matrix, a membership function can be constructed to obtain the target membership function. The target membership function is used to calculate the score value for which the reference key information is evaluated as the first key information.

[0062] This allows for the calculation of membership values ​​based on the target membership function, yielding the membership value for each reference key information. A preset membership threshold is set using empirical values ​​or historical data. Reference key information with membership values ​​exceeding the preset threshold is identified as the first key information, thus obtaining the first key information set.

[0063] In this example, a key information extraction parameter matrix is ​​constructed using the target user's core business information and confidential business information. A membership function is also constructed. Finally, the membership value is calculated based on the membership function, and the first key information set is determined based on the membership value, thus improving the accuracy of determining the first key information set.

[0064] In one possible implementation, a method for constructing a membership function using the extracted parameter matrix based on the key information to obtain the target membership function includes:

[0065] B1. Perform weight assignment on the key information extraction parameter matrix to obtain the key information extraction parameter weight matrix;

[0066] B2. Using the key information to extract the parameter weight matrix, construct the membership function to obtain the target membership function.

[0067] This involves assigning weights to each element in the key information extraction parameter matrix to obtain a key information extraction parameter weight matrix. Each element has a corresponding pre-set weight value, which improves the accuracy of subsequent target membership function construction through weight preprocessing.

[0068] The target membership function can be characterized by the following formula:

[0069]

[0070] Among them, I i,j Extracting element A from the parameter weight matrix for key information i,j Typical ranking values ​​(importance ranking values, i.e., m and element A) i,j The similarity between them); n is the number of elements in the weight matrix of the key information extraction parameters (specifically the product of i and j); μ(I) is the membership function transformed from the target membership function (importance ranking value), and m is the reference key information.

[0071] In this example, the parameter weight matrix can be extracted based on key information to construct the membership function, thereby improving the accuracy of the target membership function. Furthermore, using the target membership function for subsequent membership calculations can also improve the accuracy and efficiency of membership calculations.

[0072] In one possible implementation, the first key information within the obtained first key information can also be signed to enhance information security. Specifically, this could be:

[0073] C1. Sign the first key information in the first key information set to obtain a second key information set. The anti-tampering capability of the second key information in the second key information set is higher than that of the corresponding first key information.

[0074] C2. Divide the second set of key information into blocks to obtain a data blocks;

[0075] C3. Send the a data blocks to the server;

[0076] C4. The server extracts the second key information from the a data blocks to obtain the second key information set;

[0077] C5. The server performs signature authentication on the second key information in the second key information set and obtains the signature authentication result.

[0078] C6. If the signature authentication result is successful, the server stores the second key information set.

[0079] In this process, a random number and the first base point of the elliptic curve can be selected. The signature is then performed based on the first random number, the first base point, and the value obtained after converting the first key information, to obtain the second key information.

[0080] After obtaining the second key information, it can be sent to the server. During transmission, since there may be a large amount of second key information, it can be divided into blocks, resulting in 'a' data blocks. These 'a' data blocks are then sent to the server, which can extract the second key information from them, obtaining the second key information set. A signature authentication method matching the signature processing can be used for authentication, yielding the authentication result. After successful authentication, the second key information set is stored so that subsequent target users can retrieve it from the server for data analysis and processing. By signing the first key information, the protection of key information is enhanced, reducing the risk of information tampering.

[0081] In one possible implementation, a method for signing a first key information in a first key information set to obtain a second key information set includes:

[0082] D1. Obtain the first random number and the first base point of the elliptic curve;

[0083] D2. Determine the first signature parameters based on the first random number and the first base point;

[0084] D3. Convert the target asset data category corresponding to the first key information to obtain a second value, which is a prime number.

[0085] D4. Determine the signature public key based on the second value and the first base point;

[0086] D5. Perform a hash operation on the first key information of the target to obtain the target hash value, wherein the first key information of the target is any one of the first key information set;

[0087] D6. Perform a signature operation on the target hash value using the first signature parameter, the signature public key, and the second value to obtain the second key information corresponding to the first key information of the target, where the second value is the private key.

[0088] D7. Repeat the above steps of obtaining the first random number and the first base point of the elliptic curve to perform a signature operation on the target hash value using the first signature parameter, the signature public key, and the second value to obtain the second key information corresponding to the first key information of the target, where the second value is the private key, until the second key information set is obtained.

[0089] The first random number is a prime number. The product of the first random number and the first base point can be used to determine the first signature parameter.

[0090] Since the first key information has a corresponding target asset data category, it can be transformed using that target asset data category to obtain the second value. This second value can be processed using a preset transformation method. The product of the second value and the first base point can be used to determine the signature public key, and the second value can be used to determine the signature private key.

[0091] A general hashing method can be used to process the target's first key information to obtain the target hash value. The second key information can be determined by the ratio of the product of the first random number and the y-coordinate of the public key, the sum of the target hash values, and the second value. During verification, the sum of the product of the public key and the second key information, the product of the target hash value and the first base point, and the product of the y-coordinate of the public key and the first signature parameter can be verified. If they are the same, the signature authentication is successful; if they are different, the signature authentication fails. By determining the second value as the signature private key, the target asset data category can be transmitted simultaneously with the signing process, improving the reliability, concealment, and security of data transmission.

[0092] For examples consistent with the above embodiments, please refer to... Figure 2 , Figure 2 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0093] Acquire digital asset data of the target user, which includes digital asset data of various categories;

[0094] The digital asset data is clustered to obtain k asset data categories;

[0095] Based on the user characteristic information of the target user, m target asset data categories are extracted from k asset data categories;

[0096] Extract digital asset data corresponding to m target asset data categories to obtain a first digital asset data set;

[0097] Extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set;

[0098] Based on the first set of key information, an asset data relationship graph is constructed to obtain the target relationship graph.

[0099] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0100] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0101] For those consistent with the above, please refer to Figure 3 , Figure 3 This application provides a schematic diagram of the structure of a digital asset data processing device based on a relationship graph, as illustrated in this embodiment. Figure 3 As shown, the device includes:

[0102] The acquisition unit 301 is used to acquire the digital asset data of the target user, wherein the digital asset data includes asset data of various categories;

[0103] Clustering unit 302 is used to perform clustering processing on the digital asset data to obtain k asset data categories;

[0104] The first extraction unit 303 is used to extract m target asset data categories from k asset data categories based on the user feature information of the target user.

[0105] The second extraction unit 304 is used to extract digital asset data corresponding to m target asset data categories to obtain a first digital asset data set.

[0106] The third extraction unit 305 is used to extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set.

[0107] Construction unit 306 is used to construct an asset data relationship graph based on the first key information set to obtain the target relationship graph.

[0108] In one possible implementation, the third extraction unit 305 is specifically used for:

[0109] The first digital asset data set is subjected to key information clustering processing in the corresponding target asset data category to obtain a reference key information set corresponding to each target asset data category.

[0110] Obtain the target user's core business information and confidential business information;

[0111] Determine the first key information extraction parameter vector based on the core business information;

[0112] Determine the second key information extraction parameter vector based on the aforementioned business confidentiality information;

[0113] The first key information extraction parameter vector and the second key information extraction parameter vector are concatenated to obtain the key information extraction parameter matrix.

[0114] The membership function is constructed by extracting the parameter matrix using the key information to obtain the target membership function;

[0115] Based on the target membership function, the membership degree of the reference key information in the reference key information set corresponding to each target asset data category is calculated to obtain the membership degree value of each reference key information.

[0116] Based on the membership value of each reference key information and the preset membership threshold, the first key information is determined from the reference key information set corresponding to each target asset data category, so as to obtain the first key information set.

[0117] In one possible implementation, regarding the construction of the membership function using the key information extraction parameter matrix to obtain the target membership function, the third extraction unit 305 is specifically used for:

[0118] The key information extraction parameter matrix is ​​weighted to obtain the key information extraction parameter weight matrix.

[0119] The membership function is constructed by extracting the parameter weight matrix from the key information to obtain the target membership function.

[0120] In one possible implementation, the device is further used for:

[0121] The first key information in the first key information set is signed to obtain the second key information set. The anti-tampering capability of the second key information in the second key information set is higher than that of the corresponding first key information.

[0122] The second set of key information is divided into blocks to obtain a data blocks;

[0123] Send the a data blocks to the server;

[0124] The server extracts the second key information from the a data blocks to obtain the second key information set.

[0125] The server performs signature authentication on the second key information in the second key information set and obtains the signature authentication result.

[0126] If the signature authentication result is successful, the server stores the second set of key information.

[0127] In one possible implementation, in order to perform signature processing on the first key information in the first key information set to obtain the second key information set, the apparatus is further configured to:

[0128] Obtain the first random number and the first base point of the elliptic curve;

[0129] The first signature parameters are determined based on the first random number and the first base point;

[0130] The target asset data category corresponding to the first key information is converted to obtain a second value, which is a prime number.

[0131] The signature public key is determined based on the second value and the first base point;

[0132] A hash operation is performed on the first key information of the target to obtain the target hash value, wherein the first key information of the target is any one of the first key information sets;

[0133] The target hash value is signed using the first signature parameter, the signature public key, and the second value to obtain the second key information corresponding to the first key information of the target, where the second value is the private key.

[0134] Repeat the steps described above, from obtaining the first random number and the first base point of the elliptic curve to performing a signature operation on the target hash value using the first signature parameter, the signature public key, and the second value, to obtain the second key information corresponding to the first key information of the target, where the second value is the private key, until the set of the second key information is obtained.

[0135] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the relational graph-based digital asset data processing methods described in the above method embodiments.

[0136] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the relation graph-based digital asset data processing methods described in the above method embodiments.

[0137] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0138] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0140] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0142] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0143] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0144] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for processing digital asset data based on relational graphs, characterized in that, The method includes: Acquire digital asset data of the target user, which includes digital asset data of various categories; The digital asset data is clustered to obtain k asset data categories; Based on the user characteristic information of the target user, m target asset data categories are extracted from k asset data categories; Extract digital asset data corresponding to m target asset data categories to obtain a first digital asset data set; Extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set; Constructing an asset data relationship graph based on the first key information set to obtain a target relationship graph; extracting key information from the first digital asset data in the first digital asset data set to obtain the first key information set includes: The first digital asset data set is subjected to key information clustering processing in the corresponding target asset data category to obtain a reference key information set corresponding to each target asset data category. Obtain the target user's core business information and confidential business information; Determine the first key information extraction parameter vector based on the core business information; Determine the second key information extraction parameter vector based on the aforementioned business confidentiality information; The first key information extraction parameter vector and the second key information extraction parameter vector are concatenated to obtain the key information extraction parameter matrix. The membership function is constructed by extracting the parameter matrix using the key information to obtain the target membership function; Based on the target membership function, the membership degree of the reference key information in the reference key information set corresponding to each target asset data category is calculated to obtain the membership degree value of each reference key information. Based on the membership value of each reference key information and the preset membership threshold, the first key information is determined from the reference key information set corresponding to each target asset data category, so as to obtain the first key information set.

2. The digital asset data processing method based on relational graphs according to claim 1, characterized in that, The step of extracting the parameter matrix using the key information to construct the membership function and obtain the target membership function includes: The key information extraction parameter matrix is ​​weighted to obtain the key information extraction parameter weight matrix. The membership function is constructed by extracting the parameter weight matrix from the key information to obtain the target membership function.

3. The method for processing digital asset data based on relational graphs according to claim 1 or 2, characterized in that, The method further includes: The first key information in the first key information set is signed to obtain the second key information set. The anti-tampering capability of the second key information in the second key information set is higher than that of the corresponding first key information. The second set of key information is divided into blocks to obtain a data blocks; Send the a data blocks to the server; The server extracts the second key information from the a data blocks to obtain the second key information set. The server performs signature authentication on the second key information in the second key information set and obtains the signature authentication result. If the signature authentication result is successful, the server stores the second set of key information.

4. The digital asset data processing method based on relational graphs according to claim 3, characterized in that, The step of signing the first key information in the first key information set to obtain the second key information set includes: Obtain the first random number and the first base point of the elliptic curve; The first signature parameters are determined based on the first random number and the first base point; The target asset data category corresponding to the first key information is converted to obtain a second value, which is a prime number. The signature public key is determined based on the second value and the first base point; A hash operation is performed on the first key information of the target to obtain the target hash value, wherein the first key information of the target is any one of the first key information sets; The target hash value is signed using the first signature parameter, the signature public key, and the second value to obtain the second key information corresponding to the first key information of the target, where the second value is the private key. Repeat the steps described above, from obtaining the first random number and the first base point of the elliptic curve to performing a signature operation on the target hash value using the first signature parameter, the signature public key, and the second value, to obtain the second key information corresponding to the first key information of the target, where the second value is the private key, until the set of the second key information is obtained.

5. A digital asset data processing device based on a relational graph, characterized in that, The device includes: The acquisition unit is used to acquire the digital asset data of the target user, which includes asset data of various categories. Clustering unit, used to perform clustering processing on the digital asset data to obtain k asset data categories; The first extraction unit is used to extract m target asset data categories from k asset data categories based on the user characteristic information of the target user. The second extraction unit is used to extract digital asset data corresponding to m target asset data categories to obtain a first digital asset data set. The third extraction unit is used to extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set. The construction unit is used to construct an asset data relationship graph based on the first set of key information to obtain the target relationship graph. The third extraction unit is specifically used for: The first digital asset data set is subjected to key information clustering processing in the corresponding target asset data category to obtain a reference key information set corresponding to each target asset data category. Obtain the target user's core business information and confidential business information; Determine the first key information extraction parameter vector based on the core business information; Determine the second key information extraction parameter vector based on the aforementioned business confidentiality information; The first key information extraction parameter vector and the second key information extraction parameter vector are concatenated to obtain the key information extraction parameter matrix. The membership function is constructed by extracting the parameter matrix using the key information to obtain the target membership function; Based on the target membership function, the membership degree of the reference key information in the reference key information set corresponding to each target asset data category is calculated to obtain the membership degree value of each reference key information. Based on the membership value of each reference key information and the preset membership threshold, the first key information is determined from the reference key information set corresponding to each target asset data category, so as to obtain the first key information set.

6. The digital asset data processing device based on relational graphs according to claim 5, characterized in that, In the process of constructing the membership function using the key information extraction parameter matrix to obtain the target membership function, the third extraction unit is specifically used for: The key information extraction parameter matrix is ​​weighted to obtain the key information extraction parameter weight matrix. The membership function is constructed by extracting the parameter weight matrix from the key information to obtain the target membership function.

7. A terminal, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to invoke the program instructions to perform the method as described in any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-4.

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