Data processing device and equipment based on digital asset data relation graph
By clustering and extracting key information from digital asset data, an asset data relationship graph is constructed, which solves the problem of low accuracy in data analysis in existing technologies and achieves more efficient data analysis.
Patent Information
- Application Number
- CN202511420422.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-31
- Publication Date
- 2026-02-17
AI Technical Summary
Existing technologies lack attention to the correlation between different categories of data when analyzing and processing digital asset data, resulting in low accuracy of analysis.
By acquiring digital asset data of target users, performing clustering processing, extracting key information sets based on user characteristics, and constructing an asset data relationship graph, the accuracy of data analysis is improved.
By generating target relationship graphs, subsequent data analysis can be performed more accurately, improving the efficiency and accuracy of data analysis.
Smart Images
Figure CN121542433A_ABST
Abstract
Description
[0001] This application is a divisional application of the Chinese patent application No. 202411044364.X entitled "Digital asset data processing method based on relationship graph and related device" filed on July 31, 2024 with the China Patent Office, the whole content of which is incorporated herein by reference. TECHNICAL FIELD
[0002] The present application relates to the technical field of big data and data analysis, in particular to a data processing device and equipment based on digital asset data relationship graph. BACKGROUND
[0003] When analyzing and processing digital asset data, the existing method is to directly classify the digital asset data, and then analyze and process the data of each category respectively, which makes the data analysis and processing single, and thus the accuracy of data analysis and processing is low. SUMMARY
[0004] The present application provides a data processing device and equipment based on digital asset data relationship graph, which can extract a key information set according to the user feature information of a target user and generate a target relationship graph, thereby improving the accuracy of subsequent data analysis based on the target relationship graph.
[0005] The first aspect of the present application provides a digital asset data processing method based on relationship graph, which comprises:
[0006] Obtaining digital asset data of a target user, wherein the digital asset data includes digital asset data of multiple categories;
[0007] Clustering the digital asset data to obtain k asset data categories;
[0008] Extracting m target asset data categories from the k asset data categories according to the user feature information of the target user;
[0009] Extracting digital asset data corresponding to the m target asset data categories to obtain a first digital asset data set;
[0010] Extracting key information from the first digital asset data in the first digital asset data set to obtain a first key information set;
[0011] Constructing an asset data relationship graph according to the first key information set to obtain a target relationship graph.
[0012] In this example, by obtaining the digital asset data of the target user, the digital asset data includes various categories of digital asset data, performing clustering processing on the digital asset data to obtain k asset data categories, extracting m target asset data categories from the k asset data categories according to the user feature 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, constructing an asset data relationship graph according to the first key information set to obtain a target relationship graph, and thus the key information set can be extracted according to the user feature information of the target user and the target relationship graph can be generated, so that the accuracy of subsequent data analysis according to the target relationship graph can be improved.
[0013] A second aspect of the embodiment of the present application provides a digital asset data processing device based on a relationship graph, and the device comprises:
[0014] An acquisition unit is configured to acquire digital asset data of a target user, wherein the digital asset data includes asset data of various categories.
[0015] A clustering unit is configured to perform clustering processing on the digital asset data to obtain k asset data categories.
[0016] A first extraction unit is configured to extract m target asset data categories from the k asset data categories according to user feature information of the target user.
[0017] A second extraction unit is configured to extract digital asset data corresponding to the m target asset data categories to obtain a first digital asset data set.
[0018] A third extraction unit is configured to extract key information from the first digital asset data in the first digital asset data set to obtain a first key information set.
[0019] A construction unit is configured to construct an asset data relationship graph according to the first key information set to obtain a target relationship graph.
[0020] A third aspect of the embodiment of the present application provides a terminal, which comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are connected to each other, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions to execute the step instructions in the first aspect of the embodiment of the present application.
[0021] A fourth aspect of the embodiments of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application.
[0022] A fifth aspect of the embodiments of the present application provides a computer program product, which includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product can be a software installation package. BRIEF DESCRIPTION OF DRAWINGS
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.
[0024] Figure 1 A flowchart of a digital asset data processing method based on a relationship graph is provided for the embodiments of the present application;
[0025] Figure 2 A structural diagram of a terminal is provided for the embodiments of the present application;
[0026] Figure 3 A structural diagram of a digital asset data processing device based on a relationship graph is provided for the embodiments of the present application. DETAILED DESCRIPTION
[0027] The technical solutions of the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.
[0028] The terms "first", "second", and the like in the description and in the claims of the present application and above drawings are used for distinguishing between similar objects, not for describing a specific sequential order. The terms "comprises", "comprising", "includes", "including" and the like are synonymous with the term "comprising", and are used in the sense of "including, but not limited to". For example, a process, method, object, or apparatus that comprises a list of steps or elements is not necessarily limited to the listed steps or elements, but can include additional steps or elements not expressly listed or inherent to such process, method, object, or apparatus.
[0029] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that any of the embodiments described herein can be combined with any of the other embodiments unless specifically noted otherwise.
[0030] In order to better understand the method for processing digital asset data based on a relationship graph provided by the embodiments of the present application, the method for processing digital asset data in the prior art will be briefly introduced first. In the prior art, the digital asset data is directly classified and processed, and then the data of each category after classification is analyzed and processed respectively, so that the data analysis and processing is relatively single, and the correlation between different categories of data is not concerned, so that the accuracy of data analysis and processing is low.
[0031] To solve the above problems, the embodiments of the present application provide a method for processing digital asset data based on a relationship graph, which can extract a key information set according to the user feature information of a target user and generate a target relationship graph, so as to improve the accuracy of subsequent data analysis according to the target relationship graph.
[0032] Referring to Figure 1 , Figure 1 A flowchart of a method for processing digital asset data based on a relationship graph is provided for the embodiments of the present application. As Figure 1 shown, the method comprises:
[0033] 101, real-time acquisition of digital asset data of a target user, wherein the digital asset data comprises digital asset data of multiple categories.
[0034] The target user can be an enterprise or the like that needs to process digital asset data. The digital asset data includes customer data, sales data, transaction data, market data, supply chain data, human resource data, operation data, product data, social media data, user behavior data, marketing activity data, customer satisfaction data, and equipment data.
[0035] Specifically, the customer data includes basic information of customers, purchase history, preferences, and the like; the sales data includes sales amount, order quantity, sales channel, and the like; the transaction data includes data generated by customers during a purchase transaction; the market data includes market trends, competitor analysis, and the like; the supply chain data includes supplier information, logistics data, and the like; the human resource data includes employee information, performance data, and the like; the operation data includes production efficiency, quality control, and the like; the product data includes product characteristics, usage, and the like; the social media data includes brand reputation, customer feedback, and the like; the user behavior data includes browsing, clicking, and interacting data of a website or an application; the marketing activity data includes advertising effectiveness, conversion rate, and the like; the customer satisfaction data includes customer satisfaction evaluation of products or services; and the equipment data includes running status of enterprise equipment, maintenance records, and the like.
[0036] The above digital asset data of customers can be analyzed and processed to obtain data required by the target user. For example, the purchase preferences of customers related to the target user are obtained.
[0037] 102. Clustering the digital asset data to obtain k asset data categories.
[0038] Since the obtained digital asset data can be data that has not been classified, the digital asset data needs to be clustered to obtain k asset data categories. The asset data categories can be one of the asset data categories described in the foregoing examples, and can also be other categories. This is only an example and is not limited in particular.
[0039] The digital asset data can be clustered using a general clustering method to obtain k asset data categories.
[0040] 103. Extracting m target asset data categories from the k asset data categories according to the user feature information of the target user.
[0041] The user feature information can include enterprise type and business direction. The enterprise type can be joint venture, sole proprietorship, state-owned, private, wholly state-owned, collectively owned, joint-stock, limited liability, and the like. The business direction can be understood as the type of business currently operated by the enterprise.
[0042] According to the user feature information, the asset data category that the target user currently needs to focus on can be determined, and the asset data category is determined as the target asset data category. The asset data type that the target user currently needs to focus on can be determined according to the enterprise type and the enterprise business direction. For example, different enterprise types have their own asset data categories of interest, and the asset data categories need to be associated with the enterprise business direction.
[0043] 104. Extracting digital asset data corresponding to the m target asset data categories to obtain a first digital asset data set.
[0044] Based on the digital asset data in each asset data category during the clustering of the asset data categories in the foregoing embodiments, data extraction can be performed to obtain the first digital asset data set.
[0045] 105. Extracting key information from the first digital asset data in the first digital asset data set to obtain a first key information set.
[0046] The method of extracting key information from the first digital asset data can be: performing key information clustering processing on the first digital asset data in each target asset data category to obtain a reference key information set corresponding to each target asset data category, constructing a key information extraction parameter matrix in combination with the core business information and the business confidential information of the target user, constructing a membership function based on the constructed key information extraction parameter matrix, and finally calculating the membership degree of the reference key information corresponding to the reference key information according to the membership function, and determining the first key information set according to the membership degree. Therefore, the first key information set can be determined through matrix operation, construction of the membership function, etc., thereby improving the accuracy of the determination of the first key information set.
[0047] 106. Constructing an asset data relationship graph based on the first key information set to obtain a target relationship graph.
[0048] 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.
[0049] 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.
[0050] 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.
[0051] 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:
[0052] 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.
[0053] A2. Obtain the target user's core business information and confidential business information;
[0054] A3. Determine the first key information extraction parameter vector based on the core business information;
[0055] A4. Determine the second key information extraction parameter vector based on the aforementioned business confidentiality information;
[0056] 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.
[0057] A6. Using the key information to extract the parameter matrix, construct the membership function to obtain the target membership function;
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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.
[0065] 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:
[0066] B1. Perform weight assignment on the key information extraction parameter matrix to obtain the key information extraction parameter weight matrix;
[0067] B2. Using the key information to extract the parameter weight matrix, construct the membership function to obtain the target membership function.
[0068] 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.
[0069] The target membership function can be characterized by the following formula:
[0070]
[0071] 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.
[0072] 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.
[0073] 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:
[0074] 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.
[0075] C2. Divide the second set of key information into blocks to obtain a data blocks;
[0076] C3. Send the a data blocks to the server;
[0077] C4. The server extracts the second key information from the a data blocks to obtain the second key information set;
[0078] C5. The server performs signature authentication on the second key information in the second key information set and obtains the signature authentication result.
[0079] C6. If the signature authentication result is successful, the server stores the second key information set.
[0080] 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.
[0081] 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.
[0082] 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:
[0083] D1. Obtain the first random number and the first base point of the elliptic curve;
[0084] D2. Determine the first signature parameters based on the first random number and the first base point;
[0085] D3. Convert the target asset data category corresponding to the first key information to obtain a second value, which is a prime number.
[0086] D4. Determine the signature public key based on the second value and the first base point;
[0087] 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;
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] Acquire digital asset data of the target user, which includes digital asset data of various categories;
[0095] The digital asset data is clustered to obtain k asset data categories;
[0096] Based on the user characteristic information of the target user, m target asset data categories are extracted from k asset data categories;
[0097] Extract digital asset data corresponding to m target asset data categories to obtain a first digital asset data set;
[0098] Extract key information from the first digital asset data in the first digital asset data set to obtain the first key information set;
[0099] Based on the first set of key information, an asset data relationship graph is constructed to obtain the target relationship graph.
[0100] 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.
[0101] 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.
[0102] 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:
[0103] 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;
[0104] Clustering unit 302 is used to perform clustering processing on the digital asset data to obtain k asset data categories;
[0105] 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.
[0106] 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.
[0107] 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.
[0108] 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.
[0109] In one possible implementation, the third extraction unit 305 is specifically used for:
[0110] 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.
[0111] Obtain the target user's core business information and confidential business information;
[0112] Determine the first key information extraction parameter vector based on the core business information;
[0113] Determine the second key information extraction parameter vector based on the aforementioned business confidentiality information;
[0114] 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.
[0115] The membership function is constructed by extracting the parameter matrix using the key information to obtain the target membership function;
[0116] 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.
[0117] 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.
[0118] 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:
[0119] The key information extraction parameter matrix is weighted to obtain the key information extraction parameter weight matrix.
[0120] The membership function is constructed by extracting the parameter weight matrix from the key information to obtain the target membership function.
[0121] In one possible implementation, the device is further used for:
[0122] 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.
[0123] The second set of key information is divided into blocks to obtain a data blocks;
[0124] Send the a data blocks to the server;
[0125] The server extracts the second key information from the a data blocks to obtain the second key information set.
[0126] The server performs signature authentication on the second key information in the second key information set and obtains the signature authentication result.
[0127] If the signature authentication result is successful, the server stores the second set of key information.
[0128] 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:
[0129] Obtain the first random number and the first base point of the elliptic curve;
[0130] The first signature parameters are determined based on the first random number and the first base point;
[0131] The target asset data category corresponding to the first key information is converted to obtain a second value, which is a prime number.
[0132] The signature public key is determined based on the second value and the first base point;
[0133] 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;
[0134] 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.
[0135] 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.
[0136] 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.
[0137] 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.
[0138] 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.
[0139] 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.
[0140] 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.
[0141] 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.
[0142] 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.
[0143] 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.
[0144] 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.
[0145] 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 data processing device based on a digital asset data relationship 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.
2. The data processing device based on a digital asset data relationship graph according to claim 1, characterized in that, 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.
3. The data processing device based on a digital asset data relationship graph according to claim 2, 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.
4. The data processing apparatus based on a digital asset data relationship graph according to any one of claims 1-3, characterized in that, The device is also used for: 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.
5. The data processing device based on a digital asset data relationship graph according to claim 4, characterized in that, In the aspect of performing signature processing on the first key information in the first key information set to obtain the second key information set, the apparatus is specifically used for: 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.
6. A data processing method based on a digital asset data relationship graph, 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; Based on the first set of key information, an asset data relationship graph is constructed to obtain the target relationship graph.
7. The data processing method based on a digital asset data relationship graph according to claim 6, characterized in that, The step of 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.
8. The data processing method based on digital asset data relationship graphs according to claim 7, 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.
9. 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 5-8.
10. 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 5-8.