Digital asset data processing method based on relation graph and related device

Abstract

The embodiment of the invention provides a digital asset data processing method based on a relation graph and a related device, and the method comprises the steps: obtaining digital asset data of a target user, the digital asset data comprising various types 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 digital asset data corresponding to the m target asset data categories to obtain a first digital asset data set; performing key information extraction on first digital asset data in the first digital asset data set to obtain a first key information set; the asset data relation graph is constructed according to the first key information set, the target relation graph is obtained, and the accuracy of subsequent data analysis according to the target relation graph can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of big data and data analysis, and specifically to a digital asset data processing method and related devices based on a relationship graph. Background Art

[0002] The existing analysis and processing of digital asset data usually directly classifies the digital asset data, and then analyzes and processes the data of each category after classification separately, which makes the data analysis and processing relatively simple, resulting in low accuracy during data analysis and processing. Summary of the invention

[0003] The embodiments of the present application provide a digital asset data processing method and related device based on a relationship graph, which can extract a key information set according to user characteristic 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.

[0004] A first aspect of an embodiment of the present application provides a method for processing digital asset data based on a relationship graph, the method comprising:

[0005] Acquire digital asset data of a target user, wherein the digital asset data includes digital asset data of multiple categories;

[0006] Performing clustering processing on the digital asset data to obtain k asset data categories;

[0007] Extracting m target asset data categories from the k asset data categories according to the user characteristic information of the target user;

[0008] Extracting the digitized asset data corresponding to the m target asset data categories to obtain a first digitized asset data set;

[0009] Extracting key information from the first digitized asset data in the first digitized asset data set to obtain a first key information set;

[0010] An asset data relationship map is constructed according to the first key information set to obtain a target relationship map.

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

[0012] A second aspect of an embodiment of the present application provides a digital asset data processing device based on a relationship graph, the device comprising:

[0013] An acquisition unit, used to acquire digital asset data of a target user, wherein the digital asset data includes asset data of multiple categories;

[0014] A clustering unit, used for clustering the digital asset data to obtain k asset data categories;

[0015] A first extraction unit, configured to extract m target asset data categories from the k asset data categories according to user characteristic information of the target user;

[0016] A second extraction unit is used to extract the digitized asset data corresponding to the m target asset data categories to obtain a first digitized asset data set;

[0017] A third extraction unit, configured to extract key information from the first digitized asset data in the first digitized asset data set to obtain a first key information set;

[0018] A construction unit is used to construct an asset data relationship map according to the first key information set to obtain a target relationship map.

[0019] A third aspect of an embodiment of the present application provides a terminal, comprising 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 comprises program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiment of the present application.

[0020] The 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, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiments of the present application.

[0021] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A schematic diagram of a process flow of a digital asset data processing method based on a relationship graph is provided for an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0025] Figure 3 A structural schematic diagram of a digital asset data processing device based on a relationship graph is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

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

[0029] In order to better understand the digital asset data processing method based on the relationship graph provided in the embodiment of the present application, the following first briefly introduces the method for analyzing and processing digital asset data in the existing scheme. In the existing scheme, the digital asset data is usually directly classified and processed, and then each category of the classified data is analyzed and processed separately, which makes the data analysis and processing relatively simple and does not pay attention to the correlation between different categories of data, so that the accuracy of data analysis and processing is low.

[0030] In order to solve the above-mentioned problems, an embodiment of the present application provides a digital asset data processing method based on a relationship graph, which can extract a key information set according to 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.

[0031] See also Figure 1 , Figure 1 A schematic diagram of a process flow of a digital asset data processing method based on a relationship graph is provided for an embodiment of the present application. Figure 1 As shown, the method includes:

[0032] 101. Acquire digital asset data of a target user in real time, wherein the digital asset data includes digital asset data of multiple categories.

[0033] The target users may 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, operation data, product data, social media data, user behavior data, marketing activity data, customer satisfaction data, equipment data, etc.

[0034] Specifically, customer data includes basic customer information, purchase history, preferences, etc.; sales data: such as sales amount, order quantity, sales channel, etc.; transaction data: data generated by customers during purchase transactions; market data: market trends, competitor analysis, etc.; supply chain data: supplier information, logistics data, etc.; human resources data: employee information, performance data, etc.; operational data: production efficiency, quality control, etc.; product data: product features, usage, etc.; social media data: brand reputation, customer feedback, etc.; user behavior data: website or application browsing, clicking, interaction and other data; marketing campaign data: advertising effectiveness, conversion rate, etc.; customer satisfaction data: customer satisfaction evaluation of products or services; equipment data: operating status of enterprise equipment, maintenance records, etc.

[0035] The digital asset data of the customer can be analyzed and processed to obtain the data required by the target user, for example, the target user's customer-related purchase preferences.

[0036] 102. Perform clustering processing on the digital asset data to obtain k asset data categories.

[0037] Among them, since the acquired digital asset data may be data that has not been classified, it is necessary to cluster the digital asset data to obtain k asset data categories. The asset data category can be one of the asset data categories shown in the above example, and of course it can also be other categories. This is only for example and is not specifically limited.

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

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

[0040] The user characteristic information may include enterprise type, enterprise business direction, etc. Enterprise type may be, for example, joint venture, sole proprietorship, state-owned, private, state-owned, collectively owned, joint-stock, limited liability, etc. Enterprise business direction may be understood as the type of business currently operated by the enterprise.

[0041] The asset data category that the target user currently needs to focus on can be determined based on the user feature information, so that the asset data category is determined as the target asset data category. The type of asset data that the target user currently needs to focus on can be determined based on the enterprise type and the enterprise business direction. For example, different enterprise types have asset data categories that they focus on, and the asset data category needs to be associated with the enterprise business direction, etc.

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

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

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

[0045] The method for 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, obtaining a reference key information set corresponding to each target asset data category, and then constructing a key information extraction parameter matrix in combination with the core business information and business confidentiality information of the target user, constructing a membership function based on the constructed key information extraction parameter matrix, and finally calculating the membership corresponding to the reference key information according to the membership function, and determining the first key information set according to the membership. Therefore, the first key information set can be determined by matrix operations, the construction of membership functions, etc., which improves the accuracy of determining the first key information set.

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

[0047] The method for constructing an asset data relationship map based on the first key information set may be: extracting the asset data category of each first key information in the first key information set, constructing a map reference point based on the asset data category, and constructing an asset data relationship map based on the reference point according to the association relationship between each first key information. The first key information in the category is arranged near the map reference point, and its arrangement area is distributed in a circular shape, with the reference point as the center of the circle. Then, based on the association relationship between the first key information point near each reference point and the key information points near other reference points, the arrangement distance between each reference point is determined (the stronger the association, the shorter the arrangement distance, and the weaker the association, the longer the arrangement distance). After determining the arrangement distance, a connection process is performed based on the arrangement distance and the circular area of ​​the reference point, thereby forming an asset data relationship map.

[0048] After constructing the asset data relationship map, the target user can use the asset data relationship map to perform subsequent data analysis and processing, etc. This can improve the efficiency of the target user in subsequent data analysis and processing.

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

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

[0051] A1. Performing 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 core business information and business confidential information of the target user;

[0053] A3. Determine a first key information extraction parameter vector according to the core business information;

[0054] A4. Determine a second key information extraction parameter vector according to the business confidential information;

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

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

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

[0058] A8. According to 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] Among them, a general key information clustering processing method can be used to perform key information clustering processing on the first digitized asset data set and the first digitized asset data in the corresponding target asset data category to obtain a reference key information set.

[0060] The core business information and business confidential information can be obtained through pre-input by the target user. Such business information and confidential information can be understood as data that the target user needs to process securely. The first key information extraction parameter vector can be determined based on the core business information, and the second key information extraction parameter vector can be determined based on the business confidential information. The vector scales of the first key information extraction parameter vector and the second key information extraction parameter vector are the same. The core business information includes L core businesses, then the first key information extraction parameter vector can be (W1, W2, W3, ..., WL), W1 is the business description information of the first core business, there are L businesses that need to be handled confidentially in the business confidential information, 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 handled confidentially.

[0061] The first key information extraction parameter vector and the second key information extraction parameter vector can be processed by vertical splicing to obtain a key information extraction parameter matrix. The key information extraction parameter matrix can be weighted to obtain a key information extraction parameter weight matrix, and a membership function can be constructed according to the key information extraction parameter weight matrix to obtain a target membership function. The target membership function is a function for calculating the score value of the reference key information being evaluated as the first key information.

[0062] The membership value can be calculated according to the target membership function to obtain the membership value of each reference key information. The preset membership threshold is set by experience value or historical data, and the reference key information corresponding to the membership value higher than the preset membership threshold is determined as the first key information, thereby 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 business confidential information, and a membership function is 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, thereby improving the accuracy of determining the first key information set.

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

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

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

[0067] Among them, each element in the key information extraction parameter matrix can be weighted to obtain a key information extraction parameter weight matrix. Each element has its corresponding weight value, which is pre-set, so that the accuracy of subsequent target membership function construction can be improved based on weight preprocessing.

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

[0069]

[0070] Among them, I i,j Extract element A in the parameter weight matrix for key information i,j Typical ranking value (importance ranking value, that is, m and element A i,j similarity between them); n is the number of elements in the key information extraction parameter weight matrix (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 according to the key information to construct the membership function, thereby obtaining the target membership function, thereby improving the accuracy of the target membership function. At the same time, the subsequent use of the target membership function for membership calculation can also improve the accuracy and efficiency of the membership calculation.

[0072] In a possible implementation, the first key information in the acquired first key information may also be signed to improve the security of the information. Specifically, it may be:

[0073] C1. Signing the first key information in the first key information set to obtain a second key information set, wherein the tamper-proof capability of the second key information in the second key information set is higher than the tamper-proof capability of the corresponding first key information;

[0074] C2, dividing the second key information set into blocks to obtain a data blocks;

[0075] C3, sending the a data blocks to the server;

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

[0077] C5. The server performs signature authentication on the second key information in the second key information set to obtain a signature authentication result;

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

[0079] Among them, a random number and a first base point of an elliptic curve may be selected, and signature processing may be performed based on the first random number, the first base point and a numerical value obtained after converting the first key information to obtain the second key information.

[0080] After obtaining the second key information, the second key information can be sent to the server. When sending, since there may be a large amount of second key information, the second key information set can be processed in blocks to obtain a data blocks. Then the a data blocks are sent to the server, and the server can extract the second key information from the a data blocks to obtain the second key information set. A signature authentication method that matches the signature processing can be used to perform signature authentication to obtain a signature authentication result. After the authentication is successful, the second key information set is stored so that the subsequent target user can extract the second key information set from the server for data analysis and processing. By performing signature processing on the first key information, the protection of key information can be enhanced and the risk of information tampering can be reduced.

[0081] In a possible implementation, a method for performing signature processing on first key information in the first key information set to obtain a second key information set includes:

[0082] D1, obtaining a first random number and a first base point of an elliptic curve;

[0083] D2. Determine a first signature parameter according to 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, where the second value is a prime number;

[0085] D4. Determine a signature public key according to the second value and the first base point;

[0086] D5. performing a hash operation on the target first key information to obtain a target hash value, wherein the target first key information 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 second key information corresponding to the target first key information, where the second value is a private key;

[0088] D7. Repeat the steps of obtaining the first random number and the first base point of the elliptic curve, performing a signature operation on the target hash value using the first signature parameter, the signature public key, and the second value, obtaining the second key information corresponding to the target first key information, wherein the second value is a private key, until a 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 determined as the first signature parameter.

[0090] Since the first key information has its corresponding target asset data category, the target asset data category can be used for conversion processing to obtain a second value, which can be processed using a preset conversion processing method, which can be a pre-set conversion method. The product of the second value and the first base point can be determined as a signature public key, and the second value can be determined as a signature private key.

[0091] A general hash operation method can be used to operate the target first key information to obtain the target hash value. The product of the first random number and the ordinate of the public key, the ratio of the sum of the target hash value and the second value can be determined as the second key information. When verifying, it can be verified whether 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 sum of the ordinate of the public key and the first signature parameter are the same. If they are the same, the signature authentication is successful. If they are different, the signature authentication fails. If the second value is determined as the signature private key, the target asset data category can be transmitted and processed while signing, which improves the reliability, concealment, and security of data transmission.

[0092] For the above embodiments, please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application, as shown in the figure, includes a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for executing the following steps;

[0093] Acquire digital asset data of a target user, wherein the digital asset data includes digital asset data of multiple categories;

[0094] Performing clustering processing on the digital asset data to obtain k asset data categories;

[0095] Extracting m target asset data categories from the k asset data categories according to the user characteristic information of the target user;

[0096] Extracting the digitized asset data corresponding to the m target asset data categories to obtain a first digitized asset data set;

[0097] Extracting key information from the first digitized asset data in the first digitized asset data set to obtain a first key information set;

[0098] An asset data relationship map is constructed according to the first key information set to obtain a target relationship map.

[0099] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that in order to realize the above functions, the terminal includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

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

[0101] In line with the above, see Figure 3 , Figure 3 The present application provides a schematic diagram of a digital asset data processing device based on a relationship graph. Figure 3 As shown, the device comprises:

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

[0103] A clustering unit 302, configured to perform clustering processing on the digital asset data to obtain k asset data categories;

[0104] A first extraction unit 303 is used to extract m target asset data categories from the k asset data categories according to the user characteristic information of the target user;

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

[0106] A third extraction unit 305 is used to extract key information from the first digitized asset data in the first digitized asset data set to obtain a first key information set;

[0107] The construction unit 306 is used to construct an asset data relationship map according to the first key information set to obtain a target relationship map.

[0108] In a possible implementation, the third extraction unit 305 is specifically configured to:

[0109] Performing key information clustering processing on the first digitized asset data set in the corresponding target asset data category to obtain a reference key information set corresponding to each target asset data category;

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

[0111] Determine a first key information extraction parameter vector according to the core business information;

[0112] Determine a second key information extraction parameter vector according to the business confidential information;

[0113] Concatenate the first key information extraction parameter vector and the second key information extraction parameter vector to obtain a key information extraction parameter matrix;

[0114] The key information extraction parameter matrix is ​​used to construct a membership function to obtain a target membership function;

[0115] Calculating the membership of the reference key information in the reference key information set corresponding to each target asset data category according to the target membership function to obtain the membership value of each reference key information;

[0116] According to 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 to obtain the first key information set.

[0117] In a possible implementation, in the aspect of constructing a membership function by using the key information extraction parameter matrix to obtain a target membership function, the third extraction unit 305 is specifically used to:

[0118] Performing weight assignment processing on the key information extraction parameter matrix to obtain the key information extraction parameter weight matrix;

[0119] The key information extraction parameter weight matrix is ​​used to construct a membership function to obtain a target membership function.

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

[0121] Performing a signature process on the first key information in the first key information set to obtain a second key information set, wherein the tamper-proof capability of the second key information in the second key information set is higher than the tamper-proof capability of the corresponding first key information;

[0122] Processing the second key information set in blocks to obtain a data blocks;

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

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

[0125] The server performs signature authentication on the second key information in the second key information set to obtain a signature authentication result;

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

[0127] In a possible implementation manner, 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 further configured to:

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

[0129] Determine a first signature parameter according to the first random number and the first base point;

[0130] Convert the target asset data category corresponding to the first key information to obtain a second value, where the second value is a prime number;

[0131] Determine a signature public key according to the second value and the first base point;

[0132] Performing a hash operation on the target first key information to obtain a target hash value, wherein the target first key information is any one of the first key information set;

[0133] Performing a signature operation on the target hash value using the first signature parameter, the signature public key, and the second value to obtain second key information corresponding to the target first key information, where the second value is a private key;

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

[0135] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the digital asset data processing methods based on relationship graphs recorded in the above method embodiments.

[0136] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any one of the digital asset data processing methods based on relationship graphs recorded in the above method embodiments.

[0137] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0138] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

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

[0141] In addition, the functional units in the various embodiments of the application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0142] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially 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, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0143] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0144] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for processing digital asset data based on a relationship graph, characterized in that: The method comprises: Acquire digital asset data of a target user, wherein the digital asset data includes digital asset data of multiple categories; 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 characteristic information of the target user; Extracting the digitized asset data corresponding to the m target asset data categories to obtain a first digitized asset data set; Extracting key information from the first digitized asset data in the first digitized asset data set to obtain a first key information set; An asset data relationship map is constructed according to the first key information set to obtain a target relationship map.

2. The method for processing digital asset data based on relationship graph according to claim 1, characterized in that: The step of extracting key information from the first digital asset data in the first digital asset data set to obtain a first key information set includes: Performing key information clustering processing on the first digitized asset data set in the corresponding target asset data category to obtain a reference key information set corresponding to each target asset data category; Obtaining the target user's core business information and business confidential information; Determine a first key information extraction parameter vector according to the core business information; Determine a second key information extraction parameter vector according to the business confidential information; Concatenate the first key information extraction parameter vector and the second key information extraction parameter vector to obtain a key information extraction parameter matrix; The key information extraction parameter matrix is ​​used to construct a membership function to obtain a target membership function; Calculating the membership of the reference key information in the reference key information set corresponding to each target asset data category according to the target membership function to obtain the membership value of each reference key information; According to 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 to obtain the first key information set.

3. The method for processing digital asset data based on relationship graph according to claim 2, characterized in that: The step of using the key information extraction parameter matrix to construct a membership function to obtain a target membership function includes: Performing weight assignment processing on the key information extraction parameter matrix to obtain the key information extraction parameter weight matrix; The key information extraction parameter weight matrix is ​​used to construct a membership function to obtain a target membership function.

4. The method for processing digital asset data based on a relationship graph according to any one of claims 1 to 3, characterized in that: The method further comprises: Performing a signature process on the first key information in the first key information set to obtain a second key information set, wherein the tamper-proof capability of the second key information in the second key information set is higher than the tamper-proof capability of the corresponding first key information; Processing the second key information set in blocks to obtain a data blocks; Sending the a data blocks to the server; The server extracts the second key information from the a data blocks to obtain a second key information set; The server performs signature authentication on the second key information in the second key information set to obtain a signature authentication result; If the signature authentication result is successful, the server stores the second key information set.

5. The method for processing digital asset data based on relationship graph according to claim 4 is characterized in that: The step of performing signature processing on the first key information in the first key information set to obtain a second key information set includes: Obtain a first random number and a first base point of an elliptic curve; Determine a first signature parameter according to the first random number and the first base point; Convert the target asset data category corresponding to the first key information to obtain a second value, where the second value is a prime number; Determine a signature public key according to the second value and the first base point; Performing a hash operation on the target first key information to obtain a target hash value, wherein the target first key information is any one of the first key information set; Performing a signature operation on the target hash value using the first signature parameter, the signature public key, and the second value to obtain second key information corresponding to the target first key information, where the second value is a private key; Repeat the steps of obtaining the first random number and the first base point of the elliptic curve, performing a signature operation on the target hash value through the first signature parameter, the signature public key, and the second value, obtaining the second key information corresponding to the target first key information, wherein the second value is the private key, until a second key information set is obtained.

6. A digital asset data processing device based on a relationship graph, characterized in that: The device comprises: An acquisition unit, used to acquire digital asset data of a target user, wherein the digital asset data includes asset data of multiple categories; A clustering unit, used for clustering the digital asset data to obtain k asset data categories; A first extraction unit, configured to extract m target asset data categories from the k asset data categories according to user characteristic information of the target user; A second extraction unit is used to extract the digitized asset data corresponding to the m target asset data categories to obtain a first digitized asset data set; A third extraction unit, configured to extract key information from the first digitized asset data in the first digitized asset data set to obtain a first key information set; A construction unit is used to construct an asset data relationship map according to the first key information set to obtain a target relationship map.

7. The digital asset data processing device based on relationship graph according to claim 6 is characterized in that: The third extraction unit is specifically used for: Performing key information clustering processing on the first digitized asset data set in the corresponding target asset data category to obtain a reference key information set corresponding to each target asset data category; Obtaining the target user's core business information and business confidential information; Determine a first key information extraction parameter vector according to the core business information; Determine a second key information extraction parameter vector according to the business confidential information; Concatenate the first key information extraction parameter vector and the second key information extraction parameter vector to obtain a key information extraction parameter matrix; The key information extraction parameter matrix is ​​used to construct a membership function to obtain a target membership function; Calculating the membership of the reference key information in the reference key information set corresponding to each target asset data category according to the target membership function to obtain the membership value of each reference key information; According to 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 to obtain the first key information set.

8. The digital asset data processing device based on relationship graph according to claim 7 is characterized in that: In the aspect of constructing a membership function by using the key information extraction parameter matrix to obtain a target membership function, the third extraction unit is specifically used for: Performing weight assignment processing on the key information extraction parameter matrix to obtain the key information extraction parameter weight matrix; The key information extraction parameter weight matrix is ​​used to construct a membership function to obtain a target membership function.

9. A terminal, characterized in that: The method 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 interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

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