Data impact assessment methods, devices, computer equipment and storage media

Abstract

This disclosure provides a data influence assessment method, apparatus, computer equipment, and storage medium, relating to the fields of data circulation, data processing, and big data artificial intelligence. The data influence assessment method includes: acquiring seller data; performing on-chain processing on the seller data to obtain on-chain processing results; detecting the seller's NFT encoded data based on the on-chain processing results; obtaining a uniqueness coefficient based on the NFT encoded data; acquiring basic information about the buyer and seller during the transaction process, including: buyer information, seller information, seller data information, NFT encoding, purchase method, and purchase date; preprocessing the basic information to obtain preliminary data; constructing a knowledge graph based on the preliminary data; obtaining calculation parameters based on the knowledge graph; and calculating an influence score based on the calculation parameters and the uniqueness coefficient. This invention can guarantee the uniqueness of data during data transactions and accurately assess the influence of data through quantification.

Description

Technical Field

[0001] This invention relates to the fields of data circulation, data processing and big data artificial intelligence technology, and in particular to data influence assessment methods, data influence assessment devices, computer equipment and storage media. Background Technology

[0002] Currently, there is no clear quantitative method for assessing the influence of data in the field of data circulation. Most assessments are qualitative, such as third-party notarization or comprehensive calculations based on buyer evaluations. In addition, there is a formula for calculating the influence of academic papers, which only considers the two dimensions of citation count and time. However, data transactions involve data packaging and reprocessing, so this formula cannot be particularly suitable for assessing the influence in the data transaction process.

[0003] The impact of data needs to be accurately assessed during the data circulation process, and the current data transaction process has certain shortcomings in assessing the impact of data. Summary of the Invention

[0004] The main objective of this disclosure is to provide a data influence assessment method, apparatus, computer equipment, and storage medium to ensure the uniqueness of data during data transactions and to accurately assess the influence of data through quantification.

[0005] To achieve the above objectives, a first aspect of this disclosure provides a data influence assessment method, comprising:

[0006] Obtain seller data;

[0007] The seller data is processed on the blockchain to obtain the processing result.

[0008] The seller's NFT encoded data is detected based on the on-chain processing results;

[0009] The uniqueness coefficient is obtained based on the NFT encoded data;

[0010] Obtain the basic information of the buyer and seller during the transaction process from the seller data. The basic information includes: buyer information, seller information, seller data information, NFT code, purchase method, and purchase date.

[0011] The basic information is preprocessed to obtain preliminary data;

[0012] A knowledge graph is constructed based on the preliminary data;

[0013] Based on the knowledge graph, obtain the calculation parameters;

[0014] The influence score of the seller's data is calculated based on the calculation parameters and the uniqueness coefficient.

[0015] In some embodiments, on-chain processing of the seller data includes:

[0016] The seller's data is stored on a decentralized storage network using documents with hash values ​​stored offline.

[0017] Alternatively, the seller's data can be directly stored in an NFT.

[0018] In some embodiments, obtaining the uniqueness coefficient based on the NFT encoded data includes:

[0019] If the seller's data does not have the NFT encoding, then a first coefficient is obtained and used as the uniqueness coefficient; wherein the first coefficient is 0.5.

[0020] If the seller data obtains the NFT encoding, then the second coefficient is obtained and used as the uniqueness coefficient; wherein the second coefficient is 1.

[0021] In some embodiments, the data preprocessing of the basic information to obtain preliminary data includes:

[0022] The basic information is reviewed, filtered, and sorted to obtain preliminary information;

[0023] Preliminary data is obtained by logically expressing preliminary information using a relational database with a two-dimensional table structure.

[0024] In some embodiments, a knowledge graph is constructed based on the preliminary data, including:

[0025] A knowledge graph is constructed based on the transaction behavior data of the buyers and sellers in the preliminary data.

[0026] In some embodiments, the step of obtaining computational parameters based on the knowledge graph includes: a time coefficient, a breadth coefficient, and a depth coefficient.

[0027] In some embodiments, calculating the influence score of the seller data based on the calculation parameters includes:

[0028] Obtain the preset calculation formula;

[0029] The influence score of the seller's data is obtained by substituting the calculation parameters into the calculation formula.

[0030] The calculation formula is as follows:

[0031]

[0032] Where S is the influence score, k is the uniqueness coefficient, and W... t D is the breadth coefficient of the knowledge graph constructed from basic information within a time period t. tn W is the depth coefficient of the nth branch of the knowledge graph constructed from the basic information within time period t. t-1 D is the breadth coefficient of the knowledge graph constructed from the basic information in the previous time period of time period t. (t-1)n is the depth coefficient of the nth branch of the knowledge graph constructed from the basic information in the previous time period of time period t, where n is the branch number in the knowledge graph.

[0033] To achieve the above objectives, a second aspect of this disclosure provides a data influence assessment apparatus, comprising:

[0034] The data acquisition module is used to acquire seller data;

[0035] The data upload module is used to upload the seller data to the blockchain and obtain the upload result.

[0036] The uniqueness coefficient acquisition module is used to obtain the uniqueness coefficient based on the judgment result of whether NFT encoding can be obtained;

[0037] The basic information acquisition module is used to acquire the basic information of the buyer and seller in the transaction process of the seller data;

[0038] The data processing module performs data preprocessing and structuring on the basic information to obtain preliminary data;

[0039] A knowledge graph construction module is used to construct a knowledge graph based on the preliminary data;

[0040] The calculation parameter acquisition module is used to acquire calculation parameters based on the knowledge graph.

[0041] The influence score calculation module is used to calculate the influence score of the seller data based on the calculation parameters and the uniqueness coefficient.

[0042] To achieve the above objectives, a third aspect of this disclosure provides a computer device, comprising:

[0043] At least one memory;

[0044] At least one processor;

[0045] At least one program;

[0046] The program is stored in the memory, and the processor executes the at least one program to implement the method described in the first aspect of this disclosure, for example.

[0047] To achieve the above objectives, a fourth aspect of this disclosure provides a storage medium that is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform:

[0048] As described in the first aspect above.

[0049] The data influence assessment method, apparatus, computer equipment, and storage medium proposed in this disclosure include: acquiring seller data; performing on-chain processing on the seller data to obtain on-chain processing results; detecting the seller's NFT encoded data based on the on-chain processing results; obtaining a uniqueness coefficient based on the NFT encoded data; acquiring basic information of the buyer and seller during the transaction process, including buyer information, seller information, seller data information, NFT encoding, purchase method, and purchase date; performing data preprocessing on the basic information to obtain preliminary data; constructing a knowledge graph based on the preliminary data; obtaining calculation parameters based on the knowledge graph; and calculating the influence score of the seller data based on the calculation parameters and the uniqueness coefficient. The technical solution provided by this disclosure can guarantee the uniqueness of data during data transactions and accurately assess the influence of data through quantification. Attached Figure Description

[0050] Figure 1 This is a flowchart of the data influence assessment method provided in the embodiments of this disclosure.

[0051] Figure 2 yes Figure 1 The flowchart for step 107 in the process.

[0052] Figure 3 yes Figure 1 The knowledge graph constructed in step 107.

[0053] Figure 4 This is a block diagram of a data influence assessment device according to an embodiment of the present disclosure.

[0054] Figure 5 This is a schematic diagram of the hardware structure of a computer device provided in an embodiment of this disclosure.

[0055] Figure 6 yes Figure 1 The flowchart for step 106 in the process. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.

[0057] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0059] First, let's analyze some of the terms used in this application:

[0060] Non-fungible tokens (NFTs): Common tokens (such as BTC, ETH, etc.) are fungible; each BTC is indistinguishable and interchangeable. A key characteristic of NFTs is that each NFT has a unique and irreplaceable identifier, is not interchangeable, has a minimum unit of 1, and is indivisible. Each NFT maps to a unique serial number on a specific blockchain, is immutable, indivisible, and cannot be substituted for another. These characteristics make NFTs an excellent medium for digital art; each NFT represents a specific digital artwork or a limited-edition copy, recording its immutable on-chain rights. Therefore, NFTs are fundamentally different from fungible tokens such as cryptocurrencies. They are backed by the actual value of digital goods but do not possess any monetary attributes such as payment functionality. NFTs are unique because they are based on blockchain technology and, from their inception, establish a unique mapping relationship with a specific digital good, serving as a unique proof of rights for that digital good on a specific blockchain.

[0061] Knowledge Graph: Knowledge graph is an important branch of artificial intelligence technology. It is a structured semantic knowledge base used to describe concepts and their relationships in the physical world in symbolic form. Its basic building blocks are the "entity-relationship-entity" triple, as well as entities and their related attribute-value pairs. Entities are interconnected through relations to form a network of knowledge structures.

[0062] Uniform Resource Locator (URL): A method of representing the location of information on the World Wide Web service of the Internet.

[0063] The InterPlanetary File System (IPFS) is a network transport protocol designed to create persistent and distributed storage and sharing of files. It is a content-addressable peer-to-peer hypermedia distribution protocol.

[0064] Currently, there is no clear quantitative method for assessing data influence in the field of data circulation. Most assessments are qualitative, such as third-party notarization or comprehensive calculations based on buyer evaluations. In addition, there is a formula for calculating the influence of academic papers, which only considers the two dimensions of citation count and time. However, data transactions involve data packaging and reprocessing, so this formula cannot be particularly suitable for assessing the influence in the data transaction process. Furthermore, unlike academic papers, data is reproducible, so determining the uniqueness of data is an important indicator for calculating influence.

[0065] The process of data trading requires ensuring the uniqueness of the data and accurately assessing its impact. Current data trading methods cannot effectively guarantee the uniqueness of the data, and the assessment of the data's impact also has certain shortcomings.

[0066] Based on this, the embodiments of this disclosure provide a data influence assessment method, apparatus, computer equipment, and storage medium, which can ensure the uniqueness of data during data transactions and accurately assess the influence of seller data through quantitative calculation formulas.

[0067] This disclosure provides a data influence assessment method, apparatus, computer equipment, and storage medium, which are specifically described through the following embodiments. First, the data influence assessment method in this disclosure is described.

[0068] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0069] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0070] The data influence assessment method provided in this disclosure relates to the field of big data and artificial intelligence technology, and particularly to the field of data trading. The data influence assessment method provided in this disclosure can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, or smartwatch, etc.; the server can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms; the software can be an application implementing the data influence assessment method, but is not limited to the above forms.

[0071] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0072] Figure 1 This is an optional flowchart of the data influence assessment method provided in the embodiments of this disclosure. Figure 1 The methods may include, but are not limited to, steps 101 to 109.

[0073] Step 101: Obtain seller data;

[0074] Step 102: Process the seller's data on the blockchain to obtain the processing result.

[0075] Step 103: Detect the seller's NFT encoding data based on the on-chain processing results;

[0076] Step 104: Obtain the uniqueness coefficient based on the NFT encoded data;

[0077] Step 105: Obtain basic information about the buyer and seller during the transaction process;

[0078] Step 106: Perform data preprocessing on the basic information to obtain preliminary data;

[0079] Step 107: Construct a knowledge graph based on the preliminary data;

[0080] Step 108: Obtain the calculation parameters based on the knowledge graph;

[0081] Step 109: Calculate the influence score of the seller's data based on the calculation parameters and uniqueness coefficient.

[0082] Currently, in the field of data trading, there is no clear quantitative method for assessing data influence. Most assessments are qualitative, such as third-party notarization or comprehensive calculations based on buyer evaluations. Furthermore, while there are existing formulas for calculating the influence of academic papers, these only consider citation counts and time. However, data trading involves data packaging and reprocessing, making these formulas unsuitable for assessing influence in data trading. Additionally, unlike academic papers, data is reproducible; therefore, ensuring data uniqueness is a crucial indicator for calculating influence. This disclosure embodiment obtains seller data and related information to execute the data influence assessment method of this disclosure embodiment based on that seller data and related information.

[0083] This embodiment of the disclosure, based on the non-homogeneous nature of NFTs and combined with knowledge graphs and data influence calculation formulas, ensures the uniqueness of data during data transactions and can accurately assess the influence of seller data through quantitative calculation formulas.

[0084] In step 101 of some embodiments, the seller data may be a text document, image, video, or other data that can be stored on a computer hard drive.

[0085] In step 102 of some embodiments, the seller data is processed on the blockchain to obtain a processing result. Specifically, the on-chain processing of the seller data generally includes at least one of the following steps:

[0086] Most of the data is stored off-chain, and the data's URL points to NFTs on the blockchain.

[0087] Alternatively, documents with hash values ​​can be stored offline, storing the seller's data on a decentralized storage network, such as using IPFS to create addressable data hashes, or Content Identifiers (CIDs). These CIDs serve both as a way to retrieve data and as a way to ensure data validity. When a user retrieves data, the data is automatically reproduced on the user's computer to ensure that the data matches the original CID requested by the user. This process ensures that the received data is completely consistent with the requested data. If a malicious node attempts to transmit false data, the CID generated on the user's end will be different, alerting the user that they are receiving fraudulent data.

[0088] Alternatively, users can store seller data directly in the NFT.

[0089] In step 104 of some embodiments, a uniqueness coefficient is obtained based on the NFT encoded data;

[0090] Specifically, if the seller data does not have the NFT encoding, then a first coefficient is obtained and used as the uniqueness coefficient; wherein the first coefficient is 0.5;

[0091] If the seller data obtains the NFT encoding, then the second coefficient is obtained and used as the uniqueness coefficient; wherein the second coefficient is 1.

[0092] In step 105 of some embodiments, the seller data is obtained from the basic information of the buyer and seller during the transaction process. Specifically, the basic information of the buyer and seller generally includes: buyer information and seller information, seller data information, NFT code, and purchase date.

[0093] Please see Figure 6 In step 106 of some embodiments, the basic information of the buyer and seller is preprocessed. Specifically, the data preprocessing generally includes:

[0094] Step 701: Review, filter, and sort the basic information to obtain preliminary information;

[0095] Step 702: Logically express the preliminary information using a two-dimensional table structure in a relational database to obtain preliminary data.

[0096] In this embodiment, the basic information of the buyer and seller is reviewed, filtered, and sorted to clean up useless data and facilitate data structuring.

[0097] The data structuring of the aforementioned basic information involves taking the pre-processed basic information (unstructured data), extracting and classifying the attributes of the data in the basic information, and transforming it into preliminary data (structured data), namely row data, which is stored in a database and can be logically expressed using a two-dimensional table structure.

[0098] In step 107 of some embodiments, a knowledge graph is constructed based on preliminary data. Specifically, the basic unit of the knowledge graph is the entity-relationship-entity triple. In this embodiment, the entity represents the corresponding user, and the edge between entities represents the relationship between two entities, which in this embodiment generally represents the transaction relationship between buyers and sellers. In this embodiment, the basic information obtained after data structuring can be directly used to construct the knowledge graph. Specifically, through a series of automated or semi-automated technical means, knowledge elements are extracted from the preliminary data, namely a large number of entities and relationships between entities, which in this embodiment are the transaction relationships between users.

[0099] Specifically, refer to Figure 2 In this embodiment of the disclosure, the construction of the knowledge graph includes, but is not limited to, the following steps:

[0100] Step 501, Knowledge Modeling;

[0101] Step 502: Knowledge Storage;

[0102] Step 503: Information Extraction;

[0103] Step 504: Knowledge Integration;

[0104] Step 505: Knowledge Calculation.

[0105] Specifically, step 501, knowledge modeling, includes: based on the application attributes, knowledge characteristics, and actual needs of the scenario, business abstraction and business modeling are performed according to the knowledge graph pattern, mainly entity definition, relationship definition, and attribute definition. In some embodiments of the present invention, entities are generally buyers and sellers, relationships are transaction relationships between buyers and sellers, and attributes include, but are not limited to, the identity information of buyers and sellers, NFT encoding, seller data information, and purchase method and purchase date.

[0106] Specifically, step 502, knowledge storage, includes: Currently, mainstream knowledge storage solutions include two types: single-mode and hybrid storage. There are generally two storage methods to choose from. One is to store knowledge using a standardized storage format such as RDF (Resource Description Framework), commonly used by companies like Jena. The other method is to use a graph database for storage, commonly used by companies like Neo4j.

[0107] Specifically, step 504, knowledge fusion, includes: since the basic information has been pre-structured in some embodiments of the present invention, only knowledge merging is required, mainly involving "merging external knowledge bases", handling conflicts between the data layer and the schema layer; and "merging relational databases" using methods such as RDB2RDF.

[0108] Specifically, step 505, knowledge computation, includes: after obtaining a series of basic factual expressions, acquiring a gridded knowledge system. (Refer to...) Figure 3 , Figure 3 It is a knowledge graph constructed based on the circulation of a seller's data in the data trading market within a certain time period.

[0109] In some embodiments, computational parameters are obtained based on the knowledge graph, including time coefficient, breadth coefficient, and depth coefficient.

[0110] Specifically, the time parameter is an attribute of the transaction relationship between two entities (users), indicating when the transaction occurred, used to construct the knowledge graph for its time period; the breadth coefficient is the number of branches in the knowledge graph starting from the seller's NFT data and extending to each end user. Figure 3 The breadth factor is 4, and the 4 branches are as follows:

[0111] ① Seller data NFT - User A - User B;

[0112] ② Seller data NFT - User C - User D - User E;

[0113] ③ Seller data NFT - User F - User G;

[0114] ④ Seller data NFT - User F - User H;

[0115] The depth coefficient is the number of entities in each branch. Specifically, in branch ①, the depth coefficient is 3 for seller data NFT-User A-User B; and in branch ②, the depth coefficient is 4 for seller data NFT-User C-User D-User E.

[0116] In step 109 of some embodiments, the influence score of the seller's data is calculated based on calculation parameters and a uniqueness coefficient, including...

[0117] Obtain the preset calculation formula;

[0118] Substitute the calculation parameters into the calculation formula to obtain the influence score of the seller's data;

[0119] The calculation formula is as follows:

[0120]

[0121] Where S is the influence score, k is the uniqueness coefficient, and W... t D is the breadth coefficient of the knowledge graph constructed from basic information within a time period t. tn W is the depth coefficient of the nth branch of the knowledge graph constructed from the basic information within time period t. t-1 D is the breadth coefficient of the knowledge graph constructed from the basic information in the previous time period of time period t. (t-1)n is the depth coefficient of the nth branch of the knowledge graph constructed from the basic information in the previous time period of time period t, where n is the branch number in the knowledge graph.

[0122] Specifically, refer to Figure 3 If the seller's data has been uploaded to the blockchain and encoded as an NFT, then within this time period, the uniqueness coefficient k is 1; the breadth coefficient W... t The value is 4, D tn The numbers are 3, 4, 3, and 3 respectively. It should be noted that the branch number represented by n is only for distinguishing different branches and does not need to be used to describe a specific order or sequence.

[0123] The technical solution provided in this disclosure involves: acquiring seller data; processing the seller data on the blockchain to obtain the processing result; detecting the seller's NFT encoding data based on the processing result; obtaining a uniqueness coefficient based on the NFT encoding data; acquiring basic information about the buyer and seller during the transaction process, including buyer information, seller information, seller data information, NFT encoding, purchase method, and purchase date; preprocessing the basic information to obtain preliminary data; constructing a knowledge graph based on the preliminary data; obtaining calculation parameters based on the knowledge graph; and calculating the seller's data influence score based on the calculation parameters and the uniqueness coefficient. This disclosure, based on the non-fungible nature of NFTs and combined with a knowledge graph and data influence calculation formula, ensures the uniqueness of data during data transactions and can accurately assess the influence of seller data through a quantitative calculation formula.

[0124] The technical solution provided in this disclosure, combining the non-fungible nature of NFTs with knowledge graphs and data influence calculation formulas, ensures the uniqueness of data during data transactions. It also accurately assesses the influence of seller data through quantitative calculation formulas, improving user data security and providing users purchasing data with reliable data influence references, thus enhancing user experience. The recommendation method provided in this disclosure can solve the problem of unreliable data in traditional data transactions.

[0125] This disclosure also provides a data influence assessment apparatus, which can implement the above-described data influence assessment method, with reference to... Figure 4The device includes:

[0126] 401. Data Acquisition Module, used to acquire seller data;

[0127] 402. Data upload module, used to upload seller data to the blockchain and obtain the upload results;

[0128] 403. Data detection module, used to detect the seller's NFT encoded data based on the on-chain processing results;

[0129] 404. Uniqueness Coefficient Acquisition Module, used to obtain the uniqueness coefficient based on the NFT encoded data;

[0130] 405. Basic Information Acquisition Module, used to acquire basic information about the buyer and seller;

[0131] 406. Data processing module: performs data preprocessing on basic information to obtain preliminary data;

[0132] 407. Knowledge Graph Construction Module, used to construct a knowledge graph based on preliminary data;

[0133] 408. Calculation parameter acquisition module, used to obtain calculation parameters based on the knowledge graph;

[0134] 409. Influence Score Calculation Module: This module is used to calculate the influence score of seller data based on calculation parameters and uniqueness coefficient.

[0135] The specific implementation of the data influence assessment device in this embodiment is basically the same as the specific implementation of the data influence assessment method described above, and will not be repeated here.

[0136] This disclosure also provides a computer device, including:

[0137] At least one memory;

[0138] At least one processor;

[0139] At least one program;

[0140] The program is stored in the memory, and the processor executes the at least one program to implement the data influence assessment method described in the embodiments of this disclosure. The computer device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), in-vehicle computers, etc.

[0141] Please see Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0142] The processor 601 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0143] The memory 602 can be implemented in the form of ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 602 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 using the data influence assessment method of the embodiments of this disclosure.

[0144] The input / output interface 603 is used to implement information input and output;

[0145] Communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved via wired means (e.g., USB, Ethernet cable) or wireless means (e.g., mobile network, Wi-Fi, Bluetooth).

[0146] Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604);

[0147] The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0148] This disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the above-described data influence assessment method.

[0149] The data influence assessment method, apparatus, computer equipment, and storage medium disclosed in this embodiment acquire seller data; perform on-chain processing on the seller data to obtain on-chain processing results; detect the seller's NFT encoded data based on the on-chain processing results; obtain a uniqueness coefficient based on the NFT encoded data; acquire basic information of the buyer and seller in the transaction process, including: buyer information, seller information, seller data information, NFT encoding, purchase method, and purchase date; perform data preprocessing on the basic information to obtain preliminary data; construct a knowledge graph based on the preliminary data; obtain calculation parameters based on the knowledge graph; and calculate the influence score of the seller data based on the calculation parameters and the uniqueness coefficient. This ensures the uniqueness of data during the data transaction process and can accurately assess the influence of seller data through a quantitative calculation formula.

[0150] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0152] It will be understood by those skilled in the art that Figure 1-4 The technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure. They may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0153] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0155] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0156] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods 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 apparatuses or units may be electrical, mechanical, or other forms.

[0158] 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.

[0159] Furthermore, the functional units in the various embodiments of this 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 functional unit.

[0160] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. 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 storage medium and includes multiple 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 storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0161] The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present disclosure. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present disclosure shall be within the scope of the claims of the present disclosure.

Claims

1. A method for assessing data influence, characterized in that, include: Obtain seller data; The seller data is processed on the blockchain to obtain the processing result. The seller's NFT encoded data is detected based on the on-chain processing results; The uniqueness coefficient is obtained based on the NFT encoded data; Obtain the basic information of the buyer and seller during the transaction process from the seller data. The basic information includes: buyer information, seller information, seller data information, NFT code, purchase method, and purchase date. The basic information is preprocessed to obtain preliminary data; A knowledge graph is constructed based on the preliminary data; Based on the knowledge graph, calculation parameters are obtained; the calculation parameters include: time coefficient, breadth coefficient, and depth coefficient; the time coefficient is used to indicate when the transaction occurred; the breadth coefficient is the number of branches in the knowledge graph starting from the NFT encoded data of the seller data to each end user; the depth coefficient is the number of users in each branch; Obtain the preset calculation formula; The influence score of the seller's data is obtained by substituting the calculation parameters into the calculation formula. The calculation formula is as follows: ， Where S is the influence score and k is the uniqueness coefficient. The breadth coefficient of the knowledge graph constructed from the basic information within a time period t is given. Let be the depth coefficient of the nth branch of the knowledge graph constructed from the basic information within time period t. The breadth coefficient is the ratio of the knowledge graph constructed from the basic information in the previous time period (t). The depth coefficient of the nth branch of the knowledge graph constructed from the basic information in the previous time period is t, where n is the branch number in the knowledge graph; if the seller data has been uploaded to the blockchain to obtain the NFT encoding, then the uniqueness coefficient k is 1 in the time period.

2. The data influence assessment method according to claim 1, wherein the on-chain processing of the seller data includes at least one of the following steps: The seller's data is stored off-chain, and the URL of the seller's data points to an NFT on the blockchain. The seller's data is stored offline on a decentralized storage network using documents with hash values; or directly stored in an NFT.

3. The data influence assessment method according to claim 1, wherein the NFT encoded data includes both the seller data not having obtained the NFT encoding and the seller data having obtained the NFT encoding, and the uniqueness coefficient obtained based on the NFT encoded data includes: If the seller data does not have the NFT encoding, then the first coefficient is obtained and used as the uniqueness coefficient; The first coefficient is 0.5; If the seller data obtains the NFT encoding, then the second coefficient is obtained and used as the uniqueness coefficient; wherein the second coefficient is 1.

4. The data influence assessment method according to claim 1, wherein the data preprocessing of the basic information includes: The basic information is reviewed, filtered, and sorted to obtain preliminary information; Preliminary data is obtained by logically expressing the preliminary information using a relational database with a two-dimensional table structure.

5. The data influence assessment method according to claim 1, wherein constructing a knowledge graph based on the preliminary data includes: A knowledge graph is constructed based on the transaction behavior data of the buyers and sellers in the preliminary data.

6. A data influence assessment device, characterized in that, include: The data acquisition module is used to acquire seller data; The data upload module processes the seller's data for on-chain processing to obtain the on-chain processing result. The data detection module is used to detect the seller's NFT encoded data based on the on-chain processing results; A uniqueness coefficient acquisition module is used to acquire a uniqueness coefficient based on the NFT encoded data; The basic information acquisition module is used to acquire the basic information of the buyer and seller when the seller data is traded between the buyer and seller. The data processing module performs data preprocessing on the basic information to obtain preliminary data; A knowledge graph construction module is used to construct a knowledge graph based on the preliminary data; The calculation parameter acquisition module is used to acquire calculation parameters based on the knowledge graph. The calculation parameters include: a time coefficient, a breadth coefficient, and a depth coefficient. The time coefficient is used to indicate when the transaction occurred. The breadth coefficient is the number of branches in the knowledge graph from the NFT encoded data of the seller data to each end user. The depth coefficient is the number of users in each branch. The influence score calculation module is used to obtain a preset calculation formula; the calculation parameters are substituted into the calculation formula to calculate the influence score of the seller data; The calculation formula is as follows: ， Where S is the influence score and k is the uniqueness coefficient. The breadth coefficient of the knowledge graph constructed from the basic information within a time period t is given. Let be the depth coefficient of the nth branch of the knowledge graph constructed from the basic information within time period t. The breadth coefficient is the ratio of the knowledge graph constructed from the basic information in the previous time period (t). The depth coefficient of the nth branch of the knowledge graph constructed from the basic information in the previous time period is t, where n is the branch number in the knowledge graph; if the seller data has been uploaded to the blockchain to obtain the NFT encoding, then the uniqueness coefficient k is 1 in the time period.

7. A computer device, characterized in that, include: At least one memory; At least one processor; At least one program; The program is stored in the memory, and the processor executes the at least one program to achieve the following: The method as described in any one of claims 1 to 5.

8. A storage medium, said storage medium being a computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform: The method as described in any one of claims 1 to 5.

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