Data asset retrieval and income distribution method and device, equipment and medium

Abstract

The invention discloses a data asset retrieval and income distribution method and device, equipment and a medium, data assets constructed by all portrait co-construction parties are received, a data asset library is formed, the data assets comprise a plurality of target data items, the structured data storage mode provides a basis for comprehensively and accurately describing data features, and the data asset retrieval and income distribution efficiency is improved. The problem that in the prior art, data product description information is not comprehensive is solved. By receiving the retrieval instruction and the demand instruction of the data demand side, accurate data retrieval and demand matching are achieved, and the tedious process that a user screens and verifies data in mass data is avoided. And finally, through a data opening confirmation instruction and an income accounting mechanism, flexible sharing and collaborative application of data are supported, the problem that a data product lacks flexibility and universality in the prior art is solved, meanwhile, a co-construction party is stimulated to actively participate in data sharing, and maximum mining and improvement of data values are promoted.

Description

A method, apparatus, device, and medium for data asset retrieval and revenue distribution. Technical Field

[0001] This application relates to the field of data asset technology, and in particular to a method, apparatus, device and medium for data asset retrieval and revenue distribution. Background Technology

[0002] With the rapid development of information technology, data has become a crucial production factor, playing a key role in various industries and application scenarios. The circulation and sharing of data are of great significance for promoting industry development, enhancing enterprise competitiveness, and advancing social progress. To achieve effective data circulation and maximize its value, the industry is actively exploring various data processing and circulation technologies and models. Data products, as a common form of data circulation, are widely used in different fields. When identifying and retrieving data, users primarily rely on the descriptive information of data products to obtain the required data to meet the needs of specific industries or application scenarios.

[0003] Current technologies primarily focus on pricing data and distributing it through data products. In this model, data providers evaluate and price their data, then package it into data products and release them to the market. When users need data, they browse and analyze the descriptions of these products to find those that meet their needs, and then purchase and use the data within them. This technology, to a certain extent, promotes the circulation and realization of data value, provides a feasible path for the commercial application of data, and drives the development of the data industry.

[0004] However, existing technologies also have some obvious shortcomings. First, the descriptive information in data products often has limitations, making it difficult to comprehensively and accurately reflect all the characteristics and value of the data. This can lead to users missing potentially useful data when identifying and retrieving it, or requiring them to spend a lot of time and effort filtering and verifying the validity of the data. Second, this model lacks flexibility and universality. Data products are usually designed for specific industries or application scenarios, making it difficult to meet the diverse data needs of different users in different scenarios. Furthermore, existing technologies do not adequately support the joint processing and collaborative application of data, making it difficult for different users to effectively share and utilize their respective data assets, thus limiting the further mining and enhancement of data value. Summary of the Invention

[0005] This application provides a method, apparatus, device, and medium for data asset retrieval and revenue distribution to solve the aforementioned technical problems.

[0006] In view of this, the first aspect of this application provides a data asset retrieval and revenue distribution method, the method comprising: S101, receiving data assets constructed by various profile co-builders through a data asset construction method to form a data asset library, wherein the data assets include several target data items, the target data items including definition information items, measurement information items, description information items, and user-defined information items, wherein the definition information items are used to describe the unique identifier and / or attributes of the target item, the measurement information items are used to describe the life cycle stages of the target item, the description information items are used to describe the content of the target item, and the user-defined information items are assigned by the user to any one of the definition information items, measurement information items, or description information items according to their actual function; S102, receiving a data asset retrieval instruction sent by a data requester, the data asset retrieval instruction carrying several retrieval target data items, the retrieval target data items including retrieval... S103. Define information items and / or retrieve measurement information items; S104. Search the data asset database according to the data asset retrieval instruction, match at least one data asset that conforms to the data asset retrieval instruction, generate an item profile based on at least one data asset, and provide the retrieval results to the data requester; S105. Receive the data request instruction sent by the data requester based on the retrieval results, the data request instruction carrying the names of several target data items; S106. Generate a data opening confirmation instruction for each target data item based on the data request instruction and the accurate data value of each target data item, and send it to several profile co-builders who provide the accurate data values ​​of each target data item; S107. Receive the data opening confirmation results from each profile co-builder, confirm the actual opened target data items based on the data opening confirmation results from each profile co-builder, and calculate the actual revenue of each profile co-builder.

[0007] Optionally, step S103 specifically includes: searching the data asset database according to the search information in the data asset search instruction; if there are several target data items in the data assets that are exactly the same as those in the search information, then generating an item profile based on at least one data asset, determining the accurate data values ​​of all target data items in the item profile, and feeding back the first search result to the data requester, wherein the first search result contains the names of all target data items contained in the constructed item profile.

[0008] Optionally, step S103 further includes: if there are no several search target data items in the data assets that are exactly the same as those in the search information, then the item profile cannot be generated, and the second search result is fed back to the data requester, in which no matching result is found.

[0009] Optionally, in step S104, the data request instruction may further include the data usage period and the data usage scope.

[0010] Optionally, step S105 specifically includes: determining several profile co-builders corresponding to each profile co-builder based on the data demand instruction and the accurate data value of the target data item; sending a data opening confirmation instruction to each profile co-builder, the data opening confirmation instruction carrying each target data item corresponding to the profile co-builder, each profile co-builder independently determining whether to open the corresponding target data item, and feeding back the data opening confirmation result, the data opening confirmation result including a confirmation opening identifier for the target data item, and if a confirmation opening identifier exists, also including a confirmation opening time.

[0011] Optionally, step S106 specifically includes: receiving data opening confirmation results sent by each portrait co-builder; when all portrait co-builders corresponding to each target data item have reported data opening confirmation results or the preset confirmation time limit has been reached, confirming the actual opened target data item based on the received data opening confirmation results of each target data item; and calculating the actual revenue of each portrait co-builder based on the actual opened target data item and its corresponding data opening confirmation results.

[0012] Optionally, the step of confirming the actual open target data item based on the received data openness confirmation results of each target data item specifically involves: when there is only one profile co-builder for the target data item, the target data item is confirmed as actually open based on the confirmation openness identifier for the target data item in the data openness confirmation result of the profile co-builder; otherwise, the target data item is not open. When there are two or more profile co-builders for the target data item, if one of the profile co-builders has a confirmation openness identifier for the target data item in its data openness confirmation result, the target data item is confirmed as actually open, and the openness status of the profile co-builder with the confirmation openness identifier in the data openness confirmation result is marked as actively open; otherwise, it is marked as passively open. The openness result of each profile co-builder is recorded, and the openness result includes the name of the target data item actually opened, the confirmation openness time, and the openness status.

[0013] Optionally, the calculation of the actual revenue of each portrait co-builder based on the actual open target data items and the data open confirmation results of their corresponding portrait co-builders is specifically as follows: based on a preset revenue calculation model, the actual revenue for each actually opened target data item is calculated in combination with the open results of each portrait co-builder. The parameters in the preset revenue calculation model include data source, data release time sequence, data open time sequence, and open status.

[0014] A second aspect of this application provides a data asset retrieval and revenue distribution device, comprising: a data asset library construction unit, configured to receive data assets constructed by various profiling co-builders using a data asset construction method to form a data asset library, wherein the data assets include several target data items, and the target data items include definition information items, measurement information items, description information items, and user-defined information items, wherein the definition information items are used to describe the unique identifier and / or attributes of the target items, the measurement information items are used to describe the life cycle stages of the target items, the description information items are used to describe the content of the target items, and the user-defined information items are assigned by the user to any one of the definition information items, measurement information items, or description information items according to their actual function; and a first receiving unit, configured to receive a data asset retrieval instruction sent by a data requester, the data asset retrieval instruction carrying several retrieval target data items, including retrieval definition information items. The system includes: a first processing unit for retrieving measurement information items; a second receiving unit for retrieving data asset information from a data asset database according to a data asset retrieval instruction, matching at least one data asset that matches the data asset retrieval instruction, generating an item profile based on at least one data asset, and providing the retrieval results to the data requester; a third processing unit for receiving a data request instruction from the data requester based on the retrieval results, the data request instruction carrying the names of several target data items; a fourth processing unit for generating a data opening confirmation instruction for each target data item based on the data request instruction and the accurate data values ​​of the target data items, and sending it to several profile co-builders who provide the accurate data values ​​of each target data item; and a fifth processing unit for receiving the data opening confirmation results from each profile co-builder, confirming the actual opened target data items based on the data opening confirmation results from each profile co-builder, and calculating the actual revenue of each profile co-builder.

[0015] A third aspect of this application provides a data asset retrieval and revenue distribution device, the device comprising a processor and a memory: the memory is used to store program code and transmit the program code to the processor; the processor is used to execute the steps of the data asset retrieval and revenue distribution method as described in the first aspect above, according to the instructions in the program code.

[0016] A fourth aspect of this application provides a computer-readable storage medium for storing program code for performing the method described in the first aspect above.

[0017] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: This application provides a data asset retrieval and revenue distribution method, apparatus, device, and medium. By receiving data assets constructed by various profiling co-builders and forming a data asset library, the data assets include several target data items, which contain definition information items, measurement information items, description information items, and self-defined information items. This structured data storage method provides a foundation for comprehensively and accurately describing data characteristics, avoiding the problem of incomplete descriptive information in data products in the prior art. Furthermore, by receiving retrieval instructions and data demand instructions from data demanders, accurate data retrieval and demand matching are achieved, improving the efficiency and accuracy of data retrieval and avoiding the tedious process of users filtering and verifying data from massive amounts of data. Finally, through data open confirmation instructions and revenue accounting mechanisms, flexible data sharing and collaborative applications are supported, solving the problem of the lack of flexibility and universality of data products in the prior art. At the same time, it incentivizes co-builders to actively participate in data sharing, promoting the maximization and enhancement of data value. Attached Figure Description

[0018] Figure 1 is a flowchart of the data asset retrieval and revenue distribution method in the embodiments of this application; Figure 2 is a structural schematic diagram of the data asset retrieval and revenue distribution device in the embodiments of this application; Figure 3 is a structural schematic diagram of the data asset retrieval and revenue distribution equipment in the embodiments of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0020] In today's digital age, with the globalization of supply chains and the fragmentation of data, the construction of product profiles has become a crucial aspect of data management and business decision-making. Traditional product profile construction methods primarily rely on a three-tiered data management model, where the application layer, management layer, and support layer interact through heterogeneous data transmission protocols. However, existing technologies face numerous challenges in building product profiles. Because any user can assign values ​​to a product's profile, a single data item may contain data values ​​from multiple sources, and these data values ​​may have different data structures. This makes it difficult to achieve completeness, accuracy, and efficiency in the product profile construction process, thereby impacting profile-based decision support and business process optimization.

[0021] To address this issue, existing technologies have proposed a method and apparatus for constructing item profiles (as described in patent CN111582799B). This method involves judging data relationships in received standard data, identifying inclusion, parallelism, and contradictions among data values, and selecting accurate data values ​​based on preset rules and priorities, thereby constructing a more accurate item profile. This method reduces complexity and improves construction efficiency through distributed services and node-based profile construction, providing a technical foundation for the efficient generation of item profiles.

[0022] Building upon this foundation, this application further proposes an innovative method for data asset retrieval and revenue distribution. This method not only inherits the accurate identification and processing capabilities of existing technologies for data relationships but also introduces dynamic data retrieval, a self-decision-making mechanism for profile co-creation parties, and a revenue distribution model. Through these innovations, this application not only improves the accuracy and usability of item profiles but also provides a new path for realizing the commercial value of data assets, further promoting data-driven business decisions and the efficient utilization of data assets.

[0023] For ease of understanding, please refer to Figure 1. Figure 1 is a flowchart of the data asset retrieval and revenue distribution method in this embodiment of the application. As shown in Figure 1, specifically: S101, receive data assets constructed by each profile co-builder through the data asset construction method to form a data asset library. The data assets include several target data items, which contain definition information items, measurement information items, description information items, and user-defined information items. Among them, definition information items are used to describe the unique identifier and / or attributes of the target item, measurement information items are used to describe the life cycle stages of the target item, description information items are used to describe the content of the target item, and user-defined information items are assigned by the user to any one of the definition information items, measurement information items, or description information items according to their actual function. It should be noted that the profile co-builders refer to the parties involved in constructing the data assets, who provide their respective data resources to jointly construct a complete data asset library. These co-builders can be partners, data suppliers, etc.

[0024] Data Asset Repository: This is a centralized platform for storing all data assets. It integrates data from different co-construction parties, forming a comprehensive and rich data resource pool.

[0025] The target data items include: Definition information items: These uniquely identify the target item, ensuring that the data item accurately points to a specific object. For example, for a product, definition information could be the product's model number, name, barcode, etc., which clearly distinguishes different products.

[0026] Measurement information items describe a specific stage or state of the target item in its life cycle, helping to understand the dynamic changes of data items at different stages. For example, the production date, batch number, and sales time of a product reflect the quantitative characteristics of the product in the production, sales, and other stages.

[0027] Description information items: These further enrich the content of the target data items, providing more detailed descriptions of attributes and characteristics. For example, product color, size, functions, user reviews, etc., provide richer context for data analysis and application.

[0028] Different parties involved in profile building may have different classifications of the data items contained in the same data assets. For example, data item 1 may be a definition data item in company A, but a description data item in company B. The specific classification is defined and constituted by the profile building parties themselves.

[0029] The parties who co-create the profile have control over the data assets they build and can independently authorize the data assets they control to other users, supporting the authorization of the same data to different users.

[0030] Based on the "data value" itself, data assets built according to the same standard allow users to identify and use data assets authorized by different users, and to jointly process them.

[0031] By integrating data assets from all parties, a comprehensive and standardized data asset repository is formed, providing a foundation for subsequent data retrieval, sharing, and application.

[0032] S102. Receive a data asset retrieval instruction sent by the data requester. The data asset retrieval instruction carries several target data items for retrieval, including retrieval definition information items and / or retrieval measurement information items. It should be noted that the data requester may be a cross-border e-commerce enterprise, which may need to retrieve data assets related to a specific product (such as a watch of a certain brand) to understand the demand, price trends and other information of the product in different markets.

[0033] The format of the retrieved information can be structured, such as through a table containing product names, target markets, and the type of data to be retrieved (such as sales data, user review data, etc.).

[0034] Data asset retrieval commands can be sent through a dedicated interface, which can be a web-based API. The data requester can call this interface programmatically, passing the retrieval information as a parameter.

[0035] Data requesters can send search commands through a graphical user interface (GUI). This interface provides various options for data requesters to choose from, such as selecting product categories or market scope via drop-down menus, and then converts these selections into search information for submission.

[0036] Besides the data requester sending instructions directly, an intermediary data service provider can also send them on their behalf. The data requester informs the data service provider of their needs, and the data service provider generates and sends data asset retrieval instructions based on its own understanding and optimization of the data needs.

[0037] For data requesters who frequently send similar search requests, search templates can be provided. Data requesters only need to fill in key change information, such as new product names or time ranges, to quickly generate search instructions.

[0038] Added a priority setting function for search commands. Data requesters can set the priority of search commands according to the urgency of their needs, and the data asset retrieval system processes search commands in priority order.

[0039] S103. Search the data asset database according to the data asset retrieval instruction, match at least one data asset that conforms to the data asset retrieval instruction, generate an item profile based on the at least one data asset, and provide the retrieval results to the data requester. Further, step S103 specifically includes: searching the data asset database according to the retrieval information in the data asset retrieval instruction; if several target data items in the data asset are identical to those in the retrieval information, generate an item profile based on at least one data asset, determine the accurate data values ​​of all target data items in the item profile, and provide the first retrieval result to the data requester. The first retrieval result includes the names of all target data items contained in the constructed item profile.

[0040] Furthermore, step S103 also includes: if there are no several search target data items in the data assets that are exactly the same as those in the search information, then the item profile cannot be generated, and the second search result is fed back to the data requester, in which no matching result is found.

[0041] It should be noted that a data asset retrieval system can be a large database storing data assets from various data sources (such as e-commerce platforms and market research institutions). The retrieval process can involve using techniques such as keyword matching and attribute filtering to search for data assets in the database that match the search criteria.

[0042] Different data assets can hit the same search information; therefore, all data assets that hit the search information can be combined to form an item profile.

[0043] In addition to defining and measuring information, data retrieval information can also include data quality requirements. For example, if the data requester requires that the accuracy of the retrieved data assets be no less than 90%, then the system needs to assess the quality of the data assets during the retrieval process, and only data assets that meet the quality requirements will be retrieved.

[0044] Add real-time update functionality for data assets. For data assets with high time sensitivity, such as real-time market data, the data asset retrieval system can establish a real-time connection with the data source to ensure that the retrieved data assets are up-to-date.

[0045] Provides a detailed preview function for data assets. When providing search results to data requesters, it can also provide a detailed preview of the data assets, such as data samples and statistical analysis results, to help data requesters better assess the value of the data assets.

[0046] S104. Receive a data request instruction sent by the data requester based on the search results. The data request instruction carries the names of several target data items. Further, in step S104, the data request instruction also includes the data usage period and the data usage scope.

[0047] It should be noted that the target data item name in the data request instruction can be a specific field in the data asset. For example, in a product sales data asset, the target data item name could be "monthly sales revenue" or "sales region".

[0048] The data usage period can be a specific date range, such as from January 1, 2025 to December 31, 2025. The data usage scope can be the scenario in which the data will be used, such as using it only for market analysis reports and not for commercial advertising. The data usage scope can also be the target system that retrieves the data after this search, such as an order management system.

[0049] When sending a data request instruction, the data requester can specify the duration and scope of data usage in detail. For example, by adding a note to the instruction, they can state that the data is for internal research only and must not be disclosed to any third party.

[0050] The data usage period and scope can be set using default values. The data asset retrieval system sets default values ​​for the data usage period and scope based on the nature of the data asset and the type of data requester. Data requesters can use the default values ​​directly if they have no objections; otherwise, they can modify the default values ​​if they have special requirements.

[0051] Data request instructions can be sent in an encrypted manner to ensure their security. The data requester encrypts the instruction using a specific encryption algorithm, and the data asset retrieval system decrypts and processes the instruction upon receipt.

[0052] In addition, a data request instruction review function can be added. For data request instructions involving sensitive information or large amounts of data, a dedicated reviewer can conduct the review, including the legality of the purpose of data use and the reasonableness of the scope of data use.

[0053] The system provides a data request instruction modification log function. If the data requester modifies the data request instruction, the system will record information such as the modification time and content for future reference.

[0054] S105. Based on the data demand instruction and the accurate data values ​​of the target data items, generate a data opening confirmation instruction for each target data item and send it to several profile co-builders who provide the accurate data values ​​of each target data item. Further, step S105 specifically includes: determining several profile co-builders corresponding to each target data item based on the data demand instruction and the accurate data values ​​of the target data items; sending a data opening confirmation instruction to each profile co-builder, the data opening confirmation instruction carrying the target data items corresponding to the profile co-builder, allowing each profile co-builder to independently determine whether to open the corresponding target data items, and providing feedback on the data opening confirmation result. The data opening confirmation result includes a confirmation opening identifier for the target data items; if a confirmation opening identifier exists, it also includes the confirmation opening time.

[0055] It should be noted that identifying the various parties contributing to the profiles for each target data item can be achieved through the ownership information of the data assets. For example, a target data item, "monthly sales," may be provided by multiple e-commerce platforms, and these e-commerce platforms are the parties contributing to the profiles.

[0056] Data sharing confirmation instructions can be sent to the profile co-builders via email, instant messaging tools, or other means. The target data items carried in the instructions can be the name of the data item and related descriptive information, such as "Target data item name: Monthly sales revenue, Description: This data item reflects the sales amount of the product within one month".

[0057] After receiving the data openness confirmation instruction, the parties involved in the profile creation can independently determine whether to open the corresponding target data items based on their own data policies and business considerations. For example, an e-commerce platform may not open certain data items due to reasons such as data security or business competition.

[0058] Data access confirmation instructions can be sent via smart contracts. A smart contract is a contract with automatically executing terms; it executes automatically when the conditions stipulated in the contract are met. In this scenario, the smart contract can automatically determine whether to grant access to data items based on the data request instructions and the openness policy of the profiling co-builders.

[0059] For situations involving a large number of parties collaborating on profile creation, a batch-based approach to sending data access confirmation instructions can be adopted. The parties are divided into several batches according to certain rules (such as data importance, cooperation history, etc.), and instructions are sent to each batch separately to improve processing efficiency.

[0060] Add a feedback deadline setting function for data openness confirmation instructions. The data asset retrieval system can set a feedback deadline for data openness confirmation instructions. If the profile co-builder does not provide feedback within the deadline, the system can process it according to preset rules (such as default not open or ignoring openness).

[0061] It provides a tracking function for data release confirmation instructions. Data requesters can view the sending, receiving, and feedback status of data release confirmation instructions in real time to keep track of the progress of data release.

[0062] S106. Receive the data opening confirmation results from each portrait co-builder, and confirm the actual target data items to be opened based on the data opening confirmation results from each portrait co-builder, and calculate the actual revenue of each portrait co-builder.

[0063] Furthermore, step S106 also includes: receiving data opening confirmation results sent by each portrait co-builder; when all portrait co-builders corresponding to each target data item have reported data opening confirmation results or the preset confirmation time limit has been reached, confirming the actual opened target data item based on the received data opening confirmation results of each target data item; and calculating the actual revenue of each portrait co-builder based on the actual opened target data item and its corresponding data opening confirmation results.

[0064] Further, the step of confirming the actual open target data item based on the received data openness confirmation results of each target data item is as follows: when there is only one profile co-builder for the target data item, the target data item is confirmed as actually open based on the confirmation openness identifier for the target data item in the data openness confirmation result of the profile co-builder; otherwise, the target data item is not open. When there are two or more profile co-builders for the target data item, if there is a confirmation openness identifier for the target data item in the data openness confirmation result of one of the profile co-builders, the target data item is confirmed as actually open, and the openness status of the profile co-builder with the confirmation openness identifier in the data openness confirmation result is marked as actively open; otherwise, it is marked as passively open. The openness result of each profile co-builder is recorded, and the openness result includes the name of the target data item actually opened, the confirmation openness time, and the openness status.

[0065] Furthermore, the calculation of the actual revenue of each portrait co-builder based on the actual open target data items and the data open confirmation results of their corresponding portrait co-builders is specifically as follows: based on a preset revenue calculation model, the actual revenue for each actually opened target data item is calculated in combination with the open results of each portrait co-builder. The parameters in the preset revenue calculation model include data source, data release time sequence, data open time sequence, and open status.

[0066] It should be noted that receiving the data access confirmation results from each profile co-builder can be achieved through a dedicated data interface. Profile co-builders send the data access confirmation results to this interface in a specific format (such as JSON), and the data asset retrieval system receives and parses the results.

[0067] When calculating the actual revenue of each co-creator of the profile, the pre-set revenue calculation model can be designed based on factors such as data source, data release time sequence, data access time sequence, and access status. For example, for some rare data, the price coefficient in the revenue calculation model can be set higher.

[0068] The results of each profile co-creation party's open access can be stored in a database for easy future querying and statistics. The recorded content can include the name of the profile co-creation party, the name of the target data item opened, the confirmation time of opening, and the opening status.

[0069] The confirmation of the target data items to be actually made available can be based not only on the data availability confirmation results from the profiling co-builders, but also on the feedback from the data requesters. If a data requester believes that certain data items, although agreed to be made available by the profiling co-builders, do not meet their actual needs, they can raise objections, and a decision on whether to make them available will be made after consultation.

[0070] When calculating the actual revenue of each party involved in the portrait sharing project, a third-party evaluation agency can be introduced. This agency will assess the revenue based on the actual data and market conditions to ensure the fairness of the revenue calculation.

[0071] In addition, an appeal mechanism for the confirmation of data openness can be added. If a party involved in the profile creation disagrees with the confirmation of data openness, they can file an appeal, and the data asset retrieval system will investigate and process the appeal based on its content.

[0072] It provides a visualization function for revenue calculation results. The actual revenue of each profile co-builder is displayed in the form of charts, such as bar charts and pie charts, so that profile co-builders and data requesters can intuitively understand the revenue distribution.

[0073] Based on the data asset retrieval and revenue distribution method provided in the above embodiments of this application, this application also provides an application example 1 for cross-border e-commerce company A to retrieve product sales data, including: Company A wants to know the sales data of a certain sports shoe sold in the European market, and generates a data asset retrieval instruction through its internal data management system. The retrieval information carried in the instruction includes the product name "sports shoe", the target market "Europe", and the expected data type "monthly sales".

[0074] After receiving the instruction, the data asset retrieval system searches the database based on the search information. It finds a pre-existing product profile for the athletic shoe in the European market, including data items such as "monthly sales volume" and "sales region." The system generates the first search result and a reference price of 80 yuan for the first profile, and sends this information back to Company A.

[0075] Based on the search results, Company A determined that it needed the "monthly sales" data item, set the data usage period to the entire year of 2025, and the data usage scope to the internal market analysis report, and then sent a data request instruction.

[0076] Based on the data request instructions, the data asset retrieval system identified European e-commerce platforms B and C as the co-builders of the "monthly sales revenue" data item. It then sent data access confirmation instructions, including the "monthly sales revenue" data item, to both e-commerce platforms B and C.

[0077] E-commerce platform B confirmed its data release, agreeing to release the "Monthly Sales Revenue" data item on January 1, 2025. E-commerce platform C also confirmed its agreement, releasing the data on January 5, 2025. The data asset retrieval system confirmed that the actual data item released was "Monthly Sales Revenue" and calculated the actual revenue for e-commerce platforms B and C based on a pre-set revenue calculation model. The model considered the data source (e-commerce platform B's data quality is higher, with a weighting coefficient of 1.2, while e-commerce platform C's weighting coefficient is 1), the data release time (earlier releases result in higher revenue; e-commerce platform B's time coefficient is 1.1, and e-commerce platform C's time coefficient is 1), and the release status (both were actively released, with a status coefficient of 1). The final calculated actual revenue for e-commerce platform B is 80 × 1.2 × 1.1 × 1 = 105.6 yuan, and for e-commerce platform C, it is 80 × 1 × 1 × 1 = 80 yuan. Simultaneously record the opening results of e-commerce platform B and e-commerce platform C, including the name of the target data item "monthly sales", the confirmed opening time is January 1, 2025 and January 5, 2025 respectively, and the opening status is actively opened in both cases.

[0078] Based on the data asset retrieval and revenue distribution method provided in the above embodiments of this application, this application also provides a second application example of cross-border e-commerce enterprise B retrieving product user review data, including: Enterprise B wants to know the user review data of a certain electronic product (such as a tablet computer) sold in the Asian market, and generates a data asset retrieval instruction through its internal system. The retrieval information carried in the instruction includes the product name "tablet computer", the target market "Asia", and the data type expected to be retrieved "user review data".

[0079] After searching, the data asset retrieval system found that no existing product profile for the tablet computer in the Asian market existed. Based on the search information, it was determined that a new product profile could be constructed. Relevant data was collected from multiple data sources (such as e-commerce platforms, social media, etc.) to generate a second search result containing several data items (such as "positive review rate" and "negative review keywords"), along with a reference price of 120 yuan for the second product profile, which was then fed back to Company B.

[0080] Based on the search results, Company B determined that it needed the "positive review rate" data item, set the data usage period to the second half of 2025, and the data usage scope to be a reference for product improvement. Then, it sent a data request instruction.

[0081] The data asset retrieval system identified e-commerce platform D and social media platform E as the co-builders of the profile corresponding to the "positive review rate" data item. Data access confirmation instructions were sent to both e-commerce platform D and social media platform E, with the "positive review rate" data item included in the instructions.

[0082] E-commerce platform D has confirmed the data release results, agreeing to release the "positive review rate" data item, with the release date set for July 1, 2025.

[0083] Social media platform E provided feedback on the data openness confirmation result. Although it did not actively open the "positive review rate" data item, e-commerce platform D had already agreed to open it, and the data asset retrieval system confirmed the target data item as actually open according to the rule (when there are two or more profile co-builders for the target data item, if one of the profile co-builders has a confirmation openness mark for the target data item in its data openness confirmation result, then the target data item is confirmed as actually open). Therefore, social media platform E's openness status was marked as passively open.

[0084] The data asset retrieval system calculates the actual revenue of e-commerce platform D and social media platform E based on a preset revenue calculation model. The model considers the data source (e-commerce platform D has a data weight coefficient of 1.5, and social media platform E has a data weight coefficient of 1.3), data access time (e-commerce platform D has a time coefficient of 1, and social media platform E has a time coefficient of 0.8 due to passive access), and access status (e-commerce platform D is actively accessed, with a status coefficient of 1, and social media platform E is passively accessed, with a status coefficient of 0.9). The final calculated actual revenue for e-commerce platform D is 120 × 1.5 × 1 × 1 = 180 yuan, and the actual revenue for social media platform E is 120 × 1.3 × 0.8 × 0.9 = 112.32 yuan.

[0085] Simultaneously record the opening results of e-commerce platform D and social media platform E, including the name of the target data item "positive review rate". The confirmed opening time of e-commerce platform D is July 1, 2025, and the opening status is active opening; the opening status of social media platform E is passive opening. Since it is passive opening, its opening time is based on the opening time of e-commerce platform D, which is also July 1, 2025.

[0086] Please refer to Figure 2, which is a schematic diagram of the data asset retrieval and revenue distribution device in this embodiment of the application. As shown in Figure 2, specifically: a data asset library construction unit 201 is used to receive data assets constructed by each profile co-builder through the data asset construction method to form a data asset library. The data assets include several target data items, which include definition information items, measurement information items, description information items, and self-defined information items. Among them, the definition information items are used to describe the unique identifier and / or item attributes of the target item, the measurement information items are used to describe the life cycle stages of the target item, the description information items are used to describe the content of the target item, and the self-defined information items are assigned by the user to any one of the definition information items, measurement information items, or description information items according to their actual function; a first receiving unit 202 is used to receive a data asset retrieval instruction sent by the data requester. The data asset retrieval instruction carries several retrieval target data items, including retrieval definition information items, measurement information items, and description information items. The data asset database includes: a first processing unit 203, a second receiving unit 204, and a third processing unit 206. The first processing unit 203 is configured to search the database according to a data asset retrieval instruction, match at least one data asset that conforms to the data asset retrieval instruction, generate an item profile based on the at least one data asset, and provide the retrieval results to the data requester. The second receiving unit 204 is configured to receive a data request instruction sent by the data requester based on the retrieval results, the data request instruction carrying the names of several target data items. The second processing unit 205 is configured to generate a data opening confirmation instruction for each target data item based on the data request instruction and the accurate data values ​​of the target data items, and send it to several profile co-builders who provide the accurate data values ​​of each target data item. The third processing unit 206 is configured to receive the data opening confirmation results from each profile co-builder, confirm the actual opened target data items based on the data opening confirmation results from each profile co-builder, and calculate the actual revenue of each profile co-builder.

[0087] Another embodiment of the present invention provides a data asset retrieval and revenue distribution device, as shown in FIG3. The device 10 includes one or more processors 110 and a memory 120. FIG3 is described using one processor 110 as an example. The processor 110 and the memory 120 can be connected by a bus or other means. FIG3 is shown using a bus connection as an example.

[0088] Processor 110 is used to perform various control logics of device 10, and can be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), microcontroller, ARM (AcornRISC Cachine) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. Furthermore, processor 110 can also be any conventional processor, microprocessor, or state machine. Processor 110 can also be implemented as a combination of computing devices, such as a combination of DSP and microprocessor, multiple microprocessors, one or more microprocessors combined with DSP and / or any other such configuration.

[0089] The memory 120, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions corresponding to the method for constructing the multilingual phoneme representation model in this embodiment of the invention. The processor 110 executes various functional applications and data processing of the device 10 by running the non-volatile software programs, instructions, and units stored in the memory 120, thereby implementing the method for constructing the multilingual phoneme representation model in the above-described method embodiment.

[0090] The memory 120 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created according to the use of the device 10. Furthermore, the memory 120 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 120 may optionally include memory remotely located relative to the processor 110, and these remote memories may be connected to the device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0091] One or more units are stored in memory 120. When executed by one or more processors 110, the following steps are implemented: S101, receiving data assets constructed by each profile co-builder through a data asset construction method to form a data asset library. The data assets include several target data items, which contain definition information items, measurement information items, and description information items. The definition information items are used to describe the unique identifier and / or attributes of the target items, the measurement information items are used to describe the life cycle stages of the target items, and the description information items are used to describe the content of the target items; S102, receiving a data asset retrieval instruction sent by a data requester. The data asset retrieval instruction carries several retrieval target data items; S103, retrieving the data assets according to the data asset retrieval instruction. The system performs a search in the database, matching at least one data asset that matches the data asset search instruction, and generates an item profile based on at least one data asset, then sends the search results back to the data requester; S104: Receives a data request instruction sent by the data requester based on the search results, the data request instruction carrying the names of several target data items; S105: Generates a data opening confirmation instruction for each target data item based on the data request instruction and the accurate data values ​​of each target data item, and sends it to several profile co-builders who provide the accurate data values ​​of each target data item; S106: Receives the data opening confirmation results from each profile co-builder, confirms the actual opened target data items based on the data opening confirmation results from each profile co-builder, and calculates the actual revenue of each profile co-builder.

[0092] This invention provides a non-volatile computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are executed by one or more processors, they implement any one of the data asset retrieval and revenue distribution methods described in the above embodiments.

[0093] As examples, non-volatile storage media can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) as external cache memory. By way of illustration and not limitation, RAM can be obtained in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The memory components or memories disclosed in the operating environment described herein are intended to include one or more of these and / or any other suitable types of memory.

[0094] This application provides a method, apparatus, device, and medium for data asset retrieval and revenue distribution. By receiving data assets constructed by various profiling co-builders and forming a data asset library, the data assets include definition information, measurement information, and descriptive information. This structured data storage method provides a foundation for comprehensively and accurately describing data characteristics, avoiding the problem of incomplete descriptive information in existing data products. Furthermore, by receiving retrieval instructions and data request instructions from data requesters, precise data retrieval and demand matching are achieved, improving the efficiency and accuracy of data retrieval and avoiding the tedious process of users filtering and verifying data from massive amounts of data. Finally, through data openness confirmation instructions and revenue calculation mechanisms, flexible data sharing and collaborative applications are supported, solving the problem of insufficient flexibility and universality of data products in existing technologies. Simultaneously, it incentivizes co-builders to actively participate in data sharing, promoting the maximization and enhancement of data value.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] 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, for example, 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.

[0097] 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.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, 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 an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0099] 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.

[0100] 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.

[0101] 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 several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0102] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0103] It should be noted that if any software tools or components not belonging to our company appear in the embodiments of this application, they are merely for illustrative purposes and do not represent actual use.

Claims

1. A method for data asset retrieval and revenue distribution, characterized in that, include: S101. Receive data assets constructed by various profiling co-construction parties using data asset construction methods to form a data asset library. The data assets include several target data items, which contain definition information items, measurement information items, description information items, and user-defined information items. Definition information items describe the unique identifier and / or attributes of the target item; measurement information items describe the lifecycle stages of the target item; description information items provide a content description of the target item; and user-defined information items are assigned by the user as any one of the definition, measurement, or description information items based on their actual function. S102. Receive data asset retrieval instructions sent by data requesters. These instructions carry several target data items for retrieval, including definition information items and / or measurement information items. S10 3. Retrieve data in the data asset database according to the data asset retrieval instruction, match at least one data asset that matches the data asset retrieval instruction, generate an item profile based on at least one data asset, and provide the retrieval results to the data requester; S104. Receive the data request instruction sent by the data requester based on the retrieval results, the data request instruction carrying the names of several target data items; S105. Generate a data opening confirmation instruction for each target data item based on the data request instruction and the accurate data values ​​of the target data items, and send it to several profile co-builders who provide the accurate data values ​​of each target data item; S106. Receive the data opening confirmation results from each profile co-builder, confirm the actual opened target data items based on the data opening confirmation results from each profile co-builder, and calculate the actual revenue of each profile co-builder.

2. The data asset retrieval and revenue distribution method according to claim 1, characterized in that, Step S103 specifically includes: searching the data asset database according to the search information in the data asset search instruction; if there are several target data items in the data assets that are exactly the same as those in the search information, then generating an item profile based on at least one data asset, determining the accurate data values ​​of all target data items in the item profile, and feeding back the first search result to the data requester. The first search result includes the names of all target data items contained in the constructed item profile.

3. The data asset retrieval and revenue distribution method according to claim 2, characterized in that, Step S103 further includes: if there are no several search target data items in the data assets that are exactly the same as those in the search information, then the item profile cannot be generated, and the second search result is fed back to the data requester, in which no matching result is found.

4. The data asset retrieval and revenue distribution method according to claim 1, characterized in that, In step S104, the data requirement instruction also includes the data usage period and the data usage scope.

5. The data asset retrieval and revenue distribution method according to claim 1, characterized in that, Step S105 specifically includes: determining several profile co-builders corresponding to each profile co-builder based on the data demand instruction and the accurate data values ​​of the target data items; sending a data opening confirmation instruction to each profile co-builder, the data opening confirmation instruction carrying each target data item corresponding to the profile co-builder, each profile co-builder independently determining whether to open the corresponding target data items, and feeding back the data opening confirmation result, the data opening confirmation result including a confirmation opening identifier for the target data items, and if a confirmation opening identifier exists, also including the confirmation opening time.

6. The data asset retrieval and revenue distribution method according to claim 5, characterized in that, Step S106 specifically includes: receiving data opening confirmation results sent by each portrait co-builder; when all portrait co-builders corresponding to each required target data item have reported data opening confirmation results or the preset confirmation time limit has been reached, confirming the actual opened target data item based on the received data opening confirmation results of each required target data item; and calculating the actual revenue of each portrait co-builder based on the actual opened target data item and its corresponding portrait co-builder's data opening confirmation results.

7. The data asset retrieval and revenue distribution method according to claim 6, characterized in that, The specific steps for confirming the actual open target data items based on the received data openness confirmation results of each target data item are as follows: When there is only one profile co-builder for a target data item, the target data item is confirmed as actually open based on the confirmation openness identifier for the target data item in the data openness confirmation result of the profile co-builder; otherwise, the target data item is not open. When there are two or more profile co-builders for a target data item, if one of the profile co-builders has a confirmation openness identifier for the target data item in its data openness confirmation result, the target data item is confirmed as actually open, and the openness status of the profile co-builder with the confirmation openness identifier in the data openness confirmation result is marked as actively open; otherwise, it is marked as passively open. Record the open results of each profile co-construction party. The open results include the name of the target data item that was actually opened, the confirmed open time, and the open status.

8. The data asset retrieval and revenue distribution method according to claim 7, characterized in that, The calculation of the actual revenue of each profile co-builder based on the actual open target data items and the data open confirmation results of their corresponding profile co-builders is specifically as follows: Based on a preset revenue calculation model, the actual revenue for each actually opened target data item is calculated in combination with the open results of each profile co-builder. The parameters in the preset revenue calculation model include data source, data release time sequence, data open time sequence, and open status.

9. A data asset retrieval and revenue distribution device, characterized in that, include: The data asset repository construction unit receives data assets constructed by various profiling co-builders using data asset construction methods, forming a data asset repository. Each data asset includes several target data items, which contain definition information items, measurement information items, description information items, and user-defined information items. Definition information items describe the unique identifier and / or attributes of the target item; measurement information items describe the lifecycle stages of the target item; description information items provide a content description of the target item; and user-defined information items are assigned by the user as any one of the definition, measurement, or description information items based on their actual function. The first receiving unit receives data asset retrieval instructions sent by data requesters. These instructions carry several target data items for retrieval, including definition information items and / or measurement information items. The first processing unit… The system comprises three parts: a first unit for retrieving data assets from a data asset database according to a data asset retrieval instruction, matching at least one data asset that matches the instruction, generating an item profile based on the at least one data asset, and providing the retrieval results to the data requester; a second receiving unit for receiving a data request instruction sent by the data requester based on the retrieval results, the instruction carrying the names of several target data items; a second processing unit for generating a data opening confirmation instruction for each target data item based on the data request instruction and the accurate data values ​​of the target data items, and sending it to several profile co-builders who provide the accurate data values ​​of each target data item; and a third processing unit for receiving the data opening confirmation results from each profile co-builder, confirming the actual opened target data items based on the confirmation results, and calculating the actual revenue of each profile co-builder.

10. A data asset retrieval and revenue distribution device, characterized in that, The device includes a processor and a memory: the memory is used to store program code and transmit the program code to the processor; the processor is used to execute the data asset retrieval and revenue distribution method according to any one of claims 1-8 according to the instructions in the program code.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the data asset retrieval and revenue distribution method according to any one of claims 1-8.

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