Commercial body contribution data processing method

Abstract

The invention provides a business contribution data processing method. The business contribution data processing method comprises the following steps: acquiring information of a second business having a smart contract with a first business according to a decentralized identifier of the first business; traversing the smart contract execution data of the first business body and the second business body in the first settlement period; wherein the smart contract execution data is execution data generated after the first business body is executed by the smart contract used in the second business body by the right affirmation data assets; generating contribution value data of a first commercial body according to the intelligent contract execution data; and loading the contribution value data to a second commercial body contribution data account book of the first commercial body. According to the invention, quantitative use of data value contributions among commercial bodies can be realized.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and in particular to a method for processing data contributed by business entities. Background Technology

[0002] With the rapid development of information technology, the digital transformation of business activities is accelerating, and collaboration between businesses through data-driven transactions is becoming increasingly common and profound. In existing e-commerce transactions, to improve the success rate of business activities and transactions and attract more business users, businesses often return virtual currencies or profit sharing to e-commerce users, thus completing a positive loop in the e-commerce model, attracting more users, promoting more e-commerce transactions, and achieving rapid user acquisition. As e-commerce develops towards a decentralized model, blockchain technology is merging with e-commerce operations, recording the aforementioned virtual currencies and profit sharing returned to e-commerce users in the blockchain nodes. This makes e-commerce transactions more transparent and fair, effectively promoting the development of e-commerce.

[0003] Among the publicly disclosed related technologies, such as the patent titled "A Method for Managing E-commerce Points Transactions Based on Blockchain Technology" (application number CN202010391349.8), a solution for managing points generated in e-commerce transactions using blockchain technology is provided. This patent's method includes user points and merchant-specific points, both stored using blockchain technology. User points include points from different merchants. User points are traded between merchants and a points platform, while merchant-specific points are traded between the points platform and a third-party escrow service. This patent utilizes blockchain technology to store points, making them relatively more secure and reliable. User points are issued separately by multiple merchants, and merchant-specific points are jointly issued by multiple merchants. The exchange between user points issued by multiple merchants and merchant-specific points jointly issued by multiple merchants allows points to circulate freely between different merchants, facilitating convenient point circulation.

[0004] However, as commercial activities deepen, a large amount of commercial data is generated. The use and development of this data, stored on the blockchain, will become a new high-value data asset area, especially in blockchain-based commercial projects. For example, in the internet advertising industry, advertising platforms, advertisers, and media outlets frequently need to share user profile data and advertising performance data to achieve precision marketing. In the supply chain management field, manufacturing, logistics, and sales companies need to exchange production progress data, logistics data, and sales inventory data in real time to optimize the entire supply chain process. This commercial data, as the data assets of commercial entities, also has significant economic value when licensed for use.

[0005] In conclusion, how to fully leverage the advantages of blockchain technology to achieve more efficient and rational commercial use of business data and promote the formation of an intelligent new business ecosystem has become an important issue to be addressed in the evolution of the current new business economy. Summary of the Invention

[0006] This invention provides a method for processing commercial entity contribution data, which enables efficient and quantifiable use of the contribution value of commercial entity ownership data assets among commercial entities.

[0007] In a first aspect, embodiments of the present invention provide a method for processing business entity contribution data, comprising: obtaining information about a second business entity with which it has a smart contract based on a decentralized identifier of a first business entity; traversing the smart contract execution data of the first and second business entities in a first settlement cycle; wherein the smart contract execution data is the execution data generated after the smart contract of the first business entity's confirmed data assets is used in the second business entity; generating contribution value data of the first business entity based on the smart contract execution data; and loading the contribution value data into the second business entity contribution data ledger of the first business entity.

[0008] Optionally, the step of obtaining information about a second business entity with which a smart contract exists based on the decentralized identifier of the first business entity in this embodiment of the invention includes: registering the first business entity to generate the decentralized identifier of the user; generating a smart contract between the first business entity and the second business entity using a contract editor based on the decentralized identifier of the first business entity user, and deploying it to a blockchain node; triggering a query for the second business entity with a smart contract with the first business entity; and querying the smart contract deployed to the blockchain node based on the decentralized identifier of the first business entity to obtain information about the second business entity with a smart contract with the first business entity.

[0009] Optionally, the data assets of the first business entity that are confirmed to be owned by the first business entity in this embodiment of the invention include at least one of the following: intellectual property data assets belonging to the first business entity; user privacy data assets of the first business entity; user behavior and interaction data assets of the first business entity; user business operation data assets of the first business entity; and user relationship and evaluation data assets of the first business entity.

[0010] Optionally, the generation of execution data after the smart contract for the first commercial entity's confirmed data assets in the second commercial entity is executed includes: generating a first encryption factor; encrypting at least one of the following as its commercial activity data: intellectual property data assets belonging to the first commercial entity, user privacy data assets of the first commercial entity's users, user behavior and interaction data assets of the first commercial entity's users, user business operation data assets of the first commercial entity's users, and user relationship and evaluation data assets of the first commercial entity's users, to obtain first ciphertext data; encrypting the first encryption factor using the first commercial entity's user's public key to obtain first encryption factor ciphertext data; obtaining the hash value of the metadata corresponding to the commercial activity data in the first ciphertext data, and encrypting the hash value using the first commercial entity's user's private key to generate a first digital signature of the first commercial entity's user; uploading the first ciphertext data, the first encryption factor ciphertext data, and the first digital signature to the blockchain of the commercial entity's ecosystem for storage; generating a smart contract, adding usage code to the confirmed first ciphertext data, the first encryption factor ciphertext data, and the first digital signature data; triggering the usage code in the smart contract to generate the execution data of the smart contract.

[0011] Optionally, the smart contract execution data in this embodiment of the invention includes at least one of: smart contract execution data for promotion services; smart contract execution data for e-commerce transactions; and smart contract execution data for business cooperation.

[0012] Optionally, the step of generating contribution value data for the first business entity based on the smart contract execution data in this embodiment of the invention includes: verifying that the first digital signature data in the smart contract is the digital signature of the first business entity; verifying the use of the first encrypted data and the first encrypted factor encrypted data of the first business entity in the smart contract execution data; collecting traffic statistics data and / or corresponding financial revenue data of the first encrypted data and the first encrypted factor encrypted data in the smart contract execution data being decrypted and used by the second business entity; and generating contribution value data for the first business entity based on the traffic statistics data and / or the corresponding financial revenue data, and the settlement code for data assets in the smart contract.

[0013] Optionally, the step of generating contribution value data of the first business entity based on the smart contract execution data in this embodiment of the invention includes: verifying the decentralized identifiers of the first and second business entities in the smart contract; collecting promotion service execution traffic data, business transaction data, or business cooperation result data between the first and second business entities in the smart contract execution data; and generating contribution value data of the first business entity based on the promotion service execution traffic data, business transaction data, or business cooperation result data, and the pre-set settlement code in the smart contract.

[0014] Optionally, embodiments of the present invention further include: identifying intellectual property data assets, user privacy data assets, user behavior and interaction data assets, and user business operation data assets belonging to the first business entity based on a large digital asset identification model, and extracting feature data of the first business entity's digital assets; matching a third business entity with digital asset analysis needs based on the feature data of the first business entity's digital assets; triggering the usage code in the smart contract to generate execution data of the smart contract between the first business entity and the third business entity, thereby enabling the third business entity to use the first business entity's digital assets.

[0015] Optionally, the step of generating contribution value data of the first business entity based on the smart contract execution data in this embodiment of the invention includes: calculating the contribution degree of the first business entity in real time based on the smart contract execution data; adjusting the profit-sharing calculation weight parameter of the first business entity in real time based on the contribution degree of the first business entity; and calculating the contribution value data of the first business entity based on the profit-sharing calculation weight parameter of the first business entity in response to the request of the first business entity.

[0016] Optionally, embodiments of the present invention further include: acquiring first off-chain trusted data related to the first business entity and uploading it to the blockchain; calculating the contribution of the first business entity in real time based on the first off-chain trusted data and the smart contract execution data; adjusting the profit-sharing calculation weight parameters of the first business entity in real time based on the contribution of the first business entity; and calculating the contribution value data of the first business entity to be distributed based on the profit-sharing calculation weight parameters of the first business entity in response to the request of the first business entity.

[0017] This invention involves a first business entity traversing the execution data of its smart contracts with a second business entity during a first settlement cycle. This smart contract execution data is generated after the execution of the smart contract in the second business entity where the data assets of the first business entity are registered. Based on this smart contract execution data, contribution value data for the first business entity can be generated. This allows for the efficient and quantifiable determination of the contribution value of registered data assets between business entities, which is then attributed to the second business entity's contribution data ledger for subsequent use, thereby making the business entity ecosystem more robust. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for processing commercial entity contribution data according to Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the structure implemented in the commercial ecosystem provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the contribution value ledger of business entity B provided in Embodiment 3 of the present invention; Figure 4 A flowchart illustrating the generation process of an enterprise's industry contribution value ledger as provided in Embodiment 4 of the present invention; Figure 5 This is a schematic diagram illustrating the process of a business entity confirming its data assets, as provided in Embodiment 5 of the present invention. Figure 6 A schematic diagram of the process for generating contribution values ​​for commercial entity ownership confirmation data assets provided in Embodiment Six of the present invention; Figure 7 This is a schematic diagram illustrating the process of how commercial entity ownership data is used in a trusted data environment to generate contribution values ​​through smart contracts, as provided in Embodiment 7 of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0022] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0023] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0024] In this embodiment of the invention, by analyzing the execution data of smart contracts between the first and second commercial entities in the commercial entity ecosystem during the settlement cycle, especially the execution data generated after the smart contract in which the data assets of the first commercial entity are confirmed are used in the second commercial entity, the contribution value of the confirmed data assets of the first commercial entity can be quantitatively determined. This creates a credible business environment for subsequent commercial activities such as rebates for contributions to the first commercial entity in the commercial ecosystem, thereby making the commercial entity ecosystem more robust.

[0025] Example 1: Figure 1 This is a schematic diagram illustrating a business entity contribution process according to Embodiment 1 of the present invention. The business entity contribution in this Embodiment 1... The steps include the following: Step 100: Obtain information about the second business entity with which the first business entity has a smart contract based on the decentralized identifier of the first business entity. First, in the business entity ecosystem of this invention, the first business entity registers and generates its decentralized identifier (DID) within the ecosystem. For example, e-commerce company A, as the first business entity, generates a unique DID for itself through a specific blockchain registration system, such as "DID:0x123abc..." (the complete format data is not shown; it is merely illustrative, and similar data will be processed in the same way below). Those skilled in the art can refer to the DID (Decentralized Identifier) ​​generation algorithm to generate the DID. Then, based on the decentralized identifier of the first business entity user, a smart contract between the first business entity and the second business entity is generated through a contract editor and deployed to the blockchain node. Assuming that e-commerce company A has a cooperative relationship with logistics company B (as the second business entity), A sets the cooperation terms in the contract editor based on its own decentralized identifier, such as the scope of data sharing and usage permissions, and deploys the generated smart contract to the blockchain node. Next, a query is triggered by the second business entity that has a smart contract with the first business entity. When e-commerce company A has business needs, such as querying the transportation progress of a certain batch of goods, a query operation against logistics company B will be triggered. Finally, based on the decentralized identifier of the first business entity, the smart contract deployed in the blockchain node is queried to obtain information about the second business entity that has a smart contract with the first business entity. E-commerce company A, through its own decentralized identifier, queries the blockchain node for information related to its smart contract with logistics company B, including B's corporate identity, cooperation terms, etc.

[0026] In this embodiment, preferably, the step of obtaining information about a second business entity with which it has a smart contract based on the decentralized identifier of the first business entity includes: Steps: Register the first business entity to generate the user's decentralized identifier. The first business entity (e.g., e-commerce company A) registers through a dedicated decentralized identity registration system. This system, based on blockchain technology, generates a globally unique decentralized identifier for company A. During registration, company A needs to provide basic information such as its company name, business license registration number, and legal representative information. This information is encrypted and, along with other relevant information (such as a registration timestamp and a system-generated random number), is used in conjunction with a specific algorithm to generate a decentralized identifier conforming to blockchain standards, such as "DID:0x123abc...".

[0027] Steps: Based on the decentralized identifier of the first business entity's user, generate a smart contract between the first and second business entities using the contract editor, and deploy it to the blockchain node. Suppose e-commerce company A has a partnership with financial lending service company B (as a secondary business entity) to convert e-commerce users into financial lending customers, providing them with consumer-type commercial loans. Company A uses its decentralized identifier to initiate the smart contract creation process in the contract editor.

[0028] In the contract editor, Company A first sets the basic framework for cooperation, including the purpose of cooperation (such as the generation of loan contracts, repayment plans, liability for breach of contract, etc.) and the term of cooperation (such as from January 1, 2024 to December 31, 2024).

[0029] In order to accurately provide reliable loans to e-commerce users and fully analyze their repayment ability and credit, it is necessary to obtain user ownership data of e-commerce users on Company A's e-commerce platform, such as e-commerce user order data, including order number, product name, customer shipping address and other information. This data is considered as user data assets and requires user authorization before financial loan service company B can use it.

[0030] Based on these settings, the contract editor automatically generates smart contract code. Company A reviews the generated code to ensure it meets the cooperation requirements, and then deploys the smart contract to a blockchain node. The blockchain node verifies and stores the smart contract, ensuring its uniqueness and immutability within the blockchain network.

[0031] Steps: Trigger a query from the second business entity that has a smart contract with the first business entity. When an e-commerce company A has a user requesting a loan, such as applying for a batch of loans for payment, it will trigger a query operation against financial loan service provider B through a smart contract system. Specifically, company A sends a query request to the blockchain network, which includes company A's decentralized identifier, the relevant identifier of financial loan service provider B (defined in the smart contract), and the specific content of the query.

[0032] Steps: Query the smart contract deployed in the blockchain node based on the decentralized identifier of the first business entity to obtain information about the second business entity that has a smart contract with the first business entity. After receiving a query request from Company A, the blockchain network searches for relevant contract information in its stored smart contracts based on Company A's decentralized identifier. Upon finding the smart contract with financial lending service company B, it retrieves information such as company B's corporate identity, cooperation terms, and loan data application and usage records, and returns this information to Company A. Based on the returned information, Company A can understand relevant details about financial lending service company B, such as company B's basic information, the progress of the cooperation between the two parties, and the history of data interaction.

[0033] Step 101: Traverse the smart contract execution data of the first business entity and the second business entity during the first settlement period, wherein the smart contract execution data is the execution data generated after the smart contract of the first business entity's confirmed data assets is used in the second business entity. In this embodiment, it is assumed that within one month (the first settlement cycle), the smart contract between e-commerce company A and financial loan service company B involves the use of e-commerce users' e-commerce order data (one of A's confirmed data assets). During the service process, financial loan service company B, according to the provisions of the smart contract, acquires and processes the e-commerce users' e-commerce order data. For example, it evaluates in real time the e-commerce users' monthly payment amount, product category, whether they are high-quality loan users, payment time, loan record information, etc. These smart contract evaluations and analyses generate execution data, such as which confirmed data of the e-commerce users are used, whether to grant loans to the e-commerce users based on the confirmed data, the loan amount, the specific repayment method, and repayment progress information. E-commerce company A traverses this execution data to understand the good or bad performance of the e-commerce users' financial loan service orders.

[0034] Preferably, in this embodiment, the data assets for which the first business entity has been granted ownership include various types. Taking e-commerce company A as an example, the data assets may include the following types: Intellectual property data assets belonging to the first business entity: E-commerce company A may own its own trademark and patent-related data assets, such as trademark registration information and patent technical documents. In cooperation with financial loan service company B, if the use of these intellectual property data assets is involved, such as using company A's trademark in financial loan service advertisements, the relevant data interactions and usage will be recorded in the smart contract execution data.

[0035] The first business entity's user privacy data assets: Company A's user privacy data assets include users' personal information, such as name, contact information, and address. When this data is shared with financial lending service company B to ensure the accuracy and timeliness of loans, it will be processed strictly in accordance with the encryption and usage methods stipulated in the smart contract, and the execution data generated by the relevant operations will record the usage of privacy data.

[0036] The first business entity's user behavior and interaction data assets: This includes user behavior and interaction data from Company A's e-commerce platform, such as browsing history, purchase records, and reviews. In its collaboration with financial lending service company B, this data is used to predict users' loan repayment ability and creditworthiness, and the corresponding usage is reflected in the smart contract execution data.

[0037] The first business entity's user business operation data assets include data on the activities of Company A's e-commerce users on its e-commerce platform. This includes data on users' purchases of VIP coupons, linked payment cards, credit card usage, business operations as merchants on the platform, and cooperation data with the platform. When this data is used in cooperation with financial lending service company B, it is further used to assess the loan amount available to e-commerce users, repayment methods, etc., thereby generating corresponding smart contract execution data.

[0038] The first business entity's user relationship and evaluation data assets include, for example, user satisfaction ratings during service use by Company A, and social relationship data between users. If this data is used to further optimize and evaluate the loan amount and repayment method for e-commerce users, the relevant usage will be recorded in the smart contract execution data.

[0039] Preferably, in this embodiment, the aforementioned intellectual property data assets, user privacy data assets, user behavior and interaction data assets, and user business operation data assets belonging to the first business entity can also be identified and processed using a large digital asset identification model to extract the feature data of the first business entity's digital assets.

[0040] For example, the first business entity (e-commerce company A) uses a large-scale digital asset identification model to identify and extract features from its various data assets. For intellectual property data assets belonging to e-commerce company A, such as data related to its unique product recommendation algorithms, the model analyzes the algorithm's logical structure and innovativeness, extracting features such as algorithm complexity and the percentage improvement in prediction accuracy. For user privacy data assets, the model identifies features such as data sensitivity and encryption requirements. For example, user delivery address data is marked as highly sensitive and requires strong encryption. For user behavior and interaction data assets, such as browsing paths and purchase intervals on the e-commerce platform, the model extracts features such as behavioral patterns and preferences. For instance, identifying a user's high browsing frequency for a certain type of product within a specific time period indicates a high interest in that product. For user business operation data assets, such as inventory turnover rate and seasonal sales fluctuations, the model extracts features such as operational efficiency and market trend relevance.

[0041] Next, based on the extracted feature data of the first business entity's digital assets, a third business entity with digital asset analysis needs is matched. Assume the large-scale digital asset identification model analyzes that e-commerce company A's user behavior and interaction data assets can easily enable personalized needs analysis for e-commerce users, such as analyzing personalized recommendation needs. The model will search within the business ecosystem and find a professional data analysis company C (the third business entity). This company has extensive experience in user behavior analysis and algorithm optimization capabilities, and its business operations require analysis of digital assets similar to those of company A, thus having a need for such analysis.

[0042] Furthermore, e-commerce company A generates a smart contract with data analytics company C using its decentralized identifier via a contract editor. The contract specifies the scope of data sharing (e.g., user behavior and interaction data within a specific time period), the size of data assets (e.g., data asset version, compressed file size), the frequency of data asset usage, and the rights and obligations of both parties regarding data usage fees. After generating the smart contract, it is deployed to a blockchain node. Contract execution and data generation are then triggered: e-commerce company A triggers the usage code within the smart contract, providing the relevant data assets to data analytics company C according to the contract stipulations. For example, company A transmits encrypted user browsing history and purchase records from the past three months to data analytics company C. Data analytics company C processes the data in its secure data analytics environment; for instance, data analytics company C uses machine learning algorithms to analyze user behavior patterns and build more accurate analysis and recommendation models. In this process, the usage of data assets generated, such as the size of the data package used (e.g., 500MB), the duration of data usage (e.g., one week), the scope of the data (e.g., e-commerce user behavior and interaction feedback evaluation data), and the settlement and billing of data asset usage (e.g., monthly settlement fee of 10 contribution points), all serve as execution data for smart contracts. This execution data is recorded on the blockchain to ensure its transparency and immutability.

[0043] Preferably, in this embodiment, before the smart contract is deployed, artificial intelligence technologies (such as machine learning and deep learning algorithms) are used to predict risks in the execution process and data interaction of the smart contract. Preferably, a risk prediction model is constructed by learning from a large amount of historical smart contract data and relevant business data. For example, for a smart contract between e-commerce company A and data analysis company C, the model can analyze factors such as data analysis company C's past analysis results, data analysis market feedback data, data analysis market environment data, and potential fraud patterns to predict potential risks in this cooperation, such as analytical bias and data misuse risks. Based on the risk prediction results, the smart contract automatically adjusts its execution strategy and contribution value protection measures. If a high risk is predicted, the smart contract may increase the frequency of monitoring data usage by data analysis company C, monitor more execution data details, and require a certain percentage of contribution value to be reserved for guaranteeing the metal content in the contribution value calculation. During the cooperation process, if a risk event occurs, such as discovering that data analysis company C has misused data to falsify information and provide false analysis reports, the smart contract will automatically deduct its contribution value for guaranteeing the metal content and recalculate the contribution value for compensation based on the loss.

[0044] In this embodiment, preferably, the confirmation of user data assets of enterprise A can be further refined to the confirmation of the aforementioned user privacy data assets, user behavior and interaction data assets, user business operation data assets, and user relationship and evaluation data assets of specific users of the business entity. For example, the aforementioned data of an e-commerce user of business entity A in the e-commerce business is confirmed, and the right to use such data assets is authorized to the e-commerce user. The right to use these confirmed asset data is then confirmed to the e-commerce user. When these confirmed asset data are used subsequently, a contribution value will be calculated and recorded in the e-commerce user's account, rather than in the contributor account of business entity A. Here is a practical example: Confirmation and use of user privacy data assets: When an e-commerce user registers on the e-commerce enterprise A's platform, they will provide personal information, such as name "Zhang San", contact information "138xxxxxxxx", and delivery address "Beijing Chaoyang District Business Center District, etc." These user privacy data assets will be encrypted and a unique privacy data identifier will be generated, such as "PID:0x456def..." (The complete format data is not shown; it is only for illustration purposes. Similar data will be processed in the same way below). Simultaneously, the authorization status of this data will be recorded, meaning that the user authorizes e-commerce company A to use this data under specific conditions (simply to ensure accurate delivery of goods). When the user authorizes e-commerce company A to share these confirmed data assets with financial loan service company B, a contribution value will be generated, and the data flow, purpose of use, and usage time will be recorded in detail in the smart contract execution data. For example, during the delivery of an e-commerce order, financial loan service company B needs to obtain the delivery address of user "Zhang San". The smart contract execution data will record, "On May 10, 2024, user Zhang San's delivery address data (Chaoyang District, Beijing, etc.) was obtained by financial loan service company B for order delivery." For example, regarding the confirmation and use of e-commerce user behavior and interaction data assets: e-commerce company A will continuously track user behavior and interaction data on the platform. For example, user "Zhang San's" browsing history includes browsing the Apple mobile phone product page under the electronics category on May 8, 2024, and a purchase record showing that he bought a top-of-the-line iPhone 16 smartphone on April 20, 2024, with comments such as "good user experience." This data will be analyzed and processed to generate a unique behavioral and interaction data identifier for the user, such as "BID:0x789ghi..." (The complete format data is not shown; it is only for illustration purposes, and similar data will be processed in the same way below). The user owns the rights to this data and can choose to authorize financial lending service company B to use this data to evaluate the generation of its loan services.The user data of user "Zhang San" of e-commerce company A is then transferred to user "Zhang San". Subsequent use requires authorization from e-commerce user "Zhang San". When using this transferred asset data, the contribution value will be calculated and recorded in the account of e-commerce user "Zhang San", rather than in the contributor account of business entity A.

[0045] Preferably, the process of confirming the ownership of the e-commerce user's data in e-commerce business entity A in this embodiment is similar to the technical implementation of the data ownership confirmation process in e-commerce business entity A in this embodiment. The only difference is that the e-commerce user is replaced by a user of the business entity. A unique DID identifier is generated for the user in the business entity ecosystem, and then the ownership of the data is confirmed and the contribution value is calculated after commercial use. This will not be elaborated further here.

[0046] Preferably, regarding the authorization of ownership confirmation data, when a first business entity (such as e-commerce company A) collaborates with a second business entity (such as authorized financial loan service company B), e-commerce company A can not only set a fixed data authorization scope and quantity during smart contract creation in the traditional way, but also dynamically adjust the scope of authorized data based on the real-time monitoring function of ownership confirmation data usage in the blockchain, according to the progress of loan business and feedback on ownership confirmation data usage. For example, for a business request with a large loan amount, if authorized financial loan service company B requires more e-commerce user ownership confirmation data for optimization and evaluation, e-commerce company A can temporarily expand the authorization of relevant ownership confirmation data to financial loan service company B through a smart contract on the blockchain, under certain security verification conditions (such as verifying the current business load and data usage compliance of logistics company B), such as adding e-commerce user consumption data for a longer period of time, or e-commerce user consumption record data of larger amounts. For the adaptive adjustment of the smart contract in this embodiment, the smart contract has built-in data analysis code, which can analyze key data indicators in the cooperation process in real time. Taking the cooperation between e-commerce company A and financial loan service company B as an example, if it is found that the credit assessment accuracy of financial loan service company B for e-commerce users is consistently lower than a set threshold for a period of time, and no loan business is successfully matched, the smart contract will automatically trigger adjustment clauses, authorizing more confirmed user data to financial loan service company B. For example, it may enable financial loan service company B to access sensitive user data, enable access to user data for a longer period of time, and enable access to user data for specific search result fields, until its credit assessment accuracy returns to a normal level. At the same time, in the calculation of contribution value, the increase in the amount of confirmed data due to the adjustment of the use of confirmed data will improve the calculation result of contribution value, thereby realizing the dynamic optimization of smart contract and precise control over the use of confirmed data, and also ensuring the business results of financial loan service company B.

[0047] Step 102: Generate contribution value data for the first business entity based on the smart contract execution data. In the aforementioned loan service cooperation scenario, e-commerce company A verifies the legality of relevant data in the smart contract, such as verifying that the first digital signature data in the smart contract is its own digital signature, and verifying the use of its own first encrypted data and first encryption factor encrypted data in the smart contract execution data. Then, it collects traffic statistics and / or corresponding financial revenue data on the decryption and use of the first encrypted data and first encryption factor encrypted data in the smart contract execution data by the second business entity. For example, it counts the number of times financial loan service company B decrypts and uses user ownership data, and calculates, according to the cooperation agreement, the contribution value to be generated for e-commerce company A after a certain amount of ownership data is used for a certain period of time. Preferably, if the use of e-commerce user ownership data results in financial loan service company B obtaining an additional loan increase from e-commerce company A that is higher than the ordinary loan user acquisition conversion rate (corresponding to additional financial revenue data compared to the ordinary market conversion revenue rate), it can further generate a corresponding contribution value for e-commerce company A. Finally, based on the aforementioned data usage statistics and / or corresponding financial revenue data, and the data asset usage settlement code in the smart contract, the contribution value data for e-commerce company A (the first business entity) is generated. Assuming that, based on the settlement code, financial loan service company B decrypts and uses 100M of user rights confirmation data 10 times, the contribution value is 1 point, and the contribution value is 5 points for every 1000 yuan of additional loan increase. If financial loan service company B decrypts and uses 100M of user rights confirmation data 20 times within a month, and the additional loan increase is 2000 yuan, then the contribution value of e-commerce company A is (20 ÷ 10 × 1 point + (2000 ÷ 1000) × 5 points = 12 points).

[0048] Preferably, the smart contract execution data in this embodiment of the invention may include the processing of different types of execution data, preferably at least one of promotion service smart contract execution data, e-commerce transaction smart contract execution data, and business cooperation smart contract execution data. Examples are given below for illustration: 1. Promotion service smart contract execution data Suppose e-commerce company A and marketing organization C sign a smart contract for promotional services. Marketing organization C generates a series of execution data during the promotion process. For example, marketing organization C records data such as the platform used for ad placement, the placement time, the number of impressions, and the number of clicks. This data, as execution data of the promotional service smart contract, utilizes user ownership data assets within e-commerce company A, such as user preferences, user purchase history, and user location. Marketing organization C analyzes this data to achieve precise ad placement, thereby achieving high ad conversion efficiency.

[0049] 2. E-commerce transaction smart contract execution data Take, for example, the e-commerce transaction smart contract in the collaboration between e-commerce company A and apparel design company B. Apparel design company B obtains sales-related data related to clothing from e-commerce company A, such as sales volume changes of sportswear (verified by e-commerce company A), data on the most popular women's clothing styles and sizes, and annual sales ranking data. This data can be used in apparel design company B's business. Furthermore, through the e-commerce transaction smart contract, authorization is obtained to use these verified data assets, thereby forming the execution data of the e-commerce transaction smart contract.

[0050] 3. Execution data of smart contracts for business cooperation Suppose e-commerce company A signs a smart contract for e-commerce business cooperation with brand product provider D. The cooperation involves selling a new brand product on e-commerce company A's platform. During the cooperation, e-commerce company A generates business cooperation execution data. For example, during e-commerce company A's promotional activities (e.g., July 1, 2024 – July 10, 2024) and promotional methods (e.g., discounts, gifts, etc.), the new brand product becomes a best-selling item, with sales reaching 10,000 units. This promotional activity planning information and sales volume data are recorded as part of the execution data. Based on the sales target achievement data in the smart contract execution data, contribution value data for e-commerce company A based on the e-commerce business cooperation smart contract with brand product provider D is generated. This data helps e-commerce company A to better conduct its sales activities. The contribution value is calculated based on the confirmed sales activity data and specific sales volume data of e-commerce company A, ensuring the accuracy and authenticity of e-commerce company A's contribution value data.

[0051] Preferably, in this embodiment, the calculation of the business entity's contribution value data based on the execution data of the smart contract can be achieved through the following steps.

[0052] Verify the decentralized identifiers of the first and second business entities in the smart contract: For example, e-commerce company A verifies the correctness of its own and marketing organization C's decentralized identifiers in the smart contract. For instance, it compares company A's decentralized identifier "DID:0x123abc..." recorded in the smart contract with its actual decentralized identifier, and simultaneously verifies the decentralized identifier of marketing organization C to ensure the accuracy of both parties' identities.

[0053] The data collected includes promotion service execution traffic data, business transaction data, or business cooperation result data between the first and second business entities in the smart contract execution data. For the promotion service execution traffic data, business transaction data, or business cooperation result data mentioned above, please refer to the embodiments of these data disclosed in the above examples, which will not be repeated here.

[0054] Based on the traffic data, business transaction data, or business cooperation result data of the promotion service execution, and the pre-set settlement code in the smart contract, the contribution value data of the first business entity is generated: For example, the contribution value calculation corresponding to the traffic data of the promotion service: Marketing organization C will analyze the ownership data assets of e-commerce company A, and then complete the accurate implementation of its advertising placement, realize a precise order sales generation, and generate a contribution value of 2 points from each order generation based on the pre-set settlement code in the smart contract.

[0055] For example, the contribution value calculation corresponding to commercial transaction data: According to the cooperation agreement between e-commerce company A and clothing design company B, clothing design company B obtains sales-related data of clothing in e-commerce transactions confirmed by e-commerce company A. This data can be used in the business of clothing design company B. According to the amount of data obtained (forming smart contract execution data records), for example, 100M of raw data obtained and used, a contribution value of 2 points is given. These contributor calculation logics are written into the pre-set settlement code in the smart contract, thereby forming the automatic and efficient generation of commercial entity contribution value data.

[0056] Contribution value calculation corresponding to business cooperation outcome data: For example, e-commerce company A's promotional activity planning information, sales volume data, etc., are recognized as its data assets and recorded as part of the execution data. Based on the sales target completion data in the execution data of business cooperation smart contracts, contribution value data of e-commerce company A based on the e-commerce business cooperation smart contract signed with brand product provider D will be generated. For example, information on organizing an online promotional activity for 10 days, information on a sales activity with a price reduction of 100,000, and information on 10,000 orders completed during the promotional activity will each generate a contribution value of 10 points, which will be recorded in e-commerce company A's ledger for brand product provider D, ensuring the accuracy and authenticity of e-commerce company A's contribution value data.

[0057] Preferably, in this embodiment, for calculating the contribution value of e-commerce company A's authorized data assets, a data value assessment model based on big data analysis and artificial intelligence algorithms is further developed. This model can monitor changes in external factors such as market environment, industry trends, and user needs in real time, and dynamically assess the value of the data assets of the first business entity (such as e-commerce company A). For example, during market fluctuations in the e-commerce industry, the value of e-commerce company A's user credit risk data may change. The model will reassess the value of its confirmed user data assets based on factors such as market risk index and industry default rate. Based on the dynamic assessment results of data value, the contribution value calculation rules in the smart contract will be automatically adjusted. If the data value increases, the contribution value calculation weight of e-commerce company A will increase accordingly in cooperation with the second business entity (such as brand product provider D), and vice versa. For example, when the user credit risk data of e-commerce company A becomes more critical to the product design improvement assessment of brand product provider D during market fluctuations, the contribution value corresponding to each use of confirmed user data will increase, ensuring that the contribution value can accurately reflect the actual value of the data in different market environments, promoting fair cooperation and optimal resource allocation between business entities.

[0058] Preferably, in this embodiment, the step of generating contribution value data of the first business entity based on the smart contract execution data can be performed as follows: calculating the contribution of the first business entity in real time based on the smart contract execution data; adjusting the profit-sharing calculation weight parameter of the first business entity in real time based on the contribution of the first business entity; and calculating the contribution value data of the first business entity based on the profit-sharing calculation weight parameter in response to the request of the first business entity.

[0059] Furthermore, preferably, in this embodiment, first off-chain trusted data related to the first business entity can be acquired and uploaded to the blockchain; based on the first off-chain trusted data and the smart contract execution data, the contribution of the first business entity is calculated in real time; based on the contribution of the first business entity, the profit-sharing calculation weight parameters of the first business entity are adjusted in real time; in response to the request of the first business entity, the contribution value data of the first business entity to be distributed is calculated based on the profit-sharing calculation weight parameters of the first business entity. Preferably, this can be implemented, for example, with the following technical solution: Introduce an off-chain trusted data access layer: Integrate oracles (e.g., Chainlink) to upload off-chain trusted data (such as user KYC (Know Your Customer) authentication and offline event participation) to the chain as a supplementary dimension for the calculation of the Dynamic Profit Sharing Index (DPSI); oracle data must be verified by multiple nodes to ensure authenticity.

[0060] Multi-dimensional weighted algorithm upgrade: On the basis of the original algorithm, OffchainYJgo is added to score off-chain data according to weight (such as KYC certification ×2, offline event participation ×3); the main contract calls OffchainYJgo through a call, and the scores are linearly added with the on-chain algorithm scores to obtain a more comprehensive DPSI.

[0061] For details on the calculation of other contribution values, please refer to the technical solutions disclosed in other parts of this embodiment.

[0062] Step 103: Load the contribution value data belonging to the first business entity into the second business entity contribution data ledger. E-commerce company A assigns the calculated contribution value data to its own contribution data ledger for apparel design company B, marketing organization C, and brand product provider D, and then summarizes, records, and loads the data into the ledger. The ledger records detailed information such as the time the contribution value was generated and related business information (e.g., order number) for subsequent querying and statistical analysis.

[0063] Preferably, in this embodiment, the right to continuously generate contribution value data for the first business entity from the confirmed data asset within the smart contract can be used to generate a virtual redemption certificate. For example, based on the aforementioned cooperation scenario between e-commerce company A and clothing design company B, within a three-month cooperation period, based on the usage of data each month in the smart contract execution data and the corresponding contribution value calculation results, it is determined that e-commerce company A has a stable contribution value acquisition capability, such as 10 points, within this monthly period. This capability to acquire 10 points of contribution value each month is then transformed into a redemption certificate. E-commerce company A can use this redemption certificate to pre-deposit 10 points of contribution value per month for three months into its ledger. This virtual redemption certificate is relative to clothing design company B. E-commerce company A can also generate such virtual redemption certificates with other business entities and aggregate them into its ledger. In this way, e-commerce company A can obtain contribution value in advance for commercial use based on the stable usage of its data assets.

[0064] Preferably, in this embodiment, the contribution value data in the ledger of e-commerce company A and / or the virtual redemption warrants (i.e., the expected future contribution value) can be converted into corresponding equity certificate data of a business entity B with an equity relationship with e-commerce company A after the contribution value reaches a threshold within a certain period of time. This equity certificate data can obtain corresponding dividends from the business entity. That is, e-commerce company A's virtual redemption warrants can be exchanged for rights based on their stable contribution value, thus participating in the equity of business entity B; or e-commerce company A can participate in the equity of business entity B with a contribution value that reaches a certain threshold.

[0065] Preferably, in this first embodiment, a detailed traceability chain is further established for the flow of each confirmed data asset between various business entities, and recorded in the blockchain blocks. Starting from the generation of data by the first business entity (e.g., e-commerce company A), information such as the data generation time, source, and data content summary is recorded. Each time data is transferred to another business entity (e.g., clothing design company B), the traceability chain is updated, recording key information such as the data recipient, purpose of use, and usage time. For example, when marketing organization C evaluates the product design data of clothing design company B, it can trace back to the fact that clothing design company B's product design used clothing e-commerce data from e-commerce company A. The blockchain will record detailed information about the use and flow of e-commerce company A's data. Furthermore, an independent contribution value audit smart contract code is established. This code can periodically or on-demand audit the calculation and allocation of contribution values ​​between business entities. During the audit process, the data traceability chain is used to verify whether the use of data complies with the smart contract agreement and whether the contribution value calculation is accurate. For example, if it is found that clothing design company B uses data from e-commerce company A beyond the agreed scope, but the corresponding penalties or adjustments are not reflected in the contribution value calculation, the audit smart contract will automatically issue an alert and make corrections according to preset rules to ensure the fairness and transparency of contribution value calculation and maintain the trust relationship between business entities.

[0066] Example 2: To more clearly reveal the structure of the commercial ecosystem involved in this invention, Figure 2 This is a schematic diagram of the structure implemented in the commercial ecosystem provided in Embodiment 2 of the present invention.

[0067] The business entity ecosystem in Embodiment 2 of the present invention includes: a business entity management platform 200, a first business entity 201 and its database 2011, a second business entity 202 and its database 2021, a third business entity 203 and its database 2031, and a fourth business entity 204 and its database 2041 in a trusted data source.

[0068] The Business Entity Management Platform 200 includes a User Center Module 2001, which manages user information within the business entity ecosystem. It's important to note that users on the Business Entity Management Platform 200 can be business entity users or individual users who use or participate in businesses or organizations within the business entity. In this business ecosystem encompassing e-commerce companies, suppliers, consumer loan providers, and data analytics providers, the User Center Module stores registration information, login credentials, and user permission settings for each company. For example, the operational permissions of e-commerce company A's staff and supplier company's product designers are managed by the User Center Module.

[0069] The Business Entity Management Platform 200 includes a Business Entity Management Module 2002, which is primarily used to manage the basic information, business scope, and cooperative relationships of a business entity. For example, e-commerce company A can set its company information in this module, including company size and business scope; it can also manage its cooperative relationships with product suppliers, consumer loan providers, and data analytics providers, such as setting the start time of cooperation and the main terms of the cooperation agreement. These can be completed in the Contract Editor Module 20042 and the Smart Contract Module 2005.

[0070] The business entity management platform 200 includes a data asset management module 2003, which is primarily responsible for managing the business entity's data assets. Taking e-commerce company A as an example, it possesses a large amount of e-commerce user data. The classification, storage location, and access permissions of these data assets are all handled by the data asset management module. For instance, e-commerce users' personal information, purchase records, and other data are classified and stored according to certain rules. Access to this data is only permitted with the authorization of the e-commerce user, or with the authorization of e-commerce company A's staff (such as data analysts from e-commerce company A when conducting data analysis). The specific content of this user-owned data, and how it is used in smart contracts to form smart contract execution data and calculate contribution values, can be found in the disclosure in Example 1.

[0071] The Business Entity Management Platform 200 includes a toolset 2004, which specifically includes a payment tool module 20041. This module plays a crucial role in e-commerce transaction scenarios, handling payment-related operations. When an e-commerce user makes a purchase on e-commerce company A's platform, the payment tool module 20041 ensures the secure flow of funds, such as by interacting with banking systems or third-party payment platforms to complete the payment. Toolset 2004 also includes a contract editor module 20042, which allows businesses to easily create and edit smart contracts. For example, when e-commerce company A collaborates with a product supplier, the contract editor module allows for detailed setting of smart contract terms, including data authorization scope, product improvement goals, and profit distribution methods, transforming the rules of business cooperation into executable smart contract code. Furthermore, toolset 2004 also includes a 20043 encryption / decryption privacy protection tool module, which focuses on protecting the privacy and security of business entity data. Within the business entity ecosystem, data from various businesses needs to be encrypted and protected during interaction. For example, when e-commerce company A transmits its e-commerce user data to product suppliers for analysis, the encryption / decryption privacy protection tool module will encrypt the data to ensure its confidentiality during transmission and prevent data leakage and malicious tampering.

[0072] The business entity management platform 200 includes a smart contract module 2005, which specifically includes a smart contract deployment module 20051. This module allows businesses to deploy smart contracts created through the contract editor module 20042 within blockchain projects, based on their specific business needs. For example, when e-commerce company A collaborates with a consumer loan provider, it sets contract terms through the contract editor module 20042, such as specific data sharing details, credit assessment standards, and risk-sharing mechanisms. These terms are then converted into smart contract code and deployed in the smart contract deployment module 20051.

[0073] The smart contract module 2005 specifically includes a smart contract execution data module 20052, which is responsible for executing deployed smart contracts and generating execution data. When a product supplier acquires e-commerce user data assets according to the contract, analyzes and processes them, and improves product design, the smart contract execution data module 20052 will automatically execute the corresponding operations. For example, the specific execution status of e-commerce company A providing authorized user data to the product supplier, and the product supplier's use of this authorized data to improve product design, will generate and record smart contract execution data. Other specific scenarios and schemes for generating smart contract execution data can be specifically described using the scheme in Example 1, and will not be repeated here.

[0074] The smart contract module 2005 specifically includes a contribution value settlement module 20053, which is used to calculate and manage contribution value data between business entities. In the cooperation between e-commerce company A and the data analytics provider, this module calculates the contribution value of e-commerce company A and the data analytics provider based on the data generated during the execution of the smart contract, such as the data analytics provider's use of e-commerce user data and the resulting business value, and records it in the corresponding ledger. Other specific scenarios and schemes for generating contribution value settlement based on smart contract execution data can be specifically described using the corresponding scheme in Example 1, and will not be repeated here.

[0075] The Business Entity Management Platform 200 also includes Consortium Blockchain & Data Storage 2006, which provides a secure and reliable data storage environment. It employs consortium blockchain technology to ensure data integrity and immutability. Data from various business entities across the entire business ecosystem is stored here. For example, e-commerce user data from e-commerce company A, product design data from product suppliers, user credit data from consumer loan providers, and analysis report data from data analytics providers are all stored on the consortium blockchain. Only authorized nodes can access and modify this data. Ideally, smart contracts can be deployed on the blockchain nodes of Consortium Blockchain & Data Storage 2006, as well as on associated blockchain nodes.

[0076] The business entity management platform 200 also includes the first business entity 201 (e-commerce company A) and database 2011. E-commerce company A, as the first business entity, has its database 2011 storing a large amount of e-commerce user data. This data is stored in the database after ownership verification. For example, e-commerce users' registration information (including name, contact information, etc.), purchase history (type of goods purchased, price, time, etc.), browsing history (product pages viewed, dwell time, etc.) and other data are all securely stored in the database.

[0077] The business entity management platform 200 also includes a second business entity 202 (a product supplier designing and manufacturing mobile phones) and a database 2021. The product supplier, acting as the second business entity, stores product design-related data in its database 2021, such as the phone's hardware configuration, appearance design, and functional parameters, as well as the product supplier's own user data. When collaborating with e-commerce company A, the platform uses smart contracts and related tool modules to obtain and analyze user data authorized by e-commerce company A. For example, it analyzes e-commerce users' preferences regarding phone screen size, battery life, and camera functions, thereby improving phone product design and even customizing phones for e-commerce users. Throughout this process, all data interactions are securely conducted under the protection of the consortium blockchain, and operation records are stored on the consortium blockchain.

[0078] The business entity management platform 200 also includes a third business entity 203 (a consumer loan provider offering consumer loan services to e-commerce users) and a database 2031. The consumer loan provider, as the third business entity, stores user loan-related data in its database 2031, such as loan application records, repayment records, and risk assessment model parameters. When collaborating with e-commerce company A, company A authorizes the consumer loan provider to analyze and process its verified user data (e.g., e-commerce user consumption data). The consumer loan provider analyzes data such as e-commerce users' consumption habits, spending amounts, and frequency to assess their credit scores and loan repayment capabilities, thereby improving the success rate of its consumer loan matching business. For example, based on the stability and amount of e-commerce users' spending on the e-commerce platform, the provider judges the user's economic strength and repayment ability, thus providing more accurate loan amounts and interest rates. During data interaction, data security is ensured through encryption and decryption privacy protection modules, and all operations are executed according to smart contracts, with relevant data stored in the consortium blockchain.

[0079] The business entity management platform 200 also includes a fourth business entity 204 (a data analysis provider that can monetize user data through commercial analysis) and its database 2041, all within a trusted data source. The data analysis provider, as the fourth business entity, stores intermediate results, model parameters, and the final data analysis report in its database 2041. When collaborating with e-commerce company A, company A authorizes the data analysis provider to analyze and process its verified user data (e.g., e-commerce user consumption data, see the example of verified user data in Implementation Example 1). The data analysis provider performs large-scale model analysis based on specific business needs. For example, by analyzing a large amount of e-commerce user consumption data, it can uncover trends and patterns in user consumption behavior and generate data analysis reports. These reports can be sold as commodities to generate profit, such as to other companies with market research needs. Throughout the entire data processing and transaction process, the consortium blockchain ensures data storage security and the immutability of operations, while smart contracts ensure the fair allocation of rights among all parties.

[0080] The following is an example of the workflow of the Business Entity Management Platform 200 in a business scenario: E-commerce company A uses the data asset management module 2003 within the Business Entity Management Platform 200 to determine the scope of e-commerce user data to be authorized to product suppliers, such as e-commerce users' browsing history and purchase history of mobile phone-related products. It then uses the contract editor module 20042 to create a smart contract for data authorization, stipulating that product suppliers can use this data to improve mobile phone product design, but cannot disclose the data or use it for other unauthorized purposes. The data is then encrypted using the encryption / decryption privacy protection tool module 20043 before being transferred from e-commerce company A's database 2011 to the product supplier's database 2021. Next, after receiving the authorized data, the product supplier analyzes it internally. For example, by analyzing e-commerce users' preferences for purchasing high-end mobile phones, it discovers that users have a high demand for night mode in mobile phone photography. Based on the analysis results, the product supplier improves the phone design, such as increasing the aperture of the phone camera, optimizing night scene shooting algorithms, and may even customize phones for some users based on specific user data provided by e-commerce company A. After the product supplier generates an improved or customized mobile phone design, it sells the product as a bestseller through e-commerce company A. The smart contract execution data module 20052 in the business entity management platform 200 evaluates the sales performance of the improved mobile phone from e-commerce company A based on preset smart contract terms. For example, if the improved mobile phone design meets market expectations, achieves sales of 10,000 units within 6 months, and the price is increased by 10%, exceeding the preset smart contract terms, then the contribution value settlement module 20053 calculates the corresponding contribution value from the supplier's use of e-commerce company A's user data assets based on the sales performance of the improved mobile phone from e-commerce company A (such as the expected sales growth brought about by the design improvement), and records it in the ledger. This contribution value can subsequently be linked to e-commerce company A's revenue.

[0081] The following is another example of the workflow of the Business Entity Management Platform 200 in a business scenario: a collaboration between e-commerce company A and a consumer loan provider. E-commerce company A's data asset management module 2003 filters out the consumption data of e-commerce users, such as the amount, frequency, and time interval of consumption across different product categories, and identifies this data as user data assets. A smart contract is created with the consumer loan provider through the contract editor module 20042, clearly stipulating that the purpose of the data use is to assess the user's loan repayment ability, and that the consumer loan provider must ensure data security. The encrypted e-commerce user consumption data is transmitted from e-commerce company A's database 2011 to the consumer loan provider's database 2031. Upon receiving the data, the consumer loan provider uses its internal risk assessment model and data analysis methods to evaluate the e-commerce user's loan repayment ability and generate a credit score. For example, by analyzing the user's stable consumption on high-end goods, it is determined that the user has high economic strength and repayment ability, and a higher credit score is awarded. Based on the credit score assessment, the consumer loan provider optimizes its consumer loan matching strategy, such as offering more favorable loan interest rates and higher loan amounts to users with high credit scores, thereby improving the success rate of consumer loan matching. When an e-commerce user chooses to use a consumer loan for payment on the e-commerce platform, the payment tool module 20041 interacts with the consumer loan provider's payment system to complete the loan payment operation. The smart contract execution data module 20052 evaluates the consumer loan provider's performance in business matching (such as whether the loan default rate has decreased or whether the business volume has increased). The contribution value settlement module 20053 calculates the contribution value of e-commerce company A to the consumer loan provider based on the evaluation results and records it in the ledger. Then, e-commerce company A returns the contribution value to the accounts of e-commerce users who used its user data, thus realizing a closed loop where e-commerce users' data assets are used and corresponding contribution value revenue is generated.

[0082] The following is another example of the workflow of the Business Entity Management Platform 200 in a business scenario: a collaboration between e-commerce company A and a data analytics provider. E-commerce company A's data asset management module 2003 determines the e-commerce user consumption data to be authorized to the data analytics provider, such as user consumption behavior data and product preference data within a specific time period. Then, using the contract editor module 20042, it formulates a detailed smart contract stipulating that the data analytics provider can use this data for business analysis, but the ownership and usage rights of the analysis reports must be allocated according to the agreement, and data security must be guaranteed. Next, with the help of the encryption / decryption privacy protection tool module 20043, the e-commerce user consumption data is transferred from e-commerce company A's database 2011 to the data analytics provider's database 2041. Then, the data analytics provider uses advanced data analysis technologies such as big data models to analyze the received data. For example, through the analysis of a large amount of e-commerce user consumption data, it discovers the consumption trends of users of different age groups for certain product categories in specific seasons, such as the peak consumption of fashion apparel by young users in summer. Based on the analysis results, it generates detailed data analysis reports, which may include market trend analysis, summaries of user behavior patterns, and the discovery of potential business opportunities. Data analytics providers sell the generated data analytics reports as commodities to other businesses in need, generating economic benefits. According to the smart contract, the smart contract execution data module 20052 monitors and evaluates the data analytics provider's data analysis work and report transactions. The contribution value settlement module 20053 calculates the contribution value of both parties based on the data analytics provider's data analysis results and their resulting commercial value (such as revenue from report sales) and records it in the ledger. Preferably, regarding the revenue from report sales, e-commerce company A receives its corresponding contribution value according to the proportion stipulated in the smart contract. For example, e-commerce company A might receive a certain percentage (10%) of the report sales revenue as contribution value data, which is then evenly distributed to the accounts of all e-commerce company users. This realizes the revenue generation of e-commerce company users' data assets after their use, enabling the quantifiable monetization of user data assets on the e-commerce platform.

[0083] Ultimately, through the collaborative work between various modules and databases, the entire business ecosystem achieved efficient data sharing, smooth business operations, and reasonable calculation and management of contribution values, thus promoting the healthy development of the entire business ecosystem.

[0084] Example 3: To more clearly reveal the working details of the contribution value ledger of the business entity of this invention, see [link to documentation]. Figure 3 , Figure 3 A schematic diagram of the contribution value ledger of business entity B provided in Embodiment 3 of the present invention is given.

[0085] See Figure 3 In this embodiment, business entity A is transaction entity number 00001, which represents a specific transaction identity of business entity A in the ledger. For example, this is the identifier related to a specific cooperative project or business transaction between business entity A and business entity B.

[0086] The RMB wallet displays financial information related to Business Entity A. This involves the inflow and outflow of funds during cooperation with Business Entity B. For example, in some business transactions, funds may be allocated or transferred based on contribution value, converting contribution value into cash for withdrawal.

[0087] The contribution value ledger records detailed information about Business Entity A's contribution values. My Information includes basic information about Business Entity A, such as company name and registration information, used to clearly record the entity's identity in the ledger. Authentication Information details Business Entity A's authentication within the partnership, such as whether it has passed specific business standard authentication or authentication information related to data usage and contribution value calculation. The Data Presentation section displays data related to contribution values. For example, it presents data on Business Entity A's data asset usage and data interaction frequency in its partnership with Business Entity B; this data is crucial for calculating contribution values. The Shelf section is Business Entity A's page for displaying and managing its sales products. Authorized Personnel Management records information on personnel with management authority over Business Entity A's contribution value ledger. These personnel are responsible for reviewing, updating, and maintaining the data in the ledger to ensure its accuracy and security. The Contract Templates section provides a centralized collection of contract templates that Business Entity A can use, or contract templates that Business Entity A is modifying and maintaining, for use in smart contract editing.

[0088] In addition, Figure 3 The contract type section displays the types of smart contracts involved between Business A and Business B; here, "All" is selected. For example, it can also include contracts based on different business cooperation models, such as sales cooperation contracts and data sharing contracts. Different contract types will affect the calculation method and related rules for contribution values. Additionally, 2022-10-02 to 2024-10-10 represents the time range covered by the ledger. Within this time period, all relevant business activities and contribution value data between Business A and Business B are recorded.

[0089] Below are the records for all contribution value ledger earnings of Business Entity A in March and April, along with the corresponding contract name (customer acquisition contract) and the corresponding contribution value earnings amount: This indicates that the contract type being executed between Business Entity A and Business Entity B is a customer acquisition contract. Under this type of contract, the business cooperation goal of both parties may be to jointly attract new customers or users. For example, Business Entity A may be an e-commerce platform, and Business Entity B may be a marketing agency; the two parties cooperate to conduct customer acquisition activities to attract new consumers to shop on the e-commerce platform. The total revenue and expenditure data for the month are also recorded.

[0090] The withdrawable contribution value record shows the withdrawable revenue that business entity A earns from business entity B based on its contribution value. This means that during the cooperation with business entity B, business entity A can extract corresponding revenue based on its contribution value. It also shows today's contribution value revenue of 11 and the sum of this month's contribution value revenue of 2233, which can be in virtual currency or equivalent RMB. Preferably, business entity A also maintains a corresponding contribution value ledger at, for example, business entity C. In this embodiment, only the contribution value ledger data of business entity A at business entity B is shown.

[0091] Through such detailed ledger records, business entity A and business entity B can clearly understand their respective contributions, relevant business data, and revenue information during the cooperation process. This provides a transparent and quantifiable basis for their cooperation, which helps to promote fairness and sustainability in the partnership.

[0092] Example 4: To more clearly reveal the working details related to the contribution value ledger of the business entity involved in this invention, see [link to relevant documentation]. Figure 4 , Figure 4 This is a flowchart illustrating the generation process of an enterprise's industry contribution value ledger, as provided in Embodiment 4 of the present invention.

[0093] See Figure 4 The flowchart in Embodiment 4 of this invention includes the following steps: a registration and login step. For example, an enterprise (taking business entity A as an example) first needs to register and log in within the business entity ecosystem. This step involves the enterprise providing its basic information, such as its name, business license information, and legal representative information. The system will verify and store this information. Only after completing registration and login can the enterprise participate in subsequent industry activities and establish its own industry contribution value ledger.

[0094] Complete the enterprise certification process: After successful registration and login, enterprises need to undergo further certification. This may include enterprise qualification certification, industry standard certification, etc. For example, an e-commerce company may need to provide relevant e-commerce operating licenses, tax registration certificates, and other documents for certification to ensure that the company complies with relevant industry standards and requirements. Only certified enterprises will have their data and contributions to industry activities recognized and effectively recorded.

[0095] The steps to determine if a company has joined an industry organization: After a company completes certification, the system will determine whether the company has already joined the relevant industry organization. If the company has already joined, for example, business entity A is already a member of an e-commerce industry alliance, then it will calculate and record its industry contribution value according to the rules and procedures of that industry organization. The system will generate a dedicated industry contribution value ledger for the company and display it. If the company has not yet joined, it will have the opportunity to apply to join the industry organization, as outlined in the steps below.

[0096] The steps for applying to join an industry organization and signing its bylaws are as follows: If a company decides to join an industry organization, it needs to submit an application and sign the relevant bylaws. The bylaws will stipulate the organization's purpose, objectives, the rights and obligations of its members, and the calculation and distribution rules for industry contribution value, among other important content. After signing the bylaws, the company officially becomes a member of the industry organization, subject to its rules, and participates in the industry contribution value calculation system. A template of the bylaws will be provided in the e-commerce system, managed uniformly by the industry organization's administrators, and its maintenance and enforcement will be determined through voting by the governance body.

[0097] Governance Voting Procedure: A company's application to join and the signing of its articles of association require a vote by the industry organization's governance body. The governance body will comprehensively consider factors such as the company's qualifications, industry influence, and potential contributions to the industry organization. If the vote passes, the company will be approved to join the industry organization, and the contribution value ledger will begin to be generated.

[0098] Once a company is approved to join an industry organization, the system will generate a dedicated industry contribution value ledger for it. This ledger will record various data and contribution value information related to the company's activities within the industry. For example, the ledger will record data on business collaborations between the company and other member companies, the use of data assets, and contributions to industry development. Simultaneously, the data in the ledger will be presented to the company in an appropriate manner so that it can promptly understand its contributions to the industry.

[0099] Through the above steps, enterprises can complete the generation process of an industry contribution value ledger within the business ecosystem, thereby achieving quantitative recording and management of their contributions to industry activities and providing important reference for enterprises in industry cooperation and development. This ledger data is recorded and managed on the server side of the business ecosystem, and ideally, this ledger data is stored in the blockchain blocks of the business ecosystem.

[0100] Example 5: To more clearly reveal the working details of how businesses establish ownership of their data assets in this invention, see [link to relevant documentation]. Figure 5 The present invention provides a schematic diagram of the process for a commercial entity to confirm its data assets in Embodiment 5 of the present invention.

[0101] Figure 5 The flowchart details each step, revealing the smart contract-related operations of the business entity (the first business entity) on the blockchain node within the ecosystem, including: The first encryption factor generation step: Within its ecosystem, the first business entity (e.g., e-commerce company A) generates a first encryption factor when it comes to the ownership and use of data assets. This encryption factor is a key element for subsequent data encryption processing. For example, company A might generate a unique encryption factor based on its own algorithms and rules, combined with the current timestamp, business-related random numbers, etc., such as "EF12345..." (The complete format data is not shown; it is merely illustrative, and similar data will be processed in the same way below).

[0102] The first encryption factor encrypts the business activity data to obtain the first ciphertext data. The steps are as follows: Company A uses the generated first encryption factor to encrypt its business activity data. This business activity data may include user order data, product information data, marketing activity data, etc. Assuming Company A encrypts a batch of order data, it uses a specific encryption algorithm to process the order data with the first encryption factor to obtain the encrypted first ciphertext data, such as "ciphertext order data: CT1234..." (The complete format data is not shown; it is only for illustrative purposes. Similar data will be processed in the same way below.)

[0103] The first step in requesting the user's public key for business entity A is as follows: To further ensure data security and integrity, company A will request its own user public key. This public key is one of company A's identity identifiers within the blockchain ecosystem, used for subsequent encryption operations and data verification. This user public key for company A also has a corresponding user private key, forming a pair, preferably a public-private key pair generated using the RSA (Rivest-Shamir-Adleman) algorithm, asymmetric encryption.

[0104] The steps to encrypt the first encryption factor using the public key of the first business entity to obtain the ciphertext data of the first encryption factor are as follows: Enterprise A uses its own public key to encrypt the first encryption factor, thus obtaining the ciphertext data of the first encryption factor. For example, encrypting "EF12345..." with the public key yields "EF ciphertext: EK1234..." (The complete format data is not shown; it is only for illustrative purposes. Similar data will be processed in the same way below).

[0105] The steps for obtaining the hash value of the metadata corresponding to business activity data are as follows: For encrypted business activity data (such as the first ciphertext data), Company A will obtain the hash value of its corresponding metadata. Metadata can be some key attributes of the data, such as the order number in order data, the product number in product information, etc. By calculating the hash value, a unique identifier can be provided for the data, used for subsequent data verification and integrity checks. For example, if the order number of order data is "123456", its hash value is calculated to be "Hash1234..." (The complete format data is not shown; it is only for illustrative purposes. Similar data will be processed in the same way below. Those skilled in the art can participate in the generation of hash values ​​using hash algorithms).

[0106] The first step in generating the first digital signature is as follows: Company A uses its own user private key to encrypt the obtained hash value, generating the first digital signature. This digital signature is a way for Company A to authorize and authenticate the data, ensuring the source and integrity of the data. For example, encrypting "Hash1234..." with the private key yields "Digital Signature: DS1234..." (The complete format data is not shown; it is only for illustrative purposes. Similar data will be processed in the same way below).

[0107] The steps for uploading the first encrypted data, the first encryption factor encrypted data, and the first digital signature are as follows: Company A uploads the encrypted first encrypted data (e.g., "CT1234..."), the first encryption factor encrypted data (e.g., "EK1234..."), and the first digital signature (e.g., "DS1234...") to the blockchain of the business ecosystem for storage. This ensures the secure storage of data on the blockchain, and other authorized businesses can use this information to verify and use the data.

[0108] Through the above steps, in this example, the business entity can complete the process of confirming ownership of its data assets, ensuring the security, integrity, and verifiability of the data, and providing a reliable foundation for subsequent data use and contribution value calculation. The ownership confirmation process in this embodiment can be used in other embodiments that require confirmation of ownership of data by business entities or users.

[0109] Example 6: To more clearly reveal the details of the contribution value generation process for commercial entity ownership data assets involved in this invention, see [link to relevant documentation]. Figure 6 The present invention provides a schematic diagram of the process for generating contribution value of commercial entity ownership data assets according to Embodiment Six of the present invention.

[0110] The process for generating contribution value for commercial entity ownership data assets in Embodiment Six of this invention includes the following steps: Deploying smart contracts involves the following steps: First, within the business ecosystem, the first business entity (e.g., e-commerce company A) and related business entities (e.g., the fourth business entity, financial institution B) generate a smart contract using a contract editor and deploy it to the blockchain node. For example, company A and financial institution B sign a cooperation agreement on supply chain finance. They use the contract editor to set the rights and obligations of both parties, data sharing rules, and fund settlement methods, generating a smart contract which is then deployed to the blockchain node.

[0111] The steps for generating smart contract execution data are as follows: During business execution, corresponding execution data is generated according to the smart contract's settings. Continuing with the supply chain finance cooperation example above, when company A provides financial institution B with relevant order data and operational data (as data assets with confirmed ownership), financial institution B uses this data to conduct risk assessments, loan approvals, and other operations. The data generated during these operations constitutes the smart contract's execution data. For example, financial institution B's credit rating results for company A and loan approval data are all examples of execution data.

[0112] The steps to verify that the first digital signature data in a smart contract is the digital signature of the first business entity are as follows: Company A needs to verify the legality of the relevant data in the smart contract. First, it needs to verify whether the first digital signature data in the smart contract is its own digital signature. This is to ensure the source and integrity of the data; only if the signature is correct can it be said that the data was provided by Company A and has not been tampered with. For example, a verification algorithm can be used to compare whether the digital signature in the smart contract is consistent with the digital signature generated by Company A itself.

[0113] The steps to verify the use of the first encrypted data and the first encrypted factor encrypted data of the first business entity in the smart contract execution data are as follows: Next, verify whether the first encrypted data and the first encrypted factor encrypted data of company A are correctly used in the smart contract execution data. For example, check whether financial institution B correctly used the encrypted order data and encryption factor-related data provided by company A when conducting risk assessment.

[0114] The steps for obtaining traffic statistics and / or corresponding financial revenue data used by the second business entity to decrypt the first encrypted data and the first encrypted factor encrypted data are as follows: Company A obtains relevant data from the smart contract execution data regarding the decryption and use of the first encrypted data and the first encrypted factor encrypted data by financial institution B. For example, it counts the number of times financial institution B decrypts and uses order data (traffic statistics) and calculates the additional sales revenue of Company A resulting from financial support provided by financial institution B (corresponding financial revenue data) according to the cooperation agreement.

[0115] Based on traffic statistics and / or corresponding financial revenue data, and using the settlement code of the data assets in the smart contract, the steps to generate the contribution value data of the first business entity are as follows: Finally, Enterprise A calculates and generates its own contribution value data based on the obtained traffic statistics and / or corresponding financial revenue data, and using the settlement code of the data assets in the smart contract. Assuming that, according to the settlement code, financial institution B decrypts and uses order data 10 times, the contribution value is 2 points; and that for every 1000 yuan of additional sales revenue generated by financial institution B's financial support, the contribution value is 5 points. If financial institution B decrypts and uses order data 20 times within a cooperation period, resulting in an additional sales revenue of 3000 yuan, then Enterprise A's contribution value data would be (20 ÷ 10) × 2 points + (3000 ÷ 1000) × 5 points = 19 points.

[0116] The steps for loading and displaying contribution value data in the ledger of the first business entity are as follows: Company A loads the calculated 19 contribution value points into its own ledger for display. The ledger records the time when the contribution value was generated and relevant business information (such as the name of the cooperative project, the data assets involved, etc.) for subsequent querying and statistical analysis. Through the above steps, based on the confirmed data assets of the business entities, the process of generating and recording contribution values ​​is completed, providing a quantitative basis for the data value contribution between business entities.

[0117] Example 7: To more clearly reveal the working details of how the commercial entity ownership data involved in this invention is used in a trusted data environment to generate contribution values ​​through smart contracts, see [link to relevant documentation]. Figure 7 , Figure 7 A schematic diagram of the process of generating contribution values ​​by smart contracts in a trusted data environment using the commercial entity ownership confirmation data provided in Embodiment 7 of the present invention is given.

[0118] The process of using commercial entity ownership confirmation data in a trusted data environment to generate contribution values ​​through smart contracts in Embodiment 7 of the present invention includes the following steps: The contract editor generates a trusted smart contract between a first business entity (e.g., e-commerce company A) and a fourth business entity (e.g., a professional data analytics firm C) in the trusted data environment, and deploys it to the blockchain node. The first business entity uses the contract editor to set cooperation terms, such as data usage permissions, analysis methods, and result feedback formats, based on its cooperation needs with the fourth business entity (e.g., a professional data analytics firm C). This generates a trusted smart contract. The smart contract is then deployed to the blockchain node. For example, e-commerce company A might want to authorize its data assets to data analytics firm C to analyze its e-commerce user behavior data and generate valuable data reports for revenue. Both parties clearly define the encrypted data transmission method, analysis time period, and report format in the contract, and then deploy the contract to the blockchain.

[0119] The steps for generating smart contract execution data are as follows: During the collaboration, data analytics firm C analyzes and processes the proprietary data assets (such as user behavior data) provided by company A according to the provisions of the smart contract. The data generated by these operations constitutes the smart contract's execution data. For example, firm C might generate user behavior analysis reports, user group classification data, and user feedback data under different marketing strategies as execution data.

[0120] The steps for collecting traffic statistics and / or corresponding financial revenue data of the first business entity's confirmed data assets being decrypted and used by the fourth business entity are as follows: Company A collects relevant data from the smart contract execution data regarding its own confirmed data assets being decrypted and used by data analysis agency C. For example, the number of times agency C decrypts and uses user behavior data (traffic statistics), and the specific revenue (corresponding financial revenue data) obtained from the sale and use of the analysis report provided by agency C, calculated according to the cooperation agreement. For example: Suppose that e-commerce company A's 500MB of e-commerce user data is analyzed by agency C to generate a 2023 annual analysis report on the mobile phone market, which is then sold externally that year, generating 10,000 yuan in revenue.

[0121] Based on traffic statistics and / or corresponding financial revenue data, and using settlement codes for data assets in smart contracts, the steps for generating contribution value data for the first business entity are as follows: E-commerce company A calculates and generates its own contribution value data based on the collected traffic statistics and / or corresponding financial revenue data, and using settlement codes for data assets in smart contracts. For example, according to the settlement code, agency C decrypts and uses user behavior data 10 times, corresponding to a contribution value of 3 points. Since agency C's analysis report provides a contribution value of 4 points for every 10,000 yuan of sales revenue, if agency C decrypts and uses user behavior data 15 times within a cooperation period, and the analysis report shows a sales revenue of 20,000 yuan, then company A's contribution value data is (15 ÷ 10 × 3 points + (2 ÷ 1) × 4 points = 12.5 points).

[0122] The steps for loading and displaying contribution value data in the ledger of the first business entity are as follows: Company A loads the calculated contribution value of 12.5 points into its own ledger for display. The ledger will record the time when the contribution value was generated and related business information (such as the name of the cooperative project, the data assets involved, etc.) for subsequent querying and statistical analysis.

[0123] Through the above steps, when the ownership data of commercial entities is used in a trusted data environment, the process of generating and recording contribution values ​​is completed, providing a quantitative basis for the data value contribution between commercial entities.

[0124] Example 8: To more clearly reveal the working details of generating contribution value data of a business entity based on smart contract execution data in the present invention, a complete description of a preferred embodiment of the contribution value generation system is now provided in Embodiment 8 of the present invention.

[0125] The contribution value generation system in this embodiment mainly realizes real-time, transparent, automatic and fair profit distribution on the blockchain. The pluggable contribution calculation rules also enable the system to be reused in multiple scenarios and smoothly upgraded.

[0126] The system involves the following modules: a) Profit pool management module, used to receive and lock distributable funds / points; b) Contribution calculation module, used to calculate the Dynamic Profit Sharing Index (DPSI) of each participant within the statistical period based on traceable on-chain behavioral data. c) Weighting adjustment module, used to automatically update the weight of each participant in future profit sharing based on DPSI; d) Profit-sharing execution module, used to complete the allocation of funds / points in one go or in batches according to the latest weight when the on-chain trigger conditions are met; e) Anti-manipulation module, used to limit and verify the DPSI change rate, new address observation period and data validity.

[0127] The following is a brief introduction to each module: 1. Profit Pool Management Module 1.1 Funds / Points Receiving: Supports transfers of native tokens and points tokens (or numerical records).

[0128] 1.2 Accounting Model: A dual-track system of "share-ledger" is adopted, separating the total global share from the user share to avoid frequent iterations.

[0129] 1.3 Asset Whitelist: Only designated funds / points are allowed to enter the profit pool to prevent malicious "airdrop lock-up" attacks.

[0130] 1.4 Fund Lock-up: Each profit distribution snapshot is taken up to the actual distribution period, and arbitrary withdrawals are prohibited to ensure distribution stability.

[0131] 1.5 Events: All fund inflows and outflows emit PoolIn() / PoolOut() events for easy front-end monitoring.

[0132] 2. Contribution Calculation Module 2.1 On-chain event collection: Block logs are automatically parsed using predefined event signatures. Preferably, the automatically parsed block logs contain smart contract execution data from the blockchain, such as transaction data generated by an e-commerce transaction through smart contract execution.

[0133] 2.2 Statistical period: Supports three modes: "fixed number of blocks", "natural day", and "external manual epoch".

[0134] 2.3 Original contribution algorithm registration: The contribution algorithm interface is deployed as an independent contract, and the main contract calls it in read-only via call.

[0135] 2.4 Data cleaning: Filter duplicate events, blacklisted addresses, and outliers within the same tx.

[0136] 2.5 Weight Normalization: The original scores are summed and then scaled proportionally to uint32 to avoid overflow.

[0137] 2.6 DPSI storage: The latest valid value is recorded using mapping, and the history of the last two periods is retained for easy rollback.

[0138] Further preferably, the first off-chain trusted data related to the first business entity is uploaded to the blockchain. The contribution calculation module is then configured to: calculate the contribution of the first business entity in real time based on the first off-chain trusted data and the smart contract execution data; adjust the profit-sharing calculation weight parameters of the first business entity in real time based on its contribution; and, in response to a request from the first business entity, calculate the contribution value data of the first business entity to be utilized based on its profit-sharing calculation weight parameters.

[0139] 3. Weight Adjustment Module 3.1 Rate limit: The increase in DPSI in a single period shall not exceed maxIncreaseBps (e.g., default 3000 = 30%).

[0140] 3.2 New Address Observation Period: Only 50% of the weight of the address participating for the first time will be effective within the observationBlocks.

[0141] 3.3 Weight Snapshot: Before triggering profit sharing, call snapshotWeights() to write the global weights to the Snapshot contract.

[0142] 3.4 Rollback Mechanism: If an overflow or anomaly occurs during calculation, the system can roll back to the previous valid snapshot, which requires a vote from onlyOwner or DAO.

[0143] 4. Profit Sharing Execution Module 4.1 Triggering conditions: Choose one of the following: time, funds / points threshold, or manual triggering. Manual triggering requires onlyRole.

[0144] 4.2 Snapshot and Lock: Lock the pool first and then take a snapshot of the weights to ensure the stability of funds during the allocation period.

[0145] 4.3 Allocation Algorithm: The "pull" mode is adopted, where users claim the request themselves, and ReentrancyGuard is used to prevent re-entrancy.

[0146] 4.4 Precision handling: Multiply first and then divide, using the "cumulative numerator + denominator" method to avoid losing fractional parts.

[0147] 4.5 Events and Callbacks: Distributed events are sent upon completion of distribution, and can optionally call back to participating contracts to prevent external failures from blocking the process.

[0148] 5. Anti-manipulation and risk control module 5.1 Transaction frequency limit: The same address can record a maximum of 10 contribution events per second within a specified time period to prevent short-term volume manipulation.

[0149] 5.2 Amount Anomaly Detection: If the amount / sorting exceeds the specified value, the event will be notified in a timely manner.

[0150] 5.3 Blacklist / Graylist: Addresses can be set to the blacklist (events are directly discarded) or the graylist (weight × 10%), and their status is recorded on the chain.

[0151] 5.4 Upgrade Delay: All critical parameters must be time-locked before they take effect to prevent governance surprise attacks.

[0152] Ideally, the above modules should have their processing logic implemented in the form of contracts on the blockchain.

[0153] The following four-point mechanism is used in this embodiment: 1. Dynamic Profit Sharing Index We introduce an on-chain computable index—the Dynamic Profit Sharing Index (DPSI)—to quantify the actual contribution of each participant within a given period.

[0154] Contribution can be calculated by weighting one or more of the following on-chain data: ① Governance participation (such as voting weight, user satisfaction, etc. These dimensions can be set by the system platform when managing governance, and the data values ​​of each governance dimension can be calculated in real time based on the execution data of smart contracts on the chain). ② The index update frequency is configurable (by block, by day, by event). 2. Automatic adjustment mechanism for the weight of the profit pool The smart contract automatically adjusts the weights of each participant in the profit pool based on DPSI.

[0155] The sources of funds for the profit-sharing pool can be: the establishment of the fund pool, taxes, etc.

[0156] The trigger condition for profit sharing can be set as follows: ① Time period (e.g., the 1st of each month) ② Funding threshold (e.g., the cumulative amount in the pool exceeds a certain value) ③ External call (triggered by authorized address) 3. Anti-manipulation mechanism On-chain behavior traceability: All data used to calculate DPSI must come from traceable on-chain events to prevent off-chain forgery.

[0157] Weight change rate limit: The DPSI change amplitude is subject to a maximum slope limit to prevent short-term volume manipulation.

[0158] Cold start protection: New participants need to go through an observation period during which their contribution weight gradually takes effect.

[0159] 4. Pluggable contribution module (modular design) It allows for customized contribution calculation rules for different business scenarios, and can be accessed through a registration module to the main contract.

[0160] In this embodiment, the preferred method for calculating the main dynamic profit-sharing index is as follows: Dynamic Profit Sharing Index Calculation Process The Dynamic Profit Sharing Index (DPSI) calculation process follows a six-step main thread: "On-chain events --> Data cleaning --> Algorithm scoring --> Rate capping --> Normalized storage --> Boundary and exception handling".

[0161] On-chain event collection Data source: Relying on confirmed on-chain logs, the confirmed block logs are automatically parsed using predefined event signatures. Preferably, the automatically parsed block logs contain smart contract execution data from the blockchain, such as transaction data generated by an e-commerce transaction through smart contract execution.

[0162] Predefined signature: The system maintains an "event-algorithm" mapping table with the key being bytes32 eventSig and the value being the IYJgo contract address.

[0163] Scope of resolution: each new block triggers harvest(), scanning in the order fromBlock ~ toBlock, only processing logs sent by whitelisted contract addresses to reduce invalid calculations.

[0164] Deduplication key: Use txHash + logIndex as the unique key and store it in mapping(bytes32 => bool)harvested to ensure that the same event will not be counted repeatedly.

[0165] Data cleaning The original log is verified line by line in memory. Events are considered "valid" if they meet the following four criteria: repetition, blacklist, outlier, and frequency limit. The cleaned valid events are written to a temporary circular buffer ValidEvent[] for use in the next scoring step. The buffer only retains the data of the current period and is automatically released after the period ends, saving storage.

[0166] Algorithm scoring Plugin call: The main contract calls the registered IYJgo contract in read-only mode via call (not delegatecall), passing in three parameters: participant, fromBlock, and toBlock, and returns uint256 rawScore.

[0167] Multi-dimensional weighting: The official documentation provides three default algorithms, and weights can be freely combined: SalesYJgo: Transaction events are scored linearly by amount, with power-law decay (if the same buyer makes multiple purchases on the same day, the subsequent score is ^0.8).

[0168] EngagementYJgo: For example, Play+Like+Comment are scored 1, 5, and 3 points respectively, with a 24-hour sliding window for deduplication.

[0169] ReferralYJgo: For example, incoming funds are scored on a 1:1 basis, and the score is only counted after the referred person locks up their funds for 1 hour to prevent flash loans.

[0170] Accumulation method: The same address can accept scores from multiple algorithms. The main contract linearly accumulates the scores according to the "algorithm weight table" to obtain aggregateRaw.

[0171] Rate capping To prevent short-term volume manipulation from causing a surge in DPSI, the system is set with a "hard cap on single-period price increase": Read the previous cycle prevDpsi (snapshot solidified) and calculate the upper limit.

[0172] Normalization and on-chain storage Normalization: The capped newDpsi is scaled proportionally to uint32-level shares to reduce storage and computational overhead. The total EpochScore is calculated all at once at the end of the same period to ensure that the global proportion remains unchanged.

[0173] Boundary and Exception Handling Overflow protection: aggregateRaw uses uint256, and checks totalEpochScore != 0 before normalization; otherwise, the DPSI of all users remains unchanged for this period.

[0174] Algorithm anomaly: If the external IYJgo performs a revert, it will automatically roll back to "zero score" instead of blocking the main process, thus ensuring system availability.

[0175] Rollback Channel: The DAO can vote to write back the entire DpsiRecord to the previous snapshot to deal with algorithm vulnerabilities or malicious attacks.

[0176] In this embodiment, the lifecycle of the profit sharing execution is preferably as follows: Monitoring conditions --> Trigger successful --> Lock pool (2h) --> Snapshot weight --> Calculate share --> User claim --> Optional callback --> Unlock & remainder roll in --> New profit-sharing cycle begins.

[0177] Ideally, the monitoring conditions are selected from three options: time, funds / points threshold, or manual triggering. Manual triggering requires selecting `onlyRole`, and the smart contract execution will trigger the aforementioned monitoring conditions. Next, a snapshot and lock are performed: the pool is locked for 2 hours, then the weight is snapshotted to ensure fund stability during the allocation period. The allocation algorithm then employs a "pull" mode, with users making their own claims, and ReentrancyGuard prevents re-entry. Precision handling is performed: multiplication is performed before division, using a "cumulative numerator + denominator" method to avoid losing fractional amounts. Events and callbacks: A `Distributed` message is sent upon completion of allocation, with the option to call back participating contracts to prevent external failures from disrupting the process.

[0178] Example Nine ： To more clearly reveal the working details of generating contribution value data of a business entity based on smart contract execution data in the present invention, the preferred implementation of Embodiment Nine of the present invention in a decentralized profit-sharing scenario of an e-commerce platform (such as Web3 e-commerce) is now disclosed.

[0179] In this embodiment, DPSI is calculated based on the merchant's sales revenue, service rating, number of repeat purchases by consumers, number of new users recommended, and the platform's traffic support. The platform commission revenue is automatically allocated every Sunday at 8 PM.

[0180] Preferably, this embodiment nine includes the following technical implementation details: Profit Sharing Pool Management: Receives platform commissions (such as 5% of product sales), and only allows funds to be transferred in through e-commerce contracts; profit sharing snapshot time is 7 PM every Sunday, and funds are locked after the snapshot until distribution is completed.

[0181] Contribution calculation: Collect on-chain Order (order completion), Review (service rating), Invite (invitation of new users), and Promote (traffic support) events. SalesYJgo calculates the merchant score (sales revenue × 0.8 + service rating × 5), EngagementYJgo calculates the consumer score (repurchase count × 3 + number of new users invited × 10), and ReferralYJgo calculates the platform score (traffic support duration × 0.2).

[0182] In this embodiment, preferably, the Order (order completion), Review (service rating), Invite (invite new users), and Promote (traffic support) events collected on the blockchain are all collected based on the execution data of the smart contract, and the contribution of the corresponding business entity is calculated in real time. Data cleaning: Filter order events with a value of less than 10 USDC, and graylist merchants with a service rating of less than 3 points (weight × 10%).

[0183] Weighting Adjustment: Initial weights are 50% for merchants, 30% for consumers, and 20% for the platform; if a consumer's number of new user invitations increases by 25% in a single period, the weight will increase to 35% in the next period; new merchants have a 7-day observation period with a weight of 30% during this period. In this embodiment, the profit-sharing calculation weight parameters for each business entity are adjusted in real time based on their contribution.

[0184] Profit sharing is executed periodically (every Sunday at 8 PM). A snapshot is taken, and the share is calculated. Users claim their share through the e-commerce DApp. If the distribution fails, the system retryes three times and records the result, with the remainder rolling over to the next week. In this embodiment, in response to the business entity's DApp claim for profit sharing, the contribution value data of the business entity is calculated based on the business entity's profit sharing calculation weight parameters.

[0185] Anti-manipulation: Limit the number of Invite events recorded by the same consumer to a maximum of 5 times per day; orders exceeding 1000 USDC will trigger an audit; adjustments to commission rates require a vote by the platform's DAO and a 48-hour timelock to take effect.

[0186] Preferably, in this embodiment, off-chain trusted data related to the business entity is further acquired and uploaded to the blockchain; based on the off-chain trusted data and the smart contract execution data, the contribution of the business entity is calculated in real time; based on the contribution of the business entity, the profit-sharing calculation weight parameters of the business entity are adjusted in real time; in response to the request of the business entity, the contribution value data of the business entity to be distributed is calculated based on the profit-sharing calculation weight parameters of the business entity. Preferably, this is implemented, for example, with the following technical solution: Introduce an off-chain trusted data access layer: Integrate oracles (such as Chainlink) to put off-chain trusted data (such as user KYC authentication and offline activity participation) on the chain as a supplementary dimension for DPSI calculation; oracle data needs to be verified by multiple nodes to ensure authenticity.

[0187] Multi-dimensional weighted algorithm upgrade: On the basis of the original algorithm, OffchainYJgo is added to score off-chain data according to weight (such as KYC certification ×2, offline event participation ×3); the main contract calls OffchainYJgo through a call, and the scores are linearly added with the on-chain algorithm scores to obtain a more comprehensive DPSI.

[0188] Data priority settings: On-chain data has higher priority than off-chain data (on-chain score accounts for 70%, off-chain score accounts for 30%), to prevent off-chain data forgery from affecting profit sharing; if oracle data is abnormal, OffchainYJgo is automatically blocked, and only on-chain data is used to calculate DPSI. For other contribution value calculations, please refer to the technical solutions disclosed in other embodiments of this invention.

[0189] The system device embodiments described in the above examples are merely illustrative. The units described as separate components may or may not be physically separate. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A contribution from a business entity, comprising: Information about the second business entity with which it has a smart contract is obtained based on the decentralized identifier of the first business entity; Iterate through the smart contract execution data of the first and second business entities during the first settlement cycle; The smart contract execution data mentioned above refers to the execution data generated after the smart contract of the first business entity's data assets, for which ownership has been confirmed, is used in the second business entity. Based on the smart contract execution data, the contribution value data of the first business entity is generated; The contribution value data belonging to the first business entity is loaded into the contribution data ledger of the second business entity.

2. The method according to claim 1, characterized in that, The process of obtaining information about a second business entity with which it has a smart contract based on the decentralized identifier of the first business entity includes: Register the first business entity to generate the user's decentralized identifier; Based on the decentralized identifier of the first business entity's user, a smart contract between the first and second business entities is generated through a contract editor and deployed to the blockchain node; Trigger a query from a second business entity that has a smart contract with the first business entity; Based on the decentralized identifier of the first business entity, query the smart contract deployed in the blockchain node to obtain information about the second business entity that has a smart contract with the first business entity.

3. The method according to claim 1, characterized in that, The data assets for which the first commercial entity has been granted ownership include: Intellectual property data assets belonging to the first commercial entity; The user privacy data assets of the first business entity; The user behavior and interaction data assets of the first commercial entity; The first business entity's user business operation data assets; At least one of the user relationship and rating data assets of the first business entity.

4. The method according to claim 3, characterized in that, The execution data generated after the smart contract used by the first business entity to acquire the data assets in the second business entity includes: Generate the first encryption factor; Based on the first encryption factor, at least one of the following data assets belonging to the first business entity—intellectual property data assets, user privacy data assets, user behavior and interaction data assets, user business operation data assets, and user relationship and evaluation data assets—is used as its business activity data to encrypt the first ciphertext data. The first encryption factor is encrypted using the public key of the first business entity user to obtain the ciphertext data of the first encryption factor; Obtain the hash value of the metadata corresponding to the business activity data in the first encrypted data, and use the private key of the first business entity user to encrypt the hash value to generate the first digital signature of the first business entity user; The first ciphertext data, the first encryption factor ciphertext data, and the first digital signature are uploaded to the blockchain of the business ecosystem for storage. Generate a smart contract, and add the usage code of the first encrypted data, the first encryption factor encrypted data, and the first digital signature data after the rights have been confirmed; The code in the smart contract is triggered to generate the execution data of the smart contract.

5. The method according to claim 1, characterized in that, The smart contract execution data includes: Promote service smart contract execution data; E-commerce transaction smart contract execution data; At least one of the execution data of a business cooperation smart contract.

6. The method according to claim 4, characterized in that, The process of generating the contribution value data of the first business entity based on the smart contract execution data includes: Verify that the first digital signature data in the smart contract is the digital signature of the first business entity; Verify the use of the first ciphertext data and the first encryption factor ciphertext data of the first business entity in the smart contract execution data; Collect traffic statistics and / or corresponding financial revenue data of the first encrypted data and the first encrypted factor encrypted data used by the second business entity to decrypt the smart contract execution data. Based on the traffic statistics and / or corresponding financial revenue data, and using the settlement code of the data assets in the smart contract, the contribution value data of the first business entity is generated.

7. The method according to claim 5, characterized in that, The process of generating the contribution value data of the first business entity based on the smart contract execution data includes: Verify the decentralized identifiers of the first and second business entities in the smart contract; Collect promotion service execution traffic data, business transaction data, or business cooperation result data between the first and second business entities from the smart contract execution data; Based on the traffic data, business transaction data, or business cooperation result data of the promotion service execution, and the pre-set settlement code in the smart contract, the contribution value data of the first business entity is generated.

8. The method according to claim 4, characterized in that, Further includes: Based on the digital asset identification model, the intellectual property data assets, user privacy data assets, user behavior and interaction data assets, and user business operation data assets belonging to the first business entity are identified, and the characteristic data of the first business entity's digital assets are extracted. Based on the characteristic data of the digital assets of the first business entity, match the third business entity with digital asset analysis needs; The usage code in the smart contract is triggered to generate the execution data of the smart contract between the first business entity and the third business entity, thereby enabling the third business entity to use the digital assets of the first business entity.

9. The method according to claim 1, characterized in that, The process of generating the contribution value data of the first business entity based on the smart contract execution data includes: Based on the smart contract execution data, the contribution of the first business entity is calculated in real time; Based on the contribution of the first business entity, the profit-sharing calculation weight parameters of the first business entity are adjusted in real time; In response to the request of the first business entity, the contribution value data of the first business entity to be utilized is calculated based on the profit-sharing weight parameters of the first business entity.

10. The method according to claim 9, characterized in that, Further includes: Acquire trusted off-chain data related to the first business entity and upload it to the blockchain; The contribution of the first business entity is calculated in real time based on the first chain of trusted data and the smart contract execution data. Based on the contribution of the first business entity, the profit-sharing calculation weight parameters of the first business entity are adjusted in real time; In response to the request of the first business entity, the contribution value data of the first business entity to be utilized is calculated based on the profit-sharing weight parameters of the first business entity.

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