Data asset operation technology platform, operation methods, storage media and electronic devices

Abstract

This invention discloses a data asset operation technology platform, operation method, storage medium, and electronic device. The platform includes: an asset classification module for acquiring data assets to be processed and their market value, classifying the data assets, and generating corresponding asset identifiers for each type of data asset; an asset storage module for storing the market value and asset identifier of each type of data asset on the main chain of a blockchain and storing the data assets on a sub-chain of the blockchain; a storage adjustment module for periodically calculating a reliability index and a storage efficiency index, and dynamically adjusting the storage sub-chain of each type of data asset based on the reliability index and storage efficiency index; and an asset operation module for performing asset operation management based on the dynamically adjusted currently stored data assets. Using this invention, efficient management and value maximization of data assets can be achieved, promoting the sustainable operation and development of data assets.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular to a data asset operation technology platform, operation method, storage medium and electronic device. Background Technology

[0002] With the advent of the big data era, data assets have become a crucial driver of the new round of economic growth. The generation, storage, and management of data are becoming increasingly important across various industries, especially in finance, healthcare, logistics, and the internet. Data is not only a carrier of information but also possesses market value, capable of generating revenue through various forms of transactions. Traditional data management methods typically rely on centralized databases and manual review, resulting in issues such as data redundancy, inadequate management, and poor security. Therefore, how to efficiently and reliably operate and manage data assets has become a pressing issue for enterprises and organizations. Summary of the Invention

[0003] The purpose of this invention is to provide a data asset operation technology platform, operation method, storage medium and electronic device to address the shortcomings of existing technologies. By introducing blockchain technology, dynamic storage adjustment mechanism and asset operation management module, it can help enterprises achieve efficient management and value maximization of data assets, and promote the sustainable operation and development of data assets.

[0004] One embodiment of this application provides a data asset operation technology platform, the platform comprising:

[0005] The asset classification module is used to obtain the data assets to be processed and their market value, classify the data assets, and generate corresponding asset identifiers for each class of data assets.

[0006] The asset storage module is used to store the market value and asset identifier of each type of data asset in the main chain of the blockchain and store the data assets in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains.

[0007] The storage adjustment module is used to periodically calculate a reliability index that represents the reliability of data interaction between different sub-chains and a storage efficiency index that reflects the storage efficiency of each sub-chain, and dynamically adjust the storage sub-chain of each type of data asset based on the reliability index and the storage efficiency index.

[0008] The asset operation module is used for asset operation management based on the dynamically adjusted currently stored data assets.

[0009] Optionally, generating a corresponding asset identifier for each category of classified data assets includes:

[0010] For each type of data asset, the type information of that data asset is encoded to obtain classification coding information;

[0011] The attribute information of this type of data asset is encoded and weighted to obtain attribute encoding information;

[0012] Obtain the current timestamp and a random number for this type of data asset. Based on the classification coding information, the attribute coding information, the current timestamp, and the random number, generate the asset identifier corresponding to this type of data asset using a preset hash function.

[0013] Optionally, the periodic calculation of a reliability index representing the reliability of data interaction between different subchains and a storage efficiency index reflecting the storage efficiency of each subchain includes:

[0014] For each subchain, the number of successful interactions and the total number of interaction attempts between the subchain and other subchains are periodically obtained within the corresponding time period to calculate a reliability index that represents the reliability of data interactions between different subchains;

[0015] Periodically obtain the storage volume, usage frequency, and storage cost of the data assets stored in the sub-chain within the corresponding time period to calculate the storage efficiency index, which reflects the storage efficiency of each sub-chain.

[0016] Optionally, dynamically adjusting the storage sub-chain for each type of data asset based on the reliability index and the storage efficiency index includes:

[0017] For each subchain, if the reliability index of the subchain is less than a preset reliability threshold or the storage efficiency index is less than a preset efficiency threshold, the data assets in the subchain are stored in other subchains so that the reliability index of the other subchain in the next calculation is greater than the preset reliability threshold and the storage efficiency index is greater than the preset efficiency threshold.

[0018] Another embodiment of this application provides a data asset operation method, the method comprising:

[0019] The process involves acquiring the data assets to be processed and their market value, classifying the data assets, and generating a corresponding asset identifier for each class of data assets.

[0020] For each type of data asset, the market value and asset identifier of the data asset are stored in the main chain of the blockchain, and the data asset is stored in a sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains;

[0021] The reliability index, which represents the reliability of data interaction between different subchains, and the storage efficiency index, which reflects the storage efficiency of each subchain, are calculated periodically. Based on the reliability index and the storage efficiency index, the storage subchain of each type of data asset is dynamically adjusted.

[0022] Asset operation and management are carried out based on the dynamically adjusted currently stored data assets.

[0023] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any of the preceding claims when running.

[0024] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0025] Compared with existing technologies, this invention provides a data asset operation technology platform, comprising: an asset classification module for acquiring data assets to be processed and their market value, classifying the data assets, and generating corresponding asset identifiers for each classified data asset; an asset storage module for storing the market value and asset identifier of each data asset class in the main chain of the blockchain and storing the data assets in a sub-chain of the blockchain; a storage adjustment module for periodically calculating the reliability index and storage efficiency index, and dynamically adjusting the storage sub-chain of each data asset class based on the reliability index and storage efficiency index; and an asset operation module for performing asset operation management based on the dynamically adjusted currently stored data assets. By introducing blockchain technology, a dynamic storage adjustment mechanism, and an asset operation management module, this platform helps enterprises achieve efficient management and value maximization of data assets, promoting the sustainable operation and development of data assets. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of a data asset operation technology platform provided in an embodiment of the present invention;

[0027] Figure 2 A hardware structure block diagram of a computer terminal for a data asset operation method provided in an embodiment of the present invention;

[0028] Figure 3 This is a flowchart illustrating a data asset operation method provided in an embodiment of the present invention. Detailed Implementation

[0029] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0030] This invention first provides a data asset operation technology platform, see [link to relevant documentation]. Figure 1 It can include:

[0031] The asset classification module 101 is used to obtain the data assets to be processed and their market value, classify the data assets, and generate corresponding asset identifiers for each class of data assets.

[0032] The asset classification module plays a crucial role in the data asset operation technology platform. This module is responsible for acquiring all data assets to be processed and their corresponding market values, and classifying these assets according to preset classification criteria. Each data asset has a known market value, and the module categorizes data assets into different types based on these market values. Through effective classification, the module generates a unique asset identifier for each type of data asset. This identifier not only contains information about the asset type but also provides a foundation for subsequent data management, transactions, and monitoring, helping to ensure the traceability and security of data assets.

[0033] The asset classification module first integrates multiple data sources (such as internal business systems, market data interfaces, and third-party data providers) to obtain all data assets to be processed and their corresponding market value. These data assets can be structured or unstructured, depending on the company's business needs.

[0034] Next, the module will perform a classification process, categorizing the acquired data assets based on their characteristics (such as data type, purpose, and industry classification). For example, the module can classify data assets into categories such as customer data, sales data, and operational data. Each type of data asset will be meticulously recorded to ensure that each data asset and its market value are clearly defined. To improve processing efficiency, the module can employ existing classification algorithms combined with a manual review mechanism to ensure the accuracy of the classification.

[0035] Finally, after classification, the asset classification module generates a unique asset identifier for each type of data asset. This identifier integrates classification information, market value information, asset type, timestamp, and other data, and is processed using a preset hash function to ensure that each identifier is unique and unpredictable. In this way, different types of assets and their market value can be effectively identified and tracked in subsequent asset management and operation, providing a solid data foundation for enterprise decision-making.

[0036] Specifically, a corresponding asset identifier is generated for each category of data assets after classification. This can be achieved by encoding the type information of each data asset category to obtain classification coding information. ;

[0037] This step encodes the type information of each data asset, converting different types of asset information into numerical form. This process enables computers to efficiently process and store this information. Simultaneously, this encoding method facilitates subsequent classification and identifier generation, providing standardized input for subsequent data processing.

[0038] By encoding data asset types, different types of data assets can be systematically managed and organized. This process not only improves the manageability and traceability of data assets but also provides foundational data for subsequent identifier generation and classification operations. Ultimately, this enables data assets in a blockchain environment to be accurately identified and efficiently managed.

[0039] First, the system establishes a set of rules or standards to identify different types of data assets. For each type of data asset, the system analyzes its characteristic information (such as data type, industry classification, etc.). Next, it uses predefined encoding methods (such as binary encoding, decimal encoding, or other forms of encoding) to convert this characteristic information into numerical form. The encoded data is stored in a standard format to ensure consistency and accuracy in subsequent operations. For example, if there are three asset types (A, B, C), A can be encoded as 1, B as 2, and C as 3. Finally, the generated classification codes will provide the necessary identification for subsequent operations.

[0040] The attribute information of this type of data asset is encoded and weighted to obtain attribute encoding information. ;

[0041] The attribute information of each type of data asset is encoded, and corresponding weights are applied to different attributes to calculate comprehensive attribute coding information. Attribute coding represents the characteristic combination of that type of data asset, effectively reflecting its characteristics and value. This weighted processing method quantifies the influence of different attributes on asset characteristics, helping to comprehensively consider the relative importance of each attribute when generating asset identifiers. This process further enhances the accuracy of the identifiers and their effectiveness in subsequent management, enabling enterprises to make reasonable decisions based on the actual situation of the data assets. One type of attribute coding information can be:

[0042]

[0043] in, Let be the encoded value of the i-th attribute. Let be the weight of the i-th attribute, and n be the number of attributes. This formula uses a product operation to weight each attribute, aiming to comprehensively consider the contribution of each attribute of the data asset to the overall characteristics. In this way, the system can more accurately reflect the characteristics of each type of data asset.

[0044] Get the current timestamp of this type of data asset. and a random number According to the classification coding information The attribute encoding information The current timestamp and the random number Using a preset hash function Generate asset identifiers corresponding to this type of data asset. .

[0045] By combining classification coding information, attribute coding information, current timestamps, and random numbers, a unique asset identifier is generated using a pre-defined hash function. This identifier will be used to uniquely identify various data assets, providing security and validity for data storage and sharing. The generated asset identifier ensures the uniqueness and non-copyability of each data asset, avoiding identifier conflicts. By adding timestamps and random numbers, this mechanism enhances the security and resistance to attacks of the identifier, especially in blockchain applications, ensuring the authenticity and immutability of assets.

[0046] First, the system collects and stores the classification and attribute codes for each type of data asset. Then, the system obtains the current timestamp (representing the specific time of execution) and a securely generated random number, the randomness and security of which are ensured by a cryptographic student generator. Next, this information is combined according to a preset concatenation rule to form a string, which is then passed to a preset hash function. The hash function processes the concatenated content to generate a unique asset identifier, which will be stored and used for subsequent data management. One possible asset identifier is:

[0047]

[0048] in, This is a string concatenation operation. Using this formula, the system will generate a unique asset identifier using a hash function. This identifier actively combines the asset's classification information, attribute information, and environmental dynamics (timestamp and random number), ensuring the uniqueness and security of the identifier. This design allows the identifier to not only point to the asset itself but also reflect the background information of its generation.

[0049] The significance of the asset classification module lies in its optimization of data asset management processes, enabling enterprises to utilize and operate these assets more efficiently. By acquiring and classifying each data asset and its market value, enterprises can clearly understand the actual value of each type of asset, thereby making more accurate judgments in asset allocation, investment decisions, and risk management. Furthermore, unique asset identifiers provide strong support for subsequent data interaction in blockchain scenarios, ensuring the security, transparency, and immutability of asset information. The implementation of this module not only enhances the identifiability of data assets but also strengthens enterprises' competitiveness in the digital economy.

[0050] The asset storage module 102 is used to store the market value and asset identifier of each type of data asset in the main chain of the blockchain and store the data assets in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains.

[0051] The asset storage module is a key component of the data asset operation technology platform, designed to ensure that the market value and asset identifier of each type of data asset can be securely and efficiently stored in the blockchain system. This module constructs a hierarchical storage structure by storing the market value and its corresponding asset identifier on the main chain of the blockchain, while storing the actual data assets on the sub-chains. The main chain is responsible for recording and maintaining the basic information of all assets, ensuring transparency and trustworthiness, while each sub-chain undertakes specific data storage tasks. This hierarchical architecture not only improves the flexibility and scalability of data storage but also provides fundamental support for data interaction between different sub-chains, thereby achieving efficient management and scheduling of data assets.

[0052] In practical implementation, the asset storage module first receives the market value and corresponding asset identifier for each type of data asset generated by the asset classification module. The system then structures this information according to the data type and characteristics, and selects suitable main and sub-chains for storage. The main chain stores core asset information such as asset identifier, market value, classification information, and creation time, ensuring the immutability and verifiability of all information.

[0053] When storing data assets on a subchain, the system determines the subchain based on the specific characteristics and usage scenarios of the data. For example, frequently accessed data assets can be stored on a performance-optimized subchain, while less frequently used assets can be stored on a low-cost subchain. Each subchain is responsible for a specific type of data asset while ensuring interoperability with other subchains, supporting data interaction between different subchains. This design allows data assets to be flexibly transferred and shared across multiple subchains, improving data access speed and processing efficiency.

[0054] During storage, the system also monitors the access frequency, storage costs, and usage of data assets, updating storage decisions in real time. The storage module sets a series of storage rules and strategies to address the dynamic changes in data assets, ensuring the system remains efficient and secure at all times. When new transactions or updates occur to data assets, the asset storage module automatically records these changes through smart contracts and updates relevant information in the main chain and sub-chains, guaranteeing data consistency and integrity.

[0055] The introduction of this asset storage module significantly enhances the traceability and transparency of data assets. By storing market value and asset identifiers on the main chain, accurate monitoring and assessment of the value of data assets can be maintained throughout their entire lifecycle. Furthermore, through the design of sub-chains, the platform can flexibly manage different types of data assets, avoiding congestion on the main chain and thus improving the overall performance and efficiency of the system. The mutual support and interaction capabilities between sub-chains enhance the overall reliability of the system, helping enterprises respond flexibly to rapidly changing data environments and improving the utilization and optimized management of data assets.

[0056] The storage adjustment module 103 is used to periodically calculate a reliability index that represents the reliability of data interaction between different sub-chains and a storage efficiency index that reflects the storage efficiency of each sub-chain, and dynamically adjust the storage sub-chain of each type of data asset according to the reliability index and the storage efficiency index.

[0057] The primary function of the storage adjustment module is to provide intelligent decision support for the storage allocation of data assets. This module monitors the operational status of each part of the system in real time by periodically calculating two core indices: the reliability of data interaction between different sub-chains and the storage efficiency of each sub-chain. By analyzing this data, the module can identify which sub-chains perform well during data interaction and which have performance bottlenecks, and then dynamically adjust the storage sub-chain for each type of data asset to ensure that assets are stored on the most suitable sub-chain. This process not only incorporates historical data but also comprehensively considers the current network status to ensure efficient and secure data access.

[0058] The introduction of the storage adjustment module significantly improved the platform's overall performance and the liquidity of data assets. By regularly evaluating and dynamically adjusting storage locations, it ensures that data assets are always stored in the optimal sub-chain, avoiding data access delays and resource waste caused by improper storage. Simultaneously, this module optimizes storage resource utilization, reduces operating costs, and enhances the overall system's scalability and flexibility. By maintaining efficient asset flow and access, the platform meets user needs while enhancing its market competitiveness, bringing greater commercial value to data assets.

[0059] Specifically, a reliability index, representing the reliability of data interactions between different subchains, and a storage efficiency index, reflecting the storage efficiency of each subchain, are calculated periodically. For each subchain, the number of successful interactions between that subchain and other subchains within a corresponding time period can be obtained periodically. Total number of interaction attempts To calculate a reliability index representing the reliability of data interaction between different subchains. One calculation formula is:

[0060]

[0061] This formula aims to assess the reliability of subchains by quantifying the ratio of successful interactions between them. By introducing the ratio of successful interactions to total interaction attempts, presented as a percentage, performance comparisons between different subchains become more intuitive and easier to understand.

[0062] In this step, the system first needs to collect interaction data from different subchains within a specified time period, including the number of successful interactions and the total number of interaction attempts. This data forms the basis for evaluating the reliability of data interactions between subchains. By calculating the ratio of successful interactions to total interaction attempts, a reliability index is derived, reflecting the trust status between subchains.

[0063] Calculating the interaction reliability index helps the system identify which sub-chains have relatively stable data interactions and which have high failure rates. This allows for more scientific decisions regarding data asset storage and scheduling, helps optimize resource allocation and coordination between sub-chains, and thus improves the overall system performance and the adaptability of each sub-chain.

[0064] In its implementation, the system will schedule regular tasks (e.g., hourly or daily) to extract interaction data for a specific time period from blockchain records. Automated scripts will calculate the number of successful interactions (A_success) and the total number of interaction attempts (A_total), and then calculate the interaction reliability index (RI_interaction) according to a formula. This process can be automated using database querying and data processing tools to ensure the accuracy and real-time nature of the statistical data. Simultaneously, the system needs to store historical interaction data for trend analysis and long-term monitoring, helping to optimize system performance.

[0065] Periodically obtain the storage volume, usage frequency, and storage cost of the data assets stored in the sub-chain within the corresponding time period to calculate the storage efficiency index, which reflects the storage efficiency of each sub-chain. One possible calculation formula is:

[0066]

[0067] in, Let j be the storage amount of the j-th type of data asset. Let j be the frequency of use of the data asset of type j. Let be the storage cost of the j-th type of data asset, and m be the total number of data asset types. This formula evaluates the storage efficiency of the subchain by comprehensively considering the storage volume, usage frequency, and storage cost of various types of data assets, thereby achieving a balance between maximizing the value of data assets and effectively utilizing resources.

[0068] In this step, the system periodically collects information on the data assets stored on each sub-chain, including the storage volume, usage frequency, and storage cost of each type of data asset. This data is crucial for calculating the storage efficiency index. Through this information, the system can comprehensively evaluate the resource utilization performance of each sub-chain. The calculation of the storage efficiency index helps the system identify sub-chains that perform well and poorly in data storage. By optimizing storage efficiency, the system can improve the utilization rate of storage resources and reduce unnecessary storage costs, thereby making the entire data asset management process more efficient and economical.

[0069] The system will periodically (e.g., daily or weekly) extract relevant information about stored data assets from the blockchain, including storage volume (S_data,j), usage frequency (U_data,j), and storage cost (C_storage,j). Then, it will calculate the Storage Efficiency Index (SEI) using a formula. This process can be automated using data analysis tools such as Python or R to ensure accurate data collection and processing. Simultaneously, the system should support customizable timeframes to allow for flexible analysis of storage efficiency based on business needs.

[0070] Specifically, based on the reliability index and the storage efficiency index, the storage sub-chains for each type of data asset are dynamically adjusted. For each sub-chain, if the reliability index of the sub-chain is less than a preset reliability threshold or the storage efficiency index is less than a preset efficiency threshold, the data assets in the sub-chain are stored in other sub-chains so that the reliability index of the other sub-chain in the next calculation is greater than the preset reliability threshold and the storage efficiency index is greater than the preset efficiency threshold.

[0071] In this step, the system dynamically adjusts based on the previously calculated reliability and storage efficiency indices. If the reliability or storage efficiency index of a certain subchain falls below a preset threshold, the system triggers data migration, moving the data assets in that subchain to other subchains with better performance. This process aims to ensure that data assets are always stored in the optimal environment, improving the overall system performance and stability.

[0072] This dynamic adjustment mechanism not only optimizes data storage strategies, ensuring data assets can be accessed quickly and efficiently when needed, but also effectively reduces performance issues caused by improper storage, thereby improving user experience and system responsiveness. Through intelligent data migration, enterprises can maintain high flexibility and adaptability, enhancing their competitiveness in an ever-changing market environment.

[0073] Specifically, the system is configured with a periodic monitoring module that automatically collects the latest reliability and storage efficiency data for each subchain at set time intervals (e.g., hourly or daily). Performance metrics for all subchains are stored in a central database for historical data analysis and comparison. During the initial system implementation, administrators should set reasonable reliability and storage efficiency thresholds based on business needs and historical performance data. These thresholds can be based on industry standards or internal data analysis results to ensure the system can promptly identify subchains with poor performance. At the end of each monitoring cycle, the system automatically analyzes the latest performance metrics of each subchain and compares them to the preset thresholds. If the reliability index of a subchain falls below the reliability threshold, or the storage efficiency index falls below the efficiency threshold, the system will trigger a data migration request. This request will immediately generate an alert to notify the system administrator.

[0074] Once a data migration request is triggered, the system must identify a suitable target subchain to accept the data. This selection process is based on a comprehensive evaluation of reliability and storage efficiency, ensuring that the target subchain's performance metrics exceed preset thresholds. The algorithm calculates the reliability and storage efficiency indices of all candidate subchains and prioritizes those with the highest performance metrics as the data migration destination. After determining the target subchain, a detailed data migration plan is generated, including the types of data assets to be migrated, the quantity to be migrated, and the migration time window. The system also considers data dependencies to ensure that the migration process does not affect normal business operations and ensures data integrity. Data migration will be implemented through smart contracts to ensure the automation and security of the entire process. During the migration, the system will perform real-time data verification and validation to prevent data loss or errors. Before the migration begins, the system will back up the data on the current subchain to ensure rapid recovery should the migration fail.

[0075] After data migration is complete, the system will periodically monitor the performance of the migrated target subchain to ensure that its reliability and storage efficiency indices remain above the set thresholds. Simultaneously, the data status and performance metrics of the atomic chains will also be tracked post-migration to ensure no additional burden is incurred due to the migration. The system records all migration operations on the blockchain, ensuring data immutability and traceability. These records will provide a basis for future performance evaluations and storage strategy adjustments. Administrators can adjust future threshold settings and migration strategies based on post-migration performance feedback, optimizing the entire data asset management process.

[0076] Through the above implementation steps, the system can effectively realize the dynamic adjustment of data assets between different sub-chains, improve the overall storage resource utilization efficiency, and ensure data security and service quality.

[0077] The asset operation module 104 is used for asset operation management based on the dynamically adjusted currently stored data assets.

[0078] The asset operation module is a core component of the data asset operation technology platform. It aims to formulate and implement effective operational management strategies based on dynamically adjusted current stored data assets. This module provides data-driven decision support for enterprises by monitoring the storage status, market value, and usage of various data assets in real time. Through the analysis and mining of data assets, the asset operation module helps enterprises identify potential market opportunities and optimize asset allocation, thereby improving overall operational efficiency and economic value.

[0079] The implementation of the asset operations module is of significant strategic importance for enhancing an enterprise's data asset management capabilities. First, it enables comprehensive management of data assets, ensuring that asset usage aligns with market demands and maximizing the value of data assets. Second, this module effectively responds to market changes by dynamically adjusting storage strategies in real time, thereby improving the utilization efficiency and profitability of data assets. Ultimately, this module enables enterprises to maintain a leading position in fierce market competition, driving business innovation and achieving sustainable growth.

[0080] Specifically, this module integrates multiple internal and external data sources to collect key performance indicators (KPIs) of various data assets in real time, including market value, usage frequency, user feedback, and transaction records. After thorough cleaning and processing to ensure accuracy and completeness, this data is input into the data analysis platform. Next, utilizing data mining and machine learning algorithms, the module can conduct in-depth analysis of the assets, identify asset classes with high growth potential and feasible market opportunities, and formulate corresponding operational strategies based on industry trends and market demands. These strategies may include dynamic pricing, promotional activities, marketing, and channel optimization to maximize the value of the data assets.

[0081] During strategy implementation, the asset operation module monitors market feedback and operational results in real time to evaluate the effectiveness of each strategy. When a strategy fails to achieve expected results, the system can automatically adjust. For example, if sales of a certain type of data asset fall short of expectations, the module can respond quickly by adjusting pricing, increasing advertising, changing sales channels, or launching related styles to enhance market competitiveness. Simultaneously, the module regularly generates detailed operational reports covering asset market performance, revenue, and analysis of the effectiveness of operational strategies. These reports provide management with crucial decision-making support, promoting strategy iteration and optimization.

[0082] Furthermore, to further enhance user experience, the module will establish a user feedback mechanism to regularly collect and analyze user feedback on their experience and satisfaction with data assets. Based on this, the module can promptly adjust its products and services to improve user engagement and loyalty. Risk management is also a crucial component of this module, with a corresponding risk assessment mechanism established to evaluate market risks, storage risks, and legal compliance risks associated with data assets. This will allow for the timely identification of potential problems and the implementation of preventative measures to ensure the security and stability of asset operations. Through these comprehensive measures, the asset operation module not only optimizes data asset management processes but also enhances the company's competitiveness and economic benefits in the market.

[0083] This invention also provides a data asset operation method, which can be applied to electronic devices, such as computer terminals, specifically ordinary computers.

[0084] The following detailed explanation uses a computer terminal as an example. Figure 2 This is a hardware structure block diagram of a computer terminal for a data asset operation method provided in an embodiment of the present invention. Figure 2 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0085] Non-volatile storage media can store operating systems and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform any data asset management method.

[0086] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0087] Internal memory provides an environment for the execution of computer programs on non-volatile storage media, which, when executed by a processor, enable the processor to perform any data asset management method.

[0088] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0089] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0090] See Figure 3 The present invention provides a data asset operation method, which may include the following steps:

[0091] S301, Obtain the data assets to be processed and their market value, classify the data assets, and generate corresponding asset identifiers for each class of data assets.

[0092] S302, for each type of data asset, the market value and asset identifier of the data asset are stored in the main chain of the blockchain, and the data asset is stored in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains;

[0093] S303, periodically calculate the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index reflecting the storage efficiency of each subchain, and dynamically adjust the storage subchain of each type of data asset based on the reliability index and the storage efficiency index.

[0094] S304 manages asset operations based on dynamically adjusted currently stored data assets.

[0095] As can be seen, the data asset operation method provided by this invention involves acquiring the data assets to be processed and their market value, classifying the data assets, and generating corresponding asset identifiers for each class of data assets; for each class of data assets, storing the market value and asset identifier of the data asset class in the main chain of the blockchain, and storing the data asset class in a sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains; periodically calculating a reliability index representing the reliability of data interaction between different sub-chains, and a storage efficiency index reflecting the storage efficiency of each sub-chain, and dynamically adjusting the storage sub-chain of each class of data assets based on the reliability index and the storage efficiency index; and performing asset operation management based on the dynamically adjusted currently stored data assets. This method, by introducing blockchain technology, a dynamic storage adjustment mechanism, and an asset operation management module, helps enterprises achieve efficient management and value maximization of data assets, promoting the sustainable operation and development of data assets.

[0096] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0097] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps:

[0098] S301, Obtain the data assets to be processed and their market value, classify the data assets, and generate corresponding asset identifiers for each class of data assets.

[0099] S302, for each type of data asset, the market value and asset identifier of the data asset are stored in the main chain of the blockchain, and the data asset is stored in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains;

[0100] S303, periodically calculate the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index reflecting the storage efficiency of each subchain, and dynamically adjust the storage subchain of each type of data asset based on the reliability index and the storage efficiency index.

[0101] S304 manages asset operations based on dynamically adjusted currently stored data assets.

[0102] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0103] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0104] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0105] S301, Obtain the data assets to be processed and their market value, classify the data assets, and generate corresponding asset identifiers for each class of data assets.

[0106] S302, for each type of data asset, the market value and asset identifier of the data asset are stored in the main chain of the blockchain, and the data asset is stored in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains;

[0107] S303, periodically calculate the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index reflecting the storage efficiency of each subchain, and dynamically adjust the storage subchain of each type of data asset based on the reliability index and the storage efficiency index.

[0108] S304 manages asset operations based on dynamically adjusted currently stored data assets.

[0109] The above description, based on the embodiments shown in the figures, details the structure, features, and effects of the present invention. The above description is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified to have equivalent changes, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A data asset operation technology platform, characterized in that, The platform includes: The asset classification module is used to obtain the data assets to be processed and their market value, classify the data assets, and generate corresponding asset identifiers for each class of data assets. The asset storage module is used to store the market value and asset identifier of each type of data asset in the main chain of the blockchain and store the data assets in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains. The storage adjustment module is used to periodically calculate a reliability index that represents the reliability of data interaction between different sub-chains and a storage efficiency index that reflects the storage efficiency of each sub-chain, and dynamically adjust the storage sub-chain of each type of data asset based on the reliability index and the storage efficiency index. The asset operation module is used for asset operation management based on the dynamically adjusted currently stored data assets.

2. The platform according to claim 1, characterized in that, The process of generating corresponding asset identifiers for each category of classified data assets includes: For each type of data asset, the type information of that data asset is encoded to obtain classification coding information; The attribute information of this type of data asset is encoded and weighted to obtain attribute encoding information; Obtain the current timestamp and a random number for this type of data asset. Based on the classification coding information, the attribute coding information, the current timestamp, and the random number, generate the asset identifier corresponding to this type of data asset using a preset hash function.

3. The platform according to claim 2, characterized in that, The periodic calculation of a reliability index, representing the reliability of data interaction between different subchains, and a storage efficiency index, reflecting the storage efficiency of each subchain, includes: For each subchain, the number of successful interactions and the total number of interaction attempts between the subchain and other subchains are periodically obtained within the corresponding time period to calculate a reliability index that represents the reliability of data interactions between different subchains; Periodically obtain the storage volume, usage frequency, and storage cost of the data assets stored in the sub-chain within the corresponding time period to calculate the storage efficiency index, which reflects the storage efficiency of each sub-chain.

4. The platform according to claim 3, characterized in that, The step of dynamically adjusting the storage sub-chain for each type of data asset based on the reliability index and the storage efficiency index includes: For each subchain, if the reliability index of the subchain is less than a preset reliability threshold or the storage efficiency index is less than a preset efficiency threshold, the data assets in the subchain are stored in other subchains so that the reliability index of the other subchain in the next calculation is greater than the preset reliability threshold and the storage efficiency index is greater than the preset efficiency threshold.

5. A data asset operation method, characterized in that, The method includes: The process involves acquiring the data assets to be processed and their market value, classifying the data assets, and generating a corresponding asset identifier for each class of data assets. For each type of data asset, the market value and asset identifier of the data asset are stored in the main chain of the blockchain, and the data asset is stored in a sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains; The reliability index, which represents the reliability of data interaction between different subchains, and the storage efficiency index, which reflects the storage efficiency of each subchain, are calculated periodically. Based on the reliability index and the storage efficiency index, the storage subchain of each type of data asset is dynamically adjusted. Asset operation and management are carried out based on the dynamically adjusted currently stored data assets.

6. The method according to claim 5, characterized in that, The process of generating corresponding asset identifiers for each category of classified data assets includes: For each type of data asset, the type information of that data asset is encoded to obtain classification coding information; The attribute information of this type of data asset is encoded and weighted to obtain attribute encoding information; Obtain the current timestamp and a random number for this type of data asset. Based on the classification coding information, the attribute coding information, the current timestamp, and the random number, generate the asset identifier corresponding to this type of data asset using a preset hash function.

7. The method according to claim 6, characterized in that, The periodic calculation of a reliability index, representing the reliability of data interaction between different subchains, and a storage efficiency index, reflecting the storage efficiency of each subchain, includes: For each subchain, the number of successful interactions and the total number of interaction attempts between the subchain and other subchains are periodically obtained within the corresponding time period to calculate a reliability index that represents the reliability of data interactions between different subchains; Periodically obtain the storage volume, usage frequency, and storage cost of the data assets stored in the sub-chain within the corresponding time period to calculate the storage efficiency index, which reflects the storage efficiency of each sub-chain.

8. The method according to claim 7, characterized in that, The step of dynamically adjusting the storage sub-chain for each type of data asset based on the reliability index and the storage efficiency index includes: For each subchain, if the reliability index of the subchain is less than a preset reliability threshold or the storage efficiency index is less than a preset efficiency threshold, the data assets in the subchain are stored in other subchains so that the reliability index of the other subchain in the next calculation is greater than the preset reliability threshold and the storage efficiency index is greater than the preset efficiency threshold.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 5-8 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 5-8.

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