Data asset operation technology platform, operation method, storage medium and electronic equipment

Abstract

The invention discloses a data asset operation technology platform, an operation method, a storage medium and electronic equipment, and the platform comprises an asset classification module which is used for obtaining to-be-processed data assets and market values thereof, carrying out the classification of the data assets, and generating a corresponding asset identifier for each type of data assets; the asset storage module is used for storing the market value and the asset identifier of each type of data assets to a main chain of the block chain and storing the type of data assets to a sub-chain of the block chain for each type of data assets; the storage adjustment module is used for periodically calculating a reliability index and a storage efficiency index, and dynamically adjusting the storage sub-chain of each type of data assets according to the reliability index and the storage efficiency index; and the asset operation module is used for carrying out asset operation management based on the dynamically adjusted currently stored data assets. By means of the data asset management method and device, efficient management and value maximization of the data assets can be achieved, and sustainable operation and development of the data assets are promoted.

Description

Technical Field

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

[0002] With the advent of the big data era, data assets have become an important driving force for a new round of economic growth. The generation, storage and management of data have become increasingly important in various industries, especially in the fields of finance, medical care, logistics and the Internet. Data is not only a carrier of information, but also has market value and can generate revenue through various forms of transactions. Traditional data management methods are usually based on centralized databases and manual audits, which have problems such as data redundancy, irregular management and poor security. Therefore, how to operate and manage data assets efficiently and reliably has become an urgent problem for enterprises and organizations to solve. Summary of the invention

[0003] The purpose of the present invention is to provide a data asset operation technology platform, operation method, storage medium and electronic device to solve the deficiencies in the prior art. 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] An embodiment of the present application provides a data asset operation technology platform, the platform comprising: An asset classification module, used to obtain the data assets to be processed and their market values, classify the data assets, and generate corresponding asset identifiers for each type of classified data assets; An asset storage module, for storing the market value and asset identifier of each type of data asset in the main chain of the blockchain, and storing the data asset in a subchain of the blockchain, wherein the blockchain supports data interaction between different subchains; A storage adjustment module, which is used to regularly calculate a reliability index representing the reliability of data interaction between different subchains and a storage efficiency index representing the storage efficiency of each subchain, and dynamically adjust the storage subchain of each type of data asset according to the reliability index and the storage efficiency index; The asset operation module is used to perform asset operation management based on the dynamically adjusted currently stored data assets.

[0005] Optionally, generating a corresponding asset identifier for each type of classified data asset includes: For each type of data asset, encode the type information of the data asset to obtain classification coding information; Encode and weight each attribute information of the data asset to obtain attribute encoding information; Obtain the current timestamp and a random number of the data asset of this type, and generate an asset identifier corresponding to the data asset of this type using a preset hash function based on the classification coding information, the attribute coding information, the current timestamp and the random number.

[0006] Optionally, the periodic calculation of a reliability index representing the reliability of data interaction between different subchains and a storage efficiency index representing 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 within the corresponding time period are regularly obtained to calculate the reliability index representing the reliability of data interaction between different subchains; The storage capacity of the data assets stored in the subchain within the corresponding time period, the usage frequency and storage cost of the data assets are regularly obtained to calculate the storage efficiency index reflecting the storage efficiency of each subchain.

[0007] Optionally, dynamically adjusting the storage subchain of each type of data asset according to the reliability index and the storage efficiency index includes: For each sub-chain, if the reliability index of the sub-chain is less than the preset reliability threshold or the storage efficiency index is less than the 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 calculated next time is greater than the preset reliability threshold and the storage efficiency index is greater than the preset efficiency threshold.

[0008] Another embodiment of the present application provides a data asset operation method, the method comprising: Obtaining data assets to be processed and their market values, classifying the data assets, and generating corresponding asset identifiers for each type of classified data assets; For each type of data asset, the market value of the data asset and its asset identifier 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; Regularly calculate the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index representing the storage efficiency of each subchain, and dynamically adjust the storage subchain for each type of data asset based on the reliability index and the storage efficiency index; Asset operation management is carried out based on the dynamically adjusted currently stored data assets.

[0009] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0010] Yet another embodiment of the present 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 execute any of the methods described above.

[0011] Compared with the prior art, the present invention provides a data asset operation technology platform, including: an asset classification module, which is used to obtain data assets to be processed and their market value, classify data assets, and generate corresponding asset identifiers for each type of classified data assets; an asset storage module, which 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 this type of data asset in the sub-chain of the blockchain; a storage adjustment module, which is used to regularly calculate the reliability index and storage efficiency index, and dynamically adjust the storage sub-chain of each type of data asset according to the reliability index and storage efficiency index; an asset operation module, which is used to perform asset operation management based on the currently stored data assets after dynamic adjustment, so as to help enterprises achieve efficient management and value maximization of data assets and promote sustainable operation and development of data assets by introducing blockchain technology, dynamic storage adjustment mechanism and asset operation management module. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of the structure of a data asset operation technology platform provided by an embodiment of the present invention; Figure 2 A hardware structure block diagram of a computer terminal for a data asset operation method provided by an embodiment of the present invention; Figure 3 A flowchart of a data asset operation method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0014] The embodiment of the present invention first provides a data asset operation technology platform, see Figure 1 , which may include: The asset classification module 101 is used to obtain the data assets to be processed and their market values, classify the data assets, and generate corresponding asset identifiers for each type of classified data assets; The asset classification module plays a vital role in the data asset operation technology platform. This module is responsible for obtaining all pending data assets and their corresponding market values, and classifying these data assets according to the preset classification standards. Each data asset has a known market value, and the module divides 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 the type information of the asset, but also provides a basis for subsequent data management, transactions and monitoring, helping to ensure the traceability and security of data assets.

[0015] The asset classification module can first obtain all pending data assets and their corresponding market values ​​by integrating multiple data sources (such as internal business systems, market data interfaces, third-party data providers, etc.). These data assets can be structured or unstructured data, depending on the business needs of the enterprise.

[0016] Next, the module will implement classification operations and classify the acquired data assets into different types based on their characteristics (such as data type, purpose, industry classification, etc.). 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 recorded in detail to ensure that each data asset and its market value can be clearly stated. In order to improve processing efficiency, the module can use existing classification algorithms combined with manual review mechanisms to ensure the accuracy of classification. Finally, after the classification is completed, the asset classification module generates a unique asset identifier for each type of data asset. This identifier combines classification information, market value information, asset type, timestamp and other data, and uses a preset hash function for processing to ensure that each identifier is unique and unpredictable. In this way, in the subsequent asset management and operation process, different types of assets and their market values ​​can be effectively identified and tracked, providing a solid data foundation for corporate decision-making.

[0017] Specifically, a corresponding asset identifier is generated for each type of data asset after classification. For each type of data asset, the type information of the data asset can be encoded to obtain the classification coding information. ; This step converts different types of asset information into numerical form by encoding the type information of each type of data asset. This process enables computers to effectively process and store this information. At the same time, this encoding method facilitates subsequent classification and identifier generation, providing standardized input for subsequent data processing.

[0018] 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 basic data for subsequent identifier generation and classification operations. Ultimately, data assets in the blockchain environment can be accurately identified and efficiently managed.

[0019] First, the system sets a set of rules or standards to determine the different types of data assets. For each type of data asset, the system analyzes its characteristic information (such as data type, industry classification, etc.). Then, these characteristic information are converted into numerical form using predefined encoding methods (such as binary encoding, decimal encoding or other forms of encoding). The encoded data will be stored in a standard format to ensure the consistency and accuracy of 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. Ultimately, the generated classification code will provide the necessary identification for subsequent operations.

[0020] Encode the attribute information of this type of data assets and perform weighted processing to obtain attribute encoding information ; The attribute information of each type of data asset is encoded, and corresponding weights are applied to different attributes to calculate the comprehensive attribute encoding information. The attribute encoding represents the characteristic combination of this type of data asset, which can effectively reflect its characteristics and value. This weighted processing method will quantify the impact of different attributes on asset characteristics, which will help to comprehensively consider the relative importance of each attribute when generating asset identifiers. This process further enhances the accuracy of the identifier and its effectiveness in the subsequent management process, allowing enterprises to make reasonable decisions based on the actual situation of data assets. One type of attribute encoding information can be:

[0021] in, is the encoding value of the i-th attribute, is the weight of the ith attribute, and n is 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.

[0022] Get the current timestamp of the data asset and a random number , according to the classification coding information , the attribute encoding information , the current timestamp and the random number , using the preset hash function Generate an asset identifier corresponding to this type of data asset .

[0023] By combining classification coding information, attribute coding information, current timestamp and random number and other data, a unique asset identifier is generated using a preset hash function. This identifier will be used to uniquely identify various data assets, providing security and effectiveness for data storage and sharing. The generated asset identifier ensures the uniqueness and non-replicability of each data asset, avoiding identifier conflicts. By adding timestamps and random numbers, this mechanism enhances the security and anti-attack capabilities of the identifier, especially in blockchain applications, ensuring the authenticity and non-tamperability of assets.

[0024] First, the system will collect and store the classification coding information and attribute coding information of each type of data asset. Then, the system obtains the current timestamp (indicating the specific time of execution) and a securely generated random number, which can be ensured to be random and secure by a cryptographic generator. Next, this information will be combined according to the preset splicing rules to form a string and passed into the preset hash function. The hash function will process the spliced ​​content to generate a unique asset identifier, which will be stored and used for subsequent data management. An asset identifier can be:

[0025] in, is a string concatenation operation. Through this formula, the system will use the hash function to generate a unique asset identifier, which actively combines the asset's classification information, attribute information, and environmental dynamics (timestamp and random number) to ensure 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.

[0026] The significance of the asset classification module lies in its optimization of the management process of data assets, enabling enterprises to use 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. In addition, the unique asset identifier provides strong support for subsequent data interaction in blockchain scenarios, ensuring the security, transparency and non-tamperability of asset information. The implementation of this module not only improves the identifiability of data assets, but also promotes the competitiveness of enterprises in the digital economy.

[0027] 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 asset in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains; The asset storage module is a key component of the data asset operation technology platform, which aims to ensure that the market value and asset identifier of each type of data asset can be stored safely and efficiently in the blockchain system. This module builds a hierarchical storage structure by storing the market value and its corresponding asset identifier in the main chain of the blockchain, and storing the actual data assets in the sub-chain of the blockchain. The main chain is responsible for recording and maintaining the basic information of all assets to ensure transparency and credibility, 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 basic support for data interaction between different sub-chains, thereby realizing efficient management and scheduling of data assets.

[0028] In specific implementation, the asset storage module will first receive the market value of each type of data asset and its corresponding asset identifier generated by the asset classification module. The system will structure this information according to the type and characteristics of the data, and select the appropriate main chain and sub-chain for storage. The main chain will store the core information of the asset, such as asset identifier, market value, classification information, creation time, etc., to ensure the immutability and verifiability of all information. When storing data assets in a subchain, the system will determine the subchain for storage based on the specific characteristics of the data and the usage scenario. For example, for data assets that are accessed frequently, you can choose to store them on a performance-optimized subchain, while less frequently used assets can be stored in a low-cost subchain. Each subchain is responsible for a certain type of data assets, while ensuring its interoperability with other subchains and supporting data interaction between different subchains. This design allows data assets to be flexibly transferred and shared between multiple subchains, improving data access speed and processing efficiency. When storing, the system will also monitor the access frequency, storage cost and usage of data assets, and update storage decisions in real time. The storage module will set a series of storage rules and strategies to cope with the dynamic changes of data assets and ensure that the system is always in an efficient and secure state. When data assets generate new transactions or updates, the asset storage module will automatically record these changes through smart contracts and update relevant information in the main chain and sub-chain to ensure data consistency and integrity.

[0029] The introduction of this asset storage module greatly enhances the traceability and transparency of data assets. By storing market value and asset identifiers in the main chain, it is possible to accurately monitor and evaluate the value of data assets throughout their life cycle. In addition, through the design of sub-chains, the platform can flexibly manage different types of data assets, avoiding congestion on the main chain, thereby improving the overall performance and efficiency of the system. The mutual support and interaction capabilities between sub-chains improve the overall reliability of the system, help enterprises respond flexibly in a rapidly changing data environment, and improve the utilization and optimization of data assets.

[0030] The storage adjustment module 103 is used to regularly calculate the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index representing the storage efficiency of each subchain, and dynamically adjust the storage subchain of each type of data asset according to the reliability index and the storage efficiency index; The main function of the storage adjustment module is to provide intelligent decision support for the storage allocation of data assets. The module monitors the operating status of various parts of the system in real time by regularly calculating two core indexes: the reliability of data interaction between different subchains and the storage efficiency of each subchain. By analyzing these data, the module can identify which subchains perform well in the data interaction process and which subchains have performance bottlenecks, and then dynamically adjust the storage subchain of each type of data asset to ensure that the assets are stored on the most suitable subchain. This process not only combines historical data, but also comprehensively considers the current network status to ensure the efficiency and security of data access.

[0031] After the introduction of the storage adjustment module, the overall performance of the platform and the liquidity of data assets have been significantly improved. By regularly evaluating and dynamically adjusting the storage location, data assets are always stored in the optimal subchain, avoiding data access delays and resource waste caused by improper storage. At the same time, the module optimizes the utilization of storage resources, reduces operating costs, and improves the scalability and flexibility of the overall system. By maintaining efficient flow and access of assets, the platform not only meets user needs, but also improves market competitiveness and brings greater commercial value to data assets.

[0032] Specifically, the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index representing the storage efficiency of each subchain are calculated regularly. For each subchain, the number of successful interactions between the subchain and other subchains within the corresponding time period can be obtained regularly. and total interaction attempts , to calculate the reliability index representing the reliability of data interaction between different sub-chains ; One calculation formula is:

[0033] This formula aims to evaluate the reliability of subchains by quantifying the ratio of successful interactions between them. The ratio of the number of successful interactions to the total number of interaction attempts is introduced and presented in percentage form, making the performance comparison between different subchains more intuitive and easier to understand.

[0034] In this step, the system first needs to collect the interaction data of different sub-chains within a specified time period, including the number of successful interactions and the total number of interaction attempts. These data are the basis for evaluating the reliability of data interaction between sub-chains. By calculating the ratio of the number of successful interactions to the total number of interaction attempts, the reliability index is obtained to reflect the trust status between sub-chains.

[0035] Calculating the interaction reliability index can help the system identify which subchains have relatively stable data interactions and which have a higher failure rate. This can make data asset storage and scheduling decisions more scientific, help optimize resource allocation and coordination between subchains, and thus improve the performance of the overall system and the mutual adaptability of each subchain.

[0036] In the specific implementation, the system will set up regular scheduling tasks (such as every hour or every day) to extract interaction data within a specific time period from the blockchain records. The automated script will calculate the number of successful interactions (A_success) and the total number of interaction attempts (A_total), and calculate the interaction reliability index (RI_interaction) according to the formula. This process can be automated using database query and data processing tools to ensure the accuracy and real-time nature of the statistical data. At the same time, the system needs to save historical interaction data for trend analysis and long-term monitoring to help tune system performance.

[0037] Regularly obtain the storage volume of the data assets stored in the subchain within the corresponding time period, the usage frequency and storage cost of the data assets, so as to calculate the storage efficiency index reflecting the storage efficiency of each subchain A calculation formula can be:

[0038] in, is the storage capacity of the jth type of data assets, is the usage frequency of the jth type of data assets, is the storage cost of the jth type of data asset, and m is 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, so as to achieve a balance between maximizing the value of data assets and effectively utilizing resources.

[0039] In this step, the system will regularly collect relevant information about the data assets stored on each subchain, including the storage volume, usage frequency, and storage cost of each type of data asset. These data are the key basis for calculating the storage efficiency index. With this information, the system can comprehensively evaluate the performance of each subchain in terms of resource utilization. The calculation of the storage efficiency index can help the system identify subchains 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, making the entire data asset management process more efficient and economical.

[0040] The system will extract relevant information about the storage data assets from the blockchain on a regular basis (for example, daily or weekly), including storage volume (S_data,j), usage frequency (U_data,j), and storage cost (C_storage,j). Then, the storage efficiency index (SEI) is calculated using a formula. This process can use data analysis tools such as Python or R to perform automated statistics to ensure accurate data collection and processing. At the same time, the system should be able to support custom time ranges so that storage efficiency can be flexibly analyzed according to business needs.

[0041] Specifically, according to the reliability index and the storage efficiency index, the storage subchain of each type of data assets is dynamically adjusted. 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 calculated next time is greater than the preset reliability threshold, and the storage efficiency index is greater than the preset efficiency threshold.

[0042] In this step, the system dynamically adjusts based on the reliability index and storage efficiency index calculated previously. If the reliability index or storage efficiency index of a subchain is lower than the preset threshold, the system will trigger data migration and migrate the data assets in the subchain to other subchains with better performance. This process is designed to ensure that data assets are always stored in the optimal environment and improve the performance and stability of the overall system.

[0043] This dynamic adjustment mechanism not only optimizes data storage strategies and ensures that 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 a high degree of flexibility and adaptability and enhance their competitiveness in a constantly changing market environment.

[0044] Specifically, the system is configured with a regular monitoring module to automatically collect the latest reliability and storage efficiency data of each subchain at set time intervals (such as every hour or every day). The performance indicators of all subchains will be stored in the central database for historical data analysis and comparison. In the early stages of system implementation, managers should set reasonable reliability thresholds and storage efficiency thresholds based on business needs and historical performance data. These thresholds can be based on industry standards or the results of internal data analysis to ensure that the system can promptly detect subchains with poor performance. At the end of each monitoring cycle, the system automatically analyzes the latest performance indicators of each subchain and compares them with the preset thresholds. If the reliability index of a subchain is lower than the reliability threshold, or the storage efficiency index is lower than the efficiency threshold, the system will trigger a data migration request. This request will immediately generate an alarm to notify the system administrator.

[0045] When a data migration request is triggered, the system needs to identify the target subchain that is suitable for receiving the data. This selection process will be based on a comprehensive evaluation of reliability and storage efficiency to ensure that the performance indicators of the target subchain are above the preset threshold. The algorithm will calculate the reliability index and storage efficiency index of all candidate subchains, and give priority to those with the highest performance indicators as the destination of data migration. After the target subchain is determined, a detailed data migration plan will be generated, including information such as the type of data assets to be migrated, the number of migrations, and the migration time window. The system also needs to consider the dependencies of the data to ensure that the normal operation of the business is not affected during the migration process, and to ensure the integrity of the data. Data migration will be implemented through smart contracts to ensure the automation and security of the entire process. During the migration process, 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 of the current subchain to ensure that it can be quickly restored if the migration fails. After the data migration is completed, the system will regularly monitor the performance of the migrated target subchain to ensure that its reliability index and storage efficiency index are maintained above the set threshold. At the same time, after the migration, the data status and performance indicators of the ATOS chain must also be tracked to ensure that there is no additional burden caused by the migration. The system records all migration operations on the blockchain to ensure that the data cannot be tampered with and is traceable. These records will provide a basis for future performance evaluation and storage strategy adjustments. Managers can adjust future threshold settings and migration strategies based on performance feedback after the migration to optimize the entire data asset management process.

[0046] Through the above implementation steps, the system can effectively realize the dynamic adjustment of data assets between different sub-chains, improve the utilization efficiency of overall storage resources, and ensure data security and service quality.

[0047] The asset operation module 104 is used to perform asset operation management based on the dynamically adjusted currently stored data assets.

[0048] The asset operation module is one of the core components of the data asset operation technology platform. It aims to formulate and implement effective operation management strategies based on the current stored data assets after dynamic adjustment. 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 can help enterprises identify potential market opportunities and optimize asset allocation, thereby improving overall operational efficiency and economic value.

[0049] The implementation of the asset operation module is of great strategic significance for improving the data asset management capabilities of enterprises. First, it can achieve comprehensive management of data assets, ensure that the use of assets matches market demand, and maximize the value of data assets. Secondly, this module can effectively respond to market changes and improve the utilization efficiency and profit level of data assets by dynamically adjusting storage strategies in real time. Ultimately, this module enables enterprises to maintain their leading position in the fierce market competition, drive business innovation and achieve sustainable growth.

[0050] Specifically, this module integrates a variety of internal and external data sources to collect key performance indicators of various data assets in real time, including market value, usage frequency, user feedback, transaction records, etc. After being fully cleaned and processed to ensure its accuracy and completeness, these data will be input into the data analysis platform. Next, using data mining and machine learning algorithms, the module can conduct in-depth analysis of assets, identify asset categories with high growth potential and feasible market opportunities, and formulate corresponding operating strategies based on industry trends and market demand. These strategies may include dynamic pricing, promotional activities, marketing and channel optimization to maximize the value of data assets. During the implementation of the strategy, the asset operation module will monitor market feedback and operational results in real time to evaluate the effectiveness of each strategy. When it is found that a strategy fails to achieve the expected results, the system can automatically adjust it. For example, if the sales volume of a certain type of data asset is lower than expected, the module can respond quickly to adjust pricing, increase advertising, change sales channels or launch related styles to enhance market competitiveness. At the same time, the module regularly generates detailed operational reports covering the market performance, revenue and effectiveness analysis of the asset's operational strategy. These reports will provide important decision-making basis for management and promote the iteration and optimization of strategies. In addition, in order to further enhance the user experience, the module will also establish a user feedback mechanism to regularly collect and analyze user experience and satisfaction with data assets. On this basis, the module can adjust products and services in a timely manner to enhance user engagement and loyalty. Risk management is also an important part of this module. A corresponding risk assessment mechanism will be established to assess the market risk, storage risk and legal compliance risk of data assets, identify potential problems in a timely manner and take preventive measures to ensure the security and stability of asset operations. Through the above series of comprehensive measures, the asset operation module not only optimizes the management process of data assets, but also enhances the competitiveness and economic benefits of enterprises in the market.

[0051] An embodiment of the present invention also provides a data asset operation method, which can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0052] The following describes it in detail by taking running on a computer terminal as an example. Figure 2 The hardware structure block diagram of a computer terminal of a data asset operation method provided by an embodiment of the present invention. Figure 2 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0053] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, which, when executed, can cause the processor to execute any data asset operation method.

[0054] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0055] The internal memory provides an environment for the operation of computer programs in non-volatile storage media. When the computer program is executed by the processor, the processor can execute any data asset operation method.

[0056] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0057] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0058] See also Figure 3 , an embodiment of the present invention provides a data asset operation method, which may include the following steps: S301, obtaining data assets to be processed and their market values, classifying the data assets, and generating corresponding asset identifiers for each type of classified data assets; S302, for each type of data asset, storing the market value of the data asset and its asset identifier in the main chain of the blockchain, and storing the data asset in a subchain of the blockchain, wherein the blockchain supports data interaction between different subchains; S303, regularly calculating a reliability index representing the reliability of data interaction between different subchains and a storage efficiency index representing the storage efficiency of each subchain, and dynamically adjusting the storage subchain for each type of data asset based on the reliability index and the storage efficiency index; S304, performing asset operation management based on the dynamically adjusted currently stored data assets.

[0059] It can be seen that the data asset operation method provided by the present invention obtains the data assets to be processed and their market values, classifies the data assets, and generates corresponding asset identifiers for each type of data assets after classification; for each type of data assets, the market value of the data assets of this type and their asset identifiers are stored in the main chain of the blockchain, and the data assets of this type are stored in the sub-chain of the blockchain, wherein the blockchain supports data interaction between different sub-chains; 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 are regularly calculated, and the storage sub-chain of each type of data assets is dynamically adjusted according to the reliability index and the storage efficiency index; asset operation management is performed based on the currently stored data assets after dynamic adjustment, so that by introducing blockchain technology, dynamic storage adjustment mechanism and asset operation management module, enterprises can achieve efficient management and value maximization of data assets, and promote sustainable operation and development of data assets.

[0060] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0061] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S301, obtaining data assets to be processed and their market values, classifying the data assets, and generating corresponding asset identifiers for each type of classified data assets; S302, for each type of data asset, storing the market value of the data asset and its asset identifier in the main chain of the blockchain, and storing the data asset in a subchain of the blockchain, wherein the blockchain supports data interaction between different subchains; S303, regularly calculating a reliability index representing the reliability of data interaction between different subchains and a storage efficiency index representing the storage efficiency of each subchain, and dynamically adjusting the storage subchain for each type of data asset based on the reliability index and the storage efficiency index; S304, performing asset operation management based on the dynamically adjusted currently stored data assets.

[0062] An embodiment of the present invention further 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 execute the steps in any one of the above method embodiments.

[0063] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0064] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S301, obtaining data assets to be processed and their market values, classifying the data assets, and generating corresponding asset identifiers for each type of classified data assets; S302, for each type of data asset, storing the market value of the data asset and its asset identifier in the main chain of the blockchain, and storing the data asset in a subchain of the blockchain, wherein the blockchain supports data interaction between different subchains; S303, regularly calculating a reliability index representing the reliability of data interaction between different subchains and a storage efficiency index representing the storage efficiency of each subchain, and dynamically adjusting the storage subchain of each type of data asset according to the reliability index and the storage efficiency index; S304, performing asset operation management based on the dynamically adjusted currently stored data assets.

[0065] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A data asset operation technology platform, characterized in that: The platform includes: An asset classification module is used to obtain the data assets to be processed and their market values, classify the data assets, and generate corresponding asset identifiers for each type of classified data assets; An asset storage module, for storing the market value and asset identifier of each type of data asset in the main chain of the blockchain, and storing the data asset in a subchain of the blockchain, wherein the blockchain supports data interaction between different subchains; A storage adjustment module, which is used to regularly calculate a reliability index representing the reliability of data interaction between different subchains and a storage efficiency index representing the storage efficiency of each subchain, and dynamically adjust the storage subchain of each type of data asset according to the reliability index and the storage efficiency index; The asset operation module is used to perform asset operation management based on the dynamically adjusted currently stored data assets.

2. The platform according to claim 1, characterized in that Generating a corresponding asset identifier for each type of classified data asset includes: For each type of data asset, encode the type information of the data asset to obtain classification coding information; Encode and weight each attribute information of this type of data asset to obtain attribute encoding information; Obtain the current timestamp and a random number of the data asset of this type, and generate an asset identifier corresponding to the data asset of this type using a preset hash function based on the classification coding information, the attribute coding information, the current timestamp and the random number.

3. The platform according to claim 2, characterized in that: The periodic calculation of the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index representing 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 within the corresponding time period are regularly obtained to calculate the reliability index representing the reliability of data interaction between different subchains; The storage capacity of the data assets stored in the subchain within the corresponding time period, the usage frequency and storage cost of the data assets are regularly obtained to calculate the storage efficiency index reflecting the storage efficiency of each subchain.

4. The platform according to claim 3, characterized in that: The dynamically adjusting the storage subchain of each type of data asset according to the reliability index and the storage efficiency index includes: For each sub-chain, if the reliability index of the sub-chain is less than the preset reliability threshold or the storage efficiency index is less than the 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 calculated next time 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 comprises: Obtaining data assets to be processed and their market values, classifying the data assets, and generating corresponding asset identifiers for each type of classified data assets; For each type of data asset, the market value of the data asset and its asset identifier 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; Regularly calculate the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index representing the storage efficiency of each subchain, and dynamically adjust the storage subchain for each type of data asset based on the reliability index and the storage efficiency index; Asset operation management is carried out based on the dynamically adjusted currently stored data assets.

6. The method according to claim 5, characterized in that Generating a corresponding asset identifier for each type of classified data asset includes: For each type of data asset, encode the type information of the data asset to obtain classification coding information; Encode and weight each attribute information of this type of data asset to obtain attribute encoding information; Obtain the current timestamp and a random number of the data asset of this type, and generate an asset identifier corresponding to the data asset of this type using a preset hash function based on the classification coding information, the attribute coding information, the current timestamp and the random number.

7. The method according to claim 6, characterized in that The periodic calculation of the reliability index representing the reliability of data interaction between different subchains and the storage efficiency index representing 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 within the corresponding time period are regularly obtained to calculate the reliability index representing the reliability of data interaction between different subchains; The storage capacity of the data assets stored in the subchain within the corresponding time period, the usage frequency and storage cost of the data assets are regularly obtained to calculate the storage efficiency index reflecting the storage efficiency of each subchain.

8. The method according to claim 7, characterized in that The dynamically adjusting the storage subchain of each type of data asset according to the reliability index and the storage efficiency index includes: For each sub-chain, if the reliability index of the sub-chain is less than the preset reliability threshold or the storage efficiency index is less than the 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 calculated next time 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 according to any one of claims 5 to 8 when executed.

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

Images