Cold and hot layering method and system for RWA data, computer equipment and storage medium

Abstract

The invention relates to a cold and hot layering method and system for RWA data, computer equipment and a storage medium, and the method comprises the steps: jointly constructing a cold and hot layering rule corresponding to each preset asset type according to a basic index of a preset technical dimension and a dynamic index of a preset business dimension; loading a corresponding target cold and hot layering rule from the cold and hot layering rule based on to-be-processed RWA data obtained from the RWA data platform; according to the bound score calculation function and the associated score basis function, obtaining a cold and hot layering total score of the target cold and hot layering rule; and performing cold and hot stratification on the to-be-processed RWA data based on the cold and hot stratification total score. Through the method and the device, the problem that cold and hot layering is not accurate enough due to limitation of a single fixed index in the related technology is solved, optimization of the index from the technical dimension and the service dimension is completed, and thus the accuracy of cold and hot layering is improved.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method, system, computer equipment, and storage medium for hot and cold stratification of RWA data. Background Technology

[0002] In traditional big data platforms, the implementation of hot and cold data stratification schemes relies on access frequency or time as indicators to determine the hotness or coldness of data. For example, data that has been accessed frequently recently is marked as "hot data," while data that has not been accessed or modified for a long time is marked as "cold data." However, in RWA (Real World Asset) data platforms, RWA data exceeds the complexity and scale of data in traditional big data platforms; moreover, the hotness or coldness of RWA data is not solely determined by access frequency. This limits existing hot and cold data stratification schemes to a single fixed indicator when dealing with RWA data, resulting in inaccurate stratification.

[0003] There is currently no effective solution to the problem that related technologies are limited to a single fixed index, resulting in inaccurate hot and cold stratification. Summary of the Invention

[0004] This embodiment provides a method, system, computer device, and storage medium for hot and cold stratification of RWA data, in order to solve the problem that related technologies are limited to a single fixed index, resulting in inaccurate hot and cold stratification.

[0005] Firstly, this embodiment provides a method for hot and cold stratification of RWA data, including:

[0006] Based on the preset basic indicators of the technical dimension and the preset dynamic indicators of the business dimension, a hot and cold stratification rule corresponding to each preset asset type is jointly constructed; each indicator in the basic indicators and the dynamic indicators is bound to a corresponding score calculation function, and a score basis function is registered to obtain the value of the score basis.

[0007] Based on the RWA data to be processed obtained from the RWA data platform, the corresponding target cold and hot stratification rule is loaded from the cold and hot stratification rule; according to the bound score calculation function and the associated scoring basis function, the total cold and hot stratification score of the target cold and hot stratification rule is obtained.

[0008] Based on the overall score for hot and cold stratification, the RWA data to be processed is stratified into hot and cold stratifications.

[0009] In some embodiments, the method further includes:

[0010] The basic metrics are determined from a technical perspective; these basic metrics include data storage space usage, access frequency, and change frequency.

[0011] A basic indicator library is constructed based on the aforementioned basic indicators.

[0012] In some embodiments, the method further includes:

[0013] The dynamic indicators are determined by the aforementioned business dimensions; the dynamic indicators include asset price, asset collateral valuation volatility, asset trading frequency, asset liquidation status, asset type, and asset dividend status.

[0014] A dynamic indicator library is constructed from the aforementioned dynamic indicators.

[0015] In some embodiments, the step of jointly constructing hot and cold stratification rules corresponding to each asset type based on preset technical dimension basic indicators and preset business dimension dynamic indicators includes:

[0016] Set corresponding weight coefficients for the indicators in each original hot and cold stratification rule; the original hot and cold stratification rule corresponds to the asset type; the indicators in the original hot and cold stratification rule include basic indicators and indicators in the dynamic indicators;

[0017] Based on the basic indicators, the dynamic indicators, and the corresponding weighting coefficients, a hot and cold stratification rule corresponding to each of the asset types is jointly constructed.

[0018] In some embodiments, the method further includes:

[0019] The scoring criteria for the configured scoring calculation function are based on internal events of the RWA data platform, on-chain data sources, and off-chain data sources.

[0020] The internal events are generated by packaging the real-time operating status of the RWA data platform;

[0021] The on-chain data source consists of multiple smart contract data from multiple blockchains connected to the RWA data platform;

[0022] The off-chain data source is the off-chain real-world asset data of the RWA data platform.

[0023] In some embodiments, obtaining the total cold / hot stratification score of the target cold / hot stratification rule based on the bound scoring calculation function and the associated scoring basis function includes:

[0024] The associated scoring criteria function is invoked to obtain the value of the scoring criteria from internal events of the RWA data platform, on-chain data sources, and off-chain data sources;

[0025] Substitute the value of the scoring basis into the bound scoring calculation function to calculate the score of each indicator in the target hot and cold stratification rule;

[0026] The overall score for the target hot and cold stratification rule is obtained by combining the scores of each indicator.

[0027] In some embodiments, the process of stratifying the RWA data to be processed into hot and cold categories based on the overall hot and cold stratification score includes:

[0028] The total score for hot and cold data stratification is compared with a preset stratification threshold to determine whether the RWA data to be processed belongs to hot data; the stratification threshold is determined by the remaining storage performance of the high-speed storage system.

[0029] Based on the comparison results, the RWA data to be processed is stored or migrated between hot and cold layers.

[0030] Secondly, this embodiment provides a hot and cold stratification system, including an RWA data platform and a rules engine;

[0031] The RWA data platform is used to provide RWA data to be processed.

[0032] The rule engine is used to jointly construct hot and cold stratification rules corresponding to each preset asset type based on preset basic indicators of technical dimensions and preset dynamic indicators of business dimensions; each indicator in the basic indicators and the dynamic indicators is bound to a corresponding score calculation function and registered with a score basis function for obtaining the value of the score basis.

[0033] Based on the RWA data to be processed obtained from the RWA data platform, the corresponding target cold and hot stratification rule is loaded from the cold and hot stratification rule; according to the bound score calculation function and the associated scoring basis function, the total cold and hot stratification score of the target cold and hot stratification rule is obtained.

[0034] Based on the overall score for hot and cold stratification, the RWA data to be processed is stratified into hot and cold stratifications.

[0035] Thirdly, this embodiment provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the hot and cold stratification method for RWA data described in the first aspect above.

[0036] Fourthly, this embodiment provides a storage medium storing a computer program that, when executed by a processor, implements the hot and cold stratification method for RWA data described in the first aspect above.

[0037] Compared with related technologies, the hot and cold stratification method, system, computer equipment, and storage medium provided in this embodiment for RWA data jointly construct hot and cold stratification rules corresponding to each preset asset type based on preset basic indicators of technical dimensions and preset dynamic indicators of business dimensions. Each indicator in the basic and dynamic indicators is bound to a corresponding score calculation function and a score basis function is registered to obtain the value of the scoring basis. Based on the RWA data to be processed obtained from the RWA data platform, the corresponding target hot and cold stratification rule is loaded from the hot and cold stratification rules. According to the bound score calculation function and the associated score basis function, the total hot and cold stratification score of the target hot and cold stratification rule is obtained. Based on the total hot and cold stratification score, the RWA data to be processed is stratified into hot and cold stratification, which solves the problem of insufficient accuracy of hot and cold stratification caused by being limited to a single fixed indicator in related technologies. By using basic indicators of technical dimensions combined with dynamic indicators of business dimensions, the hot and cold stratification rules jointly constructed by these two dimensions of indicators are optimized. Furthermore, by setting a score calculation function and a score basis function for each indicator to anchor the real changes in RWA data, the accuracy of hot and cold stratification is improved.

[0038] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0039] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0040] Figure 1 This is a hardware structure block diagram of a terminal device for a hot and cold data stratification method for RWA provided in one embodiment of this application;

[0041] Figure 2 This is a flowchart of a method for hot and cold stratification of RWA data provided in an embodiment of this application;

[0042] Figure 3 This is a flowchart of step S210;

[0043] Figure 4 This is a flowchart of calculating the total score for hot and cold stratification provided in one embodiment of this application;

[0044] Figure 5 This is a structural block diagram of a thermal stratification system provided in an embodiment of this application. Detailed Implementation

[0045] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0046] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0047] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of the terminal for the hot / cold stratification method for RWA data in this embodiment. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0048] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the hot / cold stratification method for RWA data in this embodiment. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, thereby implementing the aforementioned method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0049] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0050] This embodiment provides a method for hot and cold stratification of RWA data. Figure 2 This is a flowchart of the hot and cold stratification method for RWA data in this embodiment, as follows: Figure 2 As shown, the process includes the following steps:

[0051] Step S210: Based on the preset basic indicators of the technical dimension and the preset dynamic indicators of the business dimension, jointly construct the hot and cold stratification rules corresponding to each preset asset type; each indicator in the basic indicators and dynamic indicators is bound to a corresponding score calculation function, and a score basis function is registered to obtain the value of the score basis.

[0052] Step S220: Based on the RWA data to be processed obtained from the RWA data platform, load the corresponding target hot and cold stratification rule from the hot and cold stratification rules; according to the bound score calculation function and the associated score basis function, obtain the total hot and cold stratification score of the target hot and cold stratification rule;

[0053] Step S230: Based on the overall score of hot and cold stratification, perform hot and cold stratification on the RWA data to be processed.

[0054] Specifically, the basic metrics are pre-set from a technical perspective and are fixed indicators; they can be the same metrics used in the traditional Web2 domain for data hot / cold stratification. Dynamic metrics are pre-set from a business perspective and are dynamically changing indicators; they can adapt to the diversity of asset types and the complexity of scenarios in RWA data. To adapt to the diverse asset types and complex scenarios of RWA data, a hot / cold stratification rule corresponding to each asset type is constructed by jointly using basic and dynamic metrics. When setting basic and dynamic metrics, a scoring calculation function is bound to each metric, and a scoring basis function is associated with each metric to obtain the value of the scoring basis. For example, for the "number of visits" metric in the dynamic metrics, its bound scoring function is a piecewise function: "When the number of visits is 0, the score is 0.1; when the number of visits is between 1 and 10, the score is 0.1 + 0.3 × (number of visits / 10); when the number of visits is between 11 and 50, the score is 0.4 + 0.3 × ((number of visits - 10) / 40); when the number of visits exceeds 50, the score is 0.7 + 0.3 × min(1, (number of visits - 50) / 100)". Here, the number of visits is the scoring basis of this scoring function; the value of this scoring basis (number of visits) is obtained by a pre-registered scoring basis function; the method of obtaining it can be by receiving the number of visits pushed from the RWA data platform through the scoring basis function, etc.

[0055] The joint construction can be considered as combining basic indicators and dynamic indicators into a hot / cold stratification rule for each asset type; weights and calculation coefficients can be added during the combination process. In this embodiment, there are no restrictions on the joint construction method.

[0056] The RWA data to be processed refers to the RWA data that needs to be processed within the RWA data platform. This includes RWA data that needs to be stratified but has not yet been stratified, as well as RWA data that has been stratified but may need to be migrated. For the RWA data platform, the RWA database to be processed can be obtained by the RWA data platform itself, or transmitted to the RWA data platform by other platforms or servers, without any restrictions.

[0057] After acquiring the RWA data to be processed, its corresponding asset type can be determined (provided through preprocessing; or directly provided by the RWA data platform to improve data stratification efficiency). Then, the corresponding target hot / cold stratification rule is loaded from the hot / cold stratification rules. At this point, the target hot / cold stratification rule is executed to calculate the current total hot / cold stratification score. Specifically, based on the bound scoring calculation function and the associated scoring basis function, the total hot / cold stratification score of the target hot / cold stratification rule is obtained. Since the total hot / cold stratification score can characterize the hot / cold status of the RWA data to be processed, it can be used to stratify the RWA data to be processed into hot / cold stratifications.

[0058] It is important to know that the actual data source to which the scoring criteria are anchored changes, therefore the calculated total score for hot and cold stratification also changes in reality.

[0059] In related technologies, the implementation idea of ​​data hot / cold stratification schemes is to determine the hotness or coldness of data based on access frequency or time dimension. For example, data that has been accessed frequently recently is marked as "hot data," while data that has not been accessed or modified for a long time is marked as "cold data." When dealing with RWA data, this scheme is limited to a single fixed indicator, resulting in inaccurate hot / cold stratification. In this embodiment, the metadata of the RWA data to be processed is obtained from the RWA data platform; based on preset technical dimension basic indicators and preset business dimension dynamic indicators, hot and cold stratification rules corresponding to each preset asset type are jointly constructed; each indicator in the basic indicators and dynamic indicators is bound to a corresponding score calculation function, and a score basis function is registered to obtain the value of the scoring basis; based on the metadata of the RWA data to be processed, the corresponding target hot and cold stratification rules are loaded; based on the bound score calculation function and the associated score basis function, the total score of the target hot and cold stratification rules is obtained; based on the total score of hot and cold stratification, the RWA data to be processed is stratified into hot and cold stratification, which solves the problem of insufficient accuracy of hot and cold stratification caused by being limited to a single fixed indicator in related technologies. The hot and cold stratification rules are constructed by combining the technical dimension basic indicators with the business dimension dynamic indicators, which optimizes the indicators; and by setting a score calculation function and a score basis function for each indicator to anchor the real changes in RWA data, the accuracy of hot and cold stratification is improved.

[0060] The steps described above are explained in detail below:

[0061] In some embodiments, the hot and cold stratification method for RWA data further includes the following steps:

[0062] The basic metrics are determined from a technical perspective; these metrics include data storage space usage, access frequency, and change frequency.

[0063] A basic indicator library is constructed from basic indicators.

[0064] Specifically, the basic metrics determined from the technical dimensions are those used in the traditional Web2 domain for data hot / cold stratification; a basic metric library is then built from these basic metrics. The basic metrics in this library include, but are not limited to: data storage space usage, access count, and change count; where access count includes cumulative access count and access count in the last N days; and change count refers to the number of changes in the last N days. This basic metric library can be categorized under the rule manager.

[0065] Details for each basic metric are as follows: Data storage space usage: The smaller the storage space usage, the higher the score. Cumulative access count: The more cumulative access counts, the higher the score. Access count in the last N days: The more access counts, the higher the score. Number of changes in the last N days: The more changes, the higher the score.

[0066] This embodiment utilizes the basic indicators in the basic indicator library to associate with technical dimensions, providing an accurate basis for the scoring calculation of data stratification.

[0067] In some embodiments, the hot and cold stratification method for RWA data further includes the step of:

[0068] Dynamic indicators are determined from a business perspective; these dynamic indicators include asset prices, asset collateral valuation volatility, asset trading frequency, asset liquidation status, asset type, and asset dividend status.

[0069] A dynamic indicator library is built from dynamic indicators.

[0070] Specifically, the business dimension refers to the association with the business characteristics of RWA data. Dynamic indicators determined by the business dimension can adapt to the diversity of RWA data types and the complexity of scenarios. A dynamic indicator library is then constructed from these dynamic indicators. The dynamic indicators in the library include, but are not limited to: asset collateral valuation volatility, asset trading frequency, asset liquidation status, asset type, and asset dividend status; among which, asset trading frequency can be the asset trading frequency over the most recent N days. This dynamic indicator library can be categorized under the rule manager.

[0071] Details of each dynamic indicator are as follows: Asset Price: The higher the RWA digital token price, the higher the score. Asset Collateral Valuation Volatility: The higher the volatility, the higher the score. Asset Trading Frequency in the Last N Days: The higher the frequency, the higher the score. Asset Liquidation Status: The lower the score for liquidated assets, the higher the score for unliquidated assets. Asset Type: The higher the score for high-risk assets (such as investment assets), the lower the score for low-risk assets (such as fixed assets). Asset Dividend Status: The higher the score during the dividend period, the lower the score outside the dividend period.

[0072] This embodiment utilizes dynamic indicators from the dynamic indicator library and associates them with business dimensions to adapt to the diversity of RWA data types and the complexity of scenarios, providing a basis for real changes in tiered scoring.

[0073] It should be noted that when setting basic and dynamic indicators, a score calculation function will be bound to each indicator. These score calculation functions include, but are not limited to, linear functions, piecewise functions, logarithmic functions, or time-decay-based functions; the form of the score calculation function is related to the type of indicator.

[0074] For example, for the metric "Number of visits in the last N days", the score calculation function is a piecewise function. Specifically: when the number of visits is 0, the score is 0.1; when the number of visits is between 1 and 10, the score is 0.1 + 0.3 × (number of visits / 10); when the number of visits is between 11 and 50, the score is 0.4 + 0.3 × ((number of visits - 10) / 40); when the number of visits exceeds 50, the score is 0.7 + 0.3 × min(1, (number of visits - 50) / 100).

[0075] For example, the score calculation function for the "Asset Price" indicator is a logarithmic function. Specifically: Score = 0.2 + 0.8 × log(1 + Price / Benchmark Price) / log(1 + Price Cap / Benchmark Price); where the benchmark price can be set according to the historical average price of the RWA asset. Through this logarithmic function, the higher the RWA asset price, the closer the "Asset Price" indicator score is to 1.

[0076] Examples of scoring functions for other indicators will not be repeated here.

[0077] In other embodiments, the dynamic indicator may also include legal risk. Details of this indicator are: the more complex the controlled jurisdiction, the higher the score.

[0078] Furthermore, the dynamic indicators in the dynamic indicator library are pluggable. The RWA data platform can add or disable a dynamic indicator based on different data types, asset risks, market conditions, and other attributes, thereby avoiding redundant indicator design.

[0079] In some of these embodiments, such as Figure 3 As shown, step S210 involves jointly constructing hot and cold stratification rules corresponding to each asset type based on preset basic indicators of the technical dimension and preset dynamic indicators of the business dimension, including the following steps:

[0080] Step S211: Set corresponding weight coefficients for the indicators in each original hot and cold stratification rule; the original hot and cold stratification rule corresponds to the asset type; the indicators in the original hot and cold stratification rule include the indicators in the basic indicators and the indicators in the dynamic indicators.

[0081] Step S212: Based on the basic indicators, dynamic indicators and corresponding weight coefficients, jointly construct the hot and cold stratification rules corresponding to each asset type.

[0082] Specifically, in addition to the basic indicator library and the dynamic indicator library, the rule manager also has a rule factory. The rule factory executes step S210, which is responsible for managing all the hot and cold stratification rules for RWA data. Each hot and cold stratification rule can be constructed by combining different basic indicators and dynamic indicators.

[0083] When jointly constructing rules, a weighting coefficient can be set for each indicator (basic and dynamic indicators) used in the original hot and cold stratification rules (rules before joint construction). Since the indicators in the hot and cold stratification rules corresponding to each asset type are pre-set, multiplying and summing the weighting coefficients of each indicator in the hot and cold stratification rules yields the corresponding hot and cold stratification rules. This ensures a strong correlation between the hot and cold stratification rules and the data of each asset in both technical and business dimensions. In other embodiments, hot and cold stratification rules can also be jointly constructed using other calculation methods or by combining empirical coefficients; there are no limitations on this.

[0084] This embodiment enables the joint construction of hot and cold stratification rules for each asset type from both technical and business perspectives, thereby enhancing the binding strength between hot and cold stratification and the real business risks and value changes of RWA assets.

[0085] Furthermore, the weights of different indicators within the same rule can follow the same dimension, allowing for subsequent normalization of different weight coefficients and simplifying the resources required for subsequent calculations. For example, the same dimension could be percentage usage, summing to 1. For instance, to construct a hot / cold stratification rule for high-risk assets, the following weight coefficients could be set: cumulative visits 25%, asset price 30%, asset collateral valuation volatility 25%, and asset liquidation status 20%. During subsequent normalization, this set of weights will be converted into standardized weights summing to 1: cumulative visits 0.25, asset price 0.3, asset collateral valuation volatility 0.25, and asset liquidation status 0.2, which will then be used to calculate the final hot / cold stratification score.

[0086] In some embodiments, the hot and cold stratification method for RWA data further includes the following steps:

[0087] The scoring criteria for the configuration scoring function are based on internal events of the RWA data platform, on-chain data sources, and off-chain data sources;

[0088] Internal events are generated by packaging the real-time operational status of the RWA data platform;

[0089] The on-chain data source consists of multiple smart contract data from multiple blockchains connected to the RWA data platform;

[0090] The off-chain data source is the off-chain real-world asset data from the RWA data platform.

[0091] In this embodiment, the scoring basis of the scoring calculation function can be configured in the data source manager, including but not limited to internal events of the RWA data platform, on-chain data sources, and off-chain data sources. These three sources are managed by the internal event bus, the on-chain data source connection pool, and the off-chain data source connection pool, respectively.

[0092] When registering a new metric, a corresponding score calculation function is bound to it, and a score basis function is also registered to retrieve the value of the score basis. Each score calculation function bound to a metric must perform calculations based on the value of that score basis. Therefore, before calculating the metric's score, the associated score basis function must be called to obtain the value of the score basis; then, this score basis value is substituted into the score calculation function to obtain the score calculation result; thus, the metric's score is obtained. For example, for the metric "Number of visits in the last N days," its associated score basis is "number of visits." To obtain the value of this score basis, a score basis function needs to be registered to retrieve the number of visits to a specific RWA data point pushed by the gateway module of the RWA data platform in the last N days. For the metric "Asset price," its score basis is the real-time on-chain price of the RWA token. To obtain the value of this real-time on-chain price, a score basis function needs to be defined to monitor and parse the market state of the RWA on-chain liquidity pool.

[0093] Specifically, within the RWA data platform, these three scoring criteria include, but are not limited to:

[0094] Internal events: These are generated by packaging the real-time operational status of various modules within the RWA data platform. Examples include the storage space usage of a specific RWA data point pushed by the RWA data platform's storage module, and the number of accesses to a specific RWA data point in the last N days pushed by the RWA data platform's gateway module.

[0095] On-chain data sources: These are contract data from the RWA data platform, which can originate from multiple smart contracts across multiple blockchains. The types of contract data primarily include RWA token transaction events, market status information from RWA on-chain liquidity pools, and RWA on-chain dividend and liquidation events. After parsing, this data can be used to calculate the aforementioned dynamic indicators, such as asset prices, asset liquidation status, and asset dividend status.

[0096] Off-chain data sources: These mainly refer to the off-chain real-world asset data of the RWA data platform. The sources of off-chain real-world asset data often vary significantly between different types of RWA data platforms. Examples include: asset metadata from real-world asset custodians, collateral valuation information from real-world asset exchanges, and legal documents and audit reports from compliant institutions. Similarly, after parsing, this data can be used to calculate the aforementioned dynamic indicators, such as asset collateral valuation volatility, asset type, and legal risk.

[0097] This embodiment addresses the limitations of traditional data platforms that rely solely on internal logs or a single data source to assess data hotness / coldness. By integrating three types of scoring criteria—internal events, on-chain data sources, and off-chain data sources—this embodiment simultaneously captures the real-world asset status and on-chain token status of RWA data, making the hot / coldness tiering mechanism comprehensive.

[0098] In some of these embodiments, such as Figure 4 As shown, step S220, which obtains the total cold and hot stratification score of the target cold and hot stratification rule based on the bound score calculation function and the associated scoring basis function, includes the following steps:

[0099] Step S221: Call the associated scoring basis function to obtain the scoring basis value from the internal events of the RWA data platform, on-chain data sources and off-chain data sources;

[0100] Step S222: Substitute the values ​​of the scoring criteria into the bound scoring calculation function to calculate the scores of each indicator in the target hot and cold stratification rule;

[0101] Step S223: Combine the scores of each indicator to obtain the total score of the target hot and cold stratification rule.

[0102] Before executing steps S221 to S223, based on the RWA data to be processed obtained from the RWA data platform, the corresponding target cold and hot stratification rules are loaded from the cold and hot stratification rules. The RWA data to be processed has an asset type. Based on the asset type of the RWA data to be processed, the corresponding target cold and hot stratification rules are loaded from each cold and hot stratification rule. Then, steps S221 to S223 are executed to complete the scoring. Specifically:

[0103] The associated scoring criteria function is invoked to obtain the values ​​of the scoring criteria associated with each indicator in the target hot and cold stratification rule (which can be one or more of the following: internal events of the RWA data platform, on-chain data sources, and off-chain data sources). The scoring criteria values ​​are obtained from the internal events of the RWA data platform, on-chain data sources, and off-chain data sources. These scoring criteria are also involved in the score calculation functions bound to each indicator. Therefore, substituting the values ​​of these scoring criteria into the score calculation functions yields the scores for each indicator in the target hot and cold stratification rule. The scores of each indicator are then summed to obtain the total hot and cold stratification score for the target hot and cold stratification rule.

[0104] For example: Suppose a financial RWA dataset needs to be stratified for hot / cold status. The dynamic scorer's execution process is as follows: First, load the corresponding high-risk RWA rule based on the RWA data (asset type: financial). This rule includes four indicators: cumulative visits, asset price, collateral valuation volatility, and asset liquidation status. Second, call the associated scoring basis function to obtain the scoring basis values ​​for each indicator from the RWA data platform: cumulative visits are 25, asset price is $120, collateral valuation volatility is 15%, and liquidation status is not liquidated. Then, substitute the above scoring basis values ​​into the scoring calculation function for each indicator to calculate the score: cumulative visits score is 0.55, asset price score is 0.75, volatility score is 0.8, and liquidation status score is 0.9. Finally, perform a weighted summation according to weights of 0.25, 0.3, 0.25, and 0.2 to obtain the final hot / cold status score of 0.715.

[0105] This embodiment achieves multi-dimensional evaluation by integrating on-chain and off-chain data, combining basic and dynamic indicators, and assigning weights to them. This gives the overall cold / hot stratification score the practical significance of representing RWA data as cold or hot data, and can be directly used to guide the cold / hot stratification of RWA data.

[0106] In some of these embodiments, the RWA data to be processed is stratified into hot and cold categories based on the overall hot and cold stratification score, including the following steps:

[0107] The total score for hot and cold stratification is compared with the preset stratification threshold to determine whether the RWA data to be processed belongs to hot data; the stratification threshold is determined by the remaining storage performance of the high-speed storage system.

[0108] Based on the comparison results, the RWA data to be processed will be stored or migrated between hot and cold layers.

[0109] Specifically, to further utilize the remaining storage performance of the high-speed storage system, the lower and upper limits of the tiering threshold are adjusted based on the remaining storage performance of the high-speed storage system. For example, if the lower limit of the tiering threshold is 0.3 and the upper limit is 0.7, then the tiering threshold is "0.3 ≤ tiering threshold ≤ 0.7". The more remaining storage performance of the high-speed storage system, the closer it is to 0.7; otherwise, it is closer to 0.3. That is, data below 0.3 is considered cold data; data above 0.7 is considered hot data; the range between 0.3 and 0.7 is determined by the remaining storage performance of the high-speed storage system and can dynamically change during storage to fully utilize storage performance and improve storage resource utilization. Here, "cold" and "hot" tiers refer to dividing the storage space of the high-speed storage system, with a portion of the storage space designated as a cold tier and a portion as a hot tier. In other embodiments, a low-speed storage system can also be set as a cold tier; this is not a limitation.

[0110] After determining the stratification threshold, the total score of hot and cold stratification is compared with the preset stratification threshold to determine whether the RWA data to be processed belongs to hot data.

[0111] For example: if the stratification threshold is 0.6 and the total score for hot and cold stratification is 0.715, then the RWA data to be processed corresponding to this total score is hot data, and the RWA data to be processed is stored in the hot stratum. If the total score for hot and cold stratification is 0.215, then the RWA data to be processed corresponding to this total score is cold data, and the RWA data to be processed is stored in the cold stratum.

[0112] Furthermore, before storage, it can be determined whether the RWA data to be processed is RWA incremental data or RWA stock data; if it is RWA stock data, it is decided whether to store the RWA incremental data directly to the cold layer or the hot layer; if it is RWA incremental data, it is decided whether to migrate the RWA stock data between the cold and hot layers.

[0113] This embodiment can match the remaining storage capacity of the high-speed storage system to complete the partitioned storage of RWA data to be processed, thereby improving the management value of hot data.

[0114] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0115] This embodiment also provides a hot and cold stratification system, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that perform a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0116] Figure 5 This is a structural block diagram of the hot and cold stratification system in this embodiment, as shown below. Figure 5 As shown, the system includes an RWA data platform and a rules engine;

[0117] The RWA data platform is used to provide RWA data to be processed.

[0118] The rules engine is used to jointly construct hot and cold stratification rules corresponding to each asset type based on preset technical dimension basic indicators and preset business dimension dynamic indicators. Each indicator in the basic indicators and dynamic indicators is bound to a corresponding score calculation function and registered with a score basis function for obtaining the value of the score basis.

[0119] Based on the RWA data to be processed obtained from the RWA data platform, the corresponding target hot and cold stratification rules are loaded from the hot and cold stratification rules; according to the bound score calculation function and the associated score basis function, the total hot and cold stratification score of the target hot and cold stratification rule is obtained.

[0120] Based on the overall score for hot and cold stratification, the RWA data to be processed is stratified into hot and cold stratifications.

[0121] Specifically, the RWA data platform includes storage modules, gateway modules, monitoring modules, and data acquisition modules. The rules engine includes a rules manager, a data source manager, and a dynamic scorer.

[0122] Based on the above hot and cold stratification system, the process for processing the RWA data to be processed is as follows:

[0123] Data Preprocessing Stage: When the RWA data platform's data acquisition module collects incremental RWA data, or when the monitoring module scans existing RWA data from the storage layer, the RWA data platform preprocesses the current RWA data to be processed, generating or parsing metadata about the data, including but not limited to RWA data type, asset information, source chain, and associated contract information. The RWA data to be processed and its metadata are then distributed to the rule engine for further processing.

[0124] In the hot and cold stratification total score calculation stage: the rule engine processes the metadata of the RWA data to be processed, loads the corresponding target hot and cold stratification rule from the hot and cold stratification rules; and obtains the total hot and cold stratification score of the target hot and cold stratification rule based on the bound score calculation function and the associated score basis function.

[0125] Cold / Hot Tiling Decision and Execution Phase: The storage module, based on the total cold / hot tier score of the processed RWA data and the tiering threshold determined by the remaining storage performance of the high-speed storage system, decides whether to directly store the incremental RWA data in the cold tier or the hot tier, and whether to migrate the existing RWA data between the cold and hot tiers.

[0126] This embodiment addresses the limitations of traditional platforms where hot and cold data stratification logic is fixed and lacks flexibility. Instead, a dynamic scorer is designed to automatically load indicators, obtain scoring criteria, calculate weighted scores, and dynamically output popularity results based on different rules. This achieves programmability and automation of the hot and cold data stratification logic, enhancing the scalability of the RWA data platform when processing new types of RWA data.

[0127] In some embodiments, the rule manager in the rule engine is also used to determine basic metrics from a technical perspective; these basic metrics include data storage space usage, access count, and change count.

[0128] A basic indicator library is constructed from basic indicators.

[0129] In some of these embodiments, the rule manager in the rule engine is also used to determine dynamic indicators from a business perspective; dynamic indicators include asset price, asset collateral valuation volatility, asset trading frequency, asset liquidation status, asset type, and asset dividend status.

[0130] A dynamic indicator library is built from dynamic indicators.

[0131] In some embodiments, the rule manager in the rule engine is also used to set corresponding weight coefficients for the indicators in each original hot and cold stratification rule; the original hot and cold stratification rule corresponds to the asset type; the indicators in the original hot and cold stratification rule include indicators in the basic indicators and dynamic indicators.

[0132] Based on basic indicators, dynamic indicators, and corresponding weighting coefficients, a hot and cold stratification rule corresponding to each asset type is jointly constructed.

[0133] In some of these embodiments, the data source manager in the rules engine is used to configure the scoring basis of the scoring calculation function to be internal events of the RWA data platform, on-chain data sources, and off-chain data sources;

[0134] Internal events are generated by packaging the real-time operational status of the RWA data platform;

[0135] The on-chain data source consists of multiple smart contract data from multiple blockchains connected to the RWA data platform;

[0136] The off-chain data source is the off-chain real-world asset data from the RWA data platform.

[0137] In some of these embodiments, the dynamic scorer in the rules engine is also used to call associated scoring basis functions to obtain the values ​​of the scoring basis from internal events of the RWA data platform, on-chain data sources, and off-chain data sources.

[0138] Substitute the values ​​of the scoring criteria into the bound scoring calculation function to calculate the scores of each indicator in the target hot and cold stratification rule;

[0139] By combining the scores of each indicator, the overall score for the target hot and cold stratification rule is obtained.

[0140] This embodiment addresses the limitations of traditional platforms where hot and cold data stratification logic is fixed and lacks flexibility. The dynamic scorer in this embodiment can automatically load indicators, obtain scoring criteria, calculate weighted scores, and dynamically output popularity results according to different rules. This achieves programmability and automation of hot and cold data stratification logic, enhancing the scalability of the RWA data platform when processing new types of RWA data.

[0141] In some embodiments, the storage module is further configured to compare the total score of hot and cold stratification with a preset stratification threshold to determine whether the RWA data to be processed belongs to hot data; the stratification threshold is determined by the remaining storage performance of the high-speed storage system.

[0142] Based on the comparison results, the RWA data to be processed is stored between the hot and cold layers.

[0143] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0144] This embodiment also provides a computer 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.

[0145] Optionally, the computer 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.

[0146] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0147] S1, based on the preset basic indicators of the technical dimension and the preset dynamic indicators of the business dimension, jointly constructs the hot and cold stratification rules corresponding to each preset asset type; each indicator in the basic indicators and dynamic indicators is bound to a corresponding score calculation function, and a score basis function is registered to obtain the value of the score basis.

[0148] S2, based on the RWA data to be processed obtained from the RWA data platform, load the corresponding target hot and cold stratification rules from the hot and cold stratification rules; according to the bound score calculation function and the associated score basis function, obtain the total hot and cold stratification score of the target hot and cold stratification rule;

[0149] S3, based on the overall score of hot and cold stratification, performs hot and cold stratification on the RWA data to be processed.

[0150] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0151] Furthermore, in conjunction with the hot and cold data stratification method for RWA provided in the above embodiments, this embodiment can also provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the hot and cold data stratification methods for RWA provided in the above embodiments.

[0152] It should be noted that all information and data involved in this application are authorized by the user or fully authorized by all parties and will be used legally.

[0153] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0154] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0155] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0156] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for hot and cold stratification of RWA data, characterized in that, include: Based on the preset basic indicators of the technical dimension and the preset dynamic indicators of the business dimension, a hot and cold stratification rule corresponding to each preset asset type is jointly constructed; each indicator in the basic indicators and the dynamic indicators is bound to a corresponding score calculation function, and a score basis function is registered to obtain the value of the score basis. Based on the RWA data to be processed obtained from the RWA data platform, the corresponding target cold and hot stratification rule is loaded from the cold and hot stratification rule; according to the bound score calculation function and the associated scoring basis function, the total cold and hot stratification score of the target cold and hot stratification rule is obtained. Based on the overall score for hot and cold stratification, the RWA data to be processed is stratified into hot and cold stratifications.

2. The method for hot and cold stratification of RWA data according to claim 1, characterized in that, The method further includes: The basic metrics are determined from a technical perspective; these basic metrics include data storage space usage, access frequency, and change frequency. A basic indicator library is constructed based on the aforementioned basic indicators.

3. The method for hot and cold stratification of RWA data according to claim 1, characterized in that, The method further includes: The dynamic indicators are determined by the aforementioned business dimensions; the dynamic indicators include asset price, asset collateral valuation volatility, asset trading frequency, asset liquidation status, asset type, and asset dividend status. A dynamic indicator library is constructed from the aforementioned dynamic indicators.

4. The method for hot and cold stratification of RWA data according to any one of claims 1 to 3, characterized in that, The process involves jointly constructing hot and cold stratification rules corresponding to each asset type based on preset technical dimension basic indicators and preset business dimension dynamic indicators, including: Set corresponding weight coefficients for the indicators in each original hot and cold stratification rule; the original hot and cold stratification rule corresponds to the asset type; the indicators in the original hot and cold stratification rule include basic indicators and indicators in the dynamic indicators; Based on the basic indicators, the dynamic indicators, and the corresponding weighting coefficients, a hot and cold stratification rule corresponding to each of the asset types is jointly constructed.

5. The method for hot and cold stratification of RWA data according to claim 4, characterized in that, The method further includes: The scoring criteria for the configured scoring calculation function are based on internal events of the RWA data platform, on-chain data sources, and off-chain data sources. The internal events are generated by packaging the real-time operating status of the RWA data platform; The on-chain data source consists of multiple smart contract data from multiple blockchains connected to the RWA data platform; The off-chain data source is the off-chain real-world asset data of the RWA data platform.

6. The method for hot and cold stratification of RWA data according to claim 4, characterized in that, The step of obtaining the total cold and hot stratification score of the target cold and hot stratification rule based on the bound scoring calculation function and the associated scoring basis function includes: The associated scoring criteria function is invoked to obtain the value of the scoring criteria from internal events of the RWA data platform, on-chain data sources, and off-chain data sources; Substitute the value of the scoring basis into the bound scoring calculation function to calculate the score of each indicator in the target hot and cold stratification rule; The overall score for the target hot and cold stratification rule is obtained by combining the scores of each indicator.

7. The method for hot and cold stratification of RWA data according to claim 4, characterized in that, The process of stratifying the RWA data to be processed into hot and cold categories based on the overall hot and cold stratification score includes: The total score for hot and cold data stratification is compared with a preset stratification threshold to determine whether the RWA data to be processed belongs to hot data; the stratification threshold is determined by the remaining storage performance of the high-speed storage system. Based on the comparison results, the RWA data to be processed is stored or migrated between hot and cold layers.

8. A hot and cold stratification system, characterized in that, Including the RWA data platform and rules engine; The RWA data platform is used to provide RWA data to be processed. The rule engine is used to jointly construct hot and cold stratification rules corresponding to each preset asset type based on preset basic indicators of technical dimensions and preset dynamic indicators of business dimensions; each indicator in the basic indicators and the dynamic indicators is bound to a corresponding score calculation function and registered with a score basis function for obtaining the value of the score basis. Based on the RWA data to be processed obtained from the RWA data platform, the corresponding target cold and hot stratification rule is loaded from the cold and hot stratification rule; according to the bound score calculation function and the associated scoring basis function, the total cold and hot stratification score of the target cold and hot stratification rule is obtained. Based on the overall score for hot and cold stratification, the RWA data to be processed is stratified into hot and cold stratifications.

9. A computer 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 steps of the hot and cold stratification method for RWA data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the hot and cold stratification method for RWA data as described in any one of claims 1 to 7.

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