A blockchain-based digital asset processing method

Abstract

The application discloses a kind of digital asset processing methods based on blockchain, it is related to blockchain technical field, including the following steps: S0, event listening acquisition module is deployed at the signal transceiver port and observation interface of on-chain node cluster;P2P communication listening acquisition module is deployed in the node middle section corresponding to the level close to data entrance and exit side;Fund flow impulse acquisition module is deployed on settlement channel;S1, each acquisition module works and collects asset risk signal, each acquisition module exports acquisition result with initial acquisition frequency and sampling granularity, and whether asset risk phenomenon occurs is judged by corresponding analysis module, S2, when some acquisition module detects that some form of risk occurs, the acquisition frequency and sampling granularity of other types of acquisition module closest to the topological position of current acquisition module are adjusted, the application has the characteristics of fine identification.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, specifically to a blockchain-based digital asset processing method. Background Technology

[0002] With the rapid growth of digital assets, the blockchain system is taking on more and more value recording and state verification functions in clearing, settlement, and cross-chain transmission. However, in existing technologies, abnormal fluctuations or risk states of digital assets usually rely on single-dimensional monitoring methods, such as statistics based solely on transaction frequency or contract call volume. This lack of synchronous correlation analysis of multi-source on-chain and off-chain information leads to insufficient comprehensiveness and real-time performance in risk detection.

[0003] Currently, mainstream asset monitoring platforms generally collect data using fixed sampling frequencies and uniform sampling granularity, resulting in delayed responses to complex risk phenomena such as sudden trading spikes, abnormal arbitrage flows, and oracle biases. Because different risk events vary significantly in time scale and data density, using too low a sampling frequency can easily miss short-term, high-frequency trading anomalies; while using too high a sampling frequency leads to wasted computing resources and on-chain data redundancy. Furthermore, existing systems lack a coordinated adjustment mechanism between sampling modules, making it impossible to dynamically adjust the sampling strategies of other related modules based on sudden anomalies from a particular data source, hindering the formation of a unified time benchmark and intensity scale among multi-source information. Therefore, designing a refined identification method for blockchain-based digital asset processing is essential. Summary of the Invention

[0004] The purpose of this invention is to provide a blockchain-based digital asset processing method to solve the problems mentioned in the background section.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a blockchain-based digital asset processing method, comprising the following steps:

[0006] S0. Deploy event monitoring and acquisition modules at the signal transmission and reception ports and observation interfaces of the on-chain node cluster; deploy P2P communication monitoring and acquisition modules near the data entry and exit points and in the middle of the nodes corresponding to their levels; deploy fund flow pulse acquisition modules on the settlement channel; deploy metadata consistency acquisition modules in the internal data layer of the on-chain node cluster.

[0007] S1. Each acquisition module works and acquires asset risk signals. Each acquisition module outputs acquisition results with the initial acquisition frequency and sampling granularity, and the corresponding analysis module determines whether an asset risk phenomenon has occurred.

[0008] S2. When a certain acquisition module detects a certain form of risk, the acquisition frequency and sampling granularity of other acquisition modules that are closest to the current acquisition module in topology are adjusted so that the acquisition modules that need to be adjusted are consistent with the current acquisition module in terms of acquisition frequency and sampling granularity.

[0009] S3. As time goes on, the collection frequency and sampling granularity of the collection modules that need to be adjusted are dynamically adjusted based on whether the same asset risk signals continue to be collected.

[0010] S4. When the acquisition frequency and sampling granularity of each acquisition module are not uniform, the data alignment processing module is used to process the acquired data and normalize the acquisition frequency and sampling granularity.

[0011] S5. Based on the metadata consistency analysis results interpreted from on-chain metadata, the form of asset risk is determined.

[0012] According to the above technical solution, in S0, it is specified which collection method corresponds to each type of risk:

[0013] S0-1, the transaction pulse risk of short-term high-frequency calls and abnormal repeated transactions is judged primarily by contract event monitoring, with P2P communication monitoring and fund flow pulse collection as auxiliary judgment methods.

[0014] S0-2, liquidity spike risks such as concentrated instantaneous deposits and withdrawals, abnormal arbitrage flows, and large short-term transfers are identified by using fund flow pulse collection as the primary judgment method and contract event monitoring as an auxiliary judgment method.

[0015] For the propagation and diffusion risks of S0-3, broadcast storm, and aggressive diffusion, P2P communication monitoring is used as the primary judgment method, while fund flow pulse collection and contract event monitoring are used as auxiliary judgment methods.

[0016] S0-4, off-chain dependent consistency risks such as oracle bias and content hash drift are addressed by using metadata consistency collection as the primary judgment method and contract event listening as a secondary judgment method.

[0017] According to the above technical solution, the unification of sampling frequency and sampling granularity in S2 specifically involves:

[0018] S2-1. Each data collection module, by default, collects the risk form corresponding to its primary judgment method, and at its default collection frequency. and sampling granularity Output the collected results, where The number of data collection modules is determined by the sampling granularity, which determines whether the system collects data once for each block, once for each transaction, or once for each log event. The finer the granularity, the more sensitive the detection; the coarser the granularity, the lighter the calculation, but it may miss instantaneous changes.

[0019] S2-2, Order No. Each acquisition module collects asset risk signals, and its default acquisition frequency is [missing information]. The default sampling granularity is The initial probability of this type of asset risk signal occurring is... At this point, it is necessary to... The acquisition frequency and sampling granularity of each acquisition module were adjusted. The default acquisition frequency and sampling granularity before the adjustment were as follows: and This allows for the adjustment of the sampling frequency. Adjusted sampling granularity .

[0020] According to the above technical solution, the dynamic adjustment in S3 specifically includes:

[0021] S3-1, Order No. Each acquisition module maintains the acquisition frequency. and sampling granularity continued During the time period, if Within the time period When the acquisition module collects the same type of asset risk signal again, observe the... If each acquisition module simultaneously acquires the same type of asset risk signal, and if so, the probability of this type of asset risk signal occurring is increased, with the adjusted probability being... ,in For the first When the first acquisition module is used as an auxiliary judgment method, it is related to the first... The statistical gain factor resulting from each acquisition module acquiring the same form of asset risk signal, if the first... If each acquisition module fails to simultaneously acquire the same type of asset risk signal, then... ;

[0022] S3-2, If in Within the time period When the first acquisition module fails to acquire the same type of asset risk signal, the second... The acquisition frequency and sampling granularity of each acquisition module are determined by... and To its initial value and Gradually recovering, making the current distance The elapsed time at the end of the time period is Then the sampling frequency at this time and sampling granularity The calculation formulas are as follows: when hour, ,when hour, ,when hour, ,when hour, ,in , This is the time conversion factor.

[0023] According to the above technical solution, in step S4, the normalization processing of the sampling frequency and sampling granularity specifically involves:

[0024] S4-1, Firstly in When the time period ends, the first The and the first The first sampling time of each acquisition module is aligned, at which point both acquisition modules sample simultaneously. The next sampling will only collect data that meets the criteria. and Data collected at the least common multiple of the time points; other data are not collected.

[0025] S4-2, the first The and the first The maximum and minimum values ​​of the data collected by each acquisition module are mapped to... Within the specified range, the dimensional differences in sampling granularity between different acquisition modules are eliminated.

[0026] According to the above technical solution, in S5, the determination of the form of asset risk specifically involves: before each acquisition module acquires an asset risk phenomenon, the metadata consistency acquisition module outputs various consistency deviation indicators as follows: ,in This refers to the number of consistency deviation indicators involved in asset risk. When a certain form of asset risk is detected, if this form of risk leads to... Changes have occurred, and Actual collection The probability of this form of asset risk occurring depends on the consistency deviation. ,in This is the consistency deviation impact coefficient; if no data was collected... Changes in consistency deviation Combined with S3-1 The final probability is calculated when hour, The threshold for probability judgment is used to determine if this type of risk is valid and an early warning is required.

[0027] According to the above technical solution, the system used in this method includes a signal acquisition module, a signal processing module, and a risk determination module. The signal acquisition module is used to collect contract event streams, P2P propagation indicators, and high-frequency deposit and withdrawal risk signals, and to collect data from multiple dimensions by combining consistency deviation indicators in metadata. The signal processing module is used to perform unified time benchmark and intensity normalization processing on the acquisition frequency and sampling granularity, dynamically adjust the characteristics of the two output parameters based on the subsequent acquisition results, and perform multi-source data fusion processing. The risk determination module is used to comprehensively determine the form of asset risk based on the processed data.

[0028] According to the above technical solution, the signal acquisition module includes a contract event monitoring acquisition module, a P2P communication monitoring acquisition module, a fund flow pulse acquisition module, a metadata consistency acquisition module, a contract event flow analysis module, a P2P propagation indicator analysis module, a high-frequency deposit and withdrawal analysis module, and a metadata consistency analysis module. The contract event monitoring acquisition module, the P2P communication monitoring acquisition module, and the fund flow pulse acquisition module are respectively used to collect contract event flows, P2P propagation indicators, and high-frequency deposit and withdrawal risk signals. The metadata consistency acquisition module is used to collect metadata consistency detected in on-chain metadata. The contract event flow analysis module, the P2P propagation indicator analysis module, and the high-frequency deposit and withdrawal analysis module are respectively used to analyze risk signals. The metadata consistency analysis module is used to analyze the consistency of various types of metadata.

[0029] The signal processing module includes a sampling frequency adjustment module, a sampling granularity adjustment module, a data alignment processing module, a risk form correspondence module, and a timing module. The timing module is used to count the duration after a certain risk form occurs. The sampling frequency adjustment module and the sampling granularity adjustment module are used to adjust the sampling frequency and sampling granularity of each sampling module, respectively. The data alignment processing module is used to normalize the sampling frequency and sampling granularity of each sampling module.

[0030] The risk assessment module includes a metadata consistency assessment module and a risk form assessment module. The metadata consistency assessment module is used to analyze the metadata consistency changes detected on the chain to assist in the assessment of the risk form, and the risk form assessment module is used to determine the risk form with the highest probability.

[0031] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: By deploying event monitoring and acquisition modules, P2P communication monitoring and acquisition modules, fund flow pulse acquisition modules, and metadata consistency acquisition modules in the node cluster, settlement channel, and data layer respectively, this invention constructs a multi-dimensional data acquisition system that can simultaneously capture multiple risk signals such as transaction behavior, propagation topology, fund flow, and metadata status. Compared with traditional single-indicator monitoring methods, this invention significantly improves the comprehensiveness of asset anomaly identification. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0033] Figure 1 This is a schematic diagram of the overall modular structure of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see Figure 1 This invention provides a technical solution: a blockchain-based digital asset processing method, comprising the following steps:

[0036] S0. Deploy event monitoring and acquisition modules at the signal transmission and reception ports and observation interfaces of the on-chain node cluster; deploy P2P communication monitoring and acquisition modules near the data entry and exit points and in the middle of the nodes corresponding to their levels; deploy fund flow pulse acquisition modules on the settlement channel; deploy metadata consistency acquisition modules in the internal data layer of the on-chain node cluster.

[0037] S1. Each acquisition module works and acquires asset risk signals. Each acquisition module outputs acquisition results with the initial acquisition frequency and sampling granularity, and the corresponding analysis module determines whether an asset risk phenomenon has occurred.

[0038] S2. When a certain acquisition module detects a certain form of risk, the acquisition frequency and sampling granularity of other acquisition modules that are closest to the current acquisition module in topology are adjusted so that the acquisition modules that need to be adjusted are consistent with the current acquisition module in terms of acquisition frequency and sampling granularity.

[0039] S3. As time goes on, the collection frequency and sampling granularity of the collection modules that need to be adjusted are dynamically adjusted based on whether the same asset risk signals continue to be collected.

[0040] S4. When the acquisition frequency and sampling granularity of each acquisition module are not uniform, the data alignment processing module is used to process the acquired data and normalize the acquisition frequency and sampling granularity.

[0041] S5. Based on the metadata consistency analysis results interpreted from on-chain metadata, determine the form of asset risk;

[0042] In S0, it is clearly stated which data collection method corresponds to each type of risk:

[0043] S0-1, the transaction pulse risk of short-term high-frequency calls and abnormal repeated transactions is judged primarily by contract event monitoring, with P2P communication monitoring and fund flow pulse collection as auxiliary judgment methods.

[0044] S0-2, liquidity spike risks such as concentrated instantaneous deposits and withdrawals, abnormal arbitrage flows, and large short-term transfers are identified by using fund flow pulse collection as the primary judgment method and contract event monitoring as an auxiliary judgment method.

[0045] For the propagation and diffusion risks of S0-3, broadcast storm, and aggressive diffusion, P2P communication monitoring is used as the primary judgment method, while fund flow pulse collection and contract event monitoring are used as auxiliary judgment methods.

[0046] S0-4, off-chain dependent consistency risks such as oracle bias and content hash drift are assessed by using metadata consistency collection as the primary judgment method and contract event listening as the secondary judgment method.

[0047] In S2, the unification of sampling frequency and sampling granularity is specifically as follows:

[0048] S2-1. Each data collection module, by default, collects the risk form corresponding to its primary judgment method, and at its default collection frequency. and sampling granularity Output the collected results, where The number of data collection modules is determined by the sampling granularity, which determines whether the system collects data once for each block, once for each transaction, or once for each log event. The finer the granularity, the more sensitive the detection; the coarser the granularity, the lighter the calculation, but it may miss instantaneous changes.

[0049] S2-2, Order No. Each acquisition module collects asset risk signals, and its default acquisition frequency is [missing information]. The default sampling granularity is The initial probability of this type of asset risk signal occurring is... At this point, it is necessary to... The acquisition frequency and sampling granularity of each acquisition module were adjusted. The default acquisition frequency and sampling granularity before the adjustment were as follows: and This allows for the adjustment of the sampling frequency. Adjusted sampling granularity ;

[0050] In S3, the dynamic adjustment is specifically as follows:

[0051] S3-1, Order No. Each acquisition module maintains the acquisition frequency. and sampling granularity continued During the time period, if Within the time period When the acquisition module collects the same type of asset risk signal again, observe the... If each acquisition module simultaneously acquires the same type of asset risk signal, and if so, the probability of this type of asset risk signal occurring is increased, with the adjusted probability being... ,in For the first When the first acquisition module is used as an auxiliary judgment method, it is related to the first... The statistical gain factor resulting from each acquisition module acquiring the same form of asset risk signal, if the first... If each acquisition module fails to simultaneously acquire the same type of asset risk signal, then... ;

[0052] S3-2, If in Within the time period When the first acquisition module fails to acquire the same type of asset risk signal, the second... The acquisition frequency and sampling granularity of each acquisition module are determined by... and To its initial value and Gradually recovering, making the current distance The elapsed time at the end of the time period is Then the sampling frequency at this time and sampling granularity The calculation formulas are as follows: when hour, ,when hour, ,when hour, ,when hour, ,in , This is a time conversion factor;

[0053] In S4, the normalization of the sampling frequency and sampling granularity is performed as follows:

[0054] S4-1, Firstly in When the time period ends, the first The and the first The first sampling time of each acquisition module is aligned, at which point both acquisition modules sample simultaneously. The next sampling will only collect data that meets the criteria. and Data collected at the least common multiple of the time points; other data are not collected.

[0055] S4-2, the first The and the first The maximum and minimum values ​​of the data collected by each acquisition module are mapped to... Within the range, eliminate the dimensional differences in sampling granularity between different acquisition modules;

[0056] In S5, the determination of the form of asset risk is specifically as follows: Before each asset risk phenomenon is collected, the metadata consistency collection module outputs various consistency deviation indicators as follows: ,in This refers to the number of consistency deviation indicators involved in asset risk. When a certain form of asset risk is detected, if this form of risk leads to... Changes have occurred, and Actual collection The probability of this form of asset risk occurring depends on the consistency deviation. ,in This is the consistency deviation impact coefficient; if no data was collected... Changes in consistency deviation Combined with S3-1 The final probability is calculated when hour, The probability threshold is used to determine if this type of risk is valid and an early warning is required.

[0057] The system used in this method includes a signal acquisition module, a signal processing module, and a risk assessment module. The signal acquisition module is used to collect contract event streams, P2P propagation indicators, and high-frequency deposit and withdrawal risk signals, and to collect data from multiple dimensions by combining consistency deviation indicators in metadata. The signal processing module is used to perform unified time benchmark and intensity normalization processing on the acquisition frequency and sampling granularity, dynamically adjust the characteristics of the two output parameters based on the subsequent acquisition results, and perform multi-source data fusion processing. The risk assessment module is used to comprehensively determine the form of asset risk based on the processed data.

[0058] The signal acquisition module includes a contract event monitoring acquisition module, a P2P communication monitoring acquisition module, a fund flow pulse acquisition module, a metadata consistency acquisition module, a contract event flow analysis module, a P2P propagation indicator analysis module, a high-frequency deposit and withdrawal analysis module, and a metadata consistency analysis module. The contract event monitoring acquisition module, the P2P communication monitoring acquisition module, and the fund flow pulse acquisition module are used to collect contract event flows, P2P propagation indicators, and high-frequency deposit and withdrawal risk signals, respectively. The metadata consistency acquisition module is used to collect metadata consistency detected in on-chain metadata. The contract event flow analysis module, the P2P propagation indicator analysis module, and the high-frequency deposit and withdrawal analysis module are used to analyze risk signals, respectively. The metadata consistency analysis module is used to analyze the consistency of various types of metadata.

[0059] The signal processing module includes a sampling frequency adjustment module, a sampling granularity adjustment module, a data alignment processing module, a risk form correspondence module, and a timing module. The timing module is used to count the duration after a certain risk form occurs. The sampling frequency adjustment module and the sampling granularity adjustment module are used to adjust the sampling frequency and sampling granularity of each sampling module, respectively. The data alignment processing module is used to normalize the sampling frequency and sampling granularity of each sampling module.

[0060] The risk assessment module includes a metadata consistency assessment module and a risk form assessment module. The metadata consistency assessment module is used to analyze the metadata consistency changes detected on the chain to assist in the assessment of the risk form, while the risk form assessment module is used to determine the risk form with the highest probability.

[0061] A dynamic parameter tuning model is introduced between the acquisition frequency and the sampling granularity. When any acquisition module detects a sudden risk signal, it can adaptively adjust the sampling rate and resolution level of other modules to ensure that all types of data remain consistent in the spatiotemporal dimension, thereby improving the response capability to short-term high-frequency events. This allows the system to maintain processing efficiency under high load conditions and increase sampling density when risks occur, balancing real-time performance and computational cost.

[0062] A dynamic confidence correction model was established during the risk assessment process. When the auxiliary acquisition module and the main acquisition module detect the same type of risk signal at the same time, the system automatically increases the confidence of the risk event, and vice versa. This achieves self-learning judgment based on statistical gain, thereby improving the accuracy and stability of risk identification.

[0063] S4-1. When the detection node is located in the topology of the risk event source, only the case where the number of risk occurrences is within the normal range is considered. As the number of risk occurrences accumulates, the risk event source, due to repeated data calls and continued activity, will be transmitted to the detection node's detection priority. With detection cycle Number of times the current risk event source has been triggered Proportional, that is ,in To trigger an increase in the conversion factor for activity level, High detection nodes allow for higher sampling frequency and granularity to detect all risks;

[0064] Neighboring nodes often share dependencies such as entry points, gateways, bridges, and oracles. If a node malfunctions, its nearest neighbors are most likely to encounter the same source of the malfunction. Prioritizing monitoring can cut off the same-source diffusion chain. Attackers often penetrate laterally along the shortest path in the topology. S4-2: When the detection node is located in another location, it is based on the topological coordinates of the current detection node. And the location coordinates of the nearest risk event source to the current detection node. Calculate the distance between the current detection node and the nearest risk event source in the topological coordinate system. ,get ,in This is the attenuation coefficient between priority and topological distance.

[0065] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0066] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A blockchain-based digital asset processing method, characterized in that: Includes the following steps: S0. Deploy event listening and acquisition modules at the signal transmission and reception ports and observation interfaces of the on-chain node cluster; Deploy P2P communication monitoring and acquisition modules near the data entry and exit points and in the middle of the nodes corresponding to their levels; deploy fund flow pulse acquisition modules on the settlement channel; and deploy metadata consistency acquisition modules in the internal data layer of the on-chain node cluster. S1. Each acquisition module works and acquires asset risk signals. Each acquisition module outputs acquisition results with the initial acquisition frequency and sampling granularity, and the corresponding analysis module determines whether an asset risk phenomenon has occurred. S2. When a certain acquisition module detects a certain form of risk, the acquisition frequency and sampling granularity of other acquisition modules that are closest to the current acquisition module in topology are adjusted so that the acquisition modules that need to be adjusted are consistent with the current acquisition module in terms of acquisition frequency and sampling granularity. S3. As time goes on, the collection frequency and sampling granularity of the collection modules that need to be adjusted will be dynamically adjusted based on whether the same asset risk signals continue to be collected. S4. When the acquisition frequency and sampling granularity of each acquisition module are not uniform, the data alignment processing module is used to process the acquired data and normalize the acquisition frequency and sampling granularity. S5. Based on the metadata consistency analysis results interpreted from on-chain metadata, determine the form of asset risk; In S3, the dynamic adjustment specifically refers to: S3-1, Order No. Each acquisition module maintains the acquisition frequency. and sampling granularity continued During the time period, if Within the time period When the acquisition module collects the same type of asset risk signal again, observe the... If each acquisition module simultaneously acquires the same type of asset risk signal, and if so, the probability of this type of asset risk signal occurring is increased, with the adjusted probability being... ,in For the first When the first acquisition module is used as an auxiliary judgment method, it is related to the first... The statistical gain factor resulting from each acquisition module acquiring the same form of asset risk signal, if the first... If each acquisition module fails to simultaneously acquire the same type of asset risk signal, then... ; S3-2, If in Within the time period When the first acquisition module fails to acquire the same type of asset risk signal, the second... The acquisition frequency and sampling granularity of each acquisition module are determined by... and To its initial value and Gradually recovering, making the current distance The elapsed time at the end of the time period is Then the sampling frequency at this time and sampling granularity The calculation formulas are as follows: when hour, ,when hour, ,when hour, ,when hour, ,in , This is the time conversion factor.

2. The method for processing digital assets based on blockchain according to claim 1, characterized in that: In S0, it is specified which collection method corresponds to each type of risk: S0-1, the transaction pulse risk of short-term high-frequency calls and abnormal repeated transactions is judged primarily by contract event monitoring, with P2P communication monitoring and fund flow pulse collection as auxiliary judgment methods. S0-2, liquidity spike risks such as concentrated instantaneous deposits and withdrawals, abnormal arbitrage flows, and large short-term transfers are identified by using fund flow pulse collection as the primary judgment method and contract event monitoring as an auxiliary judgment method. For the propagation and diffusion risks of S0-3, broadcast storm, and aggressive diffusion, P2P communication monitoring is used as the primary judgment method, while fund flow pulse collection and contract event monitoring are used as auxiliary judgment methods. S0-4, off-chain dependent consistency risks such as oracle bias and content hash drift are addressed by using metadata consistency collection as the primary judgment method and contract event listening as a secondary judgment method.

3. The blockchain-based digital asset processing method according to claim 2, characterized in that: In S2, the unification of sampling frequency and sampling granularity specifically involves: S2-1. Each data collection module, by default, collects the risk form corresponding to its primary judgment method, and at its default collection frequency. and sampling granularity Output the collected results, where The number of data collection modules is determined by the sampling granularity, which determines whether the system collects data once for each block, once for each transaction, or once for each log event. Coarser granularity results in lighter computation but may miss instantaneous changes. S2-2, Order No. Each acquisition module collects asset risk signals, and its default acquisition frequency is [missing information]. The default sampling granularity is The initial probability of this type of asset risk signal occurring is... At this point, it is necessary to... The acquisition frequency and sampling granularity of each acquisition module were adjusted. The default acquisition frequency and sampling granularity before the adjustment were as follows: and This allows for the adjustment of the sampling frequency. Adjusted sampling granularity .

4. The blockchain-based digital asset processing method according to claim 3, characterized in that: In step S4, the normalization processing of the sampling frequency and sampling granularity is specifically performed as follows: S4-1, Firstly in When the time period ends, the first The and the first The first sampling time of each acquisition module is aligned, at which point both acquisition modules sample simultaneously. The next sampling will only collect data that meets the criteria. and Data collected at the least common multiple of the time points; other data are not collected. S4-2, the first The and the first The maximum and minimum values ​​of the data collected by each acquisition module are mapped to... Within the specified range, the dimensional differences in sampling granularity between different acquisition modules are eliminated.

5. The blockchain-based digital asset processing method according to claim 4, characterized in that: In step S5, determining the form of asset risk specifically involves: before each acquisition module acquires an asset risk phenomenon, the metadata consistency acquisition module outputs various consistency deviation indicators as follows: ,in This refers to the number of consistency deviation indicators involved in asset risk. When a certain form of asset risk is detected, if this form of risk leads to... Changes have occurred, and Actual collection The probability of this form of asset risk occurring depends on the consistency deviation. ,in This is the consistency deviation impact coefficient; if no data was collected... Changes in consistency Combined with S3-1 The final probability is calculated when hour, The threshold for probability judgment is used to determine if this type of risk is valid and an early warning is required.

6. The method for processing digital assets based on blockchain according to claim 5, characterized in that: The system employed in this method includes a signal acquisition module, a signal processing module, and a risk assessment module. The signal acquisition module is used to collect contract event streams, P2P propagation indicators, and high-frequency deposit and withdrawal risk signals, and to collect data from multiple dimensions by combining consistency deviation indicators in metadata. The signal processing module is used to perform unified time benchmark and intensity normalization processing on the acquisition frequency and sampling granularity, dynamically adjust the characteristics of the two output parameters based on subsequent acquisition results, and perform multi-source data fusion processing. The risk assessment module is used to comprehensively determine the form of asset risk based on the processed data.

7. A blockchain-based digital asset processing method according to claim 6, characterized in that: The signal acquisition module includes a contract event monitoring acquisition module, a P2P communication monitoring acquisition module, a fund flow pulse acquisition module, a metadata consistency acquisition module, a contract event flow analysis module, a P2P propagation indicator analysis module, a high-frequency deposit and withdrawal analysis module, and a metadata consistency analysis module. The contract event monitoring acquisition module, the P2P communication monitoring acquisition module, and the fund flow pulse acquisition module are respectively used to collect contract event flows, P2P propagation indicators, and high-frequency deposit and withdrawal risk signals. The metadata consistency acquisition module is used to collect metadata consistency detected in on-chain metadata. The contract event flow analysis module, the P2P propagation indicator analysis module, and the high-frequency deposit and withdrawal analysis module are respectively used to analyze risk signals. The metadata consistency analysis module is used to analyze the consistency of various types of metadata. The signal processing module includes a sampling frequency adjustment module, a sampling granularity adjustment module, a data alignment processing module, a risk form correspondence module, and a timing module. The timing module is used to count the duration after a certain risk form occurs. The sampling frequency adjustment module and the sampling granularity adjustment module are used to adjust the sampling frequency and sampling granularity of each sampling module, respectively. The data alignment processing module is used to normalize the sampling frequency and sampling granularity of each sampling module. The risk assessment module includes a metadata consistency assessment module and a risk form assessment module. The metadata consistency assessment module is used to analyze the metadata consistency changes detected on the chain to assist in the assessment of the risk form, and the risk form assessment module is used to determine the risk form with the highest probability.

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